ALBERT

Description: Indices, codes and associated performance measures for extremes of precipitation and temperature (Tmin = minimum temperature; Tmax = maximum temperature). Credible information about the properties and changes of extreme events on the regional and local scales is of prime importance in the context of future climate change. Within the EU‐COST Action VALUE a comprehensive validation framework for downscaling methods has been developed. Here we present validation results for extremes of temperature and precipitation from the perfect predictor experiment that uses reanalysis‐based predictors to isolate downscaling skill. The raw reanalysis output reveals that there is mostly a large bias with respect to the extreme index values at the considered stations across Europe, clearly pointing to the necessity of downscaling. The performance of the downscaling methods is closely linked to their specific structure and setup. All methods using parametric distributions require non‐standard distributions to correctly represent marginal aspects of extremes. Also, the performance is much improved by explicitly including a seasonal component, particularly in case of precipitation. With respect to the marginal aspects of extremes the best performance is found for model output statistics (MOS), weather generators (WGs) as well as perfect prognosis (PP) methods using analogues. Spell‐length‐related extremes of temperature are best assessed by MOS and WGs, spell‐length‐related extremes of precipitation by MOS and PP methods using analogues. The skill of PP methods with transfer functions varies strongly across the methods and depends on the extreme index, region and season considered.

Description: This article presents the results of the comprehensive VALUE perfect predictor experiment, a downscaling exercise for Europe with predictors and boundary conditions from reanalysis data. The performance of a wide range of statistical downscaling, bias correction and weather generator methods to represent temporal aspects of local weather is evaluated and compared with reanalysis data as well as regional climate model simulations. Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short‐term variations, spells and variability from interannual to long‐term trends. The EU‐COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long‐term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors.

Description: Five state‐of‐the‐art reanalysis‐driven regional climate model experiments are evaluated against three different observational reference data sets for two variables (temperature and precipitation) and for eight sub‐regions of the European continent. Overall, we find the influence of observational uncertainty to be smaller than that of model uncertainty. For individual regions and seasons, however, model evaluation can considerably depend on the chosen reference and final model ranks can be strongly influenced. The influence of uncertainties in gridded observational reference data on regional climate model (RCM) evaluation is quantified on a pan‐European scale. Three different reference data sets are considered: the coarse‐resolved E‐OBS data set, a compilation of regional high‐resolution gridded products (HR) and the European‐scale MESAN reanalysis. Five high‐resolution ERA‐Interim‐driven RCM experiments of the EURO‐CORDEX initiative are evaluated against each of these references over eight European sub‐regions and considering a range of performance metrics for mean daily temperature and daily precipitation. The spatial scale of the evaluation is 0.22°, that is, the grid spacing of the coarsest data set in the exercise (E‐OBS). While the three reference grids agree on the overall mean climatology, differences can be pronounced over individual regions. These differences partly translate into RCM evaluation uncertainty. For most cases observational uncertainty is smaller than RCM uncertainty. Nevertheless, for individual sub‐regions and performance metrics observational uncertainty can dominate. This is especially true for precipitation and for metrics targeting the wet‐day frequency, the pattern correlation and the distributional similarity. In some cases the spatially averaged mean bias can also be considerably affected. An illustrative ranking exercise highlights the overall effect of observational uncertainty on RCM ranking. Over individual sub‐domains, the choice of a specific reference can modify RCM ranks by up to four levels (out of five RCMs). For most cases, however, RCM ranks are stable irrespective of the reference. These results provide a twofold picture: model uncertainty dominates for most regions and for most performance metrics considered, and observational uncertainty plays a minor role. For individual cases, however, observational uncertainty can be pronounced and needs to be definitely taken into account. Results can, to some extent, also depend on the treatment of precipitation undercatch in the observational reference.

Description: The VALUE perfect predictor experiment SDMs are examined following the so‐called “regime‐oriented” technique, focused on relevant atmospheric circulation features and processes, from large to local scales. Overall, SDMs show a reasonable performance representing the large spectra of atmospheric phenomena analysed, although the PP methods reveal a more differentiated behaviour than MOS. As expected, MOS methods are unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Although PP methods are conditioned on predictors typically representing the large‐scale circulation, many PP methods frequently fail in capturing the process sensitivity. In the figure, winter NAO conditioned biases for precipitation for the raw model outputs and the different MOS and PP methods. In (c, d) boxes span the 25–75% range, the whiskers the maximum value within 1.5 times the interquartile range, values outside that range are plotted individually; average results over the different PRUDENCE regions are indicated by a coloured horizontal bar (see the colours in the bottom legend). Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called “regime‐oriented” technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities.

Description: Abstract Based on twelve Senegalese stations of the Global Summary of the Day (GSOD) database (1979‐2014), Heat Waves (HW) are defined for each station in spring (March‐April‐May, the hottest season in Senegal) as the daily maximum temperature (Tx), minimum temperature (Tn), or average apparent temperature of the day (AT), exceeding the corresponding 95% mobile percentile for at least three consecutive days. A hierarchical cluster analysis used to regionalize HW in these 12 stations is applied to simultaneous occurrences of daily temperature peaks over their 95% mobile percentiles. Three homogeneous zones of 4 stations each are identified (Zone 1, Zone 2 and Zone 3), from west (Atlantic coastline) to east (inland Senegal). Atmospheric circulation associated with HW is assessed through composites of ERA‐Interim deseasonalized anomalies, with the start date of each HW in each zone used as a reference. The main pattern controlling the presence of HW in Senegal consists in positive pressure anomalies centered around the strait of Gibraltar, promoting easterly to northeasterly wind anomalies. This causes higher temperatures in the three zones of Senegal, and lower temperatures and drier air over the central Sahel. This pattern is opposite to that characteristic of HW in the Central Sahel shown in previous studies. From Zone 1 to Zone 3, the temperature and moisture patterns are shifted to the east while pressure anomalies weaken drastically. Nighttime Tn‐HW are characterized by higher water vapor contents than daytime Tx‐HW, corroborating and complementing previous studies over the Sahel. These HW patterns are close to the canonical mode of intra‐seasonal atmospheric variability over Senegal. This article is protected by copyright. All rights reserved.

Description: This paper shows how well‐established time series methods can be used to simulate climate temperature series and networks that match the major prominent statistical features found in temperature data. We started simple, showing how to generate a single fair draw from a fixed season, regardless of that season's statistical distribution. Thereafter, we moved to scenarios where a whole series was replicated, where several series were replicated in tandem, and where aspects of GCM‐generated data were mixed into the computations. Abstract It is often desirable to simulate a single temperature series or a collection (network) of temperature series. Accurate simulations can enhance our understanding of temperature trends and variabilities. Simulation can also be used to generate data with known specifications, which are useful in assessing climate data processing routines such as quality control and homogenization algorithms. Possessing multiple realistic temperature series is often beneficial as only one natural record of our climate is available. The current popularity of general circulation models (GCMs) demonstrates how important simulation techniques are. However, even with sophisticated downscaling, it is difficult to replicate station temperature data that have realistic seasonal cycles as well as realistic temporal and spatial correlations. This paper reviews and studies statistical time series methods that can replicate a single series or a network of series from data. The methods are purely statistical—no atmospheric dynamics are involved—and attempt to produce replicates of the records under study. The work here develops (a) methods that simulate temperatures from a fixed season (say a particular day or month of the year) that match the distributional characteristics of the observed data; (b) methods for simulating an entire series that matches the series' temporal autocorrelations and seasonal cycle; and (c) methods for simulating a network of series that reproduce the data's observed seasonal cycles and spatial and temporal autocorrelations. Applications are given throughout, including one where a GCM series and local station data are used in tandem to describe long‐term trends and inject realistic station‐level short‐term fluctuations. This paper can be used as a tutorial for the simulation of a single climate observation, an entire climate series, or a network of multiple climate series simultaneously. Extensions of the ideas that involve GCMs are also examined.

Description: Abstract The changes in the frequency, duration, and intensity of summer heatwaves over Central Asia during the periods 1917–2016 were studied. On average, the frequency, duration, and intensity of heatwaves showed significant positive trends during the period 1917–2016, with enhanced rates during the last 50 years. During 1967–2016, the heatwave indices increased significantly in most of Central Asia, especially in the western part. The number of heatwaves has increased by 1.3 times since the 1960s. Remarkable changes in the frequency and duration of heatwaves occurred during the 1990s in association with the inter‐decadal shift in the Silk Road pattern of atmospheric circulation around 1997. The results based on the ERA‐Interim reanalysis dataset were well‐matched with the station observations during the period 1979–2016, whereas those based on the NCEP‐NCAR dataset were less well matched. Heatwaves in Central Asia were closely related to a zonal wave circulation pattern at 500 hPa with a center of positive geopotential height anomalies over Central Asia. This anomalous circulation pattern was rapidly enhanced during the 1990s, suggesting that large‐scale patterns of atmospheric circulation had a role in modulating the occurrence of heatwaves in Central Asia. This article is protected by copyright. All rights reserved.

Description: Time series of annual TEC GPPC6, BESS GPPC5, MOD17A3 GPPC5, and TEC GPPC5 in China from 2000 to 2015 with linear trend k and significance P. Abstract Differences, arising from differences in gross primary production (GPP) model structures and driving forces, have fuelled arguments concerning interannual changes of GPP in China since 2000. To better investigate the interannual variability of GPP and its covariance with climate factors in China, this study adopted a multi‐model analysis based on three GPP models (i.e., Terrestrial Ecosystem Carbon flux model [TEC], Breathing Earth System Simulator model [BESS], and MOD17 GPP model). The results show that annual GPP in China increased by 0.021–0.057 Pg C year−1 from 2000 to 2015 attributable to atmospheric‐CO2 fertilization effects and favourable climate change, that is, increasing precipitation (Pr) and temperature (Ta). However, northern China and southern China had a large difference in the amplitude of these GPP changes; annual GPP increased by 0.017–0.039 Pg C year−1 in northern China but only 0.001–0.018 Pg C year−1 in southern China. Northern China and southern China occupy contrasting climate zones and this contrast produced different interannual variability of GPP through different mechanisms. Northern China has a dry climate with GPP changes sensitive to Pr. As a result, more Pr along with higher Ta in northern China produced the strong uptrend of GPP from 2000 to 2015. In contrast, southern China has a wet climate with its GPP sensitive to solar radiation and Ta. For the interval of 2000–2015, decreasing radiation plus drought exerted a negative influence on GPP in southern China. This study highlights the diverse mechanisms in which climate change affects GPP in dry and wet climate zones. A robust multi‐model analysis is preferred to reduce uncertainties arising from a single GPP model and its driving data.

Description: We have discovered that ENSO caused the significant dipole autumn precipitation anomalies in eastern China (EC) in the early decades (1951–1981); El Niño/La Niña introduces more/less precipitation in the southern EC, but less/more in the northern EC. However, the significant dipole pattern disappears in the recent decades (1985–2015). The decadal variations in the ENSO impacts are due to the decadal changes of SST anomalies related to La Niña in the tropical western North Pacific. In the figure, composite anomalies of SON precipitation (mm/day) in (a, c) El Niño and (b, d) La Niña years during (a, b) the first and (c, d) second periods, respectively. The anomalies significant at the 90% confidence level are stippled. Abstract By using the observation, reanalysis data and numerical simulation, the inter‐decadal variations in El Niño–Southern Oscillation (ENSO) impacts on the eastern China (EC) precipitation during its developing autumn in the past 65 years have been investigated. Results show that ENSO is related to the significant dipole precipitation anomalies in EC in the early decades (1951–1981); El Niño/La Niña introduces more/less precipitation in the southern EC, but less/more in the northern EC. However, the significant dipole pattern disappears in the recent decades (1985–2015), mainly owing to loss of the significant positive influences of La Niña on the northern EC precipitation. Comparison of the atmospheric circulation anomalies in East Asia related to La Niña in the two periods shows that there are anticyclonic circulation anomalies in the northeastern Asia near Japan in the first period, while they shift to Mongolia in the second period. Hence, in the first period, the northern EC is under the influences of anomalous southwesterlies along the southwestern flank of the anticyclonic circulation anomalies that advect more moisture from the south to the northern EC and lead to more precipitation there. In contrast, in the second period, the northern EC is under the influences of anomalous northeasterlies along the southeastern edge of the anticyclonic circulation anomalies near Mongolia that impede the northwards transport of moisture and are not conducive to surplus precipitation. The westwards shift of the anticyclonic circulation anomalies may be closely related to the higher SST and thus convective anomalies related to La Niña in the tropical western North Pacific (WNP) east of the Philippines in the second than the first periods. The sensitivity experiment forced by the positive SST anomalies in the tropical WNP comparable to the observed in an atmospheric general circulation model can successfully simulate the anticyclonic circulation anomalies around Mongolia.

Description: Abstract Climate change has influenced the glaciers and water resources in the Hindukush‐Karakorum‐Himalaya region. The relatively short instrumental record of northern Pakistan makes long‐term climate change assessments difficult to make. In this paper, tree‐ring width chronologies were developed from two stands of spruce species (Picea smithiana) in the Karakoram region of northern Pakistan. The results of the correlation analysis revealed that tree‐ring growth of spruce was limited by annual (June to May) precipitation. Based on the regional chronology, we developed an annual precipitation reconstruction for the period 1540‐2016 CE. The precipitation reconstruction equation accounts for 40% of the regional precipitation variance during the instrumental period 1946‐2016. Dry episodes with rainfall below the 477‐year average occurred from 1569 to 1577, 1598 to 1612, 1621 to 1621, 1638 to 1654, 1673 to 1680, 1697 to 1720, 1728 to 1739, 1753 to 1761, 1777 to 1793, 1801 to 1840, 1860 to 1874, 1914 to 1932, 1960 to 1985, 1998 to 2011. Wet episodes occurred from 1540 to 1568, 1578 to 1597, 1613 to 1620, 1632 to 1637, 1655 to 1672, 1681 to 1696, 1721 to 1727, 1740 to 1752, 1762 to 1776, 1794 to 1800, 1841 to 1859, 1875 to 1913, 1933 to 1959, 1986 to 1997. Furthermore, the results of spatial correlation analyses show that the reconstructed precipitation represents the regional precipitation variations for northern Pakistan and nearby high‐altitude mountains. The out‐of‐phase relationship in tree‐ring records of the Karakoram region and surrounding areas suggest that the Karakoram was under the control of large‐scale ocean‐atmosphere‐land circulations on a decadal timescale in the past centuries. In addition, by the comparison between the precipitation reconstruction and the instrumental data, we found that the drought risk of Karakoram has increased in the past 70 years and that extreme events are likely to become more severe and more frequent under the backdrop of climatic warming. This article is protected by copyright. All rights reserved.

Description: Abstract The Yellow and Yangtze River basins are the most important basins in China due to their environment and natural and socioeconomic conditions, and they are highly sensitive to extreme climate events. In this study, we used daily maximum and minimum temperature data gathered from 892 meteorological stations to analyze the spatiotemporal variations in temperature extremes in the Yellow and Yangtze River basins during 1961 to 2014 and identified correlations with the geographic location and atmospheric circulation patterns. The results indicated that annual mean maximum and minimum temperatures (TXm and TNm), most warm extremes and some cold extremes (minimum of the minimum temperature and maximum temperature, TNn and TXn) generally showed low values in the northwestern region of the Yellow and Yangtze basins, while the diurnal temperature range (DTR) and some cold extremes (ice days, ID0 and frost days, FD0) were generally higher in the northwest of the two basins. TXm, TNm, all warm extremes and some cold extremes (TNn and TXn) showed significant increasing trends in both basins. The increase in the extreme temperature was between 0.17‐0.38°C/decade in the Yellow basin, while the interval was 0.15‐0.38°C/decade in the Yangtze basin. DTR decreased by ‐0.07 days/decade in the former and ‐0.05 days/decade in the latter. Furthermore, warm event days increased by 1.38~3.52 and 1.42~2.65 days/decade in the Yellow and Yangtze basins, respectively. At the same time, the cold event days decreased by ‐1.90~‐3.47 and ‐0.20~‐2.70 days/decade in the respective basins. In terms of the spatial patterns, most stations that displayed significant trends were located over the Loess Plateau in the Yellow River basins; Sichuan basin and the lower reaches of the Yangtze River basins. Moreover, almost all of the temperature extremes exhibited the largest trends in spring and winter in the two basins. Most temperature extremes showed strong correlations with longitude and altitude and the Atlantic Multidecadal Oscillation (AMO) displayed significant correlations with almost all of the temperature extremes, except DTR, TNn and TXn; the East Atlantic/Western Russia (EA/WR) was also related to many temperature extremes. Finally, most temperature observations showed abrupt changes in the 1990s, and the timing in the Yangtze River basin was generally later than that in the Yellow River basin. This article is protected by copyright. All rights reserved.

Description: Abstract The Arctic is a cloudy place. It has been recognized that the Arctic cloud cover is sensitive to different climatic factors such as sea ice extent and atmospheric circulation indices. Moreover, several influential climate feedbacks, e.g. the summertime cloud‐radiation feedback, have been recognized. Yet, the cloud cover studies were limited in time to the satellite era observations and fragmentary data sets from meteorological stations. Here, we present the complete long‐term cloud records from 86 meteorological stations in the Eurasian Arctic. The stations are located on the coast and islands of the region from the Barents to Chukchi Seas. Thus, this study is complementing and extending the study by Chernokulsky et al. (J. Clim, 2017) where the cloud data from the Norwegian through Kara Seas were presented. Our data set comprises the entire period of observations at each station. However, we present the area‐wide analysis only over the historical period of 1936‐2012 when there were sufficient density of stations and cloud records for the coherent analysis. The total cloud cover, which on multiannual average constitutes 69–74% in different areas, increases in the warmer periods. The strongest increase is found in the convective cloud cover, particularly in the Chukchi Sea. We observe statistical evidence of transition between stratocumulus and convective cloud types. The cloud characteristics reveal the strongest correlations with the Atlantic circulation indices and the sea ice concentration in all Eurasian Arctic areas. The correlations with the Pacific circulation indices are much less significant. The obtained cloud data sets disclose much smaller scale features and variability, which deserve further research. This article is protected by copyright. All rights reserved.

Description: In this work we have detected that the occurrence of strong surface wind events associated to convection (CSSWE) during austral spring in northeastern Argentina (NEA) is related to the activity of the leading pattern of intraseasonal (IS) activity of OLR anomalies in South America, the SIS pattern. CSSWE are favoured when the SIS index is positive, related to a cyclonic IS anomaly in upper levels favouring ascent in NEA, and also in lower levels related to northerly advection in NEA. Abstract The relationship between intraseasonal variability (IS; 10–90 days) and days which registered convection‐associated strong surface wind events (CSSWE) over northeastern Argentina (NEA) was studied. The climatological behaviour of these strong wind events showed a higher duration and occurrence in austral spring. CSSWE were categorized as a function of the wet and dry phases of the spring‐season intraseasonal (spring‐SIS) index, which describes the activity of the leading pattern of IS‐filtered outgoing longwave radiation (IS‐OLR) during that season in eastern South America. A modulation of the IS variability over localized and mesoscale phenomena as the CSSWE was found, showing significant peaks of wind variability in that timescale, and especially the submonthly timescale. The CSSWE were categorized according to the phases of the spring‐SIS pattern and most of them occurred before or during a wet phase, especially for the longer CSSWE. Moreover, the detection of CSSWE days during and before a dry phase was scarce. Rossby wave trains were observed to organize the circulation on intraseasonal timescales that configure regional cyclonic anomalies in such way that favours the development of CSSWE, promoting mid‐level ascents over NEA and northerly advection of humidity to the region. Together with the composites of IS‐OLR anomalies and the spectra of wind velocity, they support the fact that the higher‐frequency IS variability is the primary influence for the development of CSSWE.

Description: In many remote areas of the world there is a lack of data and reliability of the snow cover series. A new procedure of quality control for manual snow depth data was applied. The proposed approach is more suitable for detection of suspicious snow depth events at different time scales where only snow depth values are recorded. Abstract The aim of this work is to develop, from a high‐resolution climate analysis, a quality control standard methodology applied to manual snow cover (HS) series managed by the Snow Survey Database in New Brunswick (Canada). The database collected snow depth data biweekly starting at the end of January until the end of April. A 30‐year (1981–2010) analysis of 60 weather stations belonging to two independent meteorological networks was performed. A quality control of the climatic series was performed to evaluate the homogeneity. Three snow depth climatic areas were defined by means of two geostatistical methods (Kriging and Cluster analyses) applied on monthly snow depth, precipitation and temperature data series. Then, for each cluster, the climatological thresholds that characterize a snow fall event during the cold months were detected. Subsequently, a quality control on the daily snow depth series recorded during the January to April period was performed. For each daily series, outlier values were identified by checking both the sudden day‐to‐day changes and extreme thresholds (95th percentile). The quality control was then carried out to the manual series and the observed doubtful events were compared with the snow depth values recorded in the nearby stations. The results show that for the daily snow depth series, the highest number of suspect events was recorded during the months of March and April, and the analysis also shows that there are rain‐on‐snow events. As for the manual records, questionable snow depth errors randomly distributed in the series were highlighted. Finally, in order to improve the spatial distribution of stations located in the Canadian territory, the results give evidence that, thanks to the high‐resolution climatic analysis, the proposed approach provides all the benchmarks required to conduct a quality control of snow depth series in absence of other auxiliary variables.

Description: (a) Spatial distribution of the first EOF mode of summer rainfall anomaly (shade; mm/day). (b) the corresponding principal components. (c) Power spectrum of PC1 during 1979–2012, and the dashed line in (c) represents the 95% confidence level. Abstract This study investigates the interannual variability of rainfall over the middle and lower reaches of Yangtze River Valley (MLYRV) and that over the South China Sea and Philippine Sea (SCS‐PS) during boreal summer (June–August) from 1979 to 2012. Results exhibit out‐of‐phase rainfall variations between the MLYRV and the SCS‐PS, which is closely related to the tropical zonal sea surface temperature gradient (ZSG) between the northern Indian Ocean (NIO; 5°–25°N, 60°–100°E) and the equatorial central and eastern Pacific (CEP; 5°S–5°N, 180°–130°W). The ZSG can explain as much as 40% of the total variance of the summer rainfall over the MLYRV and the SCS‐PS in the past 40 years. This is much higher than that explained by the NIO SST (24%), CEP SST (14%) and Niño‐3.4 index (16%) alone. A positive ZSG between the warm NIO and the cold CEP tends to increase rainfall over the MLYRV and decrease rainfall over the SCS‐PS, whereas a negative ZSG between the cold NIO and the warm CEP is generally favourable for rainfall over the SCS‐PS and unfavourable for rainfall over MLYRV. The close connection between the ZSG and the out‐of‐phase interannual rainfall variation over the two regions can be explained by influences of the ZSG on atmospheric circulation over East Asia. A positive ZSG induces anomalous easterlies and Walker‐like circulation in the tropics, which results in an anomalous subsidence and boundary layer divergence over the SCS‐PS. As a result, summer rainfall decreases in this region. Meanwhile, moisture transport increases due to the anomalously strong southwesterlies along the northwestern flank of the intensified western Pacific subtropical high, providing more precipitable water to the MLYRV region. In contrast, a negative ZSG induces surface westerlies and favourable environmental condition for rainfall over the SCS‐PS. Dry descending flow induced by local anomalous Hadley circulation develops over the MLYRV around 30°N, which is unfavourable for rainfall over the MLYRV. The mechanism further examined using numerical experiments. These thermal‐dynamical processes induced by the ZSG work together to cause the out‐of‐phase interannual changes of rainfall between the MLYRV and the SCS‐PS, suggesting that the ZSG is highly indicative of the interannual change of summer rainfall in the two regions.

Description: In this study, the contrast in meteorological conditions and climate factors responsible for the active tropical cyclone (TC) seasons over Arabian Sea (upper panel) and the Bay of Bengal (lower panel) is investigated. Such contrast occurred mostly during post‐monsoon. It is found that more active TC seasons over the Arabian Sea is related to northeast monsoon development. Abstract Climatologically, tropical cyclone (TC) activity in the North Indian Ocean (NIO) is asymmetric between the Arabian Sea (AS) and Bay of Bengal (BoB) basin. For the 172 TCs formed over NIO during 1983–2015, only 56 formed over AS and the rest (116) over BoB. During the period, AS was very active in a few years (but with quiet BoB season), and the opposite occurred in some others. It is found that this contrast occurred mostly during the post‐monsoon season of October–December. The meteorological and climate factors that accounted for these contrasting AS and BoB TC seasons are analysed. While climate variability such as the El Niño Southern Oscillation and Indian Ocean Dipole have known influences to NIO TC activity, results reveal that no single climate mode can well explain the TC development concentrating on AS or BoB only. Instead, it is found that variability of the northeast monsoon is an important factor responsible for the difference between the two basins. Excess moisture is available over the AS due to anomalous low‐level flow from the equatorial IO in the years in which there are more TCs in that basin, and dryer condition is over the BoB. In these years, there is likely excess northeast monsoon rainfall. The relationship is opposite between post‐monsoon BoB TC activity and the northeast monsoon. Nevertheless, the anomalous flow during active AS TC seasons is similar to that occurs during positive Indian Ocean Dipole, and thus this climate variability may be responsible for redistributing the moisture content in the NIO.

Description: The climatology (1982–2010) of (a) JJA precipitation (mm day−1, shading) and 850 hPa winds (m s−1, vectors) from observations, and the corresponding forecast bias (0‐month lead) for (b) CFSv2, (c) CanCM3, (d) CanCM4, and (e) the three‐model ensemble mean. (f)–(j) as in (a)–(e) but for 200 hPa winds. Abstract The abilities of three models (Climate Forecast System version 2 [CFSv2], Canadian Coupled Climate Model version 3 [CanCM3] and Canadian Coupled Climate Model version 4 [CanCM4]) in the North American Multimodel Ensemble for the East Asian summer monsoon (EASM) forecast were evaluated with their 29‐year hindcast data (1982–2010). Many EASM features including monsoon precipitation centres, large‐scale monsoon circulations and monsoon onset and retreat are generally captured by the three models and their ensemble mean, and the multimodel ensemble has the best performance. Since the East Asian domain includes the tropical western North Pacific summer monsoon (WNPSM) and the subtropical continent monsoon, two well‐known monsoon indices, the WNPSM index (WNPSMI) and EASM index (EASMI), and their associated low‐level winds and precipitation anomalies are well forecasted by the models. However, the forecast performance generally decreases as the leads increase, and the performance of EASMI is not as good as that of WNPSMI. CFSv2 forecasts well at leads up to 6 months whereas the skill of CanCM3 (CanCM4) decreases rapidly when the lead increases to 2 months (3 months). The failure of CanCM3 is mainly attributed to the poor forecast of the relationship of EASMI with the El Niño‐Southern Oscillation and Northern Indian Ocean‐western North Pacific (WNP) sea surface temperature anomaly. However, the causes of the poor forecast of CanCM4 for EASMI require further investigation. Sources of the forecast error (FE), which is the difference between model and observation for monsoon precipitation, are more significant than those of the predictability error (PE), which originates from the initial condition error, indicating that model deficiency plays a dominant role in limiting the EASM precipitation forecast. However, the PE cannot be neglected over the tropical western Pacific in CFSv2, over the WNP in CanCM3 and over the Tibetan Plateau in CanCM4. As the lead time increases, the FE does not remarkably change whereas the PE decreases significantly.

Description: A rainfall‐conditioned weather generator, ICAAM‐WG, was developed based on the Climate Research Unit daily weather generator. We demonstrated its validity, together with the Spatial–Temporal Neyman‐Scott Rectangular Pulses model, in simulating not only mean climatology, but also extremely wet and dry events as well as temperature extremes and heat waves. ICAAM‐WG (SIM6), which used second‐order autoregressive processes for temperature simulation, outperformed ICAAM‐WG (SIM4), which used first‐order autoregressive processes, in the simulation of heatwave extremes. Abstract Downscaling is usually necessary for robust hydrological impact assessments. This may be undertaken using a wide range of methods, including a combination of dynamical and statistical‐stochastic downscaling. This study uses the Spatial–Temporal Neyman‐Scott Rectangular Pulses model—RainSimV3, the precipitation‐conditioned daily weather generator—ICAAM‐WG, and the change factor approach for downscaling synthetic climate scenarios for robust hydrological impact assessment at middle‐sized basins. The ICAAM‐WG was developed based on the concept of the Climate Research Unit daily weather generator (CRU‐WG), motivated by the need for improved representation of heat waves by downscaling methods given the positive feedback between low soil moisture and high air temperature. We demonstrated the validity of the proposed methodology in the 705‐km2 Mediterranean climate basin in southern Portugal. The results show that, for the control period 1980–2010, both RainSimV3 and ICAAM‐WG reproduced not only the mean climatology, but also extreme wet and low precipitation events, as well as the extremes of temperature and heat waves. We found that downscaling with ICAAM‐WG (SIM6), which uses second‐order autoregressive processes for the simulation of temperature during consecutive dry and wet days, outperformed ICAAM‐WG (SIM4), which used only first‐order autoregressive processes, leading to improved simulation of heat waves. ICAAM‐WG (SIM6) well reproduced observed heatwave extremes with return periods of up to 30 years; however, ICAAM‐WG (SIM4) overestimated these extremes substantially. This indicates the importance of incorporating second‐order autoregressive processes in the simulation of heatwave length. In the context of climate warming, the proposed methodology provides a tool to improve downscaled projections of future extremes with confidence intervals for not only wet events but also dry spells and heat waves.

Description: This study presents the first climatology of drought events for mainland Spain and the Balearic Islands considering different drought timescales (1, 3, 6 and 12 months) and two different drought indices, that is, Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI). In northern Spain, the drought episodes are more frequent than in the centre and south. But the drought duration tends to be shorter in the north, and the drought magnitude also tends to be lower. The general spatial patterns of drought indices show strong differences, as a function of the timescales. Drought duration and magnitude were in general higher for the SPEI than for the SPI. Abstract This study characterizes the climatology of drought events over the mainland Spain and the Balearic Islands using high‐resolution (1.21 km2) meteorological data from 1961 to 2014. The climatology of drought was assessed based on two widely‐recognized drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation‐Evapotranspiration Index (SPEI), considering four different timescales (1‐, 3‐, 6‐ and 12‐months). Drought events were simply defined as sequences of months with negative values of the indices. We analysed the spatial and temporal variability of the frequency, duration and magnitude of the drought events. In general, the frequency of drought events is higher in the northern than in the southern regions. Conversely, the average duration and magnitude of the drought events in central and southwestern regions duplicate those recorded in northern areas. Although drought characteristics exhibit a general north–south gradient irrespective of the drought timescale and the drought index analysed, we found important spatial differences in terms of both drought duration and severity. As opposed to the SPI, the SPEI shows, on average, higher drought durations and magnitudes at 1‐, 3‐ and 6‐months timescales. Albeit of the absence of significant temporal changes in drought duration or magnitude at the regional scale, a nonsignificant tendency toward higher drought duration and magnitude is observed over the majority of Spain. Our result provide valuable guidance to stakeholders and decision‐makers on detecting, monitoring and adapting to drought impacts at local, regional and national scale in Spain.

Description: The future projections of precipitation changes over Thailand based on seven ensemble members of the Southeast Asia Regional Climate Downscaling (SEACLID)/CORDEX Southeast Asia multi‐model simulations showed a tendency towards wetter and drier condition over northern‐central‐eastern and southern region, respectively, during dry months (December to March). However, during wet months (June to September), drier condition was projected for the entire country. Abstract This paper highlights detailed projected changes in rainfall over Thailand for the early (2011–2040), middle (2041–2070) and late (2071–2099) periods of the 21st century under the representative concentration pathways (RCP) 4.5 and RCP 8.5 using the high‐resolution multi‐model simulations of the Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia. The ensemble mean is calculated based on seven members consisting of six general circulation models (GCMs) and three regional climate models (RCMs). Generally, the ensemble mean precipitation agrees reasonably well with observations, best represented by the Global Precipitation Climatology Center (GPCC) data, over Thailand during the historical period (1976–2005). However, inter‐model variations can be large among ensemble members especially during dry months (December to March) for northern‐central‐eastern parts, and throughout the year for the southern parts of Thailand. Similarly for future projection periods, inter‐model variations in the sign and magnitude of changes exist. The ensemble means of projected changes in rainfall for both RCPs during dry months show distinct contrast between the northern‐central‐eastern parts and the southern parts of Thailand with generally wetter and drier conditions, respectively. The magnitude of change can be as high as 15% of the historical period, which varies depending on the sub‐region, season, projection period, and RCP scenario. In contrast, generally drier conditions are projected during the wet season (June to September) throughout the country for both RCPs where the rainfall reduction can be as high as 10% in some areas. However, the magnitude of projected rainfall changes of some individual models can be much larger than the ensemble means, exceeding 40% in some cases. These projected changes are related to the changes in regional circulations associated with the winter and summer monsoons, which are projected to weaken. The drier (wetter) condition is associated with the enhanced subsidence (rising motion).

Description: The variability of circulation episodes (sequence of days with an unaltered circulation form), seasonality and long‐term fluctuations in Vangengeim‐Girs macroform frequencies and the number of episodes are presented. Bidirectional transitions (e.g., W ↔ E) between episodes are random. Seasonal variation is pronounced, especially as regards W form, with its maximum frequency in autumn and winter (see figure). An increase in the number of episodes is observed. Four circulation epochs in the years 1949–2010 have been separated: three meridional and the latest—zonal. Abstract This paper presents the results of an investigation of the variability in macro‐circulation forms at the mid‐tropospheric level distinguished in the Vangengeim‐Girs (V‐G) classification. The number of days and of uninterrupted periods of at least 2 days (so‐called episodes) with V‐G forms were analysed based on the daily atmospheric circulation catalogue for the period 1949–2010. The average number of circulation episodes per year (~51) and their average persistence (6–9 days) were determined. When analysing the short‐term variability, the frequency of transitions between macroforms: W ↔ E, E ↔ C and W ↔ C was calculated. It was found that these sequences were random in character. Changes in average frequencies of V‐G macroforms in an annual cycle were evaluated and the seasons of dominant zonal (W) and meridional (E, C) circulation patterns distinguished. In the years 1949–2010, four circulation epochs were separated, differing in the macroform frequency quotients—fW/fE, fW/fC and fE/fC. Three epochs were meridional (until 1988) and the last one is zonal. A growing trend was observed in the number of circulation episodes, indicating an increase in the frequency of changes of circulation forms. This rise was attributed to the growing frequency of the W episodes and the decreasing number and persistence of E episodes in the last two decades of the analysed period.

Description: Abstract This study reports the improvements in the diurnal rainfall phase over the Southern Great Plains (SGP) with the Community Atmosphere Model by incorporating two convective initiation designs into the Zhang–Mcfarlane scheme. To capture the convection initiation of the nocturnal rainfall regime, the triggers emphasized the importance of suppression of convective inhibition and identification of elevated buoyancy for initiating convection. Consistent with the single‐column study of Wang et al. (2015), the model rainfall bias reduced with an increase in the heavy nocturnal rainfall and with the suppression of the spurious daytime convection in global domain experiments. We found that, with the climate model resolution of 1°, the nocturnal convection over the SGP can be well represented when the destabilization mechanisms of potentially buoyant layer at the low‐level troposphere is appropriately captured. Such destabilization is caused by the moisture convergence between the Rocky Mountains–SGP circulations and the uplifted low‐level jets from the Gulf of Mexico. Similar improvements are also found in other regions on the lee side of high terrains, such as the Tibetan Plateau.

Description: The diurnal cycle of convection and rainfall over the Maritime Continent land regions shows strong amplitude during afternoon hours. However, over the coast and ocean regions it is strong in the early morning. The diurnal variation of rainfall, convection, and precipitation features over the region have greater amplitude during boreal winter than summer. Abstract This study investigates the diurnal cycle of rainfall, convection, and precipitation features (PFs) over the Maritime Continent (MC). The study uses Tropical Rainfall Measuring Missions (TRMM) Multi‐satellite Precipitation Analysis (TMPA; product 3b42), TRMM PFs, and convective classifications from the International Satellite Cloud Climatology Project (ISCCP) data. Together, these satellites dataset paint a comprehensive picture of the diurnal cycle of rainfall and convection over the MC consistent with past research. Isolated convection initiates around midday over the higher terrain of the large islands (Java, Borneo, and Papua New Guinea). The convection becomes more organized through the afternoon and evening, leading to peak rainfall over the islands around 1800–2100 local standard time (LST). Over the next few hours, some of that rainfall transitions to stratiform rain over land. The convection then propagates offshore overnight with rainfall peaking along the coast around 0300–0600 LST and then over ocean around 0600–0900 LST. ISCCP data suggests that the overnight and early morning convection is more associated with isolated convective cells than the remnants of mesoscale convective systems. The coastal and oceanic diurnal ranges also seem to be larger in stratiform rainfall, in contrast to land where convective rainfall dominates. Seasonally the diurnal variation of rainfall, convection, and PFs over the region have greater amplitude during DJF (December, January, and February) than JJA (June, July, and August). Given the MC's critical role in the global climate, examining variations in these cycles with respect to the Madden–Julian Oscillation and equatorial waves may ultimately lead to improved subseasonal weather forecasts.

Description: The RegCM4 downscaling simulations show a good performance in capturing the observed autumn rainfall in West China and related extremes. The RegCM4 ensemble projects an increase (a decrease) of autumn rainfall amount and wet days in northwestern (southeastern) West China under RCP4.5 by the middle and the end of this century. The projected increase in northwestern West China is associated with the enhancement of zonal water vapour transport and atmospheric unstable stratification, while the decrease in southeastern West China is related to the reduction of moisture supply. Ensemble projected percentage changes (relative to 1986–2005) in (a, b) rainfall amount and (c, d) wet days over West China during autumn of (left‐hand panels) 2046–2065 and (right‐hand panels) 2080–2099. Hatched regions indicate above the three fourth ensemble members agree on the sign of changes. Autumn rainfall in West China, the last rainy season of China, exerts profound impacts on the economic society, therefore, researches on its changes in the context of a warmer world are crucial for better adaptation to climate change. Using the dynamical downscaling performed by the regional climate model RegCM4 from four global climate models, this study firstly evaluated the fidelity of the RegCM4 simulations on the mean and extreme aspects of autumn rainfall in West China, and then projected their changes during the middle and the end of the 21st century under the RCP4.5 scenario. The evaluations indicate a good performance of the RegCM4 downscaling simulations in reproducing the observed spatial distribution of autumn rainfall amount, wet days, maximum consecutive 5‐day precipitation (RX5day), total extremely wet‐day precipitation (R95p), and consecutive dry days (CDD). Under the RCP4.5 scenario, relative to 1986–2005, the amount of autumn rainfall and the frequency of wet days are projected to increase in the northwestern part of West China and to decrease in its southeastern flank, concurrent with greater changes by the end of the 21st century than by the middle of the 21st century. Such an increase is closely associated with the enhancement of zonal water vapour transport and atmospheric unstable stratification, while the projected decrease is largely related to the reduction of moisture supply. Similar changes are also projected for the precipitation extremes Rx5day and R95p. Corresponding to the changing pattern of autumn rainfall, the CDD is projected to decrease in the northwestern part and to increase in the southeastern part of West China.

Description: Spatial distributions of the dominant PFT types in China generated from (a) the old GLC2000 and (b) the new vegetation parameters. Abstract Accurate vegetation cover data are important for realistic simulation of regional climate. The default vegetation parameters from Global Land Cover 2000, currently incorporated into global climate models and used in regional climate model RegCM, are not realistic for China, which may have contributed to serious bias in surface climate simulation. In this study, a new set of vegetation parameters considering the Plant Functional Type (PFT) fractions and the corresponding monthly leaf area index (PFT_LAI), were developed based on the land cover and MODIS LAI data sets. The regional climate model RegCM4.5 coupled with the land surface model CLM4.5 were utilized to test the performance of the new vegetation parameters by comparing simulations with observations using different surface parameters. The surface energy balance was analysed to examine the effects of changed vegetation parameters on regional climate. The results showed that the new parameters were more accurate than the GLC2000 parameters when describing the distribution of crops, grassland, and forests over China. The improved vegetation parameters reduced model biases for winter air temperature and precipitation over southern China by 0.9°C and 8%, respectively, and reduced the winter temperature and summer precipitation biases over northeastern China by approximately 0.7°C and 8%, respectively. More accurate surface albedo are the main reasons for reductions in model bias. However, certain biases, such as the cold and dry bias over the Tibetan Plateau, still remained in the simulation results using our new vegetation data.

Description: This study examines the 1950–2017 temporal changes in climate extremes in Israel. A thorough homogenization (detection and correction) routine was developed and implemented on long‐term daily records. Based on this new daily homogenized dataset, 38 extreme indices were calculated. For example, the trends of the warm spell duration index (WSDI) showed slight increases in some stations during the period 1950–2017, while for the last 30 years trends have been much stronger throughout the entire country. Trends in the WSDI during 1950–2017 (left panel) and 1988–2017 (right panel). Upward red triangles represent increasing trends. Significant changes (p ≤ .05) are indicated by filled triangles (unit: days/decade). Abstract This study examines the 1950–2017 temporal changes in climate extremes in Israel, which is located in the East Mediterranean (EM), a region which suffers from a scarcity of long and reliable datasets. It is well known that most long‐term records are affected by artificial shifts most commonly caused by station relocation, instrumental modification and local environmental changes. Therefore, for the first time, a thorough homogenization (detection and correction) routine was developed and implemented in the long‐term records. Consequently, a new daily adjusted dataset has been generated, including 34 temperature stations and 60 precipitation stations. Based on this comprehensive dataset, 38 extreme indices recommended by the Expert Team on Climate Change Detection and the Expert Team on Sector‐specific Climate Indices have been calculated. These indices will help various sectors to plan properly mitigation actions and adaptation for climate change, in addition to facilitating future studies for the EM. The results showed highly significant changes in temperature extremes associated with warming, especially for those indices derived from the daily minimum temperature (TN, 1950–2017), whereas the maximum temperature (TX) exhibited a similar increasing magnitude of the TN (~0.55°C/decade) in the last 30 years. The warming trends, which are non‐monotonic, seem to have been particularly strong since the early 1990s. The coastal area is characterized by higher heat stress during the nighttime, while mountains exhibit a strong tendency towards increasing temperatures during the noon hours. A reduction in the total precipitation amount and in the number of wet days with a tendency towards more intense wet days was found. Although all the regional trends of the precipitation indices were not statistically significant (p ≤ .05), they showed a fine spatial coherence.

Description: Recent declines of mean wind speed in the Swiss Alps lie well within the long‐term variability. Current regional climate model simulations with 12‐ to 50‐km grid resolution are mostly able to represent the seasonality of wind speed on the Swiss Plateau but miss important details in the Alps. No significant changes in mean wind speed are projected until the end of the 21st century, but major revisions of the results are possible with higher resolution simulations, in particular over complex terrain. Abstract Near‐surface seasonal and annual mean wind speed in Switzerland is investigated using homogenized observations, Twentieth Century Reanalysis (20CRv2c) data and raw model output of a 75 member EURO‐COoRdinated Downscaling EXperiment regional climate model (RCM) ensemble for present day and future scenarios. The wind speed observations show a significant decrease in the Alps and on the southern Alpine slopes in the period 1981–2010. However, the 20CRv2c data reveal that the recent trends lie well within the decadal variability over longer time periods and no clear signs of a systematic wind stilling can be found for Switzerland. The ensemble of RCMs shows large biases in the annual mean wind speed over the Jura mountains, and some members also show large biases in the Alps compared to station observations. The spatial distribution of the model biases varies strongly between the RCMs, while the resolution and the driving global model have less impact on the pattern of the model bias. The RCMs are mostly able to represent the seasonality of wind speed on the Plateau but miss important details in complex terrain related to local wind systems. Most models show no significant changes in near‐surface mean wind speed until the end of the 21st century. The model ensemble changes range from a 7% decrease to a 6% increase with an ensemble mean decrease of 1 to 2%. Due to model biases, the scale mismatch between model grid and station observations and the missing representation of local winds in the simulations, the changes need to be interpreted with utmost care. Future assessments might lead to major revisions even for the sign of the projected changes, in particular over complex terrain.

Description: Climate data may contain inhomogeneities of two different types: free floating Brownian motion and random deviations from a fixed baseline. The variance of difference time series between two climate stations is analysed for different time lags. In this way, the two signals can be separated and quantified. Random‐deviation‐type inhomogeneities produce a saturating exponential function; Brownian motion breaks cause a linear increase, whereas the noise affects a lag independent offset. U.S. climate stations include a strong Brownian motion component. Abstract Climate data are affected by inhomogeneities due to historical changes in the way the measurements were performed. Understanding these inhomogeneities is important for accurate estimates of long‐term changes in the climate. These inhomogeneities are typically characterized by the number of breaks and the size of the jumps or the variance of the break signal, but a full characterization of the break signal also includes its temporal behaviour. This study develops a method to distinguish between two types of breaks: random deviations from a baseline and Brownian motion. Strength and frequency of both break types are estimated by using the variance of the spatiotemporal differences in the time series of two nearby stations as input. Thus, the result is directly obtained from the data without running a homogenization algorithm to estimate the break signal from the data. This opens the possibility to determine the total number of breaks and not only that of the significantly large ones. The application to German temperature observations suggests generally small inhomogeneities dominated by random deviations from a baseline. U.S. stations, on the other hand, also show the characteristics of a strong Brownian‐motion‐type component.

Description: Tanzanian mangroves are susceptible to climate variability and change. The purpose of this research is to derive an improved understanding of the climatic features and forcing mechanisms that in turn support the availability of critical mangrove ecosystem services in coastal Tanzania. Specifically, this study identifies the main monsoonal patterns of hydroclimatic variables across coastal Tanzania and adjacent areas in which mangrove vegetation is important and endangered. Abstract Climatic controls regulate the coupled natural and human systems in coastal Tanzania, where mangrove wetlands provide a wealth of ecosystem services to coastal communities. Previous research has explained the precipitation seasonality of eastern Africa in terms of the local monsoons. This research examines a wider range of hydroclimatic variables, including water vapour flux, evapotranspiration, runoff, and ocean salinity, and the sources of low‐frequency atmosphere–ocean variability that support mangrove productivity and associated ecosystem services. Results confirm previous work suggesting that the northeast monsoon (kaskazi) largely corresponds to the “short rains” of October–December and extends through February, while the southeast monsoon (kusi) corresponds to the “long rains” of March–May and the drier June–September. The Indian Ocean Dipole (IOD) and, to a lesser extent, El Niño–Southern Oscillation (ENSO) are important modulators not only of precipitation (as has been shown previously) but also of water vapour flux, evapotranspiration, runoff, and salinity variability. During kaskazi, positive (negative) hydroclimatic anomalies occur during positive (negative) IOD, with a stronger IOD influence occurring during its positive phase, when seasonal anomalies of precipitation, evapotranspiration, and runoff exceed +50, 25, and 100%, and nearby salinity decreases by 0.5 practical salinity units. During kusi, the contrast between the positive and negative IOD modes is subtler, and the pattern is dictated more by variability in “long rains” months than in the dry months. The coincidence of the positive IOD and El Niño amplify this hydroclimatic signal. Because previous work suggests the likelihood of increased tendency for positive IOD and increased moisture variability associated with El Niño events in the future, wetter conditions may accompany the kaskazi, with less change expected during the kusi. These results advance understanding of the key environmental drivers controlling mangrove productivity and wetland spatial distribution that provide ecosystem services essential to the well‐being of the human population.

Description: Partial correlation coefficient for JFM cyclone frequency versus El Niño (excluding IOD). Correlation (shaded), (hatched) for (a) CFS_C1 (b) CFS_C2 (c) CFSR (d) ERA_INT. This manuscript shows that the climate version of NOAA's CFS model is capable of reproducing a seasonal climatology of storm track frequency and intensity for the Southern Hemisphere. With regards to teleconnections, the model shows a good response to ENSO and the AAO, but not to the IOD. The model's insufficient response to the IOD is due to its inability to capture the IOD's high wave‐number as demonstrated by composite analysis of the 200 hPa stream function. Abstract Evaluating cyclone tracks in climate models represents an excellent way to evaluate their ability to simulate synoptic‐scale phenomena. Cyclone tracks were generated from two free‐runs from the National Centers for Environmental Prediction Climate Forecast System (CFSv1) model for the Southern Hemisphere (SH), and compared with cyclone tracks generated from CFS reanalysis and ERA Interim data from 1979 to 2016. It is demonstrated that CFSv1 is capable of simulating realistic SH cyclone track climatology for both intensity and frequency. The CFSv1's ability to capture interannual variability is also highlighted. Specifically, the impacts of the Antarctic Oscillation (AAO), El Niño/Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) on cyclone track frequency and intensity were assessed. The CFSv1 exhibits an annular structure in frequency and intensity in response to the AAO. For the reanalysis data, AAO cyclone frequency is less annular in the South Pacific especially during Austral Summer, possibly due to a positive trend in the AAO in recent decades. To test this, a reconstruction of cyclone tracks for ERA40 data from 1958 to 2001 produces a more annular structure. The response of cyclone tracks due to ENSO is fairly robust, with the reanalysis datasets and one member of the CFSv1 producing a pattern of cyclone variability consistent with the Pacific South American teleconnection pattern. In contrast, the cyclone frequency and intensity response to the IOD shows little agreement between reanalysis and CFSv1. An examination of 200‐hPa stream function supports the CFS model producing a teleconnection response to ENSO but not the IOD, possibly due to anomalous heating generated from the IOD being too small. Our results suggest that assessing interannual variability of cyclone tracks in current state‐of‐the‐art models be done with large‐number ensembles when possible, especially when considering sensitivity to initial conditions and the magnitude of external forcing.

Description: This study assessed climate change impacts on the dependence between precipitation and temperature (P–T dependence) over global land areas from observations, and the performance of the current‐generation Coupled Model Intercomparison Project (CMIP5) models in simulating these patterns. In general, the CMIP5 models perform well in simulating the overall pattern of the P–T dependence (around 87–94% of global land areas). However, they capture both the sign and change of P–T dependence for relatively small regions (around 41–47% of global land areas). Abstract Precipitation and temperature are physically related to each other with significant dependences that vary with seasons at regional and global scales. The dependence between precipitation and temperature (P–T dependence) plays a central role in characterizing the joint behaviour of the two variables. Along with extensive studies on the variation in the mean, variance, or extremes of precipitation and temperature under global warming, it is of particular interest to understand the climate‐change impacts on the covariability of precipitation and temperature. The aim of this study therefore is to assess climate change impacts on the P–T dependence over global land areas from observations, and the performance of the current‐generation Coupled Model Intercomparison Project (CMIP5) models in simulating these patterns. In general, the CMIP5 models correctly simulate the overall pattern of the P–T dependence (around 87–94% of global land areas). However, they capture both the sign and change of P–T dependence for relatively small regions (around 41–47% of global land areas). Results from this study may provide useful insights for the future development of climate models.

Description: (a) Map of homogeneous regions and meteorological subdivisions of India, and (b) the mean summer monsoon rainfall distribution using Indian Institute of Tropical Meteorology/India Meteorological Department (IITM/IMD) data. Abstract In global warming, India is adversely affected by weather extremes. Summer monsoon contributes about 70% of annual mean rainfall to India by mode of an ensemble of synoptic disturbances and intense events. These events bring extreme amounts of rainfall in very few days. We have studied the anomalies of rainfall events for wet days (〉0 mm rainfall), dry days (=0 mm), little rainfall days (〉0 to 〈20 mm), moderate rainfall days (≥20 mm to 〈60 mm), heavy rainfall days (≥ 60 to 〈100 mm) and very heavy rainfall days (≥ 100 mm) during the period, 1901–1930; 1931–1960; 1961–1990 and 1991–2015 using India Meteorological Department (IMD) rainfall data. In the present scenario, even though an extensive increase in frequency of lows forming in the Bay of Bengal is observed, only a few are intensified into depressions and above stages. Alternatively, monsoon circulation is trailing its strength; therefore extreme (heavy and very heavy rainfall) events came into existence to balance the mean rainfall activity. The rise in these events may be due to an increasing inconsistency of the strength of low‐level jet, an increase of sea surface temperature over the Arabian Sea and dynamic flow of moisture supply to inland from neighbourhood seas. An increase in the frequency of extreme rainfall events is seen over Konkan & Goa, Madhya Maharashtra, Jammu & Kashmir, central Northeast India (CNEI) and west central India (WCI) in the recent years (25 years) compared to previous 9 decades. Whereas, a decrease in the frequencies of little and moderate rainfall events are observed over the parts of the Western Ghats, Northeast India, WCI and CNEI. There is an increasing trend in the inconsistency of daily rainfall activity due to an increase in the frequency of extreme events in the global warming era.

Description: Twenty‐five‐year low‐pass‐filtered time series of estimated June through December sum precipitation (black line), mean June–December NINO4 index (dark grey), mean January–March AO index (dashed black line), and mean February–April NAO index (light grey) over the period 1825–2012 (a); 25‐year centred moving correlation coefficients of the estimated precipitation with mean June–December NINO4 index (dark grey), mean January–March AO index (dashed black line), and mean February–April NAO index (light grey) over the same period (b). Abstract An improved knowledge of long‐term climatic variations over the Altai‐Dzungarian region will increase our understanding of the current climate and help to predict the effects of global warming on future water availability in this region. We sampled 77 Larix sibirica Ledeb. trees at upper and lower treelines in the southern Mongolian Altai mountains and reconstructed temperature and precipitation for longer periods than previous studies from this area. We reconstructed mean June–July air temperatures for the period 1402–2012 and June–December precipitation for the period 1569–2012 based on tree ring width chronologies. The temperature and precipitation reconstructions explain 39.7 and 41.3% of the respective station observation variance during the common periods. The precipitation reconstruction shows alternating wet and dry conditions during the Little Ice Age (1580–1874) followed by more stable conditions until a late 20th century wetting. The temperature reconstruction attributes the warmest period to the 20th century, which follows cooler periods related to volcanic and low solar activities during the Little Ice Age. Long‐term climatic variation and change over the Altai‐Dzungarian region is inferred from the analysis of the combined temperature and precipitation reconstructions for the common period 1580–2012. Accordingly, this region has become warmer since 1875 as the number of warm/moist and warm/dry years increased by 2 and 14%, respectively, while the number of cool/moist and cool/dry years both decreased by 8% compared to the Little Ice Age. Our findings also reveal a late 20th century cool and wet period, which has also been observed across other mountainous areas of China and Nepal. This period was most probably caused by volcanic‐induced cooling and coincided positive phases of the Arctic Oscillation and North Atlantic Oscillation promoting an intensified subtropical westerly jet and a positive summer rainfall anomaly over the Altai‐Dzungarian region.

Description: Strong nonlinear relationship for snow cover duration with mean temperature in GB, but not with precipitation. Relationship used to map average changes in snow cover duration in recent decades and into the future. Locational analysis shows important geographical and altitudinal differences in inter‐annual variability that are related to dominant synoptic influences. Abstract Snow cover is an important indicator of climate change but constraints on observational data quality can limit interpretation of spatial and temporal variability, especially in mountain areas. This issue was addressed using archived data from the Snow Survey of Great Britain to infer key climate relationships which were then used to reference larger‐scale patterns of change. Data analysis using nonlinear (logistic) regression showed average changes in yearly snow cover were strongly related to mean temperature rather than precipitation values. Inferred change shows long‐term decline in average yearly snow cover with greatest declines in some mountain areas, notably in northern England, that can be related to their position on the most temperature‐sensitive segment of the logistic curve. Further declines in snow cover were projected in the future: a central ensemble projection from HadRM3 climate model showed average yearly snow cover predominantly confined to Great Britain mountain areas by the 2050s. However, inter‐annual variability means some years can deviate significantly from average snow cover patterns. Site‐based analysis showed this variability has distinctive geographical variations and different influences for mountains compared to adjacent valleys. Comparison of inter‐annual variability with Lamb weather‐type frequency and North Atlantic Oscillation index shows the influence of large‐scale airflow patterns on snow cover duration. Most notable is the role of northwesterly and northerly flows in explaining snowy years on mountains exposed to that direction, compared to influence of easterly flows at lower levels. Future changes will therefore depend on dominant annual/decadal circulation patterns in addition to long‐term declines from climate warming.

Description: Abstract The 50‐year pentad mean simulation outputs of an atmosphere–ocean coupled general circulation model, the Scale INTeraction EXperiment (SINTEX)‐the Frontier Research Center for Global Change (FRCGC) model (SINTEX‐F model), are used to understand processes of the northward‐propagation of intraseasonal oscillations (ISOs) over the South China Sea (SCS) during the pre‐monsoon season. A quite different mean state is found in the SCS before the monsoon onset relative to that of summertime, which is consistent with the observations. Then, 18‐year SCS ISOs, with a relatively larger strength and significant northward component, are selected, and the composite and lead–lag correlation analyses are conducted to explore a possible mechanism for the northward propagation of ISOs in the pre‐monsoon period. Two mechanisms contribute to the northward propagation of the SCS ISOs. One is the vorticity advection by the barotropic southerly mean flow. First, a barotropic vorticity is induced by vertical advection inside the convection centre. During the pre‐monsoon period, the west sides of the upper tropospheric high and the western Pacific subtropical high cover the SCS, thus, there are southerly flows both in the upper and lower troposphere over the SCS. Then the barotropic vorticity induced inside the convection centre shifts to the north because of the advection by the barotropic southerly mean flow. The other is the planetary boundary layer (PBL) moisture advection by the mean flow that lead to a meridional phase leading of the ISO humidity. The northward shift of the PBL moisture promotes a convective instability ahead of the convection centre and leads to the northward propagation of ISOs over the SCS.

Description: The position and intensity of the Atlantic High can have a strong influence on seasonal rainfall. When this anticyclone is intensified, rainfall increases in central Argentina in winter and in northern Cuyo during spring. In addition, when it is displaced north of its mean position, autumn rainfall is favoured in Patagonia. These parameters of the anticyclone were also used to foretell seasonal rainfall anomalies and results suggest that there is some predictability, especially during spring in almost every region. Abstract The objective of this work is to quantify the influence of the position and intensity of the Atlantic High (AH) on seasonal rainfall in Argentina. Monthly precipitation data in 68 stations from the National Meteorological Service of Argentina, the Secretariat of Water Resource and the Territorial Authority of the Limay, Neuquén, and Negro rivers basins (AIC) were used. Correlation was the methodology used to identify the link between seasonal rainfall and some indices especially defined to detect the position and intensity of the anticyclone. Precipitation composites of the years when the value of these indices was extremely high (greater than their second tercile) and extremely low (lower than their first tercile) were built for every season. They were analysed and compared with humidity anomaly transport composites for the same sets of years in order to study circulation patterns associated with seasonal rainfall anomalies. Results indicate that when the AH is intensified, winter rainfall increases in central Argentina and spring rainfall increases in northern Cuyo region. When the AH is displaced towards the north of its mean position, autumn rainfall is favoured in the northeast and central and south Patagonia, meanwhile winter and spring rainfall decreases in central Argentina. When the AH is displaced towards the east of its mean position, summer rainfall decreases in central Andes. Finally, AH indices were used to detect the influence on rainfall in advance. Correlations between seasonal rainfall and the defined indices that characterize the anticyclone in the previous month suggest that there is some rainfall predictability, especially during spring in almost every region in Argentina.

Description: Human activity has increased warming that gives rise to heat‐related deaths by about a degree in all continents, projected to increase to 3° by 2100. There are at least 10 days of dangerous warming in a year, but more in tropical regions. If societies were to adapt to the present‐day climate, warming associated with increases in mortality would reduce by about a degree in 2100, but the effect on the frequency of heat mortality days would be more moderate. In the figure, maps of high risk warming (HRW) and high risk days (HRD) index trends for the periods 1900–2020 (a, c) and 1900–2100 (b, d). The plotted trends show the mean estimate of 10 CMIP5 models. The model simulations were extended to the end of the century with the RCP4.5 scenario. Abstract Increasing extreme temperatures linked to human influence amplify thermal stress and can lead to decreases in work productivity and increases in heat‐related mortality. However, studies assessing in a formal statistical way the contribution of climate change to such impacts remain sparse. Two new indices are introduced here that measure the effect of anthropogenic climate change on the intensity and frequency of health‐relevant heat extremes. Maximum daily temperature data from observations and climate models are used to compute annual index values in different regions around the world. The models employed in the study are evaluated against observational data and only the 10 best are retained for the analysis. Human‐induced warming that leads to an increase in heat‐related deaths has reached about a degree in all continents and is projected to exceed 3° by 2100. All regions currently experience at least 10 additional days per year when thermal deaths are expected to occur, but the number is several times higher in warmer tropical regions, where it is estimated to exceed 100 days by the end of the century. Significant positive trends may also arise in smaller‐scale areas, as shown for central England. Adaptation to the warmer present‐day climate would take the edge off the intensity of warming conducive to a rise in heat mortality by 2100, reducing it by about a degree, but would have a more moderate effect on the frequency of heat mortality days. Index values are also computed with data from the Hadley Centre's attribution system, and annual assessments of the associated impacts are made, which are envisaged to become part of a developing climate service. A first application to the United Kingdom for two recent years demonstrates the kind of attribution information that can be made available to users.

Description: The TDN decreases with increasing the solar elevation angle in a large scale. Understanding the temperature difference between neighbouring days (TDN) is beneficial for reducing the harmful impacts of sudden temperature changes on human health and establishing adaptation policies considering global TDN characteristics. This study analyses the spatiotemporal characteristics of TDN in Mainland China on the basis of the daily observation data from 839 stations in 1961–2017. Results show that the TDN increases with the latitude from south to north. Meanwhile, TDN in winter is higher but lower in summer. These characteristics potentially arise from the variations in solar elevation angle, that is, a decreased TDN corresponds to an increased solar elevation angle in most parts of China. Areas with high solar elevation angle are usually characterized by low TDN, which implies that temperature is stable for neighbouring days in areas with high solar elevation angle. Moreover, the variation rate of the annual mean TDN is negative in most parts of China, especially in the north. These findings are expected to provide an overview of the spatiotemporal characteristics of TDN and support future studies on the impacts of TDN variations on regional environmental changes.

Description: Year 2016 is the warmest followed subsequently by year 2015 and 2014. The consecutive emergence of the record three warmest years posed a debate on whether the hiatus has faded away. In the study, we present an analysis of the global annual mean surface temperature anomaly in 2014, 2015 and 2016 by using the EEMD method. We found that the interannual component contributed less to total surface temperature anomalies in 2014–2016 than in 1998, while the Decadal‐to‐Multidecadal (DM) component and the long‐term warming trend contributed more. The larger contribution from the DM component and the long‐term warming trend implies that warmer years like 2014–2016 may occur more frequently in the near future. We conclude that the so‐called warming hiatus has faded away. Abstract We present an analysis of the global annual mean surface temperature anomaly in 2014, 2015, and 2016 based on five datasets of historical observational records of surface temperature. These three years are the three warmest on record in all but one of the datasets. The largest warming occurred over land, especially at high latitudes. Since the strong El Niño event that occurred in 2015/2016 was similar to the 1997/1998 El Niño, we compared the 2014–2016 period with 1998, the warmest year in the 20th century. The contribution to the annual mean surface temperature anomaly of climate variations at different time scales was assessed using ensemble empirical mode decomposition. Results based on the HadCRUT4 dataset suggest that the interannual component may have contributed an anomaly of −0.01°C in 2014, 0.12°C in 2015, and 0.06°C in 2016. These values are substantially lower than the contribution in 1998 (0.18°C). In comparison, the combined contribution of the decadal‐to‐multidecadal (DM) component and the long‐term warming trend was 0.64°C in 2014, 0.70°C in 2015, and 0.77°C in 2016, which are substantially greater than that in 1998 (0.41°C). Similar results were obtained using the other four datasets. The larger contribution from the DM component and the long‐term warming trend implies that warmer years like 2014–2016 may occur more frequently in the near future. We conclude that the so‐called warming hiatus has faded away.

Description: The probable maximum precipitation (PMP) in Thailand was estimated using generalized method and Hershfield's statistical method. For the generalized method, most PMP values were found higher in the east and decreased towards the west. PMP estimation using the generalized method is 10% higher than that of the statistical method. The study results were applied to estimate the PMP of six dams in Thailand, which showed that the generalized method resulted in PMPs that were closer to the design values. Abstract The objective of this research was to estimate probable maximum precipitation (PMP) for the duration of 1 to 3 days in Thailand. Rainfall data were collected from 217 meteorological stations. Two methods for estimating PMP were applied: the generalized method which analysed actual storms data using meteorological process, and the Hershfield's statistical method which applied the frequency analysis of the annual maximum rainfall. Both methods were commonly used and were recommended by the World Meteorological Organization (WMO) for estimating PMP. PMP estimation results were presented as isohyetal maps. For the generalized method, most PMP values were found higher in the east and decreased towards the west. However, there was no clear trend with the statistical method in the PMP's spatial distribution. An analysis of the 3‐day PMP averages showed that PMP estimation using the generalized method is 10% higher than that of the statistical method. The study results were then applied to estimate PMP of six large dams in Thailand and compared with the ones used in the design of the spillways. It was found that PMP estimation based on the generalized method was closer to the value used in the spillway design of those dams than PMP estimation based on the statistical method.

Description: The homogenization of snow depth time series (in the figure for the station of Bad Gastein, Austria) is very important to perform reliable climatological analysis and investigate the sensitivity of snow to the ongoing increasing temperatures in the Alps. In this work, we compared the homogenization methods HOMOP and standard normal homogeneity test using Austrian mean seasonal snow depth time series. The results are quite encouraging since the two methodologies agree in most of the cases, showing a good reliability in detecting breakpoints in the snow depth time series. Despite the importance of snow in alpine regions, little attention has been given to the homogenization of snow depth time series. Snow depth time series are generally characterized by high spatial heterogeneity and low correlation among the time series, and the homogenization thereof is therefore challenging. In this work, we present a comparison between two homogenization methods for mean seasonal snow depth time series available for Austria: the standard normal homogeneity test (SNHT) and HOMOP. The results of the two methods are generally in good agreement for high elevation sites. For low elevation sites, HOMOP often identifies suspicious breakpoints (that cannot be confirmed by metadata and only occur in relation to seasons with particularly low mean snow depth), while the SNHT classifies the time series as homogeneous. We therefore suggest applying both methods to verify the reliability of the detected breakpoints. The number of computed anomalies is more sensitive to inhomogeneities than trend analysis performed with the Mann–Kendall test. Nevertheless, the homogenized dataset shows an increased number of stations with negative snow depth trends and characterized by consecutive negative anomalies starting from the late 1980s and early 1990s, which was in agreement with the observations available for several stations in the Alps. In summary, homogenization of snow depth data is possible, relevant and should be carried out prior to performing climatological analysis.

Description: Heat waves impact a large swath of the human population by increasing mortality and morbidity and stressing the energy infrastructure. Furthermore, cities amplify these impacts by being subject to synergistic urban–atmosphere feedback processes that enhance the urban heat island phenomenon. Here, we present highly detailed results of statistical and dynamically downscaled projections of heat wave metrics for New York City using a state‐of‐the‐art model that incorporates highly detailed urban canopy parameters and surface processes, highlighting their geospatial heterogeneity. Abstract Heat waves impact a wide array of human activities, including health, cooling energy demand, and infrastructure. Cities amplify many of these impacts by concentrating large populations and critical infrastructure in relatively small areas. In addition, heat waves are expected to become longer, more intense, and more frequent in North America. Here, we evaluate combined climate and urban surface impacts on localized heat wave metrics throughout the 21st century across two emissions scenarios (RCP4.5 and RCP8.5) for New York City (NYC), which houses the largest urban population in the United States. We account for local biases due to urban surfaces via bias correcting with observed records and urbanized 1‐km resolution dynamical downscaling simulations across selected time periods (2045–2049 and 2095–2099). Analysis of statistically downscaled global model output shows underestimation of uncorrected summer daily maximum temperatures, leading to lower heat wave intensity and duration projections. High‐resolution dynamical downscaling simulations reveal strong dependency of changes in event duration and intensity on geographical location and urban density. Event intensity changes are expected to be highest closer to the coast, where afternoon sea‐breezes have traditionally mitigated summer high temperatures. Meanwhile, event duration anomaly is largest over Manhattan, where the urban canopy is denser and taller.

Description: Precipitation deficits in the southern Peruvian highlands often occur during El Niño events, but not all El Niño events lead to droughts. El Niño events with droughts show stronger upper‐level zonal wind anomalies, whereas El Niño events without droughts do not show such anomalies. Atmospheric simulations suggest that also the flavour of El Niño events matters, which needs to be further corroborated by model studies and palaeoclimatological research. Abstract Precipitation deficits remain a concern to the rural population in the southern Peruvian highlands and knowledge about their occurrence is lacking because of scarce data availability. For mountainous regions with sparse station networks, reanalyses can provide valuable information; however, known limitations in reproducing precipitation are aggravated due to unresolved topographical effects. In this study, we assess in a first step the representation of precipitation during the rainy season (January–February–March) in seven reanalysis data sets in comparison to a newly generated gridded precipitation data set for Peru. In a second step, we assess summer precipitation deficits in Peru during the second half of the 20th century. In the reanalyses data sets, we find biases strongly influenced by the topography of the models and low correlations for the rainy season. Thus, reanalyses do not solve the problem of data scarcity for this region either. Furthermore, we confirm that El Niño is not a sufficient stratification criterion for precipitation deficits during the rainy season (JFM) in the southern Peruvian highlands. Based on observational records and reanalyses, a considerable fraction of inter‐annual variability of precipitation can be explained through upper‐tropospheric zonal wind anomalies. Westerly wind anomalies, often related to the warming of the troposphere during an El Niño event, lead to dry conditions, but not all El Niño events produce these westerly wind anomalies. Atmospheric simulations indicate differences between precipitation deficits in central Pacific and eastern Pacific El Niño flavours, which cannot be addressed in observations due to reduced record length: Droughts in the southern Peruvian Andes during eastern Pacific El Niño events seem to be related to a stronger warming in the troposphere above the central Pacific ocean, whereas this is not the case for droughts during central Pacific El Niño events. These results, however, need to be further corroborated by model studies and palaeoclimatological research.

Description: The robust negative correlation between the Siberian High (SH) intensity and winter surface air temperature (SAT) in Northeast China has been widely used for winter climate seasonal forecasting. However, this traditional viewpoint varies when considering the changes in SH spatial extension during boreal winter. Here, a newly defined SH index representing both SH intensity and spatial extension shows two distinct influences of a stronger SH on the SAT in Northeast China. The stronger SH with a decreasing eastern edge, which features a meridional see‐saw dipole of abnormal circulations centered over the Arctic and Siberia, respectively, results in a warmer winter over Northeast China. In this situation, the decreasing SH is linked to the enhanced Arctic Polar vortex via a tropospheric‐stratospheric interaction. However, a stronger SH with an expanding eastern edge is coupled with the East Asian trough (EAT) and results in a colder winter in Northeast China. The SH and EAT anomalies are embedded in a zonally oriented wave‐train originating from the North Atlantic in the troposphere. Because of the diversity of impacts, the SH intensity and zonal extension should be jointly considered for seasonal forecasting of winter SAT anomalies in Northeast China. This article is protected by copyright. All rights reserved.

Description: Abstract Although the 2015–16 El Niño was expected to cause a severe Paraná River flood similar to that associated with the 1997–98 El Niño, the Paraná flood of 2016 was considerably weaker than that of 1998. Here we explain this by analyzing Paraná discharge data (1904–2017) to assess the contributions of discharge cycles with different frequencies and with different associated climate modes to the formation of the 1998 and 2016 floods. We found that the 2016 flood was relatively weak mainly because the following Paraná discharge cycles contributed less to form the 2016 flood than to form the 1998 flood: (i) a 3–5 year cycle linked to El Niño/Southern Oscillation (ENSO), (ii) a 9 year cycle related to the North Atlantic Oscillation (NAO), and (iii) a 31–85 year cycle associated with the Interdecadal Pacific Oscillation (IPO). This finding suggests that, besides ENSO, two other climate modes (NAO and IPO) may have acted to weaken the 2016 flood. This article is protected by copyright. All rights reserved.

Description: Abstract Soil moisture (SM) is a key factor in the exchanging process of the hydrological cycle, which is rather difficult to be directly observed. In‐situ measurements on SM, however, are subjected to the point scales. Use of land surface models (LSMs) has been a promising way to explore SM variations, especially for poorly gauged high mountain regions such as the Yarlung Zangbo River (YZR) basin located in Southeast Tibetan Plateau (TP). This study made an attempt to investigate the spatiotemporal variations of SM and discuss hydro‐meteorological factors impacting SM evolution based on the Global Land Data Assimilation Systems (GLDAS) outputs during the period 1970–2009. Results show that: (1) GLDAS datasets have high agreement and low bias with in‐situ measurements and consistent spatial distribution with ERA reanalysis datasets; (2) an abrupt change of SM is detected in 1992 and a significantly decreasing trend happens during 1970–2009 and 1993–2009; (3) precipitation is the dominant climatic factor controlling SM during the period of 1970–2009, whereas surface air temperature is the critical factor for the significant change of SM. Due to the significantly increasing of surface air temperature since 1992, its impact on SM increased by ~91% than that before 1992. Evapotranspiration (ET) and snow water equivalent (SWE) are also taken into consideration, showing relatively weak influence on SM, which may be due to the dynamic process of coupled SM‐ET or low snow cover area fraction across the YZR basin. Findings in this study have important implications for SM variations in poorly gauged high mountain regions which may largely influences downstream water availability. This article is protected by copyright. All rights reserved.

Description: Abstract Previous studies indicated that the meridional (northerly or southerly) wind anomalies over East China play an important role in modulating interannual variation of the winter haze pollution in the North China Plain (NCP) mainly via changing surface wind speed and humidity. Here, we report that the factors for the formation of the meridional wind anomalies over East China related to interannual variation of winter haze pollution experienced a significant interdecadal change around the mid‐1990s. Before the mid‐1990s, two upstream atmospheric wave trains contribute to generation of the meridional wind anomalies over East China via inducing significant geopotential height anomalies over northeast Asia. The first occurred over mid‐latitude Eurasia and propagated eastward into East Asia, resembling the East Atlantic‐west Russia (EAWR) pattern. The second propagated eastward along the subtropical Asian jet. Furthermore, during this period, the change in the intensity of East Asian trough (EAT) was closely linked with interannual variation of the winter haze variation in the NCP. By contrast, after the mid‐1990s, the atmospheric wave train along the Asian subtropical jet was not observed. Furthermore, the connection between the EAT intensity and the winter time NCP haze variation was weak. The mid‐latitude EAWR‐like pattern as well as the El Niño‐Southern Oscillation‐related sea surface temperature anomalies in the tropical Pacific were possible factors that explain the meridional wind anomalies over East China. Understanding the change in the atmospheric anomalies contributing to interannual variation of the haze are essential for the prediction of haze in the NCP. This article is protected by copyright. All rights reserved.

Description: ABSTRACT Clouds remain as the main uncertainty source in climate studies. Specifically, over urban and other polluted regions, where their properties are subject to anthropic influence, more studies are needed. This study focuses on clouds at the Southeast of Brazil, especially on the diurnal and annual cycles, and trends of cloud amount in one of the biggest metropolitan areas of the world, the Metropolitan Area of São Paulo (MASP). For this purpose, 59 years of visual observations of cloud amount and type, made from surface at MASP, are analyzed. In addition, trends of total cloud amount in the state of São Paulo, Brazil, are estimated using 30 years of total cloud amount, adding 9 synoptic meteorological stations. In order to assess the reliability of the visual observations, annual‐average time series of cloud amount are compared with ones from the International Satellite Cloud Climatology Project (ISCCP) and solar irradiation reaching the surface. The results show that the diurnal cycle of total cloud amount at MASP is highly influenced by low clouds, especially stratiform clouds, which are the predominant cloud type. Moreover, the seasonality of the diurnal cycles was also analysed, with higher amplitude and late‐afternoon occurrence in spring‐summer. In winter‐autumn, the maxima of cloud amount diurnal cycles occur at early morning with lower amplitude. The low cloud amount at MASP has undergone an increase, especially in the last 30 years, evidenced when compared annual‐average time series of visual observations and solar irradiation. In contrast, a decrease of total cloud amount in the state of São Paulo, mainly since the beginning of 1990s, is observed in ISCCP and visual observations databases. This article is protected by copyright. All rights reserved.

Description: Abstract In Assam, Local Severe Convective Storms (LSCS) are recognised as exceptionally powerful and destructive meteorological events resulting in both death and loss of property, as well as livelihood. A valuable aid to assessing and managing LSCS lies in a reliable database of historical severe storms. In this paper, we investigate the temporal and spatial characteristics of Local Severe Convective Storms(LSCS) in the state of Assam lying in the North Eastern Province of India and provide a climatology of LSCS for the state, with respect to: distribution, storm types, frequency, seasonality, and time of occurrence. This was accomplished by developing a LSCS database through a systematic scanning of newspapers and other available sources. This historical database on LSC S dating from 1962‐2016 was used to identify the areas where the threat and risks from these storms is maximum. Our findings show that LSCS occur throughout the state of Assam, but there are unique geographical areas where the propensity for the occurrence severe to intense local convective storm is much higher. From the monthly distribution of LSCS events, as have been found by previous researchers, the most active month is April. The monthly distributions of hail follow similar pattern as the total number of LSCS events, however seasonality of lightning differ from other LSCS categories. The probability of LSCS is not uniform throughout the day and tend to reach their most vigorous development during the latter part of the evening and night hours. The observed seasonal pattern of LSCS day's distribution corresponds with the time of the year when convective heating of the lower atmosphere is at its highest in the region, thereby increasing instability of the atmosphere. In addition, climatological mean wind at upper level (200hPa) show influence of subtropical westerlies and at 925 hPa indicate possible low‐level moisture transport for the peak LSCS months. Therefore, high storm frequencies are to be anticipated. Although November, December and January show the lowest frequency during the entire year, it is still surprisingly high considering that convectional heating and instability of the atmosphere has decreased substantially by then. The results from this study could be applied to produce a hazard map of LSCS for the state of Assam. Such a hazard map will benefit numerous stakeholders, in particular, to direct disaster management authority in terms of interventions for LSCS risk in the state of Assam. This article is protected by copyright. All rights reserved.

Description: ABSTRACT This study examines the meteorological and climatological features of the Inami‐kaze in the Tonami Plain, the strongest local winds in Japan. The plain faces the Sea of Japan on the north side, while the remaining three sides are surrounded by high mountain ranges. An interview survey to local residents and the distribution of windbreak trees both indicate that the Inami‐kaze is localized to a surprisingly small region near the Inami town (about 6‐km parallel to the mountain ridge and 3‐km perpendicular to it), at the base of a very small saddle‐like topography in the mountain ridge. Wind statistics with a dense observation network during 2006–2014 (28 Inami‐kaze events) supports the above finding. Indeed, the wind speed is mostly (95 %) below 11 m/s at the Tonami town, just 5 km away from the Inami town, while the Inami‐kaze (≥ 15 m/s) blows at the Inami town. Such a spatial pattern should be formed as a result of downslope windstorm with hydraulic jumps, which is indicated from the internal Froude number change across the mountain (0.9 at the upstream, 1.4 at the mountain top and 0.3 at downstream). The required synoptic atmospheric condition for the Inami‐kaze is the pressure pattern “extratropical cyclone in the Sea of Japan.” The sufficient condition is the SSW–SSE wind (≥ 25 m/s) or south wind (around 20 m/s) at the 800‐pressure level over the mountain chain, indicating the allowed directional window is very narrow. On the other hand, the Inami‐kaze has an aspect of foehn. While the Inami‐kaze occur, the potential temperature at the Inami town tends to be higher by 4.7 K on average than in the windward plain on the Pacific Ocean side. This article is protected by copyright. All rights reserved.

Description: Abstract The relationship between the character of atmospheric blocking and surface temperature has not been studied in depth for Turkey. Here, these relationships are investigated for the period 1977 ‐ 2016. The seasonal mean temperature anomalies for all stations during blocked days varies between ‐2.1o C and 0.8o C. There are four main patterns representing the mean seasonal temperature anomalies for all stations during blocked and non‐blocked days. The annual cycle for each group is nearly opposite, and this indicates the impact of blocking on observed temperature, as blocked days comprised 30% of the study period. When focusing on the spatial distribution of mean seasonal anomalies, the winter and fall seasons show that, almost all stations have negative temperature anomalies although anomalies are close to zero during warm seasons (spring and summer). The composite analysis shows that the western part of the country is strongly affected by cold air advection during upstream blocking events and the eastern part of the country is affected by warm temperature advection for downstream blocking events. There is a statistically significant (95% confidence level) negative correlation between blocking intensity and temperature anomalies in all seasons except spring. There is no relationship between both blocking duration and longitudinal extent and the seasonal mean temperature anomaly except during winter, which has a significant negative correlation. The temperature anomaly distribution stratified by season shows that strong positive anomalies are rarely observed in all seasons. Only winter and spring were associated with very strong positive anomalies and only at a few stations. Rex‐type atmospheric blocking events are observed during the period of not only the maximum temperature anomaly but also for minimum anomalies. However, the location of the blocking event differed from the typical situation above, with the cold and warm events being located downstream and upstream of Turkey, respectively. This article is protected by copyright. All rights reserved.

Description: Abstract Changes in precipitation frequency can have a major impact on many different sectors including agriculture, tourism, and recreation. This study investigates trends in precipitation days at first‐order weather stations across the conterminous United States from 1951–2015. The Mann‐Kendall test and sliding window correlation analysis are used to examine trends over time. Future precipitation days are forecasted via usage of a stepwise auto‐regressive model. The Mann‐Kendall test found that the majority of the Northeast and Midwestern states show upward trends in precipitation days, while negative trends are located in the Southeast and in clusters throughout the Northwest. Sliding window correlation analysis was used to detect the decade when most of the change in precipitation days occurred. The northeastern United States had more significant changes during the earlier decades whereas the center part of the country had more significant changes in the later decades. Most stations saw more decades with positive (increasing) trends in precipitation days. Precipitation days are expected to increase for most of the United States into the future. This article is protected by copyright. All rights reserved.

Description: Abstract In this study, the wind and the surface waves in the Indian Ocean (IO) during 1979‐2017 are studied based on the ECMWF ERA5 Reanalysis data. Long‐term statistical analysis of extreme waves is carried out based on Generalised Extreme Value distribution using annual maxima and the spatial distribution of return levels for 50 and 100‐years are studied. In general, the ERA5 significant wave height (Hs) and maximum wave height (Hmax) show a good agreement with measured buoy data in the coastal (bias~0.29 m) and deep waters (bias~0.18 m). During the tropical cyclone, underestimation of Hs and Hmax in the ERA5 data compared to buoy data is 2.7% and 1.4%, but in general the bias is large (~0.69 m). Swell domination is observed in larger regions of the IO, whereas wind‐seas are comparable to swells in the Southern Ocean. The meridional wind speed largely influences the spatial pattern of Hs in the North IO. The stronger winds over the Southern Ocean play a major role in generating higher waves at higher latitudes. Maximum value of the 100‐year return level for Hs in IO is 17.8 m, whereas highest value of the Hs is 16.7 m and Hmax is 32.0 m. Severe wave events are common at 50‐60° S and only during 25% of the time in a year, Hmax is less than 5 m in this region. Ratio of the Hmax to Hs varies from 1.46 to 2.3 with a mean value of 1.87. The 100‐year return value of Hs changed by ‐4 to 5 m, when the length of the dataset is decreased from 39 years (1979‐2017) to recent 20 years. On an average, from 1979 to 2017, the annual average Hmax increased by 0.73 cm/year. In major areas of the IO, a clear decrease of the Hs is observed during 1991‐2017, whereas during 1979‐2017, an increase in Hs is found. This article is protected by copyright. All rights reserved.

Description: Abstract Wine production and quality are highly sensitive to local weather variability and climatic conditions. To assess these characteristics, this research examines high‐resolution bioclimatic zoning over 50 protected Denominations of Origin (DOs)/sub‐regions in mainland Portugal through the analysis of two selected bioclimatic indices (Dryness and Huglin indices). The analysis is based on a new very high‐resolution dataset over mainland Portugal and for a baseline period (1981–2015). Climate change projections are also assessed for two scenarios (RCP4.5 and RCP8.5) and using a 5‐member climate model ensemble over the future periods of 2041–2070 and 2071–2,100. A principal component analysis (PCA) was applied to the time mean spatial patterns of the two selected bioclimatic indices, for the baseline period (1981–2015) and only over the planted vineyard cover areas in each region, isolating a new optimized combined index which was used for subsequent analysis. The results for the present conditions highlight the spatial variability of Portuguese DO/sub‐regions. This study also shows that for the future periods, and regardless of the scenario, the wine sector in Portugal will likely see important bioclimatic changes across most DOs. Increases in the growing‐season mean temperatures in all the Portuguese winemaking DO/sub‐regions, accompanied by increasing severe dryness, are projected in future climates, mainly in south‐eastern Portugal and along the upper Douro Valley (Douro Superior) in north‐eastern Portugal. These DO/sub‐regions are projected to become much drier than currently so that irrigation or the introduction of new varieties are likely adaptation measures to maintain the viability and sustainability of regional viticulture in future decades. This article is protected by copyright. All rights reserved.

Description: Abstract Melting snow and glacier ice in the Himalaya forms an important source of water for people downstream. Incoming longwave radiation (LWin) is an important energy source for melt, but there are only few measurements of LWin at high elevation. For the modelling of snow and glacier melt, the LWin is therefore often represented by parameterisations that were originally developed for lower elevation environments. With LWin measurements at eight stations in three catchments in the Himalaya, with elevations between 3980‐6352 m.a.s.l., we test existing LWin parameterisations. We find that these parameterisations generally underestimate the LWin, especially in wet (monsoon) conditions, where clouds are abundant and locally formed. We present a new parameterisation based only on near‐surface temperature and relative humidity, both of which are easy and inexpensive to measure accurately. The new parameterisation performs better than the parameterisations available in literature, in some cases halving the root‐mean‐squared error. The new parameterisation is especially improving existing parameterisations in cloudy conditions. We also show that the choice of longwave parameterisation strongly affects melt calculations of snow and ice. This article is protected by copyright. All rights reserved.

Description: Abstract Ural blocking (UB) event is an important large‐scale weather system dominating cold waves in East Asia. This paper investigates the thermal‐dynamical variations characteristics associated with UB evolution via 32 UB events in winter during 1979‐2016. The results show that transport of stationary heat and momentum changes prominently from three and six days before and after UB establishment. Specifically, on three days prior to the UB event establishment, an apparent convergence of stationary heat flux ({v*T*}), which can extend from lower‐middle troposphere to the tropopause, is observed at around 60°N in the Urals region. The diagnosis show that the convergence of stationary heat flux weakens the meridional temperature gradient () and thermal wind, and thus the zonal westerly weakens. The convergence of ({v*T*}) is also in favour of formation of the warm centre structure of UB event. Meanwhile, convergence of momentum flux reverses to be a divergence in the Urals region (60°N), which contributes to decrease of the zonal wind. The heat and momentum flux reduce the intensity of the westerly and are beneficial for the establishment of UB event. Along with UB event establishment, the convergence of stationary heat flux weakens, and the divergence of momentum flux strengthens, accompanying with decrease of wind speed at middle latitudes and maintaining of the UB event. Six days later, the convergence of stationary heat flux disappears and divergence of momentum flux changes to be convergence and then the UB event collapses. Further analyses also suggest that the polar front jet (PFJ) enhances (weakens) coincidently before (after) the establishment of UB, which is attributed to the strengthening (weakening) of the convergence of ({u*v*}) around 70°N. This article is protected by copyright. All rights reserved.

Description: Abstract Drought being a complex natural hazard is challenging to monitor. Although standardized procedures for drought characterization are commonly used, the uncertainty about accurate drought characterization under various procedures always exists. Therefore, for effective drought mitigation policies, it is necessary to compare the performance of new and the existing drought procedures. In this study, we proposed a new procedure – The Drought Intensity Pattern Determinate (DIPD) to examine and compare the spatial‐temporal intensities of various drought episodes based on Standardized Drought Indices (SDI). The DIPD framework provides a new tool – The Drought Concentration Index (DCI) to understand the annual intensity of drought classes by alluding the observed frequencies of drought episodes. At the preliminary level of assessment, DIPD is employed at 52 meteorological stations of Pakistan, and compared three commonly used standardized drought indices: Standardized Precipitation Index (SPI), Standardized Evapotranspiration Index (SPEI), and Standardized Precipitation Temperature Index (SPTI). Exploratory analysis shows a mixed picture as all the drought indices behaving similar for some patterns of drought classes, whilst some classes show significant discrepancies. In addition, we assessed and compared the spatial distributions using spatial Poisson lognormal model by assuming SDI as a non‐Gaussian process for individual total classes of observed DCI. Results show that the proposed method has ability to incorporate the inner variability in patterns of drought classes. In summary, the DIPD provided a new regional based comparative framework, which accounts the all possible episodes of drought classes and effect of inner discrepancies among quantitative values of SDI. This article is protected by copyright. All rights reserved.

Description: Abstract Persistent and periodic northerly low‐level jets have been identified in the South Asian mid‐latitudes and sub‐tropics, which are modified by the presence of Makran mountain ranges adjacent to the central south coast of Pakistan, where they blow predominantly in the west‐northwest to northeast directions. These winds lead to the generation of a new wave system and propagate through the Arabian Sea as “makran swells”. They are prevalent during October to May. The measurements along the west coast of India reveal systematic variations in the wave spectra relating the makran events. This has been further verified through the WaveWatch III simulations forced by the CFSR winds. The significant wave heights up to 4.5 m were hindcasted in the northern Arabian Sea during these events. Once generated, the makran swells propagate to the eastern Arabian Sea and western Arabian Sea with directional sectors of 300‐360° and 0‐60°, respectively. The characteristic features and the spatial extension of the makran swells are particularly analysed. This article is protected by copyright. All rights reserved.

Description: Abstract As semi‐enclosed and shallow seas, the Bohai and Yellow Seas (hereafter, BYS)’ marine ecosystems are more easily affected by natural and anthropogenic climate changes than those in the open oceans. An accurate assessment of sea surface temperature (SST) change is crucial for the near‐shore water environment. In this work, new high‐quality monthly mean SST time series from 11 in situ coastal hydrological stations at BYS for the period of 1960–2012 have been generated. Monthly mean SST time series were initially subjected to penalized maximum t (PMT) test, using homogeneous monthly mean surface air temperature (SAT) series as references. Homogenized monthly mean SST series were obtained by adjusting all significant change points which have been supported by historic metadata. Our study shows that there are 29 significant change points in these 11 SST series. The majority of them are caused by instrument changes and station relocations—both account for about 51.7% and 31%, respectively. Then, homogeneous SST series are used to assess the long‐term trends. As the regional average, the annual mean homogeneous SST series shows a rapid warming trend, with the rate of 0.21°C per decade. Compared to the homogeneous SST series, the original SST series underestimate the long‐term trend (only 0.13°C per decade), indicating the artificial change points usually result in a warm‐bias in the early period measurements. Seasonally, the most significant warming occurs in boreal winter during the period analyzed. The relationships of the significant warming in boreal winter and atmospheric circulation modes have also been investigated. Results reflect the remarkable and direct influence of the East Asian Trough (EAT) on SST along the coastal BYS. This article is protected by copyright. All rights reserved.

Description: Abstract This paper analyzes climate trends in satellite‐blended model reanalyses for the east Antilles islands of Guadeloupe, Dominica and Martinique (14‐17°N, 62.5‐60.5°W). Trends are studied for the dry winter (JFM) and wet summer (JAS) seasons, for parameters including: aerosol profile, tropospheric NO2 concentration, rainfall and hydrology, outgoing longwave radiation, potential evaporation, sea level, air and sea temperature and salinity in the upper ocean. Climate change emerges in the form of 2‐4 and 6‐8 yr surges in near‐surface temperature and mid‐tropospheric subsidence. While near‐surface specific humidity exhibits a rising trend especially in summer, the mid‐troposphere is drying. Coastal zones show little trend in rainfall, but cloud forests on the volcanic slopes are getting wetter. Faster rising trends are noted for sea surface height, and for sea temperatures in low salinity zones that affect tropical cyclone intensification. This work quantifies the spatial and temporal nature of climate variability in the east Antilles islands of Guadeloupe, Dominica and Martinique and advocates for increasing integration of mesoscale analysis into studies on local socio‐economic vulnerability. This article is protected by copyright. All rights reserved.

Description: • Investigated the ability of Coupled Model Intercomparison Project phase 5 (CMIP5) models in representing the differences in decaying phases of El Niño.• Diversity among CMIP5 models in representing variations in south Asian and east Asian monsoon associated with modulations of El Niño decay phase are discussed.• This study highlights that correct representation of differences in decaying phase of El Niño and associated teleconnections in CMIP5 models is important. Abstract The present study examined the ability of Coupled Model Intercomparison Project phase 5 (CMIP5) models in representing the differences in decaying phases of El Niño and their impact on south Asia and east Asia monsoon during June and July (JJ), the early summer monsoon months. El Niño decay is classified into three categories, based on the timing of decay with respect to the summer season after the peak phase of El Niño. Analysis suggests that many CMIP5 models are able to capture the differences in the decaying phase of El Niño. Observed rainfall anomalies are positive over most parts of south and east Asia regions during the early decay (ED) years mainly due to anomalous Tropical Indian Ocean (TIO) Sea Surface Temperature (SST) warming and La Niña like forcing from the eastern Pacific. Most models show significant skill in capturing the positive rainfall anomalies over south Asia and TIO SST warming in ED years. However, many models have failed to represent east Asian rainfall anomalies due to weak Western North Pacific (WNP) anticyclone and its association with El Niño‐Southern Oscillation. In case of mid‐decay (MD) years, observed rainfall anomalies over south Asia is negative especially in the monsoon trough region and positive over east Asian region. Low‐level divergence induced by anomalous WNP anticyclone extending to head Bay of Bengal and Gangetic Plain region in MD years caused low rainfall over south Asia. These features are however not well organized in many CMIP5 models. In no decay (ND) years, the rainfall anomalies over south and east Asia regions are negative in almost all the CMIP5 models, which is consistent with the observations. This study highlights the importance of proper representation of differences in the decaying phase of El Niño and associated teleconnections in CMIP5 models.

Description: Distributions of air temperature at 2 m above ground and mean radiant temperature for different local climate zones (LCZs) in Toulouse Metropolitan Region (southern France). Thermal climatic parameters are taken from simulations with the mesoscale atmospheric model Méso‐NH for calm and sunny summer days. Distinct thermal characteristics are identified for different LCZs, notably the warmer built‐up LCZs during the night and the lower mean radiant temperatures for compact LCZ 1/2/3 and dense trees (LCZ A) due to shading in the day. Abstract To build healthy, resilient, and climate‐responsive cities, planners need ways to understand the local complexities of urban thermal climates. To assist in meeting this need, this study employs the simple classification of “local climate zones” (LCZs) to conduct a spatiotemporal thermal climatic analysis of the Toulouse Metropolitan Region (France) under warm and dry summer conditions. Simulations are performed using the mesoscale atmospheric model Méso‐NH. These simulations provide a city‐wide spatial coverage of 2‐m air temperature (T2M), mean radiant temperature (MRT), and Universal Thermal Climate Index (UTCI). Model parameters describing the urban morphology are initialized based on administrative databases and independent of LCZ maps, which allows for an evaluation of whether the distributions of the modelled thermal climatic parameters will differ between LCZs. The results show that different LCZs possess significantly different distributions of T2M and MRT, confirming the suitability of the LCZ scheme for discerning the thermal environment of Toulouse. Compact urban settings (LCZ 1/2/3) show the highest T2M throughout the day and a nocturnal temperature difference of up to 2.8 K compared to rural settings. The MRT of LCZ 1/2/3 in the late afternoon (1700–2000 LST (UTC + 2)) can be as much as 6.3 K lower than it is for LCZs with open settings due to shading by dense urban structures. Additional analysis reveals that the intra‐LCZ variabilities of T2M and MRT may be explained by the distance to the city centre. Finally, the thermal stress in different LCZs is assessed with the modelled UTCI. Among the built LCZs, the probability of strong heat stress is the highest for open high/mid‐rise (LCZ 4/5) and lowest for sparsely built (LCZ 9) and open low‐rise (LCZ 6) settings. For land cover type LCZs, dense trees (LCZ A) are the most favourable for daytime outdoor human thermal comfort.

Description: In this study, seasonal and diurnal variations of cloud occurrences, cloud types, cloud vertical structure and ice clouds have been investigated and compared between three subregions of the Tibetan Plateau based on CloudSat and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation satellite retrievals. The three key features that were established are firstly the high contribution of stratiform ice cloud layers in the westerly dominated north, secondly the importance of the monsoon season which outweighs day–night differences and affects the examined cloud parameters in all regions and finally the significant regional differences of cloud characteristics within the plateau. Abstract This sequence of papers, consisting of two parts, examines temporal and spatial variations of convection and precipitation over the Tibetan Plateau (TP) based on recent satellite observations. Here in Part 1, seasonal and diurnal variations of cloud vertical structure and cloud properties have been derived from four combined CloudSat and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation satellite data sets and compared between three subregions in the TP which are marked by different dominating large‐scale atmospheric circulations and moisture sources. The results show that the plateau is generally dominated by low‐level single‐layer clouds and stratiform cloud types. Cloud occurrence frequencies peak during the summer monsoon season between May and September and are generally higher during daytime compared with nighttime in all the three subregions. The fraction of detected ice cloud layers in the TP domain exceeds 50% during all months and 80% between January and April. While ice cloud layers occur as altostratus clouds in the westerly dominated north and transition zone, high‐level cirrus cloud occurs frequently accompanied by lower level cumulus clouds in the monsoon‐dominated south, especially during nighttime. This study complements previous satellite observations of clouds over the TP and reveals firstly the high contribution of stratiform ice cloud layers in the westerly dominated north, secondly the importance of the monsoon season which outweighs day‐night differences and affects the examined cloud parameters in all regions and finally the significant regional differences of cloud characteristics within the plateau. It is therefore suggested to focus on the relative importance of stratification, mesoscale convective systems and advection in future studies on hydro‐climatic changes in the TP region.

Description: Dynamic process is revealed in the opposite zonal movements of the South Asian high and the western North Pacific subtropical high on interannual time scale. Southeastward shift of the South Asian high leads to strong downward vorticity advection over southern China and the northern South China Sea, forming the negative vorticity anomalies over this region in the middle level, which result in the westward extension of the western North Pacific subtropical high. Abstract The South Asian high (SAH) and the North Pacific subtropical high (NPSH) are two crucial systems affecting the summer rainfall over East Asia. Features of the relative vorticity of the SAH and its dynamic effect on zonal extension of the western NPSH (WNPSH) are investigated on interannual timescales using data diagnosis and numerical model experiments. Results show that two climatological centres of negative relative vorticity are observed along the northern flank of the SAH over the Tibetan Plateau and northern West Asia at 200 hPa during boreal summer. The relatively more intense centre over the Tibetan Plateau (TPV) shows a pronounced southeast‐northwest (SE‐NW) variation, indicating a SE‐NW shift of the SAH. When the SAH shifts southeastward, an anomalous anticyclone occurs over eastern China at 200 hPa. In the middle troposphere, this anomalous anticyclone is located over southern China and the northern South China Sea, leading to a westward extension of the WNPSH. A diagnostic analysis of the vorticity equation indicates that the negative relative vorticity anomalies at 500 hPa are mainly caused by the downward advection of the mean relative vorticity by anomalous sinking motions. When the SAH extends southeastward, intense convergence on the southeastern flank of the anomalous upper‐level anticyclone induces descending motions that cause downward advection of mean negative vorticity. Consequently, negative vorticity anomaly is formed at 500 hPa, leading to a westward extension of the WNPSH. Results from the experiments using an idealized anomalous atmospheric general circulation model further demonstrate that the upper‐level anomalous anticyclone associated with a southeastward extension of the SAH triggers an anomalous anticyclone at the middle level, causing a westward extension of the WNPSH.

Description: (Top) Anomalies of the autumn PDSI in North China obtained by regression on PDSI_ave during (a) positive and (b) negative PDO phases. The interval of the contours is 0.4. Anomalies significant at the 95% confidence level are dotted. (bottom) The time series of the normalized autumn area‐averaged PDSI (barcharts) (PDSI_ave). The thick black line represents the smoothed autumn PDO index. Abstract This study assesses the impact of the Pacific Decadal Oscillation (PDO) on the severity of autumn droughts in North China (ADNC) during the period of 1962–2013. The ADNC are defined using the Palmer drought severity index (PDSI). The results show that the ADNC are more severe during negative PDO phases than during positive PDO phases. During the negative PDO phase, both the autumn precipitation and the Penman–Monteith reference evaporation (PET_pm) play roles in the significant severity of the ADNC. In contrast, during positive PDO phases, the droughts over North China are impacted to a greater degree by the climate conditions in previous seasons. Regression analysis shows that the atmospheric circulation anomalies associated with ADNC are quite different over North China under different PDO phases. Specifically, during negative PDO phases, anomalous low‐pressure systems are observed over the East Asia‐western Japan area, indicating a strengthened East Asian trough (EAT). Anomalous northerly winds bring cold and dry air from high‐latitude inland regions to North China, leading to severe ADNC during this period. Meanwhile, significant descending motion is observed in the upstream region of the EAT and the area of North China, accompanied by reduced cloudiness and reduced precipitation over these regions. In contrast, during positive PDO phases, the ADNC‐associated atmospheric circulation anomalies over North China are quite weak. Further analysis shows that during the negative phase of PDO years, the El Niño Southern Oscillation can impact the severity of ADNC through the centre of the Pacific‐East Asian teleconnection pattern over northeastern Asia.

Description: Time trends in temperatures in the US states are examined from 1895 to 2017. Results indicate that with the exception of 10 states, in the remaining 48 temperatures anomalies have increased across time. Figure 1 Degrees of increase in the averaged temperature anomalies for 38 US. states over 100 years. Summary of data extracted from Table 4. Alaska and Hawaii has not been examined. Abstract This paper investigates the time trend coefficients in the temperatures in 48 US states using monthly data from January 1895 to December 2017, as well as in their anomalies with respect to the base period 1901–2000. For this purpose, we use techniques based on fractional integration, which is a more general approach than the standard methods used in the literature based on integer differentiation. The results indicate that with the exception of 10 states, in the remaining 38, the temperature anomalies have increased across time, with the increase being higher than the one expected under the other more standard approaches. The highest increases correspond to New Jersey and Rhode Island, with an increase of approximately 2.9°C over the last 100 years.

Description: Comparison of evapotranspiration (ET) data sets obtained from IMD inputs, ERA‐I and CRU sources show substantial discrepancies. ET of IMD could explain the monsoon features over India compared to other data sets. The reported disparities play a key role in selecting the data sets for different applications. Mean seasonal ET over (a) MCR (b) IPR during SW and NE monsoon seasons of India from 1979 to 2014 obtained from IMD, ERS‐I and CRU data sets. Abstract In this study, we have computed the evapotranspiration (ET) from the input variables of India Meteorological Department (IMD) for different stations in Monsoon Core Region (MCR) of India and Indian Peninsular Region (IPR) and compared with the ERA Interim (ERA‐I) and CRU ET data sets. While studying the discrepancies among the data sets, rainfall (source: IMD gridded), relative humidity (source: ERA Interim gridded), air temperature (source: IMD gridded) and soil moisture (source: TRMM/LPRM/TMI‐Model) were made use to illustrate the ET variations. When compared with IMD ET, our results show the CRU ET is underestimated but maintained the close pattern over MCR and IPR during South West (SW) monsoon (June–September) and North East (NE) monsoon (October–December) period, respectively. ERA‐I ET bounded to have mixed response over MCR and are higher than the IMD ET over IPR. Daily comparison of the IMD and ERA‐I ET data sets shows a large bias during the beginning of SW monsoon (June month) compared to other months. Site wise correlations show the substantial positive correlations between IMD and CRU ET over MCR than IPR. Overall analysis shows the monsoon features were better explained by the variations in IMD ET compared to CRU and ERA‐I ET data sets. The reported disparities among the data sets play an important role in the choice of selection for different applications such as water resource assessments, crop water requirements, monitoring of droughts etc.

Description: Abstract The intraseasonal variability (ISV) of the genesis potential index (GPI) and its relationship with tropical cyclone (TC) genesis over the western North Pacific (WNP) are examined for the summer (May‐October) during 1982 to 2016. The distribution of GPI is separated into three subregions as South China Sea (SCS), western part of WNP (WWNP) and eastern part of WNP (EWNP). All three regions have a statistically significant power spectrum peak in the 10‐20‐day period, associated with 20‐30‐ and 50‐70‐day signals in SCS and 20‐30‐day signal in WWNP. The ISV of GPI in the SCS is the most intense among these three regions, whereas the weakest one is in the EWNP. On average, ISVs of GPI (including 10–20‐day, 20‐30‐day and 30‐70‐day components) account for about 28% of the total variance of GPI. About 83% of TCs form when at least one ISV component is in its wet phase. We further examine the spatial patterns of GPI intraseasonal oscillation through empirical orthogonal function analysis performed on the 10‐90‐day filtered GPI data. There are 61% of TC formations in the first three spatial modes during the typical phases with anomalous PC. The ISV of GPI can indicate the TC subseasonal genesis on both frequency and location. Regression analysis on different time scales, which is based on either the three subregions or statistical spatial modes, is able to conclude that oscillations of GPI are associated with intraseasonal oscillation (ISO) signals from different regions: 10‐20‐day from WNP; 30‐70‐day from Indian Ocean; 20‐30‐day from both WNP and Indian Ocean. In the boreal summer, the northward propagation of ISO accompanied with anomalous convection and circulation influences GPI intraseasonal oscillation in the WNP, which can be a good indicator for TCs genesis. Such relationship can be applied to develop extended‐range forecasting of tropical cyclogenesis. This article is protected by copyright. All rights reserved.

Description: Differences in the rates of change between temperature extremes and average temperatures can have serious implications on society. For September through November, climate models from the CMIP5 archive project that cold extremes will warm sometimes over 5°C more than the mean for parts of North America, Europe and Russia. This is similarly true for boreal winter and spring. For these seasons, the mean is projected to warm at a faster rate than warm extremes in many mid‐ to high‐latitude Northern Hemisphere regions. Abstract Changes in characteristics of the distribution of temperature, in addition to shifts towards warmer temperatures, can have substantial implications for society and the environment. A change in shape, for example, can be caused by differences in the rates of warming of cold and warm extremes relative to average temperatures. These rates of warming vary both spatially and temporally, with strong geographic and seasonal differences in how local extremes are changing relative to local average temperatures. In this paper, we investigate how seasonal warm and cold tails of both daily maximum and minimum temperatures are changing relative to corresponding seasonal mean temperatures. Analysing gridded observations over land areas and climate models from the CMIP5 archive, we show that in recent decades, the greatest differences between warming rates of extremes and the mean occur in the cold tails of the distribution for many regions in the Northern Hemisphere extratropics during all seasons except boreal summer. Globally, June through August show the smallest differences between extremes and mean warming rates. While the climate models show relatively low spatial correlations in their change patterns with each other as well as the observations for past decades, future simulated changes are shown to be systematic and robust, with a clear signal in the warming of extremes relative to the mean. The strongest differences between warming rates of extremes and the mean are projected for Northern Hemisphere mid‐ to high‐latitude regions, where the cold tails warm more than the average for all seasons except boreal summer. This is especially widespread in autumn and spring, where the models predict cold extremes to warm at least 3°C more than mean temperatures for much of the Northern Hemisphere extratropics. The results presented here highlight the importance of considering all aspects of the distribution when analysing changes in temperature.

Description: Abstract Global temperature change is strongly affected by internal climate variability (ICV). The temporal change of the ICV on the decadal to multi‐decadal scales is referred as the decadal modulated oscillation (DMO) that plays a dominated role in the occurrence of enhanced warming and warming hiatus. However, investigation on the DMO in modern historical period has received limited attention. In this study, the ensemble empirical mode decomposition (EEMD) method was applied to the surface air temperature (SAT) during the boreal cold season to extract the DMO signal in the past century. Two most sensitive areas of DMO trend over northern Eurasia and northwestern North America were identified and used to build a time series of regionally‐enhanced DMO. It showed an obvious decadal periodic oscillation at 11‐23 years and exhibited increasing amplitude. In addition, regression analysis using Niño3.4, Pacific Decadal Oscillation (PDO), Atlantic Multi‐decadal Oscillation (AMO), and Arctic Oscillation (AO) revealed a major role of the AO in DMO over the mid‐to‐high latitudes in the Northern Hemisphere (NH). However, such strong oscillation signal has not been detected in most of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and the extracted regionally‐enhanced DMO are capable of improving the predictability of SAT over the mid‐to‐high latitudes in the NH.

Description: The Madden–Julian Oscillation exhibited the strongest and the most significant correlation with precipitation throughout California, whereas the El Niño/Southern Oscillation and East Atlantic/West Russia pattern dominated throughout the Carolinas, using a computation window size of 60 months and a lag time of 12 months. Correlation strength (Pearson's r‐value) 〉 .80 while significance (p‐value) 〈 .05 at several sites; optimal beginning months ranged from December to March. Abstract Long‐term changes in precipitation in California and North and South Carolina are correlated to low‐frequency oscillations of several hydroclimate indices (HCIs) through correlation analysis that utilizes longer sliding window sizes compared to previous studies to reduce higher‐frequency noise in each time series. HCIs that are considered include the El Niño/Southern Oscillation (ENSO), the Madden–Julian Oscillation (MJO), the North Atlantic Oscillation, the Pacific‐Decadal Oscillation, among others. Multi‐year accumulations of precipitation at several point locations were correlated to these HCIs temporally averaged over the same period. The sliding window size, lag time, and beginning month were varied to optimize the correlation for each site and HCI; a 60‐month window size and 12‐month lag time were found to result in the highest correlation. Correlation strength was characterized by the Pearson's r statistic, while correlation significance was estimated through a permutation experiment that employs a bootstrapping technique, resulting in a p‐value between 0 and 1. Using a 60‐month sliding window size and 12‐month lag time, it was found that the MJO exhibited the strongest and the most significant correlation with accumulated precipitation throughout California, whereas similar correlation was found with ENSO throughout the Carolinas; correlation strength exceeded a Pearson's r of .80, while correlation significance was p 〈 .05 at several sites. Optimal beginning months ranged from December to March for a majority of sites. This study underscores the potential of low‐frequency climate oscillations that manifest themselves in the long‐range dependence of precipitation on tropical disturbances.

Description: Bars showing the interannual variability in TCs over BoB during the period 1972–2015. The overlaid black and red solid lines with dots represent the ACE and PDI values, respectively. Abstract The present paper investigates the impact of El Niño‐Southern Oscillation (ENSO) on the Bay of Bengal tropical cyclone (TC) activity and associated alterations in environmental conditions during post‐monsoon (October–December) season for a period of 44 years (1972–2015). The analysis reveals that the post‐monsoon season TCs frequency, accumulated cyclone energy (ACE) and power dissipation index (PDI) values are negatively correlated with the Niño 3.4 sea surface temperature (SST) anomalies (significant at the 95% confidence level). La Niña years are characterized by more frequent and intense cyclonic events compared with El Niño years. The mean ACE and PDI values are approximately two times higher in La Niña than El Niño years. The mean number of TC days per year is also higher in La Niña (7.64 days) than El Niño (3.68 days) years (significant at 95% confidence level). In addition, a significant shift in genesis locations, tracks and landfalling locations of TCs has been observed under different ENSO phases. The mean genesis location of TCs have shifted eastward with tendency of more recurving tracks in La Niña than El Niño years. The presence of strong convective activity, reduced vertical wind shear, high SST (≥28°C), enhanced mid‐tropospheric relative humidity and low‐level cyclonic circulation aids the TCs formation and strengthening during La Niña and vice‐versa in El Niño conditions.

Description: Changes of spatiotemporal patterns of snowfall is analysed systematically. Multiple snowfall/rainfall partitioning methods and precipitation data sets are used. Frequency of snowfall intensity is increased with decreasing of extreme snowfall intensity. Upwards trend in snowfall may be caused by the increase of atmospheric water content. Abstract The spatiotemporal pattern of precipitation is significantly changing with global climate change. Snowfall is a solid phase of precipitation and an important water resource. With two gridded data sets of APHRO (Asia Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation of Water Resources) and CN05.1, this study analyses the changes in the spatiotemporal pattern of snowfall in a snow‐dominant region of China from 1961 to 2015. The results indicate the significant increasing trend of winter snowfall in horizontal and altitude dimension in snow‐dominant regions, but the winter snowing season length shortened. For the frequency of snowfall intensity level, light, and heavy snowfall and snowstorms increased, but moderate snowfall showed no change. However, the intensity of extreme snowfall in once‐in‐a‐century was decreasing in all of the snow‐dominant regions. In the altitude dimension, the increasing trend in snow‐dominant conditions was not uniform, which may be related to change in air temperature and water vapour through the vertical atmospheric levels. The upwards trend in snowfall may be caused by the increase of atmospheric water content rather than the change of snowy weather conditions. In addition, the change values of climate indices can also contribute to snowfall increasing in snow‐dominant regions.

Description: International Journal of Climatology, Volume 39, Issue 8, Page i-iv, 30 June 2019.

Description: Median and mean correlations (Pearson and Spearman) for each of the 20 regions described in Table 2 and for each of the 3 models described in Table 1. The correlation values are obtained by comparing bias‐corrected (through 5‐year‐out cross‐validation) model hindcasts with CRU values. Southern Africa's results are marked with “13” and for the Limpopo River catchment area values (median Pearson) an “o” is used. The list shows the regions ranked in terms of highest to lowest median Pearson values according to the GFDL model. Abstract Some of the biggest emerging market economies include countries in South America, Asia and Africa. Broad‐scale political and developmental similarities (e.g., societally impactful developmental challenges related to climate variability) offer opportunities for comparative research resulting in potentially improved understanding of the complexities of various climate adaptation interventions including disaster risk reduction. Countries or geographical regions of the world significantly affected by climate extremes may consider collaboration on issues such as understanding and modelling of the climate system, especially when there is a common, dominant and somewhat plausible climate mode such as the El Niño–Southern Oscillation (ENSO) affecting the regions' climate variability. Better ENSO and subsequent climate predictions alone, however, are not enough to reduce the risks associated with such events. The socio‐economic and political context in which climate finds expression and in which climate forecasts have potential value also need to be understood. Here we present seasonal precipitation forecast skill over 20 geographical regions including emerging or developing regions, but also a few developed regions, in order to rank their ENSO‐related seasonal rainfall predictability in an attempt to cluster regions of similar ENSO climate predictability. We then also provide some of the broad contours to investigate the level of human “development” within these clusters in order to begin to understand some of the socio‐economic factors that configure vulnerabilities. Such profiles begin to show some areas of macro‐level vulnerability that may then provide further possible inter‐area collaborations, albeit at very gross level scales.

Description: The climate system will occur an abrupt shift from a relatively warm state to a cold one in the zero‐dimensional global energy balance model when the relative radiative forcing approaches its critical point by decreasing the control parameter linearly with time and keeping other parameters unchanged. Our results indicate that the skewness indicator picked up an increase of the skewness coefficient prior to this transition. In the figure, a simulated abrupt climate change by the zero‐dimensional global energy balance model, and the skewness coefficients for a sliding widows with fixed size of 2006. (a) The climate model approaches its threshold by linearly reducing the relative radiative forcing μ from 1 to 0.963 in 5000 steps. (b) The skewness as a function of relative radiative forcing μ in the zero‐dimensional climate system. Abstract As a dynamical system approaches its critical threshold, the probability density distribution of the system will change significantly. Therefore, it is possible to present an early warning signal based on the changing skewness before reaching the critical threshold. Based on a zero‐dimensional climate model and several typical fold models, this paper systematically studies the influence of noise and missing data on the performance of the skewness coefficient as an early warning signal of an abrupt climate change. The results in three types of fold models show that the skewness coefficient has anti‐noise ability to some extent, but strong noise will significantly reduce the magnitude of the skewness coefficient and the time for early warning will also be shortened. In some cases, strong noise even will lead to the result that the skewness does not work in warning an impending abrupt change. However, the influence of strong noise on skewness is insignificant in the zero‐dimensional climate model. Therefore, the influence of strong noise needs to be considered in the practical application of the skewness coefficient as an early warning signal of an abrupt change. In addition, the results of all the models also indicate that different degrees of the missing data have no statistically significant effect on the warning performance of the skewness coefficient, even when the length of the missing data is up to 20% of the total sample size used in the present paper.

Description: An index for the variability of mesoscale sea surface temperature anomalies is proposed. The storm track shifts southwards (northwards) during the positive (negative) phase of index. The variability of mesoscale SSTA impacts the meridional shift of storm track through both classic baroclinic adjustment mechanism and the vertical mixing mechanism. Composite plots of meridional eddy heat flux for the (a) positive phase and (b) negative phase of the MSSTA index (shading; km/s). The contours represent the climatological meridional eddy heat flux (interval = 2 km/s). The stippling indicates the region where the statistical significance is more than 90%. Abstract In this study, an index for the variability of mesoscale sea surface temperature (SST) anomalies over the Kuroshio and Oyashio confluence region (KOCR) is proposed based on the high‐resolution SST data set. The positive phase of the new index indicates that the mesoscale SST anomalies are strong and feature double peaks of variance within the KOCR, and the negative phase of the index denotes weak mesoscale SST anomalies and a broader single peak of variance. Composite analysis is conducted on the relation between the variability of mesoscale SST and the North Pacific storm track. The mechanism behind the relation is then investigated. We find that the storm track is shifted southwards (northwards) during the positive (negative) phase of the index. The variability of mesoscale SST impacts first the turbulent heat fluxes out of the ocean, changing the near‐surface baroclinicity with the large‐scale zonal wind. The baroclinic energy conversion resembles the baroclinicity anomaly, modulating the anomaly of the storm track along the southern side of its climatological peak.

Description: The central highlands of Eritrea account for 18% (22,569 km2) of the total surface area (124,320 km2) of the country. However, it inhabits 65% of the total population (about 5 million), where 80% of the population depends mainly on agriculture and livestock production. Thus, the study of climate change and prediction of extreme weather condition is timely and vital for the Eritrean community. Abstract Meteorological series (daily precipitation, minimum and maximum air temperature) for Asmara (Eritrea) for the last 100 years (1914–2015) are analysed. The data were quality‐controlled and homogenized using publicly available data from surrounding countries as well as newly recovered data from 12 stations in Eritrea. Overall, the Asmara data showed a consistent pattern and there were no outliers outside of four standard deviations from the corresponding reference. Climate indices were calculated using the program RClimDex. Overall, 8 indices for description of the air temperature data and 10 for precipitation data were calculated. The analyses of averages and indices reveal large climatic variations in the central highlands of Eritrea. The results indicate significant changes in air temperature since 1943, with daily minimum and maximum air temperature increasing at a similar rate of 0.22 and 0.19°C/decade, respectively. The diurnal air temperature range shows a non‐significant decreasing trend over the study period. No significant variation was found in the annual total and the seasonal precipitation over the last century. Significant trends were detected for some daily precipitation indices, although the lack of reference series prevents an evaluation of their reliability.

Description: Number of stations with available data between 1979 and 2015 (left) and distribution of the station altitudes (right) Abstract A new high‐resolution (5 km) gridded daily precipitation dataset for Tunisia between 1979 and 2015 is introduced. This product combines 960 rain gauges with the SAFRAN analysis to produce the precipitation gridded data. A validation approach on two different datasets reveals that the SAFRAN analysis outperforms other standard interpolation methods such as Inverse Distance, Nearest Neighbors, Ordinary Kriging or Residual Kriging with altitude. When compared to EOBS, a widely used gridded dataset over Europe, a strong negative bias in EOBS precipitation is found. However due to the aridity and the low density of rain gauges in south Tunisia, results in this region must be analyzed with care. The SAFRAN product could be useful for various purposes such as climate model evaluation, climate studies, hydrological modelling to support the planning and management of surface water resources in Tunisia.

Description: Classification of daily winter (DJFM) 500 hPa geopotential heights from 1949 to 2012, using self‐organizing maps, reveals dominant mid‐tropospheric circulation patterns affecting hydroclimatic variables conducive to snowmelt in western Canada. A ridge of high pressure over western Canada is associated with a greater probability of above‐freezing temperatures and rainfall, compared with zonal flow or a trough of low pressure over the same region. Abstract Winter thaw episodes, especially when accompanied by rain, can significantly deplete the winter snowpack, which is a critical water storage component in the mountainous headwater regions of the major river basins of western Canada. Here we identify the characteristic synoptic‐scale mid‐tropospheric atmospheric circulation regimes that tend to foster such extreme hydrologic events using self‐organizing map analysis of meteorological reanalysis data from 1949 to 2012. Daily winter 500 hPa geopotential height fields over the Pacific Ocean and western Canada are classified into 12 dominant synoptic types, for which conditional probabilities of above‐freezing temperatures and rainfall are then calculated and mapped using daily high‐resolution gridded data. Results show that above‐freezing surface air temperatures and rain events in winter are commonly associated with the occurrence of a ridge of high pressure over western Canada, which induces southwesterly advection of relatively warm, moist maritime air masses into the continental interior, and that the intensity and spatial footprint of the surface climate response is related to the strength and position of the ridge. Conversely, the development of a ridge of high pressure over the Pacific Ocean and adjacent trough of low pressure over western Canada, which favours northwesterly to westerly mid‐tropospheric flow over the continental interior in winter, tends to suppress the occurrence of above‐freezing temperatures and rain. The synoptic type most strongly associated with winter thaw and rain events underwent a statistically significant step‐change increase in mean frequency in 1977, accompanied by a corresponding step‐change decrease in the frequency of the dominant synoptic type depicting westerly (zonal) circulation, coinciding with a well‐documented shift to a positive phase of the Pacific Decadal Oscillation.

Description: Abstract We present a new dataset of quality controlled and homogenized daily maximum (Tmax) and minimum (Tmin) temperature for Italy. The dataset includes 144 Tmax and 139 Tmin long‐term series, covering the period 1961‐2017. First, the paper provides a description of data sources and quality controls implemented for the detection of erroneous daily observations. Next, the temperature records used for this work are introduced. Following strict data continuity and completeness requirements, we identified more than 500 time series with at least 20 years of valid data (raw dataset), which were spatially partitioned using a hierarchical clustering approach. For each cluster, the time series homogeneity was assessed using two different statistical automatic approaches: ACMANT and Climatol. The results of the homogenization process are illustrated only for the long‐term series subset. Both homogenization methods revealed the presence of non‐climatic discontinuities in most of the temperature series. Although Climatol detected a slightly lower number of breakpoints than ACMANT, the two methods are in good agreement with respect to the statistics which describe the number and timing of the breakpoints. Since no metadata are available, the plausibility of the homogenized time series was evaluated using different statistical measures: RMSE, Spearman correlation coefficient and trends estimation. Our results show that the homogenized datasets are more spatially coherent than the raw time series. In particular, the analysis of the annual temperature trends shows more realistic and reliable climatic patterns when the homogenized datasets are considered. For our data, the homogenization process only marginally changes the annual and seasonal warming trend values found for the area‐averaged anomaly raw series. The Tmax and Tmin homogenized dataset will be regularly updated and is intended to be used for a variety of climate studies that require data at daily resolution, as the analysis of climate extremes. This article is protected by copyright. All rights reserved.

Description: Abstract The tropical Pacific climate state response to both the 21st‐century greenhouse gas forcing and orbital forcing on a glacial–interglacial timescale tends to resemble either an El Niño‐ or La Niña‐like pattern. This study reveals that so long as an El Niño‐ or La Niña‐like change in the tropical climate state occurs, changes in two important negative feedback components of the El Niño‐Southern Oscillation (ENSO) system, dynamical damping by mean thermal advection (MA) and thermo‐dynamical damping (TD), largely offset each other. For example, under the El Niño‐like condition, weaker trade winds due to a relaxed zonal sea surface temperature (SST) gradient reduce the mean zonal and meridional currents in the equatorial Pacific oceanic mixed layer, causing a reduction in MA, while wider expansion and enhanced activity of climatological convective clouds due to a warmer ocean surface intensifies negative SST‐cloud‐shortwave feedback. As a result, a change in ENSO activity in changing climate is mainly ruled out not by the change in negative feedback effect, but by the change in positive feedback effect.

Description: (a–b) Geopotential height of 100 hPa and geopotential height anomaly fields for composite February cases 1989–2018 when the GBI was below −1.0 SD, implying a relocation of the polar vortex toward Greenland. (c–d) The same for 500 hPa. Abstract Winter weather in the subarctic and lower latitudes can be influenced by the repositioning of the polar vortex away from being centred near the North Pole, extending over regional locations of subarctic continents. One example was the “Beast from the East” event in Eurasia in March 2018, which brought snow to much of Europe. We are interested in extended (week to a month) North American weather events, and especially the impacts from a location of the polar vortex centre over and near Greenland. For a tropospheric polar vortex location index, we use low 500 hPa geopotential heights (GPH) over greater Greenland from values of the Greenland Blocking Index (GBI) below negative 1.0 SD (1951–2018 base period). February is a preferred month with 10 low GBI events beginning 1989. Composite 100 and 500 hPa GPH for these 10 cases show hemispheric‐wide features with a trough/ridge/trough pattern extending from eastern Siberia eastward to Greenland and spanning both the stratospheric and tropospheric polar vortex. Associated extreme weather as seen in 2015 and 2018 include cold temperatures on the eastern United States, warm monthly temperatures (〉5.0°C anomalies) in California with drought conditions, and record sea ice loss in the winter Bering Sea. Results support the concept that November–December has a regional tropospheric pathway for Arctic/mid‐latitude weather interactions due to delayed autumn sea ice freeze up, whereas January–March has a more hemispheric pathway related to stratospheric polar vortex movement that is delayed into late winter.

Description: The weather on Ben Nevis—the highest mountain in the British Isles, 1,345 m above mean sea level—sometimes shows episodes of remarkably low relative humidity (RH) with few precedents anywhere else in the British Isles. We are able to quantify this for the first time using a high‐quality series of hourly dry‐ and wet‐bulb observations, made on the summit. These observations were made between 1883 and 1904, but have only just become available to modern science, thanks to thousands of volunteers who worked to rescue this unique and exemplary data set from published volumes. Abstract The weather on Ben Nevis—the highest mountain in the British Isles at 1345 m above mean sea level—sometimes shows episodes of remarkably low relative humidity (RH) with few precedents anywhere else in the British Isles. We are able to quantify this for the first time using a high‐quality series of hourly dry‐ and wet‐bulb observations, made on the summit. These observations were made between 1883 and 1904, but have only just become available to modern science, thanks to thousands of volunteers who worked to rescue this unique and exemplary data set from published volumes. Careful examination and analysis of the original observations using modern psychrometric theory revealed several occasions where we are confident that the summit RH fell close to zero as a result of anticyclonic subsidence. Three case histories are examined in some detail. The 19th‐century Ben Nevis humidity records are also compared with contemporary automatic weather station data from two high‐altitude Scottish mountain sites.

Description: Abstract Climate change is of major relevance to wine production as most of the wine‐growing regions of the world are located within relatively narrow latitudinal bands with average growing‐season temperatures limited to 13–21 °C. This study focuses on the incidence of climate variables and indices that are relevant both for climate change assessment and for grape production, with emphasis on grapevine bioclimatic indices and extreme events (e.g. cold waves, storms, heatwaves). Dynamical downscaling of European Reanalysis‐Interim (ERA‐Interim) and Max Planck Institute Earth System low‐resolution (MPI‐ESM‐LR) global simulations forced with a Representative Concentration Pathway 8.5 (RCP8.5) greenhouse gas (GHG) emission scenario was performed with the Weather Research and Forecast (WRF) model to a regional scale including the Douro Valley of Portugal for recent‐past (1986–2005) and future periods (2046–2065, 2081–2100). The number, duration and intensity of events were superimposed over critical phenological phases estimated by using a specific local grapevine varietal phenological model in order to assess their positive or negative implications for wine production in the region. An assessment of the relevance of climate parameters and indices and their progression in recent‐past and future climate scenarios with regard to the potential impact on wine production was performed. Results indicate a positive relation between higher growing‐season heat accumulations and greater vintage yields. A moderate incidence of very hot days (daily maximum temperature above 35 °C) and drought from pre‐véraison phenological conditions have a positive association with vintage ratings. However, the mid‐ and long‐term WRF‐MPI RCP8.5 future climate scenarios reveal shifts to warmer and drier conditions, with the mean growing‐season temperature (GST) not remaining within range for quality wine production in the long‐term future climate scenario. These results indicate potential impacts that suggest a range of strategies to maintain wine production and quality in the region.

Description: Temperatures from CERA‐20C and ERA‐Interim correlate quite well with those from observed stations in Sichuan during 1979–2010, but significant cold biases between reanalysis data sets and observations are captured. The cold biases of the surface air temperature derived from ECMWF reanalysis could be mainly attributed to the elevation differences between stations and reanalysis model. It is demonstrated that the elevation correction using model internal lapse rates is an effective method to diminish the influence of altitude differences on temperature biases and calibrate the surface air temperature errors of the reanalysis. Abstract Surface air temperatures (SATs) derived from the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim and CERA‐20C reanalysis data sets are compared with data from 43 observation stations in Sichuan for 1979–2010. The results show (a) the temperatures from the ERA‐Interim and CERA‐20C data sets are strongly correlated with those from the observation stations, although significant cold biases are seen on both annual and seasonal timescales. (b) The biases in SATs are predominately influenced by the differences between the actual topography and the topography used in the reanalysis models. Larger differences in temperature are observed in the plateau and mountainous regions of Sichuan. We confirmed larger SAT biases at high altitudes by categorizing the elevation into four bands, each with a spacing of 1,000 m. (c) We reduced the biases resulting from elevation by using an elevation correction method with internal lapse rates derived from different reanalysis pressure levels. The annual mean bias was reduced from −2.86 to −0.75°C for the ERA‐Interim data set and from −5.27 to −2.21°C for the CERA‐20C data set. After calibration, the correlation coefficients between the difference in SAT (observed minus reanalysis data) and the difference in elevation (station elevation minus model elevation) decreased from −0.97 and −0.91 to −0.29 and −0.30 for the ERA‐Interim and CERA‐20C data sets, respectively. These significant differences should not be ignored in the application of reanalysis data sets to climate research. The evaluation and calibration of reanalysis data sets are essential before making assessments of regional climate change, especially over regions with complex topography.

Description: The results obtained based on six RCMs simulations indicate that in Romania extreme precipitation events are expected to decrease in duration, and increase in intensity or frequency over three future periods compared to the historical reference period (1961–1990). In the figure, projected changes of five extreme precipitation indices compared to the reference period (%): (a) 2021–2040, RCP4.5; (b) 2021–2040, RCP8.5; (a) 2041–2070, RCP4.5; (b) 2041–2070, RCP8.5; (a) 2071–2100, RCP4.5; (b) 2071–2100, RCP8.5. Abstract This paper presents the results of a study focused on the projected changes in extreme precipitation indices in Romania during three future periods: 2021–2040, 2041–2070, and 2071–2100. We investigated changes in future climate based on historical and modelled data sets of daily precipitation. We compared the values calculated for the three future periods with those obtained for the historical reference period 1961–1990. The historical observation data recorded at 30 weather stations and data extracted from six regional climate model outputs (ALADIN53, CCLM4‐8‐17, RACMO22E, RCA4, REMO2009, and WRF331F) under moderate (RCP4.5) and pessimistic (RCP8.5) scenarios were employed. Five indices established by the Expert Team for Climate Change Detection Monitoring and Indices were analysed: consecutive dry days, consecutive wet days, heavy precipitation days, very heavy precipitation days, and annual total wet‐day precipitation. The series of indices were generated using ClimPACT2 software. Based on bias correction methods, the quality of the modelled data considerably increased, and the errors obtained for the daily precipitation varied in the range of ±0.05 to ±1.22 mm/day. The main findings revealed an increase in three of the extreme precipitation indices based on all models considered compared to the historical period. The most important increase (more than 50%) compared to the historical period was detected in the frequency of very heavy precipitation days and the annual total wet‐day precipitation. They were followed by the heavy precipitation days index. The consecutive dry days index indicated mainly no change or a slow decrease, while inconsistencies among the results of regional climate models were detected for the consecutive wet days index. For all indices except the consecutive dry days index, the ALADIN53 and WRF331F models provided the highest projected values for both scenarios. The dispersion of values obtained as model output for each location was higher for RCP8.5 than for RCP4.5.

Description: Abstract From 1961 to 2015, annual and seasonal precipitation have shown a significant increase over Northwest China (NW). The change of precipitation is a combination result of the changing recycling precipitation (Pw) contributed by local evapotranspiration (ET) and changing precipitation contributed by advection moisture (Po). In this paper, we calculated the ET based on the observation data of meteorological stations and the complementary relationship model. Then used an improved precipitation recycling model to analyse influential factors on the variation of precipitation over the NW from aspects of ET and advection moisture. The annual precipitation recycling ratio (ρ) fluctuates between 4% and 10% and shows an increasing trend at a rate of 0.3% /10a with a significance level of 0.01. ET, which is the internal supplier of the precipitable water, and advection moisture, which is the outside supplier of the precipitable water, both have displayed significantly increasing trends over the NW. The advection moisture contributes precipitation much more than ET, no matter in the annual scale or seasonal scales. In view of the spatial distribution, Pw shows an increasing trend over the whole NW, but distinctly opposite spatial patterns of Po are found between the western area of NW (WNW) and the eastern area of NW (ENW). Such differences caused the precipitation is characterized by a wetting trend in WNW but a drying trend in ENW.

Description: (a) MCSs number (points) and local regression (line); (b) Precipitation (mm; points) and local regression (line). The confidence intervals at 95% are in shaded. MCSs data from 2000 to 2013: Rehbein et al. (2017). Precipitation data source: Climate Prediction Center (CPC) Unified Gauge‐Based Analysis of Global Daily Precipitation. Abstract The Green Ocean Amazon (GoAmazon2014/5) scientific program focused on the influence of aerosols and surface fluxes on tropical cloud formation. This major research effort gathered high‐quality environmental data over the central Amazon basin during 2014 and 2015. The present work is a contribution to the GoAmazon2014/5 investigations with an emphasis on the behaviour of the most important mechanism of precipitation over the tropics: mesoscale convective systems (MCSs). To provide a background, MCSs' tracks obtained from infrared satellite images over the entire Amazon basin in 2014–2015 are compared with climatological values. The number of MCSs and precipitation in the basin is about 50% lower than compared to the climatology for 2000–2013. We argue that the below average occurrence of MCSs during the GoAmazon2014/5 program can be explained, at least in part, by the effects of positive anomalies in sea surface temperature over the equatorial Pacific Ocean, negative moisture transport toward the Amazon basin, and by the anticyclonic phase of the mode of interannual and intraseasonal variability over South America. Special attention is given to the 99 MCSs that occurred over the GoAmazon2014/5 site. The impact of MCSs on the meteorological variables over the GoAmazon2014/5 sites is examined, and the contribution of MCSs to rainfall over that region is estimated to be about 70% of the total. Finally, the synoptic and thermodynamic conditions related to the MCSs' genesis and dissipation are discussed. It is suggested that in days with reduced MCS genesis over the GoAmazon2014/5 region, the ventilation over the continent by easterly winds from the relative cold South Atlantic Ocean favours convection at locations near the ocean as compared to those inland.

Description: Abstract The prediction skill and source of the predictability of the East Asian summer monsoon (EASM) system are examined in this work based on four state‐of‐the‐art seasonal climate forecast models including BCC_CSM1.1, ECMWF_SYS4, NCEP_CFS2 and TCC_CPS2. The prediction of the climatology and interannual EASM pattern and the impact on the prediction are further investigated. It is noted that the four models have some skill in predicting summer rainfall in the East Asia, however, the skill is low on average and also largely regional dependence. The interannual variation of EASM measured by monsoon circulation index is well reproduced, implying that the broad‐scale feature/pattern of EASM has higher predictability than the detailed spatial variation of EASM rainfall. The possible sources of predictability of the interannual variability of EASM are associated with the El Niño‐Southern Oscillation (ENSO) and the north Indian Ocean (NIO) sea surface temperature (SST) anomalies. The correlation pattern of rainfall with the NIO SST is characterized by a tripole pattern from south to north of East Asia, which is different from the correlation distribution of the southern‐northern dipole with ENSO, suggesting that NIO SST may exert influence on the EASM independently. The major biases in climatology of EASM in the models are the northward shift of the western Pacific subtropical high (WPSH) and weak monsoonal southerly over the coast of East Asia, which leads to the prediction bias of the Meiyu/Baiu/Changma (MBC) rainfall belt. The prediction of the interannual EASM pattern presents two deficiencies: too weak rainfall variability and northward shift of the dipole rainfall pattern (opposite variation between MBC and the northwestern Pacific), that may be caused by the biases of WPSH in the models.

Description: Trends of 16 climate extreme indices based on daily maximum and minimum temperatures during the period 1987–2016 at 28 stations across the Levant region were evaluated. A dominant warming for the last three decades, with more intense changes for minimum than for maximum temperatures was experienced. Significant increasing trends for summer days (SU25) and tropical nights (TR20) were found. North Sea‐Caspian pattern is the main driver of extreme temperature indices over the study area. Abstract The temporal and spatial trends of 16 climate extreme indices based on daily maximum and minimum temperatures during the period 1987–2016 at 28 stations distributed across Israel and Palestine territories in the Levant region were annually and seasonally analysed. The nonparametric Man‐Kendall test and the Sen's slope estimator were employed for the trend analysis. Results showed that the region has significantly experienced a dominant warming trend for the last three decades, with more intense changes for minimum temperatures than for maximum. At annual scale, maximum values of minimum temperatures exhibited significant increasing trends up to 0.68°C/decade. For percentile‐based extreme temperature indices, changes detected were more pronounced than those for the absolute extreme temperature indices, with 93 and 89% of stations significantly showed increasing trends in TX90p and TN90p, respectively. The duration and fixed threshold extreme indices confirmed the trend toward a warming, with the 86% of the stations exhibited significant increasing trends in the annual occurrence of summer days (SU25) and tropical nights (TR20). Moreover, 57% of stations showed significant increasing trends in their very summer days (SU30) index. At seasonal scale, the analysis of trends for extreme temperature indices showed intense and broad significant increasing trends in all absolute extreme temperature indices. In summer, more than 75% of total stations exhibited significant increasing trends for warm days and warm nights (TX90p and TN90p). In winter and spring, 71% of the total stations also showed significant increasing trends in SU25 index, whereas the percentage of stations reached 82% in summer and 64% in autumn for significant increasing trends in TR20 index. Finally, the influence of large‐scale circulation patterns on temperature extremes was examined. The results highlighted the presence of significant correlations between most of the selected extreme temperature indices and the North Sea‐Caspian pattern at annual and seasonal scales.

Description: Abstract As an ecologically sensitive region, the humid‐to‐arid climate transition zone in the eastern and central north China (ECNC) has experienced varied vegetation change in recent decades, but the exact impacts of climate change and human activities remain uncertain. Based on the Normalized Difference Vegetation Index (NDVI), we established the trend of vegetation change in the past three decades (1982‐2013) in the ECNC, and examined the impact of climate and non‐climate factors on vegetation growth within the different eco‐regions. For the study period, the climate in ECNC became significantly warmer and slightly drier, and the overall NDVI increased significantly, but with great spatial variations. Our results suggest that temperature increase has promoted vegetation growth in places that are colder and/or wetter, and with higher vegetation coverage. Precipitation increase has promoted vegetation growth in drier places with sparse vegetation and inhibited growth in wet places with high vegetation coverage. As different eco‐regions typically have different vegetation coverage and occupy different climate zones, their response to climate change also varies. For the study period, increasing temperature and decreasing precipitation promoted vegetation growth in the forest of northern ECNC. Increasing temperature and precipitation led to NDVI increase in the grassland in the south, whereas grassland in the north and west showed no significant change despite temperature increase and precipitation decrease. Cropland responded mostly positively to temperature increase, although correlations between NDVI and climate factors were generally weaker. Using multiple regression models, we found that 60% of the NDVI increase was attributed to climate factors whereas the remaining 40% was likely caused by human activities. Although farming practices and crop rotations might have caused significant decrease in NDVI in small areas, human impact largely led to significant NDVI increase in the grass‐crop transition zones, most likely due to ecological restoration programs. This article is protected by copyright. All rights reserved.

Description: Abstract In climatology, there is a clear need for more reliable data, especially in regions where no meteorological stations exist. Different statistical methods as well as regional climate models are usually used for covering areas with limited data. However, important biases between real and simulated climate parameters are observed, especially with respect to extremes. The present study introduces a new statistical method that combines triangular irregular networks and copulas for the simulation of extreme maximum and minimum temperatures. According to the new method, a studied region can be divided into triangles, and a data series can be simulated for every unknown x‐point in each triangle. The simulation of new data series is based on both the distances between the unknown point and the triangle vertices and the observational data available at each vertex. The statistical evaluation of this new method was successful and further demonstrated that the size of the triangle as well as the climatic characteristics of the stations at the triangle vertices can significantly affect the final results. The present investigation proposes the triangular irregular network‐copula method for the simulation of extreme temperatures, as the projections provided by this method are shown to approach the observed extremes values for individual meteorological stations.

Description: In this paper, an analysis of the spatial and temporal variability of the daily precipitation concentration in the Sardinia region, the second largest island in the Mediterranean Sea, has been performed for the period 1922–2011 by applying the Concentration Index. Results show that the region can be divided in two parts with opposite behaviours: the western side characterized by a more uniform temporal distribution of rainfall and the eastern side with the most critical intensity and aggressiveness of rainfall. The marked positive trend of the Concentration Index detected indicates a tendency towards more intense and aggressive rainfall. Abstract Recent studies evidenced that extreme daily rainfall often increases in spite of a decrease of the total rainfall. For this reason, the analysis of daily precipitation concentration is an important issue in climate research and can be useful to evaluate risks linked to extreme rainfall events. In this paper, an analysis of the spatial and temporal variability of the daily precipitation concentration in the Sardinia region, the second largest island in the Mediterranean Sea, has been performed for the period 1922–2011 by applying the Concentration Index. First, the CI has been evaluated at annual and seasonal scale. Then, the coefficient of variation, and the 5th and 95th percentiles of the annual CI values were calculated and mapped as a set of descriptors of variability and extreme characterization of precipitation concentration across the study area. Finally, in order to evaluate the temporal evolution of the CI, a trend analysis has been also performed using the Mann–Kendall test. Results show CI values ranging from a minimum of 0.477 to a maximum of 0.723. Spatially, the region can be divided in two parts with opposite behaviours: the western side characterized by a more uniform temporal distribution of rainfall intensity and the eastern side with the most critical intensity and aggressiveness of rainfall. From a seasonal point of view, this spatial gradient was evidenced in winter, spring and autumn while summer showed a more homogeneous spatial distribution of CI. The spatial distribution of the coefficient of variation did not show any spatial gradient between the two sides of the region, both at annual and seasonal scale. As regards the trend analysis, a marked positive trend of the CI has been detected, thus indicating a tendency towards more intense and aggressive rainfall. Copyright © 2019 John Wiley & Sons, Ltd.

Description: The number of spring phenological time series of herbaceous plants with advanced or delayed trends at 20 stations in Inner Mongolia, China. Abstract In a natural ecosystem or as climatic climax community, grassland is relatively dry with a strongly seasonal climate, and sensitive to climatic changes. Most of the previous studies used remote sensing data to investigate the phenological response of grassland to climate change, while ground‐based studies covering a large geographic area were limited. In this study, using the long‐term phenological data (1981–2012) of 16 herbaceous species observed at 20 stations in Inner Mongolia, China, we first investigated the trends in three spring phenophases, including the dates of bud‐burst, first leaf unfolding, and 50% of leaf unfolding. Subsequently, multiple linear regressions between phenophases and four climatic factors (spring temperature, soil moisture, chilling temperature, and insolation) were performed to determine the relative importance of each factor on the spring phenology. To validate the resulted regression coefficients, we developed a controlled environment to investigate the factors regulating the leaf unfolding time of one dominant species (Leymus chinensis). The results showed that the study area became warmer and drier from 1981 to 2012. However, the overall changes in spring phenophases were not apparent, as there was a similar proportion of significant earlier or later trends. Such phenological changes were driven by multiple climatic factors. The warmer temperature would advance the spring phenophases, while lower soil moisture would delay them. The impact of soil moisture was significant in the experimental data, but not significant in the observation data. In addition, leaf unfolding became faster when L. chinensis experienced more chilling days, but this effect was difficult to be detected in observation data due to the weak sensitivity of the leaf unfolding time to chilling days. These findings can help us to understand how the spring phenology of typical herbaceous species responds to multiple climatic factors under the background of climate change.

Description: Contribution rate of climatic factors to the trend of aridity index in eight climate regions over the period of 1999–2017. Abstract Intense anthropogenic climate changes are expected to increase atmospheric aridity in the 21st century. The aridity index (AI), defined as the ratio of annual precipitation (Pre) to atmospheric evaporation (potential evapotranspiration [PET]), represents an efficient indicator of climatic changes. However, the variations and underlying drivers of AI values have not been comprehensively compared in different climatic regions. Using the AI calculated on the basis of bias‐corrected precipitation and optimized PET over the period of 1999–2017 and two climate model projections for the coming century, we investigated the response of the AI to climate change and quantified the contributions of climatic factors to AI variations in eight climatic regions in China, that is, the northwest (NW), north‐centre (NC), northeast (NE), North China Plain (NCP), east (E), southeast (SE), southwest (SW) and Tibet Plateau (TP). The results indicated that the AI values in seven of the eight climate regions exhibited negative trends from 1999 to 2017, with mean values ranging from −0.0008 in SW to −0.0414 in NC, while the AI values in the TP region showed a significant positive trend, with a value of 0.0124. Pre was the dominant factor for the variations in AI values in all climate regions, with contribution rates from 65 to 308%, followed by decreasing solar radiation in the NW, NC, E, SE and SW regions; deceasing wind speed in NE and NCP; and deceasing actual vapour pressure in the TP. The effect of increasing temperature on the AI trend was offset by other climate factors. By the end of the 21st century, under the Representative Concentration Pathway 8.5 emission scenario, the AI will significantly increase in five of the eight regions to values approximately 16.5% higher than those during 1999–2017, and this increase in the AI will be dominated by increasing PET. Overall, the shift in the dominant AI factor from Pre in recent years to PET in the future indicates that more attention should be given to the response of the AI to global warming. Furthermore, regional differences in climate change and AI values during 2018–2,100 will inevitably influence water availability and urgently require the development of adaptation strategies for different climatic regions.

Description: Leading modes of variability in PDSI. (a) Patterns of the leading PCs at 131 stations. (b) Principle component time series (EOFs) associated with the leading PCs. (c) Time series of PDSI at 131 stations arranging by subregions. (d) Variation of the Oceanic Nino Index (ONI) monthly time series; the grey shade indicates the threshold SST of ±0.5C that categorizes the ENSO phase as El Niño (red) and La Niña (blue). Abstract Drought may have severe societal, economic, and environmental consequences. However, the space–time characteristics of drought over Vietnam remain poorly understood. In this study, we investigate the spatio‐temporal variability of drought using the Palmer Drought Severity Index (PDSI) over mainland Vietnam for the 1980–2014 period. Through data analysis at 131 stations, we identified the main characteristics, historical trends, and dominant variability of drought across seven climatic sub‐regions in Vietnam. The results show regional patterns of drought duration, inter‐arrival time, frequency, and severity, but no consistent trend of drought variation during the study period. Based on the supply and demand concepts of water balance, PDSI captures well the large frequency and severity of drought in some sub‐regions that are related to soil moisture deficit associated with high temperature and low rainfall during summer. Moreover, drought over Vietnam was predominantly controlled by climate seasonality. The linkages between drought in Vietnam and large‐scale drivers are quite different among areas, suggesting a possibility of early prediction for drought at some sub‐regions using ENSO.

Description: Climatological distributions of snow depth (units: cm) over the TP in (a) May, (b) June, (c) July, and (d) August. Black solid lines and dashed curved lines outline the areas of the TP with an average altitude greater than 2000 and 4,000 m, respectively. The red rectangle in (a) represents the area (32°–40°N, 70°–78°E), covered by thick snow, over the western TP. That rectangle has the same meaning in the following figures Abstract The atmospheric heat source/sink (AHS) and snow cover/depth over the Tibetan Plateau (TP) in late spring play important roles in modulating the evolution of Asian summer monsoons. However, quantitative estimation of the AHS over the TP is still a large challenge because of limited observational data. In this work, both data analyses and numerical simulations from the Weather Research and Forecasting model with an updated daily snow‐depth data set are conducted to explore the intrinsic connection between the AHS and snow depth over the TP in May. Data analyses indicate that only a weak negative relationship exists between snow depth and the AHS over the western TP. On the other hand, despite the overall consistency in the spatial pattern of the AHS between the reanalysis data sets and the Weather Research and Forecasting model, a strong cooling effect (about −14.4 W/m2) appears over the western TP (32°–40°N, 70°–78°E) and the Nyenchen Tanglha Mountains in the simulation, where the altitude is above 4,000 m with thick snow cover. This characteristic is opposite to that observed in reanalysis data sets. Further analysis indicates that the underestimated atmospheric net longwave radiative cooling effect that is associated with snow depth may exaggerate the atmospheric heat source in current reanalysis data sets. Large uncertainties in the AHS still exist in current state‐of‐the‐art reanalysis data sets, especially over the regions of the TP covered by snow.

Description: The increasing zonal mean and regional sea‐ice concentration (SIC) trends during 1979–2015 are vertically associated with geopotential height (GPH) and temperature trends in the troposphere, indicating that positive SIC trends are vertically associated with cooling air masses centred around 850 hPa in the troposphere which is located beneath negative GPH trends at the upper troposphere, and vice versa. This relationship also stands in extreme years for seven SIC maximum and four SIC minimum autumns from 1979 to 2015, particularly reconfirmed in the two SIC minimum autumns of 2017 and 2018. Abstract It is well‐known that the increasing zonal‐mean trend of sea‐ice concentration (SIC) surrounding Antarctica is up to 2015 so this paper first summarized this trend with opposing regional trends in each season along the latitude band 60°–70°S during 1979–2015. The surface wind trends can partly explain the observed regional SIC trends. By analysing the surface wind, as well as the tropospheric geopotential height (GPH) and temperature trends, the study revealed that the increasing zonal‐mean SIC trend and opposing regional SIC trends surrounding Antarctica are vertically associated with GPH and temperature trends in the troposphere. This relationship is also found in extreme years for seven SIC maximum and four SIC minimum autumns from 1979 to 2015, particularly reconfirmed in the two SIC minimum autumns of 2017 and 2018, indicating that a positive regional SIC anomaly is vertically associated with an anomalously cold air mass centred around 850 hPa in the troposphere which is located beneath a negative GPH anomaly at the upper troposphere, and vice versa.

Description: Abstract Taiwan's most significant natural hazards are caused by hydrological extremes resulting from excessive precipitation. The threat of extreme precipitation is posed by several different types of weather patterns that affect Taiwan. This study examined the bi‐decadal changes in rainfall by defining an extreme precipitation occurrence (EPO) for a range of event durations from 1 to 24 hours. Three major weather types affecting EPO in Taiwan were identified from 1993 to 2015: the front‐type consisting of either a frontal zone or convective systems developing with an apparent Meiyu cloudband, diurnal rainfall events when no apparent synoptic features are present, and a tropical cyclone (TC) type according to the maximum sustained wind radius of a TC. Results show that TC‐type events have the greatest overall contribution to EPO at longer (〉6h) durations. Diurnal/afternoon convection events contribute most to the shorter (〈 3h) duration EPO, while frontal/Meiyu systems prevail in the medium (3‐6h) duration. EPO of almost all durations have experienced an increase, with the 3h and 12h EPO having increased by 4.6 days each over the 23 years. However, apparent decadal‐scale variability exists in these EPO associated with the decreasing tendency of EPO after the mid‐2000s, particularly the longer duration (〉6hr) EPO associated with the TC‐type events in summer. The distinction between EPO trends for the entire island of Taiwan and for the Taipei metropolitan area alone (northern Taiwan, population of 7 million) were compared, and an intriguing interannual variation is reported in the TC‐type EPO associated with the TC season one year to a year and half just before an ENSO event. The analysis here provides refined statistical distributions of extreme rainfall and these can contribute to the revision of governmental definitions for weather disasters that are used in mitigation and response strategies. This article is protected by copyright. All rights reserved.