ALBERT

Description: A satellite-based climatology is presented of 9607 mesoscale convective systems (MCSs) that occurred over the central and southeastern United States from 1996 to 2017. This climatology is constructed with a fully automated algorithm based on their cold cloud shields, as observed from infrared images taken by GOES-East satellites. The geographical, seasonal, and diurnal patterns of MCS frequency are evaluated, as are the frequency distributions and seasonal variability of duration and maximum size. MCS duration and maximum size are found to be strongly correlated, with coefficients greater than 0.7. Although previous literature has subclassified MCSs based on size and duration, we find no obvious threshold that cleanly categorizes MCSs. The Plains and Deep South are identified as two regional modes of maximum MCS frequency, accounting for 21% and 18% of MCSs, respectively, and these are found to differ in the direction and speed of the MCSs (means of 16 and 13 m s−1), their distributions of duration and size (means of 12.2 h, 176 000 km2 and 9.6 h, 108 000 km2), their initial growth rates (means of 7.6 and 6.1 km2 s−1), and many aspects of the seasonal cycle. The lifetime patterns of MCS movement and growth are evaluated for the full domain and for the two regional modes. The growth patterns and strong correlation between size and duration allow for a parabolic function to represent the MCS life cycle quite well in summary statistics. We show that this satellite-based climatology supports previous studies identifying favorable environments for mesoscale convective systems.

Description: A multiscale dynamical model for weather forecasting and climate modeling is developed and evaluated in this study. It extends a previously established layer-averaged, unstructured-mesh nonhydrostatic dynamical core (dycore) to moist dynamics and parameterized physics in a dry-mass vertical coordinate. The dycore and tracer transport components are coupled in a mass-consistent manner, with the dycore providing time-averaged horizontal mass fluxes to passive transport, and tracer transport feeding back to the dycore with updated moisture constraints. The vertical mass flux in the tracer transport is obtained by reevaluating the mass continuity equation to ensure compatibility. A general physics–dynamics coupling workflow is established, and a dycore–tracer–physics splitting strategy is designed to couple these components in a flexible and efficient manner. In this context, two major physics–dynamics coupling strategies are examined. Simple-physics packages from the 2016 Dynamical Core Model Intercomparison Project (DCMIP2016) experimental protocols are used to facilitate the investigation of the model behaviors in idealized moist-physics configurations, including cloud-scale modeling, weather forecasting, and climate modeling, and in a real-world test-case setup. Performance evaluation demonstrates that the model is able to produce reasonable sensitivity and variability at various spatiotemporal scales. The consideration and implications of different physics–dynamics coupling options are discussed within this context. The appendix provides discussion on the energetics in the continuous- and discrete-form equations of motion.

Description: This study investigates the shapes and fall speeds of freezing and frozen raindrops through field observations using an instrument called the high-speed optical disdrometer (HOD) that we developed recently. Our field observations showed that while the shapes of all of the observed freezing raindrops and a portion of the frozen raindrops (39% of the frozen raindrops that are larger than 1.0 mm in volume equivalent diameter D) resemble the shapes of warm raindrops, majority of frozen raindrops (61% of the frozen raindrops with D 〉 1.0 mm) exhibited a distinct feature such as a spicule, bulge, cavity, or aggregation. Field observations of axis ratios (i.e., ratio of the vertical to horizontal chord) and fall speeds were compared with the predictions of available models. Separate empirical axis ratio parameterizations were developed for the freezing and frozen raindrops using the HOD field observations and extensions to an available shape model were also incorporated. For the fall speeds of freezing and frozen raindrops, field observations demonstrated a good agreement with the predictions of the available parameterizations. Frozen raindrops showed a larger scatter of fall speeds around the mean fall speed of a given drop size than those of the freezing raindrops due to the shape variety among the frozen raindrops with the aforementioned distinct features. The drag coefficients for the observed hydrometeors were compared with the predictions of the available drag coefficient models. Separate “drag coefficient–Reynolds number” relationships for freezing and frozen raindrops were developed.

Description: Soil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. The results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes.

Description: Previous studies linked the increase of the middle and low reaches of the Yangtze River (MLRYR) rainfall to tropical Indian Ocean warming during extreme El Niños’ (e.g., 1982/83 and 1997/98 extreme El Niños) decaying summer. This study finds the linkage to be different for the recent 2015/16 extreme El Niño’s decaying summer, during which the above-normal rainfalls over MLRYR and northern China are respectively linked to southeastern Indian Ocean warming and western tropical Indian Ocean cooling in sea surface temperatures (SSTs). The southeastern Indian Ocean warming helps to maintain the El Niño–induced anomalous lower-level anticyclone over the western North Pacific Ocean and southern China, which enhances moisture transport to increase rainfall over MLRYR. The western tropical Indian Ocean cooling first enhances the rainfall over central-northern India through a regional atmospheric circulation, the latent heating of which further excites a midlatitude Asian teleconnection pattern (part of circumglobal teleconnection) that results in an above-normal rainfall over northern China. The western tropical Indian Ocean cooling during the 2015/16 extreme El Niño is contributed by the increased upward latent heat flux anomalies associated with enhanced surface wind speeds, opposite to the earlier two extreme El Niños.

Description: The adequate simulation of internal climate variability is key for our understanding of climate as it underpins efforts to attribute historical events, predict on seasonal and decadal time scales, and isolate the effects of climate change. Here the skill of models in reproducing observed modes of climate variability is assessed, both across and within the CMIP3, CMIP5, and CMIP6 archives, in order to document model capabilities, progress across ensembles, and persisting biases. A focus is given to the well-observed tropical and extratropical modes that exhibit small intrinsic variability relative to model structural uncertainty. These include El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the North Atlantic Oscillation (NAO), and the northern and southern annular modes (NAM and SAM). Significant improvements are identified in models’ representation of many modes. Canonical biases, which involve both amplitudes and patterns, are generally reduced across model generations. For example, biases in ENSO-related equatorial Pacific sea surface temperature, which extend too far westward, and associated atmospheric teleconnections, which are too weak, are reduced. Stronger tropical expression of the PDO in successive CMIP generations has characterized their improvement, with some CMIP6 models generating patterns that lie within the range of observed estimates. For the NAO, NAM, and SAM, pattern correlations with observations are generally higher than for other modes and slight improvements are identified across successive model generations. For ENSO and PDO spectra and extratropical modes, changes are small compared to internal variability, precluding definitive statements regarding improvement.

Description: The atmospheric circulation during the South Asian summer monsoon season is analyzed in moist isentropic coordinates. The horizontal mass transport is sorted in terms of its equivalent potential temperature and is separated into the upper- and lower-tropospheric contributions. This technique makes it possible to trace the transport of air parcels over long distances, identify regions of convective motion in the tropics, and assess the impacts of diabatic processes. The goal here is to assess the thermodynamic characteristics of the atmospheric overturning associated with the South Asian monsoon and to connect this thermodynamic structure to horizontal transport. The monsoon is associated with a low-level inflow of warm and moist air, compensated by an upper-tropospheric outflow at high potential temperature. The South Asian monsoon differs, however, from other monsoonal systems in two important ways. First, the ascending air exhibits an unusually high equivalent potential temperature, which results in global lifting of the tropopause during the boreal summer. Second, on a seasonal basis the main monsoon regions appear to be shielded from dry air intrusion from the extratropical regions.

Description: The last comprehensive statistics of tornadoes in northern Eurasia (NE) were published more than 30 years ago. This paper introduces a new database of tornadoes in NE that spans from the tenth century to 2016. The database, compiled using various sources, contains 2879 tornado cases over land and water and includes tornado characteristics. Tornadoes are common for most regions of NE, with a density reaching four cases per 104 km2 in 1900–2016 in some regions. Tornadoes over land have distinct annual and diurnal cycles: they form mostly in May–August, with a maximum in June, and during daytime, with a maximum at 1700–1800 local time. Waterspouts form in all months with a maximum in late summer and mostly at 0900–1300 local time. Most tornadoes are weak and short lived. The Fujita-scale intensity is ≤F1 for 80% and ≥F3 for 3% out of all rated tornadoes. Half last less than 10 min. The average annual number of all tornadoes over land is around 150, including 10 and 2 tornadoes with ≥F2 and ≥F3 intensity, respectively. Annually, 1–2 tornadoes lead to casualties and result in 2.9 fatalities and 36.3 injuries. Despite the incompleteness of the dataset, our results show that tornadoes in NE, although being rare, are not as extremely rare as has been thought before. The results illustrate the substantial underestimation of tornado threat by the general public, researchers, and meteorologists, and unambiguously indicate the need for systematic assessments and forecasting of tornadoes by national weather services.

Description: The city of Jeddah, Saudi Arabia, is characterized by a hot and arid desert climate. On occasion, however, extreme precipitation events have led to flooding that caused extensive damage to human life and infrastructure. This study investigates the effect of incorporating an urban canopy model and urban land cover when simulating severe weather events over Jeddah using the Weather Research and Forecasting (WRF) Model at a convective-permitting scale (1.5-km resolution). Two experiments were conducted for 10 heavy rainfall events associated with the dominant large-scale patterns favoring convection over Jeddah: (i) an “urban” experiment that included the urban canopy model and modern-day land cover and (ii) a “desert” experiment that replaced the city area with its presettlement, natural land cover. The results suggest that urbanization plays an important role in modifying rainfall around city area. The urban experiment enhances the amount of rainfall by 26% on average over the Jeddah city area relative to the desert experiment in these extreme events. The changes in model-simulated precipitation are primarily tied to a nocturnal heat-island effect that modifies the planetary boundary layer and atmospheric instability of the convective events.

Description: Proverbs are a part of traditional knowledge that has been increasingly acknowledged to be a valuable source of information for environmental policies. Proverbs on weather convey the cumulated experience of generations that provide guidelines for agricultural practices, everyday decisions, and other situations. Besides the value the proverbs have in their cultural setting, they also serve as an indicator of objective meteorological patterns. This study presents a comprehensive evaluation of the Polish temperature-related proverbs. From the collected corpus of more than 2000 Polish proverbs, 28 were related to temperature and provided concrete predictions and so were selected for further analysis. The proverbs were tested on the basis of temperature (minimum, maximum, and mean) data from 20 weather stations, located in Poland and the neighboring countries, for the period of 1951–2012. Harbingers and forecasts were identified and coded as 0 or 1. Proverb accuracies were then compared using Heidke skill scores and proportions of fulfilled proverbs. The proverbs’ spatial and temporal contingencies were tested via generalized linear mixed models. Some proverbs provided a high proportion (up to 79%) of fulfilled proverbs. Furthermore, the accuracy of the proverbs was reversely proportional to chronological date (decreased with time), with values increasing toward the east and north directions of station locations. The observed changes in proverb accuracies may be attributed to the shift of Polish borders following the Second World War and the respective migration of the population.

Description: The evaluation of the ocean energy balance is crucial for improving the fundamental understanding of the mechanisms sustaining ocean circulation. Based on the outputs of the ROMS ocean model, the energy cycle, eddy–mean flow interactions, and energy pathways of the deep Gulf of Mexico (GoM) have been investigated in this study. The theoretical framework for the analysis is based on the energy equations for the time-mean and time-varying flow, where some of the terms were split into their horizontal and vertical components to monitor the energy pathways. Of the energy maintaining deep kinetic energy (KE), approximately 75% is transferred from the upper layer to the deep layer by vertical pressure work (PW), about 6% by the horizontal PW through the Yucatan and Florida straits, and ~19% is generated through the processes related to baroclinic instabilities. The mean circulation generates eddies in the upper layer, while eddies drive mean circulation in the deep layer. Energy is transferred downward in the eastern and western part of the Gulf, upward in the deep western-central part, and a strong westward energy transport can be observed below 2000-m depth.

Description: The response of a coastal ocean numerical model, typical of eastern boundaries, is investigated under upwelling-favorable wind forcing and with/without the presence of a submarine canyon. Experiments were run over three contrasting shelf depth/slope bathymetries and forced by an upwelling-favorable alongshore wind. Random noise in the wind stress field was used to trigger the onset of frontal instabilities, which formed around the upwelling front. Their development and evolution are enhanced over deeper (and less inclined) shelves. Experiments without a submarine canyon agree well with previous studies of upwelling frontal instabilities; baroclinic instabilities grow along the front in time. The addition of a submarine canyon incising the continental shelf dramatically changes the circulation and frontal characteristics. Intensified upwelling is channeled through the downstream side of the canyon in all depth/slope configurations. Farther downstream a downwelling area is generated, being larger and stronger on a shallow shelf. The canyon affects mainly the location of the southward upwelling jet, which is deflected inshore and accelerated after passing over the canyon. This process is accompanied by a break in the alongshore scale of the instabilities on either side of the canyon. Term balances of the depth-averaged cross-shore momentum equation reaffirm the downstream acceleration of the jet and the increased wavelength of the instabilities, and clarify the dominant balance between the advection and ageostrophic terms around the canyon.

Description: Heavy rainfall and strong wind are the two main sources of disasters that are caused by tropical cyclones (TCs), and typhoons with different characteristics may induce different agricultural losses. Traditionally, the classification of typhoon intensity has not considered the amount of rainfall. Here, we propose a novel approach to calculate the typhoon type index (TTI). A positive TTI represents a “wind type” typhoon, where the overall damage in a certain area from TCs is dominated by strong wind. On the other hand, a negative TTI represents a “rain type” typhoon, where the overall damage in a certain area from TCs is dominated by heavy rainfall. From the TTI, the vulnerability of crop losses from different types of typhoons can be compared and explored. For example, Typhoon Kalmaegi (2008) was classified as a rain-type typhoon (TTI = −1.22). The most affected crops were oriental melons and leafy vegetables. On the contrary, Typhoon Soudelor (2015) was classified as a significant wind-type typhoon in most of Taiwan (TTI = 1.83), and the damaged crops were mainly bananas, bamboo shoots, pomelos, and other crops that are easily blown off by strong winds. Through the method that is proposed in this study, we can understand the characteristics of each typhoon that deviate from the general situation and explore the damages that are mainly caused by strong winds or heavy rainfall at different locations. This approach can provide very useful information that is important for the disaster analysis of different agricultural products.

Description: Changes in tropical width can have important consequences in sectors including ecosystems, agriculture, and health. Observations suggest tropical expansion over the past 30 years although studies have not agreed on the magnitude of this change. Climate model projections have also indicated an expansion and show similar uncertainty in its magnitude. This study utilizes an objective, longitudinally varying, tropopause break method to define the extent of the tropics at upper levels. The location of the tropopause break is associated with enhanced stratosphere–troposphere exchange and thus its structure influences the chemical composition of the stratosphere. The method shows regional variations in the width of the upper-level tropics in the past and future. Four modern reanalyses show significant contraction of the tropics over the eastern Pacific between 1981 and 2015, and slight but significant expansion in other regions. The east Pacific narrowing contributes to zonal mean narrowing, contradicting prior work, and is attributed to the use of monthly and zonal mean data in prior studies. Six global climate models perform well in representing the climatological location of the tropical boundary. Future projections show a spread in the width trend (from ~0.5° decade−1 of narrowing to ~0.4° decade−1 of widening), with a narrowing projected across the east Pacific and Northern Hemisphere Americas. This study illustrates that this objective tropopause break method that uses instantaneous data and does not require zonal averaging is appropriate for identifying upper-level tropical width trends and the break location is connected with local and regional changes in precipitation.

Description: Global surface warming since 1850 has consisted of a series of slowdowns (hiatus) followed by surges. Knowledge of a mechanism to explain how this occurs would aid development and testing of interannual to decadal climate forecasts. In this paper a global climate model is forced to adopt an ocean state corresponding to a hiatus [with negative interdecadal Pacific oscillation (IPO) and other surface features typical of a hiatus] by artificially increasing the background diffusivity for a decade before restoring it to its normal value and allowing the model to evolve freely. This causes the model to develop a decadal surge that overshoots equilibrium (resulting in a positive IPO state), leaving behind a modified, warmer climate for decades. Water-mass transformation diagnostics indicate that the heat budget of the tropical Pacific Ocean is a balance between large opposite-signed terms: surface heating/cooling resulting from air–sea heat flux is balanced by vertical mixing and ocean heat transport divergence. During the artificial hiatus, excess heat becomes trapped just above the thermocline and there is a weak vertical thermal gradient (due to the high artificial background mixing). When the hiatus is terminated, by returning the background diffusivity to normal, the thermal gradient strengthens to prehiatus values so that the mixing (diffusivity × thermal gradient) remains roughly constant. However, since the base layer just above the thermocline remains anomalously warm, this implies a warming of the entire water column above the trapped heat, which results in a surge followed by a prolonged period of elevated surface temperatures.

Description: The Atmospheric Infrared Sounder (AIRS) on board NASA’s Aqua satellite provides more than 16 years of data. Its monthly gridded (Level 3) product has been widely used for climate research and applications. Since counts of successful soundings in a grid cell are used to derive monthly averages, this averaged by observations (ABO) approach effectively gives equal importance to all participating soundings within a month. It is conceivable then that days with more observations due to day-to-day orbit shift and regimes with better retrieval skills will contribute disproportionately to the monthly average within a cell. Alternatively, the AIRS Level 3 monthly product can be produced through an averaged by days (ABD) approach, where the monthly mean in a grid cell is a simple average of the daily means. The effects of these averaging methods on the AIRS version 6 monthly product are assessed quantitatively using temperature and water vapor at the surface and 500 hPa. The ABO method results in a warmer (slightly colder) global mean temperature at the surface (500 hPa) and a drier global mean water vapor than ABD method. The AIRS multiyear monthly mean temperature and water vapor from both methods are also compared with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) product and evaluated with a simulation experiment, indicating the ABD method has lower error and is more closely correlated with MERRA-2. In summary, the ABD method is recommended for future versions of the AIRS Level 3 monthly product and more data services supporting Level 3 aggregation are needed.

Description: An ensemble-based linearized forecast model has been developed for data assimilation applications for numerical weather prediction. Previous studies applied this local ensemble tangent linear model (LETLM) to various models, from simple one-dimensional models to a low-resolution (~2.5°) version of the Navy Global Environmental Model (NAVGEM) atmospheric forecast model. This paper applies the LETLM to NAVGEM at higher resolution (~1°), which required overcoming challenges including 1) balancing the computational stencil size with the ensemble size, and 2) propagating fast-moving gravity modes in the upper atmosphere. The first challenge is addressed by introducing a modified local influence volume, introducing computations on a thin grid, and using smaller time steps. The second challenge is addressed by applying nonlinear normal mode initialization, which damps spurious fast-moving modes and improves the LETLM errors above ~100 hPa. Compared to a semi-Lagrangian tangent linear model (TLM), the LETLM has superior skill in the lower troposphere (below 700 hPa), which is attributed to better representation of moist physics in the LETLM. The LETLM skill slightly lags in the upper troposphere and stratosphere (700–2 hPa), which is attributed to nonlocal aspects of the TLM including spectral operators converting from winds to vorticity and divergence. Several ways forward are suggested, including integrating the LETLM in a hybrid 4D variational solver for a realistic atmosphere, combining a physics LETLM with a conventional TLM for the dynamics, and separating the LETLM into a sequence of local and nonlocal operators.

Description: In data assimilation for NWP, accurate estimation of error covariance matrices (ECMs) and their use are essential to improve NWP accuracy. The objective of this study is to estimate ECMs of all observations and background variables using sampling statistics, and improve global NWP accuracy by using them. This study presents the first results of such all ECM refinement. ECM diagnostics combining multiple methods, and analysis and forecast cycle experiments were performed on the JMA global NWP system, where diagonal components of all ECMs and off-diagonal components of radiance observations are refined. The ECM diagnostic results are as follows: 1) the diagnosed error standard deviations (SDs) are generally much smaller than those of the JMA operational system (CNTL); 2) interchannel correlations in humidity-sensitive radiance errors are much larger than 0.2; and 3) horizontal correlation distances of AMSU-A are ~50 km, excluding channel 4. The experimental results include the following: 1) the diagnosed ECMs generally improve forecast accuracy over CNTL even without additional tunings; 2) the supplemental tuning parameter, which is the deflation factor (0.6 in SD) applied for the estimated ECMs of nonsatellite conventional data and GPS radio occultation data, statistically significantly improves forecast accuracy; 3) this value 0.6 is set as the same value as the ratio of the estimated background error SD to that in CNTL; 4) high-density assimilation (10 times) of AMSU-A is better than CNTL, not better than that with 5 times; and 5) ECMs estimated using boreal summer data can improve forecast accuracy in winter, which indicates their robustness.

Description: In the beginning of the twenty-first century, weather and climate extremes occurred more and more in extratropical summer, linked to the magnified amplitudes of quasi-stationary waves and external forcing. The study analyzes the relations between multidecadal extratropical extremes in boreal late summer and the North Atlantic (NA; 35°–65°N, 40°W–0°) multidecadal variability (NAMV) in the mid- to high latitudes. The results show that multidecadal extratropical extremes link with the intensified NAMV and the related positive–negative–positive (+ − +) zonal mode of sea surface temperature (SST). 1) The SST mode favors the eastward shift of the negative-phase NA oscillation (NNAO), with a latitudinal pattern of cyclone anomalies over the western European coast and anticyclones over Greenland; NNAO is helpful to baroclinic energy transfer and a longitudinal wavelike pattern. 2) The SST mode and the eddy-driven jet of NNAO are conducive to a southeast extension of the NA jet in close conjunction with the Afro-Asian jet, thereby enhancing the jet waveguide and barotropic energy transfer for the maintenance of a low-frequency wave. 3) The effect of the intensified NAMV on warming Europe contributes to the longitudinal temperature gradient–like “cooling ocean and warming land” pattern, which enhances the meridional wind and wave amplitude of the low-frequency wave. Based on these causes, the intensified NAMV and the + − + SST mode favor the enhancement of the low-frequency wave and quasi-resonant probability, which magnifies the amplitude of the quasi-stationary wave and enhances extratropical extremes on the decadal time scale.

Description: An extensive urban agglomeration has occurred over the Yangtze River delta (YRD) region of East China as a result of rapid urbanization since the middle 1990s. In this study, a 44-yr (i.e., 1975–2018) climatology of the summertime extreme hourly precipitation (EXHP; greater than the 90th percentile) over the YRD is analyzed, using historical land-use data, surface temperature, and hourly rain gauge observations, and then the relationship between rapid urbanization and EXHP changes is examined. Results show significant EXHP contrasts in diurnal variation and storm type roughly before and after middle July. That is, tropical cyclones (TCs) account for 16.4% of the total EXHP hours, 80.5% of which occur during the late summer, whereas non-TC EXHP accounts for 94.7% and 66.2% during the early and late summer, respectively. Increasing trends in occurrence frequency and amount of the non-TC and TC-induced EXHP are detected over the urban agglomeration. Statistically significant larger increasing trends in both the EXHP and surface temperature are observed at urban stations than those at the nearby rural stations. An analysis of 113 locally developed non-TC extreme rainfall events during 2011–18 summers also suggests the contribution of the urban heat island effects to the more occurrences of EXHP, especially over a band-shaped urban region where several major cities are distributed. This study reveals a significant correlation between rapid urbanization and increased EXHP during the past two decades over the YRD region. The results have important implications for understanding the impact of urbanization on EXHP changes in a warming climate.

Description: This study examines climatological potential vorticity streamer (PVS) activity associated with Rossby wave breaking (RWB), which can impact TC activity in the subtropical North Atlantic (NATL) basin via moisture and wind anomalies. PVSs are identified along the 2-PVU (1 PVU = 10−6 K kg−1 m2 s−1) contour on the 350-K isentropic surface, using a unique identification technique that combines previous methods. In total, 21 149 individual PVS instances are identified from the ERA-Interim (ERAI) climatology during June–November over 1979–2015 with a peak in July–August. The total number of PVSs identified in this study is more than previous PVS climatologies for this region, since the new technique identifies a wider range of cases. Variations in PVS size and intensity prompt the development of a new PVS activity index (PVSI), which provides an integrated measure of PVS activity that can improve comparisons with TC activity. For instance, PVSI has a stronger negative correlation with seasonal TC activity (r = −0.55) relative to PVS frequency, size, or intensity alone. PVSI in June–July is also positively correlated with PVSI in August–November (r = 0.67), suggesting predictive capability. Compared to the ERAI and Japan Meteorological Agency 55-Year Reanalysis (JRA-55) climatology, there are more PVSs in the Climate Forecast System Reanalysis (CFSR) but these have weaker average intensity overall. While no long-term trend in PVSI is observed in the ERAI or JRA-55 climatologies, a negative trend is observed in CFSR, which could be related to differences in near tropopause static stability early in the climatological period (1979–86) between the CFSR and ERAI datasets.

Description: This study has developed a rigorous and efficient maximum likelihood method for estimating the parameters in stochastic energy balance models (with any k 〉 0 number of boxes) given time series of surface temperature and top-of-the-atmosphere net downward radiative flux. The method works by finding a state-space representation of the linear dynamic system and evaluating the likelihood recursively via the Kalman filter. Confidence intervals for estimated parameters are straightforward to construct in the maximum likelihood framework, and information criteria may be used to choose an optimal number of boxes for parsimonious k-box emulation of atmosphere–ocean general circulation models (AOGCMs). In addition to estimating model parameters the method enables hidden state estimation for the unobservable boxes corresponding to the deep ocean, and also enables noise filtering for observations of surface temperature. The feasibility, reliability, and performance of the proposed method are demonstrated in a simulation study. To obtain a set of optimal k-box emulators, models are fitted to the 4 × CO2 step responses of 16 AOGCMs in CMIP5. It is found that for all 16 AOGCMs three boxes are required for optimal k-box emulation. The number of boxes k is found to influence, sometimes strongly, the impulse responses of the fitted models.

Description: Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

Description: Both to reconcile the large range in satellite-based estimates of the aerosol direct radiative effect (DRE) and to optimize the design of future observing systems, this study builds a framework for assessing aerosol DRE uncertainty. Shortwave aerosol DRE radiative kernels (Jacobians) were derived using the MERRA-2 reanalysis data. These radiative kernels give the differential response of the aerosol DRE to perturbations in the aerosol extinction coefficient, aerosol single-scattering albedo, aerosol asymmetry factor, surface albedo, cloud fraction, and cloud optical depth. This comprehensive set of kernels provides a convenient way to consistently and accurately assess the aerosol DRE uncertainties that result from observational or model-based uncertainties. The aerosol DRE kernels were used to test the effect of simplifying the full vertical profile of aerosol scattering properties into column-integrated quantities. This analysis showed that, although the clear-sky aerosol DRE can be had fairly accurately, more significant errors occur for the all-sky DRE. The sensitivity in determining the broadband spectral dependencies of the aerosol scattering properties directly from a limited set of wavelengths was quantified. These spectral dependencies can be reasonably constrained using column-integrated aerosol scattering properties in the midvisible and near-infrared wavelengths. Separating the aerosol DRE and its kernels by scene type shows that accurate aerosol properties in the clear sky are the most crucial component of the global aerosol DRE. In cloudy skies, determining aerosol properties in the presence of optically thin cloud is more radiatively important than doing so when optically thick cloud is present.

Description: Observations and climate models are combined to identify an anthropogenic warming signature in the upper ocean heat content changes (OHC) since 1971. We apply a new detection and attribution analysis developed by Ribes et al. (2017) which uses a symmetric treatment of the magnitude and the pattern of the climate response to each radiative forcing. A first estimate of the OHC response to natural, anthropogenic, greenhouse gas and other forcing is derived from a large ensemble of CMIP5 simulations. Observational datasets from historical reconstructions are then used to constrain this estimate. A spatio-temporal observational mask is applied to compare simulations with actual observations and to overcome reconstruction biases. Results on the 0-700 m layer from 1971 to 2005 show that the global OHC would have increased since 1971 by 2.12±0.21 10 7 J/ m 2/ y in response to GHG emissions alone. But this has been compensated for by other anthropogenic influences (mainly aerosol), which induced an OHC decrease of 0.84±0.18 10 7 J/ m 2/ y. The natural forcing has induced a slight global OHC decrease since 1971 of 0.13±0.09 10 7 J/ m 2/ y. Compared to previous studies we have separated the effect of the GHG forcing from the effect of the other anthropogenic forcing on OHC changes. This has been possible by using a new D&A method and by analyzing simultaneously the global OHC trends over 1957-1980 and over 1971-2005. This bivariate method takes advantage of the different time variation of the GHG forcing and the aerosol forcing since 1957 to separate both effects and reduce the uncertainty on their estimates.

Description: Open-ocean convection is a common phenomenon that regulates mixed layer depth and ocean ventilation in the high-latitude oceans. However, many climate model simulations overestimate mixed layer depth during open-ocean convection, resulting in excessive formation of dense water in some regions. The physical processes controlling transient mixed layer depth during open-ocean convection are examined using two different numerical models: a high-resolution, turbulence-resolving nonhydrostatic model and a large-scale hydrostatic ocean model. An isolated destabilizing buoyancy flux is imposed at the surface of both models and a quasi-equilibrium flow is allowed to develop. Mixed layer depth in the turbulence-resolving and large-scale models closely aligns with existing scaling theories. However, the large-scale model has an anomalously deep mixed layer prior to quasi-equilibrium. This transient mixed layer depth bias is a consequence of the lack of resolved turbulent convection in the model, which delays the onset of baroclinic instability. These findings suggest that in order to reduce mixed layer biases in ocean simulations, parameterizations of the connection between baroclinic instability and convection need to be addressed.

Description: Stratocumulus clouds constitute one of the largest negative climate forcings in the global radiation budget. This forcing is determined, inter alia, by the cloud liquid water path (LWP), which we analyze using a combination of Gaussian process emulation and mixed-layer theory. For nocturnal, nonprecipitating stratocumuli, we show that LWP steady states constitute an equilibrium primarily between radiative cooling and entrainment warming and drying. These steady states are approached from lower LWPs due to reduced entrainment, while higher LWPs are depleted by stronger entrainment. An analytical solution for the LWP steady state reveals not only the environmental conditions in which a stratocumulus cloud can be maintained, but also distinct analytical properties of the entrainment velocity that are required for a stable LWP steady state that opposes perturbations. In particular, the results highlight the importance of an entrainment velocity that increases strictly monotonically with the LWP if stratocumuli are to attain a stable LWP steady state. This is demonstrated through analysis of two commonly used mixed-layer entrainment parameterizations.

Description: This study presents a high-precision method, using a preconcentrator–gas chromatograph with microelectron capture detector (GC-μECD), to measure SF6 at ambient levels. Carboxen 1000 was used as an adsorbent for the preconcentrator and exhibited a high adsorption efficiency for N2O and SF6 and low adsorption efficiency for O2. This enabled the selective removal of atmospheric O2 from analytes and improved repeatability of the SF6 peak that followed the O2 peak, in a separation column of activated alumina F1. In addition, the increased sensitivity resulting from preconcentrated SF6 improved the signal-to-noise ratio. This led to better analytical precision in comparison with other measurement methods including the conventional and forecut–backflush (FCBF) methods. The precision-to-drift ratios of the conventional, FCBF, and preconcentration methods were 0.11, 0.10, and 0.03, respectively. Analytical precision of the preconcentration method was 0.08% for 10 consecutive injections; this was the best among the three methods. The long-term drift of the SF6 response was inversely proportional to the laboratory pressure. Based on this finding, room pressure can be used to correct for ECD signal drift, with an uncertainty of 0.14% over a 48-h period, using the preconcentration method. Another advantage of the preconcentration method was the excellent linearity of the SF6 response to a wide range of concentrations, including its ambient concentration.

Description: This paper describes the development and analysis of an objective climatology of warm and cold fronts over North America from 1979 to 2018. Fronts are detected by a convolutional neural network (CNN), trained to emulate fronts drawn by human meteorologists. Predictors for the CNN are surface and 850-hPa fields of temperature, specific humidity, and vector wind from the ERA5 reanalysis. Gridded probabilities from the CNN are converted to 2D frontal regions, which are used to create the climatology. Overall, warm and cold fronts are most common in the Pacific and Atlantic cyclone tracks and the lee of the Rockies. In contrast with prior research, we find that the activity of warm and cold fronts is significantly modulated by the phase and intensity of El Niño–Southern Oscillation. The influence of El Niño is significant for winter warm fronts, winter cold fronts, and spring cold fronts, with activity decreasing over the continental United States and shifting northward with the Pacific and Atlantic cyclone tracks. Long-term trends are generally not significant, although we find a poleward shift in frontal activity during the winter and spring, consistent with prior research. We also identify a number of regional patterns, such as a significant long-term increase in warm fronts in the eastern tropical Pacific Ocean, which are characterized almost entirely by moisture gradients rather than temperature gradients.

Description: Tropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment factors [daytime surface temperature (LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude, and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of precipitation is achieved with the geographically weighted regression model (GWR) and the backward-propagation artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling. The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within Hubei Province from 0.25° (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through 2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The high-resolution precipitation datasets obtained with the proposed downscaling model with GWR relying on the NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.

Description: The NOAA Science Advisory Board appointed a task force to prepare a white paper on the use of observing system simulation experiments (OSSEs). Considering the importance and timeliness of this topic and based on this white paper, here we briefly review the use of OSSEs in the United States, discuss their values and limitations, and develop five recommendations for moving forward: national coordination of relevant research efforts, acceleration of OSSE development for Earth system models, consideration of the potential impact on OSSEs of deficiencies in the current data assimilation and prediction system, innovative and new applications of OSSEs, and extension of OSSEs to societal impacts. OSSEs can be complemented by calculations of forecast sensitivity to observations, which simultaneously evaluate the impact of different observation types in a forecast model system.

Description: Seasonal changes in grass cover impact the generation of surface runoff due to the effects of grass roots on soil hydrologic properties and processes (i.e., infiltration). Using a rainfall simulator in a grass field site, we broadly investigated the influence of different initial conditions of soil moisture and grass growth stages on rainfall–runoff transformations. To parameterize the stages of grass growth, we used the height of the vegetation hveg, which is related to the leaf area index. Surprisingly, typical characteristics of runoff formation (peak flow and time to peak flow) were conditioned mainly by hveg. The runoff coefficient decreased about 40% when grass reached its maximum growth and was inversely and significantly related to the height of grass in general. Using the rainfall simulator experiments, we estimated the saturated soil hydraulic conductivity ks, a key parameter of infiltration models. We found strong relationships between ks and hveg when the Philip infiltration model was used, and we proposed a linear relationship between ks and hveg, making ks vary in time with grass growth (i.e., hveg). We compared predictions of hydrologic models at plot scale using ks varying with grass growth with predictions using a constant ks, as hydrological models commonly assume. Neglecting ks variability with grass growth can lead to errors up to 100% in surface runoff predictions at an event time scale and up to 87% at a monthly time scale. Ecohydrological models for runoff predictions should take into account the influence of grass growth dynamics on soil infiltration parameters.

Description: Recent success in assimilating cloud- and precipitation-affected satellite observations using the “all-sky” approach is thought to have benefitted from variational data assimilation, particularly its ability to handle moderate nonlinearity and non-Gaussianity and to extract wind information through the generalized tracer effect. Ensemble assimilation relies on assumptions including linearity and Gaussianity that might cause difficulties when using all-sky observations. Here, all-sky assimilation is evaluated in a global ensemble Kalman filter (EnKF) system of near-operational quality, derived from an operational four-dimensional variational (4D-Var) system. To get EnKF working successfully required a new all-sky observation error model (the most successful approach was to inflate error as a multiple of the ensemble spread) and adjustments to localization. With these improvements, assimilation of eight microwave humidity instruments gave 2%–4% improvement in forecast scores whether using EnKF or 4D-Var. Correlations from the ensemble showed that all-sky observations generated sensitivity to wind, temperature, and humidity. EnKF increments shared many similarities with those in 4D-Var. Hence both 4D-Var and ensemble data assimilation were able to make good use of all-sky observations, including the extraction of wind information. In absolute terms the EnKF forecast performance in the troposphere was still worse than that that with 4D-Var, although the gap could be reduced by going from 50 to 100 ensemble members. EnKF errors were larger in the stratosphere, where there are excessive gravity wave increments that are not connected with all-sky assimilation.

Description: The aim of this study is to investigate extreme precipitation events caused by atmospheric rivers and compare their flood impact in a warmer climate to current climate using an event-based storyline approach. The study was set up by selecting four high-precipitation events from 30 years of present and future climate simulations of the high-resolution global climate model EC-Earth. The two most extreme precipitation events within the selection area for the present and future climate were identified, and EC-Earth was rerun creating 10 perturbed realizations for each event. All realizations were further downscaled with the regional weather prediction model, AROME-MetCoOp. The events were thereafter used as input to the operational Norwegian flood-forecasting model for 37 selected catchments in western Norway, and the magnitude and the spatial pattern of floods were analyzed. The role of the hydrological initial conditions, which are important for the total flooding, were analyzed with a special emphasis on snow and soil moisture excess. The results show that the selected future extreme precipitation events affected more catchments with larger floods, compared to the events from present climate. In addition, multiple realizations of the meteorological forcing and four different hydrological initial conditions, for example, soil saturation and snow storage, were important for the estimation of the maximum flood level. The meteorological forcing (e.g., the internal variability/perturbed output) accounts for the highest contribution to the spread in flood magnitude; however, for some events and catchments the hydrological initial conditions affected the magnitudes of floods more than the meteorological forcing.

Description: We analyzed 24-h accumulated precipitation forecasts over the 4-month period from 1 May to 31 August 2013 over an area located in East Asia covering the region 15.05°–58.95°N, 70.15°–139.95°E generated with the ensemble prediction systems (EPS) from ECMWF, NCEP, UKMO, JMA, and CMA contained in the TIGGE dataset. The forecasts are first evaluated with the Method for Object-Based Diagnostic Evaluation (MODE). Then a multimodel ensemble (MME) forecast technique that is based on weights derived from object-based scores is investigated and compared with the equally weighted MME and the traditional gridpoint-based MME forecast using weights derived from the point-to-point metric, mean absolute error (MAE). The object-based evaluation revealed that attributes of objects derived from the ensemble members of the five individual EPS forecasts and the observations differ consistently. For instance, their predicted centroid location is more southwestward, their shape is more circular, and their orientation is more meridional than in the observations. The sensitivity of the number of objects and their attributes to methodological parameters is also investigated. An MME prediction technique that is based on weights computed from the object-based scores, median of maximum interest, and object-based threat score is explored and the results are compared with the ensemble forecasts of the individual EPS, the equally weighted MME forecast, and the traditional superensemble forecast. When using MODE statistics for the forecast evaluation, the object-based MME prediction outperforms all other predictions. This is mainly because of a better prediction of the objects’ centroid locations. When using the precipitation-based fractions skill score, which is not used in either of the weighted MME forecasts, the object-based MME forecasts are slightly better than the equally weighted MME forecasts but are inferior to the traditional superensemble forecast that is based on weights derived from the point-to-point metric MAE.

Description: The upper Indus River basin is characterized by biseasonal heavy precipitation falling on the foothills of major mountain ranges (Hindu Kush, Karakorm, Himalayas). Numerical studies have confirmed the importance of topography for the triggering of precipitation and investigated the processes responsible for specific events, but a systematic and cross-seasonal analysis has yet to be conducted. Using ERA5 reanalysis data and statistical methods, we show that more than 80% of the precipitation variability is explained by southerly moisture transport at 850 and 700 hPa, along the Himalayan foothills. We conclude that most of the precipitation is generated by the forced uplift of a cross-barrier flow. This process explains both wet seasons, despite different synoptic conditions, but is more important in winter. The precipitation signal is decomposed into the contribution of each altitude and each variable (wind and moisture), which exhibit different seasonality. The winter wet season is dominated by moisture transport at higher altitude, and is triggered by an increase in wind. By contrast, the summer wet season is explained by an increase in moisture at both altitudes, while wind is of secondary importance. Selected CMIP6 climate models are able to represent the observed links between precipitation and southerly moisture transport, despite important seasonal biases that are due to a misrepresentation of the seasonality in the magnitude of the southerly wind component.

Description: The Pacific–North American (PNA) teleconnection pattern has been linked both to tropical phenomena, including the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO), and to internal extratropical processes, including interactions with the zonally varying basic state and synoptic eddies. Many questions remain, however, concerning how these various relationships act, both separately and together, to yield observed PNA variability. Using linear inverse modeling (LIM), this study finds that the development and amplification of PNA anomalies largely results from the interference of modes strongly coupled to sea surface temperatures (SST), such as ENSO, and modes internal to the atmosphere, including the MJO. These SST-coupled and “internal atmospheric” modes form subspaces that are not orthogonal, and PNA growth is shown to occur via non-normal interactions. An internal atmospheric space LIM is developed to examine growth beyond this interference by removing the SST-coupled modes, effectively removing ENSO and retaining MJO variability. Optimal PNA growth in the internal atmospheric space LIM is driven by MJO heating, particularly over the Indian Ocean, and a retrograding northeast Pacific streamfunction anomaly. Additionally, the individual contributions of tropical heating and the extratropical circulation on PNA growth are investigated. The non-normal PNA growth is an important result, demonstrating the difficulty in partitioning PNA variance into contributions from different phenomena. This cautionary result is likely applicable to many geophysical phenomena and should be considered in attribution studies.

Description: Capsule Summary This paper reviews the current knowledge on detection, attribution and projection of global and regional monsoons (South Asian, East Asian, Australian, South American, North American, and African) under climate change.

Description: Meridional overturning circulation (MOC) is vital to distributing heat, freshwater, and dissolved matter in semienclosed deep marginal seas such as the East Sea (ES) (Sea of Japan). As our understanding of the ES MOC remains incomplete, we attempted to fill this research gap. We analyzed the ES MOC and its decadal change (1993–2012), employing Hybrid Coordinate Ocean Model (HYCOM) global reanalysis. We found that the ES MOC, consisting of two counterrotating overturning cells in the late 1990s, changed into a single full-depth cell in the 2000s and reverted to two cells in the 2010s. The decadal change relates to weakening of the southward western boundary current at the intermediate layer and northward eastern boundary currents at the deep abyssal layer. We propose that surface warming and salinification favored reduced intermediate water formation and enhanced bottom water formation in the northwestern ES in the 2000s and were, therefore, key to the decadal change. Conditions unfavorable to intermediate water formation and favorable to bottom water formation in the winters of the 2000s, compared with the late 1990s, enhanced northward (westward) Ekman transport in the southern (northeastern) ES, successive advection of surface warm, saline water into water formation areas, and air–sea heat and freshwater exchanges linked to the January Arctic Oscillation. Our results indicated that the ES MOC is sensitive to both external atmospheric forcing and internal ES processes, which have implications for significant changes in the response of other marginal seas and global oceans to future climate variability.

Description: In this study, a 10-yr (2008–17) radar-based mesoscale convective system (MCS) and derecho climatology for Poland is presented. This is one of the first attempts of a European country to investigate morphological and precipitation archetypes of MCSs as prior studies were mostly based on satellite data. Despite its ubiquity and significance for society, economy, agriculture, and water availability, little is known about the climatological aspects of MCSs over central Europe. Our results indicate that MCSs are not rare in Poland as an annual mean of 77 MCSs and 49 days with MCS can be depicted for Poland. Their lifetime ranges typically from 3 to 6 h, with initiation time around the afternoon hours (1200–1400 UTC) and dissipation stage in the evening (1900–2000 UTC). The most frequent morphological type of MCSs is a broken line (58% of cases), then areal/cluster (25%), and then quasi-linear convective systems (QLCS; 17%), which are usually associated with a bow echo (72% of QLCS). QLCS are the feature with the longest life cycle. Among precipitation archetypes of linear MCSs, trailing stratiform (73%) and parallel stratiform (25%) are the most common. MCSs are usually observed from April to September, with a peak in mid-July. A majority of MCSs travels from the west, southwest, and south sectors. A total of 16 derecho events were identified (1.5% of all MCS and 9.1% of all QLCS); the majority of them were produced by a warm-season QLCS, whereas only 4 were produced by cold-season narrow cold-frontal rainbands. Warm-season derechos produced a bigger impact than did cold-season events, even though their damage paths were shorter.

Description: Transport of shoreline-released tracer from the surfzone across the shelf can be affected by a variety of physical processes from wind-driven to submesoscale, with implications for shoreline contaminant dilution and larval dispersion. Here, a high-resolution wave–current coupled model that resolves the surfzone and receives realistic oceanic and atmospheric forcing is used to simulate dye representing shoreline-released untreated wastewater in the San Diego–Tijuana region. Surfzone and shelf alongshore dye transports are primarily driven by obliquely incident wave breaking and alongshore pressure gradients, respectively. At the midshelf to outer-shelf (MS–OS) boundary (25-m depth), defined as a mean streamline, along-boundary density gradients are persistent, dye is surface enhanced and time and alongshelf patchy. Using baroclinic and along-boundary perturbation dye transports, two cross-shore dye exchange velocities are estimated and related to physical processes. Barotropic and baroclinic tides cannot explain the modeled cross-shore transport. The baroclinic exchange velocity is consistent with the wind-driven Ekman transport. The perturbation exchange velocity is elevated for alongshore dye and cross-shore velocity length scales 〈 1 km (within the submesoscale) and stronger alongshore density gradient ∂ρ/∂y variability, indicating that alongfront geostrophic flows induce offshore transport. This elevated ∂ρ/∂y is linked to convergent northward surface along-shelf currents (likely due to regional bathymetry), suggesting deformation frontogenesis. Both surfzone and shelf processes influence offshore transport of shoreline-released tracers with key parameters of surfzone and shelf alongcoast currents and alongshelf winds.

Description: The air–sea exchange of heat and carbon in the Southern Ocean (SO) plays an important role in mediating the climate state. The dominant role the SO plays in storing anthropogenic heat and carbon is a direct consequence of the unique and complex ocean circulation that exists there. Previous generations of climate models have struggled to accurately represent key SO properties and processes that influence the large-scale ocean circulation. This has resulted in low confidence ascribed to twenty-first-century projections of the state of the SO from previous generations of models. This analysis provides a detailed assessment of the ability of models contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to represent important observationally based SO properties. Additionally, a comprehensive overview of CMIP6 performance relative to CMIP3 and CMIP5 is presented. CMIP6 models show improved performance in the surface wind stress forcing, simulating stronger and less equatorward-biased wind fields, translating into an improved representation of the Ekman upwelling over the Drake Passage latitudes. An increased number of models simulate an Antarctic Circumpolar Current (ACC) transport within observational uncertainty relative to previous generations; however, several models exhibit extremely weak transports. Generally, the upper SO remains biased warm and fresh relative to observations, and Antarctic sea ice extent remains poorly represented. While generational improvement is found in many metrics, persistent systematic biases are highlighted that should be a priority during model development. These biases need to be considered when interpreting projected trends or biogeochemical properties in this region.

Description: Baroclinic instabilities are important processes that enhance mixing and dispersion in the ocean. The presence of sloping bathymetry and the nongeostrophic effect influence the formation and evolution of baroclinic instabilities in oceanic bottom boundary layers and in coastal waters. This study explores two nongeostrophic baroclinic instability theories adapted to the scenario with sloping bathymetry and investigates the mechanism of the instability suppression (reduction in growth rate) in the buoyant flow regime. Both the two-layer and continuously stratified models reveal that the suppression is related to a new parameter, slope-relative Burger number Sr ≡ (M2/f2)(α + αp), where M2 is the horizontal buoyancy gradient, α is the bathymetry slope, and αp is the isopycnal slope. In the layer model, the instability growth rate linearly decreases with increasing Sr {the bulk form Sr = [U0/(H0f)](α + αp)}. In the continuously stratified model, the instability suppression intensifies with increasing Sr when the regime shifts from quasigeostrophic to nongeostrophic. The adapted theories are intrinsically applicable to deep ocean bottom boundary layers and could be conditionally applied to coastal buoyancy-driven flow. The slope-relative Burger number is related to the Richardson number by Sr = δrRi−1, where the slope-relative parameter is δr = (α + αp)/αp. Since energetic fronts in coastal zones are often characterized by low Ri, that implies potentially higher values of Sr, which is why baroclinic instabilities may be suppressed in the energetic regions where they would otherwise be expected to be ubiquitous according to the quasigeostrophic theory.

Description: Based on the Japanese 55-year reanalysis dataset, this study identifies 92 Europe–Siberia blocking high events (ESBs) over the 60 winters (November–March) from 1958/59 to 2017/18. According to the influence on the surface air temperature at 2 m over the middle and lower reaches of the Yangtze River, the ESBs are classified into three types: cold, neutral, and warm. Although cold-type ESBs are dominant, the number of warm-type ESBs is not negligible. The present study mainly focuses on the differences between cold-type and warm-type ESBs. Both the cold-type ESBs and the warm-type ESBs are characterized by height anomalies with a northwest–southeast-tilting dipole pattern over the Eurasian continent in the mid- and upper troposphere. However, the tilting dipole pattern of the warm type is located to the northwest of its cold-type counterpart, which reflects differences in the propagation of Rossby wave packets. The Siberian high is stronger in cold-type ESBs than in warm-type ESBs. The induced advection of the climatological mean air temperature by the anomalous meridional wind velocity in the lower troposphere accounts for the largest portion of the observed tendency of the air temperature for both ESB types. In addition, diabatic heating tends to counteract the local cooling tendency of air temperature over the Yangtze River region for the cold-type ESBs. Finally, cold-type ESBs are generally characterized by air parcels originating in the region to the north and northeast of the Tibetan Plateau, while warm-type ESBs are characterized by diverse trajectories.

Description: The heat engine model of tropical cyclones describes a thermally direct overturning circulation. Outflowing air slowly subsides as radiative cooling to space balances adiabatic warming, a process that does not consume any work. However, we show here that the lateral spread of the outflow is limited by the environmental deformation radius, which at high latitudes can be rather small. In such cases, the outflowing air is radially constrained, which limits how far downward it can subside via radiative cooling alone. Some literature has invoked the possibility of “mechanical subsidence” or “forced descent” in the storm outflow region in the presence of high inertial stability, which would be a thermally indirect circulation. Mechanical subsidence in the subsiding branch of a tropical cyclone has not before been observed or characterized. A series of axisymmetric tropical cyclone simulations at different latitudes and domain sizes is conducted to study the impact of environmental inertial stability on storm dynamics. In higher-latitude storms in large axisymmetric domains, the outflow acts as a wavemaker to excite an inertial wave at the environmental inertial (Coriolis) frequency. This inertial wave periodically ventilates the core of a high-latitude storm with its own low-entropy exhaust air. The wave response is in contrast to the presumed forced descent model, and we hypothesize that this is because inertial stability provides less resistance than buoyant stability, even in highly inertially stable environments.

Description: Observed wintertime atmospheric anomalies over the central North Pacific associated with the Pacific decadal oscillation (PDO) are characterized by a cold/trough (warm/ridge) structure, that is, an anomalous equivalent barotropic low (high) over a negative (positive) sea surface temperature (SST) anomaly. While the midlatitude atmosphere has its own strong internal variabilities, to what degree local SST anomalies can affect the midlatitude atmospheric variability remains unclear. To identify such an impact, three atmospheric general circulation model experiments each having a 63-yr-long simulation are conducted. The control run forced by observed global SST reproduces well the observed PDO-related cold/trough (warm/ridge) structure. However, the removal of the midlatitude North Pacific SST variabilities in the first sensitivity run reduces the atmospheric response by roughly one-third. In the second sensitivity run in which large-scale North Pacific SST variabilities are mostly kept, but their frontal-scale meridional gradients are sharply smoothed, simulated PDO-related cold/trough (warm/ridge) anomalies are also reduced by nearly one-third. Dynamical diagnoses exhibit that such a reduction is primarily due to the weakened transient eddy activities that are induced by weakened meridional SST gradient anomalies, in which the transient eddy vorticity forcing plays a crucial role. Therefore, it is suggested that midlatitude North Pacific SST anomalies make a considerable (approximately one-third) contribution to the observed PDO-related cold/trough (warm/ridge) anomalies in which the frontal-scale meridional SST gradient (oceanic front) is a key player, although most of those atmospheric anomalies are determined by the SST variabilities outside of the midlatitude North Pacific.

Description: The Lagrangian characteristics of the surface flow field arising when an idealized, anticyclonic, mesoscale, isolated deep-ocean eddy collides with continental slope and shelf topography are explored. In addition to fluid parcel trajectories, we consider the trajectories of biological organisms that are able to navigate and swim, and for which shallow water is a destination. Of particular interest is the movement of organisms initially located in the offshore eddy, the manner in which the eddy influences the ability of the organisms to reach the shelf break, and the spatial and temporal distributions of organisms that do so. For nonswimmers or very slow swimmers, the organisms arrive at the shelf break in distinct pulses, with different pulses occurring at different locations along the shelf break. This phenomenon is closely related to the episodic formation of trailing vortices that are formed after the eddy collides with the continental slope, turns, and travels parallel to the coast. Analysis based on finite-time Lyapunov exponents reveals initial locations of all successful trajectories reaching the shoreline, and provides maps of the transport pathways showing that much of the cross-shelf-break transport occurs in the lee of the eddy as it moves parallel to the shore. The same analysis shows that the onshore transport is interrupted after a trailing vortex detaches. As the swimming speeds are increased, the organisms are influenced less by the eddy and tend to show up en mass and in a single pulse.

Description: Tropical cyclone ensemble track forecasts from 153 initialization times during 2017-2018 are clustered using regression mixture models. Clustering is performed on a four-ensemble dataset (ECMWF + GEFS + UKMET + CMC; EGUC), and a three-ensemble dataset that excludes the CMC (EGU). For both datasets, five-cluster partitions are selected to analyze, and the relationship between cluster properties (size, ensemble composition) and 96-144-h cluster-mean error is evaluated. For both datasets, small clusters produce very large errors, with the least populous cluster producing the largest error in more than 50% of forecasts. The mean of the most populous EGUC cluster outperforms the most accurate (EGU) ensemble mean in only 43% of forecasts; however, when the most populous EGUC cluster from each forecast contains ≥ 30% of the ensemble population, its average cluster-mean error is significantly reduced compared to when the most populous cluster is smaller. Forecasts with a highly-populous EGUC cluster also appear to have smaller EGUC-, EGU- and ECMWF-mean errors Cluster-mean errors also vary substantially by the ensembles composing the cluster. The most accurate clusters are EGUC clusters that contain threshold memberships of ECMWF, GEFS, and UKMET, but not CMC. The elevated accuracy of EGUC CMC-excluding clusters indicates the potential utility of including the CMC in clustering, despite its large ensemble-mean errors. Pruning ensembles by removing members that belong to small clusters reduces 96-144-h forecast errors for both EGUC and EGU clustering. For 5-cluster partitions, a pruning threshold of 10% affects 49% and 35% of EGUC and EGU ensembles, respectively, improving 69-74% of the forecasts affected by pruning.

Description: Severe thunderstorms and their associated tornadoes pose significant threats to life and property, and using radar data to accurately measure the rotational velocity of circulations in thunderstorms is essential for appropriate, timely warnings. One key factor in accurately measuring circulation velocity is the azimuthal spacing between radar data points, which is referred to as the azimuthal sampling interval. Previous studies have shown that reducing the azimuthal sampling interval can aid in measuring circulation velocity; however, this comes at the price of increased computational complexity. Thus, choosing the best compromise requires knowledge of the relationship between the radar azimuthal sampling interval and the accuracy of the circulation strength as measured from the radar data. In this work, we use simulations to quantify the impact of azimuthal sampling on the strength of radar-observed circulations and show that the improvements get progressively smaller as the azimuthal sampling interval decreases. Thus, improved characterization of circulations can be achieved without using the finest possible sampling grid. We use real data to validate the results of the simulations, which can be used to inform the selection of an appropriate azimuthal sampling interval that balances the accuracy of the radar-observed circulations and computational complexity.

Description: An extension to standard ensemble Kalman filter algorithms that can improve performance for non-Gaussian prior distributions, non-Gaussian likelihoods, and bounded state variables is described. The algorithm exploits the capability of the rank histogram filter (RHF) to represent arbitrary prior distributions for observed variables. The rank histogram algorithm can be applied directly to state variables to produce posterior marginal ensembles without the need for regression that is part of standard ensemble filters. These marginals are used to adjust the marginals obtained from a standard ensemble filter that uses regression to update state variables. The final posterior ensemble is obtained by doing an ordered replacement of the posterior marginal ensemble values from a standard ensemble filter with the values obtained from the rank histogram method applied directly to state variables; the algorithm is referred to as the Marginal Adjustment Rank Histogram Filter (MARHF). Applications to idealized bivariate problems and low-order dynamical systems show that the MARHF can produce better results than standard ensemble methods for priors that are non-Gaussian. Like the original RHF, the MARHF can also make use of arbitrary non-Gaussian observation likelihoods. The MARHF also has advantages for problems with bounded state variables, for instance the concentration of an atmospheric tracer. Bounds can be automatically respected in the posterior ensembles. With an efficient implementation of the MARHF, the additional cost has better scaling than the standard RHF.

Description: We present in situ and remote observations of a Mississippi plume front in the Louisiana Bight. The plume propagated freely across the bight, rather than as a coastal current. The observed cross-front circulation pattern is typical of density currents, as are the small width (≈100 m) of the plume front and the presence of surface frontal convergence. A comparison of observations with stratified density current theory is conducted. Additionally, subcritical to supercritical transitions of frontal propagation speed relative to internal gravity wave (IGW) speed are demonstrated to occur. That is in part due to IGW speed reduction with decrease in seabed depth during the frontal propagation toward the shore. Theoretical steady-state density current propagation speed is in good agreement with the observations in the critical and supercritical regimes but not in the inherently unsteady subcritical regime. The latter may be due to interaction of IGW with the front, an effect previously demonstrated only in laboratory and numerical experiments. In the critical regime, finite-amplitude IGWs form and remain locked to the front. A critical to supercritical transition eventually occurs as the ambient conditions change during frontal propagation, after which IGWs are not supported at the front. The subcritical (critical) to critical (supercritical) transition is related to Froude number ahead (under) the front, consistently with theory. Finally, we find that the front-locked IGW (critical) regime is itself dependent on significant nonlinear speed enhancement of the IGW by their growth to finite amplitude at the front.

Description: Projecting future change of monsoon rainfall is essential for water resource management, food security, disaster mitigation, and infrastructure planning. Here we assess the future change and explore the causes of the changes using 15 models that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The multimodel ensemble projects that, under the shared socioeconomic pathway (SSP) 2–4.5, the total land monsoon rainfall will likely increase in the Northern Hemisphere (NH) by about 2.8% per one degree Celsius of global warming (2.8% °C−1) in contrast to little change in the Southern Hemisphere (SH; −0.3% °C−1). In addition, in the future the Asian–northern African monsoon likely becomes wetter while the North American monsoon becomes drier. Since the humidity increase is nearly uniform in all summer monsoon regions, the dynamic processes must play a fundamental role in shaping the spatial patterns of the global monsoon changes. Greenhouse gas (GHG) radiative forcing induces a “NH-warmer-than-SH” pattern, which favors increasing the NH monsoon rainfall and prolonging the NH monsoon rainy season while reducing the SH monsoon rainfall and shortening the SH monsoon rainy season. The GHG forcing induces a “land-warmer-than-ocean” pattern, which enhances Asian monsoon low pressure and increases Asian and northern African monsoon rainfall, and an El Niño–like warming, which reduces North American monsoon rainfall. The uncertainties in the projected monsoon precipitation changes are significantly related to the models’ projected hemispheric and land–ocean thermal contrasts as well as to the eastern Pacific Ocean warming. The CMIP6 models’ common biases and the processes by which convective heating drives monsoon circulation are also discussed.

Description: El Niño–Southern Oscillation (ENSO) events exhibit a diversity of amplitudes, spatial patterns, and life cycles, with the main ENSO periods concentrated in the 3–7-yr [low-frequency (LF)] and 2–3-yr [quasi-biennial (QB)] bands. In this study, the spatiotemporal diversity of ENSO is quantitatively examined by extracting the two ENSO modes, namely, the LF and QB components of ENSO, from the traditional Niño-3.4 index and connecting them with the spatial types of ENSO. El Niño events can be regrouped as the QB-dominated central-Pacific ENSO-like (QB-CP), LF-dominated eastern-Pacific ENSO-like (LF-EP), and LF-dominated mixing (LF-mixing) types. La Niña events with vague spatial patterns can also have the same categorization. The QB-CP and LF-EP El Niño types both have a high-amplitude QB component. Meanwhile, the former is less affected by its powerless LF component, but the latter is controlled by its strong LF component. Ocean dynamics of the two El Niño types are distinct from each other. The thermocline feedback dominates the growth of the two El Niño types and contributes to the phase transition of the LF-EP type, while the zonal advective feedback is of increasing importance in the QB-CP El Niño and mainly contributes to the phase transitions of the two El Niño types. Additionally, the LF-mixing type with ambiguous spatial features and complex life cycles is distinguished from the other two types. These results indicate that the two ENSO modes coexist in the tropical Pacific air–sea system, and their combination with changing amplitude is the key to explaining the spatiotemporal diversity of ENSO.

Description: A good understanding of the general circulation features of the oceans, particularly of the coastal waters, and ability to predict the key oceanographic parameters with good accuracy and sufficient lead time are necessary for the safe conduct of maritime activities such as fishing, shipping, and offshore industries. Considering these requirements and buoyed by the advancements in the field of ocean modeling, data assimilation, and ocean observation networks along with the availability of the high-performance computational facility in India, Indian National Centre for Ocean Information Services has set up a “High-Resolution Operational Ocean Forecast and Reanalysis System” (HOOFS) with an aim to provide accurate ocean analysis and forecasts for the public, researchers, and other types of users like navigators and the Indian Coast Guard. Major components of HOOFS are (i) a suite of numerical ocean models configured for the Indian Ocean and the coastal waters using the Regional Ocean Modeling System (ROMS) for forecasting physical and biogeochemical state of the ocean and (ii) the data assimilation based on local ensemble transform Kalman filter that assimilates in situ and satellite observations in ROMS. Apart from the routine forecasts of key oceanographic parameters, a few important applications such as (i) Potential Fishing Zone forecasting system and (ii) Search and Rescue Aid Tool are also developed as part of the HOOFS project. The architecture of HOOFS, an account of the quality of ocean analysis and forecasts produced by it and important applications developed based on HOOFS are briefly discussed in this article.

Description: A key aim of observational campaigns is to sample atmosphere–ocean phenomena to improve understanding of these phenomena, and in turn, numerical weather prediction. In early 2018 and 2019, the Atmospheric River Reconnaissance (AR Recon) campaign released dropsondes and radiosondes into atmospheric rivers (ARs) over the northeast Pacific Ocean to collect unique observations of temperature, winds, and moisture in ARs. These narrow regions of water vapor transport in the atmosphere—like rivers in the sky—can be associated with extreme precipitation and flooding events in the midlatitudes. This study uses the dropsonde observations collected during the AR Recon campaign and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) to evaluate forecasts of ARs. Results show that ECMWF IFS forecasts 1) were colder than observations by up to 0.6 K throughout the troposphere; 2) have a dry bias in the lower troposphere, which along with weaker winds below 950 hPa, resulted in weaker horizontal water vapor fluxes in the 950–1000-hPa layer; and 3) exhibit an underdispersiveness in the water vapor flux that largely arises from model representativeness errors associated with dropsondes. Four U.S. West Coast radiosonde sites confirm the IFS cold bias throughout winter. These issues are likely to affect the model’s hydrological cycle and hence precipitation forecasts.

Description: Formulas are obtained for observed circulation around and contraction rate of a Doppler radar grid cell within a surface of constant launch angle. The cell values near unresolved axisymmetric vortices vary greatly with beam-to-flow angle. To obtain reliable standard measures of vortex strength we bilinearly interpolate data to points on circles of specified radii concentric with circulation centers and compute the Doppler circulations around and the areal contraction rates of these circles from the field of mean Doppler velocities. These parameters are proposed for detection of strong tornadoes and mesocyclonic winds. The circulation and mean convergence around the Union City, Oklahoma, tornado of 24 May 1973 are computed. After doubling to compensate for the unobserved wind component, the circulation (1.1 × 105 m2 s−1) agrees with a previous photogrammetric measurement. The mature tornado was embedded in a region, 6 km in diameter, of nearly uniform strong convergence (~5.5 × 10−3 s−1) without a simultaneous mesocyclone. A model of a convergent vortex inputted to a Doppler radar emulator reproduces these results. Moving the model vortex shows that for a WSR-88D with superresolution, the circulation is relatively insensitive to range and azimuth. WSR-88D data of the 31 May 2013 El Reno storm are also analyzed. The tornado formed in a two-celled mesocyclone with strong inflow 5 km away. In the next 8 min the circulation near the axis doubled and the areal contraction rate at 5 km increased by 50%. This signified a large probability of strong tornadoes embedded in powerful storm-scale winds.

Description: Specification of suitable initial conditions to accurately forecast high-impact weather events associated with intense thunderstorms still poses a significant challenge for convective-scale forecasting. Radar data assimilation has been showing encouraging results to produce an accurate estimate of the state of the atmosphere at the mesoscale, as it combines high-spatiotemporal-resolution observations with convection-permitting numerical weather prediction models. However, many open questions remain regarding the configuration of state-of-the-art data assimilation systems at the mesoscale and their potential impact upon short-range weather forecasts. In this work, several observing system simulation experiments of a mesoscale convective system were performed to assess the sensitivity of the local ensemble transform Kalman filter to both relaxation-to-prior spread (RTPS) inflation and horizontal localization of the error covariance matrix. Realistic large-scale forcing and model errors have been taken into account in the simulation of reflectivity and Doppler velocity observations. Overall, the most accurate analyses in terms of RMSE were produced with a relatively small horizontal localization cutoff radius (~3.6–7.3 km) and large RTPS inflation parameter (~0.9–0.95). Additionally, the impact of horizontal localization on short-range ensemble forecast was larger compared to inflation, almost doubling the lead times up to which the effect of using a more accurate state to initialize the forecast persisted.

Description: Explicit forecasts of a tornado-like vortex (TLV) require subkilometer grid spacing because of their small size. Most previous TLV prediction studies started from interpolated kilometer grid spacing initial conditions (ICs) rather than subkilometer grid spacing ICs. The tornadoes embedded in the 8 May 2003 Oklahoma City tornadic supercell are used to understand the impact of IC resolution on TLV predictions. Two ICs at 500-m and 2-km grid spacings are, respectively, produced through an efficient dual-resolution (DR) and a single-coarse-resolution (SCR) EnVar ingesting a 2-km ensemble. Both experiments launch 1-h forecasts at 500-m grid spacing. Diagnostics of data assimilation (DA) cycling reveal DR produces stronger and broader rear-flank cold pools, more intense downdrafts and updrafts with finer scales, and more hydrometeors at high altitudes through accumulated differences between two DA algorithms. Relative differences in DR, compared to SCR, include the integration from higher-resolution analyses, the update for higher-resolution backgrounds, and the propagation of ensemble perturbations along higher-resolution model trajectory. Predictions for storm morphology and cold pools are more realistic in DR than in SCR. The DR-TLV tracks match better with the observed tornado tracks than SCR-TLV in timing of intensity variation, and in duration. Additional experiments suggest 1) the analyzed kinematic variables strongly influence timing of intensity variation through affecting both low-level rear-flank outflow and midlevel updraft; 2) potential temperature analysis by DR extends the second track’s duration consistent with enhanced low-level stretching, delayed broadening large-scale downdraft, and (or) increased near-surface baroclinic vorticity supply; and 3) hydrometeor analyses have little impact on TLV predictions.

Description: The representation of the winter and summer extratropical storm tracks in both hemispheres is evaluated in detail for the available models in phase 6 of the Coupled Model intercomparison Project (CMIP6). The state of the storm tracks from 1979 to 2014 is compared to that in ERA5 using a Lagrangian objective cyclone tracking algorithm. It is found that the main biases present in the previous generation of models (CMIP5) still persist, albeit to a lesser extent. The equatorward bias around the SH is much reduced and there appears to be some improvement in mean biases with the higher-resolution models, such as the zonal tilt of the North Atlantic storm track. Low-resolution models have a tendency to underestimate the frequency of high-intensity cyclones with all models simulating a peak intensity that is too low for cyclones in the SH. Explosively developing cyclones are underestimated across all ocean basins and in both hemispheres. In particular the models struggle to capture the rapid deepening required for these cyclones. For all measures, the CMIP6 models exhibit an overall improvement compared to the previous generation of CMIP5 models. In the NH most improvements can be attributed to increased horizontal resolution, whereas in the SH the impact of resolution is less apparent and any improvements are likely a result of improved model physics.

Description: Quantifying the uncertainties caused by resistance parameterizations is fundamental for understanding, improving, and developing terrestrial evapotranspiration (ET) models. Using high-density eddy covariance (EC) tower observations in a heterogeneous oasis in northwest China, this study evaluates the impacts of resistances on the estimation of latent heat flux (LE), the energy equivalent of ET, by comparing resistance parameterizations with different complexities under one- and two-source Penman–Monteith (PM) equations. The results showed that the mean absolute percent error (MAPE) for the LE estimates from the one- and two-source PM equations varied from 32% to 53%, and the uncertainties were caused mainly by the resistance parameterizations. Calibrating the parameters required in the resistance estimations could improve the performance of the PM equations; specifically, the MAPEs for the one-source PM equations were approximately 16%, whereas they were 38% for the two-source PM equations, emphasizing that multiple resistances result in increased uncertainties. The following conclusions were reached: 1) the empirical and biophysical parameters required in resistance estimations were responsible for the uncertainty; 2) increasingly complex resistance parameterizations resulted in greater uncertainties in LE estimates; and 3) models without resistance parameterizations exhibited reduced uncertainties in LE estimates.

Description: A new k-distribution scheme of longwave radiation without the correlated-k-distribution assumption is developed. Grouping of spectral points is based on the line-by-line (LBL)-calculated absorption coefficient k at a few sets of reference pressure pr and temperature θr, where the cooling rate is substantial in a spectral band. In this new scheme, the range of k(pr, θr) of a band is divided into a number of equal intervals, or g groups, in log10(kr). A spectral point at the wavenumber ν is identified with one of the g groups according to its kν(pr, θr). For each g group, a Planck-weighted k-distribution function Hg and a nonlinearly averaged absorption coefficient k¯g⁡(p,θ) are derived. The function Hg and the absorption coefficient k¯g⁡(p,θ) constitute the new k-distribution scheme. In this k-distribution scheme, a spectral point can only be identified with a g group regardless of pressure and temperature, which is different from the correlated-k distribution scheme. The k-distribution scheme is applied to the H2O, CO2, O3, N2O, and CH4 absorption bands, and results are compared with LBL calculations. To balance between the accuracy and the computational economy, the number of g groups in a band of a given gas is chosen such that 1) the difference in cooling rate is

Description: In this study, the impact of global positioning system (GPS) radio occultation (RO) data on the prediction of the genesis of 10 tropical cyclones over the western North Pacific Ocean is assessed. With the use of a nonlocal excess phase observation operator in cycling data assimilation, the probability of detection for tropical cyclogenesis is increased from 30% to 70% for the cases considered, all of which developed into typhoons. However, the probability of detection is only increased to 40% when a local observation operator is used, indicating that the observation operator can significantly influence the performance of RO data assimilation in capturing tropical cyclogenesis. A nonlocal excess phase operator, which considers the atmospheric horizontal gradients by integrating the refractivity along a ray path, gives superior performance over the local observation operator. Additional sensitivity experiments on 3 of the 10 typhoon cases show that the RO data in the vicinity of the incipient cyclones (within 500 km of the cyclone center) are most critical to successful cyclogenesis prediction. This reflects the fact that having good RO observations at the right time and place is critical for RO to have beneficial impacts on tropical cyclogenesis. Further analyses for Typhoon Nuri (2008) show that assimilation of RO data using the nonlocal operator leads to moistening of the lower and middle troposphere, organized convection, robust grid-scale vertical motions, and the development of midlevel relative vorticity, all of which are favorable for tropical cyclogenesis.

Description: Rapid-onset droughts, known as flash droughts, can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict flash droughts in advance would greatly enhance our preparation for them and potentially mitigate their impacts. Here, we investigate the prediction skill of the extreme 2012 flash drought over the U.S. Great Plains at subseasonal lead times (3 weeks or more in advance) in global forecast systems participating in the Subseasonal Experiment (SubX). An additional comprehensive set of subseasonal hindcasts with NASA’s GEOS model, a SubX model with relatively high prediction skill, was performed to investigate the separate contributions of atmospheric and land initial conditions to flash drought prediction skill. The results show that the prediction skill of the SubX models is quite variable. While skillful predictions are restricted to within the first two forecast weeks in most models, skill is considerably better (3–4 weeks or more) for certain models and initialization dates. The enhanced prediction skill is found to originate from two robust sources: 1) accurate soil moisture initialization once dry soil conditions are established, and 2) the satisfactory representation of quasi-stationary cross-Pacific Rossby wave trains that lead to the rapid intensification of flash droughts. Evidence is provided that the importance of soil moisture initialization applies more generally to central U.S. summer flash droughts. Our results corroborate earlier findings that accurate soil moisture initialization is important for skillful subseasonal forecasts and highlight the need for additional research on the sources and predictability of drought-inducing quasi-stationary atmospheric circulation anomalies.

Description: The ocean dynamic sea level (DSL) is an important component of regional sea level projections. In this study, we analyze mean states and future projections of the DSL from the global coupled climate models participating in phase 5 and phase 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively). Despite persistent biases relative to observations, both CMIP5 and CMIP6 simulate the mean sea level reasonably well. The equatorward bias of the Southern Hemisphere westerly wind stress is reduced from CMIP5 to CMIP6, which improves the simulated mean sea level in the Southern Ocean. The CMIP5 and CMIP6 DSL projections exhibit very similar features and intermodel uncertainties. With several models having a notably high climate sensitivity, CMIP6 projects larger DSL changes in the North Atlantic and Arctic associated with a larger weakening of the Atlantic meridional overturning circulation (AMOC). We further identify linkages between model mean states and future projections by looking for their intermodel relationships. The common cold-tongue bias leads to an underestimation of DSL rise in the western tropical Pacific. Models with their simulated midlatitude westerly winds located more equatorward tend to project larger DSL changes in the Southern Ocean and North Pacific. In contrast, a more equatorward location of the North Atlantic westerly winds or a weaker AMOC under current climatology is associated with a smaller weakening of the AMOC and weaker DSL changes in the North Atlantic and coastal Arctic. Our study provides useful emergent constraints for DSL projections and highlights the importance of reducing model mean-state biases for future projections.

Description: The structure of vortex Rossby waves (VRWs) and their role in the development of a secondary eyewall in Hurricane Matthew (2016) is examined from observations taken during the NOAA Sensing Hazards with Operational Unmanned Technology (SHOUT) field experiment. Radar measurements from ground-based and airborne systems, with a focus on the NASA High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) instrument on the Global Hawk aircraft, revealed the presence of ~12–15-km wavelength spiral bands breaking from the inner core eyewall in the down shear-right quadrant. The vorticity characteristics and calculations of the intrinsic phase speeds of the bands are shown to be consistent with sheared VRWs. A new angular momentum budget methodology is presented that allows an understanding of the secondary eyewall development process with narrow swath radar measurements. Filtering of the governing equations enables explicit insight into the nonlinear dynamics of scale interactions and the role of the VRWs in the storm structure change. The results indicate that the large-scale (scales〉15-km) vertical flux convergence of angular momentum associated with the VRWs dominates the time tendency with smaller effects from the radial flux term. The small-scale (scales≤15-km) vertical term produces weak, but non-negligible nonlinear forcing of the large scales primarily through the Reynolds and cross-stress components. The projection of the wave kinematics onto the low-wavenumber (zero and one) fields appears to be the more significant dynamic process. Flight-level observations show secondary peaks in tangential winds in the radial region where the VRW forcing signatures are active, connecting them with the secondary eyewall formation process.

Description: On 23 July 2014, a commercial aircraft (GE222) crashed near the Ma-Gong Airport on Penghu Island off the southwestern coast of Taiwan as it struggled to land in the stormy weather that was caused by the outer tropical cyclone rainbands (OTCRs) of Typhoon Matmo. This study aims to document the detailed aspects of airflow and precipitation of OTCRs through high-resolution radar and surface observations and to identify how these observed structures contribute to aviation weather hazards. Analyses indicate that the weather at the airport was significantly influenced by the passage of three OTCRs (R1, R2, and R3), and these rainbands share common characteristics of squall-line-like airflow and precipitation structures. As GE222 descended to approach the runway and flew immediately behind and roughly parallel to the leading edge of R3, the aircraft encountered the heaviest precipitation of the rainband and the prominent crosswind that was a manifestation of the rear-to-front flow generated locally by the rainband. The heavy rain–induced poor visibility and the occurrence of strong crosswinds were primary weather hazards affecting this flight event. Momentum budget analyses suggest that the frontward pressure gradient force provided by the near-surface, convectively generated mesohigh played a major role in driving the low-level rear-to-front flow inside the band. The results from the present study imply that closely monitoring convective activities in the outer regions of tropical cyclones and their potential transformation into squall-line-like storms is crucial to complement the routine aviation alert of severe weather under the influence of tropical cyclones.

Description: Seamless prediction means bridging discrete short-term weather forecasts valid at a specific time and time-averaged forecasts at longer periods. Subseasonal predictions span this time range and must contend with this transition. Seamless forecasts and seamless validation methods go hand-in-hand. Time-averaged forecasts often feature a verification window that widens in time with growing forecast leads. Ideally, a smooth transition across daily to monthly time scales would provide true seamlessness—a generalized approach is presented here to accomplish this. We discuss prior attempts to achieve this transition with individual weighting functions before presenting the two-parameter Hill equation as a general weighting function to blend discrete and time-averaged forecasts, achieving seamlessness. The Hill equation can be tuned to specify the lead time at which the discrete forecast loses dominance to time-averaged forecasts, as well as the swiftness of the transition with lead time. For this application, discrete forecasts are defined at any lead time using a Kronecker delta weighting, and any time-averaged weighting approach can be used at longer leads. Time-averaged weighting functions whose averaging window widens with lead time are used. Example applications are shown for deterministic and ensemble forecasts and validation and a variety of validation metrics, along with sensitivities to parameter choices and a discussion of caveats. This technique aims to counterbalance the natural increase in uncertainty with forecast lead. It is not meant to construct forecasts with the highest skill, but to construct forecasts with the highest utility across time scales from weather to subseasonal in a single seamless product.

Description: The Unified Model (UM) data assimilation system incorporates a 1D-Var analysis of cloud variables for assimilating hyperspectral infrared radiances. For the Infrared Atmospheric Sounding Interferometer (IASI) radiance assimilation, a first guess of cloud top pressure (CTP) and cloud fraction (CF) is estimated using the minimum residual (MR) method, which simultaneously obtains CTP and CF by minimizing radiance difference between observation and model simulation. In this study, we examined how those MR-based cloud retrievals behave, using “optimum” CTP and CF that yield the best 1D-Var analysis results. It is noted that the MR method tends to overestimate cloud top height while underestimating cloud fraction, compared to the optimum results, necessitating an improved cloud retrieval. An artificial neural network (ANN) approach was taken to estimate CTP as close as possible to the optimum value, based on the hypothesis that CTP and CF closer to the optimum values will bring in better 1D-Var results. The ANN-based cloud retrievals indicated that CTP and CF biases shown in the MR method are much reduced, giving better 1D-Var analysis results. Furthermore, the computational time can be substantially reduced by the ANN method, compared to the MR method. The evaluation of the ANN method in a global weather forecasting system demonstrated that it helps to use more temperature channels in the assimilation, although its impact on UM forecasts was found to be near neutral. It is suggested that the neutral impact may be improved when error covariances for the cloudy sky are employed in the UM assimilation system.

Description: This article investigates combining a WRF-ADCIRC ensemble with track clustering to evaluate how uncertainties in tropical cyclone–induced storm tide (surge + tide) predictions vary in space and time and to explore whether this method can help elucidate inundation hazard scenarios. The method is demonstrated for simulations of Hurricane Irma (2017) initialized at 1200 UTC 5 September, approximately 5 days before Irma’s Florida landfalls, and 1200 UTC 8 September. Mixture models are used to partition the WRF ensemble tracks from 5 and 8 September into six and five clusters, respectively. Inundation is evaluated in two affected regions: southwest (south and west Florida) and northeast (northeast Florida through South Carolina). For the 5 September simulations, inundation in the southwest region varies significantly across the ensemble, indicating low forecast confidence. However, clustering highlights the areas of inundation risk in south and west Florida associated with different storm tracks. In the northeast region, every cluster has high inundation probabilities along a similar coastal stretch, indicating high confidence at a ~5-day lead time that this area will experience inundation. For the 8 September simulations, track and inundation in both regions vary less across the ensemble, but clustering remains useful for distinguishing among flooding scenarios. These results demonstrate the potential of dynamical TC–surge ensembles to illuminate important aspects of storm surge risk, including highlighting regions of high forecast confidence where preparations can reliably be initiated early. The analysis also shows how clustering can augment probabilistic hazard forecasts by elucidating inundation scenarios and variability across a surge ensemble.

Description: Before the eastward-propagating rainy envelope of a Madden–Julian oscillation (MJO) arrives at the Maritime Continent (MC), some islands in the MC experience dipolar patterns of rainfall variability with opposite signs of rainfall anomalies in two neighboring regions within an island. Similar incoherent rainfall anomalies are also observed after the MJO passed the MC. The mechanisms for these dipolar patterns of rainfall anomalies are investigated by using observed and reanalysis data. It is found that the response of rainfall in the MC depends on the direction of wind anomalies and the availability of atmospheric moisture in different phases of the MJO. The low-level wind anomalies over the MC are easterlies in MJO phases 1–3, which cause above-normal rainfall over the mountainous areas in Java, and in western Borneo, western Sumatra, and western Malay Peninsula, respectively. In phases 5–6, the low-level wind anomalies are westerlies and the positive rainfall anomalies are over the eastern part of the islands. Two physical mechanisms are responsible for this phenomenon of the dipolar patterns of rainfall anomalies: 1) the monsoonal damping effect on rainfall over elongated narrow islands—an inverse relationship between the intensity of the diurnal cycle of sea breezes and valley breezes and the large-scale monsoonal wind speed, and 2) the wake effect on rainfall over large and wide islands—above-normal rainfall on the downwind wake side of an island or mountain range with respect to large-scale wind anomalies.

Description: Here we present a new theoretical framework that connects the error growth behavior in numerical weather prediction (NWP) with the atmospheric kinetic energy spectrum. Building on previous studies, our newly proposed framework applies to the canonical observed atmospheric spectrum that has a −3 slope at synoptic scales and a −5/3 slope at smaller scales. Based on this realistic hybrid energy spectrum, our new experiment using hybrid numerical models provides reasonable estimations for the finite predictable ranges at different scales. We further derive an analytical equation that helps understand the error growth behavior. Despite its simplicity, this new analytical error growth equation is capable of capturing the results of previous comprehensive theoretical and observational studies of atmospheric predictability. The success of this new theoretical framework highlights the combined effects of quasi-two-dimensional dynamics at synoptic scales (−3 slope) and three-dimensional turbulence-like small-scale chaotic flows (−5/3 slope) in dictating the error growth. It is proposed that this new framework could serve as a guide for understanding and estimating the predictability limit in the real world.

Description: This paper presents a process-oriented evaluation of precipitating stratocumulus and its transition to cumulus in version 1 of the Energy Exascale Earth System Model (E3SMv1) using comprehensive case-study observations from a field campaign of the Atmospheric Radiation Measurement program (ARM). The E3SMv1 single-column model (SCM) of the marine boundary layer and its low clouds and precipitation are compared to observations including subcloud drizzle retrievals from a combination of Doppler radar and lidar backscatter measurements. The SCM is also compared to a large-eddy simulation (LES) of the same case. The combination of advanced remote sensing observations and LES is a powerful framework to evaluate the physical parameterizations of large-scale models. Given the observed large-scale environment, the E3SMv1 SCM realistically represents the evolution of clouds and boundary layer structure during the stratocumulus-to-cumulus transition. The model well simulates the liquid water path and its diurnal cycle in the stratocumulus period as well as the two-layer vertical thermodynamic structure and lower cloud fraction in the transition period. E3SMv1’s success in simulating the cloud in the stratocumulus period permitted examination of its precipitation processes. Here problems were identified with E3SMv1 producing an unrealistically small subcloud precipitation fraction, an unrealistic double peak in the vertical profiles of precipitation mass, and drizzle that evaporates too close to the surface. Further model diagnostics determined that these unrealistic characteristics resulted from an overly long microphysics time step and an unrealistic parameterization of the precipitation fraction. These results imply that careful consideration of these issues is needed in order to better simulate precipitation processes in marine stratocumulus.

Description: This study demonstrates an implementation of the prototype quantitative precipitation R estimation algorithm using specific attenuation A for S-band polarimetric radar. The performance of R(A) algorithm is assessed, compared to the conventional algorithm using radar reflectivity Z, at multiple temporal scales. Because the factor α, defined as the net ratio of A to specific differential phase, is a key parameter of the algorithm characterized by drop size distributions (e.g., differential reflectivity Zdr dependence on Z), the estimation equations of α and a proper number of Zdr–Z samples required for a reliable α estimation are examined. Based on the dynamic estimation of α, the event-based evaluation using hourly rain gauge observations reveals that the performance of R(A) is superior to that of R(Z), with better agreement and lower variability. Despite its superiority, the study finds that R(A) leads to quite consistent overestimations of about 10%–30%. It is demonstrated that the application of uniform α over the entire radar domain yields the observed uncertainty because of the heterogeneity of precipitation in the domain. A climatological range-dependent feature of R(A) and R(Z) is inspected in the multiyear evaluation at yearly scale using rain totals for April–October. While R(Z) exposes a systematic shift and overestimation, each of which arise from the radar miscalibration and bright band effects, R(A) combining with multiple R(Z) values for solid/mixed precipitation shows relatively robust performance without those effects. The immunity of R(A) to partial beam blockage (PBB) based on both qualitative and quantitative analyses is also verified. However, the capability of R(A) regarding PBB is limited by the presence of the melting layer and its application requirement for the total span of differential phase (e.g., 3°), which is another challenge for light rain.

Description: The demand of city planners for quantitative information on the impact of climate change on the urban environment is increasing. However, such information is usually extracted from decadelong climate projections generated with global or regional climate models (RCMs). Because of their coarse resolution and unsuitable physical parameterization, however, their model output is not adequate to be used at city scale. A full dynamical downscaling to city level, on the other hand, is computationally too expensive for climatological time scales. A statistical–dynamical computationally inexpensive method is therefore proposed that approximates well the behavior of the full dynamical downscaling approach. The approach downscales RCM simulations using the combination of an RCM at high resolution (H-RES) and a land surface model (LSM). The method involves the setup of a database of urban signatures by running an H-RES RCM with and without urban parameterization for a relatively short period. Using an analog approach, these signatures are first selectively added to the long-term RCM data, which are then used as forcing for an LSM using an urban parameterization in a stand-alone mode. A comparison with a full dynamical downscaling approach is presented for the city of Brussels, Belgium, for 30 summers with the combined ALADIN–AROME model (ALARO-0) coupled to the Surface Externalisée model (SURFEX) as H-RES RCM and SURFEX as LSM. The average bias of the nocturnal urban heat island during heat waves is vanishingly small, and the RMSE is strongly reduced. Not only is the statistical–dynamical approach able to correct the heat-wave number and intensities, it can also improve intervariable correlations and multivariate and temporally correlated indices, such as Humidex.

Description: Flow along isobaths of a sloping lower boundary generates an across-isobath Ekman transport in the bottom boundary layer. When this Ekman transport is down the slope it causes convective mixing—much like a downfront wind in the surface boundary layer—destroying stratification and potential vorticity. In this manuscript we show how this can lead to the development of a forced centrifugal or symmetric instability regime, where the potential vorticity flux generated by friction along the boundary is balanced by submesoscale instabilities that return the boundary layer potential vorticity to zero. This balance provides a strong constraint on the boundary layer evolution, which we use to develop a theory that explains the evolution of the boundary layer thickness, the rate at which the instabilities extract energy from the geostrophic flow field, and the magnitude and vertical structure of the dissipation. Finally, we show using theory and a high-resolution numerical model how the presence of centrifugal or symmetric instabilities alters the time-dependent Ekman adjustment of the boundary layer, delaying Ekman buoyancy arrest and enhancing the total energy removed from the balanced flow field. Submesoscale instabilities of the bottom boundary layer may therefore play an important, largely overlooked, role in the energetics of flow over topography in the ocean.

Description: Monthly tropical sea surface temperature (SST) data are used as predictors to make statistical forecasts of cold season (November–March) precipitation and temperature for the contiguous United States. Through the use of the combined-lead sea surface temperature (CLSST) model, predictive information is discovered not just in recent SSTs but also from SSTs up to 18 months prior. We find that CLSST cold season forecast anomaly correlation skill is higher than that of the North American Multimodel Ensemble (NMME) and the SEAS5 model from the European Centre for Medium-Range Weather Forecasts (ECMWF) when averaged over the United States for both precipitation and 2-m air temperature. The precipitation forecast skill obtained by CLSST in parts of the Intermountain West is of particular interest because of its implications for water resources. In those regions, CLSST dramatically improves the skill over that of the dynamical model ensembles, which can be attributed to a robust statistical response of precipitation in this region to SST anomalies from the previous year in the tropical Pacific.

Description: The impact of land–atmosphere anomaly coupling on land variability is investigated using a new two-stage climate model experimental design called the “GLACE-Hydrology” experiment. First, as in the GLACE-CMIP5 experiment, twin sets of coupled land–atmosphere climate model (CAM5-CLM4.5) ensembles are performed, with each simulation using the same prescribed observed sea surface temperatures and radiative forcing for the years 1971–2014. In one set, land–atmosphere anomaly coupling is removed by prescribing soil moisture to follow the control model’s seasonally evolving soil moisture climatology (“land–atmosphere uncoupled”), enabling a contrast with the original control set (“land–atmosphere coupled”). Then, the atmospheric outputs from both sets of simulations are used to force land-only ensemble simulations, allowing investigation of the resulting soil moisture variability and memory under both the coupled and uncoupled scenarios. This study finds that in midlatitudes during boreal summer, land–atmosphere anomaly coupling significantly strengthens the relationship between soil moisture and evapotranspiration anomalies, both in amplitude and phase. This allows for decreased moisture exchange between the land surface and atmosphere, increasing soil moisture memory and often its variability as well. Additionally, land–atmosphere anomaly coupling impacts runoff variability, especially in wet and transition regions, and precipitation variability, although the latter has surprisingly localized impacts on soil moisture variability. As a result of these changes, there is an increase in the signal-to-noise ratio, and thereby the potential seasonal predictability, of SST-forced hydroclimate anomalies in many areas of the globe, especially in the midlatitudes. This predictability increase is greater for soil moisture than precipitation and has important implications for the prediction of drought.

Description: Although significant improvements have been made to the prediction and understanding of extreme precipitation events in recent decades, there is still much to learn about these impactful events on the subseasonal time scale. This study focuses on identifying synoptic patterns and precursors ahead of an extreme precipitation event over the contiguous United States (CONUS). First, we provide a robust definition for 14-day “extreme precipitation events” and partition the CONUS into six different geographic regions to compare and contrast the synoptic patterns associated with events in those regions. Then, several atmospheric variables from ERA-Interim (e.g., geopotential height and zonal winds) are composited to understand the evolution of the atmospheric state before and during a 14-day extreme precipitation event. Common synoptic signals seen during events include significant zonally oriented trough–ridge patterns, an energized subtropical jet stream, and enhanced moisture transport into the affected area. Also, atmospheric-river activity increases in the specific region during these events. Modes of climate variability and lagged composites are then investigated for their potential use in lead-time prediction. Key findings include synoptic-scale anomalies in the North Pacific Ocean and regional connections to modes such as the Pacific–North American pattern and the North Pacific Oscillation. Taken together, our results represent a significant step forward in understanding the evolution of 14-day extreme precipitation events for potential damage and casualty mitigation.

Description: The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.

Description: A unique feature of small mountainous rivers is that discharge can be elevated by an order of magnitude during a large rain event. The impact of time-varying discharge on freshwater transport pathways and alongshore propagation rates in the coastal ocean is not well understood. A suite of simulations in an idealized coastal ocean domain using the Regional Ocean Modeling System (ROMS) with varying steady background discharge conditions (25–100 m3 s−1), pulse amplitude (200–800 m3 s−1), pulse duration (1–6 days), and steady downwelling-favorable winds (0–4 m s−1) are compared to investigate the downstream freshwater transport along the coast (in the direction of Kelvin wave propagation) following a discharge pulse from the river. The nose of the pulse propagates rapidly alongshore at 0.04–0.32 m s−1 (faster propagation corresponds with larger pulse volume and faster winds) transporting 13%–66% of the discharge. The remainder of the discharge volume initially accumulates in the bulge near the river mouth, with lower retention for longer pulse duration and stronger winds. Following the pulse, the bulge eddy disconnects from the river mouth and is advected downstream at 0–0.1 m s−1, equal to the depth-averaged wind-driven ambient water velocity. As it transits alongshore, it sheds freshwater volume farther downstream and the alongshore freshwater transport stays elevated between the nose and the transient bulge eddy. The evolution of freshwater transport at a plume cross section can be described by the background discharge, the passage of the pulse nose, and a slow exponential return to background conditions.

Description: The most discernible and devastating impacts of climate change are caused by events with temporary extreme conditions (“extreme events”) or abrupt shifts to a new persistent climate state (“tipping points”). The rapidly growing amount of data from models and observations poses the challenge to reliably detect where, when, why, and how these events occur. This situation calls for data-mining approaches that can detect and diagnose events in an automatic and reproducible way. Here, we apply a new strategy to this task by generalizing the classical machine-vision problem of detecting edges in 2D images to many dimensions (including time). Our edge detector identifies abrupt or extreme climate events in spatiotemporal data, quantifies their abruptness (or extremeness), and provides diagnostics that help one to understand the causes of these shifts. We also publish a comprehensive toolset of code that is documented and free to use. We document the performance of the new edge detector by analyzing several datasets of observations and models. In particular, we apply it to all monthly 2D variables of the RCP8.5 scenario of the Coupled Model Intercomparison Project (CMIP5). More than half of all simulations show abrupt shifts of more than 4 standard deviations on a time scale of 10 years. These shifts are mostly related to the loss of sea ice and permafrost in the Arctic. Our results demonstrate that the edge detector is particularly useful to scan large datasets in an efficient way, for example multimodel or perturbed-physics ensembles. It can thus help to reveal hidden “climate surprises” and to assess the uncertainties of dangerous climate events.

Description: The tornado outbreak of 21–23 January 2017 caused 20 fatalities, more than 200 injuries, and over a billion dollars in damage in the Southeast United States. The event occurred concurrently with a record-breaking warm Gulf of Mexico (GoM) basin. This article explores the influence that warm GoM sea surface temperatures (SSTs) had on the tornado outbreak. Backward trajectory analysis, combined with a Lagrangian-based moisture-attribution algorithm, reveals that the tornado outbreak’s moisture predominantly originated from the southeast GoM and the northwest Caribbean Sea. We used the WRF Model to generate a control simulation of the event and explore the response to perturbed SSTs. With the aid of a tornadic storm proxy derived from updraft helicity, we show that the 21–23 January 2017 tornado outbreak exhibits sensitivity to upstream SSTs during the first day of the event. Warmer SSTs across remote moisture sources and adjacent waters increase tornado frequency, in contrast to cooler SSTs, which reduce tornado activity. Upstream SST sensitivity is reduced once convection is ongoing and modifying local moisture and instability availability. Our results highlight the importance of air–sea interactions before airmass advection toward the continental United States. The complex and nonlinear nature of the relationship between upstream SSTs and local precursor environments is also discussed.

Description: Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies - typically focusing more on tropical or mid-latitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation-dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on (1) estimating parameter values on a perfect model experiment and (2) producing calibrated 1-year SST anomaly forecast distributions using a pre-industrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which a LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.

Description: The effects of a nonlocal planetary boundary layer (PBL) scheme that considers scale dependency in the parameterized turbulent vertical transport are investigated for a case of wintertime lake-effect precipitation over Korea at gray-zone resolutions using a mesoscale model. An experiment using the scale-aware PBL scheme is compared with that using a conventional PBL scheme, which shows that the simulated precipitation amount at a resolution of less than 1 km is smaller with the scale-aware PBL scheme. The role of turbulent processes in simulating lake-effect precipitation is understood through interaction with microphysical processes. When the scale-aware PBL scheme is used, liquid water content is increased while ice water content is reduced. The higher cloud water content is because of enhanced condensation with stronger updrafts, attributed to the suppression of parameterized turbulent mixing. This results in higher rainwater content by enhancing autoconversion and accretion from cloud water to rainwater. The cloud ice content is reduced mainly because of the suppressed deposition and enhanced sublimation centered near the PBL top, and the snow content is reduced mainly because of the enhanced sublimation below and near the PBL top and suppressed growth of cloud ice to snow. The lower ice water content is mainly due to the drier PBL, attributed to the enhanced resolved (suppressed parameterized) turbulent moisture transport and enhanced condensation. The melting of a smaller amount of snow under dominant cold rain processes is responsible for the reduced surface precipitation with the scale-aware PBL scheme.

Description: The number concentration of ice particles in Arctic mixed-phase clouds is a major controlling factor of cloud lifetime. The relationships between ice nucleation mode and ice crystal habit development are not yet constrained by observations. This study uses a habit-predicting microphysical scheme within a 3D large-eddy simulation model to evaluate the relationship between immersion freezing and ice habit in a simulated Arctic mixed-phase cloud case. Three immersion freezing parameterizations are considered: a volume-dependent freezing scheme (VF), a parameterization limited to activated droplets (C-AC), and a parameterization limited to coarse aerosol particles (C-CM). Both C-AC and C-CM are based on classical nucleation theory. The freezing rate with VF is found to be greater in downdraft regions than in updraft regions due to the downdraft having a higher number concentration of large droplets. The C-AC cases show active freezing of small droplets near cloud top, whereas in the C-CM cases, mainly the 8–32-μm-sized droplets freeze in updraft regions near the cloud base. Because the initial crystal size is assumed to affect the axis ratio of hexagonal plates, the VF cases produce crystals with larger axis ratios, resulting in smaller mode radii than the C-AC cases. In all cases, irregular polycrystals dominate near cloud top and a band-like structure develops within the cloud, which qualitatively agrees with previous observations. In the VF and C-CM cases, unactivated large droplets arising from coarse-mode aerosol particles contributed significantly to the freezing rate, producing an important influence on crystal habit.

Description: This paper describes the development of convolutional neural networks (CNN), a type of deep-learning method, to predict next-hour tornado occurrence. Predictors are a storm-centered radar image and a proximity sounding from the Rapid Refresh model. Radar images come from the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) and Gridded NEXRAD WSR-88D Radar dataset (GridRad), both of which are multiradar composites. We train separate CNNs on MYRORSS and GridRad data, present an experiment to optimize the CNN settings, and evaluate the chosen CNNs on independent testing data. Both models achieve an area under the receiver-operating-characteristic curve (AUC) well above 0.9, which is considered to be excellent performance. The GridRad model achieves a critical success index (CSI) of 0.31, and the MYRORSS model achieves a CSI of 0.17. The difference is due primarily to event frequency (percentage of storms that are tornadic in the next hour), which is 3.52% for GridRad but only 0.24% for MYRORSS. The best CNN predictions (true positives and negatives) occur for strongly rotating tornadic supercells and weak nontornadic cells in mesoscale convective systems, respectively. The worst predictions (false positives and negatives) occur for strongly rotating nontornadic supercells and tornadic cells in quasi-linear convective systems, respectively. The performance of our CNNs is comparable to an operational machine-learning system for severe weather prediction, which suggests that they would be useful for real-time forecasting.

Description: Forecasters working for Australia’s Bureau of Meteorology (BoM) produce a seven day forecast in two key steps: first they choose a model guidance dataset to base the forecast on, then they use graphical software to manually edit this data. Two types of edits are commonly made to the wind fields that aim to improve how the influences of boundary layer mixing and land-sea breeze processes are represented in the forecast. In this study the diurnally varying component of the BoM’s official wind forecast is compared with that of station observations and unedited model guidance datasets. Coastal locations across Australia over June, July and August 2018 are considered, with data aggregated over three spatial scales. The edited forecast produces a lower mean absolute error than model guidance at the coarsest spatial scale (over fifty thousand square kilometres), and achieves lower seasonal biases over all spatial scales. However, the edited forecast only reduces errors or biases at particular times and locations, and rarely produces lower errors or biases than all model guidance products simultaneously. To better understand physical reasons for biases in the mean diurnal wind cycles, modified ellipses are fitted to the seasonally averaged diurnal wind temporal hodographs. Biases in the official forecast diurnal cycle vary with location for multiple reasons, including biases in the directions sea-breezes approach coastlines, amplitude biases, and disagreement in the relative contribution of sea-breeze and boundary layer mixing processes to the mean diurnal cycle.

Description: Atmospheric Rivers (AR) are narrow bands of strong horizontal transport of water vapor in the mid-latitudes that can cause extreme precipitation, which contributes to beneficial water supply and sometimes flooding. The precipitation productivity of an AR is affected by microphysical processes, including the influence of aerosols. Earlier case studies have shown that some ARs over the North Pacific contain dust from Africa and Asia that can strongly influence precipitation by acting as ice nuclei. This paper explores how commonly dust and ARs occur together, or in close proximity. A “dust score” is introduced to characterize the dustiness of the environment associated with ARs using satellite-based observations. This method is applied to days on which one or more ARs made landfall along the west coast of the U.S. between 2001 and 2018. The dust score is used to describe the seasonality and year-to-year variability of dusty-environment ARs. Dusty ARs occur primarily in the early spring (March-April) and dust is preferentially found within the cold sector of AR-associated extra-tropical cyclones. Year-to-year variability in dust score is dependent more on year-to-year variability in dust than on the frequency of AR days. This year-to-year variability is also strongly related to correlations between the frequency of ARs and the dustiness of the northeastern Pacific, motivating additional investigation into potential dynamical association between dust and ARs.

Description: Consumers of climate model information face difficulty in assessing which models and projections are best for their particular needs. This difficulty stems from the abundance of climate information, as well as the relative inaccessibility or unavailability of information concerning a given model’s quality, trade-offs, and suitability for a particular geographic region or decision-making application. Consumer reports have traditionally provided potential consumers with background knowledge and a review of available products and services to help to make decisions. As a knowledge broker for climate information in the Great Lakes region, the Great Lakes Integrated Sciences and Assessments (GLISA) team has developed a suite of climate model consumer-report-style documents to help climate information consumers make decisions when selecting models and projections for their work. To develop the reports, GLISA reviewed examples of consumer reports from other sectors, relied on the feedback and advice of our ongoing Practitioner Working Group composed of real-world consumers, and incorporated otherwise-unavailable information from model developers. Due to close, continuing partnership with our Practitioner Working Group, the content and the formatting of our climate model consumer reports respond directly to the needs of consumers. Our reports therefore evolve based on needs of the practitioners as well as with the knowledge generated by our research in usability of climate knowledge. We pose that climate model consumer reports, especially when developed in the context of trusted user–knowledge broker relationships, contribute to making climate information more relevant to and usable by practitioners.

Description: The intermittency of atmospheric turbulence plays an important role in the understanding of particle dispersal in the atmospheric boundary layer and in the statistical simulation of high-frequency wind speed in various applications. There are two kinds of intermittency, namely the magnitude intermittency (MI) related to non-Gaussianity and the less studied clusterization intermittency (CI) related to long-term correlation. In this paper, we use a 20 Hz ultrasonic data set lasting for one month to study CI of turbulent velocity fluctuations at different scales. Basing on the analysis of return time distribution of telegraphic approximation series, we propose to use the shape parameter of the Weibull distribution to measure CI. Observations of this parameter show that contrary to MI, CI tends to weaken as the scale increases. Besides, significant diurnal variations, showing that CI tends to strengthen at daytime (under unstable conditions) and weaken at nighttime (under stable conditions), are found at different observation heights. In the convective boundary layer, the mixed-layer similarity is found to scale CI exponent better than the Monin-Obukhov similarity. At night, CI is found to vary more slightly with height in the regime with large mean wind speeds than in the regime with small mean wind speeds, according to the Hockey-Stick theory.

Description: The relationship between storm-relative helicity (SRH) and streamwise vorticity ωs is frequently invoked to explain the often robust connections between effective inflow layer (EIL) SRH and various supercell updraft properties. However, the definition of SRH also contains storm-relative (SR) flow, and the separate influences of SR flow and ωs on updraft dynamics are therefore convolved when SRH is used as a diagnostic tool. To clarify this issue, proximity soundings and numerical experiments are used to disentangle the separate influences of EIL SR flow and ωs on supercell updraft characteristics. Our results suggest that the magnitude of EIL ωs has little influence on whether supercellular storm mode occurs. Rather, the transition from nonsupercellular to supercellular storm mode is largely modulated by the magnitude of EIL SR flow. Furthermore, many updraft attributes such as updraft width, maximum vertical velocity, vertical mass flux at all levels, and maximum vertical vorticity at all levels are largely determined by EIL SR flow. For a constant EIL SR flow, storms with large EIL ωs have stronger low-level net rotation and vertical velocities, which affirms previously established connections between ωs and tornadogenesis. EIL ωs also influences storms’ precipitation and cold-pool patterns. Vertical nonlinear dynamic pressure acceleration (NLDPA) is larger at low levels when EIL ωs is large, but differences in NLDPA aloft become uncorrelated with EIL ωs because storms’ midlevel dynamic pressure perturbations are substantially influenced by the tilting of midlevel vorticity. Our results emphasize the importance of considering EIL SR flow in addition to EIL SRH in the research and forecasting of supercell properties.

Description: In a 2008 paper, using satellite-derived wind speed estimates from tropical cyclones over the 25-yr period 1981–2006, we showed the strongest tropical cyclones getting stronger. We related the increasing intensity to rising ocean temperatures consistent with theory. Oceans have continued to warm since that paper was published, so the intensity of the strongest cyclones should have continued upward as well. Here I show that this is the case, with increases in the upper-quantile intensities of global tropical cyclones amounting to between 3.5% and 4.5% in the period 2007–19 relative to the earlier base period (1981–2006). All basins individually show upward intensity trends for at least one upper quantile considered, with the North Atlantic and western North Pacific basins showing the steepest and most consistent trends across the quantiles.

Description: This study evaluates the model simulation of interaction between convectively coupled tropical disturbances in the South China Sea (SCS) and Maritime Continent (MC). The Model for Prediction Across Scales (MPAS) is used to simulate the major interaction events in December 2016 with a fixed 60-km horizontal resolution and a variable 60–15-km resolution. Compared with an observational analysis, the overall spatial and temporal evolution of simulated rainfall and circulation reveals the capability of MPAS for reproducing equatorial Kelvin waves (KWs), and the interactions with equatorial Rossby waves and off-equatorial mixed Rossby–gravity (MRG)/TD-type waves up to a 5–7-day lead in both fixed 60-km and variable 60–15-km resolutions. Two interaction events are further examined. One involves an MRG/TD wave, prevailing northeasterlies, and a Borneo vortex developed in SCS during 6–11 December. The other involves a KW converging with the easterly trade wind that led to an MRG/TD-type wave and the formation of Typhoon Nock-ten during 16–20 December. The MPAS 60–15-km resolution tends to produce stronger precipitation and more coherent vorticity structures in both interaction events. Increasing the resolution to 15 km contributes to better representation of finer spatial vorticity and rainfall structures.