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This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

This paper presents the response to anthropogenic forcing in the GISS‐E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non‐interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One‐Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1‐G) and HYCOM (E2.1‐H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1‐2.6, SSP2‐4.5, SSP4‐6.0, and SSP5‐8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high‐mitigation scenarios SSP1‐1.9 and SSP1‐2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1‐H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5‐8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1‐G (−1.21 × 106 km2/°C) and E2.1‐H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5‐8.5 in the E2.1‐G model, comparable to the reduction in the corresponding CMIP5 GISS‐E2 RCP8.5 simulation. Both low‐end scenarios SSP1‐1.9 and SSP1‐2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century). Plain Language Summary: The projections of future climate change are uncertain because they are dependent on different possible scenarios of human‐caused emissions and their interaction with natural forcings, internal climate variability, and inter‐model differences. This paper presents the results of the climate model of the NASA Goddard Institute for Space Studies, GISS‐E2.1, for the anthropogenically forced climate response for the twenty first century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The sensitivity of the model response to different magnitudes of anthropogenic forcings in the twenty‐first‐century scenarios were performed using two different versions for the atmospheric composition and two different ocean general circulation models. Compared to CMIP5 GISS‐E2 versions, the CMIP6 GISS‐E2.1 climate model shows a stronger warming by 2100 in comparable scenarios due to larger effective climate sensitivity and transient climate response. Both climate models with two different ocean components project decreases in the Atlantic overturning stream function by 2100. Key Points: GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experimentsGISS‐E2.1 climate model shows a stronger warming by 2,100 in comparable Representative Concentration Pathway scenarios in CMIP5 due to larger effective climate sensitivity and stronger transient climate responseBoth coupled models, E2.1‐G and E2.1‐H, project decreases in the Atlantic overturning stream function by 2100 with the largest decrease in the warmest scenario SSP5‐8.5 in the E2.1‐G model [ABSTRACT FROM AUTHOR]

The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise‐to‐noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training data set is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere‐Ocean General Circulation Model simulations. The neural network utilized for training is a U‐Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single‐model initial‐condition large ensembles, the ensemble mean, and the U‐Net's predictions. The U‐Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale. While demonstrating effective filtering of the El Niño Southern Oscillation, the U‐Net encounters challenges in capturing the changes in the North Atlantic Ocean. This methodology holds potential for extension to other physical variables, facilitating insights into the climate change triggered by external forcings over the long term. Plain Language Summary: To anticipate future climate change, it is crucial to detect and understand the impacts of human activities. However, distinguishing the effects of anthropogenic forcing from natural climate variations in observational data is challenging. Natural climate variability, known as internal variability, arises from the chaotic nature of atmospheric and oceanic circulation, and from the interactions among the ocean, atmosphere, and land. Here, a novel approach is introduced to distinguish the changes caused by human activities from internal variability. It is applied to the surface air temperature evolution from 1901 to 2020. This method uses an artificial neural network designed to separate the internal from the human‐induced variability. An unprecedented number of climate model simulations are used, enabling precise estimation of human‐forced variability in these climate models. The spatio‐temporal variations are distinguished by applying a well‐known methodology previously used to remove noise from images. The method's performance is evaluated, revealing errors regarding the internal variability that are typically one‐fourth of the actual variations. Regions with important internal variability or with low agreement among models exhibit the largest errors. Overall, the skills are comparable to other existing approaches, but improvements are anticipated. Key Points: We present a new method to separate the forced and internal variability of the surface air temperatureWe utilize a U‐Net trained with global climate models outputs and implement a noise to noise methodology to eliminate internal variabilityThe results are assessed through the utilization of very large ensemble simulations of two distinct climate models [ABSTRACT FROM AUTHOR]

Current methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments. Plain Language Summary: Drought is affected by both rainfall and evapotranspiration. Drought indices represent drought severity compared to normal conditions as a function of time. Some drought indices are based on rainfall alone and some use both rainfall and evapotranspiration in the calculations. To understand future drought risk, simulations from climate models are needed. Unfortunately, different climate models often disagree on amounts and patterns of rainfall in the future, the disagreement being considerably more on rainfall than for evapotranspiration. In this study, we attempt to reduce the impact of these differences by developing a new method to estimate future drought. We used a mathematical method known as wavelets to estimate drought indices based on rainfall. Evapotranspiration is then used directly from the climate model and combined with the rainfall based drought index to create one overall drought index. We used projections from multiple climate models to understand if our new method led to a greater agreement in how often and severe future droughts may be. Our results confirm that the new method offers greater consistency in drought projections for the future. Key Points: Current methods for projecting drought ignore differences in uncertainty between P and ET simulationsA new basis for projecting drought is proposed that explicitly accounts for relative uncertainty between P and ETOur results show that the new method offers greater consistency in drought projections for the future [ABSTRACT FROM AUTHOR]

The arid and semi‐arid regions are highly vulnerable to climate change and variability. Agricultural production in these regions is particularly vulnerable because of its heavy dependence on climate conditions. Therefore, it is important to improve the projections of future agro‐climatic conditions. This study investigates the changes in agroclimatic conditions for growing Chinese Medicinal Yam (CMY) in the semi‐arid North China during 2031–2050 under the Representative Concentration Pathway (RCP) 4.5 and 8.5 emission scenarios. It employs high‐resolution climate data provided by the regional climate model (RCM) of Providing Regional Climates for Impacts Studies (PRECIS‐2.0). The changes in CMY yield and producing area under future climate change are simulated by the Agro‐Ecological Zone model. The simulations are based on newly calibrated cultivar parameters. The results show that the high‐resolution RCM simulation corresponds better with the observations of precipitation and temperature than the general circulation model (GCM). Because of the increased temperature and precipitation, the agroclimatic conditions for CMY are going to improve. While the traditional yam production area will maintain suitable for CMY production, the CMY production areas will further expand northward in the future. The average yield will increase by 1,196 and 1,336 kg DM/ha under RCP 4.5 and 8.5 emission scenarios, respectively. Key Points: Improving the projections of future agro‐climatic conditions in the semi‐arid North ChinaThe high‐resolution Providing Regional Climates for Impacts Studies projections correspond better with the observationsThe Chinese Medicinal Yam production areas will expand northward in the future, the average yield will increase by 1196–1336 kg DM/ha in the future [ABSTRACT FROM AUTHOR]

Many of the current, Coupled Model Intercomparison Project 6 (CMIP6), General Circulation Models (GCMs) show climate sensitivity higher than currently accepted uncertainty ranges. There is a weak correlation between increases in vertical resolution and in climate sensitivity from CMIP5. In particular, the MO Hadley Centre GCM's vertical resolution has more than doubled, and its climate sensitivity has also increased substantially. We therefore compare estimates of climate sensitivity from the CMIP6 model HadGEM3‐GC3.1‐LL, with 85 levels, and a version with 242 levels. This is far higher resolution than in any previously published simulations with a mainstream GCM, though still less than many scales important for clouds. The climate sensitivity and feedbacks, including the cloud feedback, change little. This suggests that vertical resolution did not drive recent increases in GCM climate sensitivity, though this result should be further tested in other GCMs and over a broader range of resolutions. Plain Language Summary: "Climate sensitivity" is how much climate is warmed, in the global and time average, by more CO2 in the air, largely from burning fossil fuels. However, it remains very uncertain. General Circulation Models (GCMs) are our most detailed and physically based models of climate, widely used in many sorts of study of possible climate change. "All models are wrong, but some are useful"—their complexity can make it hard to understand and so assess their behavior. They also have to approximate many physical processes on smaller scales—particularly cloud. More recent GCMs tend to have higher climate sensitivity, many higher than was ever thought likely. Cloud processes seem largely responsible. A possible cause is increased vertical resolution (calculations being done on more levels through the atmosphere) in the newer GCMs. There is a correlation between increased vertical resolution and increased climate sensitivity, but this could be by chance. We have therefore compared one new‐generation GCM with its standard vertical resolution and with 3 times higher (considerably higher than any previously published mainstream GCM work). The climate sensitivity barely changes, and the processes involved, including cloud processes, are clearly working almost identically. But other GCMs might be quite different. Key Points: Many Coupled Model Intercomparison Project 6 (CMIP6) General Circulation Models (GCMs) have climate sensitivities larger than the Intergovernmental Panel on Climate Change uncertainty range or other assessments, largely thanks to cloud feedbacksThis increase in sensitivity is correlated with an increase in vertical resolution—particularly important for cloud processesHowever, tripling the vertical resolution in one CMIP6 GCM, we found that climate sensitivity and feedbacks barely change [ABSTRACT FROM AUTHOR]

Given uncertainty in the processes involved in polar amplification, elucidating the role of poleward heat and moisture transport is crucial. The Polar Amplification Model Intercomparison Project (PAMIP) permits robust separation of the effects of sea ice loss from sea surface warming under climate change. We utilize a moist isentropic circulation framework that accounts for moisture transport, condensation, and eddy transport, in order to analyze the circulation connecting the mid‐latitudes and the Arctic. In PAMIP's atmospheric general circulation model experiments, prescribed sea ice loss reduces poleward heat transport (PHT) by warming the returning moist isentropic circulation at high latitudes, while prescribed warming of the ocean surface increases PHT by strengthening the moist isentropic circulation. Inter‐model spread of PHT into the Arctic reflects the tug‐of‐war between sea‐ice and surface‐warming effects. Plain Language Summary: A major conundrum in current climate science is to understand what Arctic changes imply for the climate and environment in mid‐latitude regions. The Polar Amplification Model Intercomparison Project (PAMIP) designed a set of climate model experiments to specifically answer this question in a carefully designed, idealized framework. PAMIP's approach is to separate historic and projected climate change into parts associated with Arctic sea ice loss and ocean surface warming and investigate how these two contributions can influence the atmosphere. To isolate these effects, only models can be used, because, in reality, sea ice loss and ocean surface warming are strongly linked together. This letter focuses on what the PAMIP experiments imply for the transport and redistribution of heat and moisture in the atmosphere. In PAMIP, we learn that Arctic sea ice loss causes the atmosphere to reduce the transport of dry and cold air away from the Arctic while ocean warming causes more transport of moist warm air toward the Arctic. These two effects are in a tug‐of‐war, suggesting that climate change can cause a mix of competing impacts on the global energy transport from the tropics to the Arctic. Key Points: Sea ice loss exports static energy from the Arctic by warming the equatorward branch of the global mass circulationSea surface warming imports static energy into the Arctic by increasing the mid‐latitude mass transport, overwhelming the sea ice effectThere is more spread amongst models for the effect of sea‐surface warming than for sea ice loss [ABSTRACT FROM AUTHOR]

Representing the West African Monsoon is a major challenge in climate modeling because of complex mechanisms of interaction across scales. We study the monsoon precipitation response to a 4K increase in sea surface temperatures using an idealized meridional‐vertical (2D) model, which allows separating regional‐ and larger‐scale influences. The 2D simulations reproduce key features of Atmospheric Model Intercomparison Project simulations and indicate that, in response to sea surface temperature increase, large‐scale changes induce a decrease of Sahel precipitation, while regional‐scale mechanisms generate a southward shift of the rainband. Large‐scale changes moisten and warm the free troposphere, while regional circulation changes increase low‐level moisture. Precipitation in the control simulations accounts for much of the variance in precipitation change for both Atmospheric Model Intercomparison Project and 2D simulations. This response is strongly connected to the large‐scale drivers of temperature and moisture, with an additional spread associated with the formulation of the convective parameterization. Plain Language Summary: The Sahel region of West Africa receives most of its annual rainfall during the monsoon season. Projections of monsoon precipitation are important to the region's growing population, yet climate models disagree on the magnitude and direction of precipitation change in future climates. This study uses a simplified model of the West African monsoon, which we show behaves similarly to complex global climate simulations. Our model allows us to disentangle monsoon changes due to directly changing sea surface temperatures from changes due to the larger‐scale response to increased temperatures. We find that both play important roles in precipitation change, the direct forcing shifts the precipitation, while the large‐scale forcing produces drying. Processes that occur on small spatial scales play an important role in producing precipitation. We investigate how a few of these are represented in our model and discover that the change in precipitation is mostly dependent on how the large‐scale forcing produces precipitation in the current climate, with a spread that is connected to how these physical processes are represented. Our study shows the usefulness of a simplified monsoon model and increases understanding of how increasing sea surface temperatures alter the West African monsoon. Key Points: A 2D monsoon model produces similar changes to general circulation models in monsoon features when sea surface temperatures are increasedSouthward shift of the rainband is connected to regional forcing, while Sahel precipitation decrease is mainly driven by larger‐scale changePrecipitation change is strongly linked to large‐scale drivers of temperature and moisture with additional spread from parameterizations [ABSTRACT FROM AUTHOR]

Many general circulation models fail to capture the observed frequency of atmospheric blocking events in the Northern Hemisphere; however, few studies have examined models in the Southern Hemisphere and those studies that have, have often been based on only a few models. To provide a comprehensive view of how the current generation of coupled general circulation models performs in the Southern Hemisphere and how blocking frequency changes under enhanced greenhouse gas forcing, we examine the output of 23 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We find that models have differing biases during winter, when blocking occurrence is highest, though models underestimate blocking frequency south of Australia during summer. We show that models generally have a reduction in blocking frequency with future anthropogenic forcing, particularly in the Australia‐New Zealand sector with the number of winter blocked days reduced by about one third by the end of the 21st century. Plain Language Summary: Atmospheric blocking is a process in which an atmospheric wave breaks, diverting the jet stream and any incoming storms to the north or south. A blocking event is characterized by high pressure on the poleward side and low pressure on the equatorward side and persists for upward of 4 days. A key feature of blocks is their persistence, which can lead to events, which have significant impacts on society including heat waves in summer and periods of extreme cold in winter. Climate models often do not simulate enough blocking events, though most research on this problem has focused on the Northern Hemisphere and less is known about the Southern Hemisphere. We survey 23 climate models and show that during winter some models simulate too many blocking events, while others simulate too few, whereas during summer, almost all models simulate too few events to the south of Australia. We also show that with higher concentrations of greenhouse gases we expect there to be less blocking, particularly to the south of Australia and over New Zealand during winter. Key Points: We provide a comprehensive survey of Southern Hemisphere blocking using a large number of coupled modelsFuture projections generally suggest an overall decrease in Southern Hemisphere blocking frequencyThere is strong model agreement on blocking frequency decreases to the south of Australia during austral winter [ABSTRACT FROM AUTHOR]

The Superparameterized Community Atmosphere Model (SPCAM) is used to identify the dynamical and organizational properties of tropical extreme rainfall events on two scales. We compare the mesoscales resolved by General Circulation Models (GCMs) and the convective scales resolved by Cloud-Resolving Models (CRMs) to reassess and extend on previous results from GCMs and CRMs in radiative-convective equilibrium. We first show that the improved representation of subgridscale dynamics in SPCAM allows for a close agreement with the 7%/K Clausius-Clapeyron rate of increase in mesoscale extremes rainfall rates. Three contributions to changes in extremes are quantified and appear consistent in sign and relative magnitude with previous results. On mesoscales, the thermodynamic contribution (5.8%/K) and the contribution from mass flux increases (2%/K) enhance precipitation rates, while the upward displacement of the mass flux profile (-1.1%/K) offsets this increase. Convective-scale extremes behave similarly except that changes in mass flux are negligible due to a balance between greater numbers of strong updrafts and downdrafts and lesser numbers of weak updrafts. Extremes defined on these two scales behave as two independent sets of rainfall events, with different dynamics, geometries, and responses to climate change. In particular, dynamic changes in mesoscale extremes appear primarily sensitive to changes in the large-scale mass flux, while the intensity of convective-scale extremes is not. In particular, the increases in mesoscale mass flux directly contribute to the intensification of mesoscale extreme rain, but do not seem to affect the increase in convective-scale rainfall intensities. These results motivate the need for better understanding the role of the large-scale forcing on the formation and intensification of heavy convective rainfall. [ABSTRACT FROM AUTHOR]