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This paper presents the methodology for the construction of the KNMI'23 national climate scenarios for the Netherlands. We have developed six scenarios, that cover a substantial part of the uncertainty in CMIP6 projections of future climate change in the region. Different sources of uncertainty are disentangled as much as possible, partly by means of a storyline approach. Uncertainty in future emissions is covered by making scenarios conditional on different SSP scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5). For each SSP scenario and time horizon (2050, 2100, 2150), we determine a global warming level based on the median of the constrained estimates of climate sensitivity from IPCC AR6. The remaining climate model uncertainty of the regional climate response at these warming levels is covered by two storylines, which are designed with a focus on the annual and seasonal mean precipitation response (a dry‐trending and wet‐trending variant for each SSP). This choice was motivated by the importance of future water management to society. For users with specific interests we provide means how to account for the impact of the uncertainty in climate sensitivity. Since CMIP6 GCM data do not provide the required spatial detail for impact modeling, we reconstruct the CMIP6 responses by resampling internal variability in a GCM‐RCM initial‐condition ensemble. The resulting climate scenarios form a detailed storyline of plausible future climates in the Netherlands. The data can be used for impact calculations and assessments by stakeholders, and will be used to inform policy making in different sectors of Dutch society. Plain Language Summary: To prepare society for the effects of future climate change, we need to know what the future climate will be like. In this paper we explain the method that is used to construct six different scenarios that describe possible future climates of the Netherlands. The scenarios make assumptions about future greenhouse gas emissions, and are based on the outcomes of climate models that simulate the response of the climate to these emissions. The KNMI'23 climate scenarios show that strongly reducing global emissions strongly reduces the expected changes in the climate of the Netherlands. In the scenario in which global emissions continue to rise until 2080, Dutch society will have to adapt to a much stronger increases in heat and precipitation extremes, increased risks of droughts with low river discharge in summer, and increased risk of flooding due to high river discharges in winter. In the coming years the climate scenario data will be used to evaluate what needs to be done to keep the country a safe place for people to live in and to thrive in, under changing climate conditions. Key Points: We present a methodology for the construction of regional climate scenarios using a storyline approach to partition uncertaintyResults from CMIP6 are reconstructed with a GCM‐RCM initial condition ensemble to produce high‐resolution scenario data for end‐usersSix scenario variants cover emission uncertainty (high, moderate, low) and uncertainty in the regional response (dry‐trending, wet‐trending) [ABSTRACT FROM AUTHOR]

Climate change is expected to increase the global occurrence and intensity of heatwaves, extreme precipitation, and flash droughts. However, it is not well understood how the compound heatwave, extreme precipitation, and flash drought events will likely change, and how global population, agriculture, and forest will likely be exposed to these compound events under future climate change scenarios. This research uses eight CMIP6 climate models to assess the current and future global compound climate extreme events, as well as population, agriculture, and forestry exposures to these events, under two climate scenarios, Shared Socioeconomic Pathways (SSP), SSP1‐2.6 and SSP5‐8.5 for three time periods: early‐, mid‐, and late‐ 21st century. Climate extremes are derived for heatwaves, extreme precipitation, and flash droughts using locational‐dependent thresholds. We find that compound heatwaves and flash drought events result in the largest increases in exposure of populations, agriculture, and forest lands, under SSP5‐8.5 late‐century projections of sequential heatwaves and flash droughts. Late‐century projections of sequential heatwaves and flash droughts show hot spots of exposure increases in population exposure greater than 50 million person‐events in China, India, and Europe; increases in agriculture land exposures greater than 90 thousand km2‐events in China, South America, and Oceania; and increase in forest land exposure greater than 120 thousand km2‐events in Oceania and South America regions when compared to the historical period. The findings from this study can be potentially useful for informing global climate adaptations. Plain Language Summary: There is lacking an understanding of how the compound heatwave, extreme precipitation, and flash drought events will likely change, and how global population, agriculture, and forest will likely be exposed to these compound events under future climate change scenarios. This paper presents a comprehensive assessment of the current and future global compound climate extreme events and population, agriculture, and forestry exposures to these events under two climate scenarios. We find that compound heatwaves and flash drought events have the largest increases in exposure of populations, agriculture, and forest lands, under a high emission scenario for late‐century projections of sequential heatwaves and flash droughts. The results revealed hot spot regions of exposure to sequential heatwaves and flash droughts and consistent increases in population, agriculture, and forest land exposures for late‐century projections. The findings from this study can potentially be useful for informing global climate adaptations. Key Points: Global compound flash drought, heat wave, and extreme precipitation are projected to increase by the end of the centuryThere are significant model agreements of compound heatwave and flash drought events compared to the other compound eventsExposures of populations, agriculture, and forestry lands to sequential heatwaves and flash droughts show the largest increases [ABSTRACT FROM AUTHOR]

A crucial factor influencing the mass balance of the West Antarctic Ice Sheet is the Amundsen Sea Low (ASL), a climatological low‐pressure region situated off the West Antarctic coast. However, albeit the deepening of the ASL since the 1950s has been attributed to anthropogenic forcing, the multi‐decadal variability of the ASL remains poorly understood, because of a lack of long observations. Here, we apply a newly developed data assimilation method to reconstruct the ASL over 1870–2000. We study the forced and internal variability of the ASL using our new reconstruction in concert with existing large ensembles of climate model simulations. Our findings robustly demonstrate that an atmospheric teleconnection originating from the tropical Indo‐Pacific is the main driver of ASL variability at the multi‐decadal time scale, with resemblance to the Interdecadal Pacific Oscillation. Since the mid‐20th century, anthropogenic forcing has emerged as a dominant contributor to the strengthening of the ASL. Plain Language Summary: Changes in the West Antarctic Ice Sheet mass balance (i.e., the difference between the gain and loss of ice mass) are partly influenced by large‐scale winds, and in particular, a climatological low‐pressure feature located off the West Antarctic coast called the Amundsen Sea Low (ASL). Yet, although the long‐term strengthening of the ASL since the mid‐20th century has been demonstrated to be related to anthropogenic forcing, our understanding of the variability of the ASL on time‐scales of decades is poorly known. In this paper, we therefore investigate the origins of this variability since 1870, and quantify the relative contributions of human‐caused climate changes and natural variability of the climate system. For this purpose, we use several ensembles of model simulations as well as new climate reconstructions that combine paleoclimate records with model simulations using a statistical method. Our results indicate that the multi‐decadal variability of the ASL is strongly driven by tropical variability in the Indo‐Pacific through atmospheric connections between this region and the Amundsen Sea. Our reconstruction, when compared with a large ensemble of model simulations, indicates that since 1950, human‐induced climate forcing has become a dominant driver of long‐term ASL variability, contributing equally to tropical variability. Key Points: The large‐scale atmospheric circulation in West Antarctica exhibits a strong multi‐decadal variability superimposed by a 20th‐century trendThe multi‐decadal variability of this large‐scale atmospheric circulation is strongly governed by the Indo‐Pacific tropical variabilitySince 1950, anthropogenic forcing has emerged as a key driver of the long‐term change of this atmospheric circulation [ABSTRACT FROM AUTHOR]

Global flood mapping has developed rapidly over the past decade, but previous approaches have limited scope, function, and accuracy. These limitations restrict the applicability and fundamental science questions that can be answered with existing model frameworks. Harnessing recently available data and modeling methods, this paper presents a new global ∼30 m resolution Global Flood Map (GFM) with complete coverage of fluvial, pluvial, and coastal perils, for any return period or climate scenario, including accounting for uncertainty. With an extensive compilation of global benchmark case studies—ranging from locally collected event water levels, to national inventories of engineering flood maps—we execute a comprehensive validation of the new GFM. For flood extent comparisons, we demonstrate that the GFM achieves a critical success index of ∼0.75. In the more discriminatory tests of flood water levels, the GFM deviates from observations by ∼0.6 m on average. Results indicating this level of global model fidelity are unprecedented in the literature. With an optimistic scenario of future warming (SSP1‐2.6), we show end‐of‐century global flood hazard (average annual inundation volume) increases are limited to 9% (likely range ‐6%–29%); this is within the likely climatological uncertainty of −8%–12% in the current hazard estimate. In contrast, pessimistic scenario (SSP5‐8.5) hazard changes emerge from the background noise in the 2040s, rising to a 49% (likely range of 7%–109%) increase by 2100. This work verifies the fitness‐for‐purpose of this new‐generation GFM for impact analyses with a variety of beneficial applications across policymaking, planning, and commercial risk assessment. Plain Language Summary: Computer models use a variety of data and physical equations to estimate the extent and depth of possible flood events. Global applications of these tools have been developed over the past decade, but they are not very good at simulating the behavior of real floods. In this paper, we address some key problems to make a global model that does a lot better than past ones. We apply new techniques to better understand how much water we need to put into the model for a given flood probability. This movement of water is simulated by the model over a more accurate map of the Earth's terrain than has been available previously, with river channels represented in a smarter way. We look at the projected changes in rainfall, river discharge, and sea levels for given levels of warming simulated by available climate models and adjust the probabilities of a given magnitude flood accordingly. The model results suggest that the effect of future climate change might be small relative to our ability to understand flood hazards today, but this depends heavily on how much carbon we emit in the coming decades. Key Points: New climate‐conditioned model framework represents fluvial, pluvial, and coastal flood hazards at high‐resolution globallyComprehensive validation studies suggest that the model is approaching local model skill in many casesEmissions reduction can hold flood hazards largely constant this century, though coastal flooding will increase drastically regardless [ABSTRACT FROM AUTHOR]

This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes. Plain Language Summary: The cloud feedback is the process by which cloud property changes in a warming climate can either further enhance warming or damp it. The Arctic is warming faster than the rest of the globe, and one of the largest sources of uncertainty in its climate projections is the cloud feedback. There are two popular methods to calculate the cloud feedback: the Adjusted Cloud Radiative Effect technique, and the Cloud Radiative Kernel technique. In this paper we compare the two methods in a suite of climate models by considering the extent to which changes in Arctic sea ice impact the cloud feedbacks. From this analysis we conclude that the Cloud Radiative Kernel method is less affected by sea ice loss. We then apply the Cloud Radiative Kernel technique to data from the two most recent generations of global climate models to investigate how polar day Arctic cloud feedbacks have changed between these generations. We find that the median value of these Arctic feedbacks is slightly positive in the newest generation of models, a change from slightly negative in the previous generation that is largely fueled by a weakening of the feedback associated with changes in cloud optical depth. Key Points: The Cloud Radiative Kernel method is less sensitive to surface albedo changes than the Adjusted Cloud Radiative Effect techniqueThe Cloud Radiative Kernel method provides poor radiative closure in a suite of global climate modelsThe median shortwave Arctic cloud feedback in recent climate models is slightly positive due to a weakened cloud optical depth feedback [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic‐scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long‐wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations. Plain Language Summary: Atmospheric rivers (ARs) are a weather pattern that brings high amounts of atmospheric water and winds in a relatively narrow region. ARs are typically considered a "west coast" phenomenon, largely because the majority of the scientific research on ARs has focused on ARs in western coastal regions: particularly the western United States (US). ARs occur in continental interiors, but there has been some debate about whether these ARs represent the same type of weather as those in western coastal regions. This paper uses two objective methods for identifying ARs and finds times when ARs are present in three locations in the eastern half of the US: Norman, OK, Bloomington, IN, and Washington, DC. Examination of weather conditions during these AR times shows remarkable similarity to conditions associated with west coast ARs. This gives strong evidence that ARs do occur in the eastern half of the US. This result is important because it suggests that ARs may be important for water resources and extreme weather in the eastern half of the US, just as they are in the western US. This result also suggests that ARs may be important for water resources and extremes in other continental interiors. Key Points: Atmospheric rivers (ARs) east of the Rockies are associated with baroclinic wavesWestern coastal ARs and eastern/midwest ARs are dynamically similarSynoptic‐scale uplift, combined with convective instability, provide efficient mechanisms for generating precipitation [ABSTRACT FROM AUTHOR]

A reference or "no‐feedback" radiative response to warming is fundamental to understanding how much global warming will occur for a given change in greenhouse gases or solar radiation incident on the Earth. The simplest estimate of this radiative response is given by the Stefan‐Boltzmann law as −4σTe‾3≈−3.8 ${-}4\sigma {\overline{{T}_{e}}}^{3}\approx -3.8$ W m−2 K−1 for Earth's present climate, where Te‾ $\overline{{T}_{e}}$ is a global effective emission temperature. The comparable radiative response in climate models, widely called the "Planck feedback," averages −3.3 W m−2 K−1. This difference of 0.5 W m−2 K−1 is large compared to the uncertainty in the net climate feedback, yet it has not been studied carefully. We use radiative transfer models to analyze these two radiative feedbacks to warming, and find that the difference arises primarily from the lack of stratospheric warming assumed in calculations of the Planck feedback (traditionally justified by differing constraints on and time scales of stratospheric adjustment relative to surface and tropospheric warming). The Planck feedback is thus masked for wavelengths with non‐negligible stratospheric opacity, and this effect implicitly acts to amplify warming in current feedback analysis of climate change. Other differences between Planck and Stefan‐Boltzmann feedbacks arise from temperature‐dependent gas opacities, and several artifacts of nonlinear averaging across wavelengths, heights, and different locations; these effects partly cancel but as a whole slightly destabilize the Planck feedback. Our results point to an important role played by stratospheric opacity in Earth's climate sensitivity, and clarify a long‐overlooked but notable gap in our understanding of Earth's reference radiative response to warming. Plain Language Summary: Earth's climate is stable because a warmer planet loses more energy to space, at infrared wavelengths invisible to the naked eye. The rate of change of this energy loss as the planet warms provides an estimate how Earth's energy balance responds to warming, which is simple enough to write on a small piece of paper. When scientists investigate the warming predicted by climate models, they often start from a similar but not identical calculation of how Earth's energy balance responds to warming. This calculation, based on model output, is about 15% less stabilizing than the simple pencil‐and‐paper estimate. In this paper, we explore the causes of this 15% difference between the pencil‐and‐paper estimate and the calculations using climate models. We show that the difference is primarily caused by the lack of assumed warming in climate models high in Earth's atmosphere, where temperatures are not closely linked to surface warming. This lack of warming acts as a hidden destabilizing feedback in current analysis of climate models. Key Points: Earth's reference radiative response, or "Planck feedback," is ∼0.5 W m−2 K−1 less stabilizing than a Stefan‐Boltzmann estimateWe find this deviation is mostly due to the assumed lack of stratospheric warming in calculations of the Planck feedbackThe lack of stratospheric warming serves as an implicit positive feedback in analysis of climate model warming [ABSTRACT FROM AUTHOR]

Summary: We synthesise the findings from 10 years of ecological restoration in the Midlands of Tasmania, Australia, captured in the series of 14 papers in this special issue of Ecological Management and Restoration. The papers illustrate how expertise from disciplines as diverse as law, economics, social sciences, the arts, education, zoology, botany, genetics, climate modelling, agriculture, spatial sciences and fire ecology are necessary to address the complex social, ecological and financial questions that underpin restoration ecology. We highlight the complexity of the task, the multi‐disciplinary and collaborative approach needed, the importance of science to inform restoration practice and the problem of achieving functional connectivity. We also outline steps that need to be taken in the next 10 years. Together, the outcomes and recommendations from these studies provide a template for restoration in similar highly cleared and degraded agricultural landscapes affected by climate change in Australia and internationally. [ABSTRACT FROM AUTHOR]

The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO2) concentration scenarios used in key climate change modeling experiments. It computes the CO2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5‐8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments. Plain Language Summary: Climate model simulations are increasingly used to understand future trends in the severity and frequency of impactful climate hazards and associated physical and socioeconomic risks. Such simulations describe physical climate change in response to scenarios of greenhouse gases and land use change. However, the scenarios most widely used today as the basis for adaptation planning have no assigned probabilities; rather, they are explicitly intended to span a range of arbitrary climate futures. This reduces the applicability of resulting hazards projections for cost/benefit analysis of adaptation investments. To support risk‐based adaptation decision‐making, this study analyzes five sets of probabilistic carbon dioxide emissions simulated by four Integrated Assessment Models (IAM), along with Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 climate model ensemble results, to estimate the probability of future greenhouse gases concentration scenarios. The results of the five IAMs simulations collated in this work vary, and although they might not be representative of the wider literature, most of them suggest that the high‐emission scenario SSP5‐8.5 becomes unlikely as we reach the second half of the century. Based on lessons learned in this exercise, we propose that new sets of IAM and climate model experiments be jointly designed to better support probabilistic climate change risk assessments. Key Points: Probabilistic Integrated Assessment Models (IAM) simulations of future carbon dioxide emissions are used to evaluate the relative probability of greenhouse gases concentration scenariosJoint coordinated IAM and climate modeling experiments should be designed to enable probabilistic climate change risk assessments [ABSTRACT FROM AUTHOR]

A mass‐conserving method to downscale precipitation from global climate models (GCMs) using sub‐grid‐scale topography and modeled 700‐mb wind direction is presented. Runoff is simulated using a stand‐alone hydrological model, with this and several other methods as inputs, and compared to runoff simulated using historical observations over the western contiguous United States. Results suggest the mitigation of grid‐scale biases is more critical than downscaling for some regions with large wet biases (e.g., the Great Basin and Upper Colorado). In other regions (e.g., the Pacific Northwest) the new method produces more realistic sub‐grid‐scale variability in runoff compared to unadjusted GCM output and a simpler downscaling method. The presented method also brings the runoff centroid timing closer to that simulated with observations for all subregions examined. Plain Language Summary: Due to limitations in computing power which necessitates coarse spatial resolution, climate models do not include many details on mountains and their impact on precipitation. For this reason, it is difficult to estimate the impact of climate change on the availability of water for human consumption in places like the western United States, where mountain snowpack is an important source of water. This paper presents a way to adjust precipitation estimates from climate models by using some simple statistics about nearby mountains and valleys. The adjusted precipitation is then used in a hydrologic model to calculate the runoff that is simulated with different precipitation inputs. Results show that in most cases, the precipitation adjustment improves estimates of the resultant runoff relative to simulations with observed precipitation. The main exceptions are in very dry areas where the climate model produces far too much precipitation. With further work, the proposed adjustment may be integrated into the climate model itself to make it easier for those managing water resources (e.g., controlling reservoir levels) to use the model output to plan and adapt to climate change. Key Points: A mass‐conserving method for downscaling orographic precipitation improves modeled runoff from the CESM2Considering upwind topography further improves modeled runoff compared to simpler adjustmentsNot tuning to individual model grid points makes this method more generalizable than many existing statistical downscaling methods [ABSTRACT FROM AUTHOR]

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

The Acadian Forest Region is a temperate‐boreal transitional zone in eastern North America which provides a unique opportunity for understanding the potential effects of climate change on both forest types. Leaf phenology, the timing of leaf life cycle changes, is an important indicator of the biological effects of climate change, which can be observed with stationary timelapse cameras known as phenocams. Using four growing seasons of observations for the species Acer rubrum (red maple), Betula papyrifera (paper/white birch) and Abies balsamea (balsam fir) from the Acadian Phenocam Network as well as multiple growing season observations from the North American PhenoCam Network we parameterized eight leaf emergence and six leaf senescence models for each species which span a range in process and driver representation. With climate models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) we simulated future leaf emergence, senescence and season length (senescence minus emergence) for these species at sites within the Acadian Phenocam Network. Model performances were similar across models and leaf emergence model RMSE ranged from about 1 to 2 weeks across species and models, while leaf senescence model RMSE ranged from about 2 to 4 weeks. The simulations suggest that by the late 21st century, leaf senescence may become continuously delayed for boreal species like Betula papyrifera and Abies balsamea, though remain relatively stable for temperate species like Acer rubrum. In contrast, the projected advancement in leaf emergence was similar across boreal and temperate species. This has important implications for carbon uptake, nutrient resorption, ecology and ecotourism for the Acadian Forest Region. More work is needed to improve predictions of leaf phenology for the Acadian Forest Region, especially with respect to senescence. Phenocams have the potential to rapidly advance process‐based model development and predictions of leaf phenology in the context of climate change. [ABSTRACT FROM AUTHOR]

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

Since the early 1990s the Pacific Walker circulation shows a multi‐decadal strengthening, which contradicts future model projections. Whether this trend, evident in many climate indices especially before the 2015 El Niño, reflects the coupled ocean‐atmosphere response to global warming or the negative phase of the Pacific Decadal Oscillation (PDO) remains debated. Here we show that sea surface temperature trends during 1980–2020 are dominated by three signals: a spatially uniform warming trend, a negative PDO pattern, and a Northern Hemisphere‐Indo‐West Pacific warming pattern. The latter pattern, which closely resembles the transient ocean thermostat‐like response to global warming emerging in a subset of CMIP6 models, shows cooling in the central‐eastern equatorial Pacific but warming in the western Pacific and tropical Indian Ocean. Together with the PDO, this pattern drives the Walker circulation strengthening in the equatorial band. Historical simulations appear to underestimate this pattern, contributing to the models' inability to replicate the Walker cell strengthening. Plain Language Summary: This paper investigates the observed changes in the tropical Pacific during the satellite era, including the recent decadal strengthening of the atmospheric zonal circulation—the Walker cell. We aim to understand the extent to which these changes represent a forced response to rising CO2 concentrations versus natural variability. We apply an approach in which we decompose the observed sea surface temperature trends into three components—a pattern associated with the Pacific Decadal Oscillation, which is part of natural variability, a uniform warming pattern, and a residual pattern. This residual pattern shows a remarkable resemblance to a forced ocean thermostat‐like transient response generated in some of the climate models, characterized by equatorial Pacific (EP) cooling, and a broad warming of the Northern Hemisphere, and the Indian Ocean and West Pacific. These results challenge studies arguing that the recent strengthening of the Pacific Walker cell can be explained simply by multi‐decadal natural variability in the tropics. Furthermore, the inability of climate models at large to fully capture this forced pattern with historical forcing puts into focus the reliability of future projections of climate change in the tropical Pacific, specifically the timing of emergence of the eastern EP warming. Key Points: A multi‐decadal strengthening of the Pacific Walker cell is observed in a wide range of indices, especially after 1990A Northern Hemisphere ‐ Indo West Pacific warming sea surface temperature pattern, which differs from the Pacific Decadal Oscillation, is evident since 1980This pattern resembles a forced response to abrupt CO2 forcing, emerging in a subset of climate models, and contributes to the Walker circulation strenthening [ABSTRACT FROM AUTHOR]

Despite lower‐than‐average total precipitation in the western states of the U.S., the 2021 "precipitation roller coaster" defined as large precipitation swings has pointed to a strong hydroclimatic intensity (HYINT). Here we examine the 2021 HYINT using an index—a product of the average precipitation intensity (INT) and dry spell length (DSL). HYINT exhibited an extremely high value in the western U.S. in 2021. INT and DSL contribute differently to the 2021 HYINT, with large spatial variability. Overall, the 2021 extreme HYINT in central California and Utah is tied more to large INT, than to DSL. Meanwhile, the historical trends in INT and DSL may have contributed to the extreme 2021 HYINT event. The fraction of attributable risk framework reveals that the 2021 extreme HYINT is more likely to occur with anthropogenic forcing (e.g., 7.3 times more likely for HYINT exceeding 1.3) than natural forcing alone. Plain Language Summary: The western U.S. is a hotspot for studying climate change impacts on the hydrological cycle. Despite lower‐than‐average total precipitation in 2021, the contrasting dryness and wetness in the western U.S. has been widely reported as a "precipitation roller coaster." In this paper we quantified the "precipitation roller coaster" using an index (hydroclimatic intensity [HYINT])—a product of average precipitation intensity during wet days and dry spell length (DSL). The study found that the 2021 extreme HYINT event was largely attributable to the combined impacts of precipitation intensity and DSL in California and Utah, with precipitation intensity playing a more important role. In contrast, the 2021 precipitation event in other western states exhibited divergent contributions from precipitation intensity and DSL. The southwestern U.S. has been identified as a hotspot for increasing HYINT, which is tied more to the increasing DSL than the precipitation intensity. The trends in DSL and precipitation intensity may have played a key role in driving the 2021 extreme HYINT event. Using climate model experiments with and without anthropogenic forcing, an extreme HYINT event in the western U.S. is more likely to occur with anthropogenic forcing. Key Points: Hydroclimatic intensity (HYINT) exhibited extremely high values in parts of the western U.S. in 2021, mainly caused by average precipitation intensityHYINT shows a significant rising trend in most of the southwestern U.S. mainly tied to a rising dry spell length trendThe extreme HYINT event is more likely to occur under anthropogenic forcing than natural forcing alone [ABSTRACT FROM AUTHOR]

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

Characteristics of, and fundamental differences between, the radiative‐convective equilibrium (RCE) climate states following the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) protocols in the Community Atmosphere Model version 5 (CAM5) and version 6 (CAM6) are presented. This paper explores the characteristics of clouds, moisture, precipitation and circulation in the RCE state, as well as the tropical response to surface warming, in CAM5 and CAM6 with different parameterizations. Overall, CAM5 simulates higher precipitation rates that result in larger global average precipitation, despite lower outgoing longwave radiation compared to CAM6. Differences in the structure of clouds, particularly the amount and vertical location of cloud liquid, exist between the CAM versions and can, in part, be related to distinct representations of shallow convection and boundary layer processes. Both CAM5 and CAM6 simulate similar peaks in cloud fraction, relative humidity, and cloud ice, linked to the usage of a similar deep convection parameterization. These anvil clouds rise and decrease in extent in response to surface warming. More generally, extreme precipitation, aggregation of convection, and climate sensitivity increase with warming in both CAM5 and CAM6. This analysis provides a benchmark for future studies that explore clouds, convection, and climate in CAM with the RCEMIP protocols now available in the Community Earth System Model. These results are discussed within the context of realistic climate simulations using CAM5 and CAM6, highlighting the usefulness of a hierarchical modeling approach to understanding model and parameterization sensitivities to inform model development efforts. Plain Language Summary: Clouds, circulations and rainfall in the tropics play an important role in Earth's climate. However, global climate models differ in their representation of these features, contributing to uncertainty in future climate projections. One useful tool to better understand model differences and inform efforts to improve models is to analyze idealized configurations. We explore two different numerical representations of an idealized atmosphere relevant to tropical regions to determine the impact on the characteristics of clouds, rainfall and circulations as well as the tropical atmospheric response to warming. We show that our idealized models mirror differences in the low‐cloud structure in the deep tropics of more realistic models. This work also finds similarities between the two numerical representations, such as a decrease in the spatial extent of high altitude clouds with warming. As the surface is warmed, both versions also show increases in the clustering of clouds, the likelihood of extreme precipitation rates, and an estimate of how much warming would occur in response to a doubling of carbon dioxide. Key Points: A new radiative‐convective equilibrium (RCE) configuration is added to the official release of Community Earth System Model (CESM) to facilitate the use of Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) in the CESM hierarchyThe Community Atmosphere Model version 5 and Community Atmosphere Model version 6 (CAM6) RCEMIP climate states and variation across SST experiments are documented in the context of the CESM hierarchyDiffering parameterizations lead to a reduction of low‐level clouds in CAM6, consistent with differences in more realistic simulations [ABSTRACT FROM AUTHOR]

In December 2022, North America experienced an unprecedented extreme cold event. However, the underlying physical mechanisms of this cold wave, and the extent to which it is driven by internal variability or external forcing, are not fully understood. Using ERA5 reanalysis data and the HadGEM3‐A‐N216 attribution simulations, we identified internal variability as the main cause, contributing −5.14 K to surface air temperature (SAT) anomalies in North America. External forcing slightly mitigated the cold by 0.42 K. An internally generated wave train from the North Pacific, influenced in combination by Pacific‐North American (PNA) and North Pacific Oscillation (NPO) teleconnection patterns, initiated this intense cyclonic event, contributing −2.18 K and −2.12 K to SAT anomalies, respectively. La Niña‐like sea surface temperature anomalies amplified this wave train and resultant cold wave. Additionally, excessive snow cover in the previous November also intensified the December cold anomalies by enhancing surface albedo and reducing solar radiation. Plain Language Summary: In December 2022, North America was hit by an exceptionally severe cold event. Scientists have been trying to understand the reasons behind this cold wave. Using detailed weather data and climate models, researchers found that natural climate variability played a major role, causing temperatures to drop by about 5.14 degrees Celsius. On the other hand, external factors like human‐induced climate change had a minor effect, slightly reducing the severity of the cold by 0.42 degrees Celsius. The study identified an atmospheric wave train pattern, originating from the North Pacific and moving toward North America, which played a crucial role in triggering this extreme weather. Further investigations showed that this wave train was influenced by specific large‐scale weather patterns in the Pacific region, namely the Pacific‐North American (PNA) and North Pacific Oscillation (NPO) patterns, which contributed to the cold temperatures by approximately 2.18 and 2.12 degrees Celsius, respectively. Additionally, colder‐than‐normal sea surface temperatures in the tropical central Pacific, associated with La Niña, strengthened the wave train. Internal thermodynamical processes, such as increased snow cover, also slightly intensified the cold wave by reflecting more sunlight and reducing the amount of solar energy reaching the surface. Key Points: Internal variability triggers 2022 December extreme cold wave in North AmericaPNA and NPO are two key teleconnections responsible for this extreme cold waveSnow cover‐SAT feedback intensifies this extreme cold wave [ABSTRACT FROM AUTHOR]

Climate models struggle to produce sea surface temperature (SST) gradient trends in the tropical Pacific comparable to those seen recently in nature. Here, we find that the magnitude of the cloud‐SST feedback in the subtropical Southeast Pacific is correlated across models with the magnitude of Eastern Pacific multi‐decadal SST variability. A heat‐budget analysis reveals coupling between cloud‐radiative effects, circulation, and SST gradients in driving multi‐decadal variability in the Eastern Pacific. Using this relationship and observed feedback estimates, we find that internal Eastern Pacific SST variability is underestimated in most models. Adjusting for model bias increases the likelihood of generating a cooling trend at least as large as observations in preindustrial control simulations by ∼ ${\sim} $56% on average. If models underestimate climate "noise," as our results suggest, this bias should be accounted for when attributing the relative importance of forced versus unforced changes in the climate. Plain Language Summary: In recent decades, observed sea surface temperatures (SSTs) have cooled in the eastern tropical Pacific and warmed in the western tropical Pacific. Historical simulations using state‐of‐the‐art climate models fail to reproduce this pattern. We find that the feedback between low‐lying clouds and SSTs is related to the magnitude of naturally occurring SST variability in the Southeast Pacific. On average, climate models that have too weak a cloud‐SST feedback in the Southeast Pacific underestimate the likelihood of multi‐decadal cooling in the eastern Pacific in preindustrial simulations. Our results suggest that biases in cloud feedbacks may be causing models to underestimate internal SST variability. Key Points: Multi‐decadal Southeast Pacific sea surface temperature trends are related to the strength of the subtropical cloud feedbackCloud radiative effects amplify multi‐decadal Pacific sea surface temperature trends by impacting circulation and surface energy fluxesCorrecting model biases in cloud feedback raises the likelihood of internally producing Southeast Pacific cooling as large as observations [ABSTRACT FROM AUTHOR]

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

Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias‐corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub‐periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade‐offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques. Plain Language Summary: Global Climate Models (GCMs) are useful tools to characterize the historical and future evolution of the Earth's climate and its impacts on water resources. Because these models contain errors and their horizontal resolution is too coarse for local impact assessments, spatial downscaling, and bias correction are required steps. In particular, bias correction methods can be trained and applied using all the available historical data or by splitting the time series (e.g., by season or months). Since there are no guidelines for selecting a temporal stratification (TS), we analyze bias‐corrected GCM outputs obtained using three types of strategies (entire period, seasons, and months) and seven bias‐correction techniques over continental Chile. We show that the choice of BCM and the TS applied can modify the projected precipitation signal and seasonality. We also propose using a simple statistical technique to identify if the TS may be a relevant decision for climate impact assessments for a given climate model. Key Points: The choice of temporal stratification (TS) for GCM bias correction is crucial for removing biases, even for GCMs with good raw seasonalityDifferent temporal stratifications used for GCM bias correction may yield different future seasonalities and signals in projected changesThe linear scaling approach can be used to easily identify GCMs whose projections are sensitive to the choice of TS [ABSTRACT FROM AUTHOR]

Identifying areas expected to remain buffered from climate change and maintain biodiversity and ecological function (i.e., climate refugia) is important for climate adaptation planning. As structurally diverse transitional zones between terrestrial and aquatic environments, riparian areas are often biological hotspots and provide critical corridors for species movement, particularly in arid and semi‐arid regions. In our study region in the western and central USA, identifying riparian areas that could serve as climate refugia is a priority for wildlife managers. We mapped areas with connected riparian habitats that, based on landscape diversity and projected changes in summer temperatures and landscape runoff, are expected to serve as climate refugia. To incorporate uncertainty and balance the need for near‐ and long‐term planning, we mapped potential refugia for 2 future time periods (2040–2069, 2070–2099) based on 2 climate models that represented divergent but plausible climate outcomes. The approach we developed is not constrained by physiology or behavior of target species and can be used to identify areas expected to fare comparatively well under a wide range of future climate scenarios. Our approach can also be used to identify areas where restoration could increase riparian connectedness and climate resilience. [ABSTRACT FROM AUTHOR]

Terrestrial, aquatic, and marine ecosystems regulate climate at local to global scales through exchanges of energy and matter with the atmosphere and assist with climate change mitigation through nature‐based climate solutions. Climate science is no longer a study of the physics of the atmosphere and oceans, but also the ecology of the biosphere. This is the promise of Earth system science: to transcend academic disciplines to enable study of the interacting physics, chemistry, and biology of the planet. However, long‐standing tension in protecting, restoring, and managing forest ecosystems to purposely improve climate evidences the difficulties of interdisciplinary science. For four centuries, forest management for climate betterment was argued, legislated, and ultimately dismissed, when nineteenth century atmospheric scientists narrowly defined climate science to the exclusion of ecology. Today's Earth system science, with its roots in global models of climate, unfolds in similar ways to the past. With Earth system models, geoscientists are again defining the ecology of the Earth system. Here we reframe Earth system science so that the biosphere and its ecology are equally integrated with the fluid Earth to enable Earth system prediction for planetary stewardship. Central to this is the need to overcome an intellectual heritage to the models that elevates geoscience and marginalizes ecology and local land knowledge. The call for kilometer‐scale atmospheric and ocean models, without concomitant scientific and computational investment in the land and biosphere, perpetuates the geophysical view of Earth and will not fully provide the comprehensive actionable information needed for a changing climate. Plain Language Summary: Terrestrial ecosystems provide a natural solution to planetary warming by storing carbon, dissipating surface heating through evapotranspiration, and other processes. That forests, in particular, influence climate is a centuries‐old premise, but its potential for planetary stewardship has not been realized. In an acrimonious controversy spanning several centuries, managing forests to purposely change climate was advocated, legislated, and resoundingly dismissed as unscientific. Similar intellectual bias is evident in today's Earth system science and the associated Earth system models, which are the state‐of‐the‐art models used to inform climate policy. The popular characterization of Earth system science lauds its interdisciplinary melding of physics, chemistry, and biology, but the models emphasize the physics and fluid dynamics of the atmosphere and oceans and present a limited perspective of terrestrial ecosystems in the Earth system. Ecologists studying the living world increasingly have a voice in Earth system science as we move beyond the physical basis for climate change to Earth system prediction for planetary stewardship. As we once again look to forests to solve a climate problem, we must surmount the disciplinary narrowness that failed to answer the forest‐climate question in the past and that continues to limit the interdisciplinary potential of Earth system science. Key Points: Nature‐based climate solutions have been advocated for centuries, but have been distorted by academic bias and colonialist prejudiceEarth system science, while recognizing the climate services of the biosphere, has a geophysical bias in interdisciplinary collaborationTo realize the potential for planetary stewardship, Earth system models must embrace the living world equally with the fluid world [ABSTRACT FROM AUTHOR]

Probabilistic projections from the UK Climate Projections 2018 are presented for four global warming levels (GWLs) at 1.5, 2, 3, and 4°C above the 1850–1900 baseline. Our results show how uncertainties associated with climate models and four representative concentration pathways (RCP) emission scenarios translate to UK regional scale changes in maximum temperature and precipitation, with data also available for minimum and mean temperatures, humidity and surface net downward shortwave radiation flux. We compare weighting the likelihood of RCPs based on (hypothetical) policy decisions, against our baseline assumption that each RCP is equally likely. Differences between weighted and unweighted GWL distributions are small, particularly in relation to the full breadth of uncertainties that are incorporated into the probabilistic projections. Finally we quantify the relative importance of scenario, model and internal variability on regional projected GWLs and show that uncertainty associated with an uncertain climate response to forcings dominates at all GWLs. Plain Language Summary: Global warming levels define the global averaged increase in temperature since the pre‐industrial period. This study looks at what the regional climate in the UK could be like in the future. We estimate how temperature and precipitation in the UK might change under four different global warming levels (GWLs) (1.5, 2, 3, and 4°C above pre‐industrial times). We account for a wide range of outcomes from both climate models and future emissions scenarios. Assuming that the range of future emissions scenarios are equally likely, the center of the ranges of years that we could reach GWLs of 1.5, 2, 3, and 4°C are 2032, 2050, 2072, and 2078 respectively. The largest influence of future climate on these range estimates comes from our understanding of how the Earth actually responds to changes in greenhouse gases. Even if we knew the exact quantity of greenhouse gas emissions we would emit in the future, we still wouldn't be able to predict the exact changes in UK climate with perfect accuracy because our understanding of the regional climate response to these emissions requires further investigation. Key Points: Probabilistic climate projections from the UK are presented for four global warming levels (GWLs) across four representative concentration pathways (RCP)We show how uncertainty associated with climate models and future emission scenarios translate to UK regional scale changesUncertainty associated with an uncertain climate response to forcings dominates at all GWLs [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning. Plain Language Summary: Global climate models (GCMs) provide information on projected changes. However, their resolution is too coarse to assess future land surface changes at the regional scale. Dynamical downscaling resolves this issue, by refining the representation of hydroclimate conditions at a higher‐resolution. However, GCMs have unique historical biases that remain despite dynamical downscaling. Typically these GCM biases are ignored, resulting in assessments at the regional scale with large uncertainty. Here, we use an approach, called emergent constraint (EC), to reduce the uncertainty in projected changes in the timing of high flow conditions across the Sierra Nevada and Cascade region of the western United States. While this approach was introduced nearly two decades ago with coarse‐resolution GCM data, this is the first time the EC approach has been used at the regional scale with dynamical downscaling. From our analysis, we find high flow events are likely to occur earlier in the future for the Sierra‐Cascade region. This conclusion contains substantial uncertainty without the use of the EC approach. While this study focuses on the timing of high flows, it opens the door to a variety of follow‐on studies, seeking to produce more robust projections of hydroclimate change across the western US. Key Points: For the first time, an emergent constraint (EC) is used to reduce uncertainty in hydroclimate projections from a dynamically downscaled ensembleWe show that snowmelt‐driven basins in the western United States are very likely to experience earlier peak flows in the futureWith EC, uncertainty in future peak flow timing is reduced by roughly 39%; however, bias corrected results can differ [ABSTRACT FROM AUTHOR]

We combined two climate‐based distribution models with three finer‐scale suitability models to identify habitat for pronghorn recovery in California now and into the future. We used a consensus approach to identify areas of suitable climate now and future for pronghorn in California. We compared the results of climate models from two separate hypotheses about their historical ecology in the state. Under the migration hypothesis, pronghorn were expected to be limited climatically by extreme cold in winter and extreme heat in summer; under the niche reduction hypothesis, historical pronghorn of distribution would have better represented the climatic limitations of the species. We combined occurrences from GPS collars distributed across three populations of pronghorn in the state to create three distinct habitat suitability models: (1) an ensemble model using random forests, Maxent, classification and regression Trees, and a generalized linear model; (2) a step selection function; and (3) an expert‐driven model. We evaluated consensus among both the climate models and the suitability models to prioritize areas for, and evaluate the prospects of, pronghorn recovery. Climate suitability for pronghorn in the future depends heavily on model assumptions. Under the migration hypothesis, our model predicted that there will be no suitable climate in California in the future. Under the niche reduction hypothesis, by contrast, suitable climate will expand. Habitat suitability also depended on the methods used, but areas of consensus among all three models exist in large patches throughout the state. Identifying habitat for a species which has undergone extreme range collapse, and which has very fine scale habitat needs, presents novel challenges for spatial ecologists. Our multimethod, multihypothesis approach can allow habitat modelers to identify areas of consensus and, perhaps more importantly, fill critical knowledge gaps that could resolve disagreements among the models. For pronghorn, a better understanding of their upper thermal tolerances and whether historical populations migrated will be crucial to their potential recovery in California and throughout the arid Southwest. [ABSTRACT FROM AUTHOR]

The impact of global warming on lightning flash rates remains relatively unknown. In this study, the South China Sea (SCS) and the surrounding areas within Southeast Asia were selected to examine the long‐term trend and future projection of lightning activity based on the currently longest satellite‐based lightning data set available and climate models. Our study revealed a reduction in the observed lightning flash rates around the SCS, with a linear trend of −0.11 fl km−2 yr−2 during 1996–2013. In contrast, the precipitation around the SCS exhibited an increasing trend and was negatively correlated with the local lightning flash rate. The sea surface temperature gradient over equatorial Pacific Ocean, latent heat flux over the equatorial Indian Ocean, local convective available potential energy, precipitation and aerosol changes collectively accounted for 82% of the variance in the lightning fluctuations over the SCS and Southeast Asia. Multiple linear regression proxies of lightning flash rates were constructed and applied to the climate models. The models indicated that lightning activity around the SCS is projected to intensify by 10% and 12% by the end of the 21st century under SSP245 and SSP370, respectively. Plain Language Summary: The projection of future changes in lightning activity is uncertain owing to various factors, and different regions exhibit distinct trajectories. In this study, the focus was on the South China Sea (SCS) and Southeast Asia, and past variations in lightning activity was examined and used to predict future variations in lightning activity. Employing a multiple linear regression approach, we identified the large‐scale thermodynamic drivers and local environmental factors that influence lightning activity around the SCS. The research findings indicated a reduction in lightning activity over the past few decades over the SCS and Southeast Asia region. The simulation results indicate that in the future under ongoing global warming, regional lightning activity will intensify. Key Points: Lightning activity and precipitation show opposing regional trends around the South China Sea (SCS)Remote thermodynamic and local environmental factors both contribute to the variability in lightning around the SCSUnder future climate change scenarios, lightning activity around the SCS is projected to gradually increase [ABSTRACT FROM AUTHOR]

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

Previous modeling studies indicate that even though marine cloud brightening under a susceptibility‐based strategy is effective in reducing the global average surface temperature, it triggers a La Niña‐like sea‐surface temperature response with cooling mostly confined within lower latitudes. Here we explore a different cloud seeding strategy involving seeding of regions with low susceptibility, primarily over the ocean in midlatitudes during the winter. Simulations with the Community Earth System Model, version 2 reveal that because the regional forcing is weaker and more widespread, cooling is more evenly distributed over the globe. This new strategy also does not result in the La Niña‐like state seen in the other strategies. Plain Language Summary: Most previous marine cloud brightening (MCB) simulations have focused on the regions more likely to induce strong cooling. A common response in these simulations across different climate models is a La Niña‐like sea surface temperature pattern which could disrupt the El Niño Southern Oscillation. We explored simulations in which MCB is deployed over the least susceptible regions rather than the most susceptible. This new seeding strategy cools the globe more evenly and no longer triggers a La Niña‐like response. Key Points: We explore the idea of deploying marine cloud brightening (MCB) over broader regions with low susceptibility to cloud seedingThis approach induces fairly uniform cooling over the globe unlike MCB that targets the most susceptible regionsThis new seeding strategy has fewer climatic side effects and does not trigger a La Niña‐like response [ABSTRACT FROM AUTHOR]

The El Niño–Southern Oscillation (ENSO) teleconnection to Europe is projected to strengthen under global warming in most climate model simulations. However, given the current difference between recent observations and historical model simulations of tropical Pacific sea surface temperature trends, with models simulating an El Niño‐like warming in recent decades which is in disagreement with observations, it is important to understand the relative contributions of changes to the teleconnection forcing and background state to the overall teleconnection change. Using idealized climate model experiments, we show that both the eastward shift of El Niño precipitation and background state changes make contributions to the overall teleconnection change. These results suggest that the ENSO–Europe teleconnection can be expected to strengthen under global warming, even if ENSO precipitation anomalies do not shift eastwards as currently projected. However, the magnitude of the strengthening may depend on how much of an eastward shift does occur. Plain Language Summary: El Niño–Southern Oscillation (ENSO) can influence the weather and climate in Europe, and this connection is expected to become stronger under global warming according to most climate models. However, there is a difference between recent observations of sea surface temperatures in the Pacific Ocean and what climate models simulate. These differences could have important implications for future projections of ENSO's impact on Europe. To understand how likely the projected future strengthening of this link is, we looked at how two factors contribute to this connection change: (a) changes in rainfall associated with El Niño in the tropical Pacific and (b) overall changes in global climate elsewhere. Using a simplified climate model, we found that both these factors play a role in making the connection between El Niño and Europe stronger. This suggests that even if the specific patterns of El Niño rainfall don't change as projected, the link between El Niño and Europe will still get stronger due to global warming. However, how much stronger it gets might depend on the actual changes in El Niño rainfall, so it's important to figure out why there's a difference between observations and the model projections of Pacific Ocean temperature trends. Key Points: The El Niño–Southern Oscillation teleconnection to Europe is projected to strengthen under global warmingThis change is driven by an eastward shift of El Niño precipitation anomalies in the tropical Pacific and background state changes globallyBackground state changes have a greater impact on European circulation than precipitation changes, particularly over central‐eastern Europe [ABSTRACT FROM AUTHOR]

We compare abrupt CO2‐quadrupling (abrupt‐4xCO2) and ‐doubling (abrupt‐2xCO2) simulations across 10 CMIP6 models. Two models (CESM2 and MRI‐ESM2‐0) warm substantially more than twice as much under abrupt‐4xCO2 than abrupt‐2xCO2, which cannot be explained by the non‐logarithmic scaling of CO2 forcing. Using an energy balance model, we show that increased warming rates within these two models are driven by both less‐negative radiative feedbacks and smaller global effective heat capacity under abrupt‐4xCO2. These differences are caused by a decrease in low cloud cover and shallower ocean heat storage, respectively; both are linked to smaller fractional declines in the Atlantic Meridional Overturning Circulation (AMOC) under abrupt‐4xCO2 (relative to abrupt‐2xCO2). On a global scale, higher climate sensitivity under larger forcing can be explained by a feedback‐temperature dependence; however, we find that forcing‐dependent spatial warming patterns due to AMOC decline are an important physical mechanism which reduces warming in a way that is not captured by a global‐mean framework. Plain Language Summary: Climate model simulations with abruptly quadrupled CO2 concentrations are often used to characterize the climate response of global climate models to greenhouse gas forcing and inform predictions of the future. In this study, we ask how relevant CO2‐quadrupling experiments are to more modest warming scenarios by comparing them to CO2‐doubling experiments. It is usually assumed that CO2‐quadrupling will produce about twice as much warming than CO2‐doubling, but this is not the case in some models. Two global climate models in particular warm much more under CO2‐quadrupling than twice the warming under CO2‐doubling. By fitting each climate model to a simpler energy balance model, we find that these two climate models are strongly impacted by the effective heat capacity (which can be impacted by ocean circulation changes) in comparison to other climate models where radiative effects (such as effects from clouds) are more impactful. Changes in the strength of an Atlantic Ocean circulation are important for both radiative effects and heat storage. Therefore, the behavior of our planet at 1.5 or 2 times CO2 cannot necessarily be estimated with quadrupling CO2 experiments. Key Points: Models with disproportionate Atlantic Meridional Overturning Circulation (AMOC) declines under abrupt CO2 doubling experience less warmingAMOC decline modulates transient warming by influencing both low cloud feedbacks and the global effective heat capacityTherefore, abrupt quadrupling of pre‐industrial CO2 simulations do not necessarily capture the behavior of lesser warming scenarios [ABSTRACT FROM AUTHOR]

We assess the impact of three bias correction approaches on present day means and extremes, and climate change signal, for six climate variables (precipitation, minimum and maximum temperature, radiation, vapour pressure and mean sea level pressure) from dynamically downscaled climate simulations over Queensland, Australia. Results show that all bias‐correction methods are effective at removing systematic model biases, however the results are variable and season‐dependent. Importantly, our results are based on fully independent cross‐validation, an advantage over similar studies. Linear scaling preserves the climate change signals for temperature, while quantile mapping and the distribution‐based transfer function modify the climate change signal and patterns of change. The Perkins score for all the values above the 95th percentile and below the 5th percentile was used to evaluate how well the climate model matches the observational data. Bias correction improved Perkins score for extremes for some variables and seasons. We rank the bias‐correction methods based on the Kling–Gupta efficiency (KGE) score calculated during the validation period. We find that linear scaling and empirical quantile mapping are the best approaches for Queensland for mean climatology. On average, bias correction led to an improvement in the KGE score of 23% annually. However, in terms of extreme values, quantile mapping and statistical distribution‐based transfer function approaches perform best, and linear scaling tends to perform worst. Our results show that, except linear scaling, all approaches impact the climate change signal. [ABSTRACT FROM AUTHOR]

Uttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs. Plain Language Summary: Uttar Pradesh is the most populated state in India, with most of the population working in the agriculture sector and having a low income. The state's vulnerability to climate change is high due to inadequate infrastructure and heavy dependence on agriculture. Rice is a crucial crop for the state, but this study shows that climate change will decrease rice yields in the future, especially for irrigated rice, due to higher temperatures and shorter growing seasons. While rainfed rice yields may increase in some regions due to increased rainfall, rice production is expected to decline overall. Key Points: Rice yield (combining irrigated and rainfed) in Uttar Pradesh, India, is projected to decrease in the future for SSP2‐4.5 and SSP5‐8.5With a projected increase in rainfall, rainfed rice yield increases in rainfall deficit zones, and irrigation decreases under both shared socioeconomic pathwaysPlanting in the early season could reduce the amount of yield loss for irrigated rice [ABSTRACT FROM AUTHOR]

Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts. Plain Language Summary: Regional warming associated with climate change is already making droughts worse in many places. This trend is expected to continue as global temperatures increase in response to continued emissions of greenhouse gases. With increasing temperatures, soils are projected to lose water to the atmosphere as it draws more and more moisture from the land surface, increasing drought risk. At the same time, climate change is also projected to shift precipitation patterns. This means it is important to understand how changes in rainfall will cause and end droughts in the future. Using state‐of‐the‐art climate models, we investigate how future warming will impact the influence of precipitation on soil moisture in the Mediterranean and Western North America. Our results suggest that current levels of monthly precipitation will be insufficient to alleviate drought conditions in a warmer world. Key Points: Warming causes declines in soil moisture in the Mediterranean and Western North AmericaThe probability of a moderate soil moisture drought increases during both low and high precipitation anomalies, even at 1°C of warmingThe ability of large positive precipitation anomalies to terminate soil moisture droughts will be substantially reduced in a warmer world [ABSTRACT FROM AUTHOR]

UK water resources face a number of challenges when planning for an uncertain future. Climate change impacts and what future droughts might look like can be a significant contributor to this uncertainty. Recent and potential future developments (e.g. ever‐finer resolutions) in climate modelling offer the potential for running bias‐corrected transient future scenarios through hydrological, hydrogeological and water supply models, providing users with droughts of differing severity, frequency, spatial extent and duration to those experienced historically, incorporating changes over time and an understanding of climate model uncertainty. The recent enhanced Future Flows and Groundwater (eFLaG) project sought to demonstrate a climate service using these transient scenarios, with the aim of enhancing the resilience of the water industry to drought events and complementing existing approaches. The project demonstrated the use of this transient climate change information within a water resource setting, using a variety of hydrological and water resource models to help illuminate potential gaps and issues with such an approach. If we are to realise the potential of transient scenarios, a number of barriers – both scientific and organisational – need to be overcome. We present a road map for the future based on outcomes from the eFLaG project, as well as ways the eFLaG projections could be used to improve system resilience in the present. [ABSTRACT FROM AUTHOR]

While previous studies have largely focused on anthropogenic warming characterized by surface air temperature, little is known about the behaviors of human‐perceived temperature (HPT), which describe the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity and/or wind speed. Here we adopted an optimal fingerprinting method to compare seasonal mean HPTs in China with those from simulations conducted with multiple climate models participating in the Coupled Model Intercomparison Project Phase 6. We found clear anthropogenic signals in the observational records of changes in both summer and winter HPTs over the period 1971–2020. Moreover, the anthropogenic greenhouse gas influence was robustly detected, with clear separation from natural and anthropogenic aerosol forcings. The anthropogenic greenhouse gas forcing plays the dominant role (>90%) of human‐perceived warming. Urbanization effects contribute slightly and moderately to the estimated trends in summer and winter HPTs, respectively, in addition to the effects of external forcing. Plain Language Summary: Human influences have been identified in the observed warming quantified by surface air temperature (SAT), but SAT alone is inadequate as a metric for human thermal comfort. Here we focus on human‐perceived temperature (HPT), which describes the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity, and/or wind speed. We isolate anthropogenic impacts on the observed increase in summer and winter HPTs in China during 1971–2020 by comparing observations with state‐of‐the‐art climate models. Results show that the influence of anthropogenic greenhouse gas is detected, with clear separation from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most (>90%) of the observed human‐perceived warming. Along with the effects of large‐scale anthropogenic forcing, urbanization effects also have a slight to moderate influence on the estimated trends in summer and winter HPTs. Our work is an early attempt to provide quantitative evidence for the physiological impacts of anthropogenic global warming and local urbanization on human beings. Key Points: The warming is quantified by human‐perceived temperature that considers the joint effects of temperature, humidity and/or wind speedHuman influence could be robustly detected in both summer and winter human‐perceived warmingThe observed increase in human‐perceived temperature is mostly attributed to anthropogenic greenhouse gas increases [ABSTRACT FROM AUTHOR]

Recent years were characterized by spatially co‐occurring hot and wet extremes around the globe, raising questions about the contribution of human‐induced global warming to the changing likelihoods of such extreme years. To characterize spatially co‐occurring extremes we investigate recent trends in global and regional land area that is concurrently affected by hot or wet annual maxima taking observational uncertainty into account. Observed trends in the land area affected by extreme events are assessed in the context of Earth System Model (ESM) simulations for present‐day and early‐industrial climate conditions in a detection and attribution setting. We compare different reanalysis and station‐based observational products to account for observational data uncertainty. At the global scale, trends of spatially co‐occurring hot or wet annual maxima in all observational products can be explained by ESM simulations driven by historical radiative forcing that accounts for human‐induced changes in the composition of the atmosphere but cannot be explained by ESM simulations that account for an early‐industrial radiative forcing. At the regional scale, trends in spatially co‐occurring hot annual maxima are in general coherent among observational products and can in most cases be attributed to human influence on the climate system. Trends in spatially co‐occurring wet annual maxima show differences in some regions, highlighting the importance of a multi‐dataset approach to overcome observational product dependencies. Despite observational uncertainty, we find robust detection and attribution results for many regions. These results can complement previous assessments on regional exposure to hot and wet events from the new IPCC AR6 report. Plain Language Summary: Recent years were characterized by spatially co‐occurring hot and wet extremes around the globe, leading to questions about the contribution of human‐induced climate change to the occurrence of such extreme years. In this study, we investigate trends in the global and regional land area that is concurrently affected by hot or wet annual maxima. Due to observational uncertainties, we investigate different observation‐based data products for temperature and rainfall. By comparing observational trends in the land area affected by extreme events to respective trends from Earth System Model ensemble simulations accounting for climate conditions of the past decades and early‐industrial climate conditions, we can evaluate their attribution to human‐induced climate change. On the global scale, observed trends of spatially co‐occurring hot or wet annual maxima cannot be simulated by climate models when considering early‐industrial climate conditions, but are captured by climate models considering recent climate conditions. This holds also true for the detection and attribution of regional trends in concurrent land area affected by hot annual maxima. Observational products agree in many regions on the detection and attribution of regional trends in concurrent land area affected by wet annual maxima, but also disagree in some regions. Key Points: Global and regional trends of spatially concurrent hot or wet annual maxima can be attributed to human‐induced climate changeIn some regions observational products show inconsistencies in the sign of detectable trends in concurrent wet annual maximaLarge regional differences in spatial exposure and steady increase in concurrent land area affected by hot or wet annual maxima is detected [ABSTRACT FROM AUTHOR]

Global warming has largely advanced spring vegetation phenology, which has subsequently affected terrestrial carbon and water cycles. However, further shifts in vegetation phenology under future climate change remain unclear. We estimated the start of the growing season (SOS) by applying multiple extraction methods based on the NDVI3g data set, and then parameterized and evaluated 11 spring vegetation phenology models that included chilling, forcing, and the photoperiod. Based on scenario data from three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) derived from eight climate models, future vegetation phenology was predicted using the phenology models. Results showed that all the phenology models performed better than the NULL model (mean of the SOS), with the performance of one‐phase models broadly matching that of two‐phase models, although the best models varied by vegetation type. The spatial pattern of simulated SOS was similar among the models, and it explained >75% of the variation. Based on the mean predicted SOS, we found that spring vegetation phenology will continue to advance under strong warming conditions (SSP245 and SSP585), but that the trend of advance will reverse at around 2060 under the SSP126 scenario. The continued trend in SOS advance is likely related to rapid forcing fulfillment under stronger warming conditions. However, under moderate warming, chilling might be reduced and it might require longer to compensate for higher forcing, which ultimately would result in SOS delay. Our findings highlight that trends will likely change under different warming conditions, potentially causing widespread impact on species interaction, biodiversity, and ecosystem function. Plain Language Summary: Phenological change, which is often seen as an indicator of climate change, has received widespread attention. However, despite this focus, comprehension of how vegetation phenology might alter under different climate change scenarios remains inadequate. We used 11 process‐based phenology models to forecast changes in the start of the growing season (SOS) in the Northern Hemisphere under 3 climate change scenarios (SSP126, SSP245, and SSP585). Results showed that the one‐phase models and the two‐phase models exhibited similar performance, which exceeded that of the NULL model (mean of the SOS). Under the strong warming scenarios (SSP245 and SSP585), spring phenology was projected to continue to advance, whereas under the moderate warming scenario (SSP126), the trend of advance was predicted to reverse at around 2060. Our findings suggest that climate change might alter the competition between species for spring resources, and thereby have widespread impact both on biodiversity and on the structure and function of ecosystems. Key Points: Process‐based models outperform statistical models in forecasting changes in spring vegetation phenology in the Northern HemisphereSimulated start of the growing season explained >75% of the percentage of varianceTrend of advance in the start of the growing season in the Northern Hemisphere will reverse by 2060 under moderate warming conditions [ABSTRACT FROM AUTHOR]

As sea ice disappears, the emergence of open ocean deep convection in the Arctic, which would enhance ice loss, has been suggested. Here, using 36 state‐of‐the‐art climate models and up to 50 ensemble members per model, we show that Arctic deep convection is rare under the strongest warming scenario. Only five models have convection by 2100, while 11 have had convection by the middle of the run. For all, the deepest mixed layers are in the eastern Eurasian basin. When that region undergoes a salinification and increasing wind speeds, the models convect; yet most models are freshening. The models that do not convect have the strongest halocline and most stable sea ice, but those that lose their ice earliest ‐because of their strongly warming Atlantic Water‐ do not have a persistent deep convection: it shuts down mid‐century. Halocline and Atlantic Water changes urgently need to be better constrained in models. Plain Language Summary: Both observations and modeling simulations suggest that deep vertical mixing (or deep convection) in winter may become the new normal in the Arctic as sea ice disappears, which further accelerates that disappearance. These simulations are often done using only one model, so here we used all models available that participated in the Climate Model Intercomparison Project Phase 6 (CMIP6), for the strongest warming scenario. We show that after removing those that are already inaccurate in the present, and even with a restrictive threshold, most models have no deep convection in the Arctic, or extremely rarely. Only five still had deep convection by the time the run finishes in 2100. We found that deep convection regions and periods are associated with a saltier, windier surface, while the rest of the Arctic and/or run freshens. The subsurface properties were crucial too: a deep halocline, that is, strongly stratified model, leads to less ice loss and no deep convection. In contrast, models with a shallow halocline and strongly warming Atlantic Water lose their ice and convect earliest, although it does not persist. As water mass properties are poorly represented in CMIP6 models, these need improving to better constrain Arctic deep convection and sea ice projections. Key Points: Oceanic deep convection does not emerge and persist in the Arctic in the majority of Climate Model Intercomparison Project Phase 6 models, despite a drop in the Nordic SeasArctic deep convection occurs only when both surface salinity and winds are increasing, year round, yet most models are fresheningA deep halocline and stable ice cover hinder deep convection, while early ice loss due to warmer Atlantific water can also stop it rapidly [ABSTRACT FROM AUTHOR]

The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal‐mean precipitation‐minus‐evaporation (P − E) under greenhouse‐gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P − E and increase P − E in the deep tropics. The surface‐albedo feedback shifts the location of maximum tropical P − E and increases P − E in the polar regions. The intermodel spread in the P − E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse‐rate and surface‐albedo feedbacks playing important roles in the polar regions. The P − E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the "wet‐gets‐wetter, dry‐gets‐drier" paradigm. Plain Language Summary: Climate feedbacks, which act to amplify or dampen global warming, play an important role in shaping how the climate system responds to changes in greenhouse‐gas concentrations. Here, we use an idealized climate model, which makes a simplified assumption about how energy is transported in the atmosphere, to examine how climate feedbacks influence the patterns of precipitation and evaporation change under global warming. We find that the cloud feedback acts to narrow the band of rainfall on the equator known as the Intertropical Convergence Zone and that the surface‐albedo feedback acts to shift the location of maximum rainfall. We also find that the cloud feedback accounts for most of the uncertainty associated with feedbacks in regional hydrological change under warming. The idealized model with a locked cloud feedback also simulates a change in precipitation and evaporation that is similar to a comprehensive climate model with an inactive cloud feedback. Key Points: A moist energy balance model (MEBM) is used to investigate the influence of climate feedbacks on regional hydrological changes under warmingCloud feedbacks act to narrow and increase tropical P − E and are the dominant source of feedback uncertainty in regional hydrological changesThe MEBM with a locked cloud feedback largely replicates a climate model with an inactive cloud feedback [ABSTRACT FROM AUTHOR]

Future climate projections suggest a poleward shift of the maximum intensity of tropical cyclones (TCs) over the western North Pacific. However, the global nature of the latitudinal change in TC genesis under global warming remains poorly understood. We show, using large‐ensemble high‐resolution atmospheric model simulations (d4PDF) with four warming scenarios, that the poleward shift is a robust change over the globe, attributable to the weakening of the Hadley circulation. The weakened ascent driven by the upper‐tropospheric warming suppresses the TC genesis within 5°–20° latitudes, whereas the weakened descent enhances the TC genesis in the poleward latitudes. We further estimate the poleward shift of TC genesis to emerge at the 2 K global warming over the Arabian Sea, South Atlantic and Pacific Oceans and at the 4 K warming over the North Pacific. The present results underscore the potential for increasing social and economic risks associated with TCs at higher latitudes. Plain Language Summary: Climate models have projected a decrease in TC genesis frequency in future warming. However, the global nature of the latitudinal change in TC genesis under global warming remains uncertain partly due to insufficient resolution as well as the ensemble size of climate model simulations. We show a global feature of the robust poleward shift of the TC genesis during the active seasons of both hemispheres scaled with the global warming level, which can be attributed to the weakening of the Hadley circulation. The weakened ascending branch of the Hadley circulation, driven by the increased upper tropospheric warming, potentially hinders TC genesis within 5°–20° latitudes. Conversely, the weakened descending branch of the Hadley circulation enhances the likelihood of TC genesis within 20°–35° latitudes. We further estimate that the signal of TC genesis is expected to emerge over high latitudes of the Arabian Sea, South Atlantic and South Pacific Oceans at the 2 K warming and at the 4 K warming over the North Pacific. The present analyses have significant implications not only for assessing the reliability of future TC‐related changes in climate models but also for estimating the increased TC‐related hazards at higher latitudes under global warming. Key Points: We project a global feature of the robust poleward shift of tropical cyclone (TC) genesis during active seasons of both hemispheresMore TC genesis at high latitudes can be attributed to the weakening of the Hadley circulationPoleward shift of TC genesis emerges at 2 K warming over Arabian Sea, South Atlantic and Pacific Oceans and at 4 K warming over North Pacific [ABSTRACT FROM AUTHOR]

Understanding the response of the Indian summer monsoon (ISM) onset to CO2 forcing is of utmost importance for rain‐fed agriculture and water management. In this study, we utilized an idealized symmetric CO2 removal scenario from the sixth phase of the Coupled Model Intercomparison Project to analyze the reversibility of monsoon onset. The results show that ISM onset is reversible but exhibits strong asymmetry: it undergoes minimal changes during the ramp‐up phase, but experiences rapid postponement as the CO2 begins to decline; Eventually, it is delayed more than 1 week when the CO2 concentration is restored to the initial level. To investigate the possible underlying mechanisms, we decomposed the climate response to CO2 forcing into the fast and slow processes. Notably, it is the enhanced slow response, which is driven by long‐term sea surface temperature (SST) changes, that dominates the asymmetric response of ISM onset. This slow response delays the ISM onset by strengthening near‐surface poleward land‐sea moist static energy contrast, thereby weakening the lower‐tropospheric monsoonal circulation. Based on the atmospheric component model simulations, we found that both the uniform SST change and patterned SST changes in the slow response contribute to the delay of ISM onset, but the latter plays a dominant role. Our results emphasize the importance of thoroughly assessing regional hydrological cycle features when designing the CO2 removal pathways. Plain Language Summary: The 2015 Paris Agreement set a target to limit global warming to 2°C by the end of the 21st century, with a preference for achieving 1.5°C. Meeting this temperature goal requires the atmospheric CO2 concentration to peak in this century and then start declining. It is crucial to understand whether the changes in the regional hydrological cycle can be reversed when we reach the global mean temperature goal. This study focuses on examining the responses of the Indian summer monsoon (ISM) onset to idealized CO2 removal (CDR) forcing, in which the atmospheric CO2 concentration decreases symmetrically after increasing. Under the symmetric CDR pathway, the ISM onset displays reversible yet asymmetric evolution, it significantly delays when the CO2 concentration returns to the pre‐industrial level. This asymmetric response is attributed to the slow process of the climate system driven by long‐term sea surface temperature change, which leads to the delay in ISM onset by weakening the lower‐tropospheric circulation and, consequently, the precipitation. The slow response lags behind the evolution of CO2 concentration and plays a dominant role in the global mean surface temperature change during the ramp‐down phase. Understanding these mechanisms is essential for addressing climate change challenges and the impacts on regional monsoonal systems. Key Points: The idealized symmetric CO2 removal forcing leads to a reversible but asymmetric Indian summer monsoon (ISM) onsetThe delayed onset of ISM during the ramp‐down period is primarily dominated by the asymmetric and lagged slow process, which is associated with the long‐term sea surface temperature changeThe slow process delays the ISM onset by enhancing the near‐surface poleward land‐sea moist static energy contrast, which subsequently weakens the monsoonal circulation [ABSTRACT FROM AUTHOR]

To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science. Plain Language Summary: While temperatures around the world continue to warm due to human‐caused climate change, some areas have observed smaller temperature trends than others. Understanding this regional variability in the rate of warming is important when assessing future projections. One location that has observed less warming is across the United States during their summer season. To evaluate temperature variability in this region using real‐world observations and climate model simulations, we use a statistical method from artificial intelligence called neural networks. The goal of the neural network setup is to learn temperature patterns across the United States and then identify whether climate change effects have exceeded the range of natural variability that has occurred in the past. This is called the timing of emergence (ToE), which is the first year that the effect has clearly appeared. We find that the average United States minimum temperature increase has already emerged in historical records. However, we find no ToE for the average maximum temperature, other than in the Western United States. Another important finding of this study is that by using higher resolution climate model data (i.e., more latitude and longitude points), we find better accuracy in the neural network predictions. Key Points: Forced temperature changes have emerged in observations during summer in the United States as detected by an artificial neural networkIncreasing spatial resolution improves neural network skill for predicting the year of a given summer temperature mapWestern United States land surface climate properties contribute to earlier timing of emergence predictions for the SPEAR climate model [ABSTRACT FROM AUTHOR]

Based on outputs of 28 coupled models from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6), we show that the response of the Southeast Asian summer monsoon to the El Niño‐Southern Oscillation (ENSO) during post‐ENSO summer will likely strengthen in a warmer climate, which can be attributed to concurrently weakened sea‐surface temperature anomalies (SSTAs) in the western equatorial Pacific (WEP). The weakened WEP SSTAs are primarily caused by enhanced latent heat damping due to increased surface wind speed anomalies, which are associated with the eastward shift of the El Niño‐induced anomalous Walker circulation due to El Niño‐like sea surface temperature change in the tropical Pacific under global warming. Besides, the climatological zonal ocean currents will slow down due to the weakening of climatological Walker circulation, which also acts to weaken the WEP SSTAs via reducing the advection of anomalous temperature by the mean current. Plain Language Summary: As an important component of the Asian monsoon system, the Southeast Asian summer monsoon (SEASM) is crucial for the livelihoods of billions of people in East Asia, and is closely connected to a climate phenomenon called El Niño‐Southern Oscillation (ENSO). Understanding how the relationship between SEASM and ENSO will change in the future is important for enhancing our knowledge of climate change in East Asia. Outputs of 28 climate models from the Phase 6 of the Coupled Model Intercomparison Project show that ENSO will exert enhanced impacts on the SEASM in a warmer climate. The enhanced influences of ENSO on the monsoon will exacerbate the reduction of rainfall over the western North Pacific during the post‐El Niño summer. We find that such projected changes are mainly caused by weakened warm sea surface temperature (SST) anomalies (SSTAs) in the western equatorial Pacific (WEP). Further analyses indicate that the change in WEP SSTAs can be linked to the El Niño‐like change in climatological SSTs in the tropical Pacific. This study depicts detailed physical processes responsible for the projected changes in ENSO's impacts on the SEASM. Key Points: The effects of the El Niño‐Southern Oscillation on the Southeast Asian summer monsoon will strengthen under global warmingThe enhanced El Niño's impacts result from the weakened warm sea‐surface temperature (SST) anomalies in the western equatorial Pacific (WEP)The weakened WEP SST anomalies are related to the eastward shift of anomalous Walker circulation and the slackened mean zonal ocean currents [ABSTRACT FROM AUTHOR]

Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC (RBC, the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use RBC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust RBC to make the modeled BC mixing state index (χmode) match the one predicted by the ML model (χML). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χmode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χML (66%). The RBC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs. Plain Language Summary: Black carbon (BC) mixing state is an important property for assessing impacts of BC on human health and climate. It describes how BC is distributed among the particle population and within individual particles. Due to computational costs, global climate models (GCMs) consider aerosols as either external mixtures (e.g., bulk models) or fully internal mixtures with uniform composition within a certain size range (e.g., an aerosol bin in sectional models or an aerosol mode in modal models). To improve BC mixing state representation, we coupled a machine learning (ML) model learned from particle‐resolved simulations to the online simulations of the Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4‐ML). We used the mass ratio of coating to BC (RBC) to determine the mass fraction of BC‐free particles and BC‐containing particles, and we adjusted RBC to make the modeled BC mixing state match ML model. The MAM4‐ML model reduces the degree of internal mixture of BC substantially. In addition, MAM4‐ML RBC is reduced by 52%, leading to a 20% reduction in BC activation fraction and a 4% increase in BC burden. This study proposes a method for the application of the ML to improve GCMs. Key Points: Machine learning model trained on particle‐resolved simulations has been coupled to CAM6 MAM4 to improve BC mixing state representationThe BC mixing state index is reduced by 19%, and the mass ratio of coating to BC is reduced by 52%, in better agreement with observationsDue to the drop in hygroscopicity of BC‐containing aerosol (9%), BC activation fraction decreased by 20%, and BC burden increased by 4% [ABSTRACT FROM AUTHOR]

High‐resolution climate change projections are increasingly necessary to inform climate policy and adaptation planning. Downscaling of global climate models (GCMs) is required to simulate the climate at the spatial scale relevant for local impacts. Here, we dynamically downscaled 15 CMIP6 GCMs to a 10 km resolution over Australia using the Conformal Cubic Atmospheric model (CCAM), creating the largest ensemble of high‐resolution downscaled CMIP6 projections for Australia. We compared the host CMIP6 models and downscaled simulations to the Australian Gridded Climate Data (AGCD) observational data and evaluated performance using the Kling‐Gupta efficiency and Perkins skill score. Downscaling improved performance over host GCMs for seasonal temperature and precipitation (10% and 43% respectively), and for annual cycles of temperature and precipitation (6% and 13% respectively). Downscaling also improved the fraction of dry days, reducing the bias for too many low‐rain days. The largest improvements were found in climate extremes, with enhancements to extreme minimum temperatures in all seasons varying from 142% to 201%, and to extreme precipitation of 52% in Austral winter and 47% in summer. The ensemble average integrated skill score improved by 16%. Temperature and precipitation biases were reduced in mountainous and coastal areas. CCAM downscaling outperformed host CMIP6 GCMs at multiple spatial scales and regions—continental Australia, Australian IPCC regions and Queensland's regions—with integrated added value ranging from 9% to 150% and higher over densely populated regions more exposed to climate impacts. This data set will be a valuable resource for understanding future climate changes in Australia. Plain Language Summary: High‐resolution climate models are used for producing climate change projections for assessing regional and local climate change impacts and for adaptation and policy formulation. We completed 15 high‐resolution climate simulations using the Conformal Cubic Atmospheric Model at a 10 km spatial resolution over the Australian continent and surrounds based on global climate models (GCMs) used in the Intergovernmental Panel on Climate Change Assessment Report 6. We evaluated the new high‐resolution data set by comparing it to observations and to their host global model across several regions within Australia. The high‐resolution simulations improved the representation of topography, seasonal temperature and annual cycles of temperature, precipitation, as well as the number of dry days, extreme precipitation and extreme minimum temperatures. The high‐resolution projections show improvement compared to GCMs consistently across spatial scales and regions, being particularly high (150%) over urban areas with high exposure to impacts. This new 10 km data set is the highest resolution, and largest ensemble of the latest climate projections for the Australian continent and surrounds and will be a valuable tool for evaluating future climate change impacts. Key Points: We dynamically downscaled 15 CMIP6 global climate model simulations over Australia to a 10 km spatial resolution using the Conformal Cubic Atmospheric modelTwo new assessment metrics are proposed to assess model performance and added value of downscalingThe integrated added value of downscaling can be as high as 150% over highly populated areas [ABSTRACT FROM AUTHOR]

Aim: The capacity for poleward range expansions beyond the tropics in corals hinges on ecophysiological constraints and resulting responses to climatic variability. We aimed to determine how future warming will affect coral habitat suitability at the poleward range edges of these foundational species in the Northwest Pacific. Location: Northwest Pacific. Methods: We generated models integrating thermal physiological constraints of corals adapted to extreme seasonality in Hong Kong, specifically the minimum annual temperature and the proportion of time annually spent at seasonal extremes. With these models, we projected habitat suitability for five coral species under current and future climatic conditions across the Northwest Pacific. Results: Climate model projections reveal an easing of thermal constraints on the leading‐edge of coral ecophysiological limits with an expansion of thermally suitable habitat poleward by 2°–7° in latitude depending on the coral species and model considered. We also highlight a potential divergence of present and future thermal regimes that may lead to a mismatch in suitability for corals currently inhabiting high latitude reefs. Main Conclusions: Understanding the thermal constraints on coral distributions and defining the potential range of corals under climate change is critical for adaptive management that focuses on coral conservation and ensuring ecosystem function of existing subtropical and temperate ecosystems. [ABSTRACT FROM AUTHOR]

Cloud and convection strongly modulate atmospheric energy budgets, but the latter's responses often vary across timescales because of complex interactions between fast and slow processes. Here, based on atmospheric model simulations at intermediate state between weather and climate timescales, we investigate how the responses in the global‐mean atmospheric energy budgets evolve over time after simultaneously perturbing various cloud‐scale processes. We find that the responses in radiative and sensible heat fluxes converge much more rapidly compared to condensation heat associated with precipitation, which is attributed to the compensating feedback effects of precipitation on longwave cooling and shortwave heating. Because of energy conservation, uncertainty in long‐term precipitation simulations can be substantially reduced by constraining the fast processes of radiative and sensible heat fluxes. These findings can help economize on computational resources required for model tuning and serve as a crucial link between the convective‐scale and equilibrium‐state outcomes within the model. Plain Language Summary: Cloud and convection occurring on short timescales can interact with processes that require much longer time to respond to small perturbations in the climate system, making it extremely difficult to understand the sources of uncertainty in climate modeling. Here, with an atmospheric model, we investigate how the simulated global‐mean atmospheric energy budgets gradually evolve over time when various cloud‐scale processes are simultaneously perturbed. We find that the responses in radiative and sensible heat fluxes converge much more rapidly compared to condensation heat associated with precipitation. As a result, a substantial reduction in uncertainty regarding long‐term precipitation simulations can be achieved by constraining the radiative and sensible heat fluxes at shorter timescales, in accordance with energy conservation principles. Our results can help modelers economize on computational resources required for model tuning and serve as a crucial link between the model physics parameterization community and the model application community. Key Points: The responses in radiative and sensible heat fluxes converge much more rapidly compared to precipitationThe rapid radiative response is attributed to the compensating feedback effects of precipitation on longwave cooling and shortwave heatingConstraining the fast processes of radiative and sensible heat fluxes can alleviate uncertainty in long‐term precipitation simulations [ABSTRACT FROM AUTHOR]

Volcanic forcing, a major natural source of climate variability, represents a challenge for current climate modeling because of the unpredictability and specificity of individual eruptions, and because of the complexity of processes linking the eruption to the climate response. Volcanic forcing is largely underrepresented in available future climate projections, which is a critical problem. The study by Man Mei Chim and Colleagues (Chim et al., 2023, https://doi.org/10.1029/2023GL103743) tackles this known unknown and reveals how climatically relevant volcanic activity may be stronger than currently thought in a future warmer climate, enhancing uncertainty of climate projections. The study exemplifies the profound implications of inaccuracies within simplified climate scenarios and motivates new research on volcanically forced climate variability. It also arouses some thoughts on climate uncertainty communication. Plain Language Summary: Volcanic eruptions are a critical part of the earth system, capable of causing large climatic fluctuations over periods from years to decades. Available scenario experiments typically neglect volcanic forcing, which may cause systematic errors in future climate projections. Man Mei Chim and Colleagues (Chim et al., 2023, https://doi.org/10.1029/2023GL103743) use a series of statistical and mathematical models to comprehensively simulate how volcanic eruptions may affect climate during the 21st century. The results indicate that a more realistic representation of volcanic eruptions yields slightly less future warming and larger uncertainty than standard projections. In other words, we are less confident of future climate changes once we account for the (unpredictable) volcanic forcing. By considering future volcano‐climate interactions in their whole complexity and revealing a substantial role for small‐to‐moderate eruptions, the study places a new important piece in the volcano‐climate puzzle. It will motivate new coordinated research to improve the simulated representation of volcano‐climate interactions not only in the ambit of future climate scenarios but in paleoclimatic investigations and idealized volcanic experiments well. The research also reminds us of limitations inherent in climate model experiments and should foster improved communication of natural climate variability and associated uncertainties. Key Points: The study by Man Mei Chim and Colleagues shows that volcanic forcing can strongly affect future climate projectionsThe study reaffirms the importance of natural variability for future climates, stimulating research on volcano‐climate interactionsVolcanic forcing remains unpredictable, which rises the question of how to communicate irreducible climate projection uncertainties [ABSTRACT FROM AUTHOR]