ALBERT

Description: The present study assessed climate change vulnerability in agricultural sector of low-lying Sagar Island of Bay of Bengal. Vulnerability indices were estimated using spatially aggregated biophysical and socio-economic parameters by applying principal component analysis and equal weight method. The similarities and differences of outputs of these two methods were analysed across the island. From the integration of outputs and based on the severity of vulnerability, explicit vulnerable zones were demarcated spatially. Results revealed that life subsistence agriculture in 11.8% geographical area (2829 ha) of the island along the western coast falls under very high vulnerable zone (VHVZ VI of 84–99%) to climate change. Comparatively higher values of exposure (0.53 ± 0.26) and sensitivity (0.78 ± 0.14) subindices affirmed that the VHV zone is highly exposed to climate stressor with very low adaptive capacity (ADI= 0.24 ± 0.16) to combat vulnerability to climate change. Hence, food security for a population of 〉22 thousands comprising 〉3.7 thousand agrarian households are highly exposed to climate change. Another 17% area comprising 17.5% population covering 20% villages in north-western and eastern parts of the island also falls under high vulnerable (VI= 61%–77%) zone. Findings revealed large spatial heterogeneity in the degree of vulnerability across the island and thus, demands devising area specific planning (adaptation and mitigation strategies) to address the climate change impact implications both at macro and micro levels.

Description: The uncertainty of global summer precipitation simulated by the 23 CMIP5 CGCMs and the possible impacts of model resolutions are investigated in this study. Large uncertainties exist over the tropical and subtropical regions, which can be mainly attributed to convective precipitation simulation. High-resolution models (HRMs) and low-resolution models (LRMs) are further investigated to demonstrate their different contributions to the uncertainties of the ensemble mean. It shows that the high-resolution model ensemble means (HMME) and low-resolution model ensemble mean (LMME) mitigate the biases between the MME and observation over most continents and oceans, respectively. The HMME simulates more precipitation than the LMME over most oceans, but less precipitation over some continents. The dominant precipitation category in the HRMs (LRMs) is the heavy precipitation (moderate precipitation) over the tropic regions. The combinations of convective and stratiform precipitation are also quite different: the HMME has much higher ratio of stratiform precipitation while the LMME has more convective precipitation. Finally, differences in precipitation between the HMME and LMME can be traced to their differences in the SST simulations via the local and remote air-sea interaction.

Description: The statistical climate downscaling model by the Turkish Water Foundation (TWF) is further developed and applied to a set of monthly precipitation records. The model is structured by two phases as spatial (regional) and temporal downscaling of global circulation model (GCM) scenarios. The TWF model takes into consideration the regional dependence function (RDF) for spatial structure and Markov whitening process (MWP) for temporal characteristics of the records to set projections. The impact of climate change on monthly precipitations is studied by downscaling Intergovernmental Panel on Climate Change-Special Report on Emission Scenarios (IPCC-SRES) A2 and B2 emission scenarios from Max Plank Institute (EH40PYC) and Hadley Center (HadCM3). The main purposes are to explain the TWF statistical climate downscaling model procedures and to expose the validation tests, which are rewarded in same specifications as “very good” for all stations except one (Suhut) station in the Akarcay basin that is in the west central part of Turkey. Eventhough, the validation score is just a bit lower at the Suhut station, the results are “satisfactory.” It is, therefore, possible to say that the TWF model has reasonably acceptable skill for highly accurate estimation regarding standard deviation ratio (SDR), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) criteria. Based on the validated model, precipitation predictions are generated from 2011 to 2100 by using 30-year reference observation period (1981–2010). Precipitation arithmetic average and standard deviation have less than 5% error for EH40PYC and HadCM3 SRES (A2 and B2) scenarios.

Description: Winter (DJF) precipitation over Northwest India (NWI) is very important for the cultivation of Rabi crops. Thus, an accurate estimation of high-resolution observations, evaluation of high-resolution numerical models, and understanding the local variability trends are essential. The objective of this study is to verify the quality of a new high spatial resolution (0.25° × 0.25°) gridded daily precipitation data set of India Meteorological Department (IMD1) over NWI during winter. An intercomparison with four existing precipitation data sets at 0.5° × 0.5° of IMD (IMD2), 1° × 1° of IMD (IMD3), 0.25° × 0.25° of APHRODITE (APRD1), and 0.5° × 0.5° of APHRODITE (APRD1) resolution during a common period of 1971–2003 is done. The evaluation of data quality of these five data sets against available 26 station observations is carried out, and the results clearly indicate that all the five data sets reasonably agreed with the station observation. However, the errors are relatively more in all the five data sets over Jammu and Kashmir-related four stations (Srinagar, Drass, Banihal top, and Dawar), while these errors are less in the other stations. It may be due to the lack of station observations over the region. The quality of IMD1 data set over NWI for winter precipitation is reasonably well than the other data sets. The intercomparison analysis suggests that the climatological mean, interannual variability, and coefficient of variation from IMD1 are similar with other data sets. Further, the analysis extended to the India meteorological subdivisions over the region. This analysis indicates overestimation in IMD3 and underestimation in APRD1 and APRD2 over Jammu and Kashmir, Himachal Pradesh, and NWI as a whole, whereas IMD2 is closer to IMD1. Moreover, all the five data sets are highly correlated (〉0.5) among them at 99.9% confidence level for all subdivisions. It is remarkably noticed that multicategorical (light precipitation, moderate precipitation, heavy precipitation, and very heavy precipitation) skill score of accuracy (〉0.8) for the four data sets against IMD1 is good for all the subdivisions as well as NWI and is more in IMD2. IMD1 performs well in capturing the relationships of winter precipitation with climate indices such as Nino 3.4 region sea surface temperature, Southern Oscillation Index, Arctic Oscillation, and North Atlantic Oscillation. The results conclude that IMD1 is useful to understand the variability trends at the local climate scale and its global teleconnections.

Description: The annual distribution and variations in dust weather occurrence (DWO) have been analyzed using monthly DWO data from 26 stations over the Tarim Basin during the period of 1961 to 2010. The results show that the DWO presents a significant decreasing trend for different parts of the Tarim Basin in recent decades. The monthly DWO has two peaks in the east and west. In the first half of the year, the peak is in April, but in the second half of the year, the peak is in September. According to the concentration period and concentration degree (CD) of DWO, we can find that the maximum DWO occurs in April in the eastern, western, and northern parts of the basin, but it occurs in May in the southern part. The dust weather season is shorter for the northern and eastern parts of the basin than those of the remaining parts. On average, the dust weather season initiates in April in the northeast and in May for the rest of the region. As an indicator for the length of dust weather season, the CD is significantly related to DWO, with a correlation coefficient of −0.51, revealing an interesting feature of regional climate change with declining DWO and declining dust weather season over the Tarim Basin. The correlation analysis exhibits that all the Arctic Oscillation, Antarctic Oscillation, and North Atlantic Oscillation have a negative relation with the DWO but a positive relation with the length of dust weather season.

Description: The accuracy of reconstructing missing daily temperature extremes in the Jaffna climatological station, situated in the northern part of the dry zone of Sri Lanka, is presented. The adopted method utilizes standard departures of daily maximum and minimum temperature values at four neighbouring stations, Mannar, Anuradhapura, Puttalam and Trincomalee to estimate the standard departures of daily maximum and minimum temperatures at the target station, Jaffna. The daily maximum and minimum temperatures from 1966 to 1980 (15 years) were used to test the validity of the method. The accuracy of the estimation is higher for daily maximum temperature compared to daily minimum temperature. About 95% of the estimated daily maximum temperatures are within ±1.5 °C of the observed values. For daily minimum temperature, the percentage is about 92. By calculating the standard deviation of the difference in estimated and observed values, we have shown that the error in estimating the daily maximum and minimum temperatures is ±0.7 and ±0.9 °C, respectively. To obtain the best accuracy when estimating the missing daily temperature extremes, it is important to include Mannar which is the nearest station to the target station, Jaffna. We conclude from the analysis that the method can be applied successfully to reconstruct the missing daily temperature extremes in Jaffna where no data is available due to frequent disruptions caused by civil unrests and hostilities in the region during the period, 1984 to 2000.

Description: Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.

Description: We present preliminary analyses of the historical (1986–2005) climate simulations of a ten-member subset of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models over Southeast Asia. The objective of this study was to evaluate the general circulation models’ performance in simulating the mean state of climate over this less-studied climate vulnerable region, with a focus on precipitation. Results indicate that most of the models are unable to reproduce the observed state of climate over Southeast Asia. Though the multi-model ensemble mean is a better representation of the observations, the uncertainties in the individual models are far high. There is no particular model that performed well in simulating the historical climate of Southeast Asia. There seems to be no significant influence of the spatial resolutions of the models on the quality of simulation, despite the view that higher resolution models fare better. The study results emphasize on careful consideration of models for impact studies and the need to improve the next generation of models in their ability to simulate regional climates better.

Description: The Turkana Low-Level Jet (LLJ) was discovered in the early 1980s, yet there are still questions about the primary forcing mechanisms that drive and sustain the jet throughout the year. A few studies have addressed these questions, but most focus on numerical simulations of mechanical forcing mechanisms, such as orography, channeling flow, and monsoon background flow. No studies have shown the effects of thermal forcing from differential heating in the regions in and around the Turkana Channel. This paper uses National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) data and the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) data in order to analyze and find relationships between temperature gradients and the strength of the Turkana LLJ. In addition to temperature, potential temperature, divergence, wind magnitude, wind fields, and vertical motion are also examined. This analysis attempts to show that thermal forcing is one of the most important factors, if not the primary factor, in the initiation and maintenance of the jet and propose that more research and model simulations should be implemented to determine the contributions from thermal forcing.

Description: Apparent changes in the temperature patterns in recent years brought many challenges to the province of Ontario, Canada. As the need for adapting to climate change challenges increases, the development of reliable climate projections becomes a crucial task. In this study, a regional climate modeling system, Providing Regional Climates for Impacts Studies (PRECIS), is used to simulate the temperature patterns in Ontario. Three PRECIS runs with a resolution of 25 km × 25 km are carried out to simulate the present (1961–1990) temperature variations. There is a good match between the simulated and observed data, which validates the performance of PRECIS in reproducing temperature changes in Ontario. Future changes of daily maximum, mean, and minimum temperatures during the period 2071–2100 are then projected under the IPCC SRES A2 and B2 emission scenarios using PRECIS. Spatial variations of annual mean temperature, mean diurnal range, and temperature seasonality are generated. Furthermore, heat waves defined based on the exceedance of local climatology and their temporal and spatial characteristics are analyzed. The results indicate that the highest temperature and the most intensive heat waves are most likely to occur at the Toronto-Windsor corridor in Southern Ontario. The Northern Ontario, in spite of the relatively low projected temperature, would be under the risk of long-lasting heat waves, and thus needs effective measures to enhance its climate resilience in the future. This study can assist the decision makers in better understanding the future temperature changes in Ontario and provide decision support for mitigating heat-related loss.