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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]

Among several devastating natural hazards, flooding is a common and serious threat to society causing huge loss of lives, properties, and infrastructure throughout the world. The intensity and frequency of this extreme weather event are expected to increase due to significant changes in the present‐day climate and land use and land cover (LULC) pattern. India has a very systematic and organized structural program and policies but lacks proper implementations, and adverse effect of climate change and the extreme event goes on in society. This paper is an analysis of floods in India and hazards due to climate change and LULC change patterns. Three models, namely "Eco‐biogeography‐based optimization (EBO), Random forest (RF), and Support vector machine (SVM)" were used to obtain the final output to prepare a "Flood susceptibility map". The result was validated through the "Receiver operating characteristics (ROC)" with "Area under curve (AUC)" values. The future rainfall scenario has been estimated by considering the "General circulation models" through different "shared socioeconomic pathways (SSPs)". The values of AUC are 0.915 (EBO), 0.887 (RF), and 0.869 (SVM), respectively. After consideration of different SSPs, the result shows that there is an increasing tendency of flood hazards in the projected period. Among all the employed modelling approaches, the EBO model has notable potential in delineating the possible flood‐prone regions for effective flood planning and management. Decision‐makers can benefit from country‐specific information and regional planner to implement sustainable and long‐term measures to overcome this type of hazardous situation. [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]

An analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Plain Language Summary: China is a vulnerable country to climate change due to its dense population, unbalanced social and economic development, shortage of water resources, and fragile ecosystems. How future precipitation will change over the region is of great concern for the general public and decision makers. This paper presents a first analysis of precipitation simulations from a set of five RCM (RegCM4) 21st century climate change projections, driven by coarse resolution general circulation models (GCMs) over China. We find that the spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by a precipitation underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in north China by both the GCMs and RegCM4 in all seasons except summer, when, weak correlations of the projected change patterns are found between the GCMs and nested RegCM4, indicating the greater role of the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Key Points: The spatial patterns of bias show common features between the GCMs and RegCM4RegCM4 provides greater spatial detail of present day precipitation simulation compared to the GCMs and finer structures of future changesThe change patterns across the RegCM4 projections show a high correlation, but not always between each pair of driving GCM and RegCM4 [ABSTRACT FROM AUTHOR]

El Niño Southern Oscillation (ENSO) events yield precipitation deficits and ensuing droughts, often damaging regional forests, in many parts of the world. The relative roles of ENSO, other natural climate changes, and anthropogenic factors on the forest clearing of Easter Island over the last millennium are still debated. Here, we analyze Easter Island precipitation changes using in situ, satellite‐derived and reanalysis products spanning the last 4–7 decades, and 46 monthly 156‐year‐long (1850–2014) simulations derived from 25 CMIP5 and 21 CMIP6 (Coupled Model Intercomparison Project phases 5 and 6) General Circulation Models. Our analysis shows that La Niña events, the cold phases of ENSO, cause precipitation deficits of −0.2 to −0.3 standard deviation (relative to long‐term mean) in all analyzed data types. ENSO‐like events are further examined over the last millennium (850–1981). A new multiproxy reconstruction of the NINO3.4 index based on proxy records from the Past Global Changes 2k database and Random Forest method is produced. Our reconstruction reveals unusual high recurrences of La Niña‐like situations during the fifteenth to seventeenth centuries, which likely induced significant precipitation deficits on the island. These situations are compared to published vegetation reconstructions based on pollen analyses derived from sedimentary cores collected in three island sites. We conclude the environmental consequences of cumulative precipitation deficits over long‐lasting La Niña‐like situations reconstructed here over the fifteenth to seventeenth centuries were likely favoring drought and forest flammability. La Niña events should be better accounted for among the causes of forest clearing on Easter Island. Plain Language Summary: Easter Island is a small remote island located in the south‐eastern Pacific Ocean. It is home of several scientific enigmas: the origin of early settlements, the construction of the giant moai statues, and causes of the forest clearing. Was the forest clearing abrupt, gradual, homogenous in time and space, human‐induced (in line with the ecocide hypothesis), or related to natural climate variability? The question we address in this paper is related to the natural climate variability hypothesis, focusing on the El Niño Southern Oscillation (ENSO) phenomenon. We analyze ENSO effects on Easter Island precipitation from three instrumental (1950–2021) and 42‐simulation data sets (1850–2014). Our analysis shows the cold phases of ENSO, also known as La Niña, cause significant precipitation deficits over the island in all analyzed data types. Then, we provide a new up‐to‐date reconstruction of ENSO over 850–1981. We found a large number of La Niña‐like situations during the fifteenth‐seventeenth centuries. These situations are compared to vegetation reconstructions derived from sedimentary cores collected in three island sites. We conclude that the environmental consequences of cumulative precipitation deficits during long‐lasting La Niña‐like situations could be an additional cause of the forest clearing of Easter Island. Key Points: Analysis of independent data sets shows precipitation deficits over Eastern Island during La Niña events over the period 1850–2021A new reconstruction of El Niño Southern Oscillation episodes reveals a large number of La Niña‐like situations during the fifteenth to seventeenth centuriesHigh repetitions of La Niña events, favoring drought, likely played a role in the forest clearing of the island, on top of other stressors [ABSTRACT FROM AUTHOR]

Abstract: The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Among other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific descriptions of these components are in companion papers) and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present‐day forcing. The suitability of the configuration for predictability on shorter time scales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2‐AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however, there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2‐AO, many aspects of the present‐day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extratropical variability and top‐of‐atmosphere and surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking. [ABSTRACT FROM AUTHOR]

This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO2 is slightly higher than previously at 2.7–3.1°C (depending on version) and is a result of lower CO2 radiative forcing and stronger positive feedbacks. Plain Language Summary: This paper describes the latest iteration of the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) climate model, which will be used for understanding historical climate change and to make projections for the future. We compare the model output to a wide range of observations over the recent era (1979–2014) and show that there has been a significant increase in how well the model performs compared to the previous version from 2014, particularly in the Southern Ocean, though some persistent biases remain. The model has a temperature response to the increase of carbon dioxide that is slightly higher than previous versions but is well within the range expected from observational and past climate constraints. Key Points: GISS‐E2.1 is an updated climate model version for use within the CMIP6 projectAtmospheric composition is calculated consistently in all model versionsResults demonstrate a significant improvement in skill in a climate model without changes to atmospheric resolution [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) observations in the North Atlantic since 1870 reveal a region of enhanced warming off the northeastern coast of North America, and a region of cooling to the south of Greenland. It has been hypothesized that these adjacent SST trends are a result of long‐term changes in the buoyancy‐driven ocean circulation—a slowdown of the Atlantic Meridional Overturning Circulation. The impacts of these historical SST trends on the atmosphere are estimated using idealized atmospheric general circulation model experiments in which the global atmosphere is exposed to modern climatological forcing minus the aforementioned regional SST trends. The local response includes a negative North Atlantic Oscillation tendency and southward shift of the wind forcing for the subtropical gyre. Due to planetary wave propagation, the regional SST trends also induce a northward shift of the intertropical convergence zone over the Indian Ocean. Implications for climate feedbacks and projections are discussed. Plain Language Summary: The ocean surface is warming over most of the planet, with a couple of notable exceptions. One is over the North Atlantic Ocean, just south of Greenland, where the temperature of the ocean surface has actually been cooling by about 1°C since 1870. Nearby, extending off the northeastern coastline of North America is a region where the ocean surface has been warming much faster than the global ocean on average—by about 1.5°C since 1870. This side‐by‐side pair of accelerated warming and cooling trends is likely due to a slowdown of the ocean's overturning circulation, which carries large amounts of heat energy from the tropics toward the poles. This paper reveals the effects of those unusual ocean temperature trends in the North Atlantic on the atmospheric circulation using a computer model of the global atmosphere. The model simulations indicate that these ocean temperature trends are causing shifts in the jet stream, changing the way the winds propel the upper ocean currents, and even have impacts quite far away by moving the tropical rain belt northward in the Indian Ocean. Key Points: The impact of historical sea surface temperature trends in the North Atlantic since 1870 is simulated with a global atmospheric modelAdjacent warming and cooling trends in the North Atlantic Ocean induce a negative NAO‐like response and southward‐shifted gyre forcingPropagation of planetary waves away from the North Atlantic induces a northward‐shifted intertropical convergence zone in the Indian Ocean [ABSTRACT FROM AUTHOR]

Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. Published studies show that climate change is projected to have a substantial negative influence on the current suitable growing areas for indigenous Arabica in Ethiopia and South Sudan. Here we use all available future projections for the species based on multiple general circulation models (GCMs), emission scenarios, and migration scenarios, to predict changes in Extent of Occurrence (EOO), Area of Occupancy (AOO), and population numbers for wild Arabica coffee. Under climate change our results show that population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. EOO and AOO are projected to decline by around 30% in many cases. Furthermore, present‐day models compared to the near future (2038), show a reduction for EOO of over 40% (with a few cases over 50%), although EOO should be treated with caution due to its sensitivity to outlying occurrences. When applying these metrics to extinction risk, we show that the determination of generation length is critical. When applying the International Union for Conservation of Nature's Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria. Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. In this paper we show that under climate change alone, population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. When applying the International Union for Conservation of Nature's Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria. [ABSTRACT FROM AUTHOR]

Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. The uncertainty in maize‐yield simulations might arise mostly from global climate models (GCMs), followed by crop models and representative concentration pathways (RCPs), the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. [ABSTRACT FROM AUTHOR]

While it is generally recognized that the projected changes in the global climate will have serious negative consequences for the Caribbean as a whole, it is becoming more and more evident that the impacts of climate change will not be uniformly felt across the region. The primary aim of this paper is to provide a review of the Caribbean climate change literature. The paper begins with a brief discussion on the shifting nature of Caribbean vulnerability within the context of the region's longstanding and continued exposure to forces of global economic and environmental change. Particular attention is paid to both the practical and discursive dimensions of vulnerability, and the specific ways the term has been understood and framed in relation to other crosscutting themes such as adaptation and resilience in the broader academic literature. In addition to providing an overview of the regional climate change science literature, the paper offers a critical review of the existing climate change impacts literature for the Caribbean. By drawing on, and taking stock of this growing body of impact studies, the paper seeks to shed light on the differential and multi-scalar drivers of social and economic vulnerabilities to climate variability and change in the Caribbean as well as highlight some of the existing knowledge gaps that need to be addressed. The paper ends by providing some brief ref lections on the emerging themes arising from the discussion and highlights a few key areas for future academic research for the Caribbean in light of existing knowledge gaps observed in the regional climate change impact literature. [ABSTRACT FROM AUTHOR]

Reconstructions of temperature and hydrology from lake sedimentary archives have made fundamental contributions to our understanding of past, present, and future climate and help evaluate general circulation models (GCMs). However, because paleoclimate observations are an indirect (proxy) constraint on climatic variables, confounding effects of proxy processes complicate interpretations of these archives. To circumvent these uncertainties inherent to paleoclimate data‐model comparison, proxy system models (PSMs) provide transfer functions between climate variables and the proxy. We here present a new PSM for lacustrine sedimentary archives. The model simulates lake energy and water balance, sensors including leaf wax δD and carbonate δ18O, bioturbation, and compaction of sediment to lend insight toward how these processes affect and potentially obfuscate the original climate signal. The final product integrates existing and new models to yield a comprehensive, modular, adaptable, and publicly available PSM for lake systems. Highlighting applications of the PSM, we forward model lake variables with GCM simulations of the last glacial maximum and the modern. The simulations are evaluated with a focus on sensitivity of lake surface temperature and mixing to climate forcing, using Lakes Tanganyika and Malawi as case studies. The PSM highlights the importance of mixing on interpretations of air temperature reconstructions from lake archives and demonstrates how changes in mixing depth alone may induce nonstationarity between in situ lake and air temperatures. By placing GCM output in the same reference frame as lake paleoclimate archives, we aim to improve interpretations of past changes in terrestrial temperatures and water cycling. Plain Language Summary: Paleoclimate data from lakes provide some of the richest records of past changes in temperature and precipitation on Earth. Indeed, the wealth of data from and global coverage of large lake systems makes these records a particularly apt target for testing the performance of global climate models. However, comparing models to lake archives is nontrivial: the two data types are starkly different, and a model is required to "translate" between them. This paper builds a framework for modeling lakes that places climate model and paleoclimate proxy measurements in the same units by accounting for all the ways in which the climate signal of interest (e.g., temperature) is modified by the lake (e.g., the heat budget of the lake or sedimentation processes). By making more direct comparisons between data and models, we hope to build connections between researchers working with climate models and researchers who produce lake records of past climate. In general, our lake model helps the climate science community interpret the drivers of past climate changes from lakes. These records from the past give us context for how the climate system may respond to anthropogenic greenhouse gas forcing in the future. Key Points: We present a publicly available forward model for lake paleoclimate archives, expanding open‐source tools for PRoxY System Modeling (PRYSM)The model simulates lake energy and water balance, sensors including TEX86, leaf wax δD, and carbonate δ18O, bioturbation, and compactionModeling the full lake system demonstrates importance of mixing, nonstationarity, and seasonality in lake paleoclimate archives [ABSTRACT FROM AUTHOR]

ABSTRACT The biases in the Global Circulation Models ( GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree ( RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970-1999. [ABSTRACT FROM AUTHOR]

Aim: The rapidly changing Arctic is ideal for investigating uncertainties in climate projections. Despite the challenges of collecting data in this region, an unprecedented large‐scale survey of shorebirds has been conducted over the last 30 years. Our study aimed to (1) develop probabilistic estimates for the change in suitable habitat for 10 Arctic shorebird species in Canada by 2075 and (2) assess the contribution of modelling decisions to the uncertainty in these estimates. Location: Arctic Canada. Methods: To evaluate uncertainty, we considered six classes of modelling decisions, yielding 216 unique projections for each species. We tested three decisions that are less commonly explored − the pool of candidate variables, a method for selecting variables, and the maximum distance of tree line dispersal, as well as the modelling algorithm, carbon emissions scenario, and global circulation model. We used a bootstrapping approach, creating a probability distribution for the proportional change in suitable habitat for each species. Results: Our findings indicated a substantial risk for 8/10 species to lose over half of their suitable breeding habitat, but this projection is much less certain than has been described previously. While much uncertainty is unexplained, we were surprised that the largest source of uncertainty among our modelling decisions was from our choice of methods for variable selection, that the other modelling decisions were relatively small sources of uncertainty, overshadowing other modelling decisions. Main Conclusions: While most scenarios predict a northward shift and significant habitat loss for Arctic‐breeding shorebirds, the Arctic Archipelago of Canada will remain an important refuge because in many other Arctic regions, there is no land farther north for these species to shift into. A comprehensive understanding of uncertainty is important for deciding if future projections can or should be used when planning climate‐resilient protected area networks. [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]

This study estimated global climate change signals at different latitudes for four main Shared Socioeconomic Pathways (SSPs). Five evaluation metrics were integrated using the Technique for Order of Preference by Similarity to Ideal Solution to quantify the historical reproducibility of 25 CMIP6 General Circulation Models (GCMs) with Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature as the reference. The most suitable GCMs for simulating climate over different latitudes, selected based on evaluation metrics, were used to prepare a multimodel ensemble and project the future annual and seasonal precipitation and temperature in the near (2031–2065) and far future (2066–2100). The results showed that GCMs estimated the historical mean temperature efficiently but underestimated the monthly precipitation compared to the reference data. The changes in precipitation and temperature at mid‐latitudes (N45.5°–60°) showed the highest variability for all scenarios. The maximum increases in both climate variables for SSP5‐8.5 were 80.5% and 4.8% at N45.5°–60°, respectively. In contrast, the temperature and precipitation at S30.5°–45° revealed a decreasing pattern. Mid‐latitude winter (S30.5°–45°) would be drier in the future than in the base period (1980–2014). This study showed that precipitation variability and the mean temperature in the northern hemisphere would be larger for SSPs with higher radiative forcing. Therefore, the results of this study help improve knowledge of global future climate change by latitudes. Plain Language Summary: Climate change is already contributing to various unpredictable phenomena in many fields. A well‐known organization that periodically evaluates the impacts of climate change and actionable response strategies, the IPCC assessment report states that climate change is already directly impacting ecosystems, water cycles, and human activities. Therefore, sufficient exploration of the future climate change is vital for systematically developing a plan for climate change mitigation and adaptation, and the Shared Socioeconomic Pathway scenario contains various factors such as social, economic, and physics, making it reasonable for projecting the future climate. This study evaluated the historical monthly temperature and precipitation reproducibility of CMIP6 General Circulation Model (GCM) using various metrics. Based on this, multi‐model ensemble was built using Technique for Order of Preference by Similarity to Ideal Solution, a multi‐criteria decision‐making technique, for a reasonable future climate assessment. The results of this study showed that the monthly precipitation of CMIP6 GCM over the historical period is overestimated than the reference data, but the monthly temperature performance is stable. For projected future climate, high latitudes in the northern hemisphere are most vulnerable to changes in temperature and precipitation, and the southern hemisphere captured robust dryness for the future. Key Points: General Circulation Models' performances are different by each latitude and their simulations were overestimated for rainfall and well‐estimated for temperatureThe region in N75‐N90 would be most vulnerable to climate change in the future, and the area in S30‐S60 would be drier in the futureVariability of the northern hemisphere would increase more for high emission scenarios but seasonal trends are more chaotic than in the past [ABSTRACT FROM AUTHOR]

Aim: The dynamic nature of climate change diminishes the effectiveness of static approaches to nature conservation. Areas that were once suitable for species may no longer be suitable, and areas that are suitable now, may be unsuitable in the future. Despite increasing global awareness of the threats posed by climate change, it remains poorly accounted for in conservation programmes, such as translocation. In this study, we project changes in climate suitability for populations of ectothermic species that have been successfully established through translocation efforts. Location: Biogeographical realms: Australasia, Holarctic, Palearctic and Nearctic. Methods: We use species distribution models (SDMs) to project changes in macroclimatic suitability across 65 translocation recipient sites involving 38 ectothermic species. We consider optimistic (SSP126) and pessimistic (SSP370) scenarios of climate change for five general circulation models spanning three time horizons from 2021–2040 up to 2061–2080. Results: Our models predict that at least 74% of recipient sites are projected to decline in climate suitability, regardless of the SSP scenario or time horizon. While recipient site suitability, scaled from 0 to 1 (low–high), was typically very high (>0.75, 39% of sites) under baseline climate conditions (1960–2010), models project a marked shift towards low suitability climates (<0.25, 40% of sites) by the middle of the century (2041–2060) onwards under the more pessimistic scenario. Relative to species' ranges, recipient sites located closer to the equator are projected to experience the most significant declines in suitability. Main Conclusions: Our results call for greater consideration of spatiotemporal factors during the recipient site selection process, so that translocated populations are more strategically placed for long‐term persistence under climate change. [ABSTRACT FROM AUTHOR]

Climate change is altering the interactions among plants and soil organisms in ways that will alter the structure and function of ecosystems. We reviewed the literature and developed a map of studies focused on how the three most common types of mycorrhizal fungi (arbuscular mycorrhizal [AM], ectomycorrhizal [EcM], and ericoid mycorrhizal [ErM] fungi) respond to elevated atmospheric carbon dioxide concentrations (eCO2), climatic warming, and changes in the distribution of precipitation. Broadly, we ask how do mycorrhizal fungi respond to climate change, how do these responses vary by fungal type, and how do mycorrhizal traits influence plant adaptation, movement, or extinction in response to climatic change? First, we found that 92% of studies were conducted in the northern hemisphere, and plant host, ecosystem type and study location were only correlated with each other in the northern hemisphere because studies across all mycorrhizal fungal types were only common in the northern hemisphere. Second, we show that temperature and rainfall variability had more variable effects than eCO2 on mycorrhizal fungal structures, but these effects were context dependent. Third, while mycorrhizal fungal types vary in their responses to climate change, it appears that warming leads to more variable responses in ectomycorrhizal than in arbuscular mycorrhizal fungi. Finally, we discuss common traits of mycorrhizal fungi that could aid in fungal and plant adaption to climate change. We posit that mycorrhizal fungi can buffer plant hosts against extinction risk, they can facilitate or retard the dispersal success of plants moving away from poor environments, and, by buffering host plants, they can enable host plant adaptation to new climates. All of these influences are, however, context dependent a finding that reflects the complex traits of mycorrhizal fungi as a group, the diversity of plant species they associate with and the variation in ecosystems in which they reside. Overall, while we point out many gaps in our understanding of the influence of climate changes on mycorrhizal fungi, we also highlight the large number of opportunities for researching plant and mycorrhizal fungal responses to and mitigation of climate changes. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional‐to‐local scales from large‐scale atmospheric fields following the perfect‐prognosis approach. Given their complexity, it is crucial to properly evaluate these methods, especially when applied to changing climatic conditions where the ability to extrapolate/generalize is key. In this work, we intercompare several DL models extracted from the literature for the same challenging use‐case (downscaling temperature in the CORDEX North America domain) and expand standard evaluation methods building on eXplainable Artificial Intelligence (XAI) techniques. Specifically, we introduce two novel XAI‐based diagnostics—Aggregated Saliency Map and Saliency Dispersion Maps—and show how they can be used to unravel the internal behavior of these models, aiding in their design and evaluation. This work advocates for the introduction of XAI techniques into deep downscaling evaluation frameworks, especially when working with large regions and/or under climate change conditions. Plain Language Summary: Due to limitations in the computational resources available, General Circulation Models (GCMs) are often used to simulate the climate system over coarse resolution grids. This hampers the applicability of GCM products in the regional‐to‐local scale, highly demanded by different socio‐economic sectors. Statistical downscaling aims to solve this problem by generating high‐resolution climate fields. Recently, machine learning techniques—particularly deep learning (DL) models—have shown promising results in this task. These models are first trained in a historical period through observational data sets, and then applied to the GCM outputs of plausible future scenarios, thus generating high‐resolution climate change products. To assess the performance of these methods, a number of evaluation metrics have been proposed considering both the skill to reproduce present climate conditions and the ability to generalize changing conditions. Here, we illustrate the possibilities of eXplainable Artificial Intelligence (XAI) techniques to expand the evaluation framework for deep downscaling methods, introducing new XAI‐derived diagnostics to unravel their internal behavior. The results show the usefulness of incorporating XAI techniques into statistical downscaling evaluation frameworks, especially when working with large regions and/or under climate change conditions. Key Points: Explainable artificial intelligence (XAI) facilitates the evaluation of deep downscaling models by unraveling their internal behaviorXAI techniques can detect structural problems not revealed by standard evaluation, which may be relevant for understanding model differencesXAI techniques can assess the relevance and locality of the predictors of deep learning models, helping to assess their physical consistency [ABSTRACT FROM AUTHOR]

This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoir command area. A statistical downscaling model and an evapotranspiration model are used with a general circulation model (GCM) output to predict the anticipated change in the monthly irrigation water requirement of a crop. Specifically, we quantify the likely changes in irrigation water demands at a location in the command area, as a response to the projected changes in precipitation and evapotranspiration at that location. Statistical downscaling with a canonical correlation analysis is carried out to develop the future scenarios of meteorological variables (rainfall, relative humidity (RH), wind speed ( U2), radiation, maximum (Tmax) and minimum (Tmin) temperatures) starting with simulations provided by a GCM for a specified emission scenario. The medium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario, to assess the likely changes in irrigation demands for paddy, sugarcane, permanent garden and semidry crops over the command area of Bhadra reservoir, India. Results from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area. RH, Tmax and Tmin are also projected to increase with small changes in U2. Consequently, the reference evapotranspiration, modeled by the Penman-Monteith equation, is predicted to increase. The irrigation requirements are assessed on monthly scale at nine selected locations encompassing the Bhadra reservoir command area. The irrigation requirements are projected to increase, in most cases, suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due to projected increase/change in other meteorological variables (viz., Tmax and Tmin, solar radiation, RH and U2). The irrigation demand assessment study carried out at a river basin will be useful for future irrigation management systems. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Aim This paper aims to project areas of olive cultivation into future scenarios. Accordingly, we first asked the question whether global circulation models ( GCMs) are able to reproduce past climatic conditions and we used historical ranges of olive cultivation as a palaeoclimate proxy. Location The Mediterranean basin. Methods We used an ecological model, calibrated and validated for modern times, to test the reliability of a general circulation model ( NCAR-CSM GCM) in reproducing past ranges of olive tree cultivation inferred from the literature, archaeo-botanical investigations and fossil pollen analyses. Results The re-constructions of olive growing areas, obtained for the Medieval Climate Anomaly (MCA, 1200-1300 AD) and the Little Ice Age (LIA, 1600-1700 AD) by coupling the outputs of NCAR-CSM to the ecological model, were in agreement to those observed. Simulations of olive growing areas for future time-windows showed that a northwards expansion of the species is expected to occur by 2100. Main conclusions These results demonstrate that the NCAR-CSM can provide an accurate reconstruction of past climate with results sensitive to climate forcing factors and thus, it is more likely to give reliable projections for the future. Additionally, the warming and drying conditions expected in the coming decades may determine changes across the Mediterranean basin that is unprecedented. [ABSTRACT FROM AUTHOR]

One way in which global climate model (GCM) output can be utilized to infer local impacts is through the use of synoptic climatology: creating a set of atmospheric patterns that capture the variability in the climate system, and then analyzing trends and variability in the frequency of these patterns moving into the future. In this paper, we demonstrate a new synoptic climatological technique for classifying atmospheric patterns that can be used in conjunction with GCM output data (in this case, the Community Climate System Model 3). We apply this method to 850-hPa temperature patterns over the contiguous United States to derive daily categorizations. A total of 15 clusters are created from the data set; once the mean GCM bias is removed, historical cluster frequencies in the GCM data set are not statistically different from those of the reanalysis data set. In the future, significant changes in frequency are observed across most of the transition season clusters, as they broaden in seasonality at the expense of winter clusters, some of which nearly entirely disappear. Changes are greater moving further into the future, and greater for the more carbon-intensive special report on emissions scenarios (SRES) scenarios (A1FI, A2) than the less-intensive scenario tested (B1). Diagnostics test how well the mean patterns of the reanalysis data set, GCM historical data set and the future GCM data sets resemble each other. For some clusters, mean bias between the historical and future data sets grows substantially by the end of the 21st century under the more carbon-intensive scenarios. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper investigates the impact of climate change on drought by addressing two questions: (1) How reliable is the assessment of climate change impact on drought based on state-of-the-art climate change projections and downscaling techniques? and (2) Will the impact be at the same level from meteorological, agricultural, and hydrologic perspectives? Regional climate change projections based on dynamical downscaling through regional climate models (RCMs) are used to assess drought frequency, intensity, and duration, and the impact propagation from meteorological to agricultural to hydrological systems. The impact on a meteorological drought index (standardized precipitation index, SPI) is first assessed on the basis of daily climate inputs from RCMs driven by three general circulation models (GCMs). Two periods and two emission scenarios, i.e., 1991-2000 and 2091-2100 under B1 and A1Fi for Parallel Climate Model (PCM), 1990-1999 and 2090-2099 under A1B and A1Fi for Community Climate System Model, version 3.0 (CCSM3), 1980-1989 and 2090-2099 under B2 and A2 for Hadley Centre CGCM (HadCM3), are undertaken and dynamically downscaled through the RCMs. The climate projections are fed to a calibrated hydro-agronomic model at the watershed scale in Central Illinois, and agricultural drought indexed by the standardized soil water index (SSWI) and hydrological drought by the standardized runoff index (SRI) and crop yield impacts are assessed. SSWI, in particular with extreme droughts, is more sensitive to climate change than either SPI or SRI. The climate change impact on drought in terms of intensity, frequency, and duration grows from meteorological to agricultural to hydrological drought, especially for CCSM3-RCM. Significant changes of SSWI and SRI are found because of the temperature increase and precipitation decrease during the crop season, as well as the nonlinear hydrological response to precipitation and temperature change. [ABSTRACT FROM AUTHOR]

The wetland ecosystem is particularly vulnerable to hydrological and climate changes. The Great Xing'an Mountain is such a region in China that has a large area of wetlands with rare human disturbance. The predictions of the global circulation model CGCM3 (the third-generation coupled global climate model from the Canadian Centre for Climate Modeling and Analysis) indicated that the temperature in The Great Xing'an Mountain will rise by 2-4°C over the next 100 years. This paper predicts the potential distributions of wetlands in this area under the current and warming climate conditions. This predication was performed by the Random Forests model, with 18 environmental variables, which will reflect the climate and topography conditions. The model has been proven to have a great prediction ability. The wetland distributions are primarily topography-driven in the Great Xing'an Mountains. Mean annual temperature, warmness index, and potential evapotranspiration ratio are the most important climatic factors in wetland distributions. The model predictions for three future climate scenarios show that the wetland area tends to decrease, and higher emission will also cause more drastic shrinkage of wetland distributions. About 30% of the wetland area will disappear by 2050. The area will decrease 62.47, 76.90, and 85.83%, respectively, under CGCM3-B1, CGCM3-A1B, and CGCM3-A2 by 2100. As for spatial allocation, wetlands may begin to disappear from the sides to the center and south to north under a warming climate. Under CGCM3-B1, the loss of wetlands may mainly occur in the south hills with flatter terrain, and some may occur in the north hills and intermontane plains. Under CGCM3-A1B, severe vanish of wetlands is predicted. Under CGCM3-A2, only a small area of wetlands may remain in the north of the high mountains. [ABSTRACT FROM AUTHOR]

Empirical scaling approaches for constructing rainfall scenarios from general circulation model (GCM) simulations are commonly used in water resources climate change impact assessments. However, these approaches have a number of limitations, not the least of which is that they cannot account for changes in variability or persistence at annual and longer time scales. Bias correction of GCM rainfall projections offers an attractive alternative to scaling methods as it has similar advantages to scaling in that it is computationally simple, can consider multiple GCM outputs, and can be easily applied to different regions or climatic regimes. In addition, it also allows for interannual variability to evolve according to the GCM simulations, which provides additional scenarios for risk assessments. This paper compares two scaling and four bias correction approaches for estimating changes in future rainfall over Australia and for a case study for water supply from the Warragamba catchment, located near Sydney, Australia. A validation of the various rainfall estimation procedures is conducted on the basis of the latter half of the observational rainfall record. It was found that the method leading to the lowest prediction errors varies depending on the rainfall statistic of interest. The flexibility of bias correction approaches in matching rainfall parameters at different frequencies is demonstrated. The results also indicate that for Australia, the scaling approaches lead to smaller estimates of uncertainty associated with changes to interannual variability for the period 2070-2099 compared to the bias correction approaches. These changes are also highlighted using the case study for the Warragamba Dam catchment. [ABSTRACT FROM AUTHOR]

The coarse resolution of general circulation models (GCMs) necessitates use of downscaling approaches for transfer of GCM output to finer spatial resolutions for climate change impact assessment studies. This paper presents a stochastic downscaling framework for simulation of multisite daily rainfall occurrences and amounts that strive to maintain persistence attributes that are consistent with the observed record. At site, rainfall occurrences are modeled using a modified Markov model that modifies the transition probabilities of an assumed Markov order 1 rainfall occurrence process using exogenous atmospheric variables and aggregated rainfall attributes designed to provide longer-term persistence. At site rainfall amounts on wet days are modeled using a nonparametric kernel density simulator conditional on previous time step rainfall and selected atmospheric variables. The spatial dependence across the rainfall occurrence and amounts is maintained through spatially correlated random numbers and atmospheric variables that are common across the stations used. The proposed framework is developed using the current climate (years 1960-2002) reanalysis data and rainfall records at a network of 45 rain gauges near Sydney, Australia, while atmospheric variable simulations of the CSIRO Mk3.0 GCM (corresponding to Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) B1, A1B and A2 emission scenarios) are used for downscaling of rainfall for the current and future (year 2070) climate conditions. Results of the study indicate wetter autumn and summer and drier spring and winter conditions over the region in a warmer climate. The best estimates of annual rainfall project little change in the number of wet days and slight increase (2% in 2070) in the rainfall amount. An increase (about 4%) in daily rainfall intensity (rain per wet day) is estimated in year 2070. Changes in rainfall intensity, wet and dry spells, and rainfall amount in wet spells suggest that the future rainfall regime will have longer dry spells interrupted by heavier rainfall events. [ABSTRACT FROM AUTHOR]

The global climate is changing. Much research has already been carried out to assess the potential impacts of climate change on plant physiology. However, effects on plant disease have not yet been deeply studied. In this paper, an empirical disease model for primary infection of downy mildew on grapevine was elaborated and used to project future disease dynamics under climate change. The disease model was run under the outputs of the General Circulation Model (GCM) and future scenarios of downy mildew primary outbreaks were generated at several sites all over the word for three future dates: 2030, 2050, 2080. Results suggested a potential general advance of first disease outbreaks, both in the Northern and Southern Hemispheres, for all three future decades considered. The advance is predicted to be from about a minimum of one day in South Africa in 2030 to a maximum of 28 days in Chile and China in 2080. The advance in the outbreak time could lead to more severe infections, due to the polycyclic nature of the pathogen. Therefore, changes in the timing and frequency of fungicide treatments could be expected in the future, with a possible increase in the costs of disease management. [ABSTRACT FROM AUTHOR]

Motivation: Climate change plays an important role in the generation and maintenance of biodiversity by driving processes such as diversification and range shifts. As a result, biodiversity patterns are often found to carry the imprints of palaeoclimatic changes. However, we still know little about the spatial and temporal variation in climate over the scale of millennia affecting eco‐evolutionary dynamics, mostly because of the scarcity of user‐friendly and freely available spatio‐temporal palaeoclimate series at such temporal scales. Here, we address this gap by presenting PALEO‐PGEM‐Series, a global spatio‐temporal dataset of the last 5 Myr, with 1 kyr resolution, spatially downscaled from emulations performed with the intermediate‐complexity atmosphere–ocean general circulation model PALEO‐PGEM. PALEO‐PGEM‐Series holds the potential to advance our understanding of the mechanisms behind the strong relationship between biodiversity and climate, a pressing need given projected biodiversity responses to anthropogenic climatic change. Main Types of Variables Contained: Spatio‐temporal series of monthly temperature and precipitation and 17 derived bioclimatic variables over the Pliocene–Pleistocene, along with standard error estimates from multiple runs of the emulator. Spatial Location and Grain: Global landmasses, at 1° × 1°. Time Period and Grain: Last 5 Myr at 1000 year resolution. Major Taxa and Level of Measurement: Not applicable. Software Format: Tab‐delimited text files and accompanying R code to derive bioclimatic variables. [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 present research focuses on temperature change signals over the Carpathian Basin with a special focus on selected lowland and mountainous subregions. High‐resolution (0.11°) EURO‐ and Med‐CORDEX regional climate model (RCM) simulations of near‐surface air temperature are analysed based on raw and bias‐adjusted data. The mini‐ensemble consists of eight RCM simulations driven by five different general circulation models for the period 1976–2099 under the high‐end RCP8.5 scenario. The high‐resolution, homogenized and quality controlled CARPATCLIM was used as a reference dataset. The selected subregions cover eight municipalities located at diverse altitudes: Bratislava, Budapest, Brassov, Debrecen, Hoverla, Novi Sad, Pécs and Poprad. The following climate indices are assessed: summer days, ice days, frost days, tropical nights, the coldest day, the warmest day, the coldest night and the warmest night. In general, for the reference period (1976–2005) bias‐adjusted RCM data showed almost perfect match with observations. Accordingly, no best performing RCM is found for all indices. The ensemble mean of the bias‐adjusted RCM simulations projects an increase (decrease) of 32% and 112% (18% and 25%) in the annual number of summer days and tropical nights (frost days and ice days) for the period 2021–2050. For 2070–2099 we can expect more frequent tropical nights (about five times) with respect to the reference period and the frequency of frost days can be halved. Profound warming manifests in the increase of the warmest temperature of day of up to 2–3°C by the near future and of 5–7°C by the end of the 21st century, which means the absolute maximum temperature can reach 44–47°C for the period 2070–2099. Our results also highlight the need for bias‐adjusted data adapted by different sectors (human health, agriculture, transport, disaster management, heritage conservation) under the national adaptation strategies. [ABSTRACT FROM AUTHOR]

Sagebrush (Artemisia spp.) ecosystems provide critical habitat for the Greater sage‐grouse (Centrocercus urophasianus), a species of conservation concern. Thus, future loss of sagebrush habitat because of land use change and global climate change is of concern. Here, we use a dynamic additive spatiotemporal model to estimate the effects of climate on sagebrush cover dynamics at 32 sage‐grouse management (core) areas in Wyoming. We use the fitted models to quantify the sensitivity of each management area to precipitation and temperature, and to make probabilistic projections of sagebrush cover from present to 2100 under three climate change scenarios. Global circulation models predict an increase in temperature and no change in precipitation for Wyoming. Sensitivity to climate varied among management areas, but the most common response (70% of management areas) was a positive effect of temperature on sagebrush performance. The combination of positive sensitivity to temperature and the predicted increase in temperature under all climate change scenarios resulted in projections of increased sagebrush cover for most management areas. We characterized management areas as "optimal" or "suboptimal" based on the percentage of grid cells in each management area with sagebrush cover exceeding a nesting habitat target value. Only 18% of management areas are projected to switch from being currently optimal to suboptimal in the future. Thirty‐five percent of management areas are projected to switch from being suboptimal to optimal. The most common outcome (47%) was for currently suboptimal management areas to remain suboptimal, even though average cover tended to increase in those areas. The direct effects of climate change appear to favor sagebrush performance in the future for most sage‐grouse core areas in Wyoming. Our approach is broadly applicable to quantitative climate change assessments where remotely sensed estimates of habitat‐defining vegetation are available. [ABSTRACT FROM AUTHOR]

Atmospheric gravity waves (GWs) span a broad range of length scales. As a result, the un‐resolved and under‐resolved GWs have to be represented using a sub‐grid scale (SGS) parameterization in general circulation models (GCMs). In recent years, machine learning (ML) techniques have emerged as novel methods for SGS modeling of climate processes. In the widely used approach of supervised (offline) learning, the true representation of the SGS terms have to be properly extracted from high‐fidelity data (e.g., GW‐resolving simulations). However, this is a non‐trivial task, and the quality of the ML‐based parameterization significantly hinges on the quality of these SGS terms. Here, we compare three methods to extract 3D GW fluxes and the resulting drag (Gravity Wave Drag [GWD]) from high‐resolution simulations: Helmholtz decomposition, and spatial filtering to compute the Reynolds stress and the full SGS stress. In addition to previous studies that focused only on vertical fluxes by GWs, we also quantify the SGS GWD due to lateral momentum fluxes. We build and utilize a library of tropical high‐resolution (Δx = 3 km) simulations using weather research and forecasting model. Results show that the SGS lateral momentum fluxes could have a significant contribution to the total GWD. Moreover, when estimating GWD due to lateral effects, interactions between the SGS and the resolved large‐scale flow need to be considered. The sensitivity of the results to different filter type and length scale (dependent on GCM resolution) is also explored to inform the scale‐awareness in the development of data‐driven parameterizations. Plain Language Summary: Gravity waves (GWs) present a challenge to climate prediction: waves on scales of O(1)–O(100) km can neither be systematically measured with conventional observational systems, nor properly represented (resolved) in operational climate models, which have a typical grid spacing on the order of 100 km. Therefore, in these climate models, small‐scale GWs must be parameterized, or estimated, based on the resolved (large‐scale) flow. The primary effects of these small‐scale waves on the resolved flow is the so‐called sub‐grid scale drag (Gravity Wave Drag [GWD]), resulting from the propagation and breaking of these waves. Existing GW parameterizations in general circulation models are all highly simplified; for example, they only account for vertical propagation of GWs. With growing computing power, a promising alternative approach is to use machine learning to develop data‐driven parameterizations. However, this requires to first generate reliable high‐resolution computer simulations and then extract GWD from these simulations. This study follows these steps, compares different extraction methods, and describes some challenges and pathways to make advances. Furthermore, our results suggest that the horizontal propagation of GWs should be included in parameterizations too, however, extra care is needed in order to extract the resulting GWD from high‐resolution data. Key Points: In a library of weather research and forecasting model simulations, we compare methods for estimating 3D gravity wave drag force that are un‐ and under‐resolved by general circulation modelsFor drag associated with vertical fluxes, different methods agree on time‐ and zonal‐mean but not on instantaneous spatiotemporal patternsDrag associated with horizontal fluxes is significant but is very sensitive to the estimation methodology [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth's hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common data set consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection. Plain Language Summary: Atmospheric rivers (ARs) are long and narrow weather features often referred to as "rivers in the sky." They often transport water from lower latitudes to higher latitudes typically across climate zones and produce precipitation necessary for local climates. Understanding ARs in a warming climate is challenging because of the variety of ways an AR can be defined on gridded data sets. Unlike weather features such as tropical cyclones where identification methodologies are similar, algorithms that determine the characteristics of ARs vary depending on the science question posed. Because there is no real consensus on AR identification methodology, we aim to quantify the algorithmic uncertainty in AR metrics and precipitation. We compare 16 different ways of defining an AR on gridded data sets and present the range of possibilities in which an AR could change under global warming. Generally, ARs are projected to increase but the amount of that increase is a function of the algorithm. Across all algorithms and focus regions, AR precipitation is projected to become more extreme. Key Points: High‐resolution historical and future simulations are used to evaluate atmospheric river detection tools (ARDT) uncertaintyARDTs mostly show increases in frequency and intensity of future atmospheric rivers (ARs) but the scale of response is dependent on algorithmic restrictivenessMost regions experience an increase in precipitation volume coming from extreme ARs [ABSTRACT FROM AUTHOR]

Previous work has found that as the surface warms the large‐scale tropical circulations weaken, convective anvil cloud fraction decreases, and atmospheric static stability increases. Circulation changes inevitably lead to changes in the humidity and cloud fields which influence the surface energetics. The exchange of mass between the boundary layer (BL) and the midtroposphere has also been shown to weaken in global climate models. What has remained less clear is how robust these changes in the circulation are to different representations of convection, clouds, and microphysics in numerical models. We use simulations from the Radiative‐Convective Equilibrium Model Intercomparison Project to investigate the interaction between overturning circulations, surface temperature, and atmospheric moisture. We analyze the underlying mechanisms of these relationships using a 21‐member model ensemble that includes both General Circulation Models and Cloud‐system Resolving Models. We find a large spread in the change of intensity of the overturning circulation. Both the range of the circulation intensity, and its change with warming can be explained by the range of the mean upward vertical velocity. There is also a consistent decrease in the exchange of mass between the BL and the midtroposphere. However, the magnitude of the decrease varies substantially due to the range of responses in both mean precipitation and mean precipitable water. We hypothesize based on these results that despite well understood thermodynamic constraints, there is still a considerable ability for the cloud fields and the precipitation efficiency to drive a substantial range of tropical convective responses to warming. Plain Language Summary: Tropical large‐scale overturning circulations are expected to weaken with warming. This weakening is the result of precipitation increasing at a slower rate than the atmospheric water vapor. Because precipitation and water vapor are important measures of how energy flows through the atmosphere it is important to understand how they will respond to a warming climate. We use two methods to calculate the change of the overturning circulation in 21 different simulations of the tropical atmosphere. This group of 21 models includes high resolution models that resolve cloud systems, and global models with grid‐spacing of about 100 km. We show that a weakening circulation that results from increasing atmospheric stability and increased water vapor is a robust result across most models. But across the group of models there is a large range of magnitudes in the response of the circulation to warming. This variability is well explained by the magnitude of the mean upward vertical velocity. Higher resolution models do not have a narrower range of responses. Narrowing this range of responses will depend on developing a better understanding of what drives the variations in atmospheric stability, surface fluxes of latent energy, and relative humidity. Key Points: The overturning tropical circulation weakens as the surface warms in the majority of radiative convective equilibrium models examinedInter‐model spread of circulation intensity and change of intensity are highly correlated with ascending velocity spread at 500 hPaVariability of both the clear‐sky heating and static stability result in large variations of the subsidence velocity [ABSTRACT FROM AUTHOR]

Global changes increasingly worry researchers and policymakers and may have irreversible impacts on Earth's biodiversity. Similar to other phytophysiognomies, natural grasslands suffer from the effects of land use changes and rising temperatures, threatening animal and plant communities. Birds, being very sensitive to these changes, are widely studied and fundamental to understand the dynamics of ecosystems in relation to climate and land use changes. The Campo Miner Geositta poeciloptera is a grassland bird endemic to the Brazilian Cerrado and threatened with extinction that has been widely studied in recent years. We analyze the decrease in its extent of occurrence (EOO) and the effects of climate and land use change to understand the environmental suitability of the species in current and future scenarios. We used 5 common algorithms to produce ecological niche models. For future predictions, we use two general circulation models for two different greenhouse gas emission scenarios with different climate policies, an optimistic (ssp245) and a pessimistic (ssp585), plus two land use models focusing on increasing farmlands and reducing native grasslands. The current EOO represents ~45% of that presented by the IUCN EOO. The models generated for the present were satisfactory (TSS = 0.77 and ROC = 0.90) and showed high environmental suitability in areas where the species is currently found and low suitability where it is already extinct. All future scenarios have reduced suitable areas for the species, and the models of a greater increase in temperature and increase in farmlands and a greater decrease in grasslands were the worse. Our results reinforce the need to care about biome awareness disparity and the importance of actively preserving grassy‐shrub areas. Apparently, the state of Minas Gerais will be the only stronghold of the species in the coming years; however, the lack of protected areas that guarantee its survival needs attention. [ABSTRACT FROM AUTHOR]

El Niño–Southern Oscillation (ENSO) is one of the most important modes of climate variability on interannual timescales. We aim to find out whether a change in ENSO frequency can be predicted for the nearer future. We analyse the unforced pre‐industrial control run and the forced 1%/year CO2 increase run for an ensemble of 43 general circulation models that participated in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We assume that the uncertainty of ENSO frequency trend estimates from an ensemble is caused by apparent trends as well as model differences. The part of the uncertainty caused by apparent trends is estimated from the pre‐industrial control simulations. As a measure for ENSO frequency, we use the number of El Niño‐ and La Niña‐like months in a moving 30‐year time window. Its linear decadal trend is calculated for every member. The multimember mean of the trend for both experiments is less than 0.7 events per decade. Given that the standard error is of the same order of magnitude, we consider this a negligible trend. The uncertainties are large in both experiments and we can attribute most of the intermember variability to apparent trends due to natural variability rather than different model reactions to CO2 forcing. This means that the impact of intermodel differences might have been overstated in previous studies. Apparent trends make it very difficult to make reliable predictions of changes in ENSO frequency based on 120‐year time series. [ABSTRACT FROM AUTHOR]

The Northeast Brazil (NEB) is considered a region that is strongly vulnerable to extreme rainfall events due to climate change. In this context, this study evaluates the performance of 12 general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the number of dry (DD) and heavy rainfall days (HRD) over NEB in the historical period (1981–2005) and their projections for the near (2016–2040) and far future (2076–2100) under three Shared Socioeconomic Pathways (SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5) scenarios. For selection of extreme rainfall days, we used the absolute threshold of less than 1 mm for DD and the 99th percentile for HRD. The results indicate that four (three) models show an overall superior performance in reproducing the dry (heavy rainfall) days, being common in both aspects only the EC‐EARTH3. Thus, the skill of the CMIP6 models for NEB varies according to the extreme rainfall conditions analysed. For the future climate (near and far), results show that dry days are project to increase over the entire NEB territory, especially during DJF and MAM and more pronounce in the east coast, with projections that these conditions will be more severe under the SSP5‐8.5 scenario. The number of dry days may increase up to 15%. For HRD, although the results indicate that the number of days with heavy precipitation will be more frequent in the future (the increase can exceed 140%), the analysis show that under the low (SSP2‐4.5) or intermediate (SSP3‐7.0) forcing scenarios the HRD tends to be higher than in the most pessimistic scenario (SSP5‐8.5). Such result was explained according to the dryness of the atmosphere. Therefore, this study shows that it might either rain too much within a short range of time or the water scarcity will be longer‐lasting in the future in NEB. [ABSTRACT FROM AUTHOR]

This study shows that North Pacific subtropical mode water (STMW) characterized by low potential vorticity can approach and impinge on the Kuroshio mainstream east of Taiwan. The results of the analyses of observations and output from eddy‐resolving models show that the STMW appears with the frequency of 32.1% and 19.9% in the Kuroshio east of Taiwan. A case study with the output of the ocean general circulation model for the earth simulator during October 2015–March 2016 indicates that mode water moves southwestward toward the Kuroshio in accordance with a vertically lens‐shaped subsurface anticyclonic eddy (SAE). After the shoreward edge of the SAE impinges on the offshore edge of the Kuroshio, the northward velocity, especially subsurface velocity, of the Kuroshio increases significantly in the subsurface layer and the Kuroshio central position (KCP) extends eastward. While the SAE moves and dissipates, the offshore edge of the Kuroshio moves inshore, and then transport decreases. Both the northward velocity and the KCP changes result in the Kuroshio transport change during the impingement of SAE in the case. Statistic analysis further demonstrate that eddies those can trap moving STMW are dominated by SAEs. When SAEs encounter the Kuroshio, their subsurface northward velocity increases by 67.8% and the KCP moves eastward, and thus the northward transport of the Kuroshio increases by 43.4%. Plain Language Summary: The Kuroshio mainstream is a strong western boundary current of the North Pacific subtropical gyre that plays an important role in regulating climate changes in the Pacific Ocean. The Kuroshio flow passes east of Taiwan Island, where there is a high probability of the occurrence of eddies. We find that North Pacific subtropical mode water, which is characterized as a low potential vorticity water, can move to the east of Taiwan Island. We further confirm that this mode water is trapped by subsurface anticyclonic eddies (SAEs) in most cases, according to an eddy‐resolving ocean hindcast simulation, forced by observed atmospheric fields in the period 1993–2017. The subsurface northward velocity increases and the Kuroshio central position moves eastward when the shoreward edge of a SAE impinges on the offshore edge of the Kuroshio, and thus increases northward transportation of the Kuroshio. Under the effect of the SAE, the Kuroshio velocity intensifies, especially in the subsurface layer. While the SAE moves toward the western boundary and dissipates, the offshore edge of the Kuroshio moves inshore, and then transportation decreases. Key Points: Subtropical mode water can move to and impinge on the Kuroshio Current east of TaiwanThe mode water impinging on the Kuroshio Current is generally accompanied by subsurface anticyclonic eddies (SAEs)Collision of SAEs leads to the Kuroshio subsurface northward velocity change dramatically east of Taiwan [ABSTRACT FROM AUTHOR]

Climate scenario data set are indispensable for assessing future climate impacts. In this study, we developed statistically downscaled climate scenarios in Japan using modified bias correction method based on five general circulation models selected from the Coupled Model Intercomparison Project (CMIP) Phase 6 to facilitate impact assessments and adaptation strategies. Modification of time window of the original correction method results in successful agreement with the observed seasonal change of variables in each grid. The original CMIP6 models have a relatively small bias compared to CMIP5 models. The advantage of CMIP6‐based bias‐corrected scenarios is its availability for multiple global circulation models, which covered wide uncertainty in CMIP6 ensembles, with various emissions scenarios for representative concentration pathway (RCP) including RCP4.5, RCP2.6, and RCP8.5. Several temperature‐related indices derived from the CMIP6‐based climate scenarios agreed well with observations. The number of extremely hot days and nights increased nonlinearly in the future with additional global warming. An increase in the global warming level from 1 to 2°C above the early 1900s would increase the probability of the number of extremely hot days per year exceeding the 2018 case by 4.1 times. The development of bias‐corrected climate scenarios facilitates the study of various climate impacts on a CMIP6 basis. Plain Language Summary: It is important to appropriately remove model biases since the impact of climate change does not always depend linearly on the climate input data. To facilitate impact assessment based on the latest data set from CMIP6, we developed bias‐corrected daily climate scenarios for eight variables using five models. The application of climate scenarios to heat mortality‐related indices showed that they reproduced historical values well and would be useful for future projections. Key Points: We developed a statistically downscaled climate scenario in Japan based on five general circulation models selected from the Coupled Model Intercomparison Project Phase 6 to facilitate impact assessmentsSimilar increasing trend was found for the temperature and precipitation between CMIP5‐ and CMIP6‐based climate scenarios, while CMIP6‐based scenario showed smaller biases for the underlying global circulation model. Addition of the moderate emission scenario for CMIP6‐based scenario enable us to consider wider range of the possible futureSeveral threshold‐based indices derived from the climate scenarios was evaluated. They agreed with the observed values for the historical period. For the future, we can discuss the possible future change of indices in comparison with the historical values. Development of the bias‐corrected climate scenarios has enabled the broader impact assessment community in Japan to study various climate impacts on a CMIP6 basis [ABSTRACT FROM AUTHOR]

This study investigates the impacts of climate change on the hydrology and soil thermal regime of 10 sub‐arctic watersheds (northern Manitoba, Canada) using the Variable Infiltration Capacity (VIC) model. We utilize statistically downscaled and bias‐corrected forcing datasets based on 17 general circulation model (GCM) ‐ representative concentration pathways (RCPs) scenarios from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to run the VIC model for three 30‐year periods: a historical baseline (1981–2010: 1990s), and future projections (2021–2050: 2030s and 2041–2070: 2050s), under RCPs 4.5 and 8.5. Future warming increases the average soil column temperature by ~2.2°C in the 2050s and further analyses of soil temperature trends at three different depths show the most pronounced warming in the top soil layer (1.6°C 30‐year−1 in the 2050s). Trend estimates of mean annual frozen soil moisture fraction in the soil column show considerable changes from 0.02 30‐year−1 (1990s) to −0.11 30‐year−1 (2050s) across the study area. Soil column water residence time decreases significantly (by 5 years) during the 2050s when compared with the 1990s as soil thawing intensifies the infiltration process thereby contributing to faster conversion to baseflow. Future warming results in 40%–50% more baseflow by the 2050s, where it increases substantially by 19.7% and 46.3% during the 2030s and 2050s, respectively. These results provide crucial information on the potential future impacts of warming soil temperatures on the hydrology of sub‐arctic watersheds in north‐central Canada and similar hydro‐climatic regimes. [ABSTRACT FROM AUTHOR]

Our limited understanding of millennial‐scale variability in the context of the last glacial period can be explained by the lack of a reliable modeling framework to study abrupt climate changes under realistic glacial backgrounds. In this article, we describe a new set of long‐run Last Glacial Maximum experiments where such climate shifts were triggered by different snapshots of ice‐sheet meltwater derived from the early stages of the last deglaciation. Depending on the location and the magnitude of the forcing, we observe three distinct dynamical regimes and highlight a subtle window of opportunity where the climate can sustain oscillations between cold and warm modes. We identify the Eurasian Arctic and Nordic Seas regions as being most sensitive to meltwater discharge in the context of switching to a cold mode, compared to freshwater fluxes from the Laurentide ice sheets. These cold climates follow a consistent pattern in temperature, sea ice, and convection, and are largely independent from freshwater release as a result of effective AMOC collapse. Warm modes, on the other hand, show more complexity in their response to the regional pattern of the meltwater input, and within them, we observe significant differences linked to the reorganization of deep water formation sites and the subpolar gyre. Broadly, the main characteristics of the oscillations, obtained under full‐glacial conditions with ice‐sheet reconstruction derived meltwater patterns, share similar characteristics with δ18O records of the last glacial period, although our experiment design prevents detailed conclusions from being drawn on whether these represent actual Dansgaard‐Oeschger events. Plain Language Summary: During the last glacial period (115,000–12,000 years before present), the baseline cold climate was continuously disturbed by intense and abrupt climate changes. They completely modified the climate for a few thousand years or so, resulting, for instance, in massive temperature shifts and complete reorganizations of ocean circulation. These abrupt changes have been observed in climate records from the Northern Hemisphere and also can be traced in records from the Southern Hemisphere. Yet, we still do not know what triggers these changes, and often cannot simulate them at the right time under known environmental conditions. In the context of the Last Glacial Maximum, a cold period 21,000 years ago with extensive ice over the Northern Hemisphere, this article analyses a new set of climate model simulations that test the effects of freshwater melting from the ice sheets at different periods of the early deglaciation (∼21,000 to 18,000 years before present). Under some conditions, the resulting experiments displayed an Atlantic Ocean that oscillates between strong and collapsed basin‐wide circulation, causing approximately 10°C of temperature change over Greenland; a behavior that resembles observed abrupt climate changes. Key Points: New set of long‐run Last Glacial Maximum general circulation model experiments showing millennial‐scale variabilityDetailed description of the impact of ice sheet reconstruction‐derived meltwater distributions on abrupt climate changesIntroduction for future studies of the underlying physical processes of abrupt climate changes [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis on the past and future changes in precipitation extremes and quantifies the relative contributions from its frequency and intensity across India, considering five extremeness levels, denoted by 95th, 99th, 99.9th, 99.95th, and 99.97th percentile. Gridded station‐based observations from the historical period (1951–2020) and simulations from 14 General Circulation Models for the future (2021–2100), participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) are considered. Apart from an overall increasing pattern of precipitation extremes, it is noticed that the contribution of frequency dominates over intensity. Specifically, the frequency of 99th percentile daily rainfall is projected to increase approximately by two‐ (SSP245) to three‐ (SSP585) times in future. We also proposed a new zoning of entire Indian mainland, identified as six Homogeneous Precipitation Zones (HPZs). HPZ‐wise analysis reveals that the increase in frequency dominates over intensity for all the HPZs with a varying extent. For instance, increase in frequency is more for the climatologically high‐precipitation zones (HPZ‐3: Western Ghats, and HPZ‐6: North‐east India), whereas increase in intensity is more for the low‐precipitation zones (HPZ‐1: North‐west India, and HPZ‐4: Peninsular India). The degree of increase gets even more pronounced under the worst scenario SSP585, indicating a potential impact of anthropogenic activities on changing precipitation extremes. Findings of this study should be accounted in the climate change adaptation and mitigation strategies for future. Plain Language Summary: There are different characteristics of extreme precipitation, such as how frequent they occur, its intensity. Owing to change in climate, these characteristics are changing as time passes, as well as from one location to another location. How has it been changed in the past and how will it be changing in future? Is there any link with the nature of rainfall in a region? These are the focus of this study. We considered entire Indian mainland as our study domain that spans across a wide range of climatology. Overall, it is noted that both frequency and intensity are increasing and it is going to increase further in future. Moreover, frequency is increasing more rapidly than intensity. However, this finding varies from region to region. The regions that receive high precipitation, in general, may experience maximum increase in frequency (northeast and western ghats region in India), whereas the regions with relatively low precipitation may experience maximum increase in intensity (southern peninsular India). Such findings are expected to be useful in many applications related to climate change impacts on water sector. Key Points: Both frequency and intensity of extreme precipitation are increasing with spatial variation and it is going to increase further in futureIn general, change in the characteristics of precipitation extremes is dominated by increase in frequency rather than intensityHigh precipitation regions experience an increase in frequency and low precipitation regions experience an increase in intensity [ABSTRACT FROM AUTHOR]

Among mechanisms accounting for atmospheric pCO2 drawdown during glacial periods, processes operating in the North Atlantic (NA) and Southern Ocean (SO) have been proposed to be critical. Their individual and synergic effects during a course of glaciation, however, remain enigmatic. We conducted simulations to examine these effects at idealized glacial stages. Under early‐glacial‐like conditions, cooling in the SO can trigger an initial pCO2 drawdown while the associated sea ice expansion has little impact on air‐sea gas exchange. Under later glacial‐like conditions, further cooling in the NA enhances ocean carbon uptake due to a stronger solubility pump, and the SO‐induced stronger deep stratification prevents carbon exchange between the deep and upper ocean. Meanwhile, strengthened dust deposition increases the SO contribution to the global biological pump, and CO2 outgassing is suppressed by fully extended sea ice cover. More carbon is then stored in the deep Pacific, acting as a passive reservoir. Plain Language Summary: CO2 is one of the most important "greenhouse" gases that drive global climate changes. Tens of thousands of years ago, during the glacial time (known as the "ice age"), atmospheric CO2 was much lower than today. Research has shown that processes in different ocean regions, such as the Southern Ocean (SO) and North Atlantic (NA), made essential contributions to the glacial CO2 drawdown. However, the interplay between these processes remains unclear. Here, using an ocean general circulation model coupled with an atmospheric box that includes active air‐sea CO2 exchange, we examined the key processes at idealized stages of a glacial cycle. We found that: at an early stage, surface cooling in the SO triggers the CO2 decrease by shortening the time for air‐sea CO2 exchange; during a late stage, the SO and NA cooling act collaboratively to store more carbon in the deep ocean, where the SO plays a more determining role. Pacific seems to be a passive carbon pool during glaciation. Sea ice expansion hinders CO2 outgassing increasingly over the course of glaciation. Further CO2 decrease is achieved by a larger contribution of the SO to the global biological carbon pump. Key Points: The stepwise glacial atmospheric pCO2 decrease is linked to a Southern Ocean (SO)‐controlled synergy of interhemispheric mechanismsSea ice prevents CO2 outgassing increasingly from the early‐ to later‐glacial timeThe SO contribution to the global biological carbon pump might increase from interglacial to glacial periods [ABSTRACT FROM AUTHOR]

Estimating and planning for the impacts of climate change on the biodiversity of protected areas is a major challenge for conservation managers. When these areas are topographically heterogenous and contain species' entire ranges, this challenge is exacerbated because the coarse spatial scales of Global Circulation Model projections provide limited information for within‐park management. South Africa's Table Mountain National Park, home to three endemic amphibian species in just ~24,500 hectares, provides a case study for identifying conservation needs under climate change. Selecting the park's herpetofauna as pilot taxa, we identified life history and demographic characteristics believed to make species more sensitive and less able to adapt to climate change. We organized these into assessment frameworks and, through a combination of literature review and expert elicitation, reviewed and used them to assess climate change vulnerability of 18 amphibian and 41 reptile species. The assessment highlighted that 73% and 67% of the park's reptile and amphibian species, respectively, had at least one high‐sensitivity and low‐adaptive capacity trait. Using ordinal and additive scoring methods, we identified the species most vulnerable to climate change and highlight the park areas containing their highest concentrations. These areas will be used to inform landscape‐scale management priorities and park use zones. The current IUCN Red List assessments for these species do not incorporate climate change vulnerability. Considering some species appear to be threatened by climate change, their conservation needs might be underestimated. Identifying the most vulnerable species and the mechanisms underpinning their vulnerability can guide the identification and prioritization of conservation needs, while the highlighted knowledge gaps inform priorities for monitoring and research. While comprehensive climate change adaptation planning for Table Mountain National Park requires additional assessment of other taxonomic groups, this trait‐based assessment example highlights a viable tool for assessing climate change vulnerability in protected areas. [ABSTRACT FROM AUTHOR]

Flood‐frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate‐informed methodology for estimating flood‐frequency curves under non‐stationary future climate conditions. The methodology develops an asynchronous, semiparametric local‐likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log‐Pearson Type‐3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR‐MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR‐MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood‐frequency quantiles resulting from the ASLLR‐MM and GCM members compare well with the flood‐frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land‐surface conditions and a multi‐level model structure that includes other basins in a region could further improve the model performance in arid basins. Plain Language Summary: Reliable projection of future flood risk enables us to assess the infrastructure risk and to develop contingency measures to reduce potential flood losses. The flooding process of any basin is linked with the relevant climatic variables to estimate the flood quantiles. Given the future projections of those climatic variables from global climate models, we propose a statistical approach to estimate flood risk. Estimated flood quantiles for both the observed and future periods have lesser uncertainty in humid basins in the east, whereas arid basins show considerable uncertainty for both periods. Key Points: A climate‐informed modeling framework is developed to estimate near‐term (10–30 years) flood risk over the conterminous United StatesThe developed framework estimates observed flood quantiles well using Global Circulation Models historical simulationsEstimated flood quantiles for both the observed and future period have lesser uncertainty in humid basins compared to the arid basins [ABSTRACT FROM AUTHOR]

The representation of the South American Monsoon System (SAMS) by general circulation models (GCMs) is of key relevance for a better understanding of the physical rationale behind the recent climate changes over South Tropical South America (STSA) and their expected changes in a global warming scenario. During the last four decades, STSA experienced a lengthening of the dry season associated with diverse forcings. In this work, a set of 16 GCMs historical Coupled Model Intercomparison Project Phase 6 coupled simulations were evaluated during 1979–2014 in terms of how well they reproduced the atmospheric circulation over STSA through a circulation‐patterns (CPs) approach. Nine CPs were first identified based on low‐level winds from the ERA5 reanalysis. Focus was put on the representation of CPs during the dry‐to‐wet transition season (July‐October). Model performance depended on the seasonal cycle and spatial structure of the CPs. GCMs adequately reproduced the different CPs, with lower skills in the transition seasons. GCMs tended to go from dry to wet conditions too quickly, evidencing deficiencies in the representation of the SAMS onset, related to a poor representation of the southerly wind intrusions to STSA and the variability of the South American low‐level jet. Some GCMs were able to associate the occurrence of anomalous dry and wet years with specific CPs, suggesting well‐represented physical mechanisms controlling precipitation variability. This study could identify a few GCMs that adequately simulated the CPs in STSA (among them, CESM2, CMCC‐CM2‐HR4 and MPI‐ESM1‐2‐HR), which is relevant for driving high‐resolution models and the analysis of future projections. Key Points: Some general circulation models (GCMs) adequately represented both the seasonal cycle and spatial structure of circulation patternsGCMs often struggled in representing the monsoon initiation and the variability of the South American low‐level jetGCMs tended to go from dry to wet conditions too quickly [ABSTRACT FROM AUTHOR]

Understanding of future changes in drought characteristics is crucial for climate change adaptation and drought impact mitigation. We analysed the projected changes in drought characteristics relative to historical drought conditions in Central Asia using the Standardized Precipitation Evapotranspiration Index based on both gridded observations and an ensemble of bias‐corrected and spatially disaggregated global circulation models (GCMs) from phase 6 of Coupled Model Intercomparison Project (CMIP6). The results suggest that precipitation and potential evapotranspiration are projected to increase across Central Asia. Even though the change in wetness may not be significant and robust, a "dry gets drier and wet gets wetter" pattern may emerge in future under different scenarios. Drought events in Central Asia's semi‐arid and arid regions (aridity index <0.5) are projected to become more frequent (>125%), with longer duration (>55%), higher severity (>74%) and intensity (>8%) by the end of the 21st century under four Shared Socioeconomic Pathway (SSP) scenarios. In the coming period of 2021–2050, Central Asia is expected to have more drought events with a longer duration but lower intensity. Comparisons between different SSP scenarios stress the importance of climate change mitigation strategies to avoid more drought occurrence, longer drought duration and higher drought severity. The long‐term mitigation and adaptation studies for increasing drought impacts are also imperative in Central Asia. [ABSTRACT FROM AUTHOR]

Data assimilation techniques are becoming increasingly popular for climate reconstruction. They benefit from estimating past climate states from both observation information and from model simulations. The first monthly global paleo‐reanalysis (EKF400) was generated over the 1600 and 2005 time period, and it provides estimates of several atmospheric fields. Here we present a new, considerably improved version of EKF400 (EKF400v2). EKF400v2 uses atmospheric‐only general circulation model simulations with a greatly extended observational network of early instrumental temperature and pressure data, documentary evidences and tree‐ring width and density proxy records. Furthermore, new observation types such as monthly precipitation amounts, number of wet days and coral proxy records were also included in the assimilation. In the version 2 system, the assimilation process has undergone methodological improvements such as the background‐error covariance matrix is estimated with a blending technique of a time‐dependent and a climatological covariance matrices. In general, the applied modifications resulted in enhanced reconstruction skill compared to version 1, especially in precipitation, sea‐level pressure and other variables beside the mostly assimilated temperature data, which already had high quality in the previous version. Additionally, two case studies are presented to demonstrate the applicability of EKF400v2 to analyse past climate variations and extreme events, as well as to investigate large‐scale climate dynamics. [ABSTRACT FROM AUTHOR]

Understanding the climate controls of precipitation δ18O in the Tibetan Plateau (TP) is crucial for paleoclimate reconstructions from a wealth of regional archives. We use the outputs of iCAM5 model to quantify the different moisture contribution to local precipitation δ18O and to understand the climate controls of precipitation δ18O in the TP based on water‐tagging. The model shows some deficiencies in simulating the spatial and temporal characteristics of precipitation δ18O and the local climatic controls across the TP. Among all the tagged source regions, South Asia and the Indian Ocean contribute the most to the precipitation δ18O in the monsoon‐controlled domain, followed by the East Asia source region. The westerlies are identified as major moisture sources to the precipitation δ18O in the westerlies‐controlled domain. South Asia and the Indian Ocean also contribute substantially for the westerlies‐controlled domain. On interannual time scales, summer precipitation δ18O in the monsoon‐controlled domain is dominated by rainout processes occurring along the moisture transport pathway, indicating that precipitation δ18O variations here potentially record changes in the regional upstream convection. The δ18O signal can be altered by changes in the moisture source location, which implies that enhanced moisture delivery from remote source regions leads to more negative precipitation δ18O due to an increase in the rainout effect during transport. Our results have implications for the interpretation of past variations of archives with precipitation stable isotopes, such as ice cores, tree rings, lake sediments, and speleothems in the TP and surrounding regions. Plain Language Summary: Precipitation δ18O signal stored in climate proxy records from the Tibetan Plateau has long been used for paleoclimate reconstructions. However, the controls on precipitation δ18O over the TP remain debated. Studies of modern‐day climate controls on precipitation δ18O is crucial for interpreting past climate variations. We use the iCAM5 global atmospheric general circulation model fitted with water‐tagging capability to quantitatively decipher different climatic controls on precipitation δ18O across the TP. The model shows some deficiencies in simulating the spatial and temporal characteristics of precipitation δ18O and the local climatic controls across the TP. Among all the tagged source regions, South Asia and the Indian Ocean are identified as the most dominant moisture sources for the precipitation δ18O in the monsoon‐controlled domain, followed by the East Asia source region. For the westerlies‐controlled domain, the highest fractional contribution is from the westerlies. Changes in the rainout of water vapor during transport are the main driver of the interannual variability of precipitation δ18O in the monsoon‐controlled southern TP domain. The δ18O signal can be altered by changes in relative moisture contributions from different source regions. The amount effect contributes little to the precipitation δ18O variability in the southern TP. Key Points: Precipitation δ18O over the Tibetan Plateau (TP) is simulated with an AGCM implemented with water‐tagging capabilitySouth Asia and the Indian Ocean (the westerlies) contribute the most to precipitation δ18O in the southern (northern) TPVariability of precipitation δ18O in the monsoon‐controlled domain is dominated by rainout processes and moisture location changes [ABSTRACT FROM AUTHOR]