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The study employed high-resolution atmospheric general circulation models (AGCM) to simulate tropical cyclones (TCs) and evaluated two TC genesis potential indices in reflecting projected TC changes in the western North Pacific (WNP) under a warming scenario. Both indices accurately represented the seasonal variation of TC genesis frequency (TCGF) and its spatial distribution in historical simulations and observation data. The widely-used TC genesis potential index (χGPI) projected a significant increase in TCGF in response to a warmer ocean surface. However, this projection conflicted with the significant reduction in the model projection due to the dominant control of SST on the χGPI. Higher SST in remote ocean basins often over dominated the destabilization effect of in-situ warmer SST and caused more stable atmospheric conditions in the WNP, resulting in fewer TC occurrences. By contrast, the revised index (χMqGPI), which considers gross moisture condensation, projected a TCGF decrease that more accurately reflected the decreasing trend of TCGF in the warming simulations by AGCM, although the degree of reduction was smaller than that derived directly from TC detection scheme. The results suggest the plausibility of using χMqGPI, based on the results of multimodel coarse-resolution CMIP6 climate models, to project future changes in TCGF in the WNP.

Idealized general circulation models (GCMs) suggest global‐mean precipitation ceases to increase with warming in hot climates because evaporation is limited by the available solar radiation at the surface. We investigate the extent to which this generalizes in comprehensive GCMs. We find that in the Community Atmosphere Model, global‐mean precipitation increases approximately linearly with global‐mean surface temperatures up to about 330 K, where it peaks at 5 mm day−1. Beyond 330 K, global‐mean precipitation decreases substantially despite increasing surface temperatures because of increased atmospheric shortwave absorption by water vapor, which decreases the shortwave radiation available for evaporation at the surface. Precipitation decreases in the tropics and subtropics but continues to increase in the extratropics because of continuously strengthening poleward moisture transport. Precipitable water increases everywhere, resulting in longer water‐vapor residence times and implying more episodic precipitation. Other GCMs indicate global‐mean precipitation might exhibit a smaller maximum rate and begin to decrease at lower surface temperatures. Plain Language Summary: Earth's climate has experienced substantial changes over its history, including periods of extremely cold temperatures where most regions contained ice, and periods of extremely warm temperatures where most regions contained no ice. In this study, we explore how precipitation changed in extremely cold and warm climates using a unique set of coupled climate model simulations. We find that global‐mean precipitation increases linearly with global‐mean surface temperatures up to 330 K, where it peaks at 5 mm day−1 and then decreases as surface temperatures further increase. This occurs because in hot climates, global‐mean precipitation is almost entirely balanced by absorbed shortwave radiation at the surface. As the climate warms, the atmosphere contains more water vapor, resulting in increased absorption of shortwave radiation within the atmosphere and decreased absorption of shortwave radiation at the surface. This decreases the energy available for surface evaporation. We show that other climate models exhibit qualitatively similar behavior but indicate global‐mean precipitation might exhibit a smaller maximum rate and begin to decrease at lower surface temperatures. These results demonstrate the need to better understand Earth's hydrological cycle in hot climates. These results also have large implications for understanding weathering in past climates and the habitability of other Earth‐like planets. Key Points: In CAM4, global‐mean precipitation increases linearly with surface temperatures up to 330 K, then decreases with higher temperaturesPrecipitation decreases at high temperatures due to increased atmospheric shortwave absorption by water vapor, decreasing surface absorptionAt high temperatures, precipitation decreases in most regions, but continues to increase in the extratropics due to eddy moisture transport [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are developed to simulate the past climate and generate future climate predictions. In the context of global warming, their important roles in identifying possible solutions to water resources planning/management are recognized by the world. However, in actual and regional implementation, due to many factors like initial and boundary conditions, parameters and model structures and so forth, there are great variabilities and uncertainties across the future climate projections of GCMs outputs, which has attracted criticism from water resources planners. Thus, the GCMs usually must be evaluated for assessing their performances in simulating the historical observations. Currently, there are many different conclusions and opinions as to whether the optimal individual model is more advantageous or whether the combined consideration of MME works better. The purpose of this paper is to compare the advantages and disadvantages between the selection of the optimal single GCM and multi‐model ensemble (MME) based on one of the more objective selection methods called TOPSIS. The results show that the performances of GCMs in simulating precipitation and temperature in different climate subregions over China are not identical. For CMIP6 GCMs simulations, the optimal precipitation GCM is CMCC‐CM2‐SR5 for EC, MIROC6 for SC, SWC and NWC, CESM2‐WACCM for NC and NEC, and FGOALS‐g3 for QTP. As for temperature, only NESM3 and BCC‐ESM1 are the optimal GCM for QTP and NWC, respectively; in other subregions, MME is better than single GCM. In general, a simple arithmetic averaging approach employed to generate the MME model is not superior to the optimal GCM, although the error metric with the observed data is reduced, at the cost of a severe compression of the interannual variability. [ABSTRACT FROM AUTHOR]

Recurring floods, droughts, heatwaves, and other hydro-meteorological extreme events are likely to be increased under the climate change scenarios. The increased risk of these extreme events might have more exposure to the population; thus, it is important to discuss such extreme events and their projected behavior under a changing climate scenario. In the present study, we have computed the extreme precipitation and temperature indices over the 10 agro-climatic zones falling under the Ganga River Basin (GRB)utilizing a high-resolution daily gridded temperature and precipitation multi-model ensembled CMIP6 dataset (0.25° × 0.25°) under global warming levels of 1.5 °C, 2 °C, and 3 °C. We found that the annual daily minimum temperature (TNN) showed a higher rise of about 67% than the maximum temperature (TXX) of 48% in GRB. The basin also experiences a greater increase in the frequency of warm nights (TN90P) of about 67.71% compared to warm days (TX90P) of 29.1% for the 3 °C global warming level. Along with extreme indices, the population exposed due to the impact of the extreme maximum temperature has also been analyzed for progressive warming levels. Population exposure to extreme temperature event (TXX) has been analyzed with 20-year return period using GEV distribution method. The study concludes that the exposed population to extreme temperature event experienced an increase from 46.99 to 52.16% for the whole Ganga Basin. Consecutive dry days (CDD) and consecutive wet days (CWD) both show a significant increasing trend, but CWD has a significant increase in the majority of the zones, while CDD shows a significant decreasing trend for some of the zones for three warming levels periods. Extreme climate indices help to understand the frequency and intensity of extreme weather events such as heavy rainfall, droughts, and heatwaves to develop early warning systems and adaptation strategies to mitigate such events., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Abstract Drought is one of the foremost outcomes of global warming and global climate change. It is a serious threat to humans and other living beings. To reduce the adverse impact of drought, mitigation strategies as well as sound projections of extreme events are essential. This research aims to strengthen the robustness of anticipated twenty-first century drought by combining different Global Climate Models (GCMs). In this article, we develop a new drought index, named Maximum Relevant Prior Feature Ensemble index that is based on the newly proposed weighting scheme, called weighted ensemble (WE). In the application, this study considers 32 randomly scattered grid points within the Tibetan Plateau region and 18 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) of precipitation. In this study, the comparative inferences of the WE scheme are made with the traditional simple model averaging (SMA). To investigate the trend and long-term probability of various classes, this research employs Markov chain steady states probability, Mann–Kendall trend test, and Sen’s Slope estimator. The outcomes of this research are twofold. Firstly, the comparative inference shows that the proposed weighting scheme has greater efficiency than SMA to conflate GCMs. Secondly, the research indicates that the Tibetan Plateau is projected to experience “moderate drought (MD)” in the twenty-first century.

The extreme temperature has become more frequent and intense due to global warming, particularly in dry regions, causing devastating impacts on humans and ecosystems. The transboundary Amu river basin (ARB) is the most vulnerable region in Central Asia (CA) to extreme weather linked to climate change. This study aimed to project warm and cold extremes in ARB for four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and two time-horizons, 2020–2059 and 2060–2099, using daily maximum (Tmax) and minimum temperature (Tmin) simulations of global climate models (GCMs) of Coupled Model Inter-comparison Project phase six (CMIP6). Results revealed that the basin's west experiences more hot extremes and the east more cold extremes. Climate change would cause a significant increase in the annual mean of Tmax and Tmin. However, the increase in mean Tmin would be much higher (5.0ºC) than the mean Tmax (4.6ºC). It would cause an increase in the hot extremes and a decrease in the cold extremes in the basin. The higher increase in the hot extremes would be in the west, while the higher decrease in the cold extreme in the basin's east. The number of days above 40℃ would increase from 45 to 60 days in the basin's west and northwest compared to the historical period. The number of days below − 20℃ would decrease up to 45 days in the basin's east. Overall, the decrease in cold extremes would be much faster than the increase in hot extremes. [ABSTRACT FROM AUTHOR]

As the capital city of Canada, Ottawa has been experiencing significant impacts of global climate change. How to adapt to future climate change is one of the biggest concerns in the city's built and natural systems. It thus requires a comprehensive understanding of possible changes in the local climate of Ottawa, which can hardly be reflected in the coarse outputs of Global Climate Models (GCMs). Therefore, a stepwise clustered downscaling (SCD) model is employed in this study to help investigate the plausible changes in daily maximum, minimum, and mean temperatures in Ottawa. Outputs from multiple GCMs under the Representative Concentration Pathways (RCPs) are used as inputs to drive the SCD model in order to develop downscaled climate projections. The performance of SCD model is evaluated by comparing the model simulations to the observations (R2 > 0.87) over the historical periods. Future temperature projections and their likely temporal trends throughout this century are analyzed in detail to explore the regional variations of global warming in Ottawa, thus to provide scientific basis for developing appropriate adaptation strategies at different management levels. The results suggest that the City of Ottawa is likely to expect significant increasing trends in temperatures (i.e., 0.18-0.38 °C per decade in maximum temperature, 0.16-0.31 °C per decade in minimum temperature, and 0.17-0.34 °C per decade in mean temperature under RCP4.5; 0.46-0.54 °C per decade in maximum temperature, 0.37-0.45 °C per decade in minimum temperature, and 0.42-0.50 °C per decade in mean temperature under RCP8.5) throughout this century. [ABSTRACT FROM AUTHOR]

The frequency, duration, and intensity of extreme thermal events are increasing and are projected to further increase by the end of the century 1,2 . Despite the considerable consequences of temperature extremes on biological systems 3-8 , we do not know which species and locations are most exposed worldwide. Here we provide a global assessment of land vertebrates' exposures to future extreme thermal events. We use daily maximum temperature data from 1950 to 2099 to quantify future exposure to high frequency, duration, and intensity of extreme thermal events to land vertebrates. Under a high greenhouse gas emission scenario (Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5); 4.4 °C warmer world), 41.0% of all land vertebrates (31.1% mammals, 25.8% birds, 55.5% amphibians and 51.0% reptiles) will be exposed to extreme thermal events beyond their historical levels in at least half their distribution by 2099. Under intermediate-high (SSP3-7.0; 3.6 °C warmer world) and intermediate (SSP2-4.5; 2.7 °C warmer world) emission scenarios, estimates for all vertebrates are 28.8% and 15.1%, respectively. Importantly, a low-emission future (SSP1-2.6, 1.8 °C warmer world) will greatly reduce the overall exposure of vertebrates (6.1% of species) and can fully prevent exposure in many species assemblages. Mid-latitude assemblages (desert, shrubland, and grassland biomes), rather than tropics 9,10 , will face the most severe exposure to future extreme thermal events. By 2099, under SSP5-8.5, on average 3,773 species of land vertebrates (11.2%) will face extreme thermal events for more than half a year period. Overall, future extreme thermal events will force many species and assemblages into constant severe thermal stress. Deep greenhouse gas emissions cuts are urgently needed to limit species' exposure to thermal extremes., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate. [ABSTRACT FROM AUTHOR]

Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energybalance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34:25±5:21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85:44±4:37 and 14:98±4:61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed. [ABSTRACT FROM AUTHOR]

The European Mediterranean Basin (Euro-Med), a region particularly vulnerable to global warming, notably lacks research aimed at assessing and enhancing the widely used remote climate detection products known as General Circulation Models (GCMs). In this study, the proficiency of GCMs in replicating reanalyzed 1981–2010 temperature data sourced from the ERA5 Land was assessed. Initially, the least data-modifying interpolation method for achieving a resolution match of 0.1° was ascertained. Subsequently, a pixel-by-pixel evaluation was conducted, employing five goodness-of-fit metrics. From these metrics, we compiled a Comprehensive Rating Index (CRI). A Multi-Model Ensemble using Random Forest was constructed and projected across three emission scenarios (SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5) and timeframes (2026–2050, 2051–2075, and 2076–2100). Empirical Bayesian Kriging, selected for its minimal data alteration, supersedes the commonly employed Bilinear Interpolation. The evaluation results underscore MPI-ESM1-2-HR, GFDL-ESM4, CNRM-CM6-1, MRI-ESM2-0, CNRM-ESM2-1, and IPSL-CM6A-LR as top-performing models. Noteworthy geospatial disparities in model performance were observed. The projection outcomes, notably divergent from IPCC forecasts, revealed a warming trend of 1 to over 2 °C less than anticipated for spring and winter over the medium–long term, juxtaposed with heightened warming in mountainous/elevated regions. These findings could substantially refine temperature projections for the Euro-Med, facilitating the implementation of policy strategies to mitigate the effects of global warming in vulnerable regions worldwide. [ABSTRACT FROM AUTHOR]

Assessment of climate change impacts on regional hydrological processes is vital for effective water resource management and planning. The 2015 Paris Agreement includes a two-headed temperature goal: “holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C”. The current study assesses the potential effect of global warming of 1.5 °C and 2 °C on river discharge, and their differences in the Upper Yangtze River Basin (UYRB) based on three hydrological models (HBV, SWAT and VIC) driven by bias-corrected climate projections from five General Circulation models (GCMs), namely GFDL, HAD, IPSL, MIROC and NOR as abbreviated, and two Representative Concentration Pathways (RCP2.6 and RCP4.5). Results indicate that all discharges (except autumn) especially winter have slight decreasing tendency (−0.3%, −2.3%, −0.8%, −6.4%, −1.2%, −0.3% respectively for annual, spring, summer, winter, 90% percentile and 10% percentile discharges) under RCP2.6 during 2020–2039 (+1.5 °C global warming) comparing to the reference period of 1986–2005. However, all discharges (except autumn and winter) especially summer have an increasing tendency (1.1%, 1.1%, 3.7%, 0.9%, 2.1% respectively for annual, spring, summer, 90% percentile and 10% percentile discharges) under RCP4.5 during 2040–2059 (+2 °C global warming). Moreover, the occurrence frequency and strength of droughts in dry season under global warming of 1.5 °C will increase, while floods in wet season under global warming of 2 °C tend to increase in UYRB. Additionally, the difference of river discharge between global warming of 2 °C and 1.5 °C is positive for almost all the time scales (1.4%, 3.5%, 4.5%, 2.1%, 2.4% respectively for annual, spring, summer, 90% percentile and 10% percentile discharges), which suggests that the increment of 0.5 °C could lead to more flood events in the UYRB. [ABSTRACT FROM AUTHOR]

Future projections of climate variables are the key for the development of mitigation and adaptation strategy to changing climate. However, such projections are often subjected to large uncertainties which make implementation of climate change strategies on water resources system a challenging job. Major uncertainty sources are General Circulation models (GCMs), post-processing and climate heterogeneity based on catchment characteristics (e.g. scares data and high-altitude). Here we presents the comparisons between different GCMs, statistical downscaling and bias correction approaches and finally climate projections, with the integration of gridded and converted (monthly to daily) data for a high-altitude, scarcely-gauged Jhelum River basin, Pakistan. Current study relies on climate projections obtained from factorial combination of 5-GCMs, 2 statistical downscaling and 2 bias correction methods. In addition, we applied bias corrected APHRODITE, converted daily data using MODAWEC model and observed data. Further, five GCMs (CGCM3, HadCM3, CCSM3, ECHAM5 and CSIRO-MK3.5) were tested to scrutinize two suitable GCMs integrated with Statistical Downscaling Model (SDSM) and Smooth Support Vector Machine (SSVM). Results illustrate that the CGCM3 and HadCM3 were suitable GCMs for selected study basin. Both downscaling techniques are able to simulate precipitation, however, SSVM performed slightly better than SDSM. We found that the integration of CGCM3 with SSVM (SSVM-CGCM3) generates precipitation and temperature better than the CGCM3 (SDSM-CGCM3) and HadCM3 (SDSM-HadCM3) with SDSM. Furthermore, the low elevation stations were influenced by monsoon, significantly prone to rise in precipitation and temperature, while high-altitude stations were influenced by westerlies circulations, less prone to climate change. The projections indicated rise in basin-wide annual precipitation by 25.51, 36.76 and 45.52 mm and temperature by 0.64, 1.47 and 2.79 °C, during 2030s, 2060s and 2090s, respectively. The methods and results of this study can be adopted to evaluate climate change implications in the catchments of characteristics similar to Jhelum River basin. [ABSTRACT FROM AUTHOR]

Climate sensitivity is generally studied using two types of models. Atmosphere-ocean general circulation models (AOGCMs) include interactive ocean dynamics and detailed heat uptake. Atmospheric GCMs (AGCMs) with slab ocean models (SOMs) cannot fully simulate the ocean's response to and influence on climate. However, AGCMs are computationally cheaper and thus are often used to quantify and understand climate feedbacks and sensitivity. Here, physical climate feedbacks are compared between AOGCMs and SOM-AGCMs from the Coupled Model Intercomparison Project phase 3 (CMIP3) using the radiative kernel technique. Both the global-average (positive) water vapor and (negative) lapse-rate feedbacks are consistently stronger in AOGCMs. Water vapor feedback differences result from an essentially constant relative humidity and peak in the tropics, where temperature changes are larger for AOGCMs. Differences in lapse- rate feedbacks extend to midlatitudes and correspond to a larger ratio of tropical- to global-average temperature changes. Global-average surface albedo feedbacks are similar between models types because of a near cancellation of Arctic and Antarctic differences. In AOGCMs, the northern high latitudes warm faster than the southern latitudes, resulting in interhemispheric differences in albedo, water vapor, and lapse-rate feedbacks lacking in the SOM-AGCMs. Meridional heat transport changes also depend on the model type, although there is a large intermodel spread. However, there are no consistent global or zonal differences in cloud feedbacks. Effects of the forcing scenario [Special Report on Emissions Scenarios A1B (SRESa1b) or the 1% CO2 increase per year to doubling (1%to2x) experiments] on feedbacks are model dependent and generally of lesser importance than the model type. Care should be taken when using SOM-AGCMs to understand AOGCM feedback behavior. [ABSTRACT FROM AUTHOR]

The responses of tropical anvil cloud and low‐level cloud to a warming climate are among the largest sources of uncertainty in our estimates of climate sensitivity. However, most research on cloud feedbacks relies on either global climate models with parameterized convection, which do not explicitly represent small‐scale convective processes, or small‐domain models, which cannot directly simulate large‐scale circulations. We investigate how self‐aggregation, the spontaneous clumping of convection in idealized numerical models, depends on cloud‐radiative interactions with different cloud types, sea surface temperatures (SSTs), and stages of aggregation in simulations that form part of RCEMIP (the Radiative‐Convective Equilibrium Model Intercomparison Project). Analysis shows that the presence of anvil cloud, which tends to enhance aggregation when collocated with anomalously moist environments, is reduced in nearly all models when SSTs are increased, leading to a corresponding reduction in the aggregating influence of cloud‐longwave interactions. We also find that cloud‐longwave radiation interactions are stronger in the majority of General Circulation Models (GCMs), typically resulting in faster aggregation compared to Cloud‐system Resolving Models (CRMs). GCMs that have stronger cloud‐longwave interactions tend to aggregate faster, whereas the influence of circulations is the main factor affecting the aggregation rate in CRMs. Plain Language Summary: The spatial organization of tropical rainstorms has major effects on weather and climate. This organization influences the duration and intensity of these convective storms, and alters the amount of radiation absorbed and emitted by the atmosphere. There is great uncertainty in the response of organization to a warming climate, and this results in one of the largest sources of uncertainty in climate predictions. Climate projections rely on either General Circulation Models (GCMs) that can represent the large‐scale motions, or smaller high‐resolution models that represent small‐scale features like cloud formations, but not the large motions. In this study, we compare convective organization in GCMs and Cloud‐system Resolving Models (CRMs) across a range of sea surface temperatures (SSTs). We find that the cloud‐radiation feedbacks that make the convective environment more favorable for further convection, and the non‐convective environment less favorable for convection, are stronger in GCMs than CRMs on average. This is related to larger cloud amounts in GCMs, leading GCMs to have typically faster organization than CRMs. We find these feedbacks which drive aggregation decrease as SST increases, yet the aggregation rate is largely insensitive to SST because of the decrease in the effect of atmospheric motions that oppose aggregation. Key Points: General Circulation Models (GCMs) aggregate faster than Cloud‐system Resolving Models (CRMs) on average due to an enhanced longwave feedbackFeedbacks tend to decrease in magnitude as sea surface temperature increases, although the rate of aggregation remains similarAggregation rate in GCMs is correlated with diabatic feedbacks, while in CRMs it is more related to advection feedbacks [ABSTRACT FROM AUTHOR]

Prospecting our future climate behavior under a wide range of possible scenarios is one of the most challenging issues in climate science. This study examines changes in monsoon's (June–September) future precipitation over three zones distributed across Sudan based on GCMs from CMIP5 and (CMIP6). The models are GISS-E2-H, IPSL-CM5A-MR, and MPI-ESM-LR (BCC-CSM2-MR, INM-CM4-9 MPI-ESM1-2-LR) in addition to the ensemble mean of each group. Given systematic errors in the GCMs simulations, we applied the Quantile Mapping bias correction technique to rectify the bias toward the real-occurred field. The bias coefficients were calculated for 1946–1975 and validated for 1976–2005. The improvement was quantified using the Skills Score technique. Overall, the GCMs simulations improved by 0–15% (45–90%) for CMIP6 (CMIP5) after bias correction (BC) was involved. BC has led to a remarkable breakthrough in the GCMs simulations manifested by a considerable improvement in the simulated precipitation, particularly in southern parts of Sudan. Furthermore, we applied the bias adjustments of the future period 2030–2099 to reduce the uncertainty margins when assessing the possible changes. The future period was divided as (i) Near Future 2025–2049, (ii) Middle Future 2050–2074, and (iii) Far Future 2075–2099. The changes were examined based on two Representative Concentration Pathways (Shared Socioeconomic Pathway) scenarios, namely: RCP4.5(SSP2–4.5) and RCP8.5(SSP5–8.5) for CMIP5 (CMIP6). In both scenarios, June–September is projected to remain the rainy season in all analyzed zones. Among all studied periods for both scenarios, the most severe changes are expected in the far future with a positive change of 60 to >80% shown in CMIP6 projection under the high emission scenario in zone3. However, the highest negative bias is expected in the near future period under the high emission scenario in CMIP5 projections in the same zone. Higher uncertainty is associated with the future projections, particularly in zone3 under the high emission scenario for both CMIP5 and CMIP6 projections. Higher agreement between the models, on the other hand, is shown in zone2 under the high emission scenario for both simulations. • GCMs simulations are afflicted by systematic bias to a level that prevents its direct interpretation. • The bias correction is a must carried out process before examining changes on hydrologic variables using GCMs. • The GCMs response to bias correction varies remarkably from CMIP5 to CMIP6 models groups. • The future precipitation is projected to be increased with unwitnessed records based on bias-adjusted CMIP6 GCMs. [ABSTRACT FROM AUTHOR]

Despite a globally uniform increase in the concentrations of emitted greenhouse gases, radiatively forced surface warming can have significant spatial variations. These define warming patterns that depend on preexisting climate states and through atmospheric and oceanic dynamics can drive changes of the hydrological cycle with global-scale feedbacks. Our study reviews research progress on the hydrological cycle changes and their effects on multiscale climate variability. Overall, interannual variability is expected to become stronger in the Pacific and Indian Oceans and weaker in the Atlantic. Global monsoon rainfall is projected to increase and the wet season to lengthen despite a slowdown of atmospheric circulation. Strong variations among monsoon regions are likely to emerge, depending on surface conditions such as orography and land-sea contrast. Interdecadal climate variability is expected to modulate the globally averaged surface temperature change with pronounced anomalies in the polar and equatorial regions, leading to prolonged periods of enhanced or reduced warming. It is emphasized that advanced global observations, regional simulations, and process-level investigations are essential for improvements in understanding, predicting, and projecting the modes of climate variability, monsoon sensitivity, and energetic fluctuations in a warming climate., (© 2020 New York Academy of Sciences.)

Pithan and Mauritsen argue that the 2009 results of Boé et al. are not consistent with current understanding of the lapse-rate feedback in the Arctic. They also argue that these results arise to an important extent from self-correlation issues. In this response, the authors argue that their results are not inconsistent with current understanding of lapse-rate feedback and demonstrate that the conclusions remain unchanged when all possibilities of self-correlation are excluded. [ABSTRACT FROM AUTHOR]

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This study investigates some of the uncertainties sources associated with the pseudo global warming (PGW) approach which was employed to project future patterns of tropical cyclones (TCs) over the Arabian Sea (AS). First, the climate variables controlling the patterns of tropical cyclones were extracted from reanalysis datasets of ERA5, ERAI, CFSR, and NCEP/NCAR. Then, each dataset was evaluated against long‐term measurements to identify the best‐performing reanalysis dataset. ERA5 showed the best performance for most of the variables. Outputs of 20 CMIP5 global climate models (GCMs) were then evaluated against the ERA5 data resulting in an ensemble of the best performing GCMs. A PGW framework was then used to project the changes in patterns of three significant historical cyclones: Gonu, Phet, and Ashobaa. In doing so, the signals of future climate variables were extracted from the GCMs ensemble to modify the initial and boundary conditions of the WRF model which was previously tuned for reproducing the historical TCs. Different tests were conducted to address the sources of uncertainty in the PGW approach, including the selection of the climate variables contributing to the computation of the signals, the selection of GCMs, and the spatial variation of signals. A considerable sensitivity of the projected track and intensity of TCs to the choice of GCMs was observed, acknowledging the importance of GCMs evaluation before calculating the signals. Moreover, it was found that among all variables, signals of sea surface temperature and air temperature have major effects on the cyclone's track and intensity. Apart from that, when the signals were applied to the domain of the WRF model uniformly, compared to applying spatially varying signals, different tracks and intensities for future TCs were also observed. Overall, the findings of this paper challenge the reliability of the projected changes in TCs patterns obtained from PGW. [ABSTRACT FROM AUTHOR]

Regional hydrological sensitivity (i.e., precipitation change per degree local surface warming) contributes substantially to the uncertainty in future precipitation projections over tropical oceans. Here, we investigate the sensitivity of relative precipitation (P*, precipitation divided by the basin average precipitation) to local sea surface temperature (SST) change by dissecting it into three components, namely the sensitivity of P* to relative SST (SSTrel, SST minus the tropical mean SST) changes, the sensitivity of P* to surface convergence changes, and the sensitivity of surface convergence to SST gradient changes. We show that the relationships between P* and SSTrel, and between P*, surface convergence, and SST gradients are largely constant during climate change. This allows us to constrain regional hydrological sensitivity based on present‐day SST‐precipitation relationships. The sensitivity of surface convergence to SST gradient changes is a main source of uncertainty in regional hydrological sensitivity and is likely underestimated in GCMs. Plain Language Summary: Understanding how precipitation will change over tropical oceans is important because these changes influence the atmospheric circulation, which in turn affects the global climate and weather patterns. Climate models disagree on their projections of precipitation changes over tropical oceans in part due to a lack of understanding on how precipitation should respond to a given amount of local surface warming. We find that the sensitivity of precipitation to future changes in local sea surface temperature (which is commonly referred to as regional hydrological sensitivity) largely depends on the present‐day relationship between precipitation and local sea surface temperature, as well as that between precipitation and the spatial gradient in sea surface temperature, and both relationships are observable and thus can serve as constraints. We find that inter‐model differences in regional hydrological sensitivity result primarily from differences in the response of surface winds to sea surface temperature gradient changes. Key Points: Regional hydrological sensitivity is an important source of uncertainty in rainfall projections over tropical oceansRegional hydrological sensitivity can be constrained by components of rainfall‐temperature relationship that stay constant during warmingUncertainty in regional hydrological sensitivity originates largely from surface convergence sensitivity to temperature gradient changes [ABSTRACT FROM AUTHOR]

Hot and dry extremes have co‐occurred with global warming, causing worse threats than single climate extremes. We analyse the compound hot and dry day (CHDD) and population exposure (Epop) across China based on observations and 11 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6. The bias‐corrected GCMs, particularly the two‐stage multivariate quantile mapping‐corrected ones, perform better than the raw ones in catching changes in CHDD but they all overestimate CHDD and Epop. More CHDDs occur in the southeast (SE), north (NC) and northwest (NW) China with increasing trends over regions stretching from the northeast (NE) to southwest (SW) China. Despite the declined CHDD, historical Epop has generally increased by the increased population. Future projections in the SSP5‐8.5 experiment indicate an overall increase of CHDD and Epop at 1.5°C warming and an increasing Epop to dangerous CHDD at 2°C warming. SW, NE and the Tibetan Plateau need effective measures of adaptation to reduce sufferings from CHDD under climate change. [ABSTRACT FROM AUTHOR]

The equilibrium climate sensitivity (ECS) of the CMIP6 global circulation models (GCMs) varies from 1.83°C to 5.67°C. Herein, 38 GCMs are grouped into three ECS classes (low, 1.80–3.00°C; medium, 3.01–4.50°C; high, 4.51–6.00°C) and compared against the ERA5‐T2m records from 1980–1990 to 2011–2021. We found that all models with ECS > 3.0°C overestimate the observed global surface warming and that spatial t‐statistics rejects the data‐model agreement over 60% (using low‐ECS GCMs) to 81% (using high‐ECS GCMs) of the Earth's surface. Thus, the high and medium‐ECS GCMs are unfit for prediction purposes. The low‐ECS GCMs are not fully satisfactory yet, but they are also found unalarming because by 2050 they predict a moderate warming (ΔTpreindustrial→2050 ≲ 2°C). Plain Language Summary: The last‐generation Coupled Model Intercomparison Projects (CMIP6) global circulation models (GCMs) are used by scientists and policymakers to interpret past and future climatic changes and to determine appropriate (adaptation or mitigation) policies to optimally address scenario‐related climate‐change hazards. However, these models are affected by large uncertainties. For example, their equilibrium climate sensitivity (ECS) varies from 1.83°C to 5.67°C, which makes their 21st‐century predicted warming levels very uncertain. This issue is here addressed by testing the GCMs' global and local performance in predicting the 1980–2021 warming rates against the ERA5‐T2m records and by grouping them into three equilibrium climate sensitivity (ECS) classes (low‐ECS, 1.80–3.00°C; medium‐ECS, 3.01–4.50°C; high‐ECS, 4.51–6.00°C). We found that: (a) all models with ECS > 3.0°C overestimate the observed global surface warming; (b) Student t‐tests show model failure over 60% (low‐ECS) to 81% (high‐ECS) of the Earth's surface. Thus, the high and medium‐ECS GCMs do not appear to be consistent with the observations and should not be used for implementing policies based on their scenario forecasts. The low‐ECS GCMs perform better, although not optimally; however, they are also found unalarming because for the next decades they predict moderate warming: ΔTpreindustrial→2050 ≲ 2°C. Key Points: The Coupled Model Intercomparison Projects, global circulation models (GCMs) are very different from each other; their equilibrium climate sensitivity (ECS) large range needs to be greatly constrained for scientific and policy purposesWe group the GCMs into three classes with low, medium, and high ECS; we test them against the ERA5‐T2m records using spatial t‐statisticsWe found that the high and medium‐ECS GCMs run too hot; the low‐ECS GCMs are not optimal yet, but also unalarming for the future [ABSTRACT FROM AUTHOR]

Global warming may increase potential evapotranspiration (ETp), reducing the water resources in Yangzi River Delta. Therefore, it is important to investigate the trend of ETp there under the background of climate change. To this purpose, the systematic biases in temperature outputs of 24 global climate models (GCMs) under 3 shared socioeconomic pathways—representative concentration pathways (SSPs) emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5)—are first corrected by using 8 bias correction methods. Then, the trend of ETp in Yangtze River Delta is projected by using 4 ETp calculation formulas (Blaney–Criddle, Hargreaves–Samani, Makkink, and Priestley–Taylor). The uncertainty of the projections is estimated and decomposed by using multi-way analysis of variance frameworks. The influence of uncertainty on the projected change signal is quantified by using the signal-to-noise ratio. The results show that all emission scenarios indicate robust increments of ETp. Specifically, relative to 1971~2000, ETp will increase by 0.14~0.17 mm d−1 (5.7~6.8%) during 2021~2050 and by 0.21~0.41 mm d−1 (8.5~16.7%) during 2061~2090, respectively. During 2021~2050, the uncertainty of ETp projections is dominantly contributed by the main effects of GCM (63%) and the ETp calculation formula (24%). During 2061~2090, it is mainly contributed by the main effect of GCM (36%), followed by the main effects of the emission scenario (34%) and the ETp calculation formula (18%). The ETp projections are generally reliable and robust during the two projection periods. [ABSTRACT FROM AUTHOR]

Introduction :Higher temperature as the result of climate change are likely to affect horticultural production. Deciduous fruit trees need winter chilling to break winter dormancy. Climate plays an important role in the successful production of deciduous fruit. Winter dormancy is one of the key factors of the annual cycle of deciduous fruit and nut trees along with the following breaking of the dormant state. This state is maintained through the winter period each year to protect against damaging cold temperatures. To be released from dormancy, trees require exposure to a predetermined quantity of cold temperatures in a process known as winter chilling or vernalization. Insufficient chilling can lead to sporadic and light bud break, poor fruit development, small fruit size and uneven ripening times. The main objective of this study is to investigate climate change effect on the winter chilling requirement (WCR) in Hamadan. Materials and Methods:This research was performed based on the General Circulation Models (BCM2, HADCM3,GFCM2 and IPCM4) and different emission scenarios (A2, B1, A1B), as recommended by the Forth Report of the IPCC. The output of the GCMs was downscaled by LARS-WG model. The hourly weather data were generated as the inputs of three different Chilling Requirement Models (CRMs), and the winter chilling trend of deciduous fruit trees were predicted for Hamadan. The projected daily temperature time series were then converted into hourly temperatures. The projected hourly temperature data were run through each of the three chill models for all four GCMs in different scenarios. Three chill models [the 0.0–7.2°C (CH), the Utah (UT), and the Utah Positive (UTPos) models] were used to investigate changes in chill accumulation in Hamadan, according to localized temperature change related to increases in global average temperatures. In addition, the winter chilling requirement time series were divided into two periods: baseline and future time. Historical daily minimum and maximum data from 1980 to 2010 were used from the Hamadan airport synoptic station. Future time horizon splittedinto early (2011-2030) and late (2031-2050) periods. For evaluating the long-term future changes in the chilling requirement, we used parametric and non-parametric tests. Results and Discussion: The model results showed a decreasing WCR trend during the recent decade. In general, the outputs of downscaled climate models predicted a decreasing WCR trend for the study site. For the time horizon of 2031-2050, this dramatic reduction in the WCR varied rom 25 percent to 40 percent. Future chill profiles differentiated between the WCR models as demonstrated through Hamadan global average temperature, causing a small decline in accumulated chill unit, with further warming causing greater decreases. This decrease in the UT models can be due to the negative effect of high temperature during this period. The study result which showed the WCR mean during early time horizon 2011-2030, was not significant but further time horizon 2031-2050 had a very significant change, as compared to the baseline. The aim of this study was to assess changes in the WCR rather than completing a model skill analysis. Through using previous climate model performance studies a justification of the addition of GCMs was described. Such defenses for model selection are recommended in all climate change impact studies. Test of model output in other scenarios and different GCMs showed an insignificant versatility between them. Conclusions: This research represents a significant update to the previous climate impact analysis of chill in cold semi-arid climate of Hamadan. It also highlights that sensitivity studies as a useful method for impact assessments. The severity and rate of decline of winter chilling requirement, depends on which chill model was used. The general trend showed decreasing of the winter chilling requirement against the winter temperature trend. Therefore, in the context of global warming, the earlier flowering dates of many deciduous tree species is likely leads to increased risk of damage during the late spring frost. For future fruit farm management, decisions can be implemented with deliberation of the likely changes in the winter chilling requirement reported here. There might be some adaptation, at least to some degree, being essential for most production areas in Hamadan and other similar climate conditions within the next 40 years. Reduction in winter chilling, prevents breaking winter dormancy, which finally may lead to serious damage to deciduous fruits.

This study investigates present and future European heat wave magnitudes, represented by the Heat Wave Magnitude Index-daily (HWMId), for regional climate models (RCMs) and the driving global climate models (GCMs) over Europe. A subset of the large EURO-CORDEX ensemble is employed to study sources of uncertainties related to the choice of GCMs, RCMs, and their combinations. We initially compare the evaluation runs of the RCMs driven by ERA-interim reanalysis to E-OBS (observation-based estimates), finding that the RCMs can capture most of the observed spatial and temporal features of HWMId. With their higher resolution compared to GCMs, RCMs can reveal spatial features of HWMId associated with small-scale processes (e.g., orographic effects); moreover, RCMs represent large-scale features of HWMId satisfactorily (e.g., by reproducing the general pattern revealed by E-OBS with high values at western coastal regions and low values at the eastern part). Our results indicate a clear added value of the RCMs compared to the driving GCMs. Forced with the emission scenario RCP8.5, all the GCM and RCM simulations consistently project a rise in HWMId at an exponential rate. However, the climate change signals projected by the GCMs are generally attenuated when downscaled by the RCMs, with the spatial pattern also altered. The uncertainty in a simulated future change of heat wave magnitudes following global warming can be attributed almost equally to the difference in model physics (as represented by different RCMs) and to the driving data associated with different GCMs. Regarding the uncertainty associated with RCM choice, a major factor is the different representation of the orographic effects. No consistent spatial pattern in the ensemble spread associated with different GCMs is observed between the RCMs, suggesting GCM uncertainties are transformed by RCMs in a complex manner due to the nonlinear nature of model dynamics and physics. In summary, our results support the use of dynamical downscaling for deriving regional climate realization regarding heat wave magnitudes. [ABSTRACT FROM AUTHOR]

In terms of five categories of East Asian summer monsoon (EASM) indices and one classic meridional wind index, we project the EASM intensity change over the 21st century using 27 global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) under the mid‐range combined scenarios of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP2‐4.5). Results show that future changes in the EASM intensity are index‐ and model‐dependent. Relative to the reference period 1985–2014, the EASM intensity strengthens slightly based on the east–west thermal contrast indices but varies little as a whole based on the southwest monsoon indices over the 21st century, with large discrepancies among the models. However, it tends to weaken over time based on the other three categories of indices, including the north–south thermal contrast indices, the shear vorticity indices, and the South China Sea monsoon indices. In terms of the meridional wind index that directly measures the EASM circulation in the lower troposphere and holds valid under future global warming, the EASM intensity strengthens significantly over the 21st century as a consequence of increases in zonal and meridional land–sea thermal contrasts between the East Asian continent and the adjacent oceans of western North Pacific and South China Sea. There are certain relationships between the trends of the meridional wind index and other EASM indices. The projections are overall consistent between the ensemble means of all available 27 models and the 14 good models as selected according to their abilities to simulate the present EASM climatology, but with different magnitudes. [ABSTRACT FROM AUTHOR]

Advances in high‐performance computing have enabled large‐eddy simulations (LES) of turbulence, convection, and clouds. However, their potential to improve parameterizations in global climate models (GCMs) is only beginning to be harnessed, with relatively few canonical LES available so far. The purpose of this paper is to begin creating a public LES library that expands the training data available for calibrating and evaluating GCM parameterizations. To do so, we use an experimental setup in which LES are driven by large‐scale forcings from GCMs, which in principle can be used at any location, any time of year, and in any climate state. We use this setup to create a library of LES of clouds across the tropics and subtropics, in the present and in a warmer climate, with a focus on the transition from stratocumulus to shallow cumulus over the East Pacific. The LES results are relatively insensitive to the choice of host GCM driving the LES. Driven with large‐scale forcing under global warming, the LES simulate a positive but weak shortwave cloud feedback, adding to the accumulating evidence that low clouds amplify global warming. Plain Language Summary: Clouds remain one of the largest uncertainties in our understanding and in predictions of climate change because it is challenging to represent their small‐scale dynamics in climate models. However, high‐resolution simulations can provide faithful simulations of clouds and turbulence in limited areas, and these can be used to calibrate climate models. So far, only a limited set of simulations has been used for calibration of climate models, with focus on a few specific locations. This study presents an experimental setup that allows the high‐resolution simulations to be run anywhere on the globe, in any climate state, driven by output from different climate models. The setup is used to create a library of high‐resolution simulations of clouds across the tropics and subtropics in both the current and a warmer climate. The library substantially expands the data set available for the calibration and evaluation of climate models. Key Points: A library of high‐resolution simulations of clouds is created with large‐eddy simulations (LES) driven by a few global climate models (GCMs)Clouds simulated by LES driven by different GCMs are more similar to one another than the corresponding GCM‐simulated cloudsUnder global warming, LES‐simulated low clouds exhibit a weak but positive feedback on warming [ABSTRACT FROM AUTHOR]