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

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

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

A 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]

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

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

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]

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]

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]

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

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

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

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

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

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]

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

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

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

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

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

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

Despite multiple advances in the understanding of the water cycle intensification in a warmer climate, climate models still diverge in their hydrological projections. Here we constrain annual runoff projections over individual and aggregated Arctic river basins. For this purpose, we use two ensembles of global climate models and two statistical methods: a regression scheme assuming similar runoff sensitivities at interannual versus climate change timescales, and a Bayesian method where models are used to derive a posterior runoff response conditioned on historical observations. While both techniques are shown to narrow model uncertainties, more or less substantially depending on rivers, the Bayesian method is less sensitive to the choice of the model ensemble and is more skillful when tested with synthetic observations. It has also been applied over the whole Arctic watershed, showing so far a limited narrowing of the inter‐model spread, but its skill will further improve with increasing climate change. Plain Language Summary: Despite considerable progress in understanding the intensification of the water cycle in response to global warming, projections of changes in the various components of the continental water balance remain highly model‐dependent. This is particularly the case for the evolution of runoff, which remains very sensitive to the way atmospheric and continental processes are represented. Here we evaluate and compare two statistical methods to constrain climate projections of annual runoff at high latitudes via available or synthetic observations. Our Bayesian method, based on historical observations, is found to be more robust than a previous method based on observed interannual climate variability. It underlines the importance of having reliable flow reconstructions to constrain runoff projections and of using empirical observational constraints with caution, as they are likely to lead to over‐confident projections. Key Points: Runoff sensitivity to temperature depends on the considered timescaleObservational constraints based on interannual variability can thus lead to overconfident projectionsObservational constraints based on historical trends can be effective if such trends are driven by human emissions of greenhouse gasses [ABSTRACT FROM AUTHOR]

Although the multi‐model average compares well with observations, individually most of the latest climate models do not simulate a realistic size of the Indo‐Pacific Warm Pool in the present‐day climate. This study explores the implications of this warm pool size bias in climate models in Northern Hemisphere winter. The warm pool size bias in phase 6 of the Coupled Model Intercomparison Project models is related to the subtropical jet and precipitation distribution, both in the present‐day climate and in response to climate change, through extratropical Rossby wave trains and tropical circulation pathways. Based on these relationships, emergent constraints are developed to observationally constrain the future subtropical jet response over Asia and the Atlantic Ocean and precipitation response over North and Central America, which can help to reduce uncertainty in future projections of these features. Thus, accurate model simulation of the warm pool in the present‐day climate is important for future projections of the subtropical jet and precipitation. Plain Language Summary: This study examines the impact of a common problem in the latest climate models where they do not accurately simulate the size of the Indo‐Pacific Warm Pool in the present‐day climate. The effects of this issue are investigated in the Northern Hemisphere winter. The warm pool size problem in the models affects the subtropical winds and precipitation, both in the current climate and in response to climate change. Based on the relationships between the present‐day warm pool size and future projections of the subtropical winds and precipitation across models, we can help to constrain future projections of the subtropical winds over Asia and precipitation over North and Central America by ruling out models with biased warm pool size. The results show that having an accurate simulation of the warm pool in the present‐day climate is crucial for more reliable future projections of subtropical winds and precipitation. Key Points: Phase 6 of the Coupled Model Intercomparison Project multi‐model‐mean reproduces the observed Indo‐Pacific warm pool size well, but there is a huge inter‐model spread in warm pool sizeModel biases in warm pool size have local and remote effects on subtropical winds and precipitation in present‐day and future climatesEmergent constraints reduce projection uncertainty of subtropical winds and precipitation by ruling out models with biased warm pool size [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

As anthropogenic activities continue to drive increases in extreme events, the fundamental solution of reducing greenhouse gas emissions remains elusive. Thus, there is growing interest in stratospheric aerosol injection (SAI) to offset some of the most dangerous consequences of climate change. Although global SAI deployment would likely be easy to detect by some metrics, the detectability of SAI on extreme events might be more difficult. We examine this question in climate model simulations of SAI; specifically, the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS‐SAI) scenario. We train a logistic regression model to predict whether a map of global extremes came from climate simulations with or without SAI. The timing of accurate predictions is a quantification of the time required to detect SAI impacts. We find that regional changes in extreme temperature and precipitation under GLENS are robustly detected within 1 and 15 years of initial SAI injection, respectively. Plain Language Summary: In light of concerns regarding increasing extremes driven by human‐induced climate change, and the limited progress to‐date of climate change solutions, a key recommendation from a recent National Academies of Science, Engineering and Medicine report is that the United States should establish a transdisciplinary research program into proposed climate intervention techniques, including stratospheric aerosol injection (SAI). SAI would increase the number of small reflective particles in the upper atmosphere to cool the climate by reflecting a small percentage of incoming solar radiation back into space. If SAI were deployed, the question arises as to when and where we might first detect regional impacts of SAI on climate extremes. Here, we begin to examine this question by analyzing climate model simulations of the 21st century both without and with SAI deployment. We train a simple statistical method to predict whether a map of climate extremes came from a world with or without SAI. By looking at the ability of the statistical model to accurately identify the presence (or absence) of SAI deployment, we find that regional changes in extreme temperatures and precipitation under SAI are robustly detected within 1 and 15 years of initial SAI deployment, respectively. Key Points: A statistical model is trained to predict whether a map of global extremes came from a Representative Concentration Pathway 8.5 or stratospheric aerosol injection (SAI) simulationThe timing of accurate predictions acts as a quantification of the time to detection of a geoengineered climateRegional changes in extreme temperatures and extreme precipitation under SAI are robustly detected within 1 and 15 years of injection [ABSTRACT FROM AUTHOR]

Observations from the RAPID array at 26.5°N suggest a weakening in the Atlantic meridional overturning circulation between 2004 and 2020. The shortness of this time series raises the question of whether this weakening should be thought of as natural variability or as related to climate change. To estimate natural variability, preindustrial control runs of the Coupled Model Intercomparison Project Phase 6 are used and the observed weakening is found to fall within the distribution of naturally occurring trends. These trends result from model variability on periods longer than the observational window, and variation between models at these frequencies is especially pronounced. Finally, adding noise to inflate model variability also inflates model trends; however, the effect on trends is sensitive only to the low frequency portion of the noise spectrum. Plain Language Summary: The Atlantic meridional overturning circulation (AMOC) is a major ocean circulation system that transports heat northward in the Atlantic basin thus contributing to the regulation of the climate system. Fluctuations in the AMOC have been linked with changes in Arctic sea ice and to variations in the climate of Europe and North America. The longest direct observational record of the AMOC spans only 15.8 years and has shown a weakening of the circulation. Whether this weakening is due to natural oscillations in the climate system or is a response to anthropogenic greenhouse gases in the atmosphere is unclear. To assess whether the ocean circulation produces spontaneously weakening signals of such intensity, long simulations of climate models run under a preindustrial scenario are used to estimate the natural variability of the AMOC. These models suggest that the current weakening of the AMOC is consistent with natural variability. An accurate representation of slow timescale fluctuations in the AMOC is what matters most to the detection of an anthropogenically driven AMOC trend. This variability, however, differs markedly across models. Key Points: The 2004–2020 observed Atlantic meridional overturning circulation (AMOC) trend is not significantly relative to Coupled Model Intercomparison Project Phase 6 (CMIP6) model natural variabilityCMIP6 models largely disagree on AMOC variability, including at decadal and longer periodsIt is periods longer than the observational window that set AMOC trend detectability thresholds [ABSTRACT FROM AUTHOR]

Arba'een, a Muslim pilgrimage, is one of the largest annual mass gatherings in the world, with a date fixed according to the lunar calendar. Most pilgrims start their long walking journey from Basra/Najaf toward Karbala (about 70–500 km) and are significantly affected by outdoor weather conditions during this period. Here, based on simulations performed using carefully‐selected climate models, we project that heat stress during the pilgrimage is likely to reach a "dangerous" level, defined according to the US National Weather Service criteria, by the end of this century. Moreover, a significant increase in consecutive occurrence of hot days and hot nights is expected within the coming decades, which may cause a high incidence of heat‐related disorders as the human body may not recover from the daytime heat loads. Our study suggests that sound adaptation measures and stringent mitigation actions must be established to ensure a safe pilgrimage in the future. Plain Language Summary: Arba'een is a religious mass gathering, drawing millions of people to the holy city of Karbala in Iraq annually. When this procession occurs in the sweltering summer, it could cause significant heat‐related morbidity and mortality. Despite the growing risk of heat stress with global warming, less attention has been paid to heat‐related issues during this religious ceremony. Without adequate adaptation and mitigation strategies to climate change, more frequent and severe heatwaves (associated with the consecutive occurrence of hot days and nights) in a warmer world may pose a substantial threat to the Arba'een participants' health. Key Points: Future climate change will likely increase the frequency and intensity of heatwaves during Arba'een at Shiite holy sites in IraqAn increase in mugginess (measured by wet‐bulb temperature) is more pronounced at night than during the day in a warmer climateWe project a rise in consecutive occurrences of hot days and nights, which could threaten human health during the Arba'een event [ABSTRACT FROM AUTHOR]

Africa's central Sahel region has experienced prolonged drought conditions in the past, while rainfall has recovered more recently. Global climate models project anything from no change to a strong wetting trend under unabated climate change; and they have difficulty reproducing the complex historical record. Here we show that when a period of dominant aerosol forcing is excluded, a consistent wetting response to greenhouse‐gas induced warming emerges in observed rainfall. Using the observed response coefficient estimate as a constraint, we find that Coupled Model Intercomparison Project Phase 6 climate models with a realistic past rainfall response show a smaller spread, and higher median, of projected future rainfall change, compared to the full ensemble. In particular, very small or negative rainfall trends are absent from the constrained ensemble. Our results provide further evidence for a robust Sahel rainfall increase in response to greenhouse‐gas forcing, consistent with recent observations, and including the possibility of a very strong increase. Plain Language Summary: Rainfall is a critical resource for a large population in the African Sahel region, but rainfall levels have strongly varied in the past. It is unclear what will happen in the next decades, because some climate models suggest little to no change in average rainfall, while other models project a strong increase due to climate change. Our study aims to narrow down this uncertainty. In the past, both greenhouse gases and aerosols influenced Sahel rainfall, to different degrees during different periods; and models may not capture both mechanisms equally well. However, future rainfall changes will likely be driven mainly by greenhouse gas levels. We identify models that closely match the rainfall change that was observed in a recent period with dominant greenhouse gas forcing. These models also tend to project stronger rainfall increases than other models. Indeed, their multi‐model mean projection is an increase by almost 50% by 2040; while none of them projects stable rainfall levels or even a reduction. Thus, it appears more likely now that rainfall in the Sahel will increase substantially over the next few decades. Key Points: We identify the response of Sahel rainfall to greenhouse gas‐induced global warming in the historical recordMany global climate models of the latest generation (Coupled Model Intercomparison Project Phase 6) do not reproduce the observed rainfall responseObservational constraint reduces model spread and implies stronger rainfall increase under further warming than in full ensemble [ABSTRACT FROM AUTHOR]

The 2021 Pacific Northwest heatwave featured record‐smashing high temperatures, raising questions about whether extremes are changing faster than the mean, and challenging our ability to estimate the probability of the event. Here, we identify and draw on the strong relationship between the climatological higher‐order statistics of temperature (skewness and kurtosis) and the magnitude of extreme events to quantify the likelihood of comparable events using a large climate model ensemble (Community Earth System Model version 2 Large Ensemble [CESM2‐LE]). In general, CESM2 can simulate temperature anomalies as extreme as those observed in 2021, but they are rare: temperature anomalies that exceed 4.5σ occur with an approximate frequency of one in a hundred thousand years. The historical data does not indicate that the upper tail of temperature is warming faster than the mean; however, future projections for locations with similar climatological moments to the Pacific Northwest do show significant positive trends in the probability of the most extreme events. Plain Language Summary: While the 2021 Pacific Northwest heatwave was reasonably well‐forecasted by weather models, it was unexpected by many in the climate community because the high temperatures were so extreme. The event has raised questions about whether the probability of very extreme events is increasing faster than would be expected based on historical warming of average temperatures. Here, we analyze the spread of temperatures around the average, which has increased by 1.5°C since 1960, to provide a rough estimate of the probability of the very extreme event of 2021, and assess whether the probability of extreme heat is changing beyond what is expected from warming of average temperatures. By drawing on climate model simulations from regions that are analogous to the Pacific Northwest, we find that similar events can be simulated by climate models, but that they are very rare: when they occur, they are often the largest event across nearly 10,000 years of data. The climate model also suggests that the most extreme events may increase in probability in the future beyond what would be expected from the average climate change signal, although we do not yet see clear evidence of this in the observations. Key Points: Summer temperatures in the Pacific Northwest are positively skewed, so hot extremes are more likely than if the distributions were normalCommunity Earth System Model version 2 (CESM2) can simulate events as extreme as the 2021 Pacific Northwest heatwave at points with similar high‐order statistics, but they are rareObservations do not indicate that the upper tail is warming more than the mean, but CESM2 projects this behavior for very extreme events [ABSTRACT FROM AUTHOR]

Emulators of Earth System Models (ESMs) are complementary to ESMs by providing climate information at lower computational costs. Thus far, the emulation of spatially resolved climate extremes has only received limited attention, even though extreme events are one of the most impactful aspects of climate change. Here, we propose a method for the emulation of local annual maximum temperatures, with a focus on reproducing essential statistical properties such as correlations in space and time. We test different emulator configurations and find that driving the emulations with global mean surface temperature offers an optimal compromise between model complexity and performance. We show that the emulations can mimic the temporal evolution and spatial patterns of the underlying climate model simulations and are able to reproduce their natural variability. The general design and the good performance for annual maximum temperatures suggest that the proposed methodology can be applied to other climate extremes. Plain Language Summary: Climate models are invaluable tools for studying climate change but take a very long time to run, even on modern supercomputers. Emulators of climate models are statistical tools that can be calibrated to mimic the behavior of complex climate models with a much lower computational demand. However, they are typically not designed to reproduce climate extremes, despite the fact that extreme climate events belong to the most impactful aspects of climate change. In this study, we propose a method for the emulation of annual maximum temperature in time and space. This method also reproduces the natural variability of climate models, even though it is driven only by global mean surface temperature. We show that the resulting emulations are very similar to the data created by climate models. In an example application, we use the emulator to examine the extreme temperatures for different climate scenarios. Key Points: We present a method for the emulation of spatially resolved annual maximum temperatures from Earth System ModelsThe emulator reproduces statistical properties and correlations in space and time and can be extended to other extreme indicatorsThe method exhibits good performance for annual maximum temperatures [ABSTRACT FROM AUTHOR]

A warmer climate impacts streamflows and these changes need to be quantified to assess future risk, vulnerability, and to implement efficient adaptation measures. The climate simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which have been the basis of most such assessments over the past decade, are being gradually superseded by the more recent Coupled Model Intercomparison Project Phase 6 (CMIP6). Our study portrays the added value of the CMIP6 ensemble over CMIP5 in a first North America wide comparison using 3,107 catchments. Results show a reduced spread of the CMIP6 ensemble compared to the CMIP5 ensemble for temperature and precipitation projections. In terms of flow indicators, the CMIP6 driven hydrological projections result in a smaller spread of future mean and high flow values, except for mountainous areas. Overall, we assess that the CMIP6 ensemble provides a narrower band of uncertainty of future climate projections, bringing more confidence for hydrological impact studies. Plain Language Summary: Greenhouse gas emissions are causing the climate to warm significantly, which in turn impacts flows in rivers worldwide. To adapt to these changes, it is essential to quantify them and assess future risk and vulnerability. Climate models are the primary tools used to achieve this. The main data set that provides scientists with state‐of‐the‐art climate model simulations is known as the Coupled Model Intercomparison Project (CMIP). The fifth phase of that project (CMIP5) has been used over the past decade in multiple hydrological studies to assess the impacts of climate change on streamflow. The more recent sixth phase (CMIP6) has started to generate projections, which brings the following question: is it necessary to update the hydrological impact studies performed using CMIP5 with the new CMIP6 models? To answer this question, a comparison between CMIP5 and CMIP6 using 3,107 catchments over North America was conducted. Results show that there is less spread in temperature and precipitation projections for CMIP6. This translates into a smaller spread of future mean, high and low flow values, except for mountainous areas. Overall, we assess that using the CMIP6 data set would provide a more concerted range of future climate projections, leading to more confident hydrological impact studies. Key Points: A comparison of hydrological impacts using Coupled Model Intercomparison Project version 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles is performed over 3,107 catchments in North AmericaThe CMIP6 ensembles provide a narrower band of uncertainty for hydrological indicators in the futureIt is recommended to update hydrological impact studies performed using CMIP5 with the CMIP6 ensemble [ABSTRACT FROM AUTHOR]

Assessing how increasing greenhouse gas concentrations may modify regional climates is an ongoing challenge. Relatively little work has examined how climate change may influence processes related to regional thunderstorm activity. This is important to consider in areas where thunderstorms are important for maintaining regional hydroclimates. This study focuses on three convection‐allowing climate simulations—namely, a retrospective simulation (1990–2005) and two possible climate change scenarios (2085–2100)—with domains encompassing the conterminous United States. Regional and seasonal variability is noted in the response of thunderstorm activity as measured by thresholds in simulated radar reflectivity factor in the two climate change scenarios. A decrease in thunderstorm activity is projected for the Southern Plains, whereas the Southeast and Midwest experience an increase in thunderstorm activity. An examination of environmental parameters related to thunderstorm activity reveals an overall increase in convective instability but spatially varying changes in convective inhibition. Plain Language Summary: More favorable conditions may exist for thunderstorm activity throughout the United States in the late 21st century. However, certain factors prevent thunderstorms from taking advantage of these conditions by suppressing their formation. This leads to a varying response to possible climate change scenarios, with less thunderstorm activity in the Great Plains, and more in the Midwest and Southeast. Key Points: A regional climate model is used to simulate two climate change scenariosFewer Southern Plains thunderstorm days, more in the Midwest and SoutheastSome increases in conditional instability are offset by convective inhibition [ABSTRACT FROM AUTHOR]

While the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the "best" CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time. Plain Language Summary: The rate of Arctic warming varied in time, while the rate of global warming was nearly constant over the period 1960–2020. This led to a variable Arctic Amplification (AA) defined as a ratio of the Arctic warming trend to mean global warming trend. In addition, the Arctic experienced sudden changes in the rate of warming with step‐like changes in annual mean Arctic Amplification around the years 1986 and 1999. Climate models do not capture the second observed sharp increase of the AA. We suggest that the first AA increase around 1986 is primarily due to external forcing (increasing concentration of carbon dioxide) while the second sharp increase around 1999 is dominated by internal climate variability which current models cannot capture accurately within the time scale of the physical phenomena. Key Points: Annual mean Arctic Amplification (AA) within the period 1970–2020 changed in steep steps around 1986 and 1999. It reached values over 4.0Even those CMIP6 models best at reproducing the AA did not reproduce the second sudden increase near 1999We conjecture that the first AA sharp increase is due to external forcing, while the second is due to internal climate variability [ABSTRACT FROM AUTHOR]

The ocean is inhomogeneous in hydrographic properties with diverse water masses. Yet, how this inhomogeneity has evolved in a rapidly changing climate has not been investigated. Using multiple observational and reanalysis datasets, we show that the spatial standard deviation (SSD) of the global ocean has increased by 1.4 ± 0.1% in temperature and 1.5 ± 0.1% in salinity since 1960. A newly defined thermohaline inhomogeneity index, a holistic measure of both temperature and salinity changes, has increased by 2.4 ± 0.1%. Climate model simulations suggest that the observed ocean inhomogeneity increase is dominated by anthropogenic forcing and projected to accelerate by 200%–300% during 2015–2100. Geographically, the rapid upper‐ocean warming at mid‐to‐low latitudes dominates the temperature inhomogeneity increase, while the increasing salinity inhomogeneity is mainly due to the amplified salinity contrast between the subtropical and subpolar latitudes. Plain Language Summary: The ocean's inhomogeneity, quantified by the spatial standard deviation (SSD) of the global water mass, is closely linked to the global ocean's physical and biogeochemical processes. Although previous studies have reported various aspects of the long‐term ocean changes, the change of the global ocean inhomogeneity as an integral measure of the water‐mass diversity remains unknown. Our study shows that the overall inhomogeneity has increased by 1.4 ± 0.1% in temperature and 1.5 ± 0.1% in salinity since 1960. The observed ocean inhomogeneity increase is attributed to anthropogenic forcing and projected to accelerate in the future. The increase in temperature inhomogeneity is mainly due to the rapid upper‐ocean warming at mid‐to‐low latitudes. The amplified salinity contrast between the subtropical and subpolar latitudes contributes to the salinity inhomogeneity increase. Key Points: The spatial inhomogeneity of global ocean thermohaline properties has increased over the past decadesThe increase is primarily caused by anthropogenic forcing and is projected to accelerate in the futureUpper‐ocean warming and amplified salinity contrast between the subtropics and subpolar regions dominate the inhomogeneity increase [ABSTRACT FROM AUTHOR]

There is low confidence in projections of Antarctic sea ice area (SIA), due to deficiencies in climate model sea ice processes. Ensemble regression techniques can help to reduce this uncertainty. We investigate relationships between SIA climatology and 21st century change in the Coupled Model Intercomparison Project, phase 6 (CMIP6) multi‐model ensemble. In summer, under a strong forcing scenario, each model loses the majority of its sea ice. Therefore, models with greater historical SIA exhibit greater reductions, so the observed climatology of SIA strongly constrains projections. Ensemble spread in historical summer SIA is smaller than in CMIP phase 5 (CMIP5), and CMIP6 gives a more robust constraint on future SIA. In winter, by 2100 under a strong forcing scenario, 40% of SIA disappears on average, and ensemble spread in historical mean SIA explains approximately half the spread in projected change. A greater winter ice loss in CMIP6 than CMIP5 is explained by the higher climate sensitivities of some CMIP6 models. Plain Language Summary: Antarctic sea ice area is important for ecosystems, human activity, and for the dynamics of the atmosphere and ocean. However, major climate change assessments such as the Intergovernmental Panel on Climate Change Sixth Assessment Report have placed little confidence in projections of Antarctic sea ice area. This is because climate models struggle to simulate many aspects of sea ice area as observed by satellites. However, not all models should be treated equally. Models with relatively more sea ice area in the recent past project greater losses in the future, especially in summer. This suggests that models with historical sea ice area closest to what we have observed may be more reliable in their future simulations. We use this information in a simple statistical model to show that, firstly, the newest climate models largely show near‐total sea ice loss in summer by the end of the 21st century, which was not universally true for older climate models. Secondly, newer climate models lose more winter sea ice than their predecessors in the same time period, which is related to the known greater global warming response to given greenhouse gas concentrations in the newer models. Key Points: We compare Coupled Model Intercomparison Project (CMIP), phase 5 and the CMIP, phase 6 (CMIP6) projections of Antarctic sea ice loss, assessing drivers of uncertainty and the robustness of projectionsUnder strong forcing, CMIP6 models lose most Antarctic summer sea ice by 2100, so projections are constrained by historical climatologyIncreased winter sea ice loss in CMIP6 is related to increased equilibrium climate sensitivity [ABSTRACT FROM AUTHOR]

The Pre‐Industrial climate model simulation is intended as an equilibrium control experiment that uses constant external forcing, yet in the Coupled Model Intercomparison Project Phase 6 (CMIP6) model HadGEM3‐GC31‐LL a distinct change in climate state occurs around year 500 of the 2000 year simulation. The global mean near surface air temperature increases by almost 0.5 K associated with a reduction in southern hemisphere sea ice area of almost 20%. Here we show this step change in the state of the climate to be a consequence of the onset of deep convection in the Weddell and Ross Sea gyres. The delayed onset of convection in the gyres is a consequence of a positive, downward, top‐of‐atmosphere radiative balance, and continual ocean heat up‐take during the model spin‐up and the initial pre‐industrial control simulation. Consequently, model spin‐up strategy should be revised to initialize pre‐industrial simulations that are in energy balance. Plain Language Summary: The Pre‐industrial control simulation of climate models is a reference for climate change simulations. Consequently it should simulate a stable climate. However, if the model is not in radiative equilibrium, the ocean continually accumulates heat, becoming unstable at high latitudes and generating large climate fluctuations. This results in a control simulation that is not a steady state. Consequently the methodologies used to initialize and formulate climate models should be revised to ensure a steady state is achieved. Key Points: The long timescales associated with deep ocean heat uptake suggests simulations may not be equilibriated before forcing is perturbedSouthern Ocean venting of heat reduces sea ice cover and increases global mean temperature, leading to a warmer climate stateNet top‐of‐atmosphere energy imbalance can have implications for the methodologies used for climate model spin‐up [ABSTRACT FROM AUTHOR]

Previous studies documented possible connections between low frequency climate modes in the Northern Hemisphere ocean basins. We use observed sea surface temperatures and 270 large ensemble climate model simulations, which allows for improved methods of separating external and internal variability, such as removing the ensemble mean from each simulation. Detrending methods for observations have also improved since some of these previous studies were conducted. We also devise a modified statistical test using bootstrapping that is tuned specifically to this analysis. With these tools, we reexamine relationships among these modes. While previous studies have argued for the existence of an inter‐basin link, our results suggest that any internal connections between these modes are indistinguishable from random noise. Further, we show that external forcing affects each region in similar ways. This suggests that anthropogenic warming can cause an indirect link between the two basins, confounding the interpretation of a potential relationship. Plain Language Summary: We reexamine possible connections between climate patterns in the North Atlantic and North Pacific oceans. Improved climate model simulations and appropriate statistical methods allow us to build on previous research of these linkages. In contrast to previous studies, no natural connections are detected. However, global warming is shown to affect each region in similar ways, suggesting that climate change could cause an indirect link between the two basins. Key Points: A multi model large ensemble archive and modified statistical assumptions provide a framework for studying climatic relationshipsAn analysis of relationships between leading Northern Hemisphere climate modes does not show any internal connectionsExternal forcing such as global warming is shown to be a possible confounding factor in climate relationships [ABSTRACT FROM AUTHOR]

Heatwave frequency, duration and intensity has increased since the 1950s, a trend likely to continue with anthropogenic climate change. Although climate model projections are an important source of information on how heatwaves and other climate extremes may evolve with climate change it is necessary to understand whether climate models are able to adequately reproduce historical statistics of these events. We provide the first global evaluation of CMIP6 models to characterize heatwave characteristics between 1950 and 2014. Further, we also include a global heatwave evaluation of the CMIP5 models, as no study currently exists. Our results demonstrate that both ensembles underestimate mean heatwave frequency with region‐dependent biases for mean heatwave duration, intensity, and cumulative heat. Comparisons between CMIP5 and CMIP6 show that improvements in skill for the heatwave metrics evaluated here are marginal, suggesting that future improvements in simulating heatwaves may only be possible with significant advances in climate modeling capabilities. Plain Language Summary: Heatwaves are increasing in response to climate change associated with human activity, a trend that is likely to continue. In order to build confidence in projected changes in heatwaves it is necessary to first understand whether the models producing these projections can represent historical heatwave characteristics. We present the first global evaluation of the current and previous suite of climate models that inform the Intergovernmental Panel on Climate Change Assessment Reports. Our analysis considers four different heatwave characteristics that measure the frequency, duration, average intensity, and cumulative intensity over the period 1950–2014. Our results show that most climate models underestimate historical heatwave frequency with regional under‐ and overestimation of heatwave duration, average intensity and cumulative intensity. Our analyses are useful for decision makers and other users of climate model projections as they show which regions and heatwave characteristics have higher skill over the present‐day period. Key Points: First global evaluation of CMIP6 and CMIP5 models for historical heatwavesHeatwave frequency is systematically underestimated by both CMIP cyclesCumulative heat is systematically overestimated by both CMIP cycles [ABSTRACT FROM AUTHOR]

During March 2021, large regions of Eastern Australia experienced prolonged heavy rainfall and extensive flooding. The maximum daily mean column integrated water vapor transport (IVT) over Sydney during this event was within the top 0.3% of all days since 1980, and the 10‐day mean IVT was in the top 0.2%. In this study, we have examined the change in frequency of extreme IVT events over Sydney in 16 climate models from the Coupled Model Intercomparison Project 6 under two Shared Socioeconomic Pathways (SSP245 and SSP585). Generalized Extreme Value modeling was used to further analyze the change in frequency of extreme IVT events. We found the probability of long duration high IVT events is projected to increase by 80% at the end of the century, but the future change in IVT is correlated to the rate of global and regional warming in each model. Plain Language Summary: During March 2021, large regions of Eastern Australia experienced prolonged heavy rainfall and extensive flooding. This was associated with strong horizontal water vapor transport over this region that persisted for approximately 10 days. The amount of water vapor transported over Sydney during this event was extreme and within the top 0.3% of all days since 1980. In this study, we used climate models to project how much more often events such as these may occur by the end of the twenty‐first century under two greenhouse gas emission scenarios. We found that the probability of long duration high water vapor transport over Sydney, as in March 2021, may increase by 80%. Key Points: Sydney's March 2021 floods were associated with persistent high water vapor transportThe probability of long‐duration high integrated water vapor transport events should increase by the end of the twenty‐first centuryDifferences in climate sensitivity within the CMIP6 ensemble may increase the spread of moisture flux projections [ABSTRACT FROM AUTHOR]

State‐of‐the‐art climate models simulate a large spread in the projected decline of Arctic sea‐ice area (SIA) over the 21st century. Here we diagnose causes of this intermodel spread using a simple model that approximates future SIA based on present SIA and the sensitivity of SIA to Arctic temperatures. This model accounts for 70%–95% of the intermodel variance, with the majority of the spread arising from present‐day biases. The remaining spread arises from intermodel differences in Arctic warming, with some contribution from differences in the local sea‐ice sensitivity. Using observations to constrain the projections moves the probability of an ice‐free Arctic forward by 10–35 years when compared to unconstrained projections. Under a high‐emissions scenario, an ice‐free Arctic will likely (>66% probability) occur between 2036 and 2056 in September and between 2050 and 2068 from July to October. Under a medium‐emissions scenario, the "likely" date occurs between 2040 and 2062 in September and much later in the 21st century from July to October. Plain Language Summary: Arctic sea ice coverage has declined substantially over the past few decades and is projected to continue to decline over the next century. These projections, however, are marred by large uncertainties which arise primarily due to differences between climate models. In this study, we use a simple model that emulates the future evolution of Arctic sea ice as simulated by climate models to explain where this uncertainty comes from. We show that biases in simulating present‐day Arctic sea ice contribute most of the uncertainty, with differences in the simulated amount of Arctic warming from climate models contributing much of the rest. We use observations to constrain our simple model and show that under a high emissions scenario it is likely the Arctic will be free of sea ice in September sometime between 2036 and 2056 and from July to October sometime between 2050 and 2068. We also show that the emissions pathway impacts the length of ice free summers in the Arctic, indicating a low‐emissions pathway will reduce the likelihood of seeing ice‐free Arctic summers. Key Points: A model relating future sea‐ice area (SIA) to present SIA and local sea‐ice sensitivity is used to explain the intermodel spread in Arctic SIA projectionsBiases in simulating present‐day SIA contribute most to the spread, with model differences in Arctic warming contributing the restUnder a high‐emissions scenario, the Arctic will likely be ice‐free in September between 2036 and 2056 and from July to October between 2050 and 2068 [ABSTRACT FROM AUTHOR]

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

Correction of atmospheric variables to remove systematic biases in global climate model (GCM) simulations before downscaling offers a means of improving climate simulation accuracy in climate change impact assessments. Various mathematical approaches have been used to correct the lateral and lower boundary conditions of regional climate models (RCMs). Most of these techniques correct only the magnitude of each variable individually over time without regard to spatial and multivariate bias. Here, we investigate how well an RCM is able to reproduce the dependence of an observed variable based on three aspects: temporal, spatial, and multivariate. Results show that the RCM simulations with univariate bias-corrected GCM boundary conditions perform well in capturing both temporal and spatial dependence. However, all RCM simulations do not show improvement in the representation of dependence between variables, indicating the need for alternatives that correct systematic biases in multivariate dependence in both lateral and lower boundary conditions. Plain Language Summary It is well understood that systematic biases within a global climate model simulation can be passed into the input boundary condition of a regional climate model (RCM). To address this, many bias correction approaches have been used to correct the lateral and lower boundary conditions of RCMs, while assuming that each variable that makes up the lateral or lower boundary is independent. This study investigates the consequences of bias correction to assess whether the dependencies in time, space, and between variables are preserved. Using correlation length, it is shown that there is improvement in spatial and temporal dependence but not in inter-variable dependence which can produce a mismatch in the physical relationships in the simulated outcomes. A more physically consistent approach that considers the relationships between the variables is needed instead of the simplistic univariate correction procedures that have been used to date. [ABSTRACT FROM AUTHOR]

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

The simultaneous occurrence of extremely wet winters at multiple locations in the same region can contribute to widespread flooding and associated socio‐economic losses. However, the spatial extent of precipitation extremes (i.e., the area in which nearby locations experience precipitation extremes simultaneously) and its future changes are largely overlooked in climate assessments. Employing new multi‐thousand‐year climate model simulations, we show that under both 2.0 °C and 1.5 °C warming scenarios, wintertime total precipitation extreme extents would increase over about 80%–90% of the Northern Hemisphere extratropics (i.e., of the latitude band 28°–78°N). Stabilizing at 1.5 °C rather than 2.0 °C would reduce the average magnitude of the increase by 1.7–2 times. According to the climate model, the increased extents are caused by increases in precipitation intensity rather than changes in the spatial organization of the events. Relatively small percentage increases in precipitation intensities (e.g., by 4%) can drive disproportionately larger, by 1–2 orders of magnitude, growth in the spatial extents (by 93%). Plain Language Summary: One of the most impact‐relevant and studied effects of global warming is the intensification of precipitation extremes. When extremely wet winters occur simultaneously at multiple locations within the same region, their cumulative impacts can be particularly high and enhanced as a result of limited resources available to cope with simultaneous damages. Despite the impacts caused by widespread extremes, climate change studies have typically disregarded the spatial extension of the extremes. Here, based on new multi‐thousand‐year climate model simulations, we show that—over most of the Northern Hemisphere extratropics, that is, most of the latitude band 28°–78°N—the spatial footprint of total wintertime precipitation extremes is projected to largely widen in the future. This widening results from a warmer, and therefore moister, atmosphere that will intensify precipitation. Holding global warming to 1.5 °C rather than 2.0 °C, in line with the Paris Agreement, would be beneficial to society as it could limit the average increase in the extension over the Northern Hemisphere extratropics by up to a factor of 2. To develop better preparedness for such extreme events, stakeholders should consider that a small increase in precipitation intensities (for example, by 4%) could result in large (by 93%) increases in spatial extent. Key Points: The spatial extent of wintertime total precipitation extremes is projected to increase over most of the Northern Hemisphere in the futureChanges in the spatial organization, that is, dependence, of precipitation extremes only marginally affect the spatial extentsIncreased precipitation magnitudes can cause a disproportionate (even 20–90 times larger) increase in the precipitation extreme extents [ABSTRACT FROM AUTHOR]

Projections of extreme precipitation based on modern climate models suffer from large uncertainties. Specifically, unresolved physics and natural variability limit the ability of climate models to provide actionable information on impacts and risks at the regional, watershed and city scales relevant for practical applications. Here, we show that the interaction of precipitating systems with local features can constrain the statistical description of extreme precipitation. These observational constraints can be used to project local extremes of low yearly exceedance probability (e.g., 100‐year events) using synoptic‐scale information from climate models, which is generally represented more accurately than the local scales, and without requiring climate models to explicitly resolve extremes. The novel approach, demonstrated here over the south‐eastern Mediterranean, offers a path for improving the predictability of local statistics of extremes in a changing climate, independent of pending improvements in climate models at regional and local scales. Plain Language Summary: Climate change impact studies are currently restrained by the limited accuracy of climate models in resolving precipitation extremes, by the computational power required to run high‐resolution models, and by the uncertainties characterizing their analysis. Here, we present a novel approach which permits to project extreme precipitation for future climatic scenarios based on the combination of coarse‐scale information from climate models with local observations. Focusing on the south‐eastern Mediterranean, we provide projections of precipitation extremes which could not yet be derived using traditional methods, such as the events occurring on average once in 100 years. The combined effect of changes in intensity and occurrence of two dominant synoptic systems is projected to increase the intensity of the 100‐year events in the coast and in the desert areas of the region and to decrease it elsewhere. The novel approach offers a path for improving the predictability of extremes in a changing climate, independent of pending improvements in climate models. Key Points: Local observations can be used to constrain the intensity distribution of precipitation events associated with different synoptic systemsThe constraints allow projecting extreme return levels at scales relevant for impact studies from synoptic information from climate modelsThe approach improves the predictability of local extremes, independent of improvements in climate models at regional and local scales [ABSTRACT FROM AUTHOR]