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We show that the seasonal cycles of clouds over the mid‐latitude oceans in the Northern Hemisphere are predictors of the responses of clouds to increasing sea‐surface temperatures globally. These regions are therefore "natural laboratories" in which the processes responsible for low‐cloud feedbacks on global scales are observed as seasonal changes in local cloud properties. We use an ensemble of configurations of a global‐climate model to show that the sensitivities of cloud‐radiative anomalies to surface temperature and lower‐tropospheric stability in the "laboratory" regions predict the models' global cloud‐radiative feedbacks. Models with greater changes in low‐clouds between seasons are shown to have stronger negative feedbacks in the mid‐latitudes, and stronger positive feedbacks from the subtropical stratocumulus. The biases in the simulated seasonal cycles, compared to observations, imply that both feedbacks are too weak in the model. The consequences of this for configuring our model to have a lower climate sensitivity are discussed. Plain Language Summary: As the Earth's climate warms, feedbacks from changes in clouds are crucial for determining the rate of warming. In some climate models, positive cloud feedbacks are known to contribute to projected temperature increases which fall outside the range that is considered plausible given our current understanding of the Earth's climate. Methods are needed which directly relate present‐day model errors to projected cloud feedbacks so that we can understand how to improve models and simultaneously obtain plausible feedback strengths. We show that in the Met Office Hadley Centres' climate model, the seasonal cycles of clouds in a few key regions, specifically the poleward ocean basins in the Northern Hemisphere, can be used to predict cloud feedbacks, globally, across a wide range of configurations of the model. These regions, particularly the North‐west Pacific, are "natural laboratories" in which the same processes that are responsible for global, cloud‐feedbacks onto global‐warming rates, occur annually as part of the local seasonal cycle. Studying clouds in these regions and model biases in predictions of these clouds will allow us to directly improve estimates of global climate change. Key Points: Seasonal cycles of clouds constrain cloud feedbacksThe northern ocean basins are natural laboratories for low‐cloud feedbacksThe hot‐model problem in HadGEM3 will not easily be solved by tuning [ABSTRACT FROM AUTHOR]

Improved seasonal precipitation forecasts can enable more effective water resource management decisions in a number of sectors, including municipal supply, agriculture, hydropower generation, and tourism. This study develops an effective straightforward statistical approach to enhance the quality of seasonal precipitation forecasts through the utilization of El Niño–Southern Oscillation (ENSO) information projected by Coupled General Climate Models (CGCMs). A stochastic weather generation (WG) model is developed to predict seasonal precipitation condition on ENSO condition. The WG model links a nonhomogeneous Markov Chain representing ENSO occurrence model to a bivariate normal distribution for seasonal precipitation conditioned on ENSO phase. Two verification metrics are suggested to measure the degree of predictability of raw, calibrated and climatological seasonal precipitation forecasts over northwest Costa Rica as a case study. Results indicate the potential to narrow the uncertainty of seasonal precipitation forecasts by incorporating CGCMs ENSO cycle information. Precipitation during the late part of the wet season (LS) has more predictability than precipitation in the early part of the wet season (ES). In addition, the degree of predictability decreases with an increase in lead time for a given forecast. A lead time of 1 year maintains a moderate level of predictability likely to support tangible benefits to various decision‐making processes. [ABSTRACT FROM AUTHOR]

Climate projections for the UK exhibit substantial uncertainty, and this uncertainty is a hindrance to robust and timely decision making on both adaptation and mitigation policy issues. A large part of the uncertainty is associated with dynamical changes of the regional atmospheric circulation rather than thermodynamic changes which are better constrained by model simulations. Of particular importance for the UK is the extent to which the North Atlantic jet will change over coming decades and the impact this will have on weather and climate in the region. In this article, we propose the use of jet‐based storylines for assessing and communicating uncertainty in climate projections for the UK, wherein changes in each impact are explicitly conditioned on changes in the North Atlantic jet. This approach provides a framework for evaluating the impacts associated with a range of plausible future climate outcomes for the UK, including outcomes that may not be well represented in the current generation of climate models, and for communicating these potential outcomes. We construct a simple yet useful set of future jet storylines for both summer and winter and for 2°C and 4°C global warming levels and illustrate the utility of the approach by evaluating the impact of each jet storyline on future changes in UK precipitation. In doing so, we demonstrate that the relationships between the jet and UK precipitation are consistent between observed interannual variability and projected changes. This finding increases our confidence in projecting changes in UK precipitation associated with each storyline. [ABSTRACT FROM AUTHOR]

Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency. Plain Language Summary: Winter storms can have devastating socioeconomic impacts across the United States. Climate models are used to understand and predict these winter storms and how they might change in the future, therefore helping to inform climate policy and emergency services. It is essential that we evaluate these climate models so that their performance and accuracy can be optimized. One way of evaluating climate models is to assess their ability in reproducing the large‐scale atmospheric conditions which occur during extreme events. Here we apply a cyclone tracking algorithm and self‐organizing maps, a machine‐learning approach, to automate the process of identifying key patterns associated with historical winter storm activity. We then use this approach to assess if CMIP6 climate models are able to reproduce the same patterns including the winter storm frequency and intensity. Our results show that, regardless of resolution, most CMIP6 models struggle to simulate the more extreme patterns and tend to favor weaker patterns that are not as conducive to cyclone formation. As a result, most models simulate too few winter storms, however, the error in storm intensity is more varied, with some models producing stronger storms and others producing weaker storms on average. Key Points: Despite resolution, CMIP6 models struggle to reproduce amplified synoptic patterns over the US, with a robust bias toward zonal patternsMost CMIP6 models produce too few cyclones over the northeastern US, while model errors are more varied when considering cyclone intensityIncreased resolution increases the number and intensity of simulated extratropical cyclones but does not improve model representation of synoptic patterns [ABSTRACT FROM AUTHOR]

In contrast to the "wet gets wetter and dry gets drier" paradigm, here, using observations and climate model simulations, we show that the mean rainfall over the semi‐arid northwest parts of India and Pakistan has increased by 10%–50% during 1901–2015 and is expected to increase by 50%–200% under moderate greenhouse gas (GHG) scenarios. The GHG forcing primarily drives the westward expansion of the Indian summer monsoon rainfall (ISMR) and is facilitated by a westward expansion of the Indian Ocean (IO) warm pool. Mechanistically, the westward expansion of ISMR is a consequence of the episodic genesis over IO and the northward propagation of an expanded Inter‐Tropical Convergence Zone on a sub‐seasonal time scale. While an adaptation strategy to increased hydrological disasters is a must, harnessing the augmented rainfall would lead to a substantial boost in food productivity, bringing transformative changes in the socio‐economic condition of people in the region. Plain Language Summary: An apparent eastward shift of the Indian monsoon has led to the arid conditions in the west and north‐west regions of India where monsoon was once active, and the Indus Valley civilizations thrived (5,300–3,300‐year BP). A reversal of the process and a westward expansion of the present‐day Indian monsoon would transform the west and north‐west India to a humid "monsoonal" climate and provide food security to the expanding population of the country. The present study shows that the Indian monsoon is indeed expanding to the west with 10% decrease of mean rainfall in the northeast and 25% increase in the west and north‐west during the historical period with a potential of 50%–100% increase in the north‐west under SSP2–4.5. Harvesting the increased rainfall has the potential for significant increase in food productivity bringing in transformative changes in the socio‐economic condition of people of the region. Key Points: Global warming could potentially cause a westward expansion of the Indian monsoonRainfall in northwest India and Pakistan has increased and will continue to rise due to greenhouse gasesIncreased monsoon rainfall can improve food productivity and socio‐economic conditions in the region [ABSTRACT FROM AUTHOR]

Most climate models simulate an intensified eastern equatorial Pacific warming in response to increasing greenhouse gas concentrations. So far, the proposed mechanisms have focused on thermodynamic aspects such as the spatial inhomogeneity of evaporative feedbacks, and future increases in upper ocean thermal stratification, partly intensified by projected Walker circulation weakening. Here we show, using earth system model freshwater perturbation experiments, that the simulated future rainfall intensification along the equator plays an important role in tropical climate change. Associated negative equatorial salinity anomalies of about −0.6 permil, which are very similar to the projected late 21st century salinity anomalies in CMIP6 SSP585 scenario, strengthen upper ocean stratification in the Pacific which leads to a flattening of the thermocline, shoaling of the equatorial undercurrent, and major shifts in tropical ocean dynamics. The resulting eastern basin equatorial warming also contributes to a marked weakening of the Pacific Walker circulation. Our analysis illustrates the importance of rainfall and salinity changes in the tropical climate system with relevance for understanding both, patterns of future climate change as well as the El Niño Southern Oscillation phenomenon. Key Points: Future increased tropical rainfall and its effect on salinity contribute to the generation of the enhanced eastern Pacific warming patternSalinity‐driven enhanced eastern Pacific warming weakens Pacific Walker circulationRainfall‐induced thermocline flattening causes a subsurface cold wedge in the western to central tropical Pacific [ABSTRACT FROM AUTHOR]

Uncertainties associated with the response of shallow clouds to global warming remain a great challenge for climate projection. Due to the small size of these clouds, parameterizations are required to represent them in both current‐ and next‐generation climate models. We present a quantitative evaluation of several important assumptions used in both mass‐flux and Assumed‐PDF Higher‐Order Closure parameterizations of shallow convection. We use large‐eddy simulations (LESs) of four different shallow convection regimes as benchmarks and apply a qt (total moisture)–w (vertical velocity) quadrant analysis technique to identify the "coherent structures" of moist and dry up/down‐drafts. The statistics of these coherent structures and the environment are then used to evaluate commonly used assumptions in mass‐flux parameterizations related to (a) the convective downdrafts and (b) the subplume variability of convective updrafts. For quantitative evaluation of the PDF closure used in AP‐HOCs, we perform offline calculations with the PDF closure used in the Cloud Layers Unified By Bi‐normals and the Simplified Higher‐Order Closure schemes by supplying the closure with statistical quantities directly calculated from the LESs. Then, the impact of parameterization assumptions embedded in the PDF closure in the same two categories is analyzed and understood through comparisons of the output from the closure with the statistics of the moist and dry up/down‐drafts from the LESs. Plain Language Summary: This study examines critically several key simplifying assumptions about convective downdrafts and small‐scale variability associated with shallow clouds that are made in two types of shallow convection parameterizations widely used in numerical weather and climate models and demonstrates quantitatively the impact of these assumptions on the parameterized cloud water content, depth, and turbulent transport. Key Points: A qt–w quadrant analysis is applied to identify convective coherent structures in large‐eddy simulations of various shallow cloud regimesStatistics from the quadrant analysis is used to evaluate assumptions in mass‐flux and Assumed‐PDF Higher‐Order Closure shallow convection parameterizationsSimplifying assumptions on convective downdrafts and subplume variability are linked to parameterization deficiencies [ABSTRACT FROM AUTHOR]

Mortality due to extreme temperatures is one of the most worrying impacts of climate change. In this analysis, we use historic mortality and temperature data from 106 cities in the United States to develop a model that predicts deaths attributable to temperature. With this model and projections of future temperature from climate models, we estimate temperature‐related deaths in the United States due to climate change, changing demographics, and adaptation. We find that temperature‐related deaths increase rapidly as the climate warms, but this is mainly due to an expanding and aging population. For global average warming below 3°C above pre‐industrial levels, we find that climate change slightly reduces temperature‐related mortality in the U.S. because the reduction of cold‐related mortality exceeds the increase in heat‐related deaths. Above 3°C warming, whether the increase in heat‐related deaths exceeds the decrease in cold‐related deaths depends on the level of adaptation. Southern U.S. cities are already well adapted to hot temperatures and the reduction of cold‐related mortality drives overall lower mortality. Cities in the Northern U.S. are not well adapted to high temperatures, so the increase in heat‐related mortality exceeds the reduction in cold‐related mortality. Thus, while the total number of climate‐related mortality may not change much, climate change will shift mortality in the U.S. to higher latitudes. Plain Language Summary: Deaths due to extreme temperatures is one of the most important impacts of climate change. Here we estimate future temperature related deaths in the US using climate projection, population projection, and adaptation assumptions. We find that in 3°C of global warming level, temperature‐related deaths will increase by a factor of five, mostly due to aging and increasing population. Below 3°C warming, impact of climate change slightly decreases temperature‐related deaths, because the decrease in cold‐related deaths exceeds the increase in heat‐related deaths. Above 3°C warming, impact of climate change depends on the level of adaptation. Furthermore, we find that temperature‐related mortality shifts Northward. This is due to increase of heat‐related deaths in Northern cities, which are currently poorly adapted to heat. Key Points: Temperature‐related deaths in the U.S. will increase by a factor of 5 with 3°C of warming, mostly due to aging and increasing populationImpact of climate change is not significant until 3°C of global warming, and it depends on the level of adaptation beyond that pointTemperature‐related deaths will shift Northward, due to increasing heat‐related deaths in poorly heat‐adapted Northern cities [ABSTRACT FROM AUTHOR]

Water vapor in the Arctic can be attributed to local evaporation and transport from mid‐latitudes. Here we investigate the response of Arctic water vapor to declining sea ice in a climate model, using an idealized warming scenario in which the Arctic becomes ice free year‐round. We find distinct monthly patterns in water vapor change with warming, with a strong influence of sea ice loss on changes to evaporation, except in summer when transports dominate. Before the Arctic becomes ice‐free, total column water increases at a rate slower than that indicated by the Clausius‐Clapeyron relationship; once ice‐free, total column water increases at a rate equal to Clausius‐Clapeyron. The exception is summer when the increase is initially faster then becomes slower when ice‐free. The boundary layer structure changes, under global warming, from stratified to well mixed in all seasons except summer, which remains largely unchanged throughout the ice‐free transition. Plain Language Summary: Surface evaporation from the Arctic Ocean is limited by the presence of sea ice, which creates a natural barrier between the ocean and atmosphere above. We show the loss of Arctic sea ice, in a climate model affects atmospheric water vapor and find that this varies throughout the year. The greatest increase in atmospheric water vapor is in winter, during the Arctic night, whilst there is very little change in summer. There are two reasons why sea ice loss does not impact water vapor in the summer: first, the summer air temperature barely changes between ice‐covered and ice‐free, whereas in winter the loss of ice cover can increase air temperatures by at least 20°C; second the summer Arctic water budget is dominated by transport of water vapor from midlatitudes rather than local evaporation. We also show changes in the vertical structure of the near‐surface atmosphere and cloud types, with a transition from stable to turbulent regimes in autumn and winter. Key Points: As Arctic sea ice declines with increased warming, Arctic evaporation and water vapor increase most in autumn and winterArctic sea ice limits water vapor availability, but once the Arctic is ice‐free, it increases with the Clausius‐Clapeyron relationshipArctic boundary layer changes from stably stratified to well mixed, during winter and autumn, but less change in spring and summer [ABSTRACT FROM AUTHOR]

The impacts of hot, dry, and compound hot‐dry extremes are significant for societies, economies, and ecosystems worldwide. Such events therefore need to be assessed in the light of anthropogenic climate change so that suitable adaptation measures can be implemented by governments and stakeholders. Here we show a comprehensive analysis of hot, dry, and compound hot‐dry extremes over global land regions using 25 Coupled Model Intercomparison Project Phase 6 models and four future emissions scenarios from 1950 to 2100. Hot, dry, and compound hot‐dry extremes are projected to increase over large parts of the globe by the end of the 21st century. Hot and compound hot‐dry extremes show the most widespread increases and dry extreme changes are sensitive to the index used. Many regional changes depend on the strength of greenhouse‐gas forcing, which highlights the potential to limit the changes with strong mitigation efforts. Plain Language Summary: Heatwaves, drought and their joint occurrences can negatively impact populations, economies, and natural systems worldwide. It is therefore of paramount importance that governments and stakeholders assess the risk from such events and adapt accordingly. In this study we use 25 climate models and four emission scenarios from 1950 to 2100 to assess how hot, dry, and compound hot‐dry extremes are expected to change in the future when compared to current climate conditions. We find that such extremes are projected to increase by the end of the 21st century over large parts of global land areas under the highest‐emission, no‐policy, climate change scenario. Hot and compound hot‐dry extremes show the most widespread increases, whereas dry extreme changes are sensitive and more regionally limited depending on the method by which they are computed. Most of the regional changes in hot, dry, and compound hot‐dry extremes can be reduced with strong climate change mitigation efforts to limit future green‐house gas emissions. Key Points: Hot extremes are projected to increase in frequency and intensity over almost all land areas by the end of the 21st centuryDrought changes depend on measure but increase robustly over central and northern South America, the Mediterranean, and southern AfricaCompound hot and dry extremes are sensitive to the drought measure but projected to increase in most regions globally [ABSTRACT FROM AUTHOR]

Lightning activity has been predicted to increase with global warming, though estimates of lightning sensitivity to a change of temperature vary widely. Since lightning is a small‐scale process, it must be represented by parametrizations in climate models. This article uses large‐scale meteorological parameters tied to thunderstorm generation to improve existing empirical models that simulate regional thunderstorm behaviour. The response of the number of thunderstorms (as presented here) to climate change is rarely analysed in global studies. This study focuses on Tropical America, and uses the ERA5 higher‐resolution reanalysis data (ERA5) to develop our empirical model. Thunderstorm data were taken from the World Wide Lightning Location Network (WWLLN) and processed using the clustering algorithm developed by Mezuman et al. (2014). The two meteorological parameters that correlated best with thunderstorm clusters in Tropical America were specific humidity (SH) and convective available potential energy (CAPE). The resulting empirical model was run from 1979 to 2019 using ERA5 reanalysis data as input. This approach approximates the long‐term trends in the behaviour of thunderstorms in the regions, in the absence of a complete historical lightning record. To our surprise, Tropical American thunderstorms exhibited a negative trend over this period, with an 8% decrease in thunderstorm clusters since the 1980s even with a rise of 1 K in temperature over the same period. The regions of largest decreases in thunderstorm activity align well with estimates of deforestation. We estimate that for every 1 Tg C lost due to deforestation, there is a 10% decrease in thunderstorm number. [ABSTRACT FROM AUTHOR]

This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision‐making. Following an unprecedented sequence of five droughts, 23 million east Africans faced starvation in 2022, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution‐based insights can be combined with the latest dynamical models to predict droughts at 8‐month lead‐times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro‐pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization "Early Warning for All" Executive Action Plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limitedfunding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African‐led EWS, and building better links between EWS and agricultural development efforts can support long‐term adaptation, reducing chronic needs for billions of dollars in reactive assistance. In Africa and beyond, climate change brings increasingly extreme sea surface temperature (SST) gradients. Using climate models, we can often see these extremes coming. Prediction, therefore, offers opportunities for proactive risk management and improved advisory services, if we can create effective societal linkages via cross‐silo collaborations. Plain Language Summary: Eastern East Africa is extremely food insecure. Millions of farmers and pastoralists rely on two meager rainy seasons that arrive twice a year. In the 13 seasons since late 2016, the region experienced eight droughts and three exceptionally wet seasons. Seven droughts were linked to very strong Pacific sea surface temperature (SST) gradients, which arose through an interaction between climate change and La Niña. Climate change will bring more extreme Pacific and Indian Ocean SST gradients. Here, for the first time, we show that these gradients can be very well predicted by the current generation of climate models. We then discuss how such information might be used to inform risk management, agriculture, and livestock management practices. The IGAD Climate Predictions and Applications Center, Ethiopian and Kenyan meteorological agencies, and other groups are providing increasingly accurate climate information. This creates opportunities for more proactive and effective agricultural and pastoral advisory services. Trust, urgency and accuracy can lower uncertainty, reduce risk aversion, and empower poor households and cash‐strapped institutions to act and innovate. Investing now in collaborative African climate systems, participatory advisory services and proactive risk management will help counter these threatening climate extremes. Key Points: Climate change and La Niña are producing extreme Pacific sea surface temperature (SST) gradients, which can be predicted very far in advanceThese Pacific SST forecasts provide robust opportunities for predicting well wet and dry outcomes in East AfricaTrust, urgency and accuracy can overcome barriers due to limited funding, uncertain tradeoffs, and inertia by improving advisory services [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 performance of the climate simulations by the regional climate model COSMO‐CLM forced by MPI‐ESM‐LR, as well as projected future temperature and precipitation conditions over northwestern Türkiye at the convection‐permitting resolution, are shown in this study. In terms of mean, maximum and minimum temperatures and daily total precipitation, the model response was examined. Comparisons with observations were made on an annual and seasonal basis. The convection‐permitting model (0.0275°) provides a satisfactory representation of annual and seasonal mean temperatures according to bias, MAE and RMSE. Better results were found for precipitation with respect to RE, MAE and RMSE. Finally, we used the RCP8.5 emission scenario to investigate future climate changes in terms of average temperature and precipitation variations for northwestern Türkiye, including Istanbul, over two projection periods (2041–2060 and 2071–2090) compared to the reference period (1991–2005). The model predicts significant warming in northwestern Türkiye, particularly in Istanbul and its environs, by the end of the century, as well as a general decrease in precipitation, especially evident in the spring and summer. The findings of this study can be used to develop climate adaptation policies based on temperature and precipitation variables in and around the study area. [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]

Climate projections in the North Atlantic region suffer from great uncertainties, and projections of precipitation are given with a large spread. Some of this uncertainty is related to projections of the North Atlantic warming hole (NAWH). The Community Earth System Model version 2 (CESM2) projects a relatively strong and extensive NAWH, with future sea surface cooling extending to Northern Scandinavia. This study investigates the relatively large winter precipitation decrease projected by CESM2 in the northeastern North Atlantic region, reinforced in a regional model. Three future scenarios from CESM2 are dynamically downscaled with the Weather Research and Forecast model. A methodology to separate convective and orographic from stratiform precipitation is applied to explore the physical mechanisms. Changes in stratiform precipitation closely relate to storm‐track changes, which varies between the scenarios. Convective precipitation decreases by up to 50% over the Norwegian Sea at the end of the century, which is robust across the scenarios. This is explained by the underlying reduced sea surface temperatures of the NAWH, leading to reduced evaporation and reduced convective activity and intensity. The orographic precipitation maximum over the Scandinavian mountains is shifted upstream, likely affected by increased static stability and flow blocking, which also relates to the NAWH. This shift is possibly also explained by more frequent rain versus snow, as well as reduced cross‐barrier wind speeds. This study contributes to highlight the importance of focusing future research efforts on the NAWH, in order to constrain future climate projections in this region. Plain Language Summary: How precipitation patterns and amounts will change in the future is of high societal importance, for example, for hydropower, drinking water, irrigation, industry, and structural design. Given the large uncertainties involved in future climate projections this is still difficult to quantify. One uncertainty is related to climate model predictions of important ocean circulation features in the North Atlantic, which further affect projections of sea surface temperatures. The Community Earth System Model version 2 projects a large area of sea surface cooling in the North Atlantic toward the end of the century. In our study we point to possible connections between this cooling and future decreases in winter precipitation in the northeastern North Atlantic region. This is mostly associated with a reduction in convective precipitation, which is dependent on the temperature of the sea surface. In this area the Scandinavian mountains contribute significantly to the precipitation received through orographic effects. We also find a future upstream shift in the maxima of orographic precipitation, which is indicated to be also (partly) connected to the sea surface cooling. These findings may help in understanding the large spread in future precipitation projections in this region and should urge for more research toward constraining them. Key Points: Winter convective precipitation decreases by up to 50% toward the end of the century in the northeastern North Atlantic regionThis is associated with reduced sea surface temperatures (the North Atlantic warming hole), reduced evaporation and convectionFuture upstream shift in orographic precipitation distribution is likely affected by increasing static stability associated with the NAWH [ABSTRACT FROM AUTHOR]

In hydrology, projected climate change impact assessment studies typically rely on ensembles of downscaled climate model outputs. Due to large modeling uncertainties, the ensembles are often averaged to provide a basis for studying the effects of climate change. A key issue when analyzing averages of a climate model ensemble is whether to weight all models in the ensemble equally, often referred to as the equal‐weights or unweighted approach, or to use a weighted approach, where, in general, each model would have a different weight. Many studies have advocated for the latter, based on the assumption that models that are better at simulating the past, that is, the models with higher hindcast accuracy, will give more accurate forecasts for the future and thus should receive higher weights. To examine this issue, observed and modeled daily precipitation frequency (PF) estimates for three urban areas in the United States, namely Boston, Massachusetts; Houston, Texas; and Chicago, Illinois, were analyzed. The comparison used the raw output of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. The PFs from these models were compared with the observed PFs for a specific historical training period to determine model weights for each area. The unweighted and weighted averaged model PFs from a more recent testing period were then compared with their corresponding observed PFs to determine if weights improved the estimates. These comparisons indeed showed that the weighted averages were closer to the observed values than the unweighted averages in nearly all cases. The study also demonstrated how weights can help reduce model spread in future climate projections by comparing the unweighted and weighted ensemble standard deviations in these projections. In all studied scenarios, the weights actually reduced the standard deviations compared to the equal‐weights approach. Finally, an analysis of the results' sensitivity to the areal reduction factor used to allow comparisons between point station measurements and grid‐box averages is provided. [ABSTRACT FROM AUTHOR]

Climate change is already having profound effects on biodiversity, but climate change adaptation has yet to be fully incorporated into area‐based management tools used to conserve biodiversity, such as protected areas. One main obstacle is the lack of consensus regarding how impacts of climate change can be included in spatial conservation plans. We propose a climate‐smart framework that prioritizes the protection of climate refugia—areas of low climate exposure and high biodiversity retention—using climate metrics. We explore four aspects of climate‐smart conservation planning: (1) climate model ensembles; (2) multiple emission scenarios; (3) climate metrics; and (4) approaches to identifying climate refugia. We illustrate this framework in the Western Pacific Ocean, but it is equally applicable to terrestrial systems. We found that all aspects of climate‐smart conservation planning considered affected the configuration of spatial plans. The choice of climate metrics and approaches to identifying refugia have large effects in the resulting climate‐smart spatial plans, whereas the choice of climate models and emission scenarios have smaller effects. As the configuration of spatial plans depended on climate metrics used, a spatial plan based on a single measure of climate change (e.g., warming) will not necessarily be robust against other measures of climate change (e.g., ocean acidification). We therefore recommend using climate metrics most relevant for the biodiversity and region considered based on a single or multiple climate drivers. To include the uncertainty associated with different climate futures, we recommend using multiple climate models (i.e., an ensemble) and emission scenarios. Finally, we show that the approaches we used to identify climate refugia feature trade‐offs between: (1) the degree to which they are climate‐smart, and (2) their efficiency in meeting conservation targets. Hence, the choice of approach will depend on the relative value that stakeholders place on climate adaptation. By using this framework, protected areas can be designed with improved longevity and thus safeguard biodiversity against current and future climate change. We hope that the proposed climate‐smart framework helps transition conservation planning toward climate‐smart approaches. [ABSTRACT FROM AUTHOR]

Europe has seen a rapid increase in the frequency and intensity of hot extremes in recent decades. In this study it is shown, using ERA5 reanalysis data 1960–2021, that the hottest summer days in North‐West Europe are warming approximately twice as fast as mean summer days. Moreover, this pattern stands out as relatively unusual across the Northern Hemisphere. It is also shown that comprehensive climate models fail to capture this difference in trends. A hypothesis is suggested to explain the differential rate of warming between the mean and hottest days, namely that the hottest days are often linked to warm advection from Iberia and North Africa, areas that are warming faster than North‐West Europe. This hypothesis can account for about 25% of the difference between ERA5 and a climate model ensemble and hence further research is needed to understand the drivers of the differing trends in mean and extreme temperature. Plain Language Summary: Extreme heat has a negative impact on society and is becoming more common with climate change. It is shown in this study that, for North‐West Europe over the last 60 years, the hottest day in each year has been warming at a faster rate than the average summer day. This suggests that slightly different processes are influencing the trends in the hottest summer days compared to average summer days. Unfortunately, state of the art climate models do not capture this difference between extreme and average summer days which makes understanding this phenomenon more difficult and means that we have to be careful when using these models to study heat extremes. In this article, I suggest an explanation for the difference between the average and hottest summer day trends seen in observations. The hottest summer days in North‐West Europe are often linked to transport of warmer air from regions further south, such as from Iberia. These more southerly areas are warming faster than North‐West Europe, hence air carried in is ever more extreme relative to the ambient air, leading to a greater trend in the hottest days. Key Points: The hottest boreal summer days are warming approximately twice as fast as mean summer days for North‐West EuropeSimulations with comprehensive climate models fail to capture this difference between trends in maximum and mean temperaturesOne possible explanation for the trend difference is an increase in the local meridional temperature gradient [ABSTRACT FROM AUTHOR]

Predictions of biodiversity trajectories under climate change are crucial in order to act effectively in maintaining the diversity of species. In many ecological applications, future predictions are made under various global warming scenarios, as described by a range of different climate models. We propose a clustering methodology to synthesize and interpret the outputs of these various predictions.We propose an interpretable and flexible two‐step methodology to measure the similarity between predicted species range maps and to cluster the future scenario predictions utilizing a spectral clustering technique. We implement and provide code for this method.We find that clustering based on predicted species range maps is mainly driven by the amount of warming rather than climate model or future scenario. We contrast this with clustering based only on predicted climate variables, which is driven primarily by climate models, that is, scenarios of the same climate model are clustered together, even when the amount of warming input to the models is varied.The differences between species‐based and climate‐based clusterings illustrate that it is crucial to incorporate ecological information to understand the relevant differences between climate models. Our findings can be used to better synthesize forecasts of biodiversity change under the wide spectrum of results that emerge when considering potential future scenarios. [ABSTRACT FROM AUTHOR]

As the future climate becomes hotter or drier, forests may be exposed to more frequent or severe droughts. To inform efforts to ensure resilient forests, it is critical to know which forests may be most exposed to future drought and where. Longer duration droughts lasting 2–3 years or more are especially important to quantify because forests are likely to experience impacts. We summarized exposure to 36‐month drought for forests across the conterminous United States using the Standardized Precipitation‐Evapotranspiration Index (SPEI) overlaid on forest inventory plot locations. Exposure was quantified under 10 scenarios that combined five modeled climates and two Representative Concentration Pathways (RCPs, 4.5 and 8.5) through 2070. Future projections indicate a tripling of the monthly spatial extent of forests exposed to severe or extreme drought—38% of forests were exposed on average by mid‐century as opposed to 11% during 1991–2020 (2041–2070). Increases in drought exposure were greatest under hotter (HadGEM2‐ES), drier (IPSL‐CM5A‐MR), and middle (NorESM1‐M) climate models, under either RCP. Projections agreed that forests in portions of the western United States, especially the southwestern United States, could face high levels of exposure. Forest types including pinyon/juniper, woodland hardwoods, and ponderosa pine were projected to be exposed to drought more than 50% of the time on average across all scenarios by mid‐century, when no forest type was exposed more than 25% of the time under any scenario during the recent period. Projections agreed less for the eastern United States, but in some scenarios, particularly under RCP 8.5, large portions of the East could be exposed to drought nearly as often as parts of the West. Moreover, a substantial portion of oak/hickory forests occur in eastern regions, where projections agree on increased drought exposure. This study provides novel insights about the changing conditions forests face in both the eastern and western United States. Our results can be combined with information about the sensitivities and adaptive capacities of forest ecosystems to prioritize drought adaptation efforts. [ABSTRACT FROM AUTHOR]

Barley (Hordeum vulgare ssp. vulgare) is cultivated globally across a wide range of environments, both in highly productive agricultural systems and in subsistence agriculture and provides valuable feedstock for the animal feed and malting industries. However, as the climate changes there is an urgent need to identify adapted barley varieties that will consistently yield highly under increased environmental stresses. Our ability to predict future local climates is only as good as the skill of the climate model, however we can look back over 100 years with much greater certainty. Historical weather datasets are an excellent resource for identifying causes of historical yield variability. In this research we combined recently digitised historical weather data from the early 20th century with published Irish spring barley trials data for two heritage varieties: Archer and Goldthorpe, following an analysis first published by Student in 1923. Using linear mixed models, we show that interannual variation in observed spring barley yields can be partially explained by recorded weather variability, in particular July maximum temperature and rainfall, and August maximum temperature. We find that while Archer largely yields more highly, Goldthorpe is more stable under wetter growing conditions, highlighting the importance of considering growing climate in variety selection. Furthermore, this study demonstrates the benefits of access to historical trials and climatic data and the importance of incorporating climate data in modern day breeding programmes to improve climate resilience of future varieties. [ABSTRACT FROM AUTHOR]

The Australian monsoon delivers seasonal rain across a vast area of the continent stretching from the far northern tropics to the semi‐arid regions. This article provides a review of advances in Australian monsoon rainfall (AUMR) research and a supporting analysis of AUMR variability, observed trends, and future projections. AUMR displays a high degree of interannual variability with a standard deviation of approximately 34% of the mean. AUMR variability is mostly driven by the El Niño‐Southern Oscillation (ENSO), although sea surface temperature anomalies in the tropical Indian Ocean and north of Australia also play a role. Decadal AUMR variability is strongly linked to the Interdecadal Pacific Oscillation (IPO), partially through the IPO's impact on the strength and position of the Pacific Walker Circulation and the South Pacific Convergence Zone. AUMR exhibits a century‐long positive trend, which is large (approximately 20 mm per decade) and statistically significant over northwest Australia. The cause of the observed trend is still debated. Future changes in AUMR over the next century remain uncertain due to low climate model agreement on the sign of change. Recommendations to improve the understanding of AUMR and confidence in AUMR projections are provided. This includes improving the representation of atmospheric convective processes in models, further explaining the mechanisms responsible for AUMR variability and change. Clarifying the mechanisms of AUMR variability and change would aid with creating more sustainable future agricultural systems by increasing the reliability of predictions and projections. This article is categorized under:Paleoclimates and Current Trends > Modern Climate Change [ABSTRACT FROM AUTHOR]

The deadly and destructive nature of tropical cyclones (TCs) makes understanding their response to future climate change of the utmost importance. TC genesis hinges on multiple factors, including an initial disturbance. African easterly waves (AEWs) have been shown to serve as such disturbances for TCs developing in the North Atlantic. It is therefore crucial to understand the relationship between AEWs and TCs and how this relationship may be affected by climate change. In this study, we examine the AEW‐TC relationship in historical and future climates using three models from the HighResMIP PRIMAVERA simulations. The AEWs and TCs were tracked in the model data using objective tracking algorithms, and AEW and TC tracks were then matched together if they were close to each other in space and time. The strength of the AEWs was measured using the eddy kinetic energy and the curvature vorticity of the waves. TC strength and intensity were measured using potential intensity and lifetime maximum 10 m windspeed. We found that future changes in the frequency of AEWs are not a good indicator of future TC activity. However, AEW strength, as well as environmental conditions conducive to strong TCs, are good indicators of AEWs that develop into TCs in both historical and future climates. Plain Language Summary: Tropical cyclones (TCs) are both deadly and destructive and it is important to understand how they will respond to future climate change. African easterly waves (AEWs) are disturbances that can develop into TCs that form in the North Atlantic. The relationship between AEWs and TCs is an important part of understanding how TCs will be affected by climate change. In this study, we examine the AEW‐TC relationship in historical and future climates using three climate models. AEWs and TCs were tracked in the model data, and AEW and TC tracks were then matched together if they were close to each other in space and time. The number of AEWs and TCs in the historical and future climates were compared. We found that future changes in the number of AEWs are not a good indicator of future changes in the number of TCs. However, AEW strength, as well as environmental conditions conducive to strong TCs, are good indicators of AEWs that develop into TCs in both historical and future climates. Key Points: Future changes in the frequency of African easterly waves (AEWs) are not a good indicator of future tropical cyclone (TC) activityAEWs that develop into TCs are stronger than non‐developing waves in historical and future climatesWave strength combined with environmental conditions are good indicators of tropical cyclogenesis from waves [ABSTRACT FROM AUTHOR]

In this study, we developed a regional coupled model for the western North Pacific (WNP) and East Asian region. The atmospheric/oceanic component of the coupled model was the Weather Research and Forecasting and the Regional Ocean Modeling System, coupled through the National Center for Atmospheric Research Coupler version 7 (CPL7). An evaluation is provided through the simulation of the climatology and interannual variability of the summer monsoon climate from 1982 to 2015. The coupled model is also compared with a solely atmospheric run, which is driven by the observed sea surface temperature (SST) as the surface boundary. The results show that the coupled model has good capability in modeling the climate in the WNP and East Asian domain, including the climatology of summer precipitation and circulation, the subseasonal (month‐to‐month) variation in the western Pacific subtropical high and associated precipitation, and the seasonal cycle of the East Asian rain belt. One merit of this model is its ability to simulate the northward and eastward shift from early to late summer. With respect to the interannual variability, the model captures the spatial pattern of the leading mode of summer precipitation, with a north–south dipole mode in the studied region. The model also reproduces the larger SST variability in the northern part of this region. The temporal correlation coefficient between the coupled simulation and observation was 0.70 for the precipitation and 0.53 for the SST leading mode. The coupled model also captures the precipitation–SST relationship, which results from reasonably considering the air–sea coupling processes in the coupled run. Plain Language Summary: Due to the resolution of global models is relatively coarse and cannot describe the regional features, especially in the monsoon regions, regional climate model is an essential solution for regional climate simulation and projection. Compared with atmospheric alone model which is driven by prescribed sea surface temperature, regional air‐sea coupled model can describe reasonably the air‐sea coupling processes and has better performance. In this study, an regional coupled model is developed based on the Community Earth System Model coupler version 7 framework. Based on our analysis, the regional coupled model reasonably reproduced most key characteristics in the East Asian monsoon region. In addition to being used in regional climate simulation and projection, it can also be conveniently coupled with global models through the coupler 7. Thus with this useful tool in future we can develop global‐regional two‐way coupling simulation system for climate predictions and projections. Key Points: A regional coupled model is developed for East Asia and western North Pacific region based on Community Earth System Model frameworkClimatology and interannual variability of the summer monsoon climate have been evaluatedThe model can well capture the climatological northward and eastward movements of the western Pacific subtropical high from June to August [ABSTRACT FROM AUTHOR]

Climate change impacts ocean nutrient availability and will likely alter the marine food web. While climate models predict decreased average ocean productivity, the extent of these changes, especially in the marginal seas upon which large human populations depend, is not well understood. Here, we reconstructed changes in seawater phosphate concentration and nitrate source over the past 400 years, which reveals a more than 50% decline in residence time of seawater phosphate, and 8%–48% decline in subsurface nitrogen supply following the coldest period of Little Ice Age. Our data indicates a link between surface ocean nutrient supply and the East Asian Summer Monsoon strength in an economically important marginal sea. As climate models predict that the East Asian Summer monsoon will strengthen in the future, our study implies that surface ocean primary productivity may increase in the South China Sea, contrary to the predicted decrease in global average ocean productivity. Plain Language Summary: Global climate models predict that surface ocean nutrient concentrations will significantly decrease over the next 100 years as our climate changes. However, in the world's marginal seas, which human populations depend on most heavily for food security, the surface ocean biogeochemical changes are still difficult to predict with global‐scale models. Here, using coral‐based paleoclimate proxies, we reconstruct changes to surface phosphate concentration and to sources of surface nitrogen over the past four centuries in the South China Sea (SCS). Combined with box model simulations, our data reveal a more than 50% decline in phosphate residence time and an 8%–48% decline in subsurface nitrate supply from the 1600s to present. Such a decline implies a significant decrease in primary productivity and likely a community shift toward nitrogen‐fixing phytoplankton. Importantly, our data indicate that the decrease in nutrient supply was driven by weakened monsoon‐induced upwelling, demonstrating strong ties between nutrient supply and monsoon strength in the SCS. Climate change is predicted to strengthen the East Asian Summer Monsoon, and our results suggest that primary productivity in this economically important region may increase, contrary to the widely predicted decreases in average global ocean productivity. Key Points: Coral skeletal phosphorus content with scattered nitrogen isotopes is reconstructed over the past 400 yearsNutrient supply declined up to 48% since the Little Ice Age in the western South China SeaThe coral record from links nutrient supply to monsoon wind, implying higher productivity under a stronger monsoon in the future [ABSTRACT FROM AUTHOR]

Four climate parameters (i.e. maximum, mean and minimum air temperature and precipitation amount) from 10 regional climate models, provided by the EURO‐CORDEX initiative, are adjusted using as reference the ROCADA gridded dataset. The adjustment was performed on a daily temporal resolution for the historical period (1971–2005), as well as for climate change scenarios based on two Representative Concentration Pathways (RCP4.5 and RCP8.5). The most accurately method for bias‐correction (BC) was selected following a 2‐fold cross‐validation approach, which was performed on historical data using two methods: Quantile Mapping (QMAP) and Multivariate Bias Correction with N‐dimensional probability (MBCn). The performances of the two methods are very similar when analysing the frequency distribution of each selected variable, whereas the comparison between (1) the intervariables correlation of the adjusted datasets, and (2) the reference dataset revealed much smaller differences for the dataset adjusted with the multivariate method, hence this was used for producing the BC scenario dataset. Based on the MBCn adjusted dataset, a climate change analysis over Romania was tested at the seasonal and annual scale. Overall, for the multimodel ensemble mean, at the country level, a substantial temperature increase is reported for both scenarios and no significant trend is revealed for precipitation amount. The adjusted RCMs are provided without any restrictions via an open‐access repository in netCDF CF‐1.4‐compliant file format. The BC climate models are archived at the 0.1° spatial resolution, in the WGS‐84 coordinate system, at a daily temporal resolution. [ABSTRACT FROM AUTHOR]

Climate envelope models have been used to evaluate the predicted impacts of climate change on species of concern and can be a useful planning tool in determining the long‐term suitability of current habitat and potential introduction sites. However, due to model complexity, these models have generally been seen as "black boxes" when it comes to understanding why they make the predictions they do. In this study, we examined current potential ocelot (Leopardus pardalis) habitat using publicly available ocelot records and CHELSA bioclimatic variables combined in an ensemble model approach. We then predicted future potential ocelot habitat under three different emission scenarios using five climate models produced at a 30 arcsec grid cell size. To better understand what was driving model predictions, we used a variety of model interpretability approaches, including variable importance values, Shapley additive explanations, interaction detection, and a local surrogate model. These approaches revealed that ocelot potential habitat was associated with at least 233 mm of precipitation during the warmest quarter (BIO18) and at least 146 mm of precipitation during the wettest month (BIO13). Shapley additive explanations, applied to four protected areas across the species range, were valuable for understanding which climatic predictors drove predicted ocelot habitat at a local scale, with the protected areas with the highest predicted values having consistently more precipitation and higher temperatures. These tools are model agnostic and can be used to assess model predictions for biological validity and to further understand potential underlying relationships. As climate change shifts species range distributions, species distribution models and the tools to interpret them may become increasingly valuable, particularly on the leading edge of range expansion. [ABSTRACT FROM AUTHOR]

Drought is one of the most serious natural disasters exacerbated by climate change. Changes in precipitation and temperature in the future increase the likelihood of drought in China. In this study, a stepwise cluster ensemble downscaling (SCED) model was developed to bias‐correct projections of temperature and precipitation from multiple RCM outputs, and further characterized the drought hazards. The developed SCED model was used to aggregate and correct the results of multiple regional climate models, and its performance was proved to be reliable by comparing with the observed results. The proposed SCED method has been applied for drought projections over the Fujian province, China. The results showed that the changes of precipitation and temperature in Fujian would have obvious spatial heterogeneous characteristics. The temperature in the southeast coastal areas will increase by up to 4°C and the precipitation will decrease by 3.1% in the late 21st century, while the temperature rises and precipitation increases in the southwest. Temperature in inland areas will be lower and precipitation will be less. The drought hazards were also characterized by both SPI and SPEI based on biased‐corrected projections from SCED model. According to the SPI and SPEI indices, although the number of dry months in Fujian province will not change significantly in future, the spatial and temporal heterogeneity may become more explicit. Moreover, the moderate drought (from SPI) may increase while the general drought may decrease (from both SPI and SPEI). For extreme droughts, there would not be visible changes detected by SPI, but an increasing trend characterized since the impact of temperature was included in SPEI. In addition, there would be an increasing trend on drought when increasing temperature and precipitation occurred simultaneously. [ABSTRACT FROM AUTHOR]

The Iberian Peninsula is a known climate change hotspot. In the last decades, the Coupled Model Intercomparison Project (CMIP) has allowed for thousands of Global Climate Model (GCM) simulations to be conducted, an important tool to assess and understand future changes in Earth's climate. The comparison of past future projections with observations provides a robust evaluation of the models' skill according to different emission scenarios. Here, a comprehensive performance assessment of GCM‐simulated mean, maximum, and minimum temperatures, and accumulated precipitation is conducted for Iberia, by retrospectively comparing historical simulations and past future projections from CMIPs 1 to 6 with reference datasets. From means to extremes, and multi‐year intra‐annual cycles to inter‐annual trends, GCM simulations are compared with Iberia0.1 and E‐OBS observational gridded datasets, and ERA5 reanalysis. The matching between the variables' distributions is assessed through the distribution added values (DAVs), a measure of gain or loss in performance between CMIPs. Results show relevant improvements in the description of the Iberian climate throughout the CMIP effort, for historical and past future periods. While the representation of intra‐annual cycles (inter‐annual trends) is enhanced after CMIP3 (CMIP2), GCMs from all CMIPs are generally able to depict the observed warming trend. Nevertheless, until 2021, a slight detrimental effect in the performance of CMIP6 models is found, in comparison with CMIP5 ones, with positive DAVs obtained only for past future temperature projections (less than 2%). A continuous monitorization of modelling accuracy for Iberia is needed, considering the increasing relevance of climate change information for adaptation strategies. [ABSTRACT FROM AUTHOR]

Although the Coupled Model Intercomparison Project 6 (CMIP6) protocol provides an experiment to estimate effective radiative forcing (ERF), it is only quantified for few models. We present new estimates of ERF for models participating in CMIP6 by applying the method developed in Fredriksen et al. (2021, https://doi.org/10.1029/2020JD034145), and validate our approach with available fixed‐SST forcing estimates. We estimate ERF for experiments with abrupt changes of CO2, 1% increase of CO2, historical forcings, and future scenarios, and demonstrate that CMIP6 ERF is lower than CMIP5 ERF at the end of the historical period, but grows faster than CMIP5 in the future scenarios, ending up at higher levels than CMIP5 at the end of the 21st century. The simulated radiative efficiency of CO2 has not changed much, suggesting that the larger future increase in CO2 concentrations in CMIP6 compared to CMIP5 is important for explaining the forcing difference. Plain Language Summary: To understand climate model responses, it is useful to separate between the drivers of climate change and their responses. We present new estimates of the drivers, called the effective radiative forcing, for the latest generation of climate models (Coupled Model Intercomparison Project 6 (CMIP6)). This estimates the energy imbalance at the top of the atmosphere and is a measure of human and natural influences on climate. Normally this requires additional climate model experiments to make these estimates, but since these have only been run for a few models, we are here aiming to make the best alternative estimates based on more widely available data, following the method in Fredriksen et al. (2021, https://doi.org/10.1029/2020JD034145). We show that our forcing estimates are growing faster during the 21st century for the new CMIP6 models than for the previous generation of models (CMIP5), and suggest this can be attributed to the higher CO2 concentrations in future scenarios for CMIP6 compared to CMIP5. Key Points: Our method to estimate effective radiative forcing based on common model diagnostics is consistent with Coupled Model Intercomparison Project 6 (CMIP6) fixed‐SST estimatesWe present coherent estimates for abrupt CO2, 1% CO2, historical, and future scenario experimentsForcing estimates for the 21st century grow faster for CMIP6 than for CMIP5 models [ABSTRACT FROM AUTHOR]

Clouds over tropical oceans are an important factor in the Earth's response to increased greenhouse gas concentrations, but their representation in climate models is challenging due to the small‐scale nature of the involved convective processes. We perform two 4‐year‐long simulations at kilometer‐resolution (3.3 km horizontal grid spacing) with the limited‐area model COSMO over the tropical Atlantic on a 9,000 × 7,000 km2 domain: A control simulation under current climate conditions driven by the ERA5 reanalysis, and a climate change scenario simulation using the Pseudo‐Global Warming approach. We compare these results to the changes projected in the CMIP6 scenario ensemble. Validation shows a good representation of the annual cycle of albedo, in particular for trade‐wind clouds, even compared to the ERA5 reanalysis. Also, the vertical structure and annual cycle of the intertropical convergence zone (ITCZ) is accurately simulated, and the simulation does not suffer from the double ITCZ problem commonly present in global climate models (GCMs). The response to global warming differs between the COSMO simulation and the analyzed GCMs. While both exhibit an overall weakening of the Hadley circulation, the narrowing of the ITCZ (known as the deep‐tropics squeeze) is not so pronounced in the kilometer‐resolution simulation, likely due to the absence of a double ITCZ bias. Also, there is a more pronounced intensification of the ITCZ at the equator in the kilometer‐resolution COSMO simulation, and a stronger associated increase in the anvil cloud fraction. Plain Language Summary: Tropical clouds are an important component of the climate system as they can either amplify or dampen human‐induced climate change. However, predicting how clouds will change in a warming climate is challenging due to their small‐scale nature. We perform high‐resolution atmospheric climate simulations over the tropical Atlantic to study what we can learn about tropical clouds from this relatively novel type of models. We validate the simulation under current climate conditions and find a good representation of tropical clouds in comparison to coarser climate models. In particular, the high‐resolution simulation does not suffer from the double intertropical convergence zone (ITCZ) problem commonly present in global climate models. We then study how tropical clouds will change in a warming climate and find differences between our high‐resolution simulation and coarser climate simulations. The tropical overturning (Hadley) circulation weakens in both model types, but the narrowing of the ITCZ (known as the deep‐tropics squeeze) is not so pronounced in the high‐resolution simulation, likely due to the absence of a double ITCZ bias. Key Points: We perform a km‐resolution climate simulation over the tropical Atlantic for current and future climate conditions using the Pseudo‐Global Warming approachWe find an accurate representation of the annual cycle of shallow cumulus clouds and a realistic structure of the intertropical convergence zone (ITCZ), without double ITCZThe ITCZ shows a stronger central intensification and weaker narrowing with warming than in the global climate models [ABSTRACT FROM AUTHOR]

An emergent consequence of climate change is its potential impacts on transatlantic flight routes and durations, with studies suggesting that future optimal paths will likely lead to decreased (increased) eastbound (westbound) journey times. However, these earlier works all rely on a class of flight planning algorithms (based on the so‐called 'shooting method') that are fundamentally different to those typically used by the aviation industry (based on the so‐called 'network method'). To help resolve any ambiguities associated with these differences and better align both the academic and operational communities, we have therefore conducted an evaluation of methods of estimating time‐optimal flight routes to relate the result obtained from the shooting method with the result obtained from the network method, using identical data from an ensemble of eight different climate models included within the CMIP5 project. Our findings suggest that, contrary to the existing literature, journey times will likely be shorter, irrespective of direction of travel and season, although the magnitude of this difference is negligible given the typical duration of transatlantic flights. Trajectory prediction (TP) results are largely dependent on the characteristics of the ensemble, their method of generation, the projected climate scenario and the temporal periods considered. Importantly, we conclude the choice of TP models is not a crucial factor in assessing the impact of climate change on flight durations—as long as this sample size (i.e., number of trajectories used in deriving statistics) is large. [ABSTRACT FROM AUTHOR]

Model output from climate projections often requires bias‐adjustment to compensate for systematic model errors. A bias‐adjustment method for extreme precipitation intensity is proposed that preserves the scaling equation for different accumulation levels from hourly to daily, using intensity‐duration‐frequency (IDF) modeling. A validation is performed within a pseudo‐reality setting, based on hourly precipitation from 28 regional climate model projections of the EURO‐CORDEX ensemble over Belgium. The scaling‐based adjustment methods improve upon previous methods, an optimal method is identified, and, analytical quantile mapping methods must be avoided due to three identified problems. The ensemble mean of the adjusted extreme precipitation intensity obeys the above‐mentioned scale‐invariance property, which is consistent with observed extreme intensities. We thus show that IDF modeling provides added value in the context of bias‐adjustment, and, that the particular IDF model proposed balances well between accuracy and the preservation of desired properties such as scale invariance and consistency among rainfall durations. Plain Language Summary: Heavy precipitation is known to have major impacts on society, including crop damage, soil erosion, landslides, and increased flood risk. In recent years, there is a growing body of evidence that human‐induced climate change has intensified extreme precipitation on the global scale. Both regional and global climate models predict that this will continue as the climate warms. Nevertheless, trend analysis or climate‐change attribution of local rainfall extremes remains particularly difficult for many reasons including high variability in space and time. Therefore it is essential to use statistical methods that make use of all available information. Climate model simulations are known to exhibit systematic biases. Here, for the first time, we use a well‐established technique in hydrology (intensity‐duration‐frequency modeling) to adjust extreme precipitation intensity as a function of the rainfall duration and event probability. Validation results with a large multi‐model ensemble illustrate that the new method improves upon existing approaches. As an illustration, we present a bias‐adjusted multi‐model climate ensemble for future projections of heavy rainfall statistics over Belgium. Key Points: New bias‐adjustment methods for subdaily extreme precipitation consistent across different precipitation durationsPseudo‐reality cross‐validation shows higher skill than existing bias‐adjustment methods for subdaily extremesApplication to regional climate model projections over Belgium shows, despite large uncertainties, consistent future increase in subdaily precipitation extremes [ABSTRACT FROM AUTHOR]

Changes in climate and land‐use and land‐cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (a) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios, (b) sensitivity, and (c) adaptive capacity. The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increases are expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Strong variation in predicted estuarine nutrient loads was observed as a function of climate model projections, while projected LULC changes were more consistently associated with elevated loads. Our findings illustrate the benefits of integrating natural and socio‐ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries. Plain Language Summary: Estuaries provide ecological, economic, and cultural benefits threatened by changes in land‐use and land cover (LULC) and climate. One way in which climate and LULC changes threaten estuaries is by altering the quality and quantity of freshwater they receive. This study developed a framework for identifying estuarine systems at risk of suffering from water quality degradation—specifically through increases in nutrients—due to climate and LULC change. Many factors capturing biological, physical, ecological, and social aspects of estuarine vulnerability to water quality change were analyzed in the framework, which was used to assess the vulnerability of 112 estuarine systems across the contiguous US to future increases in nutrients. Study findings revealed that the largest increases in future estuarine nutrient loads are expected along the Atlantic and eastern Gulf of Mexico, and the lowest increases are expected along the Pacific and western Gulf of Mexico. However, the North Atlantic and the South Pacific were associated with the greatest access to resources for mitigating ecological degradation (e.g., access to local knowledge, legislative and governmental actions), which could help estuaries in these regions better cope with the effects of LULC and climate on nutrient loads, making them potentially less vulnerable. Key Points: A framework to assess water quality vulnerability to future land‐use and climate change was developed and applied to 112 U.S. estuariesSystems along the Atlantic coast had the highest predicted exposure to nutrient loading changeThe North Atlantic and South Pacific regions had the highest adaptive capacity, which can help mitigate the effects of nutrient increase [ABSTRACT FROM AUTHOR]

Our understanding of how increasing atmospheric CO2 and climate change influences the marine CO2 system and in turn ecosystems has increasingly focused on perturbations to carbonate chemistry variability. This variability can affect ocean-climate feedbacks and has been shown to influence marine ecosystems. The seasonal variability of the ocean CO2 system has already changed, with enhanced seasonal variations in the surface ocean pCO2 over recent decades and further amplification projected by models over the 21st century. Mesocosm studies and CO2 vent sites indicate that diurnal variability of the CO2 system, the amplitude of which in extreme events can exceed that of mean seasonal variability, is also likely to be altered by climate change. Here, we modified a global ocean biogeochemical model to resolve physically and biologically driven diurnal variability of the ocean CO2 system. Forcing the model with 3-h atmospheric outputs derived from an Earth system model, we explore how surface ocean diurnal variability responds to historical changes and project how it changes under two contrasting 21st-century emission scenarios. Compared to preindustrial values, the global mean diurnal amplitude of pCO2 increases by 4.8 μatm (+226%) in the high-emission scenario but only 1.2 μatm (+55%) in the high-mitigation scenario. The probability of extreme diurnal amplitudes of pCO2 and [H+] is also affected, with 30-to 60-fold increases relative to the preindustrial under high 21st-century emissions. The main driver of heightened pCO2 diurnal variability is the enhanced sensitivity of pCO2 to changes in temperature as the ocean absorbs atmospheric CO2. Our projections suggest that organisms in the future ocean will be exposed to enhanced diurnal variability in pCO2 and [H+], with likely increases in the associated metabolic cost that such variability imposes. [ABSTRACT FROM AUTHOR]

Rise in mean temperature put a great deal of uncertainty about how weather and climate extremes may play out, particularly in India's varied climatic zones. Consequently, it is important to understand the possible changes in both magnitude and direction of weather and climate extremes like rainfall for different warming levels of 1.5 and 2°C scenarios concerning preindustrial and present levels. Hence in the present study, the precipitation behavior of seven North Indian states that is, Haryana, Himachal Pradesh, J&K, Punjab, Rajasthan, Uttar Pradesh, and Uttarakhand carefully studied using CMIP5 models. Future projections of precipitation has been done for the Paris Agreement global warming level of 1.5 and 2°C scenarios. Along with model validation and future projections of precipitation, the return period of extreme rainfall is also discussed to understand the behavior of the occurrence of extreme precipitation. Statistical analysis shows that the ensemble means have the least error as compared to the other six CMIP5 models. Therefore, future analysis has been done with the ensemble mean. Our findings show that the precipitation is likely to decrease in the 1.5°C scenarios, while it is likely to increase in the 2°C scenarios. The occurrence and intensity of extreme rainfall events are likely to be more frequent in all the models. The return period of the extreme rainfall events is likely to increase in all the states in both the warming scenarios. A three‐fold rise is likely to increase extreme rainfall events in the 2°C scenario. Key Points: Study performs a detailed analysis of rainfall behavior of the northern states India during preindustrial, present, 1.5 and 2° scenariosSix climate models were selected out of 26 CMIP5 climate models based on the capability of representing the monsoon rainfall over IndiaIn the 2°C scenario, the recurrence period for intense rainfall events will likely to increase by a factor of 3 [ABSTRACT FROM AUTHOR]

Climate change is predicted to alter the current climate suitability under which plant species and communities occur. Predictions of change have focused on individual species or entire communities, but theory indicates plants will not respond uniformly to climate change within or between communities. We developed models of the current climate suitability (the baseline) of 66 plant species characteristic of 29 plant communities of the arid Southwest, made predictions of climate suitability for the species under two climate change scenarios for the years 2041–2060 (Community Climate System Model version 1.4 [CCSM4] global climate model [GCM], Representative Concentration Pathway [RCP] 4.5 and 8.5 scenarios), and calculated changes in suitability between the future scenarios and baseline for each species. Climate change exposure for the entire community was then evaluated as the composite change of the predicted future climate suitability of the communities' characteristic species. Loss of 25% or more of favorable climate suitability was predicted for 39 (RCP4.5) and 51 (RCP8.5) species within their communities. The proportion of the study area with all species in a community having unfavorable suitability was 17.9% (RCP4.5) and 21.3% (RCP8.5) compared to 6.2% for baseline. We show that suitable climates for species within a plant community are not expected to be a single community‐wide trajectory, but rather changes in climate suitability will be unique to the species and not experienced uniformly across the extant communities. This decoupling of plant species within their traditional plant communities may lead to a cascade of unanticipated ecological responses and unprecedented challenges to resource management. Our study results can inform hypotheses of the future successional track of plant communities, characteristic species, and the decisions resource managers must make for management. [ABSTRACT FROM AUTHOR]

The Regional Atmospheric Modelling System (RAMS) driven by data from the CMIP5 Earth System Model HadGEM2‐ES was used to simulate daily precipitation over the tropical Americas for both current and future climate, including distinct scenarios (representative concentration pathways 4.5 and 8.5) and different time horizons (short‐term, mid‐term and long‐term changes). The major objective was to evaluate possible future changes in extreme events, with emphasis on the intensity of precipitation events and the duration of wet and dry spells. According to RAMS, longer dry spells are expected over most regions of the tropical Americas in the future, with indications for Northeast Brazil, Caribbean, Northern Amazon, and shorter wet spells over Central America and Amazon. With the exception of the Caribbean, there is a general tendency towards the increased frequency of intense precipitation in the tropical Americas. [ABSTRACT FROM AUTHOR]

Better weather and climate forecasts are needed to maximise the ability of societies worldwide to prepare for climate change. This paper summarises work regarding the hypothesis that increasing the resolution of global models to achieve this aim could be done with existing computer technology by reducing the precision of the model variables to increase efficiency. A method of testing this idea carried out using computer simulations of the idealised Lorenz '96 atmosphere is explained. Preliminary results suggest that high-resolution, reduced precision forecasts outperform low-resolution ones. If these results also apply to real-world models, the technique could yield substantial benefits. [ABSTRACT FROM AUTHOR]

In this work we analyse the effects of climate change on the recharge and the groundwater flow of the Urucuia Aquifer System in western Bahia State, Brazil (UAS‐BA) in the period 2041–2060. We assessed possible changes and impacts on groundwater recharge with four climate models from the sixth phase of the Coupled Model Intercomparison Project Phase 6 (CMIP6): ACCESS‐ESM1‐5, CanESM5, EC‐Earth3 and MIROC6, under scenarios SSP1‐2.6 and SSP5‐8.5 (Shared Socioeconomic Pathways—SSP). Visual Balan 2.0 aquifer recharge assessment code, and the MODFLOW hydrogeological model were used to analyse the impact of future climate changes on the recharge. The projections of relative change in precipitation, under the SSP1‐2.6 scenario, show a precipitation reduction between 5% and 20%, using ACCESS‐ESM1‐5, CanESM5 and EC‐Earth3 models. Using SSP5‐8.5 scenario, these models behave differently, while the CanESM5 and EC‐Earth3 models indicate an even greater decrease in precipitation, with values up to 35%, the ACCESS‐ESM1‐5 projects an increase in precipitation of up to 25%. Using MIROC6, though, an increase of up to 10% in precipitation in the UAS‐BA is expected, under both scenarios. Regarding temperature, all the models agree with a warming in the future varying from 2 to 3°C. Recharge projections calculated using the Visual Balan 2.0 indicated in general a reduction between 8% and 43%. Using the MODFLOW model, the results show a reduction of recharge to the aquifer varying from 3% to 45% using CanESM5, EC‐Earth3 and MIROC6. The ACCESS‐ESM1‐5 has a 26% reduction (5% increase) under SSP1‐2.6 (SSP5‐8.5). Furthermore, projections indicate a downward trend in the water table in the northern portion of UAS‐BA, reaching a maximum drop of up to 12 m with the CanESM5, under SSP5‐8.5 scenario. [ABSTRACT FROM AUTHOR]

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

Bias correction is a vital technique to correct the climate model outputs for regional studies. Prior bias correction methods such as quantile mapping (QM) may be problematic in correcting extreme values. Herein we proposed a novel bias correction method, that is, the piecewise‐quantile mapping (PQM), by combing piecewise mapping with QM, which implements bias corrections on both extreme and nonextreme data. We compared the PQM method and other six commonly used methods in correcting daily precipitation in the Mekong River Basin (MRB) and the Yangtze River Basin (YRB) from two global climate models (GCMs) (i.e., EC‐Earth3 and MPI‐ESM1‐2‐HR) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our results show that the PQM perform better in bias correction pertaining to both the precipitation percentiles and most of the extremes indices. For example, the mean absolute error of R20mm (the number of days when precipitation ≥20 mm) is significantly reduced from 21.64 to 9.03 days and from 10.96 to 5.71 days over MRB and YRB, respectively. In addition, the PQM could capture the spatial distributions of extremes indices reasonably well. The PQM developed in this study provides more accurate bias correction of the GCMs outputs, which will reduce the uncertainty in the subsequent analyses, such as climate change impacts on hydrological and biogeochemical cycles, particularly under extreme conditions. [ABSTRACT FROM AUTHOR]

Global climate change is projected to increase the probability of occurrence and severity of droughts. Increased CO2 concentration drives partial closure of plant stomata and reduces evapotranspiration. However, the impact of reduced evapotranspiration due to CO2 on future droughts characteristics in China is unclear. In this study, we have used the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model simulations (GCMs) under historical period, and four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5) to estimate drought. We used a widely applicable drought index (i.e., the self‐calibrating Palmer Drought Severity Index [scPDSI]) to evaluate the historical and future drought conditions, using the Penman–Monteith formula with and without the CO2 concentrations. The results show that the increasing trend of scPDSI slows down when the CO2 effect is considered in all scenarios, especially in Heilongjiang, northern Xinjiang, and the Qinghai–Tibet Plateau. The divergence in the slowdown trend among the different scenarios becomes greater after 2030, with higher emission scenarios implying a greater slowdown in the increasing trend of scPDSI. After considering CO2, drought characteristics such as the drought frequency, intensity, and drought area decrease. Increased CO2 concentration on vegetation physiological processes could mitigate future drought. Therefore, the effects of CO2 on plant physiology should be considered in studies of future drought trends to develop more realistic response and adaptation policies to future drought changes. [ABSTRACT FROM AUTHOR]

This study investigates the combined impacts of climate change and agricultural conservation on the magnitude and uncertainty of nutrient loadings in the Maumee River Watershed, the second‐largest watershed of the Laurentian Great Lakes. Two scenarios — baseline agricultural management and increased agricultural conservation — were assessed using an ensemble of five Soil and Water Assessment Tools driven by six climate models. The increased conservation scenario included raising conservation adoption rates from a baseline of existing conservation practices to feasible rates in the near future based on farmer surveys. This increased adoption of winter cover crops on 6%–10% to 60% of cultivated cropland; subsurface placement of phosphorus fertilizers on 35%–60% to 68% of cultivated cropland; and buffer strips intercepting runoff from 29%–34% to 50% of cultivated cropland. Increased conservation resulted in statistically significant (p ≤ 0.05) reductions in annual loads of total phosphorus (41%), dissolved reactive phosphorus (18%), and total nitrogen (14%) under the highest emission climate scenario (RCP 8.5). While nutrient loads decreased with increased conservation relative to baseline management for all watershed models, different conclusions on the true effectiveness of conservation under climate change may be drawn if only one watershed model was used. [ABSTRACT FROM AUTHOR]

For many years now, studies on climate change impacts on continental hydrology have suffered from conflicting results between different approaches. On the one hand, studies relying on widely used, simple metrics of land water availability, such as the Aridity Index and Palmer Drought Severity Index, depict predominantly drier future land surface conditions, when driven by climate change projections from global models. On the other hand, the same climate models also generate their own land surface projections, which exhibit balanced changes in land hydrology, with spatially heterogenous changes in soil moisture or runoff. Writing in Earth's Future, Scheff et al. (2022, https://doi.org/10.1029/2022ef002814) provide a comprehensive modeling assessment of the various processes responsible for these contrasted projections, resolving this conflict and thereby improving our understanding of future land hydrology. Key Points: Simple climate‐based metrics and full‐complexity land model simulations yield conflicting results regarding the future of the water cycleScheff et al. (2022) highlight the processes responsible for this discrepancyChanges in plant physiology and in rainfall distribution are key drivers of hydrological changes, which are omitted from simple metrics [ABSTRACT FROM AUTHOR]

Rapid climate change may exceed ecosystems' capacities to respond through processes including phenotypic plasticity, compositional turnover and evolutionary adaption. However, consequences of the resulting climate disequilibria for ecosystem functioning are rarely considered in projections of climate change impacts. Combining statistical models fit to historical climate data and remotely‐sensed estimates of herbaceous net primary productivity with an ensemble of climate models, we demonstrate that assumptions concerning the magnitude of climate disequilibrium are a dominant source of uncertainty: models assuming maximum disequilibrium project widespread decreases in productivity in the western US by 2100, while models assuming minimal disequilibrium project productivity increases. Uncertainty related to climate disequilibrium is larger than uncertainties from variation among climate models or emissions pathways. A better understanding of processes that regulate climate disequilibria is essential for improving long‐term projections of ecological responses and informing management to maintain ecosystem functioning at historical baselines. [ABSTRACT FROM AUTHOR]

The Updated Trewartha climate classification (TWCC) at global level shows the changes that are expected as a consequence of global temperature increase and imbalance of precipitation. This type of classification is more precise than the Köppen climate classification. Predictions included the increase in global temperature (T in °C) and change in the amount of precipitation (PA in mm). Two climate models MIROC6 and IPSL‐CM6A‐ LR were used, along with 4261 meteorological stations from which the data on temperature and precipitation were taken. These climate models were used because they represent the most extreme models in the CMIP6 database. Four scenarios of climate change and their territories were analysed in accordance with the TWCC classification. Four scenarios of representative concentration pathway (RCP) by 2.6, 4.5, 6.0 and 8.5 W/m2 follow the increase of temperature between 0.3°C and 4.3°C in relation to precipitation and are being analysed for the periods 2021–2040, 2041–2060, 2061–2080 and 2081–2100. The biggest extremes are shown in the last grid for the period 2081–2100, reflecting the increase of T up to 4.3°C. With the help of GIS (geographical information systems) and spatial analyses, it is possible to estimate the changes in climate zones as well as their movement. Australia and South East Asia will suffer the biggest changes of biomes, followed by South America and North America. Climate belts to undergo the biggest change due to such temperature according to TWCC are Ar, Am, Aw and BS, BW, E, Ft and Fi. The Antarctic will lose 11.5% of the territory under Fi and Ft climates within the period between 2081 and 2100. The conclusion is that the climates BW, Bwh and Bwk, which represent the deserts, will increase by 119.8% with the increase of T by 4.3°C. [ABSTRACT FROM AUTHOR]

Climate models are becoming increasingly sophisticated as climate scientists continually work to improve the realism with which the processes influencing Earth's climate are represented. One example is the treatment of cloud microphysics: as complexity is added to cloud microphysical schemes, Earth's energy budget can respond to changes in climate forcings, such as carbon dioxide or aerosols, in new ways. This increase in degrees of freedom has illuminated larger spread in climate sensitivity across the latest generation of climate models participating Coupled Model Intercomparison Project, Phase 6, with more high climate sensitivity models (Zelinka et al., 2020, https://doi.org/10.1029/2019gl085782). Whilst the historical record gives us just over a century of data to apply toward climate sensitivity constraints (e.g., Nijsse et al., 2020, https://doi.org/10.5194/esd-11-737-2020), the ocean is still taking up much of the heat trapped by anthropogenic greenhouse gas emissions and the climate system is far from equilibrium which limits our understanding how climate sensitivity might change in response to long‐term forced climate change. Here we discuss the valuable tests that paleoclimate reconstructions can provide the latest generation of climate models, as demonstrated by the recent study of Zhu et al., 2022, https://doi.org/10.1029/2021ms002776. Their study provides an example of the benefits for climate model development when climate models are confronted with simulating climates very different from today. Ideally the climate model development stage under future iterations of CMIP will involve such tests as an effort to constrain global climate sensitivity and the regional patterns of climate, such as polar amplification and subtropical aridification. Plain Language Summary: In each successive generation of climate models the representation of climate processes becomes more realistic and more complex. The more sophisticated characterization of cloud processes has resulted in some models warming much more in response to increasing atmospheric carbon dioxide concentrations due to stronger cloud feedbacks; referred to as having a higher climate sensitivity. The Community Earth System Model version 2 (CESM2) is one such model. When used to simulate the Last Glacial Maximum (LGM), CESM2 has a global temperature around 5°C colder than surface temperature reconstructions and previous generations of this model. By investigating the updated cloud scheme, Zhu et al., 2022, https://doi.org/10.1029/2021ms002776 were able to identify and modify issues with the cloud microphysical scheme that were contributing to this problem. They subsequently produced a new version of CESM2 which can simulate the LGM more accurately without degrading the simulation of the 20th century climate. The Zhu et al., 2022, https://doi.org/10.1029/2021ms002776 study is an example of how a paleoclimate can be used to identify issues with cloud schemes not traditionally recognized in the model development and validation process. Incorporating paleoclimate simulations earlier in the model development and validation process may help constrain cloud feedbacks and subsequently climate sensitivity. Key Points: Paleoclimates provide valuable tests for the latest generation of climate modelsZhu et al., 2022, https://doi.org/10.1029/2021ms002776 provide an example of the paleoclimate benefits for climate model developmentFuture iterations of CMIP will ideally incorporate paleoclimate tests during model development [ABSTRACT FROM AUTHOR]

Extreme heat is investigated in a series of high‐resolution time‐slice global simulations comparing the current and late‐21st century climates. An increase in climate‐relative extreme heat is found in the region surrounding the Black Sea. Similarities between the synoptic‐scale flows in current and future heat events combined with a decrease in future summer precipitation suggests that the increased future severity stems from strengthened land‐atmosphere feedbacks driven primarily by the changes in precipitation. The resulting intensification of heat events beyond the mean warming driven by climate change could generate significant future heat hazards in vulnerable regions. Given the continental cool bias in the present‐day simulations, the resulting estimates of future extreme heat are likely to be conservative. Plain Language Summary: Increases in extreme heat are a direct consequence of global warming. In some regions, however, the frequency and severity of heat waves can exceed what is expected given the rise in mean temperature alone. We examine this issue using data from a high‐resolution climate model run for 10 simulated summers under current and future (late 21st century) climate conditions. In the future simulations, extreme heat events, relative to the baseline of a much warmer climate, become more frequent and severe in a region surrounding the Black Sea. The weather patterns that lead to heat waves are very similar in the current and future climates. This similarity suggests that land‐atmosphere interactions during atmospheric heat waves amplify these events more in the future. Under warmer baseline conditions, the land dries out more rapidly during a heat event, reducing cooling from evaporation, and thus creating more severe and dangerous heat. Additionally, these model simulations are too cold over land areas, likely weakening the land‐atmosphere interactions so our projections of increased extreme heat are probably conservative. Key Points: Climate‐relative extreme heat events in certain regions are substantially amplified in a set of high‐resolution future climate simulationsThis regional amplification of extreme heat does not depend on changes in flow regimesResults suggest amplified heat may be due to stronger regional/local land surface feedbacks, resulting in faster drying in a warmer climate [ABSTRACT FROM AUTHOR]