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When selecting a subset of climate change scenarios (GCM models), the priority is to ensure that the subset reflects the comprehensive range of possible model results for all variables concerned. Though many studies have attempted to improve the scenario selection, there is a lack of studies that discuss methods to ensure that the results from a subset of climate models contain the same range of uncertainty in hydrologic variables as when all models are considered. We applied the Katsavounidis-Kuo-Zhang (KKZ) algorithm to select a subset of climate change scenarios and demonstrated its ability to reduce the number of GCM models in an ensemble, while the ranges of multiple climate extremes indices were preserved. First, we analyzed the role of 27 ETCCDI climate extremes indices for scenario selection and selected the representative climate extreme indices. Before the selection of a subset, we excluded a few deficient GCM models that could not represent the observed climate regime. Subsequently, we discovered that a subset of GCM models selected by the KKZ algorithm with the representative climate extreme indices could not capture the full potential range of changes in hydrologic extremes (e.g., 3-day peak flow and 7-day low flow) in some regional case studies. However, the application of the KKZ algorithm with a different set of climate indices, which are correlated to the hydrologic extremes, enabled the overcoming of this limitation. Key climate indices, dependent on the hydrologic extremes to be projected, must therefore be determined prior to the selection of a subset of GCM models. [ABSTRACT FROM AUTHOR]

Changes in the essential climate extremes indices and surface variables for the end of the twenty-first century are assessed in this study based on two transient climate change simulations, with and without land-use and land-cover changes (LULCC), but identical atmospheric forcing. The two simulations are performed with the 5th generation of the Canadian Regional Climate Model (CRCM5) driven by the Canadian Earth System Model for the (2006-2100)-Representative Concentration Pathway 4.5 (RCP4.5) scenario. For the simulation with LULCC, land-cover data sets are taken from the global change assessment model (GCAM) representing the RCP4.5 scenario for the period 2006-2100. LULCC in RCP4.5 scenario suggest significant reduction in cultivated land (e.g. Canadian Prairies and Mississippi basin) due to afforestation. CRCM5 climate projections imply a general warming by the end of the twenty-first century, especially over the northern regions in winter. CRCM5 projects more warm spell-days per year over most areas of the continent, and implicitly more summer days and tropical nights at the expense of cold-spell, frost and ice days whose number is projected to decrease by up to 40% by the end of the twenty-first century with respect to the baseline period 1971-2000. Most land areas north of 45°N, in all seasons, as well as the southeastern United States in summer, exhibit increases in mean precipitation under the RCP4.5 scenario. In contrast, central parts of the continent in summer and much of Mexico in all seasons show reduced precipitation. In addition, large areas of North America exhibit changes of 10 to 40% (depending on the season and geographical location) in the number of heavy precipitation days. Results also suggest that the biogeophysical effects of LULCC on climate, assessed through differences between the two simulations, lead to warmer regional climates, especially in winter. The investigation of processes leading to this response shows high sensitivity of the results to changes in albedo as a response to LULCC. Overall, at the seasonal scale, results show that intense afforestation may contribute to an additional 25% of projected changes. [ABSTRACT FROM AUTHOR]

Anthropogenic CO 2 emissions are expected to significantly impact climate patterns, including extreme heat events. The influence of plants on land-atmosphere water and energy exchanges plays a crucial role in shaping these heat extremes. The response of vegetation growth and physiology to elevated CO 2 , both directly and indirectly, will determine their contribution to future heat extremes. In this study, we employed a suite of CMIP6 earth system models (ESMs) to differentiate between the effects of radiative forcing and vegetation forcing under elevated CO 2 background on extreme heat and precipitation events in China. The CO 2 vegetation forcing may reduce transpiration and weak vegetation cooling impact, leading to a decrease in clouds and precipitation under 2 × CO 2 and 4 × CO 2 scenarios. Our findings indicate that CO 2 -driven vegetation forcing (VEG) decreases extreme precipitation and increases dry days numbers in most humid regions of China, despite the increase in transpiration resulting from the saving of soil moisture and aboveground biomass enhancement due to CO 2 fertilization. Additionally, VEG intensifies the frequency and intensity of future heatwaves over most of China. Both radiative forcing and vegetation forcing have a stronger impact on humid area than dry area. The findings indicate that solely considering the radiative impacts of CO 2 is insufficient to explain the projected variations in extreme precipitation and heat changes. It suggests that CO 2 physiological forcing might play a role in influencing the differences in projected water and energy characteristics among different climate models. [ABSTRACT FROM AUTHOR]

This study assesses the ability of climate models to represent rainy season (RS) dependent climate indices relevant for agriculture and crop-specific agricultural indices in eleven African subregions. For this, we analyze model ensembles build from Regional Climate Models (RCMs) from CORDEX-CORE (RCM_hist) and their respective driving General Circulation Models (GCMs) from CMIP5 (GCM_hist). Those are compared with gridded reference data including reanalyses at high spatio-temporal resolution (≤ 0.25°, daily) over the climatological period 1981–2010. Furthermore, the ensemble of RCM-evaluation runs forced by ERA-Interim (RCM_eval) is considered. Beside precipitation indices like the precipitation sum or number of rainy days annually and during the RS, we examine three agricultural indices (crop water need (CWN), irrigation requirement, water availability), depending on the RS' onset. The agricultural-relevant indices as simulated by climate models, including CORDEX-CORE, are assessed for the first time over several African subregions. All model ensembles simulate the general precipitation characteristics well. However, their performance strongly depends on the subregion. We show that the models can represent the RS in subregions with one RS adequately yet struggle in reproducing characteristics of two RSs. Precipitation indices based on the RS also show variable errors among the models and subregions. The representation of CWN is affected by the model family (GCM, RCM) and the forcing data (GCM, ERA-Interim). Nevertheless, the too coarse resolution of the GCMs hinders the representation of such specific indices as they are not able to consider land surface features and related processes of smaller scale. Additionally, the daily scale and the usage of complex variables (e.g., surface latent heat flux for CWN) and related preconditions (e.g., RS-onset and its spatial representation) add uncertainty to the index calculation. Mostly, the RCMs show a higher skill in representing the indices and add value to their forcing models. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of 39 CMIP5 models participating in the Coordinated Ocean Wave Climate Project phase 2 (COWCLIP2.0) for simulating extreme significant wave height (SWH) indices in the Indian Ocean (IO) for the 26-year period from 1979 to 2005, using the ERA5 wave reanalysis as observation proxy. The multiple skill metrics of bias, root mean square error (RMSE), relative error (RE), interannual variability skill-score (IVS), comprehensive rating index (CRI), and total ranking (TR) are utilized to evaluate the CMIP5 models consisting of four clusters (ECCC(s), CSIRO, ECCC(d), and JRC) over the Northern IO (NIO), SouthernTropical IO (STIO), and Southern IO (SIO) sub-domains. The three extreme SWH indices are considered: rough wave days (HsRo), high wave days (HsHi), and top decile wave spell duration indicator (HHsDI). Climatology evaluation results indicate that the ECCC(s) cluster models and MME exhibit better agreements with the ERA5 reanalysis data (with smaller biases, RMSEs, and REs) than the other clusters over all sub-domains for HsRo and HsHi indices. Whereas most models display reasonable skills at simulating interannual variability of HsRo, HsHi is poorly captured by all clusters over the NIO and STIO, with a large inter-model spread in IVS values. HHsDI is found to be simulated well by all clusters regarding the climatology pattern and interannual variability, reflecting the characteristics of percentile-based indices. Integrated assessment based on CRI and TR analysis confirms the overall superiority of ECCC(s) cluster models in simulating mean and interannual variability of extreme SWH indices over all IO subdomains. [ABSTRACT FROM AUTHOR]

The observed climate includes both the response to external forcings and internal climate variability. Thus, more reasonable approaches of model evaluation which fully take into account the influence of internal variability are desirable, with the availability of initial-condition large ensembles (LEs). Here we evaluate the newly developed FGOALS-g3 LE (with 110 realizations which differ only in the initial conditions) in terms of the climatology and historical changes of temperature and precipitation extremes, by comparing multiple observational datasets with the full ensemble spread (representing a wide variety of possibilities of internal variability). We show that internal variability does not significantly affect the evaluation of model simulated climatology, but it does for the evaluation of long-term changes. For climatology, the FGOALS-g3 LE reasonably reproduces the global spatial distributions of temperature and precipitation extremes, with pattern correlations exceeding 0.90 for temperature extremes and around 0.70 for precipitation extremes. Specifically, the FGOALS-g3 LE shows an overall warm bias in warm extremes and cold bias in cold extremes aggregated over global land; the biases in extreme precipitation intensity only exist in limited regions while the biases in extreme precipitation amount are more widespread. For the long-term change since 1950, taking internal variability into account, the FGOALS-g3 LE can reasonably simulate the observed changes in cold extremes and precipitation extremes for global land average and for most land regions; while it generally overestimates the warming trend in warm extremes. With the growing availability of initial-condition LEs, it is desirable to apply these more reasonable approaches in model evaluations to fully consider the influence of internal variability. [ABSTRACT FROM AUTHOR]

Extreme weather events are more detrimental to human culture and ecosystems than typical weather patterns. A multimodel ensemble (MME) of the top-performing global climate models (GCMs) to simulate 11 precipitation extremes was selected using a hybrid method to project their changes in Pakistan. It also compared the benefits of using all GCMs compared to using only selected GCMs when projecting precipitation extremes for two future periods (2020–2059) and (2060–2099) for four shared socioeconomic pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. Results showed that EC-Earth3-Veg, MRI-ESM2-0 and NorESM2-MM performed best among GCMs in simulating historical and projecting future precipitation extremes. Compared to the MME of all GCMs, the uncertainty in future projections of all precipitation indices of the selected GCMs were significantly smaller. The MME median of the selected GCMs showed increased precipitation extremes over most of Pakistan. The greater increases were in RX1day by 6–12 mm, RX5day by 12–20 mm, Prcptot by 40–50 mm, R95ptot by greater than 30 mm, R99ptot by more than 9 mm, R4mm ≥ 4 mm by 0–4 days, R10mm by 2–6 days, R20mm by 1–3 days, and SDII by 1 mm/day, CWD by one day, CDD by 0–4 days in the northern high elevated areas for SSP5-8.5 in the late future. These results emphasize the greater influence of climate change on precipitation extremes in the northern, high-elevation areas, which provide the majority of the country's water. This emphasizes the necessity to adopt suitable climate change mitigation strategies for sustainable development, particularly in the country's northern regions. [ABSTRACT FROM AUTHOR]

Selection of suitable general circulation models (GCMs) increases the reliability in the assessment of the impact of climate change. In a recent study, we introduced the concepts of complex networks for evaluating the performance of GCMs in simulating rainfall using clustering coefficient, which quantifies the tendency of a network to cluster. However, since networks have different properties, it is important to use any additional measure to verify the consistency in the outcomes for more reliable interpretations and conclusions. To this end, in the present study, we employ the shortest path length to evaluate the performance and ranking of GCMs for rainfall simulation. The shortest path length quantifies the efficiency of a network in transmitting information between the nodes in the network. It is generally a better measure, as its calculation involves every pair of nodes in the entire network, rather than only the nodes that are in 'clusters' with a given node of interest. We evaluate the ability of 49 GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6) to simulate the monthly rainfall in India for the period 1961–2014. We consider each grid, of the 288 grids of 1° × 1° spatial resolution across India, as a network. For each network, we treat the higher-dimensional vectors reconstructed from the scalar rainfall time series as the nodes and the connections between the vectors as the links. We determine the optimum dimension for reconstruction using the false nearest neighbor method. We consider two cases for the evaluation of GCMs: (1) Case 1—whole-year rainfall (January-December); and (2) Case 2—summer monsoon rainfall (June–September). For each grid, we rank the 49 GCMs based on the difference in the average shortest path length between the observed rainfall network and the GCM-simulated rainfall network. For each of Case 1 and Case 2, we employ the group decision-making (GDM) methodology to rank the GCMs for the entire study area, considering all the 288 grids. We then use the comprehensive rating metric (RM) value to combine the ranks obtained for the GCMs for Case 1 and Case 2 and to identify the final ranking of the GCMs. For the whole-year rainfall, the models CMCC-ESM2, NorCPM1, GFDL-ESM4, CMCC-CM2-SR5, and CESM2-WACCM, in order, occupy the top five positions. For the summer monsoon rainfall, the models CESM2-FV2, E3SM-1-1-ECA, CMCC-ESM2, EC-Earth3-Veg-LR and CMCC-CM2-HR4, in order, are the top five. The results from the RM values suggest that the models CMCC-ESM2, CESM2-WACCM, CMCC-CM2-HR4, E3SM-1-1-ECA, and BCC-ESM1 are, in order, the five best-performing models. We find that the ranks obtained for the GCMs based on the shortest path length analysis are in reasonably good agreement and consistent with those obtained using clustering coefficient, especially for the whole-year rainfall. [ABSTRACT FROM AUTHOR]

A statistical study was made of the temporal trend in extreme temperatures in the region of Extremadura (Spain) during the period 1981–2015 using a Regional Climate Model. For this purpose, a Weather Research and Forecasting (WRF) Regional Climate Model extreme temperature dataset was obtained. This dataset was then subjected to a statistical study using a Bayesian hierarchical spatio-temporal model with a Generalized Extreme Value (GEV) parametrization of the extreme data. The Bayesian model was implemented in a Markov chain Monte Carlo framework that allows the posterior distribution of the parameters that intervene in the model to be estimated. The role of the altitude dependence of the temperature was considered in the proposed model. The results for the spatial-trend parameter lend confidence to the model since they are consistent with the dry adiabatic gradient. Furthermore, the statistical model showed a slight negative trend for the location parameter. This unexpected result may be due to the internal and modeling uncertainties in the WRF model. The shape parameter was negative, meaning that there is an upper bound for extreme temperatures in the model. [ABSTRACT FROM AUTHOR]

This study examines the dominant heatwave variability over North America (NA), extracted from an empirical orthogonal function (EOF) analysis of summertime monthly warm extreme index anomalies over 1959–2021. The principal mode features a dipole structure with a large area of anomaly over northwestern NA and an anomaly of opposite sign over the southern U.S. The corresponding principal component is associated with a large-scale atmospheric wave train extending from the North Pacific to North America (NP-NA) and a northeastward injection of moisture from the subtropical western Pacific towards western NA, which are key factors in supporting the NA heatwave variability. The NP-NA wave train can be systematically reinforced and supported by synoptic-scale eddies, and may also be forced by an anomalous convection over the tropical-subtropical western Pacific. Surface radiation heating directly contributes to surface temperature anomalies and is dominated by anomalous downwelling shortwave and longwave radiations. In association with a positive phase of the heatwave variability, the NP-NA wave train brings an anticyclonic anomaly over northern NA, leading to anomalous descent, reduced total cloud cover and below-normal precipitation and surface relative humidity over northern NA. Over northwestern NA, the anomalous subsidence causes air to warm through compression. Reduced cloud cover results in increased downward shortwave radiation that is a key contributor to surface radiation heating. In addition, increase in vertically integrated water vapour through the moisture injection from the North Pacific collocates with tropospheric warming. The atmosphere has more water vapor holding capability and acts as a greenhouse gas to absorb longwave radiation, leading to increased downward longwave radiation that is the second major contributor to surface radiation heating. Processes with circulation and surface radiation anomalies of opposite signs will likewise lead to the negative heatwave variability. [ABSTRACT FROM AUTHOR]

In the coming few decades, projected increases in global temperature and humidity are generally expected to exacerbate human exposure to climate extremes (e.g., humid-heat and rainfall extremes). Despite the growing risk of humid-heat stress (measured by wet-bulb temperature), it has received less attention in East Africa, where arid and semi-arid climatic conditions prevail. Moreover, no consensus has yet been reached across models regarding future changes in rainfall over this region. Here, we screen Global Climate Models (GCMs) from CMIP5 and CMIP6 and use, for boundary conditions, simulations from only those GCMs that simulate successfully recent climatic trends. Based on these GCMs and Regional Climate Model (RCM) simulations, we project that annual mean temperature is likely to rise by 2 ℃ toward midcentury (2021–2050) ​at a faster rate than the global average (about 1.5 ℃), under the RCP8.5 and SSP5-8.5 scenarios, associated with more frequent and severe climate extremes. In particular, low-lying regions in East Africa will be vulnerable to severe heat stress, with an extreme wet-bulb temperature approaching or exceeding the US National Weather Service's extreme danger threshold of 31 ℃. On the other hand, population centers in the highlands of Ethiopia will receive significantly more precipitation during the autumn season and will see more extreme rainfall events, with implications for flooding and agriculture. The robustness of these results across all GCM and RCM simulations, and for both of CMIP5 and CMIP6 frameworks (CMIP: Coupled Model Inter-comparison Project) supports the reliability of these future projections. Our simulations of near-term climate change impacts are designed to inform the development of sound adaptation strategies for the region. [ABSTRACT FROM AUTHOR]

Climate extremes pose threats to lives and properties. It is therefore critical to assess the dynamics of climate extremes in the future. Knowledge of the appropriate models to be used for projection helps reduces uncertainties in model projections. The study assesses the evolution of 8 extreme temperature indices over the Volta Basin for the 1985–2014 period. Consequently, the ability of 40 Coupled Model Intercomparison Project Phase 6 (CMIP6) models in reproducing these extremes is assessed. The biases and the M-scores of the annual spatial distribution of indices are determined. It is observed that the temperature extremes are frequent and profound in the Sahel, indicating that the Sahel is susceptible to extreme temperature changes. The warm indices showed positive trends, while the cold indices mainly showed negative trends. Most of the models had difficulty reproducing the climatologies of the indices. However, the CMCC-ESM2, MPI-ESM-1-2-HAM, MIROC6, and AWI-CM-1-1-MR reproduced the TNN, TNX, TXN, and TXX well while the MIROC6, MRI-ESM2-0, INM-CM4-8, and MIROC6 showed robust performance in reproducing the TN10P, TN90P, TX10P, and TX90P respectively. Poor performance of models may be due to poor simulation of sub-grid features such as vegetation cover, and clouds as a result of coarse grid sizes. Warming of the basin poses a significant impact on agriculture, drought occurrence, and seasonal to annual scale precipitation distribution. Southern sections of the basin may be vulnerable to temperature-related extremes as a result of deforestation and urbanization. [ABSTRACT FROM AUTHOR]

Bias correcting and downscaling climate model simulations requires reconstructing spatial and intervariable dependences of the observations. However, the existing univariate bias correction methods often fail to account for such dependences. While the multivariate bias correction methods have been developed to address this issue, they do not consistently outperform the univariate methods due to various assumptions. In this study, using 20 state-of-the-art coupled general circulation models (GCMs) daily mean, maximum and minimum temperature (Tmean, Tmax and Tmin) from the Coupled Model Intercomparison Project phase 6 (CMIP6), we comprehensively evaluated the Super Resolution Deep Residual Network (SRDRN) deep learning model for climate downscaling and bias correction. The SRDRN model sequentially stacked 20 GCMs with single or multiple input-output channels, so that the biases can be efficiently removed based on the relative relations among different GCMs against observations, and the intervariable dependences can be retained for multivariate bias correction. It corrected biases in spatial dependences by deeply extracting spatial features and making adjustments for daily simulations according to observations. For univariate SRDRN, it considerably reduced larger biases of Tmean in space, time, as well as extremes compared to the quantile delta mapping (QDM) approach. For multivariate SRDRN, it performed better than the dynamic Optimal Transport Correction (dOTC) method and reduced greater biases of Tmax and Tmin but also reproduced intervariable dependences of the observations, where QDM and dOTC showed unrealistic artifacts (Tmax < Tmin). Additional studies on the deep learning-based approach may bring climate model bias correction and downscaling to the next level. [ABSTRACT FROM AUTHOR]

Global Climate Models are the main tools for climate projections. Since many models exist, it is common to use Multi-Model Ensembles to reduce biases and assess uncertainties in climate projections. Several approaches have been proposed to combine individual models and extract a robust signal from an ensemble. Among them, the Multi-Model Mean (MMM) is the most commonly used. Based on the assumption that the models are centered around the truth, it consists in averaging the ensemble, with the possibility of using equal weights for all models or to adjust weights to favor some models. In this paper, we propose a new alternative to reconstruct multi-decadal means of climate variables from a Multi-Model Ensemble, where the local performance of the models is taken into account. This is in contrast with MMM where a model has the same weight for all locations. Our approach is based on a computer vision method called graph cuts and consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. The performance of the graph cuts approach is assessed based on two experiments: one where the ERA5 reanalyses are considered as the reference, and another involving a perfect model experiment where each model is in turn considered as the reference. We show that the graph cuts approach generally results in lower biases than other model combination approaches such as MMM, while at the same time preserving a similar level of spatial continuity. [ABSTRACT FROM AUTHOR]

How the value of higher-resolution climate variables dynamically downscaled can affect the hydrological impact assessment has been a long standing issue. This study investigates the potential benefit of high-resolution climate data locally tailored over South Korea in terms of the reduction of uncertainties in hydrological projections. For this purpose, a large ensemble consisting of three Global Climate Model (GCM) projections and their dynamical downscaling products in different resolutions (i.e., 20 and 5 km), and four bias correction (BC) methods is fed into a semi-distributed hydrological model (HM) customized over Korean river basins. The in-depth comparison among the 45-members hydrological simulations proves the benefit in using high-resolution Regional Climate Model (RCM) for the runoff projections. While this study acknowledges the necessity of BC to remove the systematic bias in climate simulations, it is found that the high-resolution dynamical downscaling can significantly narrow the spread brought with different BC methods, thus reducing the uncertainty in the projected hydrological change. The projected runoff changes for both the mean of wet season and the high flows indicate that there will be an intensified runoff, especially for the extremes, over South Korea under the warming. Altogether, this study provides a valuable exploration of uncertainty reduction in hydrological projections from the perspective of resolution effect of dynamical downscaling, which is meaningful for hydroclimate studies and climate change impact assessment. [ABSTRACT FROM AUTHOR]

The impacts of global warming on Meiyu–Baiu extreme rainfall and the associated mid-latitude synoptic-scale weather systems over the Eastern China (EC) and the Baiu rainband (Bu) regions in East Asia have been examined, based on simulations from the 20-km Meteorological Research Institute atmospheric general circulation model (MRI-AGCM3.2S). This model was demonstrated to give realistic Asian extreme rainfall, when compared with data from the Tropical Rainfall Measuring Mission (TRMM). Here we used a novel wave-selection algorithm based on the 300 hPa wind, in order to identify upper-level propagating wave signals in conjunction with the occurrence of extreme precipitation in either EC or Bu. The same algorithm was applied for both the present (1979–2003) and future (2075–2099) climate simulations from the AGCM, so as to infer the impacts of global warming on the behavior of these systems. Results show robust decrease of intensity of systems influencing both Bu and EC in the future warmer climate. Their corresponding low-to-mid level circulation, as revealed by vertical velocity, temperature advection and sea-level pressure composites, was also found to be weakened. This is likely related to changes in the background circulation in future over the East Asian mid-latitude zone, such as the widespread increment of the seasonal mean static stability at 500 hPa. However, the wave-associated precipitation over these regions was enhanced in the future climate simulations. This can be attributed to more strong intensity rainfall, which increases as the background temperature in these regions warms, largely following the Clausius–Clapeyron relation. Therefore, changes of wave-related extreme precipitation in EC and Bu are mainly controlled by the thermodynamic effect; the latter appears to be much stronger than the potential impacts due to the slight weakening of these weather systems. [ABSTRACT FROM AUTHOR]

The magnitude asymmetry between positive and negative extreme anomalies in gross primary productivity (GPP) and the underlying mechanism have been investigated based on daily records from the data-driven and in situ datasets. Distinct magnitude asymmetries of daily GPP extreme anomalies (DGEAs) have been identified between forested and non-forested areas. Forested areas are dominated by negative asymmetries, manifesting as the magnitude of negative DGEAs significantly larger than that of their positive counterparts; while non-forested areas are dominated by positive asymmetries. These distinct asymmetric features are due to the different climatic constraints between forests and non-forests. We found that solar radiation is the dominant climatic controller of the daily variation in GPP of forests. Due to the existence of a light saturation point, vegetation productivity is less sensitive to extremely high values of solar radiation than it is to the extremely low, thus leading to the negative asymmetries. For the non-forested areas, however, the daily variation in GPP depends on the preceding 3‒6-day soil moisture conditions. Heavy precipitation is more effective at causing strong and persistent soil moisture anomalies, as soil water can penetrate to deeper soil layers. Therefore, strong positive asymmetry of precipitation anomalies results in the positive asymmetry of soil moisture anomalies and thus leads to the positive asymmetry of DGEAs. Due to the significant asymmetry in the DGEAs, the cumulative effect of DGEAs further rectifies the interannual variability of annual GPP, implying the importance of asymmetry in the DGEAs to changes of terrestrial carbon cycle. [ABSTRACT FROM AUTHOR]

This study provides a long-term (1891–2014) global assessment of precipitation trends using data from two station-based gridded datasets and climate model outputs evolved through the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively). Our analysis employs a variety of modeling groups that incorporate low- and high-top level members, with the aim of assessing the possible effects of including a well-resolved stratosphere on the model's ability to reproduce long-term observed annual precipitation trends. Results demonstrate that only a few regions show statistically significant differences in precipitation trends between observations and models. Nevertheless, this pattern is mostly caused by the strong interannual variability of precipitation in most of the world regions. Thus, statistically significant model-observation differences on trends (1891–2014) are found at the zonal mean scale. The different model groups clearly fail to reproduce the spatial patterns of annual precipitation trends and the regions where stronger increases or decreases are recorded. This study also stresses that there are no significant differences between low- and high-top models in capturing observed precipitation trends, indicating that having a well-resolved stratosphere has a low impact on the accuracy of precipitation projections. [ABSTRACT FROM AUTHOR]

The frequency and severity of climatic extremes is expected to escalate in the future primarily because of the increasing greenhouse gas concentrations in the atmosphere. This study aims to assess the impact of climate change on the extreme temperature and precipitation scenarios using climate indices in the Kashmir Himalaya. The analysis has been carried out for the twenty-first century under different representative concentration pathways (RCPs) through the Statistical Downscaling Model (SDSM) and ClimPACT2. The simulation reveals that the climate in the region will get progressively warmer in the future by increments of 0.36–1.48 °C and 0.65–1.07 °C in mean maximum and minimum temperatures respectively, during 2080s (2071–2100) relative to 1980–2010 under RCP8.5. The annual precipitation is likely to decrease by a maximum of 2.09–6.61% (2080s) under RCP8.5. The seasonal distribution of precipitation is expected to alter significantly with winter, spring, and summer seasons marking reductions of 9%, 5.7%, and 1.7%, respectively during 2080s under RCP8.5. The results of extreme climate evaluation show significant increasing trends for warm temperature-based indices and decreasing trends for cold temperature-based indices. Precipitation indices on the other hand show weaker and spatially incoherent trends with a general tendency towards dry regimes. The projected scenarios of extreme climate indices may result in large-scale adverse impacts on the environment and ecological resource base of the Kashmir Himalaya. [ABSTRACT FROM AUTHOR]

The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA's Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices. [ABSTRACT FROM AUTHOR]

In this study, the performance of sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating extreme precipitation indices over West Africa has been evaluated. Nine extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) have been used. The performance of CMIP6 models and their ensemble mean was examined by comparing the model results to that of Global Precipitation Climatology Project One-Degree Daily Dataset (GPCP) and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42 (TRMM) gridded observations during the present-day period 1997–2014 with focus on the summer months (i.e., June–July–August, JJA). Our results show that CMIP6 models reasonably reproduce the spatial patterns of the extreme precipitation indices over the entire region, although their performance is quite different between Sahel and Guinea coast subregions. The gridded observations exhibit significant differences in their estimates of the indices evaluated, and the CMIP6 models are generally closer to GPCP than to TRMM. The models broadly exhibit too many consecutive wet days (CWD) resulting in widespread overestimation over entire West Africa. Also, the heavy (R10 mm) and very heavy (R20 mm) precipitation days are considerably overestimated especially over the mountain regions. Overall, the ensemble mean outperforms any individual model at capturing mean distributions of the extreme precipitation indices, particularly in comparison to the two gridded observations. [ABSTRACT FROM AUTHOR]

We report on the characteristics of precipitation associated with three types of landfalling atmospheric rivers (ARs) over western North America in the winter season from 1980 to 2004. The ARs are classified according to three landfalling regions as southern, middle and northern types. Two main centers of precipitation are associated with the contributions by the ARs: one over Baja California linked to the southern type of the ARs, and the other over Washington State correlated with the northern and middle types of the ARs. ARs are seen to play a dominant role in the occurrences of extreme precipitation events, with a proportionately greater impact on more extreme events. Moisture flux convergence makes the dominant contribution to precipitation when ARs and extreme precipitation occur simultaneously in the studied areas. Moisture flux convergence in these cases is, in turn, dominated by the mean and transient moisture transported by the transient wind, with greater contribution from the latter, which is mainly concentrated in certain areas. The magnitude and direction of vertically integrated vapor transport (IVT) also play a role in determining the amount of precipitation received in the three regions considered. Larger IVT magnitude corresponds to more precipitation, while an IVT direction of about 220° (0° indicating east wind) is most favorable for high precipitation amount, which is especially obvious for the northern type of the ARs. [ABSTRACT FROM AUTHOR]

Studies that evaluate climate change projections over the whole of South America (SA) and including different seasons and models are scarce. In this context, the objective of this work is to assess climate projections for SA through the use of climatic indices, considering the entire continent, distinct seasons, and ensembles of models. Projections performed with the Eta regional climate model and its driving global climate models (GCMs) are analysed. From these projections, 19 climate indices based on daily precipitation and maximum and minimum temperature are computed. The results focus on two ensembles (GCMs and Eta), time slices (1980–2005 and 2050–2080), and scenarios (RCP4.5 and RCP8.5). In the validation of the present climate, it is shown that Eta adds value to GCMs. Future projections indicate, for both austral summer (DJF) and winter (JJA), an increase in the frequency and intensity of extreme events of daily rainfall over southeastern and extreme north of SA. Over the Amazon, during DJF, there is a statistically significant increase in the number of consecutive dry days and a decrease in the consecutive wet days. For northeastern Brazil, these features are more intense in JJA. The frequency of cold (warm) nights and days is projected to decrease (increase) over the whole continent and seasons. The climate change signal for the 19 climate indices is more intense under RCP8.5, and the regions more vulnerable to climate change are the Amazon, northeastern Brazil, and southeastern SA. Considering Brazil, the projections of precipitation and air temperature are also shown by biomes. [ABSTRACT FROM AUTHOR]

Based on four reanalyses or gridded data sets (ERA5, 20CR, APHRODITE and REGEN), we provide an overview of 23 Historical and 7 HighResMIP experiments' performance from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (for short, 6-Hist, HighRes) in simulating seven extreme precipitation indices over Asia defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We compare them with 28 Historical experiments in CMIP5 (5-Hist). CMIP5 and CMIP6 models are generally able to reproduce extreme precipitation's spatial distribution and their trend patterns in comparison to the benchmark data set (APHRODITE). The overall performance of individual model is summarized by a "portrait" diagram based on four statistics for each index. We divide all 58 models into three groups (A, the top 20%; B, the median 60% and C group, the last 20%) according to MR rankings (the comprehensive ranking measure). Based on the "portrait" diagram and MR rankings, models that perform relatively well for all seven extreme precipitation indices include HadCM3, HadGEM2-AO, HadGEM2-CC and HadGEM2-ES from 5-Hist, EC-Earth3, EC-Earth3-Veg from 6-Hist and ECMWF-IFS-HR, ECMWF-IFS-LR, ECMWF-IFS-MR from HighRes. The simulated performance of CMIP6 is polarized, for the top four and the last five ranking models are both from CMIP6. Compared with the counterpart models in CMIP6 and CMIP5, the improvement of PCC (pattern correlation coefficient) is more obvious. Furthermore, the dry biases of CMIP6 (both 6-Hist and HighRes) in Southern China and India and the wet biases of CMIP6 in Tibet are reduced compared to CMIP5. This may benefit from the improvement that CMIP6 models can capture the characteristics of meridional moisture flux convergence, and improve the overestimation or underestimation of meridional and zonal specific humidity eddies compared to CMIP5. [ABSTRACT FROM AUTHOR]

A new regional climate projection ensemble has been created for the Australasia region as part of the World Climate Research Programs Coordinated Regional Downscaling Experiment (CORDEX). The CORDEX-Australasia ensemble is the largest regional climate projection ensemble ever created for the region. It is a 20-member ensemble made by 6 regional climate models downscaling 11 global climate models. Overall the ensemble produces a good representation of recent climate. Consistent biases within the ensemble include an underestimation of the diurnal temperature range and an underestimation of precipitation across much of southern Australia. Under a high emissions scenario projected temperature changes by the end of the twenty-first century reach ~ 5 K in the interior of Australia with smaller increases found toward the coast. Projected precipitation changes are towards drying, particularly in the most populated areas of the southwest and southeast of the continent. The projected precipitation change is very seasonal with summer projected to see little change leaning toward an increase. These results provide a foundation enabling future studies of regional climate changes, climate change impacts, and adaptation options for Australia. [ABSTRACT FROM AUTHOR]

A collection of 10 high-impact extreme precipitation events occurring in Southeastern South America during the warm season has been analyzed using statistical (ESD) and dynamical downscaling approaches. Regional Climate Models from the CORDEX database for the South American domain at two horizontal resolutions, 50 km and 25 km, short-term simulations at 20 km and at 4 km convective-permitting resolution and statistical downscaling techniques based on the analogue method and the generalized linear model approach were evaluated. The analysis includes observational datasets based on gridded data, station data and satellite products that allow assessing the observational uncertainty that characterizes extreme events in the region. It is found that the ability of the modelling strategies in capturing the main features of the extreme rainfall varies across the events. The higher the horizontal resolution of the models, the more intense and localized the core of the rainfall event, being the location of the exit region of the low-level jet and the low-level moisture flux convergence during the initial stages of the events the most relevant features that determine models' ability of capturing the location and intensity of the core of the heavy rainfall. ESD models based on the generalized linear approach overestimate the spatial extension of the events and underestimate the intensity of the local maxima. Weather-like convective-permitting simulations depict an overall good performance in reproducing both the rainfall patterns and the triggering mechanisms of the extreme events as expected, given that these simulations are strongly controlled by the initial conditions. [ABSTRACT FROM AUTHOR]

Climate extremes can severely impact socio-economic development. Climate trends of three temperature and three precipitation climate indices were evaluated in observational data, 23 models from the 5th Coupled Model Intercomparison Projects (CMIP5), and 20 models from CMIP6. The climate indices were calculated over the whole of China, and individually over the basins of its three major rivers. The indices are the spatio-temporal evolution characteristics of annual mean temperature (Tas), minimum of daily minimum temperature (TNn), maximum of daily maximum temperature (TXx), number of tropical nights (TR), daily precipitation (Pre), very heavy precipitation days (R20mm), maximum consecutive 5-day precipitation (Rx5day) and consecutive dry days (CDD). From 1961 to 2018, most of China has warmed; Tas, TNn, TXx and TR over China has increased by 1.7 °C, 2.8 °C, 1.1 °C and 9 days, respectively. Changes of Tas, TNn and TXx over the Yellow River Basin, Yangtze River Basin and Pearl River Basin were generally similar in sign. The most significant increase of TR was seen over the Pearl River Basin. Historical Tas was well reproduced by both CMIP5 and CMIP6 over the study regions, but obvious uncertainties exist in the simulation of Pre. In general, CMIP6 models were improved from CMIP5 models. Climate projections were calculated for the 2021–2100 period. Future warming over China would be stronger with higher SSP scenarios; TNn over China would warm seven times more under the SSP5-8.5 scenario (5.6 °C) compared to the SSP1-2.6 scenario. Future wetting over China would be stronger with higher the SSP scenarios; Under the SSP5-8.5 scenario, Pre, R20mm, Rx5day would increase by 28%, 150%, and 38%, respectively. Projected changes of CDD different by region—decreasing over most of China and the Yellow River Basin, but increasing over the Yangtze and Pearl River Basins. The higher of the emission scenario, the less significant the reduction of CDD over the two basins. This suggests that the temporal distribution of precipitation over China will become more uneven in the future, especially under the higher SSP scenarios. [ABSTRACT FROM AUTHOR]

The frequency and spatial distributions of precipitation extremes (PEs) and deep convective clouds (DCC) across the Amazon region were assessed using satellite-derived data. For PEs, CHIRPS dataset for the period 1981–2018 were used to calculate a set of absolute, threshold, duration, and percentile-based threshold indices defined by the Expert Team on Climate Change Detection and Indices. DCC occurrence was assessed based on the Advanced Microwave Sounding Unit data for the period 2002–2018. In northern Amazon (north of 5 ∘ S ) PEs and DCC are more frequent ( ≥ 60 % frequency) during February–June. Averaged trends over these months have shown increase in daily rainfall above 20 mm of near 3 days over the 1981–2018 period, and an increase of 2 consecutive wet days (P ≥ 1 mm ) in the same period. South of 5 ∘ S prevalence of PEs and DCC is largely observed during November–March ( ≥ 60 % frequency), whereas the longest persistence of dry days is observed during June–August. Though all PE trends point to an intensification of rainfall in November–March, only consecutive dry days in winter (JJA) and spring (SON) show significant trends, pointing to an increase of 7 days over the 38-yr winters. Rainfall extremes over the entire Amazon region were found to be moderate to strongly correlated with the mean vertically integrated moisture divergence, and in southern Amazon also to upper level divergence and upward vertical velocity. Increased frequency of DCC were found over the whole basin ( ∼ 18 % yr - 1 ), in contrast to decreased convective overshooting (up to ∼ 15.4 % yr - 1 ). [ABSTRACT FROM AUTHOR]

The Intergovernmental Panel on Climate Change (IPCC) suggests limiting global warming to 1.5 °C compared to 2 °C would avoid dangerous impacts of anthropogenic climate change and ensure a more sustainable society. As the vulnerability to global warming is regionally dependent, this study assesses the effects of 0.5 °C less global warming on climate extremes in the United States. Eight climate extreme indices are calculated based on Coupled Model Intercomparison Project—phase 5 (CMIP5), and North American—Coordinated Regional Climate Downscaling Experiments (NA-CORDEX) with and without bias correction. We evaluate the projected changes in temperature and precipitation extremes, and examine their differences between the 1.5 and 2 °C warming targets. Under a warming climate, both CMIP5 and NA-CORDEX show intensified heat extremes and reduced cold extremes across the country, intensified and more frequent heavy precipitation in large areas of the North, prolonged dry spells in some regions of the West, South, and Midwest, and more frequent drought events in the West. Results suggest that the 0.5 °C less global warming would avoid the intensification of climate extremes by 32–46% (35–42%) for heat extremes intensity (frequency) across the country and, by 23–41% for heavy precipitation intensity in the North, South, and Southeast. The changes in annual heavy precipitation intensity are mainly contributed by winter and spring. However, impacts of the limited warming on the frequency of heavy precipitation, dry spell, and drought frequency are only evident in a few regions. Although uncertainties are found among the climate models and emission scenarios, our results highlight the benefits of limiting warming at 1.5 °C in order to reduce the risks of climate extremes associated with global warming. [ABSTRACT FROM AUTHOR]

Simulation of past climate is an important tool for the validation of climate models. The comparison with observed daily values allows us to assess the reliability of their projections on climatic extremes in a future climate. The frequency and amplitude of extreme events are fundamental aspects that climate simulations need to reproduce as they have high impacts on economy and society. The ability to simulate them will help policy makers in taking better measures to face climate change. This work aims at evaluating how six models within the High Resolution Model Intercomparison Project reproduce the trends on extreme indices as they have been observed over Europe in the 1970–2014 period. Observed values are provided by the new homogenized version of the E-OBS gridded dataset. The comparison is performed through the use of indices based on seasonal averages and on exceedances of percentile-based thresholds, focusing on six subregions. Winter-average minimum temperature is generally underestimated by models (down to − 4 °C difference over Italy and Norway) while simulated trends in seasonal averages and extreme values are found to be too cold on Eastern Europe and too warm on Iberia and Southern Europe (e.g. up to a difference of − 4% per decade on the number of Cold Nights over Spain). On the other hand the models tend to overestimate summer maximum temperatures averages in the Mediterranean Area (up to + 5 °C over the Balkans) and underestimate these at higher latitudes. Iberia, Southern and Eastern Europe are simulated with too low trends in average summer temperatures. The simulated trends are too strong on the North West part and too weak on the South East part of Europe (down to − 3%/decade on the number of Warm Days over Italy and Western Balkans). These results corroborate the findings of previous studies about the underestimation of the warming trends of summer temperatures in Southern Europe, where these are more intense and have more impacts. The high-resolution versions of the models are compared to their lower-resolution counterparts, similar to those used in the CMIP5, showing a slight improvement for the simulation of extreme winter minimum temperatures, while no significant progresses have been found for extreme summer maximum temperatures. [ABSTRACT FROM AUTHOR]

This study investigates the near-term future changes of temperature extremes in summer (June–August) and winter (December–February) seasons over mainland China in the mid-twenty-first century (FP; 2045–2055) under representative concentration pathway (RCP) 4.5 scenario relative to the present day (PD; 1994–2011) by using an atmosphere–ocean-mixed-layer coupled model MetUM-GOML1. The projected changes in hot extremes exhibit a rise in hottest day temperature (TXx) and warmest night temperature (TNx) and an increase in frequencies of summer days (SU) and tropical nights (TR). The projected changes in cold extremes show a rise in coldest day temperature (TXn) and coldest night temperature (TNn) and a decrease in frequencies of ice days (ID) and frost days (FD). The projected changes in temperature extremes in both seasons are primarily determined by changes in seasonal mean daily maximum and minimum temperature while changes in temperature variability from daily to sub-seasonal time scales play a minor role. The future changes in temperature extremes over China, being consistent with the rise in seasonal temperature, are partly due to the increase in surface downward clear sky longwave radiation through the increased greenhouse gas concentrations and enhanced water vapor in the atmosphere, and partly due to the increase in net surface shortwave radiation as a result of the decreased aerosol emissions over Asia via aerosol-radiation interactions. Moreover, the seasonal mean surface warming can further be amplified with positive feedbacks by reducing the cloud cover, leading to positive changes in shortwave radiative effect through aerosol-cloud interactions and surface-atmosphere feedbacks during summer, and by positive changes in surface clear sky shortwave radiation through snow-albedo feedbacks over northern China and southwestern China during winter. [ABSTRACT FROM AUTHOR]

An accurate description of changes in extreme rainfall events requires high resolution simulations. Regional climate models (RCMs), where GCM data are used to provide input boundary conditions, are widely used as a way to resolve finer spatial scale phenomena. A problem with this, however, is that the inherent systematic biases within the GCM simulation are transferred to the RCM through the model boundaries. In this work we focus on the impact of bias correction of lateral and lower boundary conditions on simulated extreme rainfall events. Here three bias correction approaches are investigated. In increasing order of complexity, these are corrections for the mean, mean and variance, and the nested bias correction (NBC) approach that also corrects for lag-1 autocorrelations at nested timescales. These corrections are implemented on six-hourly GCM data taken from the GCM simulations which are used to drive the RCM along the RCM lateral boundaries. To evaluate the performance of bias correction on simulation of extreme rainfall events, daily precipitation extremes indices from the World Meteorological Organization (WMO) Expert Team on Climate Risk and Sectoral Climate Indicators (ET-CRSCI) are used. The results show that bias correction on the boundary conditions produce the results in significant improvement in extremes indices. It is clear that sea surface temperature (SST) plays an important role in driving the simulation. The results indicate that within the domain (far from boundaries) the errors in precipitation extremes are strongly dependent on the RCM, with a smaller effect coming from changes in the lateral boundary conditions. [ABSTRACT FROM AUTHOR]

The precipitation behaviour at different timescales (interannual, intraseasonal and synoptic) and extreme events in a warmer scenario over South America is analysed. This study is based on a set of Regional Climate Models (RCM) from the CORDEX database and their driving Global Climate Models (GCM) from the CMIP5 Project. Historical simulations for 1979–2005 and from the RCP4.5 scenario for 2071–2100 are used. The projected changes in the precipitation behaviour are evaluated in terms of the consistency between the pairs RCM-GCM aiming to explore the added value of RCMs. The agreement between projected changes from RCMs is also evaluated as a measure of confidence of the regional climate change signal. The analysis is carried out for two extended seasons (April to September and October to March). The projected change in the mean precipitation over subtropical latitudes is associated with both changes in the frequency of rainy days and in the intensity of extreme events, while for the tropics the changes are mainly associated with changes in the wet day frequency. The increase of extreme precipitation events over the subtropics arises as a robust signal among models, while for tropical latitudes the dispersion among models is high, which reduces the confidence of the projections. In general, the consistency of the projections in the pairs RCM-GCM and the agreement among models are higher for the low frequency variability patterns during winter, while for summertime a better agreement in the projected changes of the precipitation behaviour at different timescales among RCMs is found. [ABSTRACT FROM AUTHOR]

In the present study, the Max-Planck-Institute for Meteorology's Earth System Model is used to investigate irrigation's general effect on severe and extreme hydrometeorological regimes. Our idealized simulations show a large potential to modulate the magnitude and occurrence frequency of severe and extreme precipitation rates, indicating the possibility to mitigate some of the detrimental effects of future climate change, but also a substantial risk due to the declining water availability in drying regions. Irrigation almost exclusively reduces the magnitude and occurrence frequency of severely and extremely dry conditions and has the potential to counter the drying trends that result from the 21st century increase in greenhouse gas concentrations—according to the RCP4.5 scenario. At the same time, irrigation does not only have a mitigating effect, as it increases the occurrence frequency and intensity of severely wet conditions in many regions. The study aims at irrigation's theoretical (maximum) impact and investigates a highly idealized trajectory in which global irrigation is being maximized within hydrologically sustainable limits. However, even for this scenario, we find large regions in which present-day water extractions are not sustainable as they often rely on exhaustible sources. Especially, a depletion of non-renewable ground water in South Asia would lead to a strong reduction in irrigation and, consequently, a substantial increase in the occurrence frequency of severely and extremely dry months throughout the region. [ABSTRACT FROM AUTHOR]

Brazil experiences extreme weather and climate events that cause numerous economic and social losses, and according to climate change projections, these events will increase in intensity and frequency over this century.This study adds to the body of research on Brazil's climate change by analyzing the historical patterns and projected changes in temperature and precipitation extremes across Brazil using the World Climate Research Program's Expert Team on Climate Change Detection and Indices framework. This novel approach analyzes climate extreme events over the past four decades (1980–2016) using multiple gridded observation and reanalysis datasets. Furthermore, future changes in climate extremes are analyzed from 20 downscaled Earth System Models (ESMs) at high horizontal resolution (0.25° of latitude/longitude), under two representative concentration pathway scenarios (RCP4.5 and RCP8.5). Projected changes in the extreme indices are analyzed over mid-twenty-first century (2046–2065) and end-of-twenty-first century (2081–2100) relative to the reference period 1986–2005. Results show consistent warming patterns with increasing (decreasing) trends in warm (cold) extremes in the historical datasets. A similar but more intense warm pattern is projected in the mid and end of the twenty-first century. For precipitation indices, observations show an increase in consecutive dry days and a reduction of consecutive wet days over almost all Brazil. The frequency and intensity of extremely wet days over Brazil are expected to increase according to future scenarios. Designing effective adaptation and mitigation measures in response to changes in climate extremes events depends on this improved understanding of how conditions have and are likely to change in the future at regional scales. [ABSTRACT FROM AUTHOR]

Regional Climate-Weather Research and Forecasting model (CWRF) simulations driven by the ECMWF-Interim reanalysis (ERI) showed that cumulus parameterization significantly impacts daily 95th percentile precipitation (P95) over the US Gulf States (GS) and Central-Midwest States (CM). This study compared interannual variations across ERI and five CWRF cumulus parameterization members based on CM and GS regional mean composites during P95 events. A structural equation model framework was used to build regressions of these variations among optimally selected fields to identify the underlying processes affecting P95. We discovered five distinct physical mechanisms, each involving unique interplays among water and energy supplies and surface and cloud forcings, with varying degrees of relative importance (%). In CM summer and CM and GS autumn, water supply (~ 60%), energy supply (~ 20%), and cloud forcing (~ − 20%) jointly determined P95. In GS spring and winter, surface forcing was predominant (84–87%), while energy and water supplies evenly accounted for the remaining impact. In CM spring, surface forcing was also predominant (85%), but was accompanied by energy supply alone. In GS summer, cloud forcing was predominant (− 84%), while water supply had the opposite impact (− 8%) to energy supply (6%). In CM winter, water supply (− 62%) also counteracted energy supply (31%) while cloud forcing played a positive role (7%). The seasonal reversal in the roles of water supply and cloud forcing occurred because the prevailing precipitation system changed from convective to stratiform processes. The choice of cumulus parameterization affected how water and energy supplies acted through surface and cloud forcings, thus determined CWRF's ability to simulate extreme precipitation. [ABSTRACT FROM AUTHOR]

The study assesses the performance of 24 model runs from five COordinated Regional climate Downscaling Experiment (CORDEX) regional climate models (RCMs) in simulating East Africa's spatio-temporal precipitation characteristics using a set of eight descriptors: consecutive dry days (CDD), consecutive wet days (CWD), simple precipitation intensity index (SDII), mean daily annual (pr_ANN), seasonal (pr_MAM and pr_OND) precipitation, and representatives of heavy precipitation (90p) and very intense precipitation (99p) events. Relatively better performing RCM runs are then used to assess projected precipitation changes (for the period 2071–2099 relative to 1977–2005) over the study domain under the representative concentration pathway (RCP) 8.5 scenario. The performance of RCMs is found to be descriptor and scope specific. Overall, RCA4 (r1i1p1) forced by CNRM-CERFACS-CNRM-CM5 and MPI-M-MPI-ESM-LR, REMO2009 (r1i1p1) forced by MPI-M-MPI-ESM-LR, and RCA4 (r2i1p1) forced by MPI-M-MPI-ESM-LR emerge as the top four RCM runs. We show that an ensemble mean of the top four model runs outperforms an ensemble mean of 24 model simulations and ensemble means for all runs in an RCM. Our analysis of projections shows a reduction (increase) in mean daily precipitation for MAM(OND), an increase(decrease) in CDD(CWD) events, and a general increase in SDII and the width of the right tail of the precipitation distribution (99p–90p). An increase in SDII and 99p–90p implies a possibility of occurrence of heavy and extreme precipitation incidences by the end of the twenty-first century. Our findings provide important information to support the region's climate change adaptation and mitigation efforts. [ABSTRACT FROM AUTHOR]

Recent studies indicated that the internal climate variability plays an important role in various aspects of projected climate changes on regional and local scales. Here we present results of the spreads in projected trends of wintertime North American surface air temperature and extremes indices of warm and cold days over the next half-century, by analyzing a 50-member large ensemble of climate simulations conducted with CanESM2. CanESM2 simulations confirm the important role of internal variability in projected surface temperature trends as demonstrated in previous studies. Yet the spread in North American warming trends in CanESM2 is generally smaller than those obtained from CCSM3 and ECHAM5 large ensemble simulations. Despite this, large spreads in the climate means as well as climate change trends of North American temperature extremes are apparent in CanESM2, especially in the projected cold day trends. The ensemble mean of forced climate simulations reveals high risks of warm days over the western coast and northern Canada, as well as a weakening belt of cold days extending from Alaska to the northeast US. The individual ensemble members differ from the ensemble mean mainly in magnitude of the warm day trends, but depart considerably from the ensemble mean in spatial pattern and magnitude of the cold day trends. The signal-to-noise ratio pattern of the warm day trend resembles that of the surface air temperature trend, with stronger signals over northern Canada, Alaska, and the southwestern US than the midsection of the continent. The projected cold day patterns reveal strong signals over the southwestern US, northern Canada, and the northeastern US. In addition, the internally generated components of mean and extreme temperature trends exhibit spatial coherences over North America, and are comparable to their externally forced trends. The large-scale atmospheric circulation-induced temperature variability influences these trends. Overall, our results suggest that climate change trends of North American temperature extremes are likely very uncertain and need to be applied with caution. [ABSTRACT FROM AUTHOR]

This study evaluated the performance of 25 earth system models (ESMs), statistically and dynamically downscaled to a high horizontal resolution (0.25° of latitude/longitude), in simulating extreme climate indices of temperature and precipitation for 1980–2005. Datasets analyzed include 21 statistically downscaled ESMs from the National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) and dynamically downscaled Eta Regional Climate Model simulations driven by 4 ESMs generated by the Brazilian National Institute for Space Research (INPE). Downscaled outputs were evaluated against observational gridded datasets at 0.25° resolution over Brazil, quantifying the skill in simulating the observed spatial patterns and trends of climate extremes. Results show that the downscaled products are generally able to reproduce the observed climate indices, although most of them have poorest performance over the Amazon basin for annual and seasonal indices. We find larger discrepancies in the warm spell duration index for almost all downscaled ESMs. The overall ranking shows that three downscaled models (CNRM-CM5, CCSM4, and MRI-CGCM3) perform distinctively better than others. In general, the ensemble mean of the statistically downscaled models achieves better results than any individual models at the annual and seasonal scales. This work provides the largest and most comprehensive intercomparison of statistically and dynamically downscaled extreme climate indices over Brazil and provides a useful guide for researchers and developers to select the models or downscaling techniques that may be most suitable to their applications of interest over a given region. [ABSTRACT FROM AUTHOR]

Extreme precipitation response to increasing temperature includes not only changes of frequency and intensity, but also changes of extreme precipitation interval (EPIV) and the precipitation during the neighboring daily extreme precipitations interval (EPIP). These changes have not been fully evaluated yet in observations or climate model simulations although they are very useful to understand variations of extreme precipitation. We used daily precipitation data from 669 meteorological stations during the past five decades across China and projections of 19 general circulation models from CMIP5 under the RCP4.5 and RCP8.5 scenarios to investigate variations of EPIV and EPIP. We found the national average annual EPIV increased across China during the last five decades, while annual EPIP significantly decreased. The decreases mainly occurred in southwest China, east China, and southeast China. At national and regional scales, the average annual EPIV and EPIP showed greater decreases under the RCP8.5 scenario than those under the RCP4.5 scenario from 2006 to 2100. Annual EPIP showed a stronger correlation with extreme precipitation intensity than EPIV. The national average annual EPIP had a significant positive correlation with the Western Pacific Subtropical High Area Index. The abnormal geopotential heights over western Mongolia and the western Pacific at 500 hpa as well as the abnormal SSTs in Japan Sea and the western of Pacific in rainy seasons would result in abnormal annual EPIVs and EPIPs in China. This study may provide references for flooding prediction, water resources management, and disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

The extreme temperature changes under a 0.5 °C global mean surface temperature warming increment is of great importance for climate change adaption and risk management on post-Paris-Agreement agenda. The impacts of the already happened 0.5 °C warming increment on extreme temperature can serve as essential references for the 1.5/2 °C projections. Quantifying the observed changes of climate extremes is hampered by the limitation of observational datasets in both spatial coverage and temporal continuity. The reanalysis datasets are hoped to be useful substitutes for the observations, but their performance over continental China remains unknown. In this study, we compare the extreme temperature changes associated with the past 0.5 °C warming derived from three reanalysis datasets including JRA-55, ERA and 20CR with the observation in China. Distinct increases (decreases) in warm (cold) extremes are detected in all three reanalyses in a spatially aggregated perspective as in the observation. On regional scales the reanalyses have evident spreads in regions with insufficient observational coverage such as the western China. JRA-55 shows good agreement with the observation in both spatial patterns and magnitudes of extreme temperature changes. Both ERA and 20CR show weaker consistency with the observation, particularly in western China, mainly due to less observational constraints in data assimilation. The different aerosol data used in reanalysis assimilation systems also influenced the data quality. Our results indicate that while the reanalyses can serve as useful substitutes to fill in the observational gaps, cautious should be taken in regions with sparse observations and large anthropogenic aerosol emissions. [ABSTRACT FROM AUTHOR]

Flooding events associated with extreme precipitation have had large impacts in Norway. It is well known that these heavy precipitation events affecting Norway (and other parts of Europe) are strongly associated with atmospheric rivers (ARs). We assess trends in Norwegian AR characteristics, and the influence of AR variability on extreme precipitation in Norway. We first evaluate the ability of a high-resolution global climate model (EC-Earth) to simulate ARs, compared to ERA-Interim. We evaluate the EC-Earth simulated relationship between ARs and extreme precipitation in western Norway, compared to the observed relationship. We find that EC-Earth is able to simulate well the statistics of AR events and the related precipitation. The intensity and frequency of ARs making landfall in Norway both increase by the end of the century and we find a shift in seasonality of AR events in the future period. In two regions on the west coast, the majority of winter precipitation maxima are associated with AR events (> 80% of cases). Next we assess the influence of AR variability on extreme precipitation. A non-stationary extreme value analysis indicates that the magnitude of extreme precipitation events in these regions is associated with AR intensity. Indeed, the 1-in-20 year extreme event is 17% larger when the AR-intensity is high, compared to when it is low. There is little influence of specific humidity on the variability of extreme precipitation after all variables are de-trended. Finally, we find that the region mean temperature during winter AR events increases in the future. In the future, when the climate is generally warmer, AR days will tend to make landfall when the temperature is above the freezing point. The partitioning of more precipitation as rain, rather than as snow, can have severe impacts on flooding and water resource management. [ABSTRACT FROM AUTHOR]

Arid Central Asia is highly vulnerable to extreme climate events. Information on potential future changes in extreme climate events in Central Asia is limited. In this study, the performances of models from the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating climatological extremes in Central Asia are first evaluated, and a bias correction method is employed to constrain future projections. The responses of extreme climate events over Central Asia to future warming and, in particular, the impact of 1.5 and 2 °C global warming scenarios are then assessed based on the observationally constrained projections. During the twenty-first century, coldest night (TNn), coldest day (TXn), warmest night (TNx), warmest day (TXx), 1-day maximum precipitation (RX1 day), 5-day maximum precipitation (RX5 day), and precipitation intensity (SDII) in Central Asia would robustly increase at best estimated rates of 1.93 °C, 1.71 °C, 1.18 °C, 1.25 °C, 6.30%, 5.71%, and 4.99% per degree of global warming, respectively, under Representative Concentration Pathway (RCP) 8.5. Compared with the 2 °C warming scenario, limiting global warming to 1.5 °C could reduce the intensification (relative to 1986–2005) of TNn, TNx, TXn, TXx, RX1 day, RX5 day, and SDII by 33%, 24%, 32%, 29%, 39%, 42%, and 53% from the best estimates under RCP8.5, respectively. The avoided intensification of TNn, TNx, TXn and TXx (RX1 day and SDII) would be larger (smaller) under RCP4.5. This suggests that a low warming target is necessary for avoiding the dangerous risk of extremes in this arid region. [ABSTRACT FROM AUTHOR]

Present study attempts to project extreme precipitation indices over 34 different meteorological subdivisions and six homogeneous regions such as Northwest, Central Northeast, Northeast, West Central, Peninsular India and Hilly Region during summer monsoon season in the twenty-first century. For this purpose, the Regional Climate Model version4 (RegCM4) had been run at 50 km horizontal resolution forced with the global model GFDL-ESM2 M, during reference period 1976–2005 for the model validation, and the mid- (2031–2060) and far-future (2070–2099) for projections under RCP8.5 scenario over the South Asia CORDEX domain. In this paper, model simulated precipitation has been validated against IMD, APHRODITE and NCEP/NCAR data sets. The results indicate that RegCM4 captures the important features of seasonal precipitation and various extreme indices over the study area. The RegCM4 has projected an increase in the mean seasonal precipitation by 0.56 mm/day whereas in case of GFDL model the rate is 0.39 mm/day during the far-future relative to the reference period. The heavy precipitation indices are projected to increase more frequently (0.264/decade) than the mean precipitation rate (0.01/decade) over India. The correlations between the extreme precipitation indices and the seasonal mean precipitation are found to be strong. In addition, the consecutive dry days are projected to occur more frequently (3–5 days) over West Central (Telangana, Vidarbha and Marathwada) and west Rajasthan while consecutive wet days are projected to decrease over larger parts of India during far-future. Similarly, 1 day maximum precipitation and the simple daily intensity index are projected to increase consistently from mid- to far- futures over some sub-divisions of West coast, Hilly and Northeast regions. From a spatial probability perspective, model projection indicates more frequent severe drought and flood conditions over India. [ABSTRACT FROM AUTHOR]

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of "extreme" are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes. [ABSTRACT FROM AUTHOR]

We employ a large ensemble of Regional Climate Models (RCMs) from the COordinated Regional-climate Downscaling EXperiment to explore two questions: (1) what can we know about the future precipitation characteristics over Africa? and (2) does this information differ from that derived from the driving Global Climate Models (GCMs)? By taking into account both the statistical significance of the change and the models' agreement on its sign, we identify regions where the projected climate change signal is robust, suggesting confidence that the precipitation characteristics will change, and those where changes in the precipitation statistics are non-significant. Results show that, when spatially averaged, the RCMs median change is usually in agreement with that of the GCMs ensemble: even though the change in seasonal mean precipitation may differ, in some cases, other precipitation characteristics (e.g., intensity, frequency, and duration of dry and wet spells) show the same tendency. When the robust change (i.e., the value of the change averaged only over the land points where it is robust) is compared between the GCMs and RCMs, similarities are striking, indicating that, although with some uncertainty on the geographical extent, GCMs and RCMs project a consistent future. Potential added value of downscaling future climate projections (i.e., non-negligible fine-scale information that is absent in the lower resolution simulations) is found for instance over the Ethiopian highlands, where the RCM ensemble shows a robust decrease in mean precipitation in contrast with the GCMs results. This discrepancy may be associated with the better representation of topographical details that are missing in the large scale GCMs. The impact of the heterogeneity of the GCM–RCM matrix on the results has been also investigated; we found that, for most regions and indices, where results are robust or non-significant, they are so independently on the choice of the RCM or GCM. However, there are cases, especially over Central Africa and parts of West Africa, where results are uncertain, i.e. most of the RCMs project a statistically significant change but they do not agree on its sign. In these cases, especially where results are clearly clustered according to the RCM, there is not a simple way of subsampling the model ensemble in order to reduce the uncertainty or to infer a more robust result. [ABSTRACT FROM AUTHOR]

As the second biggest economy in the world, China has been experiencing significant impacts of global climate change. Developing future projections of regional climate over China is an indispensable step for designing appropriate mitigation and adaptation strategies against future climate change. To this end, this study focuses on exploring how the regional climate over China, including the mean and extreme climate, will be affected in the context of global warming throughout this century. The RegCM model is used to develop high-resolution climate scenarios for the whole country of China driven by boundary conditions of the Geophysical Fluid Dynamics Laboratory (GFDL) model under the Representative Concentration Pathways (RCPs). RegCM performance on simulating the present climate over China is evaluated and the results indicate that it is capable of reproducing the spatial distributions of temperature and precipitation. Future projections from RegCM suggest that an increase of 2 °C in daily mean temperature is expected in China by the end of the twenty-first century under RCP4.5 while an increase of 4 °C would be seen under RCP8.5. The Tibetan Plateau is likely to expect the most substantial temperature increase as well as the most significant decrease in extreme cold climate in China. In comparison, the annual total precipitation over China is projected to increase by 58 mm/year at the end of the twenty-first century under RCP4.5 and by 71 mm/year under RCP8.5. The projected changes in precipitation show apparent spatial variability due to the influences of local topography and land cover/use. [ABSTRACT FROM AUTHOR]

Four statistical downscaling methods, that is, three quantile mapping based techniques including bias-correction and spatial downscaling (BCSD), bias-correction and climate imprint (BCCI), and bias correction constructed analogues with quantile mapping reordering (BCCAQ), and the cumulative distribution function transform (CDF-t) method, are evaluated with daily observed precipitation and surface temperature for 1961–2005 over China. The four downscaling methods improve the accuracy over the driving general climate models (GCMs) significantly in terms of spatial variability, bias, seasonal cycle, and probability density functions of daily series and extreme events. Overall, BCSD outperforms other methods in frequency distributions of daily temperature, precipitation, and extreme precipitation indices such as wet and dry spell lengths. But it comparably has larger biases in temperature-related extremes. When downscaling the seasonal and extreme precipitation, the three quantile mapping based techniques exhibit better capacity than CDF-t in terms of the spatial correlation and bias over all subregions. Whereas CDF-t methods overestimate consecutive wet days and extreme wet indices significantly, as it displays limited improvement over the driving GCMs by producing too many drizzle days using either absolute or relative threshold. All methods are equally skillful in downscaling monthly and seasonal temperature, and the temperature extremes are better reproduced by BCCI, BCCAQ and CDF-t. However, the statistical downscaling methods show limited capacity in improving the interannual variability of temperature and precipitation extremes. [ABSTRACT FROM AUTHOR]

The assessment of predictability for decadal predictions of quantities like temperature and precipitation typically focuses on regional and temporal mean values. However, changes in extremes can be different to changes in mean climate, and accordingly their predictability may be different. We use simulations from CSIRO-Mk3-6-0 (CMIP5 archive) to set up a 'perfect model' experiment to compare the predictability for mean and extreme temperature and precipitation on interannual to decadal time-scales. The results show that both the mean and likelihood of near-surface temperature extremes is potentially predictable in many regions in the first lead year, while the areas with precipitation predictability tend to be mostly in low-latitude regions during this period. On decadal time scales, significant potential skill for mean and extreme temperatures is found over the North Atlantic and Southern Ocean but also over some land areas including North Africa, Europe and North America. The general spatial patterns of predictability are very similar between the mean and extremes. However, indices of moderate temperature extremes in particular show a tendency towards higher predictability than the mean. The approach to studying predictability presented here uses international coordinated model intercomparison project simulations. However a larger number of different initializations would be required from more models to allow improved robustness of the results. [ABSTRACT FROM AUTHOR]