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

This paper compares extreme value statistics of daily maximum 10 m wind speed in winter simulated by the regional climate model COSMO‐CLM at a horizontal resolution of 15 km (C15) with the reanalyses ERA‐Interim and Arctic System reanalysis (ASR version 1 and 2) and with a satellite data set (CCMPv2). Our C15 simulation (1979/1980–2015/2016, November–April) is thereby the longest high‐resolution simulation available for the Arctic. The results show that the extreme wind speeds tend to increase over the ocean with increasing the horizontal model resolution. A horizontal resolution of ≤15 km is required to sufficiently capture all extreme wind characteristics, in particular for the tip jets and barrier winds over the Irminger Sea and in the Denmark Strait, and for the low‐level jets in the Nares Strait. Of almost equal importance are physical parameterizations of surface fluxes and of turbulence. Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic. State‐of‐the‐art global climate models and reanalyses (e.g., ERA‐Interim) underestimate the extreme near‐surface wind speeds in the Arctic and around Greenland. Regional climate models, for example, ASR/WRF or COSMO‐CLM, resolve the mesoscale wind systems that frequently produce extreme winds, which we estimated i.a. as 10‐year return levels (see the figure). Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, we analyse observed changes in characteristics of sub-daily heavy summer precipitation and erosive precipitation events for the Czech Republic, taking into consideration 51 years of data (1961-2011) for 17 stations. The sub-daily data were checked for consistency with the daily control records, and unreliable data were removed from the data set. Subsequently, four statistical tests were applied to assess the homogeneity of the data set. The trend magnitude was estimated using the Theil-Sen slope estimator, and two tests were applied to evaluate its significance. For most of the indices, the tests did not indicate inhomogeneity, with the exception of characteristics related to rainfall intensity. The trends are positive for most of the stations and most precipitation characteristics, and significant positive trends are much more frequent than negative trends. The analysis revealed a significant increase in the contribution of heavy precipitation to the precipitation total in half of the stations at short aggregation levels. [ABSTRACT FROM AUTHOR]

ABSTRACT The aim of this paper is to study the model's simulated frequency, track, intensity and location of cyclonic storms (CSs) and severe cyclonic storms (SCSs) in the Bay of Bengal (BoB), India. For the purpose, the PRECIS (Providing REgional Climates for Impacts Studies) a regional climate modelling system, of UK Met Office, is used. This model is integrated for the period 1961-1990 (baseline) and the future time period 2071-2100 (High emission scenario, A2). To run the model, the initial and lateral boundary conditions are supplied by UK Met Office. The analysis of frequency, track, intensity and location are carried out for May, June, September and October for the period 1961-1990 and 2071-2100. To evaluate the model's performance in simulating storms frequency during 1961-1990, chi square test is carried out with observed storms for the same period. The model's simulated frequency of storms is an overestimation of observations although the frequency of model's simulated storms during 2071-2100 is less than that of during 1961-1990. In general, model's simulated storms are found moving in the northwest direction from their initial location in all months during 1961-1990 and 2071-2100. In model's simulations, the drop in central pressure is relatively more in the months of May, June, September and October during 2071-2100 compared with that during 1961-1990. During 2071-2100, more intense storms may be possible in the months of May and June compared with that of September and October. It is observed that the model is able to simulate the initial locations of storms during 1961-1990 and 2071-2100 close to observations for the months of June and September, especially. [ABSTRACT FROM AUTHOR]

Classifications by self‐organizing maps (SOMs) have been used to study links between extreme weather events and atmospheric circulation. However, SOMs may often provide a too generalized image of circulation, which is too coarse to properly resolve meteorological extremes. Several approaches have been used to circumvent this limitation, including an increase in the number of nodes and/or relaxation of the stress on topological ordering of nodes, through adjusting the neighbourhood radius. Here, we (1) test whether weighting of training fields based on extremity of their circulation could lead to SOMs that would better represent circulation extremes without impairing the topological ordering of nodes, and (2) we investigate whether Sammon maps (SM), which have so far been limited to validation of SOMs in climatology, could provide a viable alternative to SOMs in studying weather extremes. We show that removing a certain percentage of circulation fields closest to the climatological mean has a greater effect on the representation of circulation extremes than more than doubling the SOM array size. Additionally, this step allows for decreasing the neighbourhood radius, the parameter responsible for topological ordering, which would not otherwise be possible without impairing the topology of the map. The SOMs are compared with two newly developed methods that discretize SMs based on arbitrarily chosen and distribution‐based bins. We demonstrate the usefulness of both methods in climate model validation and show that both methods are able to capture circulation variability in its extremes in more detail than the SOMs. The distribution‐based method does not suffer from sample‐size issues and outperforms all other classifications in extracting ERA5 patterns leading to extreme temperature minima over central Europe. Our results suggest that classifications based on SM could be a viable alternative to SOMs when SOMs lack the ability to discriminate circulation outliers leading to temperature extremes. [ABSTRACT FROM AUTHOR]

The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME‐mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME‐means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS‐g3, IPSL‐CM6A‐LR, and IPSL‐CM6A‐LR‐INCA, and one model in consecutive dry day (CDD) simulation, MPI‐ESM‐1‐2‐HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action. [ABSTRACT FROM AUTHOR]

The increasing awareness of climate change requires accurate, reliable and timely information on possible precipitation (PRE) changes to build climate resilience. This study uses the Coupled Model Intercomparison Project phase 6 (CMIP6) data to examine the effectiveness of bias correction on simulated historical mean and extreme PRE, and investigates the projected changes in extreme PRE events over southern Africa (SAF). Quantile mapping on a gamma distribution bias correction method is applied to the historical and projected CMIP6 data, with the Global Precipitation Climatology Centre (GPCC) PRE dataset as reference data. The projection analyses are conducted for the wet (December–March) and dry (May–October) seasons for two shared socioeconomic pathway (SSP) scenarios: SSP2‐4.5 and SSP5‐8.5. The results affirm that the bias correction significantly reduces (by more than 90%) the biases in the modelled mean and extreme PRE. The projected extreme PRE shows a general drying pattern over SAF. In the wet season, wet days (R1mm) are projected to decrease by 1 and 2 days by the end of the 21st century for SSP2‐4.5 and SSP5‐8.5, respectively, while very heavy PRE days (R20mm) show a slight upward pattern over SAF, reaching 0.1 and 0.4 days for the two scenarios. In contrast, the dry season exhibits a more pronounced drying tendency, with a consistent decrease in R1mm of 2 and 0.1 days by 2100 for SSP2‐4.5 and SSP5‐8.5, respectively. R20mm is projected to decrease by 4 and 0.2 days for SSP2‐4.5 and SSP5‐8.5, respectively. This implies that SAF is expected to get drier during the dry season while the PRE intensity during the wet season is projected to increase. [ABSTRACT FROM AUTHOR]

The ability of regional climate models (RCMs) to accurately simulate the current climate is increasingly important for impact assessments over Southeast Asia (SEA), identified as one of the world's most vulnerable regions to climate change. In this study, we evaluate the performance of a set of regional high‐resolution simulations from the Coordinated Regional Climate Downscaling Experiment‐SEA (CORDEX‐SEA) in simulating rainfall over the region. Simulations of the 1982–2005 seasonal mean climatology of daily precipitation and precipitation distribution over land are compared to observations from different sources (i.e., in situ‐based and satellite‐based). We also evaluate to what extent the precipitation distribution in RCMs is closer to observations than their associated forcing global climate models (GCMs). Observational estimates of precipitation over SEA have large uncertainties, making the model evaluations complicated. Despite these difficulties, our results highlight that RCMs can reproduce some complexities in the spatial distribution of seasonal rainfall but generally have a larger wet bias than GCMs. This is particularly true for the extremes in which RCMs show a large overestimation of rainfall intensity. There are some precipitation quantiles and grid points in which RCMs show limited reductions in biases compared to observations, but there is no consistency across all simulations and RCMs are generally further away from observations than their forcing GCMs. We find that greater intensity in RCMs over CORDEX‐SEA compared to their associated forcing GCMs is firstly associated with the increased supply of moisture from both local and large‐scale sources. Second, a widespread increase in convective precipitation is found across the region in RCMs. Our findings suggest that a model's ability to simulate precipitation over the region relies more on the RCM setup itself (e.g., parameterization scheme), rather than its forcing GCM. This should be considered when assessing the reliability of RCM precipitation simulations for future projections. [ABSTRACT FROM AUTHOR]

The global climate models (GCMs) performances of the recently released Coupled Model Intercomparison Project phase 6 (CMIP6) compared to its predecessor, CMIP5, are evaluated to anticipate the expected changes in climate over Egypt. Thirteen GCMs and their multi‐model ensemble (MME) of both CMIPs were used for this purpose. The future projections were compared for two radiative concentration pathways (RCP 4.5 and 8.5) and two shared socio‐economic pathways (SSP 2–4.5 and 5–8.5). The results revealed improvement in most CMIP6 models in replicating historical rainfall, maximum temperature (Tmax), and minimum temperature (Tmin) climatology over Egypt. The MME of the CMIPs revealed that both could reproduce Egypt's spatial distribution and seasonal climate variability. However, the bias in CMIP5 was higher than that for CMIP6. The uncertainties in simulating seasonal variability of rainfall and temperatures were lower for CMIP6 compared to CMIP5. The future projection of rainfall using CMIP6 MME revealed a higher reduction of precipitation (10–26 mm) in the economically crucial northern region than that estimated using CMIP5 (0–17 mm), compared to 133.5 mm rainfall in the base period. CMIP6 also projected a 0.74–1.63°C more rise in Tmax and Tmin compared to CMIP5 by the end of the century. The study indicates more aggravated scenarios of climate changes in Egypt than anticipated earlier, using the CMIP5 models. Therefore, Egypt needs to streamline the existing adaptation and mitigation measures to account for climate projections. [ABSTRACT FROM AUTHOR]

This study evaluated the rainfall historical simulations of 15 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in replicating annual and seasonal rainfall climatology, their temporal variability and trends in Bangladesh for the period 1979–2014, considering ERA5 (ECMWF Reanalysis 5th Generation) reanalysis as the reference dataset. Shannon's Entropy decision‐analysis was employed for GCMs' rating based on eight statistical indicators and a comprehensive rating metric for the final grading of the GCMs. The majority of the CMIP6 GCMs accurately reproduced the spatial feature of ERA5 rainfall. However, the GCMs underestimated annual rainfall by an average of 190.5 mm, with the highest underestimation in monsoon (131.76 mm) and least in winter (3.52 mm) seasons. Most GCMs also underestimated rainfall variability for all seasons except winter. Besides, the GCMs showed an increasing trend in pre‐monsoon and a decreasing trend in post‐monsoon rainfall like ERA5, but an opposite (negative) to ERA5 trend (positive) in monsoon season rainfall. The ensemble mean of the GCMs showed higher skill in reconstructing rainfall climatology, temporal variability and trends than the individual GCMs. The study identified MPI‐ESM1‐2‐LR, MPI‐ESM1‐2‐HR, and GFDL‐ESM4 as the most effective GCMs in reproducing precipitation over Bangladesh. The selected models' simulation can be used for climate change impact assessment in Bangladesh after bias minimization. [ABSTRACT FROM AUTHOR]

ABSTRACT Stability indices provide simple metrics to characterize tropospheric states favouring convection. Not only for regions where precipitation is mainly related to convective processes, important aspects of the climate comprising information from different levels and variables can therefore be summarized in scalar metrics. Although the linkage between tropospheric stability and convective precipitation is blurred by additional prerequisites often not resolved in atmospheric models, analysis of stability indices from global climate models (GCMs) provides a more process-orientated assessment than the separate analysis of the individual climate variables. This paper presents an assessment of GCM capabilities to simulate tropospheric stability on the Arabian Peninsula. Therefore, six stability indices (K-Index, Total Totals Index, Vertical Totals Index, Showalter Index, SWEAT and Cross Totals Index) were calculated for several GCMs from the Climate Model Intercomparison Project 3 archive for three locations. GCM indices were compared with reanalysis data from the National Centers for Environmental Prediction in collaboration with the National Center for Atmospheric Research as well as radiosoundings from the Integrated Global Radiosounding Archive. Comparison was done by means of a classification approach based on quantile values of the index distribution. Input parameters of the indices were also analysed and biases for indices as well as input parameters were identified. There are biases found for the different indices which can be attributed to some extent to biases of the input parameters. There is no input parameter which is biased in the same direction for all indices and locations. Due to the variability of the results, rankings of the models are characterized by large rank differences so that there is no overall best performing model. Nevertheless, this study provides some insight in the performance of the GCMs to simulate tropospheric stability at individual locations and so presents an alternative method of model performance assessment. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the ability of a set of large ensembles (LE) of Global Climate Model (GCM) simulations from the Multi‐Model Large Ensemble Archive, Coupled Model Intercomparison Project Phase 6 (CMIP6) and database for Policy Decision making for Future climate change in reproducing the variability and change of the austral summer precipitation observed in South America along the second part of the 20th century and beginning of the 21st. LE show similar biases in the mean austral summer rainfall and interannual variability than those detected in previous model sets, such as the Coupled Model Intercomparison Project Phase 5 (CMIP5). The positive trends in south‐eastern South America (SESA) and negative ones in the southern Andes (SAn), that are the most significant observed regional features, are identified to some extent in LE simulations. The negative trend in SAn is a feature consistently shown among different models. For all models, mean trend is negative in that particular region and larger than the inter‐member dispersion for each large ensemble. Trend magnitudes in the SESA region show a larger dispersion between models than the SAn region. While the mean trend among models is consistent with the observations, models underestimate the observed trend. The multi‐model ensemble of selected models that best reproduce both mean and interannual variability features of rainfall in South America shows an 18% larger positive mean trend in SESA rainfall than those that have the worst performance. However, internal variability uncertainty is still higher than the mean trend for both sets of multi‐model ensembles [5% (18%) for selected (non‐selected) models]. Improvements in the representation of the main features of mean rainfall and variability are needed in order to better describe and reduce the uncertainties associated with the main climate change related signals in the continent. [ABSTRACT FROM AUTHOR]

Climate impact studies often require climate data at a higher space–time resolution than is available from global and regional climate models. Weather generator (WG) models, generally designed for mesoscale applications (e.g., 101–105 km2), are popular and widely used tools to downscale climate data to finer resolution. One advantage of using WGs is their ability to generate the necessary climate variables for impact studies in data sparse regions. In this study, we evaluate the ability of a previously established state of the art WG (the AWE‐GEN‐2d model) to perform in data sparse regions that are beyond the mesoscale, using the Zambezi River basin (106 km2) in southeast Africa as a case study. The AWE‐GEN‐2d model was calibrated using data from satellite retrievals and climate re‐analysis products in place of the absent observational data. An 8‐km climate ensemble at hourly resolution, covering the period of 1976–2099 (present climate and RCP4.5 emission scenario from 2020), was then simulated. Using the simulated 30‐member ensemble, climate indices for both present and future climates were computed. The high‐resolution climate indices allow detailed analysis of the effects of climate change on different areas within the basin. For example, the southwestern area of the basin is predicted to experience the greatest change due to increased temperature, while the southeastern area was found to be already so hot that is less affected (e.g., the number of 'very hot days' per year increase by 18 and 9 days, respectively). Rainfall intensities are found to increase most in the eastern areas of the basin (1 mm·d−1) in comparison to the western region (0.3 mm·d−1). As demonstrated in this study, AWE‐GEN‐2d can be calibrated successfully using data from climate reanalysis products in the absence of ground station data and can be applied at larger scales than the mesoscale. [ABSTRACT FROM AUTHOR]

We investigate the impact of horizontal resolution, convection parameterization and lateral boundary conditions on the ability of a regional climate model ‐ RegCM4 to simulate the observed temperature and precipitation climatology over Croatia and surrounding regions. An ensemble of RegCM4 simulations over Europe has been generated including two resolutions (50 and 12.5 km), two different deep convection schemes and different boundary conditions (a reanalysis and four global climate models). Using two observational data sets (with and without the undercatch correction) shows that the magnitude of RegCM4 errors in winter precipitation also depends on observational uncertainty. While the mean precipitation amounts in high‐resolution simulations are generally overestimated in the cold part of the year, low‐resolution RegCM4 simulations are comparable to most of other EURO‐CORDEX models. When forced by ERA‐Interim, RegCM4 performs relatively poorly in reproducing temporal correlations against observations, but performs well in terms of spatial correlation. Whereas precipitation amounts from high‐resolution simulations need further improvements, the RegCM4 skill in simulating near‐surface temperature is high and well suited for impact studies. [ABSTRACT FROM AUTHOR]

Global warming acts to prolong thermal summers and shorten winters. In this work, future changes in the lengths and timing of four thermal seasons in northern Europe, with threshold temperatures 0 and 10°C, are derived from bias‐adjusted output data from 23 CMIP5 global climate models. Three future periods and two Representative Concentration Pathway (RCP) scenarios are discussed. The focus is on the period 2040–2069 under RCP4.5, which approximately corresponds to a 2°C global warming relative to the preindustrial era. By the period 2040–2069, the average length of the thermal summer increases by nearly 30 days relative to 1971–2000, and the thermal winter shortens by 30–60 days. The timing of the thermal springs advances while autumns delay. Within the model ensemble, there is a high linear correlation between the modelled annual‐mean temperature increase and shifts in the thermal seasons. Thermal summers lengthen by about 10 days and winters shorten by 10–24 days per 1°C of local warming. In the mid‐21st century, about two‐thirds of all summers (winters) are projected to be very long (very short) according to the baseline‐period standards, with an anomaly greater than 20 days relative to the late‐20th century temporal mean. The proportion of years without a thermal winter increases remarkably in the Baltic countries and southern Scandinavian peninsula. Implications of the changing thermal seasons on nature and human society are discussed in a literature review. [ABSTRACT FROM AUTHOR]

The Red Sea trough (RST) is a low‐pressure trough extending from south towards the Levant. Unlike previous synoptic classifications covering all systems that affect the region, our algorithm focuses on the RST alone. It uses sea‐level pressure (SLP) and relative geostrophic vorticity for identifying the existence of an RST and classifying it to one of three types, according to the location of the trough axis with respect to 35°E longitude. The following conditions were imposed to assure the existence of an RST: (a) north to south SLP drop across the Levant, (b) average positive sea‐level relative vorticity over the region of interest, (c) existence of a distinct and continuous trough axis from the south towards the region of interest and (d) absence of any pronounced closed cyclone near the Levant. The algorithm was applied on the NCEP/NCAR reanalysis, 2.5° × 2.5° resolution and the ERA‐Interim, 2.5° × 2.5° and 0.75° × 0.75° resolutions. An evaluation of the algorithm against subjective identification, based on the NCEP reanalysis, showed an agreement of 93% for RST identification and 79% for correct classification. The use of fine resolution data may insert noise that reduce the identification rate of RSTs but improves the axis locating. The autumn is the main season of RST, with a maximum in November and a consistent decrease towards the July minimum. The annual frequency varies among the data sources between 17.6 and 24.6%. The trough axis is shown to have a diurnal oscillation; towards the eastern coast of the Mediterranean at nighttime and eastward, inland, at noontime. No consistent long‐term trend was found for the period 1979–2016, during which the global warming was persistent. This automated algorithm is flexible in the sense that it is not confined to any predetermined spatial resolution and is applicable to operational forecast model as well as to climate model outputs. [ABSTRACT FROM AUTHOR]

Potential change in the hydrological cycles may lead to changes in extreme events such as drought due to the changes in intensity, frequency, and seasonality of precipitation and evaporative demand. In this study, projected drought scenarios and associated uncertainties in the late 21st century over the Continental United States are analysed based on seven regional climate models (RCMs) contributing to the Coordinated Regional Climate Downscaling Experiment. Meteorological drought scenarios are analysed based on standardized precipitation evapotranspiration index (SPEI). Here, we propose a new multimodel ensemble approach based on the similarity of spectral power of different frequency components of the SPEI time series. The approach combines output from different RCMs based on their ability to produce the observed power spectra as well as convergence towards the average spectra from all participating RCMs in the future climate. Furthermore, to understand the SPEI variability associated with climate teleconnection, continuous wavelet transform‐based spectral coherency between the SPEI and two climate indices: the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) is analysed. Results show that among‐model uncertainty is the dominant source of uncertainties in the projected drought scenarios with contributions as large as 97% to the total uncertainty. Fifteen to 20% more frequent droughts are projected in future climate due to a decrease in mean SPEI as well as an increase in the variability of SPEI. Observed SPEI variability is strongly associated with ENSO variability while PDO modulates the strength of correlation between the SPEI and the ENSO. Spectral analysis of future SPEI shows that the increase in the SPEI variability is due to an increase in interannual variability as well as an enhanced ENSO teleconnection over the study area. The results show the increased role of climatic variability and hence the enhanced predictability of drought scenarios in the future climate. [ABSTRACT FROM AUTHOR]

With the objective to provide a basis for regional climate models (RCMs) selection and ensemble generation for climate impact assessments, we perform the first ever analysis of climate projections for Western Nepal from 19 RCMs in the Coordinated Regional Downscaling Experiment for South Asia (CORDEX‐SA). Using the climate futures (CF) framework, projected changes in annual total precipitation and average minimum/maximum temperature from the RCMs are classified into 18 CF matrices for two representative concentration pathways (RCPs: 4.5/8.5), three future time frames (2021–2045/2046–2070/2071–2095), three geographic regions (mountains/hills/plains) and three representative CF (low‐risk/consensus/high‐risk). Ten plausible CF scenario ensembles were identified to assess future water availability in Karnali basin, the headwaters of the Ganges. Comparison of projections for the three regions with literature shows that spatial disaggregation possible using RCMs is important, as local values are often higher with higher variability than values for South Asia. Characterization of future climate using raw and bias‐corrected data shows that RCM projections vary most between mountain and Tarai plains with increasing divergence for higher future and RCPs. Warmer temperatures, prolonged monsoon and sporadic rain events even in drier months are likely across all regions. Highest fluctuations in precipitation are projected for the hills and plains while highest changes in temperature are projected for the mountains. Trends in change in annual average discharge for the scenarios vary across the basin with both precipitation and temperature change influencing the hydrological cycle. CF matrices provide an accessible and simplified basis to systematically generate application‐specific plausible climate scenario ensembles from all available RCMs for a rigorous impact assessment. [ABSTRACT FROM AUTHOR]

This study examines projected changes in surface air temperature (SAT) and precipitation over mid–high latitudes of Eurasia during boreal spring in a 1.5/2°C warmer world (relative to pre‐industrial level) using historical, RCP4.5 and RCP8.5 simulations from 16 climate models of the Coupled Model Intercomparison Project Phase 5. Results show that spatial patterns of spring SAT and precipitation changes over Eurasia in the 1.5/2°C warmer world relative to the current climate (1979–2005) are similar between the two RCPs, but with a slightly larger amplitude in the RCP8.5. Spring SAT warmings over Eurasia are faster than the global annual mean. Amplitudes of the spring Eurasian SAT warmings are larger over higher latitudes. An additional 0.5°C global warming leads to extra spring Eurasian SAT warmings, with the magnitude larger than 0.5°C. Changes in the spring maximum temperature are comparable to the mean temperature changes both in the 1.5 and 2°C warming targets. Significant increases in the mean and extreme precipitation are only seen over parts of Eurasia. An additional 0.5°C global warming does not lead to obvious changes in the mean and extreme spring precipitation. Further analyses show that there are large uncertainties in the projected changes in the inter‐annual variability of the Eurasian spring SAT and precipitation. [ABSTRACT FROM AUTHOR]

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

The climate system comprises multiple components, primarily the atmosphere, ocean and cryosphere, each incorporating physical processes that interact across scales. To help understand the behaviour of this complex system, and evaluate climate model simulations, researchers typically take a reductionist approach, focusing on individual climate processes and studying their relationships with weather and climate in different regions. While more holistic approaches, such as climate networks, have been developed to explicitly address the complexity of the climate, here we argue for the use of a new approach that accounts for multiple cross‐scale process interactions, framed with respect to specific climate outcomes of societal importance. We introduce and explore the concept of "climate process chains" (CPCs), describing their potential application using examples determined for southern Africa. Building on related theoretical concepts, and through reviewing literature on climate processes and teleconnections to southern Africa, we identify candidate CPCs for two outcomes of societal interest; a regional‐scale drought and local‐scale heavy rainfall. Focusing on such outcomes means that CPC investigations have more relevance to climate risk management contexts, as well as providing a constraint on the exploration of climate uncertainties for a region. We argue that CPCs may help to articulate relationships amongst regionally relevant climate processes across temporal and spatial scales, and discuss their potential utility in climate research, including in the evaluation of climate models and their simulations. [ABSTRACT FROM AUTHOR]

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

The ability of the seven CMIP5 models to simulate extreme precipitation events over Iran was evaluated using the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN‐CDR) data set. The criterion used to select the CMIP5 models was the availability of historical daily precipitation data (PERSIANN‐CDR) for the retrospective period 1983–2005, as well as future projections for the three representative concentration pathways emission scenarios (RCP2.6, RCP4.5, and RCP8.5) and spatial resolution higher than 2 × 2°. This is the first study to focus on extreme precipitation climate model simulations over Iran that includes high topography and different climates. The results show that CCSM4 has the highest correlation coefficients (CC = 0.85) and lowest root‐mean‐square error (RMSE = 73.6 mm) compared to PERSIANN‐CDR for the mean annual precipitation. However, HadGEM2‐ES shows the best (highest CCs between 0.67–0.79 and almost the lowest root‐mean‐square errors [RMSEs] compared to PERSIANN‐CDR) performance for intensity indices; MIROC5 ranked seventh (least CCs and almost the highest RMSEs) among the selected models. The results show that BCC‐CSM1‐1‐M captures maximum consecutive dry days (CDD) better than the other models. The probability matching method (PMM) is used to bias‐correct daily precipitation events from CMIP5 models with respect to the PERSIANN‐CDR estimations. All the model performances designed to capture the mean annual precipitation, as well as extreme intensity indices, improved after correction. The ensemble, constructed from the bias‐corrected model simulations using multiple linear regression (MLR), has the best performance for simulating the mean annual precipitation and extreme indices (CCs between 0.82 for consecutive wet days [CWD] and 0.93 for the mean annual precipitation) compared to the PERSIANN‐CDR estimations. Among the seven selected models, CCSM4 has the highest ranking (CCs between 0.70 for CWD to 0.91 for mean annual precipitation) after bias correction. [ABSTRACT FROM AUTHOR]

Potential increases in the risk of extreme weather events under climate change can have significant socio‐economic impacts at regional levels. These impacts are likely to be particularly high in South Asia where Bangladesh is one of the most vulnerable countries. Regional climate models (RCMs) are valuable tools for studying weather and climate at finer spatial scales than are typically available in global climate models. Quantitative assessment of the likely changes in the risk of extreme events occurring requires very large ensemble simulations due to their rarity. The weather@home setup within the climateprediction.net distributed computing project is capable of providing the necessary very large ensembles at regionally higher resolution, but has only been evaluated over the South Asia region for its representation of seasonal climatological and monthly means. Here, we evaluate how realistically the HadAM3P‐HadRM3P model setup of weather@home can reproduce the observed patterns of temperature and rainfall in Bangladesh with focus on the modelled extreme events. Using very large ensembles of regional simulations, we find that there are substantial spatial and temporal variations in rainfall and temperature biases compared with observations. These are highest in the pre‐monsoon, which are largely caused by timing issues in the model. Modelled mean monsoon and post‐monsoon temperatures are in good agreement with observations, whereas there is a dry bias in the modelled mean monsoon rainfall. The rainfall bias varies both spatially and with the data set used for comparison. Despite of these biases, the model‐simulated temperature and rainfall extremes in summer monsoon over Bangladesh are approximately representative of the observed ones. At the wettest parts of northeast Bangladesh, rainfall extremes are underestimated compared to GPCC and APHRODITE but are within the range of CPC observations. Therefore, the weather@home RCM, HadRM3P may provide a sufficiently reliable tool for studying the extreme weather events in Bangladesh. [ABSTRACT FROM AUTHOR]

Climate inter‐annual variability over the North American monsoon (NAM) region is associated with El Niño‐Southern Oscillation (ENSO) and Pacific decadal variability (PDV), which drive a warm season atmospheric teleconnection response. Using the North American Regional Climate Change Assessment Program (NARCCAP) simulations, previous studies have found that regional models forced with an atmospheric reanalysis (NARCCAP Phase I) represent the NAM reasonably well as a climatological feature. However, when these same regional models are forced with global climate model projections (NARCCAP Phase II), their ability to represent the NAM as a salient feature substantially degrades. The present study evaluates NAM inter‐annual climate variability through the continental‐scale patterns of summer precipitation within the NARCCAP simulations (Phases I and II), in relation to ENSO–PDV, and the presence of the driving atmospheric teleconnection response. Multivariate statistical analyses are applied to sea surface temperature and precipitation data sets to determine dominant variability at continental scale, with focus on the southwest. The analysis reveals that NARCCAP Phase I simulations are able to portray the spatial pattern of precipitation associated with ENSO–PDV in a similar way to observations. However, all NARCCAP Phase II simulations, with the exception of the HRM(Hadcm3) regional–global model pair, fail to reproduce this climate variability. Although including all possible NARCCAP model simulations to generate a multi‐model ensemble mean would increase the statistical degree of confidence in climate projections, this type of result would not increase confidence in the physical climatology of model representations of warm season climate variability. More physically based, process‐oriented metrics are needed to evaluate model quality in assessing the uncertainty of future climate change in multi‐model ensemble products used for climate change impacts assessments. [ABSTRACT FROM AUTHOR]

This study demonstrates a methodology to construct short‐duration rainfall intensity–duration–frequency (IDF) curves and to quantify the variability in the rainfall intensities for different return periods with respect to the changing climate. A dynamical downscaling approach using the regional climate model (RCM) Weather Research and Forecasting (WRF) has been used to assess present and future climates using the downscaling of an ensemble of three global climate models (GCMs) (CSIRO‐ACCESS1.3, MPI‐ESM‐MR ECHAM6 and NIES‐MIROC5) under the Coupled Model Intercomparison Project phase 5 (CMIP5). Furthermore, a statistical approach using the well‐known simple scaling method has been applied to extend the 6‐hourly WRF precipitation output to the finer temporal scale of 10 min. The short‐duration IDF curves were then constructed for the present and future climates under two representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5. A preliminary examination for this case study over Bac Ninh, an industrial area in the northern Vietnam, shows that there is a substantial increase in short‐duration rainfall intensity in the future with respect to the baseline climate. The highest increase is towards the end of the century (2071–2100) ranging from 56 to 61% for a 10‐ and 100‐year return period for 24‐hr duration, respectively, while the increase is about 40–45% for the 10‐min duration. These results strongly suggest that severe flooding in the future climate over the study region may be likely. The study results might be useful for policymakers and infrastructure planning and for insurance companies around the study area. [ABSTRACT FROM AUTHOR]

ABSTRACT: High‐resolution monthly precipitation climatologies for Italy are presented. They are based on 1961–1990 precipitation normals obtained from a quality‐controlled dataset of 6134 stations covering the Italian territory and part of the Northern neighbouring regions. The climatologies are computed by means of two interpolation methods modelling the precipitation‐elevation relationship at a local level, more precisely a local weighted linear regression (LWLR) and a local regression kriging (RK) are performed. For both methods, local optimisations are also applied in order to improve model performance. Model results are compared with those provided by two other widely used interpolation methods which do not consider elevation in modelling precipitation distribution: ordinary kriging and inverse distance weighting. Even though all the four models produce quite reasonable results, LWLR and RK show the best agreement with the observed station normals and leave‐one‐out‐estimated mean absolute errors ranging from 5.1 mm (July) to 11 mm (November) for both models. Their better performances are even clearer when specific clusters of stations (e.g. high‐elevation sites) are considered. Even though LWLR and RK provide very similar results both at station and at grid point level, they show some peculiar features. In particular, LWLR is found to have a better extrapolation ability at high‐elevation sites when data density is high enough, while RK is more robust in performing extrapolation over areas with complex orography and scarce data coverage, where LWLR may provide unrealistic precipitation values. However, by means of prediction intervals, LWLR provides a more straightforward approach to quantify the model uncertainty at any point of the study domain, which helps to identify the areas mainly affected by model instability. LWLR and RK high‐resolution climatologies exhibit a very heterogeneous and seasonal‐dependent precipitation distribution throughout the domain and allow to identify the main climatic zones of Italy. [ABSTRACT FROM AUTHOR]

Simulations of the regional climate model REMO at 25 and 50 km (0.44° and 0.22°, respectively) grid spacing are evaluated over Central Africa for the period 1998-2010. The model's ability to simulate various aspects of the observed climate is assessed with a main focus on precipitation characteristics. The results show that the REMO model has a good skill to reproduce the main regional features of the mean climate fields as well as seasonal and annual cycles, although some biases still exist. The model captures the variability in precipitation anomalies between different events associated with El Niño/Southern Oscillation. Generally, the findings remain largely insensitive to the refinement of the horizontal resolution, especially when considering the regional scale. This points the fact that the model dynamical and physical setup may have to be adjusted when setting a focus on the Central African region, in order to better represent the climate mechanisms. It is further found that REMO is capable of capturing the basic features of climatic extremes and accurately represents the shape of the frequency distribution of daily precipitation data. Daily characteristics of precipitation are simulated much better by REMO at 25 km. [ABSTRACT FROM AUTHOR]

ABSTRACT Under the framework of an project 'Building Asian Climate Change Scenarios by Multi-Regional Climate Models Ensemble', the ability of eight regional climate models and two fine-resolution global climate models to reproduce late 20th century (1981-2000) precipitation climatology is assessed. Future precipitation change (2041-2060) under the A1B scenario is also quantified by applying four different ensemble methods: equal weighting, weighted mean ( WM), reliability ensemble averaging ( REA) and performance-based ensemble averaging, after applying fourfold cross-validation using observation and multi-model-simulated precipitation. The results indicate that the ensemble of simulated precipitation outperforms any single RCM in many aspects. Among the four ensemble approaches, the WM and REA methods show better skill in improving the simulation results, and are used for ensemble prediction of regional climate in Asia. Under the A1B scenario, the WM method estimates future precipitation change of approximately 0.2 mm day−1 with less precipitation in northern and western China and northern India, and more precipitation in most other areas in Asia. The future annual precipitation will decrease by 0.1-0.5 mm day−1 in northern India, Pakistan and the central area of southern China. No significant change is found over eastern Kazakhstan, Mongolia, north-central and western China. [ABSTRACT FROM AUTHOR]

ABSTRACT An ensemble of simulations from four regional climate models ( RCMs) driven by a global reanalysis was obtained from the North American Regional Climate Change Assessment Program ( NARCCAP) and used to evaluate the ability of the RCMs to simulate the long-term (1979-2000) climatology of southerly low-level jets (S- LLJs) in the central United States. The RCM-derived S- LLJ climatologies were evaluated against rawinsonde observations for the same period. The use of a small ensemble of RCM simulations helped to identify model differences and assisted with interpretation. The RCMs generally reproduced the broad spatial patterns and temporal variations of jet frequency and average jet height and speed. No model consistently outperformed the others in all aspects of the evaluation, although differences existed between models in the placement, migration and relative strength of 'hotspots' of more frequent jet activity. In particular, three of the four models placed the centre of greatest nocturnal S- LLJ activity during the warm season in northern and central Texas, whereas for the other model the greatest jet activity was located in the south-central plains (Kansas/Oklahoma). The magnitude of a S- LLJ frequency maximum over south Texas also varied between models, with simulated frequencies exceeding observed frequencies for some models but substantially underestimating for others. The evaluation presented here highlights the potential applications of RCMs in S- LLJ research for future climate and other assessment studies that require three-dimensional data with relatively high spatial and temporal resolutions. The overall performance of the models in reproducing the long-term S- LLJ climatology supports the use of NARCCAP RCM simulations in climate assessments for the central United States where S- LLJs are an important contributor to the regional climatology. [ABSTRACT FROM AUTHOR]

ABSTRACT A total of 107 climate runs from 48 Coupled Model Inter-comparison Project 5 ( CMIP5) general circulation models ( GCMs) were evaluated for their ability to skillfully reproduce basic characteristics of late 20th century climate over Central America. The models were ranked according to metrics that take into consideration the mean and standard deviation of precipitation ( pr) and surface temperature ( tas), as well as the El Niño-Southern Oscillation ( ENSO)- pr teleconnection. Verification was performed by comparing model runs to observations and a reanalysis dataset. Based on the rankings, the best 13 models were further evaluated. Not surprisingly, the models showed better skill at reproducing mean tas patterns throughout the year. The skill is generally low for mean pr patterns, except for some models during March, April, and May. With a few exceptions, the skill was low for reproducing the observed monthly standard deviation patterns for both pr and tas. The ENSO- pr teleconnection was better simulated in the best 13 model runs compared to the sea-surface temperature global pattern characteristic of ENSO which showed low skill. The Inter-tropical Convergence Zone ( ITCZ) appeared better modeled in July than in January. In January, there were instances of a double ITCZ pattern. Some models skillfully reproduced the seasonal distribution of the Caribbean Low-Level Jet index ( CLLJ). More detailed research evaluating the specific performance of the models on a variety of time-scales and using parameters relevant to these and other climatic features of Central America is needed. This study facilitates a pre-selection of models that may be useful for this task. [ABSTRACT FROM AUTHOR]

ABSTRACT Extreme climate events over China at the end of the 21st century (2080-2099) are investigated using the regional climate model RegCM4. Model performance is validated through comparison between observations and simulations over the period 1985-2005. The results show that RegCM4 can satisfactorily reproduce the spatial distribution of extreme climate events over China. The model simulates temperature extremes more accurately than precipitation. Under the RCP8.5 (high emission) scenario, the number of frost days decreases, and both the heat wave duration index and the growing season length increase dramatically towards the end of the 21st century. Changes in extreme temperature become increasingly pronounced from South to North China, with the most significant changes occurring on the Tibetan Plateau ( TP). The proportion of heavy precipitation generally increases, except on the southern TP. The number of very heavy precipitation days increases by 25-50% in Northwest and East China. In winter, the number of consecutive dry days ( CDD) decreases in North China and increases in South China. The greatest increases in CDD are found in June, July and August ( JJA) in Southwest China. In a future that follows this scenario, drought events may be aggravated in Southwest China, and decrease in North China. In contrast, when repeating these projections under the assumption of the RCP4.5 scenario for emissions, the frequency of extreme climate events is reduced. These results suggest that reductions in greenhouse gas emissions may mitigate the effects of climate change over the coming decades. [ABSTRACT FROM AUTHOR]

ABSTRACT As both global (GCM) and regional (RCM) climate models have their own advantages, the most comprehensive picture of changes in precipitation and their uncertainty ranges can be achieved by comparing the results of both model categories. Here we have evaluated seasonal changes in indices representing excess or scarcity of precipitation in Europe on the basis of simulations performed with ten GCMs with a reasonably high spatial resolution and, for comparison, with five RCMs driven by one and the same GCM. We found no fundamental differences between the GCMs and RCMs in the projected tendency towards a more extreme precipitation climate, characterized by increases both in indices representing wet conditions and also dry conditions. Most evidently in the Northern European summer and Southern European winter, the differences in the responses of the various indices between the present sets of GCMs and RCMs could be explained by the dissimilar changes in seasonal mean precipitation. The scatter among the projected changes was in general much smaller for the RCM than for the GCM simulations. The projections of individual RCMs mainly fall within the interval determined by the GCM projections. This indicates that the GCM ensemble yielded more comprehensive estimates for the uncertainty ranges in the extreme precipitation indices. [ABSTRACT FROM AUTHOR]

ABSTRACT The western Himalayas (WH) is characterized by heterogeneous land surface characteristics and topography. During winter (December, January, and February-DJF), eastward moving low-pressure synoptic weather systems, called Western Disturbances (WDs) in Indian parlance, cause the majority of the precipitation mostly in the form of snow. The interplay between land surface/topography and WDs greatly controls precipitation distribution over the region. This study seeks to evaluate this using a mosaic-type parameterization of subgrid-scale topography and land use (sub-BATS) for regional climate simulation with a regional climate model (RegCM3). The model coarse grid cell size in the control simulation is 60 km while the subgrid cell size is 10 km. This study compares two 22 year simulations (1980-2001) during winter (DJF). The first simulation is without (CONT) and the second is with (SUB) the fine scale subgrid scheme. Representing the fine scale processes using the subgrid scheme SUB experiment simulates reduced precipitation by approximately 2 mm d−1 with comparison to CONT experiment. This estimation of reduced and closer to the corresponding observed precipitation is important for regional water budget over the WH which is primarily governed by topographic and land surface disaggregation. Validation with corresponding observations over similar elevations shows that SUB displays an improvement over CONT experiment. This relevant decrease of precipitation in SUB using disaggregation-reaggregation methodology for initial model input fields in subBATS scheme is due to better representation of the WH topography. In case of temperature, SUB experiment displays colder bias (∼2-4 °C) than the CONT over the Himalayas. This preliminary finding is important for studying regional water balance, snow melt accumulation in following summer period. [ABSTRACT FROM AUTHOR]

ABSTRACT Through the use of the climatic classification of Köppen-Trewartha (K-T), the ability to reproduce the current climate of Europe has been shown for an ensemble of 15 regional climate model simulations nested in six global climate models. Depending on the simulation, between 55.4 and 81.3% of the grid points are in agreement with observations regarding the location of climate types in current climate simulations (1971-2000). In this respect, the result of the ensemble of 15 simulations is better than that of any individual model, with 83.5% of the grid points in agreement with observations. K-T classification has also been used to analyse the projected climate change over the 21st century under the SRES-A1B emissions scenario. It was found that 22.3% of the grid points in the domain change their climate by the period 2021-2050 compared to current climate and 48.1% change by 2061-2090. The climate shifts affecting the biggest extensions are projected in Central Europe and Fennoscandia, but other smaller areas suffer more intense changes which potentially are more dangerous to vegetation and ecosystems. Generally, these changes occur at a sustained rate throughout the century, reaching speeds of up to 90 × 103 km2 decade−1 in the retreat or expansion of some climates. [ABSTRACT FROM AUTHOR]