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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]

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]

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 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]

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]

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

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Sea ice is an important and complex component of the Earth system and is considered a sensitive indicator of climate change. The seasonal sea ice cycle regulates the exchange of heat and salinity, altering the energy balance between high and low latitudes as well as the ocean and atmospheric circulation. The accurate representation of Antarctic sea ice has been considered a hot topic in the climate modelling community and lacks conclusive answers. In this paper, we evaluated the ability of 11 climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) to simulate the sea ice seasonal cycle in Antarctica in terms of area (SIA) and concentration (SIC), as well as the improvements in the most recent models' version, submitted to CMIP6. The results indicated that all models are able to accurately capture the seasonal cycle of the Antarctic SIA, with the minimum (maximum) occurring in February (September). In the Weddell Sea, Amundsen Sea, Bellingshausen Sea, and the Ross Sea, the simulated sea ice concentration revealed a large and systematic bias in February when compared to observations. In September, a large and systematic bias was found nearby the Southern Ocean's northern limit in the Polar Front. Several CMIP6 models exhibited slight improvements on the SIA and SIC estimate over the previous version (CMIP5). All models indicated a significant sea ice loss in the coming years as a response to CO2 forcing. Despite the advancements in the sea ice representation, our findings show that the models are still unable to accurately represent the regional sea ice changes [ABSTRACT FROM AUTHOR]

As a dynamical system approaches its critical threshold, the probability density distribution of the system will change significantly. Therefore, it is possible to present an early warning signal based on the changing skewness before reaching the critical threshold. Based on a zero‐dimensional climate model and several typical fold models, this paper systematically studies the influence of noise and missing data on the performance of the skewness coefficient as an early warning signal of an abrupt climate change. The results in three types of fold models show that the skewness coefficient has anti‐noise ability to some extent, but strong noise will significantly reduce the magnitude of the skewness coefficient and the time for early warning will also be shortened. In some cases, strong noise even will lead to the result that the skewness does not work in warning an impending abrupt change. However, the influence of strong noise on skewness is insignificant in the zero‐dimensional climate model. Therefore, the influence of strong noise needs to be considered in the practical application of the skewness coefficient as an early warning signal of an abrupt change. In addition, the results of all the models also indicate that different degrees of the missing data have no statistically significant effect on the warning performance of the skewness coefficient, even when the length of the missing data is up to 20% of the total sample size used in the present paper. [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]

Conceptual models demonstrate that climate can control the structural evolution of mountain belts and that Pliocene–Quaternary global climate change modulated Himalayan wedge behaviour. A key test of this hypothesis is the timing of foreland accretion. If foreland accretion pre‐dates climate change, then climate is not the primary driver of mountain building. New apatite (U–Th)/He data from NW Himalaya foreland thrust sheets suggest that accretion had occurred by the latest 4 Ma, prior to the Pliocene–Quaternary global climate change. Climate cannot, therefore, be invoked as the principal driver of Himalayan tectonics in the late Cenozoic. Statement of significance: This paper presents 66 new, single grain apatite (U–Th)/He (AHe) dates from the foreland basin that constrain NW Himalayan evolution through the late Cenozoic. We show that accretion of the NW sub‐Himalayan foreland was underway by 4 Ma at the latest, several million years earlier than previous estimates, and was followed by distributed, wedge‐internal deformation by 2 Ma. These and other published data present a consistent pattern and timing of deformation observed over nearly 600 km of the NW sub‐Himalaya. Our new constraints on orogen evolution provide key insights into the relative importance of climate and tectonics in driving orogenic systems. [ABSTRACT FROM AUTHOR]

The marine biological carbon pump (BCP) stores carbon in the ocean interior, isolating it from exchange with the atmosphere and thereby coregulating atmospheric carbon dioxide (CO2). As the BCP commonly is equated with the flux of organic material to the ocean interior, termed "export flux," a change in export flux is perceived to directly impact atmospheric CO2, and thus climate. Here, we recap how this perception contrasts with current understanding of the BCP, emphasizing the lack of a direct relationship between global export flux and atmospheric CO2. We argue for the use of the storage of carbon of biological origin in the ocean interior as a diagnostic that directly relates to atmospheric CO2, as a way forward to quantify the changes in the BCP in a changing climate. The diagnostic is conveniently applicable to both climate model data and increasingly available observational data. It can explain a seemingly paradoxical response under anthropogenic climate change: Despite a decrease in export flux, the BCP intensifies due to a longer reemergence time of biogenically stored carbon back to the ocean surface and thereby provides a negative feedback to increasing atmospheric CO2. This feedback is notably small compared with anthropogenic CO2 emissions and other carbon‐climate feedbacks. In this Opinion paper, we advocate for a comprehensive view of the BCP's impact on atmospheric CO2, providing a prerequisite for assessing the effectiveness of marine CO2 removal approaches that target marine biology. [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]

In climate science and applications, the term "metric" is used to describe the distillation of complex, multifaceted evaluations to summarize the overall quality of a model simulation, or other data product, and/or as a means to quantify some response to climate change. Metrics provide insights into the fidelity of processes and outcomes from climate models and can assist with both differentiating models' representation of variables or processes and informing whether models are "fit for purpose." Metrics can also provide a valuable reference point for co‐production of knowledge between climate scientists and climate impact practitioners. Although continued metric developments enable model developers to better understand the impacts of decisions made in the model design process, metrics also have implications for the characterization of uncertainty and facilitating analyses of underlying physical processes. As a result, comprehensive evaluation with multiple metrics enhances usability of climate information by both scientific and stakeholder communities. This paper presents examples of insights gained from the development and appropriate use of metrics, and provides examples of how metrics can be used to engage with stakeholders and inform decision‐making. This article is categorized under:Climate Models and Modeling > Knowledge Generation with ModelsThe Social Status of Climate Change Knowledge > Climate Science and Decision MakingAssessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change [ABSTRACT FROM AUTHOR]

An increase in the seasonal cycle amplitude (SCA) of atmospheric CO2 since the 1960s has been observed in the Northern Hemisphere (NH). However, the underlying dominant drivers are still debated. The peak season CO2 uptake by vegetation is critical in shaping the CO2 seasonality. Using satellite‐upscaled gross primary production (GPP) from FLUXCOM and near‐infrared reflectance of vegetation (NIRV), we demonstrate that peak GPP has increased across the NH over the last two decades. We relate this productivity increase to changes in the CO2 SCA using an atmospheric transport model. The increased photosynthesis has strongly contributed to CO2 SCA trends, but with substantial latitudinal and longitudinal variations. Despite a general increase in the CO2 SCA, there are distinct regional differences. These differences are mainly controlled by regional biosphere carbon fluxes, with the remainder explained by non‐biome factors, including large‐scale atmospheric transport, changes in fossil fuel combustion, biomass burning and oceanic fluxes. Using the global flask and in situ CO2 measurement sites, we find that SCA trends at high latitude are mainly driven by increasingly productive natural ecosystems, whereas mid latitude sites around the Midwest United States are mainly impacted by intensified agriculture and atmospheric transport. Averaging across the 15 long‐term surface sites, forests contribute 26% (7%) to the SCA trends, while crops contribute 17% (24%) and the combined shrubland, grassland and wetland regions contribute 23% (37%) for simulations driven by FLUXCOM (NIRv) ecosystem fluxes. Our findings demonstrate that satellite inferred trends of ecosystem fluxes can capture the observed CO2 SCA trend. Plain Language Summary: An increase in the seasonal cycle amplitude (SCA) of atmospheric carbon dioxide (CO2) since the 1960s has been observed in the Northern Hemisphere (NH). However, dominant drivers of the amplified CO2 seasonality are still debated. In this study, we employ satellite‐based remote sensing observations to track spatial and temporal changes in global gross primary production (GPP) of different vegetation types. Then, we use a state‐of‐art atmospheric transport model to examine whether our bottom‐up estimates of ecosystem fluxes can capture the magnitude of CO2 SCA trends at multiple surface sites. Further, we explore dominant drivers of the observed CO2 SCA trends across different locations of sites. To our best knowledge, this paper makes the first effort to link long‐term satellite remote sensing observations with ground atmospheric CO2 measurements across the NH to explore how terrestrial carbon fluxes shape and change spatiotemporal pattern of atmospheric CO2. Key Points: Peak growing season gross primary production has increased across the northern extratropics over the last two decades based on satellite observationsThe remote sensing‐based global terrestrial carbon fluxes well capture the observed CO2 seasonal cycle amplitude trends at surface sitesMajor factors driving the observed increase in the atmospheric CO2 seasonal amplitude trends vary across the location of sites [ABSTRACT FROM AUTHOR]

For years, carbon cycle scientists have sought to understand how a warming climate will impact the decay of soil carbon. However, there have been relatively few investigations of how soils will warm in comparison to the atmosphere. Three recent papers in American Geophysical Union journals have looked at expected soil warming by synthesizing predictions in the Climate Model Intercomparison Project 5 (CMIP5). Collectively, these papers show that soil warming will keep pace with air warming except where snow and ice occur, and that the magnitude of soil warming in northern latitudes is uncertain due to model variability in snow and ice extent. They also show that a considerable portion of anthropogenic warming is being stored in deep soils, and that in comparison to observations, soil heat storage is underrepresented by land models. These studies highlight how important continued investigations of soil climatology are to understanding carbon cycling and Earth energy balance. Key Points: Three recent papers examined soil warming predictions over the 21st centuryThey show air warming is a good proxy for soil warming, except at high latitudesMore efforts to measure and model soil temperatures will broadly benefit the biogeosciences and climate change science [ABSTRACT FROM AUTHOR]

Using a new modelling methodology known as "extreme event attribution," or EEA, climate scientists can now connect extreme weather to anthropogenic forcings. This paper seeks to uncover the significance of EEA for the epistemology of climate change, nature, and culture in the Anthropocene. First, we examine how EEA is emblematic of a larger turn in climate modelling, one that seeks to deploy anthropogenic climate change as an explanatory tool for an increasing number of socio‐natural phenomena. While some theorists have argued that the Anthropocene heralds the end of the nature–culture divide, we argue that EEA and similar modelling technologies seek to separate human influence from the natural variability of weather, thus establishing a new form of nature–culture divide mediated by computer simulation: a divide which we call "partitioned causality." Second, we demonstrate that partitioned causality is enabled by the relative hegemony of modelling technologies in climate change knowledge, as scientists retain substantial influence over who gets to "speak for" climate impacts. Finally, however, interviews with EEA scientists, journalists, and policymakers on the 2011–2017 California drought reveal that EEA remains a nascent scientific framework, one marked by epistemic slippage and divergent results. Thus, it serves as a powerful example of how emergent attempts to "domesticate" climate often become caught up in socio‐political conflicts around who – or what – has the power to shape discourses of climate change in the Anthropocene. This paper seeks to uncover the significance of extreme event attribution (EEA) for the epistemology of climate change, nature, and culture in the Anthropocene. We argue that, contrary to popular theories of the Anthropocene and its role in erasing the nature–culture divide, EEA and similar modelling technologies seek to separate human influence from the natural variability of weather, thus establishing a new form of nature–culture divide mediated by computer simulation – a divide we call "partitioned causality." We demonstrate that partitioned causality is enabled by the relative hegemony of modelling technologies in climate change knowledge, as scientists retain substantial influence over who gets to "speak for" climate impacts and influence. [ABSTRACT FROM AUTHOR]

Climate classifications are useful to synthesize the physical state of the climate with a proxy that can be directly related to biota. However, they present a potential drawback, namely a strong sensitivity because of the use of hard thresholds (step functions). Thus, minor discrepancies in the base data produce large differences in the type of climate. However, such an a priori limitation is also a strength because such sensitivity can be used to better gauge model performance. Although previous attempts of classifying climates of the world using global climate model outputs were encouraging, the applicability of their classifications to impact studies has been limited by past model issues. Notwithstanding the persistence of certain imperfections and limitations in current models, the high‐quality physical simulations from phase six of the Coupled Intercomparison Project (CMIP6) has opened new possibilities in the field, thanks to their improved representation of atmospheric and oceanic physics. The purpose of this paper is twofold: to show that climate classifications from CMIP6 are sufficiently robust for use in impact studies, and to use those classifications for identifying error sources and potential issues that deserve further attention in models. Thus, 52 CMIP6 climate models were evaluated by using three climate classifications schemes, classical Köppen, extended‐Köppen, and modified Thornthwaite. We first assessed model ability to reproduce present climate types by comparing their outputs with observational data. Models performed best for the Köppen and extended‐Köppen classification methods (Cohen's kappa κ = 0.7), and had moderate scores for the Thornthwaite climate classification (κ = 0.4). By tracing back the observed biases, we were able to pinpoint the misrepresentation of dry climates as a major source of error. Another finding was that most models still had some difficulties in representing the seasonal variability of precipitation over several monsoonal regions, thereby yielding the wrong climate type there. Models were also evaluated for future climate. Substantial changes in climate types are projected in the SSP5‐8.5 scenario. These changes include a shrinkage of polar/frigid climates (22%) and an increase of dry climates (7%). [ABSTRACT FROM AUTHOR]

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

The Grid‐point Atmospheric Model of the IAP LASG version 3 (GAMIL3) has been developed by upgrading the horizontal resolution, methods of parallel computation, boundary layer scheme, aerosol parameterization, convective parameterization, stratocumulus cloud fraction scheme, land component, and coupler, as well as tuning some moist physical parameters with large uncertainties. Its performance is evaluated, and the results show significant improvements compared with the previous version, GAMIL2. The simulated performance of mean states is notably enhanced, including the energy budget terms at the top of the atmosphere (TOA) and surface, shortwave/longwave cloud radiative forcing (SWCF/LWCF), precipitation, zonal wind, low‐level temperature, 500‐hPa geopotential height, and snow cover fraction in the Northern Hemisphere. The characteristics of internal variability are captured well, such as the frequency band and active areas of quasi‐biweekly (QBW) oscillation, spectral power of convectively coupled equatorial waves (CCEWs), Madden‐Julian Oscillation (MJO) eastward propagation, and heat flux response to El Niño‐Southern Oscillation (ENSO), and these variabilities are generally strengthened in GAMIL3. In addition, the anthropogenic aerosol climate effects are weakened when using the forcings recommended by CMIP6. Plain Language Summary: The Coupled Model Intercomparison Project (CMIP) provides a multimodel platform for research in climate change originating from unforced variability or in response to changes in anthropogenic forcings. The Atmospheric Model Intercomparison Project (AMIP) experiment, which is part of the sixth phase of CMIP (CMIP6), is designed to evaluate the atmospheric component in the climate system. The GAMIL3 is the atmospheric component of Flexible Global‐Ocean‐Atmosphere‐Land System Model Grid‐point version 3 (FGOALS‐g3), one of the CMIP6 models, and it has been developed from GAMIL2. This paper presents a description of the GAMIL3 model and its upgrades, the performance of the simulated mean state and internal variability, and the anthropogenic aerosol climate effects. This description is useful for understanding climate variability and change. Key Points: This paper presents GAMIL3 and the results of its AMIP simulationsOverall performance is improved, and climate variabilities are strengthenedAnthropogenic aerosol effects using the CMIP6 protocol are weaker than those from the model's default approach [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]

Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called "regime‐oriented" technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities. [ABSTRACT FROM AUTHOR]

The hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi‐source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%. The uncertainty contribution of hydrological models (H) (6.1–9.4%) ranks third, followed by emission scenarios (S) (2.9–5.9%) and bias correction methods (B) (0.2–1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2–0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern China (67.5–70.6%) and in the far future for southern China (66.1–66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3–57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2–19.5%) and lower for northern, eastern, and southern China (2.5–6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies. [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]

Low‐level mixed‐phase clouds are important for Earth's climate but are poorly represented in climate models. A one‐moment microphysics scheme from Seiki and Roh (2020, https://doi.org/10.1175/JAS-D-19-0266.1) improves the representation of supercooled water and verifies it with a single‐column model. We evaluate the performance of this scheme using a global cloud‐system‐resolving simulation. We show that the scheme has several major improvements over the original scheme on which it is based, which underestimated the generation of supercooled droplets. The new scheme suppresses the original scheme's tendency to overestimate the conversion of cloud water to rain, vapor to cloud ice, and cloud water to cloud ice. It greatly improves the previously underestimated production of low‐level mixed‐phase clouds at middle‐to‐high latitudes, particularly over the ocean at the middle latitudes of the Southern Hemisphere. It also increases the lifetime of liquid clouds, thus improving the simulation of low‐level liquid clouds in western coastal regions of the tropics. The temperature dependency of the ratio of mass fraction of liquid cloud to the sum of ice and liquid clouds, F, reveals that mixed‐phase clouds statistically develop in a much wider range of temperature (−30°C ∼ 0°C), which supports the development of more mixed‐phase clouds in our simulation. The change to a wider range of F at given temperature is expected to be important, because it allows more complex feedback processes to arise from different cloud phase regimes. An improved simulation in seasonal variation of shortwave radiation and its cloud radiative effect are also identified. Plain Language Summary: Low‐level mixed‐phase clouds are important for the Earth's radiation budget because they persistently develop especially over the Southern Ocean, and reflect sunlight back to space. However, most climate models have suffered from simulating those mixed‐phase clouds, which has been a major source of uncertainty to project a future climate change. This paper reveals that an improved modeling of mixed‐phase clouds leads to better representation of the global distribution and seasonal change of mixed‐phase clouds considerably, which also results in an improved simulation of the Earth's radiation budget. The present result is capable of contributing to improve not only our climate model, but current climate models worldwide. Key Points: We use a global cloud‐system‐resolving simulation to analyze a one‐moment microphysics scheme for representing mixed‐phase low‐level cloudsWe show that the new scheme improves the representation of supercooled water and increases the lifetime of liquid cloudsThe new scheme also better represents seasonal changes in reflection of solar radiation, compared with the previous schemes [ABSTRACT FROM AUTHOR]

Model evaluation is difficult and generally relies on analysis that can mask compensating errors. This paper defines new metrics, using clusters generated from a machine learning algorithm, to estimate mean and compensating errors in different model runs. As a test case, we investigate the Southern Ocean shortwave radiative bias using clusters derived by applying self‐organizing maps to satellite data. In particular, the effects of changing cloud phase parameterizations in the MetOffice Unified Model are examined. Differences in cluster properties show that the regional radiative biases are substantially different than the global bias, with two distinct regions identified within the Southern Ocean, each with a different signed bias. Changing cloud phase parameterizations can reduce errors at higher latitudes but increase errors at lower latitudes of the Southern Ocean. Ranking the parameterizations often shows a contrast in mean and compensating errors, notably in all cases large compensating errors remain. Key Points: A novel method for climate model evaluation is used to identify both mean errors and potential compensating errorsAs an example, we apply this methodology to investigate the quality of different cloud parameterizations over the Southern OceanChanges to the cloud phase parameterizations can reduce shortwave radiative bias regionally, but large compensating errors remain [ABSTRACT FROM AUTHOR]

Antarctic sea ice trends have to date been linked to surface winds, through sea ice motion and atmospheric thermal advection. This paper analyzes sea ice volume in 10 Coupled Model Intercomparison Project Phase 5 (CMIP5) model configurations under pre‐industrial and historical climate forcings, to compare the relative importance of ice motion and thermodynamic processes. We find that the models' responses to historical forcings is dependent on their sea ice motion formulation; models with low‐magnitude sea ice motion tend to have historical trends that are dominated by thermodynamic processes, while sea ice models with higher‐magnitude motion have more spatially variable relative contributions from dynamic and thermodynamic processes. Trends at the sea ice edge during the season of sea ice advance are generally dominated by dynamic processes, whereas during retreat thermodynamic trends dominate. The models show more disagreement in the sea ice interior. This analysis highlights the different estimates and patterns of sea ice volume among global climate models and offers insight into the drivers of sea ice volume change as well as the subsequent implications for simulated atmosphere‐sea ice‐ocean interactions. Plain Language Summary: Increasing observed Antarctic sea ice is thought to be largely driven by surface winds, through driving sea ice motion and advecting warm air southward to melt ice in some regions. However, global climate models do not reproduce observed sea ice trends. This paper analyzes the sea ice volume budget in 10 climate models to see how much simulated sea ice change is driven by dynamic processes such as wind‐driven sea ice motion, and how much is due to thermodynamic freeze and melt processes. We find that the models generally agree when it comes to mean sea ice volume change, with dynamic processes dominating the sea ice edge and thermodynamic processes dominating in the interior of the sea ice pack. However, the models disagree about trends of sea ice volume. In the sea ice interior, each process dominates approximately half the models in all sea ice sectors, and thermodynamic processes tend to dominate in models with low‐magnitude average sea ice motion. These results provide insight into the estimates and patterns of Antarctic sea ice in global climate models, helping to understand the disparity between simulated and observed sea ice trends. Key Points: Thermodynamic drivers tend to dominate trends of Antarctic sea ice volume in models with low‐magnitude mean sea ice mass transportDrivers of sea ice volume trends in models with higher‐magnitude mean sea ice mass transport have large spatial and seasonal variabilityBiases in mean Antarctic sea ice concentration are unrelated to the dominant processes driving sea ice volume trends [ABSTRACT FROM AUTHOR]

Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985–2014 relative to 1955–1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi‐model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30‐yr period is projected to increase for 2070–2099 relative to 1985–2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, respectively. Inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 and especially high for the Amazon and the Tibetan Plateau. Plain Language Summary: Compound climate extremes, such as the events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. Can climate model simulate the historical compound extremes? If yes, how the global compound extremes will change in the future? In this study, we found that the global climate model performs well in simulating temporal changes of warm/wet and warm/dry extremes during the period 1955–2014. With greenhouse gas emissions continuing to increase in the future, compound warm/dry and warm/wet extremes show a continuous increase in frequency in the next few decades, while compound cold/dry and cold/wet extremes are projected to occur less frequently. Key Points: A multi‐model ensemble of CMIP6 models performs well in simulating temporal changes of warm/wet extremesThe inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremesThe projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 [ABSTRACT FROM AUTHOR]

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Californian hydroclimate is strongly seasonal and prone to severe water shortages. Recent changes in climate trends have induced shifts in seasonality, thus exacerbating droughts, wildfires, and adverse water shortage effects on the environment and economy. Previous studies have examined the timing of the seasonal cycle shifts mainly as temperature driven earlier onset of the spring season. In this paper, we address quantitative changes in the onset, amounts, and termination of the precipitation season over the past 6 decades, as well as the large‐scale atmospheric circulation underpinning the seasonal cycle changes. We discover that the onset of the rainy season has been progressively delayed since the 1960s, and as a result the precipitation season has become shorter and sharper in California. The progressively later onset of the rainy season is shown to be related to the summer circulation pattern extending into autumn across the North Pacific, in particular, a delay in the strengthening of the Aleutian Low and later southward displacement of the North Pacific westerlies. Plain Language Summary: The rainy season over California is projected to show a distinct sharpening of the mean seasonal cycle, with winter precipitation increasing, and both autumn and spring precipitation decreasing. Our analysis of the past 6 decades of data for California suggests autumn decrease is already underway. A delayed start of the rainy season of 27 days since 1960s can exacerbate seasonal droughts and prolong the wildfire season. This delay occurs due to a number of conditions that controls precipitation: the summer circulation pattern has been extending throughout November across the North Pacific, and the wintertime strengthening of the Aleutian Low is delayed. Accordingly, the southward migration of the North Pacific jet stream as well as extratropical storm tracks are delayed, which marks the start of the California rainy season, are delayed. More work, using climate models, will be needed to provide a better understanding of atmospheric conditions across the North America and the North Pacific. However, our findings provide observational evidence for the projected rainfall change over California and inform ongoing discussion about the drying/wetting tendencies of the rainy season in California. Key Points: Rainfall data over the last 6 decades suggests a progressively delayed onset to the rainy season over CaliforniaA corresponding delay occurs in the transition from summer to wintertime circulation over the North PacificObserved autumn trends appear consistent with projected future shortening of the California rainy season [ABSTRACT FROM AUTHOR]

We present an unprecedented set of high‐resolution climate simulations, consisting of a 500‐year pre‐industrial control simulation and a 250‐year historical and future climate simulation from 1850 to 2100. A high‐resolution configuration of the Community Earth System Model version 1.3 (CESM1.3) is used for the simulations with a nominal horizontal resolution of 0.25° for the atmosphere and land models and 0.1° for the ocean and sea‐ice models. At these resolutions, the model permits tropical cyclones and ocean mesoscale eddies, allowing interactions between these synoptic and mesoscale phenomena with large‐scale circulations. An overview of the results from these simulations is provided with a focus on model drift, mean climate, internal modes of variability, representation of the historical and future climates, and extreme events. Comparisons are made to solutions from an identical set of simulations using the standard resolution (nominal 1°) CESM1.3 and to available observations for the historical period to address some key scientific questions concerning the impact and benefit of increasing model horizontal resolution in climate simulations. An emerging prominent feature of the high‐resolution pre‐industrial simulation is the intermittent occurrence of polynyas in the Weddell Sea and its interaction with an Interdecadal Pacific Oscillation. Overall, high‐resolution simulations show significant improvements in representing global mean temperature changes, seasonal cycle of sea‐surface temperature and mixed layer depth, extreme events and in relationships between extreme events and climate modes. Plain Language Summary: Although the current generation of climate models has demonstrated high fidelity in simulating and projecting global temperature change, these models show large uncertainties when it comes to questions concerning how rising global temperatures will impact local weather conditions. This is because the resolution (~100 km) at which the majority of climate models simulate the climate is not fine enough to resolve these small‐scale regional features. Conducting long‐term (multi‐centuries) high‐resolution (~10 km) climate simulations has been a great challenge for the research community due to the extremely high computational demands. Through international collaboration, we are able to address this challenge by delivering an unprecedented set of multi‐century high‐resolution climate simulations using the Community Earth System Model (CESM), capable of directly representing tropical cyclones and extreme rainfall events. In this paper, we give an overall assessment of the value and benefit of the high‐resolution CESM climate simulations by making a direct comparison to an identical set of low‐resolution CESM simulations. We showcase some of the major improvements of the high‐resolution CESM in simulating global mean temperature changes, seasonal cycle of sea‐surface temperature, and extreme events, such as tropical cyclones and relationships between tropical cyclones and El Niño‐Southern Oscillation. Key Points: An unprecedented set of multi‐century high‐resolution Community Earth System Model (CESM) simulations is describedHigh‐resolution CESM simulations reveal a potential role of Southern Ocean polynyas in multidecadal climate variabilityHigh‐resolution CESM exhibits significantly improved simulations of extreme events, such as tropical cyclones and atmospheric rivers [ABSTRACT FROM AUTHOR]

With the advent of climate change as a major challenge of our time, Earth system modeling has become highly policy‐relevant regulatory science. In this situation, the social mechanisms that play a role in any scientific endeavor become particularly exposed. By discussing historical, philosophical, and sociological (HPS) aspects of the field's current "cultures of prediction" together with the physical science community in a physical science journal, we aim to provide an entry point into HPS reasoning for climate scientists interested in reflecting on their field and science in general. This paper, first, introduces our perspective on "science as culture" and climate modeling as "regulatory science" and, second, highlights and connects relevant ideas from the three commentaries that follow it. In so doing, we hope to give a fuller picture of climate science, the interplay it engenders between HPS and the physical sciences, the distinctions that it gives rise to as compared to some of the more traditional, exact, sciences in which it is rooted and its place in society including its role in scientific policy advice. Key Points: With the advent of climate change as a major challenge of our time, Earth system modeling has become highly policy relevantIn this situation, the social mechanisms that play a role in any scientific endeavor become particularly exposedThis paper introduces perspectives on "science as culture" and highlights relevant ideas from the three subsequent commentaries [ABSTRACT FROM AUTHOR]

The Alfred Wegener Institute Climate Model (AWI‐CM) participates for the first time in the Coupled Model Intercomparison Project (CMIP), CMIP6. The sea ice‐ocean component, FESOM, runs on an unstructured mesh with horizontal resolutions ranging from 8 to 80 km. FESOM is coupled to the Max Planck Institute atmospheric model ECHAM 6.3 at a horizontal resolution of about 100 km. Using objective performance indices, it is shown that AWI‐CM performs better than the average of CMIP5 models. AWI‐CM shows an equilibrium climate sensitivity of 3.2°C, which is similar to the CMIP5 average, and a transient climate response of 2.1°C which is slightly higher than the CMIP5 average. The negative trend of Arctic sea‐ice extent in September over the past 30 years is 20–30% weaker in our simulations compared to observations. With the strongest emission scenario, the AMOC decreases by 25% until the end of the century which is less than the CMIP5 average of 40%. Patterns and even magnitude of simulated temperature and precipitation changes at the end of this century compared to present‐day climate under the strong emission scenario SSP585 are similar to the multi‐model CMIP5 mean. The simulations show a 11°C warming north of the Barents Sea and around 2°C to 3°C over most parts of the ocean as well as a wetting of the Arctic, subpolar, tropical, and Southern Ocean. Furthermore, in the northern middle latitudes in boreal summer and autumn as well as in the southern middle latitudes, a more zonal atmospheric flow is projected throughout the year. Plain Language Summary: The Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) participates for the first time with a global climate model in the Coupled Model Intercomparison Project 6 (CMIP6). The results of CMIP6 and previous model comparison projects feed into the next assessment report of the Intergovernmental Panel on Climate Change (IPCC). The IPCC assessment reports include information on past and expected climate change in the future and is written for policy‐ and decision‐makers as well as for the general public. The main characteristics of the AWI climate model are described and compared to models from previous intercomparison projects. The projected global warming in AWI‐CM is similar to the average warming predicted by climate models in the previous intercomparison project. However, the Arctic sea‐ice extent declines faster than typical previous estimates. Areas that are wet in present‐day climate become wetter, and areas that are dry in present‐day climate become drier in the future—consistent with previous climate model simulations. The ocean currents remain rather stable in the AWI climate projections, which leads to a continued warm Gulf stream and therefore an only slightly reduced warming of the North Atlantic and parts of Europe compared to other middle‐latitude regions. Key Points: The paper describes contributions of AWI‐CM, which employs a sea‐ice ocean component formulated on unstructured meshes, to CMIP6Equilibrium climate sensitivity is similar to average of CMIP5 projections; transient climate response is slightly above averageResponse patterns are similar to CMIP5 with more pronounced Arctic sea ice loss and a more stable AMOC compared to other systems [ABSTRACT FROM AUTHOR]

This work is the first of a two‐part study that aims to develop a computationally efficient bias correction framework to improve surface PM2.5 forecasts in the United States. Here, an ensemble‐based Kalman filter (KF) technique is developed primarily for nonrural areas with approximately 500 surface observation sites for PM2.5 and applied to three (GEOS‐Chem, WRF‐Chem, and WRF‐CMAQ) chemical transport model (CTM) hindcast outputs for June 2012. While all CTMs underestimate daily surface PM2.5 mass concentration by 20–50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: (1) the arithmetic mean ensemble (AME) that equally weights each model and (2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least‐square errors. While the OPE shows superior performance than the AME, the combination of either the AME or the OPE with a KF performs better than the OPE alone, indicating the effectiveness of the KF technique. Overall, the combination of a KF with the OPE shows the best results. Lastly, the Successive Correction Method (SCM) was applied to spread the bias correction from model grids with surface PM2.5 observations to the grids lacking ground observations by using a radius of influence of 125 km derived from surface observations, which further improves the forecast of surface PM2.5 at the national scale. Our findings provide the foundation for the second part of this study that uses satellite‐based aerosol optical depth (AOD) products to further improve the forecast of surface PM2.5 in rural areas by performing statistical analysis of model output. Plain Language Summary: Air quality forecasting plays an important role in informing the general public and decision‐makers on reducing exposure to air pollution. Air quality models simulating atmospheric constituents such as particulate matter with a diameter less than 2.5 μm (PM2.5) are often used to provide daily forecasts. However, these models are subject to large error and uncertainty as a result of the incomplete representation of the real atmosphere. Here, we develop a computationally efficient framework to improve model forecasts by performing bias correction on model outputs. We focus on nonrural areas in the continental United States and show that our technique improves model forecasts of surface PM2.5. In a companion paper, we focus on the application of satellite data to improve PM2.5 forecasting in rural areas. Key Points: The chemical transport models (GEOS‐Chem, WRF‐Chem, and WRF‐CMAQ) show systematically low bias of PM2.5Model output postprocessing with surface data and the ensemble Kalman filter technique improves the PM2.5 forecast at both local and urban scaleThe Successive Correction Method extends the PM2.5 forecast improvement from the local to regional scale [ABSTRACT FROM AUTHOR]

In this paper, we used a Bayesian model averaging (BMA) approach to analyse the changes in rainfall extremes in the periods 2041–2070 and 2071–2099 over northeast Bangladesh as a consequence of climate change. Climate change over this region could potentially impact agricultural production, water resources management, and the overall economy of the country. We used six regional climate models (RCMs) over the Coordinated Regional Downscaling Experiment South Asia domain. We used one medium stabilization scenario (RCP4.5) and one high‐emission scenario (RCP8.5) for projecting the extreme rainfall indices. A multi‐model ensemble mean was generated using the BMA approach. The BMA mean is a weighted average related to each RCM's predictive skill during the training period. Most of the rainfall extremes are expected to increase in both pre‐monsoon (March–May) and monsoon (June–September) seasons in the future compared with baseline (1976–2005). The average pre‐monsoon rainfall of the study area is projected to increase by 12.93 and 18.42% under RCP4.5 and 18.18 and 23.85% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. The average monsoon rainfall of the study area is projected to increase by 4.96 and 2.27% under RCP4.5 and 6.56 and 6.40% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. All the extreme indices except consecutive wet day are expected to change significantly at the 95% confidence level during the pre‐monsoon season. The study area will potentially be subjected to more frequent floods in the future both in pre‐monsoon and monsoon seasons as a consequence of climate change. Notably, the intensity and the magnitude of flash flooding in the pre‐monsoon season are expected to increase more in the future because the increase in extreme indices is more significant during that season. [ABSTRACT FROM AUTHOR]

This is the first paper that analyzes data from atmosphere model intercomparison project‐type climate simulations using a cloud‐system‐resolving global nonhydrostatic model without cumulus parameterization focussing particulaly on the relationship between clouds and circulation, and their changes due to global warming. The decrease in fractional coverage of low clouds is key to evaluating cloud radiative effects, because changes in shortwave cloud radiative effects overwhelm those of longwave cloud radiative effects. Thus, improved evaluation of low clouds is important, even in high‐resolution climate simulations. An analysis of heat redistribution by explicitly computed clouds revealed that column‐integrated heating rate due to phase changes correlates highly with vertical velocity at the altitude corresponding to 500 hPa and is closely linked to column water vapor, similar to the present climate result. Using data from year 1 to year 5, the effective climate sensitivity was evaluated to be 3.6−3.7°C. Possible convective aggregation is also examined using an index of modified subsidence fraction and characteristic changes in the number of cold pools. Despite previous idealized‐planet simulations showing more aggregated tropical convection under warmer conditions, here we show a decrease in the subsidence fraction and an increase in the number of smaller cold pools, suggesting that it is possible to realize less convective organization with warming under real atmospheric conditions. Key Points: We used global nonhydrostatic climate simulations to study how clouds and large‐scale circulations change in response to global warmingChanges in the shortwave cloud radiative effect are key to evaluating the net cloud radiative effect, even in high‐resolution simulationsWe found no evidence of an increased amount of tropical convective aggregation in response to global warming [ABSTRACT FROM AUTHOR]

In the present paper, an ensemble approach is proposed to estimate possible modifications caused by climate changes in the extreme precipitation regime, with the rain gauge Napoli Servizio Idrografico (Naples, Italy) chosen as test case. The proposed research, focused on the analysis of extremes on the basis of climate model simulations and rainfall observations, is structured in several consecutive steps. In the first step, all the dynamically downscaled EURO‐CORDEX simulations at about 12 km horizontal resolution are collected for the current period 1971–2000 and the future period 2071–2100, for the RCP4.5 and the RCP8.5 concentration scenarios. In the second step, the significance of climate change effects on extreme precipitation is statistically tested by comparing current and future simulated data and bias‐correction is performed by means of a novel approach based on a combination of simple delta change and quantile delta mapping, in compliance with the storm index method. In the third step, two different ensemble models are proposed, accounting for the variabilities given by the use of different climate models and for their hindcast performances. Finally, the ensemble models are used to build novel intensity–duration–frequency curves, and their effects on the early warning system thresholds for the area of interest are evaluated. [ABSTRACT FROM AUTHOR]

The year 2019 marks the thirtieth anniversary of the development of the first regional climate model (RCM), and here an overview is provided of the progress in regional modeling research and of the main challenges lying ahead. RCMs were primarily developed to provide fine‐scale climate information for impact studies, but they have evolved into general and multipurpose modeling tools. Among the main achievements in RCM research the focus is on: the development of community RCMs applicable to a wide variety of studies and regional contexts; the increase of model simulation length up to centennial scales and spatial resolutions up to convection‐permitting scales (few kilometers), leading to a better understanding of regional to local climate change signals; the development of fully coupled Regional Earth System Models; the inception of intercomparison projects culminating in the international Coordinated Regional Climate Downscaling Experiment; the extensive use of RCM simulations for impact assessments; and the involvement of the scientific community from developing countries in climate modeling research. Among the outstanding issues in need of more attention are the Added Value of using this downscaling technique; various technical aspects concerning RCM simulations; and uncertainties in RCM‐based climate projections. Future directions in RCM research are discussed, with highlight on: transition to convection‐permitting modeling systems; further development of Regional Earth System Models including the human component; next phase of the Coordinated Regional Climate Downscaling Experiment project; and use of RCMs in the distillation of actionable information for contribution to climate service activities. A brief historical overview of regional climate modeling is also presented. Key Point: The paper reviews accomplishments, outstanding issues, and future research directions in regional climate modeling [ABSTRACT FROM AUTHOR]

Decadal climate predictability has received considerable scientific interest in recent years, yet the limits and mechanisms for decadal predictability are currently not well known. It is widely accepted that noise due to internal atmospheric dynamics at the air‐sea interface influences predictability. The purpose of this paper is to use the interactive ensemble (IE) coupling strategy to quantify how internal atmospheric noise at the air‐sea interface impacts decadal predictability. The IE technique can significantly reduce internal atmospheric noise and has proven useful in assessing seasonal‐to‐interannual variability and predictability. Here we focus on decadal timescales and apply the nonlinear local Lyapunov exponent method to the Community Climate System Model comparing control simulations with IE simulations. This is the first time the nonlinear local Lyapunov exponent has been applied to the state‐of‐the‐art coupled models. The global patterns of decadal predictability are discussed from the perspective of internal atmospheric noise and ocean dynamics. Key Points: For the first time, the nonlinear local Lyapunov exponent method has been applied to the state‐of‐the‐art coupled climate modelsDepending on region reducing internal atmospheric noise reduces decadal variability but either increases or decreases decadal predictabilityDecadal‐scale subsurface predictability is examined for the North Atlantic region [ABSTRACT FROM AUTHOR]

Vb cyclones typically emerge in the Western Mediterranean and propagate to the Northeast into Central Europe. This paper explores the temporal characteristics of Vb cyclone occurrence based on cyclone tracks identified at the atmospheric levels of Z700 and sea level pressure, using JRA‐55 reanalysis data for the period 1959–2015. The risk of Vb occurrence was significantly high in the 1960s and has remained at a lower level since then. Vb cyclones do not occur fully randomly according to a Poisson point process. Eleven well‐separated and distinct clusters as well as 11 hiatus periods are identified, with average occurrence rates of 21.5 and 5.2 yrea−1, respectively. During the event of Vb, the large‐scale atmospheric circulation is changed into a state favoring the development of successive Vb cyclones. Clustering is very prominent in the case of Genoan Vb cyclones in summer as well as those Vb cyclones developing over the Iberian Peninsula or the North African Coast in winter. Superposition of the polar and the subtropical jet stream over the Western Mediterranean is identified as a main feature at the onset of Vb cyclones. Vb cyclone occurrence appears to be synchronized with the Northern Atlantic Oscillation (NAO; at Z500) and Arctic Oscillation (AO; at Z1000). Clusters have occurred when both NAO and AO were negative. This relation applies to Western Mediterranean cyclones not following a Vb track as well, however to a much weaker extent. In contrast, Vb cyclone frequency was particularly low from 1988 to 1997 during a sustained positive phase of both NAO and AO. Key Points: In the past 50 years, the frequency of Vb cyclones was high in the 1960s and lower since thenVb cyclones were strongly synchronized with NAO and AO; high frequency clusters occurred when NAO and AO were negativeThe coupling of the polar and the subtropical jet stream over the Western Mediterranean is a driving mechanism of the onset of Vb cyclones [ABSTRACT FROM AUTHOR]

As solar energy will be an increasingly important renewable energy source in future years, the study of how climate change affects both its temporal and spatial variability is very relevant. In this paper, we study future changes of the solar radiation resource in the Iberian Peninsula (IP) through a set of simulations from ESCENA project (generation of regionalized scenarios of climate change in Spain with high‐resolution models) until mid‐century. The evaluation of the simulations against observations indicates contrasting biases for the different regional climate models (RCMs) in terms of solar irradiation amount and its inter‐annual variability. We propose a diagnostic for the quality of solar energy resource, in which the gridpoints are classified in four categories depending on the combination of solar irradiation amount and variability. The observed large percentage of points in the optimal category (high irradiation/low variability) in the IP is well‐captured by the RCMs in general terms. The analysis of scenarios indicates a future increase in solar irradiation, although not all scenarios agree in the geographical distribution of this increase. The quality of solar energy resource is projected to increase, mostly due to a decrease in variability. This is an important result, as a more stable inter‐annual resource should decrease the need for backup sources and also reduce inter‐annual electricity price variations. Finally, results from a first approximation to the issue of the ability of solar energy to cover power demand peaks in summer show important differences between regions of the IP. However, the spatially averaged correlation of solar irradiation and summer surface temperatures for the whole IP is rather high, which is a positive result as the strong interconnections of the power grid within the IP could allow a distribution of solar power surpluses in certain regions for such high‐temperature episodes. Due to the importance of solar energy in the future we study future changes of the solar radiation over Iberian Peninsula through the simulations from ESCENA project. To evaluate the quality of solar energy resource, the gridpoints are classified in four categories depending on the combination of solar irradiation amount and variability. The observed large percentage of points in the optimal category (high irradiation/low variability) is captured by the models. The analysis of scenarios indicates a future increase in solar irradiation amount and variability, although not all scenarios agree in this respect. [ABSTRACT FROM AUTHOR]

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

This paper compares extreme value statistics of daily maximum 10 m wind speed in winter simulated by the regional climate model COSMO‐CLM at a horizontal resolution of 15 km (C15) with the reanalyses ERA‐Interim and Arctic System reanalysis (ASR version 1 and 2) and with a satellite data set (CCMPv2). Our C15 simulation (1979/1980–2015/2016, November–April) is thereby the longest high‐resolution simulation available for the Arctic. The results show that the extreme wind speeds tend to increase over the ocean with increasing the horizontal model resolution. A horizontal resolution of ≤15 km is required to sufficiently capture all extreme wind characteristics, in particular for the tip jets and barrier winds over the Irminger Sea and in the Denmark Strait, and for the low‐level jets in the Nares Strait. Of almost equal importance are physical parameterizations of surface fluxes and of turbulence. Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic. State‐of‐the‐art global climate models and reanalyses (e.g., ERA‐Interim) underestimate the extreme near‐surface wind speeds in the Arctic and around Greenland. Regional climate models, for example, ASR/WRF or COSMO‐CLM, resolve the mesoscale wind systems that frequently produce extreme winds, which we estimated i.a. as 10‐year return levels (see the figure). Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic. [ABSTRACT FROM AUTHOR]

The MaRIUS‐G2G datasets were produced for the MaRIUS (Managing the Risks, Impacts and Uncertainties of drought and water Scarcity) project, using the Grid‐to‐Grid (G2G) national‐scale hydrological model for Great Britain. There are six separate datasets, with each of three combinations of meteorological driving data (two observation‐based and one from climate model ensembles) used to produce two types of outputs (daily time‐series of natural river flow for 260 sites, and monthly 1 × 1 km grids of natural flow and soil moisture in the unsaturated zone). The driving data required by G2G are rainfall and potential evaporation (PE). Two of the datasets from observation‐based driving data use rainfall from CEH‐GEAR (CEH‐Gridded Estimates of Areal Rainfall) with PE from MORECS (Met Office Rainfall and Evaporation Calculation System), and cover the period 1960–2015, while the other two use CEH‐GEAR rainfall with PE derived from 5 km temperature data using the Oudin method, and cover 1891–2015. The two datasets based on driving data (rainfall and PE) from the climate model ensembles cover three periods: Historical Baseline (1900–2006), Near‐Future (2020–2049), and Far‐Future (2070–2099). Data for a 30‐year Baseline period (1975–2004), against which the Near‐Future and Far‐Future periods should be compared, are also available directly. There are 100 members in each ensemble, and the future periods use the RCP8.5 emissions scenario. This paper provides details of the G2G model and the different sets of meteorological driving data, as well as the availability and formatting of the output datasets. It also describes some recent and potential applications of the datasets, which have already been used to support historical and future analyses of low flow and drought characteristics across Britain, and provides some guidance on how the climate model‐driven datasets should (and should not) be used. Open Practices: This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at DOI 10.5285/5f3c1a02-d5c4-4faa-9353-e8b68ce2ace2. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki. [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]

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]

It is becoming increasingly significant for humans to predict and understand future climate changes using coupled climate system models. Although the performance and scalability of individual physical components have improved over the past few years, coupled climate systems still suffer from low efficiency. This paper focuses on the process scheduling problem for the widely applied coupled earth system model (CESM). The proposed resource allocation strategies allow components to execute on a compromised suboptimal setup and still maintain approximately the best parallel speedup. With this flexible resource allocation strategy, we further propose a coordinated process scheduling algorithm (CPSA). More notably, we propose an upgraded version called CPSA‐B, which makes efficient resource sharing configurations, including resource allocation and process layout of components. We integrate CPSA and CPSA‐B as pre‐arrangement tools into the CESM program and deploy them on the Huawei Kunpeng platform. The speedup curves of the CESM components are prepared in advance, based on sampling tests. Experimental data show that CPSA‐B reduces up to 58% of the execution time compared with the CESM default strategy. The algorithm has low complexity and can efficiently find solutions for large input sizes. [ABSTRACT FROM AUTHOR]