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Compared to independent drought or extreme heat events, compound drought and heat events (CDHEs) can pose more adverse impacts on ecosystems and human society. As the anthropogenic influence on CDHEs in China remains largely unquantified, we analyse both observed and simulated changes in the occurrence of CDHEs over China from 1961 to 2014, and perform detection and attribution analyses utilizing an optimal fingerprinting method, based on observations, reanalysis data and numerical experiments from seven Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Upward trends are found in observed CDHEs during 1961–2014 over the majority of China, especially in the last two decades. The increase in CDHEs is largely attributed to human influence. Anthropogenic forcing is detectable in the observed CDHEs changes in the whole China and its eastern region. Furthermore, a three‐signal analysis indicates that greenhouse gases play a key role in increasing CDHEs over China, while natural forcing has a negligible effect. Anthropogenic aerosol emissions also play a detectable role in offsetting the greenhouse gas‐induced increase of CDHEs over China. [ABSTRACT FROM AUTHOR]

While being successful in the detection and attribution of climate change, the optimal fingerprinting method (OFM) may have some limitations from a physics-and-dynamics-based viewpoint. Here, an analysis is made on the linearity, non-interaction, and stationary-variability assumptions adopted by OFM. It is suggested that furthering OFM needs a viewpoint beyond statistical science, and the method should be combined with theoretical tools in the dynamics and physics of the Earth system, so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system. [ABSTRACT FROM AUTHOR]

In the context of global warming, both droughts and extreme precipitation events occur frequently over China. Precipitation, being a major driving factor and a crucial component of the water cycle, is highly sensitive to the human activities. This study uses high-resolution gridded daily precipitation, categorizing precipitation into 20 equal intervals, to analyze the changes in precipitation structure across the region. The investigation reveals a significant transition from light to heavy precipitation in China from 1961 to 2014, characterized by a notable decrease in light precipitation and a substantial increase in heavy precipitation. Employing the latest generation of CMIP6 models, it is found that the models can accurately replicate this change, with GHG forcing exacerbating this trend, particularly noticeable, whereas results under AER forcing are contrary to observational outcomes. Simultaneously, the study notes that CMIP6 models exhibit limitations in simulating the spatial distribution of trends in light precipitation but perform well in reproducing the trend pattern for heavy precipitation. Attribution results suggest that the observed shift from light to heavy precipitation is predominantly a result of the combined influence of GHG and AER forcing. However, significant regional variations exist in this transition. In the future, this transition is expected to persist, with heavy precipitation showing a more pronounced increasing trend. [ABSTRACT FROM AUTHOR]

The UK contribution to the Detection and Attribution Model Intercomparison Project (DAMIP), part of the sixth phase of the Climate Model Intercomparison Project (CMIP6), is described. The lower atmosphere and ocean resolution configuration of the latest Hadley Centre global environmental model, HadGEM3‐GC3.1, is used to create simulations driven either with historical changes in anthropogenic well‐mixed greenhouse gases, anthropogenic aerosols, or natural climate factors. Global mean near‐surface air temperatures from the HadGEM3‐GC31‐LL simulations are consistent with CMIP6 model ensembles for the equivalent experiments. While the HadGEM3‐GC31‐LL simulations with anthropogenic and natural forcing factors capture the overall observed warming, the lack of marked simulated warming until the 1990s is diagnosed as due to aerosol cooling mostly offsetting the well‐mixed greenhouse gas warming until then. The model has unusual temperature variability over the Southern Ocean related to occasional deep convection bringing heat to the surface. This is most prominent in the model's aerosol only simulations, which have the curious feature of warming in the high southern latitudes, while the rest of the globe cools, a behavior not seen in other CMIP6 models. This has implications for studies that assume model responses, from different climate drivers, can be linearly combined. While DAMIP was predominantly designed for detection and attribution studies, the experiments are also very valuable for understanding how different climate drivers influence a model, and thus for interpretating the responses of combined anthropogenic and natural driven simulations. We recommend institutions provide model simulations for the high priority DAMIP experiments. Plain Language Summary: We describe the UK submission to the Detection and Attribution Model Intercomparison Project (DAMIP), using the HadGEM3‐GC3.1 climate model. The model's near‐surface temperature responses to different human and natural climate drivers are compared with other climate models and observed temperature changes. The experiments help to understand the evolution of the model's simulated historical global temperatures. One of the more interesting model features is the variability in the Southern Ocean which manifests itself as occasional surface warming due to deep ocean heat coming to the surface. This behavior, which occurs more often in simulations that cool than in simulations that warm, appears to be unusual compared to other models. The investigation of this model behavior demonstrates that DAMIP model experiments are not just useful for climate change detection and attribution, but also for understanding how a model responds to different climate drivers. Climate model participation in DAMIP is encouraged. Key Points: The UK's contribution to the Detection and Attribution Model Intercomparison project (DAMIP) is describedThe climate model's global temperature response to different anthropogenic and natural drivers is examined and compared to other modelsSouthern Ocean temperature variability is unusual and sensitive to climate driver [ABSTRACT FROM AUTHOR]

It is not clear to what extent anthropogenic activities increase meteorological drought based on regional-scale observations. This study provides a detection and attribution (D&A) analysis of external forcing on meteorological drought using the standard precipitation index for a 12-month time scale (SPI-12) on a regional scale, particularly in the Ajichay basin, from 1972 to 2020, based on models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Regularized Optimal Fingerprinting (ROF) method is performed for D&A analyses on two SPI-12 time series (inter-annual/decadal and long-term), which are decomposed by the Ensemble Empirical Mode Decomposition (EEMD). Observed annual precipitation, greenhouse gas (GHG) forcing, and anthropogenic-plus-natural (ALL) forcing show 62%, 33%, and 17% upward trends, respectively, based on the Mann-Kendall test. Additionally, the EEMD method reveals that the long-term trends of observed SPI-12, GHG, and ALL forcings exhibit nonlinear trends that have 7%, 3.5%, and 4.5% variance contribution rates of components, respectively. The scaling factor (β) presents the responses SPI-12 to external forcing using total least squares regression estimates in the ROF method. External forcing is detectable and attributable should β and an uncertainty range be greater than zero and spanning unity. The results show that for inter-annual/decadal SPI-12, greenhouse gas can be detected and separated from natural (NAT) and other anthropogenic forcings ( β =0.96 with 95% confidence interval of 0.64–1.2) in single, two, and three-signal analyses. In long-term evaluations, greenhouse gas forcing ( β =1.27 with a 95% confidence interval of 0.95–1.59) can be detected and separated from natural and other anthropogenic forcing in single, two, and three-signal analyses. [ABSTRACT FROM AUTHOR]

Abstract The UK contribution to the Detection and Attribution Model Intercomparison Project (DAMIP), part of the sixth phase of the Climate Model Intercomparison Project (CMIP6), is described. The lower atmosphere and ocean resolution configuration of the latest Hadley Centre global environmental model, HadGEM3‐GC3.1, is used to create simulations driven either with historical changes in anthropogenic well‐mixed greenhouse gases, anthropogenic aerosols, or natural climate factors. Global mean near‐surface air temperatures from the HadGEM3‐GC31‐LL simulations are consistent with CMIP6 model ensembles for the equivalent experiments. While the HadGEM3‐GC31‐LL simulations with anthropogenic and natural forcing factors capture the overall observed warming, the lack of marked simulated warming until the 1990s is diagnosed as due to aerosol cooling mostly offsetting the well‐mixed greenhouse gas warming until then. The model has unusual temperature variability over the Southern Ocean related to occasional deep convection bringing heat to the surface. This is most prominent in the model's aerosol only simulations, which have the curious feature of warming in the high southern latitudes, while the rest of the globe cools, a behavior not seen in other CMIP6 models. This has implications for studies that assume model responses, from different climate drivers, can be linearly combined. While DAMIP was predominantly designed for detection and attribution studies, the experiments are also very valuable for understanding how different climate drivers influence a model, and thus for interpretating the responses of combined anthropogenic and natural driven simulations. We recommend institutions provide model simulations for the high priority DAMIP experiments.

Near‐surface wind speed (NSWS) over China shows multiple time‐scale changes at a centennial scale, but the contributions of internal variability (IV), anthropogenic forcing (ANT), and natural forcing (NAT) to those changes remain unknown. This study investigated the contributions of IV, ANT, and NAT to NSWS changes at a centennial scale. Results show that the NSWS changes were attributed mainly to IV. IV not only modulated the interannual changes in NSWS but also determined the interdecadal transition in NSWS. The relative contributions of IV to the interannual and decadal NSWS exceeded 75.0%. ANT contributed particularly to the long‐term reduction in NSWS; especially, it has contributed 55.0% of the reduction in NSWS since 1957, serving as the major contributor to the reduction in NSWS. NAT had a small‐to‐negligible effect on China's NSWS throughout the study period. This study enhances our understanding of NSWS changes at different time scales. Plain Language Summary: Near‐surface wind speed (NSWS) is crucial because it can influence energy, water, and air move between the Earth's surface and the atmosphere, which can also affect weather and climate systems like dust storms, evaporation rates, and the water cycle. In the past decades, interannual and interdecadal changes in NSWS, as well as the long‐term trend of NSWS have been analyzed; however, the causes behind these changes are not clear. Our research focuses on understanding these changes over nearly a century. We discover that internal variability (IV) is a primary factor driving these changes in NSWS, especially in terms of its fluctuations and shifts over decades. In addition to IV, anthropogenic forcing also plays a crucial role, particularly for the decrease of NSWS since 1957. On the other hand, natural forcing seem to have a minimal or almost no impact on NSWS changes in China during the study period. This study not only enhances our understanding of NSWS changes over multiple time scales but also provides essential information for policymakers to develop climate strategies and adaptation measures. Key Points: Internal variability determines the interannual and interdecadal changes in near‐surface wind speed (NSWS) across ChinaAnthropogenic forcing is responsible for the slowdown in NSWS since 1957, its contribution reaches 55.0%Natural forcing has a small‐to‐negligible influence on the changes in NSWS [ABSTRACT FROM AUTHOR]

Due to the scarcity of observational data in the early 20th century, very limited research has explored the impact of human activities on temperature extremes at the regional scale. Here we used a newly developed homogenized near-surface air temperature dataset from the beginning of the 20th century to estimate the frequency and intensity of extreme temperatures in eastern China and evaluate their anthropogenic influence based on models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found clear increases in warm extremes and decreases in cold extremes since 1901 for both annual and seasonal mean temperatures, with more pronounced changes in recent decades. The most significant warming occurred in spring and winter, approximately double the smallest warming observed in autumn. The CMIP6 models generally replicated the century-scale warming in annual and seasonal temperature extremes, showing increases in the frequency and intensity of warm extremes and corresponding decreases in cold extremes. The optimal fingerprinting detections suggest that the century-scale warming can be clearly attributed to anthropogenic forcing, including changes in seasonal extreme temperatures. Most observed changes in extreme temperatures were attributable to anthropogenic greenhouse gas emissions, partially offset by a smaller negative impact from anthropogenic aerosol forcing, whereas natural forcing has played a minor role. These results provide important information for accurately projecting future changes in temperature extremes.

The accelerated warming over the Tibetan Plateau relative to global means has attracted considerable attention from the scientific community. Nevertheless, the timescale, seasonality and dominant causes of the Tibetan warming amplification have not been discussed. The seasonality of the Tibetan amplification effect at different timescales was revealed in this study. Based on the optimal fingerprinting attribution method, an attribution study of the Tibetan warming amplification was also conducted after selecting the outperforming model simulations in the Coupled Model Intercomparison Project (CMIP6). Results showed that Tibetan warming amplification manifested in the mid-1970s. The annual amplification during 1961–2018 was 0.13 °C per decade. The warming amplification in spring started later than all the other seasons, and the strongest warming amplification occurred in winter. The change in Tibetan warming amplification also displayed distinct decadal differences at time scales shorter than 30 years. The updated models perform better in simulating the surface air temperature change on the global land scale whilst underestimating the warming over the Tibetan Plateau region, which causes a weak amplification in the model simulation. Overall, the largest deviation comes from winter. The attribution result reveals that the stronger warming response to human influence on the Tibetan Plateau than the global land is the direct cause of the Tibetan warming amplification. This finding implies the important role of local climate feedback over the Tibetan Plateau.

Abstract Near‐surface wind speed (NSWS) over China shows multiple time‐scale changes at a centennial scale, but the contributions of internal variability (IV), anthropogenic forcing (ANT), and natural forcing (NAT) to those changes remain unknown. This study investigated the contributions of IV, ANT, and NAT to NSWS changes at a centennial scale. Results show that the NSWS changes were attributed mainly to IV. IV not only modulated the interannual changes in NSWS but also determined the interdecadal transition in NSWS. The relative contributions of IV to the interannual and decadal NSWS exceeded 75.0%. ANT contributed particularly to the long‐term reduction in NSWS; especially, it has contributed 55.0% of the reduction in NSWS since 1957, serving as the major contributor to the reduction in NSWS. NAT had a small‐to‐negligible effect on China's NSWS throughout the study period. This study enhances our understanding of NSWS changes at different time scales.

Understanding the impact of different external forcings on regional temperature changes in China and constraining future changes is important for formulating targeted climate mitigation policies. We conducted a detection and attribution analysis to examine the effects of different external forcings on the differences in warming across China and seven subregions. This analysis spanned from 1965 to 2014 and used observations as well as data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The signals of anthropogenic (ANT) forcing can be detected and separated from natural (NAT) forcing in all regions, and the effect of greenhouse gases are found to account for a significant portion of the observed warming in these regions. In the Yangtze River Basin, southeast China and southwest China, the observed warming trends are lower than other subregions. This can be attributed to the relatively weak impact of greenhouse gases, resulting in a temperature increase of only 0.51°C in the Yangtze River Basin, which accounts for 48.57% of the observed warming. While both southeast China and southwest China experience a cooling effect of aerosols, leading to temperature decreases of 0.20 and 0.53°C, respectively, even though the signal of aerosol forcing is not detected. In terms of future projection, the use of attribution constraint approach effectively corrects the biases of the CMIP6 model simulation and reduces projection uncertainty in China and most subregions under the new shared socioeconomic pathway (SSP) SSP2‐4.5 and SSP5‐8.5. The Tibetan Plateau has the largest reduction of the uncertainty range among seven subregions, with the decrease of 39.02% under SSP2‐4.5 scenario in the long‐term (2081–2098). In addition, north China and southwest China will experience the highest and lowest warming in the long‐term (2081–2098) after attribution constraints, increasing by 3.41 and 2.14°C, respectively, under SSP2‐4.5 scenario. [ABSTRACT FROM AUTHOR]

Evaluating the susceptibility of regional climates to climate change provides a framework for realistically analyzing potential future climate changes. This paper investigates the impact of human activities on variations in extreme precipitation in Iran by evaluating data provided from 286 rain-gauge stations during 1967-2010 and general circulation simulation results of the CanESM2 model. This investigation was based on six forcing factors, including natural external factors (volcanic aerosols, solar radiation), anthropogenic and a combination of them, Green House Gases (GHGs), changes in land use, and anthropogenic aerosols. Seven precipitation indices, namely Rx1day (annual maximum 1-day precipitation), Rx5day (annual maximum 5-day precipitation), R10mm (annual count of days with daily precipitation exceeding 10 mm), R20mm (annual count of days with daily precipitation exceeding 20 mm), CDD (consecutive dry days), CWD (consecutive wet days), and PRCPTOT (annual total wet day precipitation), have been analyzed via the optimal fingerprint method. The results revealed that Rx1day, Rx5day and CWD increased, while R10mm, R20mm, CDD, and PRCPTOT decreased among which CDD and Rx1day indices showed significant variations, with values of 18.4% and 10.9%, respectively. Furthermore, the obtained results implied that only the impact of anthropogenic forcing, with a value of 1.4, was detected and attributed to variations in CDD. Additionally, anthropogenic forcing caused an increase in the return period of a 20-year event by 1.9 years for CDD. Although human-induced forcing factors presented marked trends in some cases, their effects were not generally detected and attributed to the change in the observations, apart from one exception. [ABSTRACT FROM AUTHOR]

Greenhouse gases and aerosols play a major role in controlling global climate change. Greenhouse gases drive a radiative imbalance which warms the ocean, while aerosols cool the ocean. Since 1980, the effective radiation felt by the planet due to anthropogenic aerosols has leveled off, global ocean cooling due to aerosols has decelerated, and greenhouse gas‐driven ocean warming has accelerated. We explore the deceleration of aerosol‐driven ocean cooling by quantifying a time‐ and spatially varying ocean heat uptake efficiency, defined as the change in the rate of global ocean heat storage per degree of cooling surface temperature. In aerosol‐only simulations, ocean heat uptake efficiency has decreased by 43 ± 14% since 1980. The tropics and sub‐tropics have driven this decrease, while the coldest fraction of the ocean continues to sustain cooling and high ocean heat uptake efficiency. Our results identify a growing trend toward less efficient ocean cooling due to aerosols. Plain Language Summary: The composition of the atmosphere has a major impact on our climate. Greenhouse gases warm the planet, while aerosols (i.e., suspensions of particles in the atmosphere) cool the planet, and most of this change is absorbed by the oceans. Since 1980, the rate of cooling of the planet due to aerosols has plateaued. In the past few decades, the ocean has begun to equilibrate to this change, and this work explores where and when this equilibration has occurred in the ocean based on global climate models. To understand this change, we use an "ocean heat uptake efficiency" metric which describes how much additional heat builds up in the ocean for a given degree of surface temperature gain (or loss). We find that the ocean is cooling more slowly given a degree of surface cooling due to aerosols compared to the pre‐1980s. This change is largely driven by the tropics and sub‐tropics, where the ocean has stopped cooling in response to aerosol‐driven negative surface temperatures. Polar and sub‐polar regions, however, continue to cool due to aerosols. These changes are occurring alongside accelerating greenhouse gas‐driven warming, suggesting that the relative role of aerosols in cooling our climate is weakening. Key Points: Since 1980, aerosol‐driven ocean cooling has decelerated substantially, alongside a drop in ocean heat uptake efficiencyThe drop in ocean heat uptake efficiency is limited to the tropics, which may have equilibrated to ongoing aerosol‐driven radiative forcingAir‐sea fluxes into the coldest fraction of the ocean continue to offset greenhouse gas‐driven ocean warming [ABSTRACT FROM AUTHOR]

Extreme weather events and their consequential impacts have been a key feature of the climate in recent years in many parts of the world, with many partly attributed to ongoing global-scale warming. The past year, 2022, has been no exception, with further records being broken. The year was marked by unprecedented heatwaves and droughts with highly unusual spatial extent, duration and intensity, with one measure indicating an aggregated and overall intensity of extreme heat events worldwide not seen since at least 1950. The extreme drought measured by surface soil moisture covered 47.3% of global land areas in 2022, which was the second most widespread year since 1980. Here, we examine notable events of the year in five major regions of the world: China's Yangtze River region, western Europe, the western U.S., the Horn of Africa and central South America. For each event, we review the potential roles of circulation, oceanic forcing (especially the "triple-dip" La Niña) and anthropogenic climate change, with an aim of understanding the extreme events in 2022 from a global perspective. This will serve as a reference for mechanism understanding, prediction and attribution of extreme events. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are critical to the hydrological cycle of the western United States with both favorable and formidable impacts to society based on their landfalling characteristics. In this study, we provide a first-of-its-kind evaluation of how landfalling ARs may respond to several stabilized warming scenarios. To do this we combine a recently developed AR detection workflow with an ensemble of uniform high-resolution (0.25°) Community Earth System Model simulations designed to facilitate detection and attribution of extreme events with global warming. These simulations include a world that might have been in the absence of anthropogenic warming (+0◦C), a world that corresponds to present day warming (+0.85◦C), and several future worlds corresponding to +1.5◦C, +2◦C and +3◦C global warming. We show that warming increases the number of water management relevant landfalling ARs from 19.1 ARs per year at +0◦C to 23.6 ARs per year at +3◦C. Additionally, this warming intensifies the amount of water transported by landfalling ARs resulting in a decrease in the fraction of ARs that are “mostly to primarily beneficial” to water resource management (i.e., 91% of ARs at +0◦C to 78% at +3◦C) and an increase in the fraction of ARs that are “mostly or primarily hazardous” to water resource management (i.e., 2% of ARs at +0◦C to 8% at +3◦C). Shifts in AR character also have important ramifications on flood damages, whereby for every +1◦C of additional warming from present conditions annual average flood damages increase by ~$1 billion. These findings highlight the pragmatic implications of climate mitigation aimed at limiting global warming to under +2◦C.

Atmospheric rivers (ARs) are critical to the hydrological cycle of the western United States with both favorable and formidable impacts to society based on their landfalling characteristics. In this study, we provide a first-of-its-kind evaluation of how landfalling ARs may respond to several stabilized warming scenarios. To do this we combine a recently developed AR detection workflow with an ensemble of uniform high-resolution (0.25°) Community Earth System Model simulations designed to facilitate detection and attribution of extreme events with global warming. These simulations include a world that might have been in the absence of anthropogenic warming (+0◦C), a world that corresponds to present day warming (+0.85◦C), and several future worlds corresponding to +1.5◦C, +2◦C and +3◦C global warming. We show that warming increases the number of water management relevant landfalling ARs from 19.1 ARs per year at +0◦C to 23.6 ARs per year at +3◦C. Additionally, this warming intensifies the amount of water transported by landfalling ARs resulting in a decrease in the fraction of ARs that are “mostly to primarily beneficial” to water resource management (i.e., 91% of ARs at +0◦C to 78% at +3◦C) and an increase in the fraction of ARs that are “mostly or primarily hazardous” to water resource management (i.e., 2% of ARs at +0◦C to 8% at +3◦C). Shifts in AR character also have important ramifications on flood damages, whereby for every +1◦C of additional warming from present conditions annual average flood damages increase by ~$1 billion. These findings highlight the pragmatic implications of climate mitigation aimed at limiting global warming to under +2◦C.

Abstract Greenhouse gases and aerosols play a major role in controlling global climate change. Greenhouse gases drive a radiative imbalance which warms the ocean, while aerosols cool the ocean. Since 1980, the effective radiation felt by the planet due to anthropogenic aerosols has leveled off, global ocean cooling due to aerosols has decelerated, and greenhouse gas‐driven ocean warming has accelerated. We explore the deceleration of aerosol‐driven ocean cooling by quantifying a time‐ and spatially varying ocean heat uptake efficiency, defined as the change in the rate of global ocean heat storage per degree of cooling surface temperature. In aerosol‐only simulations, ocean heat uptake efficiency has decreased by 43 ± 14% since 1980. The tropics and sub‐tropics have driven this decrease, while the coldest fraction of the ocean continues to sustain cooling and high ocean heat uptake efficiency. Our results identify a growing trend toward less efficient ocean cooling due to aerosols.

A new detection and attribution method is presented and applied to the global mean surface air temperature (GSAT) from 1900 to 2014. The method aims at attributing the climate changes to the variations of greenhouse gases, anthropogenic aerosols, and natural forcings. A convolutional neural network (CNN) is trained using the simulated GSAT from historical and single‐forcing simulations of 12 climate models. Then, we perform a backward optimization with the CNN to estimate the attributable GSAT changes. Such a method does not assume additivity in the effects of the forcings. The uncertainty in the attributable GSAT is estimated by sampling different starting points from single‐forcing simulations and repeating the backward optimization. To evaluate this new method, the attributable GSAT changes are also calculated using the regularized optimal fingerprinting (ROF) method. Using synthetic non‐additive data, we first find that the neural network‐based method estimates attributable changes better than ROF. When using GSAT data from climate model, the attributable anomalies are similar for both methods, which might reflect that the influence of forcing is mainly additive for the GSAT. However, we found that the uncertainties given both methods are different. The new method presented here can be adapted and extended in future work, to investigate the non‐additive changes found at the local scale or on other physical variables. Plain Language Summary: In order to design effective adaptation policies, it is essential to have reliable estimates of the effect of anthropogenic activities on the climate. For that purpose, a new attribution method based on a neural network is designed and evaluated. The method estimates the past global mean surface air temperatures anomalies caused by the changes in the greenhouse gases concentration, the variation of anthropogenic aerosols, and the variations driven by naturally occurring phenomena. To build this estimation, the data from observations and climate models are used. This methodology is compared with another state‐of‐the‐art method. The results of both methods are evaluated and discussed. The proposed method provides better estimations in the case of large non‐additivity of the causes of climate change and can be applied to other physical variables or at the regional scale. In the case of the global mean surface air temperature, the method presented provides estimation similar to other methods. Key Points: We present a non linear method based on neural network to attribute the global mean surface air temperature variability to different forcingsWe use a convolutional neural network associated with a backward optimization to estimate the climate response to the different external forcingsThe attributable forcings are consistent with those obtained using another state‐of‐the‐art method [ABSTRACT FROM AUTHOR]

A decline of anthropogenic aerosol (AA) emission is expected worldwide over the coming decades. But the climate effects of aerosol removal and greenhouse gases (GHG) emission at regional scale are poorly distinguished and constrained. Taking the Tibetan Plateau (TP) as an instance, analyses of the state‐of‐the‐art climate models participating in the Detection and Attribution Model Intercomparison Project imply that while the observed warming from 1961 to 2020 is predominantly attributed to GHG emission, the future temperature rise will be influenced by the combined effects of persistent increase in GHG concentration and reduction of AA emission. Here, we develop a new constraint method considering the changed contribution of AA forcing. Constrained by detected individual external forcings, the joint contributions of GHG (1.74°C) and AA forcings (0.10°C) will lead to a warming around 1.85°C over the TP during mid‐century (2041–2060) relative to 1995–2014 under SSP2‐4.5 scenario, which is 0.44°C cooler than the raw projection. Plain Language Summary: The rapid warming over the Tibetan Plateau (TP) has led to increased risks to ecological environment and even the livelihoods and health of the people. A reliable projection on future warming and a deeper understanding of the contributions of the effects of anthropogenic aerosol (AA) removal and greenhouse gases (GHG) emission are necessary for climate change adaptation and mitigation activities. Here, using the state‐of‐the‐art climate models participating in the Detection and Attribution Model Intercomparison Project, we found that the while the GHG emission has and will continue to warm the TP from 1961 to 2100, the changes in AA emission have partly offset the warming trend induced by GHG forcing before the late 2000s but will accelerate the warming rate induced by GHG forcing in the future. According to on our new constraint method considering the changed emission condition of AA, the joint contributions of GHG emission (1.74°C) and AA removal (0.10°C) will lead to a warming around 1.85°C over the TP during mid‐century (2041–2060) relative to 1995–2014 under intermediate‐emission scenario. Both the effects of GHG and AA to TP future warming are overestimated by the state‐of‐the‐art climate models. Key Points: Anthropogenic aerosol removal will accelerate the Tibetan Plateau (TP) warming induced by greenhouse gas emission over the coming decadesThe TP will warm by 1.85°C (2.69°C) in 2041–2060 (2081–2100) relative to 1995–2014 under intermediate‐emission scenarioConstrained responses to individual external forcings projects weaker TP warming than the raw projections [ABSTRACT FROM AUTHOR]

Variability in the North Atlantic Oscillation (NAO) has contributed to the recent multidecadal trends observed in European climate, especially to trends in winter precipitation over Northern Europe. However, the current generation of coupled climate models struggle to reproduce the NAO's contribution to multidecadal trends, which has important implications for deriving constraints based on the comparison of observed and modeled trends. An observational constraint based on attribution results, both with and without the contribution of variability associated with the NAO, is applied to projections of Northern European precipitation and temperature, and observed NAO variability is shown to lead to a constraint that overestimates future forced changes. Only after removing the NAO variability is the observed climate change consistent with model simulations, and a tighter, unbiased observational constraint based on the forced signal (without the NAO) can be applied to future projections. Plain Language Summary: The observed precipitation and temperature across Northern Europe in winter has been increasing over the past several decades. However, the magnitude of this trend is generally not well reproduced by the latest set of global climate models. A part of the observed increase in precipitation can be related to the variability in the North Atlantic Oscillation (NAO), which is a large‐scale pattern of sea level pressure in the North Atlantic region that varies in strength each season and year, and is strongly linked to temperature and precipitation patterns across Europe, particularly in winter. In this study, we show that if the variability related to the NAO is first removed from the precipitation time series, from both the observations and the model simulations, the difference between the long‐term observed and modeled trends in precipitation is reduced. Removing the NAO variability also has an impact on the difference in observed and modeled trends in temperature. This has important implications for approaches that use the difference in observations and model simulations as a way of constraining future projections of climate change. Key Points: Variability in the North Atlantic Oscillation (NAO) has contributed to the recent multidecadal trends observed in European climateThe suite of current comprehensive global models (CMIP6) struggle to reproduce the NAO's contribution to multidecadal trendsRemoving the NAO from both observations and models provides a tighter, unbiased constraint, which can then be applied to model projections [ABSTRACT FROM AUTHOR]

Abstract A new detection and attribution method is presented and applied to the global mean surface air temperature (GSAT) from 1900 to 2014. The method aims at attributing the climate changes to the variations of greenhouse gases, anthropogenic aerosols, and natural forcings. A convolutional neural network (CNN) is trained using the simulated GSAT from historical and single‐forcing simulations of 12 climate models. Then, we perform a backward optimization with the CNN to estimate the attributable GSAT changes. Such a method does not assume additivity in the effects of the forcings. The uncertainty in the attributable GSAT is estimated by sampling different starting points from single‐forcing simulations and repeating the backward optimization. To evaluate this new method, the attributable GSAT changes are also calculated using the regularized optimal fingerprinting (ROF) method. Using synthetic non‐additive data, we first find that the neural network‐based method estimates attributable changes better than ROF. When using GSAT data from climate model, the attributable anomalies are similar for both methods, which might reflect that the influence of forcing is mainly additive for the GSAT. However, we found that the uncertainties given both methods are different. The new method presented here can be adapted and extended in future work, to investigate the non‐additive changes found at the local scale or on other physical variables.

A positive trend in time has been noted in the seasonal amplitude of surface ocean pCO2 over much of the oceans, which is expected to have detrimental impacts on marine ecosystems. To determine whether or not this has an anthropogenic cause, this study investigates historical climate simulations from the Detection and Attribution Model Intercomparison Project with and without anthropogenic forcing. The simulations with anthropogenic forcing show clear evidence of positive trends, whereas the simulations with constant preindustrial atmospheric CO2 and natural external forcing give only negligible trends. A statistical analysis of five zonal latitudinal bands reveals that the trends detected over 1990–2014 in an ensemble of six observational products are attributable to anthropogenic forcing in mid‐latitudes (40°N–10°N, 10°S–40°S), while no trends are detected and modeled in the tropics and the Southern Ocean. Most models fail to represent the sign of the observed climatological mean seasonal cycle difference in high latitudes. Plain Language Summary: Global warming due to fossil fuel use is unequivocal and documented by long records. In contrast, measurements for other important Earth system parameters are only available over recent decades. A short record length makes the attribution of observed trends to human‐induced forcing challenging. Trends may be hard to identify due to chaotic Earth system variability. The seasonal amplitude of the partial pressure of CO2 (pCO2) in the surface ocean is observed to increase, with global‐scale data available since 1990. A continued increase is expected to have negative impacts on fish as the amplified seasonality leads to very high pCO2, hindering the uptake of oxygen during breathing. Here, we analyze pCO2 from climate model historical simulations forced by human‐caused and natural factors versus simulations with natural factors only. The results allow us to firmly attribute the observed trend in the pCO2 seasonal amplitude in mid‐latitudes to human activities. Key Points: Trends in seasonal amplitudes of surface ocean pCO2 are analyzed in observations and CMIP6 simulations with/without anthropogenic forcingApplying an Analysis of Covariance, the observed trends in mid‐latitude regions are firmly attributed to anthropogenic forcingThe model ensemble shows large data‐model and model‐model discrepancies in the pCO2 seasonal cycle, in particular in high‐latitude regions [ABSTRACT FROM AUTHOR]

A positive trend in time has been noted in the seasonal amplitude of surface ocean pCO2 over much of the oceans, which is expected to have detrimental impacts on marine ecosystems. To determine whether or not this has an anthropogenic cause, this study investigates historical climate simulations from the Detection and Attribution Model Intercomparison Project with and without anthropogenic forcing. The simulations with anthropogenic forcing show clear evidence of positive trends, whereas the simulations with constant preindustrial atmospheric CO2 and natural external forcing give only negligible trends. A statistical analysis of five zonal latitudinal bands reveals that the trends detected over 1990–2014 in an ensemble of six observational products are attributable to anthropogenic forcing in mid‐latitudes (40°N–10°N, 10°S–40°S), while no trends are detected and modeled in the tropics and the Southern Ocean. Most models fail to represent the sign of the observed climatological mean seasonal cycle difference in high latitudes. Plain Language Summary: Global warming due to fossil fuel use is unequivocal and documented by long records. In contrast, measurements for other important Earth system parameters are only available over recent decades. A short record length makes the attribution of observed trends to human‐induced forcing challenging. Trends may be hard to identify due to chaotic Earth system variability. The seasonal amplitude of the partial pressure of CO2 (pCO2) in the surface ocean is observed to increase, with global‐scale data available since 1990. A continued increase is expected to have negative impacts on fish as the amplified seasonality leads to very high pCO2, hindering the uptake of oxygen during breathing. Here, we analyze pCO2 from climate model historical simulations forced by human‐caused and natural factors versus simulations with natural factors only. The results allow us to firmly attribute the observed trend in the pCO2 seasonal amplitude in mid‐latitudes to human activities. Key Points: Trends in seasonal amplitudes of surface ocean pCO2 are analyzed in observations and CMIP6 simulations with/without anthropogenic forcingApplying an Analysis of Covariance, the observed trends in mid‐latitude regions are firmly attributed to anthropogenic forcingThe model ensemble shows large data‐model and model‐model discrepancies in the pCO2 seasonal cycle, in particular in high‐latitude regions [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is rapidly warming, posing potential risks to regional to global climate and ecosystems. However, it remains ambiguous to what extent anthropogenic forcing contributes to the rapid TP warming based on observation. It is also unclear for the quantitative analysis of the anthropogenic effects on the dynamic and thermodynamic warming of the TP. Here we disentangle this issue using CN05.1 observations and a probability statistics model based on long term memory. The findings demonstrate that the anthropogenically-driven trend accelerates regional warming and is dominant for the rapid TP warming. The anthropogenically-driven warming was more than 1.2 °C in the central-western TP from 1980 to 2018, with anthropogenic contribution reaching up to ~ 60% for the observed warming (1.8–2.0 °C). Furthermore, we extract the dynamic and thermodynamic warming from the observed TP warming by dynamical adjustment method and determine the anthropogenic effect of each. The results show that anthropogenic contribution of thermodynamic warming exceeds 50% in most parts of TP, with over 70% in the southwest. The thermal effect is the main way of anthropogenic impact on the rapid TP warming. [ABSTRACT FROM AUTHOR]

The Southwestern U.S. has been in a megadrought for the past two decades, stressing vegetation across the region and leading to poor forage conditions on rangelands. How has human‐induced climate change affected air temperatures and atmospheric evaporative demand in the greater Four Corners region of the Southwest, and what have the corresponding impacts been to vegetative productivity? We address this question via a multi‐step climate change detection and attribution analysis. We first relate evaporative demand (quantified by Vapor Pressure Deficit, or VPD) and precipitation to the Normalized Difference Vegetation Index (NDVI) in six unique plant‐precipitation zones. VPD is strongly negatively related to NDVI, and the strength of this relation is similar in magnitude to precipitation. We then use a large ensemble of climate change simulations to estimate the magnitude of VPD increases attributable to human‐induced climate change. We find that modest increases in air temperatures have led to large increases in VPD, with the influence of climate change about twice the inter‐annual standard deviation by 2020. Finally, we estimate the reductions in NDVI due to increased VPD and present the reductions in terms of total annual Aboveground Net Primary Productivity. We conclude that human‐induced warming has roughly doubled the magnitude of recent vegetation deficits, especially in mid‐precipitation areas (receiving 250–500 mm of precipitation annually), concentrated in New Mexico. These results indicate that increased temperatures from human‐caused climate change are having persistent and damaging impacts on vegetation productivity, with significant implications for ranchers and other land users in the region. Plain Language Summary: Numerous communities in the Southwestern U.S. depend on rangelands to support their livelihoods. As air temperatures rise in this semi‐arid environment, increased atmospheric evaporative demand stresses grasses and shrubs, which are important forage sources for livestock and other animals. Here, we show that human‐induced temperature increases have already driven substantial increases in atmospheric evaporative demand, especially in the hottest and driest areas of the region. We also show that these increases in evaporative demand have contributed to large and rapidly increasing reductions in rangeland productivity, with the greatest sensitivity in areas receiving moderate amounts of precipitation. While precipitation deficits are a major driver of recent vegetation deficits, climate change‐induced warming has also magnified the impact of droughts, often doubling the magnitude of productivity deficits. Our findings indicate that increased temperatures from human‐caused climate change have had and will have an increasingly damaging effect on rangeland vegetation, with large implications for both local ecosystems and communities that depend on southwestern rangeland resources. Key Points: Climate change‐enhanced Vapor Pressure Deficits (VPD) are negatively impacting vegetation productivity on Southwestern U.S. rangelandsRelatively modest temperature increases have roughly doubled the magnitude of vegetation productivity deficits in recent yearsThe largest impacts are concentrated in New Mexico [ABSTRACT FROM AUTHOR]

Compound hot and dry events (CHDEs) pose an increasing threat to global warming and have received growing attention in recent decades. We conducted a detection and attribution analysis (D&A) using the optimal fingerprint technique to analyze the changes in observed CHDEs at continental and sub‐continental scales from 1961 to 2014. The responses of CHDEs changes to anthropogenic (ANT), greenhouse gases (GHG), aerosols (AER), and natural (NAT) forcings were obtained from the Coupled Model Intercomparison Project phase 6 forcing experiment. In a two‐signal D&A analysis that considered ANT and NAT simultaneously, ANT signals were robustly detected and separated from NAT in all continental domains and more than 65% of subcontinental domains (29 out of 44 domains). The trend attributed to the ANT forcing is comparable to the observed trend over domains where the ANT signal is detectable. We also conducted a three‐signal D&A analysis that regressed observation onto GHG, AER, and NAT simultaneously. The GHG signals were detectable in all continents except Oceania, and it contributed dominantly to the observed trend over 22 out of 29 regions where ANT signals had emerged. Multivariate biases correction can largely reduce the models' biases in simulating the marginal distribution and the dependence structure, which can provide a more accurate future risk of CDHEs. We found that limiting global warming to 1.5℃ rather than 2.0℃ can significantly mitigate the impact of compound events in most areas of global land, particularly in South America, Africa, and Oceania. Plain Language Summary: The severity of compound hot and dry events (CDHEs) is an important property of CDHEs, its changes can pose a great threat to agriculture production. Previous studies demonstrated that the anthropogenic influence has increased the probability of CDHEs over most regions across the globe by comparing observation with ALL forcing and natural‐forcing simulations. However, how much the long‐term trend in the severity of CDHEs is attributed to anthropogenic forcing is unsolved. We used the optimal fingerprint technology with the newly released models from Coupled Model Intercomparison Project phase 6 to quantify the contribution of different external forcing to the trend of the severity of CHDEs. The detectable anthropogenic signal has been found on six continents and more than 50% of sub‐continents. Greenhouse gases (GHG) are found to contribute dominantly to anthropogenic‐induced changes. Additional strict control regulations on GHG emissions are thus suggested to mitigate its impact on the intensification of CDHEs. Key Points: The anthropogenic influence on the intensified of compound hot and dry events on six continents and more than 65% of 44 sub‐continents have been robustly detectedAnthropogenic greenhouse gases dominantly contribute to the observed changes over the regions with anthropogenic signal detectableLimiting global warming to 1.5℃ can largely avoid the impact of compound events in most global land areas, especially over tropical regions [ABSTRACT FROM AUTHOR]

Abstract A decline of anthropogenic aerosol (AA) emission is expected worldwide over the coming decades. But the climate effects of aerosol removal and greenhouse gases (GHG) emission at regional scale are poorly distinguished and constrained. Taking the Tibetan Plateau (TP) as an instance, analyses of the state‐of‐the‐art climate models participating in the Detection and Attribution Model Intercomparison Project imply that while the observed warming from 1961 to 2020 is predominantly attributed to GHG emission, the future temperature rise will be influenced by the combined effects of persistent increase in GHG concentration and reduction of AA emission. Here, we develop a new constraint method considering the changed contribution of AA forcing. Constrained by detected individual external forcings, the joint contributions of GHG (1.74°C) and AA forcings (0.10°C) will lead to a warming around 1.85°C over the TP during mid‐century (2041–2060) relative to 1995–2014 under SSP2‐4.5 scenario, which is 0.44°C cooler than the raw projection.

Abstract Variability in the North Atlantic Oscillation (NAO) has contributed to the recent multidecadal trends observed in European climate, especially to trends in winter precipitation over Northern Europe. However, the current generation of coupled climate models struggle to reproduce the NAO's contribution to multidecadal trends, which has important implications for deriving constraints based on the comparison of observed and modeled trends. An observational constraint based on attribution results, both with and without the contribution of variability associated with the NAO, is applied to projections of Northern European precipitation and temperature, and observed NAO variability is shown to lead to a constraint that overestimates future forced changes. Only after removing the NAO variability is the observed climate change consistent with model simulations, and a tighter, unbiased observational constraint based on the forced signal (without the NAO) can be applied to future projections.

There is a growing research interest in understanding extreme weather in the context of anthropogenic climate change, posing a requirement for new tailored climate data products. Here we introduce the Climate of the 20th Century Plus Detection and Attribution project (C20C + D&A), an international collaboration generating a product specifically intended for diagnosing causes of changes in extreme weather and for understanding uncertainties in that diagnosis. The project runs multiple dynamical models of the atmosphere-land system under observed historical conditions as well as under naturalised versions of those observed conditions, with the latter representing how the climate system might have evolved in the absence of anthropogenic interference. Each model generates large ensembles of simulations with different initial conditions for each historical scenario, providing a large sample size for understanding interannual variability, long-term trends, and the anthropogenic role in rare types of weather. This paper describes the C20C + D&A project design, implementation, strengths, and limitations, and also discusses various activities such as this special issue of Weather and Climate Extremes dedicated to “First results of the C20C + Detection and Attribution project”.

There is a growing research interest in understanding extreme weather in the context of anthropogenic climate change, posing a requirement for new tailored climate data products. Here we introduce the Climate of the 20th Century Plus Detection and Attribution project (C20C + D&A), an international collaboration generating a product specifically intended for diagnosing causes of changes in extreme weather and for understanding uncertainties in that diagnosis. The project runs multiple dynamical models of the atmosphere-land system under observed historical conditions as well as under naturalised versions of those observed conditions, with the latter representing how the climate system might have evolved in the absence of anthropogenic interference. Each model generates large ensembles of simulations with different initial conditions for each historical scenario, providing a large sample size for understanding interannual variability, long-term trends, and the anthropogenic role in rare types of weather. This paper describes the C20C + D&A project design, implementation, strengths, and limitations, and also discusses various activities such as this special issue of Weather and Climate Extremes dedicated to “First results of the C20C + Detection and Attribution project”.

Abstract The Southwestern U.S. has been in a megadrought for the past two decades, stressing vegetation across the region and leading to poor forage conditions on rangelands. How has human‐induced climate change affected air temperatures and atmospheric evaporative demand in the greater Four Corners region of the Southwest, and what have the corresponding impacts been to vegetative productivity? We address this question via a multi‐step climate change detection and attribution analysis. We first relate evaporative demand (quantified by Vapor Pressure Deficit, or VPD) and precipitation to the Normalized Difference Vegetation Index (NDVI) in six unique plant‐precipitation zones. VPD is strongly negatively related to NDVI, and the strength of this relation is similar in magnitude to precipitation. We then use a large ensemble of climate change simulations to estimate the magnitude of VPD increases attributable to human‐induced climate change. We find that modest increases in air temperatures have led to large increases in VPD, with the influence of climate change about twice the inter‐annual standard deviation by 2020. Finally, we estimate the reductions in NDVI due to increased VPD and present the reductions in terms of total annual Aboveground Net Primary Productivity. We conclude that human‐induced warming has roughly doubled the magnitude of recent vegetation deficits, especially in mid‐precipitation areas (receiving 250–500 mm of precipitation annually), concentrated in New Mexico. These results indicate that increased temperatures from human‐caused climate change are having persistent and damaging impacts on vegetation productivity, with significant implications for ranchers and other land users in the region.

Abstract A positive trend in time has been noted in the seasonal amplitude of surface ocean pCO2 over much of the oceans, which is expected to have detrimental impacts on marine ecosystems. To determine whether or not this has an anthropogenic cause, this study investigates historical climate simulations from the Detection and Attribution Model Intercomparison Project with and without anthropogenic forcing. The simulations with anthropogenic forcing show clear evidence of positive trends, whereas the simulations with constant preindustrial atmospheric CO2 and natural external forcing give only negligible trends. A statistical analysis of five zonal latitudinal bands reveals that the trends detected over 1990–2014 in an ensemble of six observational products are attributable to anthropogenic forcing in mid‐latitudes (40°N–10°N, 10°S–40°S), while no trends are detected and modeled in the tropics and the Southern Ocean. Most models fail to represent the sign of the observed climatological mean seasonal cycle difference in high latitudes.

Abstract Compound hot and dry events (CHDEs) pose an increasing threat to global warming and have received growing attention in recent decades. We conducted a detection and attribution analysis (D&A) using the optimal fingerprint technique to analyze the changes in observed CHDEs at continental and sub‐continental scales from 1961 to 2014. The responses of CHDEs changes to anthropogenic (ANT), greenhouse gases (GHG), aerosols (AER), and natural (NAT) forcings were obtained from the Coupled Model Intercomparison Project phase 6 forcing experiment. In a two‐signal D&A analysis that considered ANT and NAT simultaneously, ANT signals were robustly detected and separated from NAT in all continental domains and more than 65% of subcontinental domains (29 out of 44 domains). The trend attributed to the ANT forcing is comparable to the observed trend over domains where the ANT signal is detectable. We also conducted a three‐signal D&A analysis that regressed observation onto GHG, AER, and NAT simultaneously. The GHG signals were detectable in all continents except Oceania, and it contributed dominantly to the observed trend over 22 out of 29 regions where ANT signals had emerged. Multivariate biases correction can largely reduce the models' biases in simulating the marginal distribution and the dependence structure, which can provide a more accurate future risk of CDHEs. We found that limiting global warming to 1.5℃ rather than 2.0℃ can significantly mitigate the impact of compound events in most areas of global land, particularly in South America, Africa, and Oceania.

• Over the past century, observed summer-autumn CDHE severity in MLRYR has risen significantly. • 86.7% of the observed increase in the CDHE severity can be attributed to anthropogenic warming. • If global warming exceeds 3.5℃, the CDHE severity is projected to be higher than historical record-breaking events. The middle and lower reaches of the Yangtze River (MLRYR), one of the most densely populated and economically developed regions in China, have suffered several severe compound dry-hot events (CDHEs) during the summer-autumn seasons in recent decades. However, it is unclear for spatiotemporal evolutions in this season's CDHE severity and anthropogenic influence on them. Here, we calculated the Standardized Compound Event Indicator (SCEI) to quantify the CDHE severity during the summer-autumn season (August to November). During the past twelve decades, significant decreases in SCEI (–0.81 per century) in MLRYR have been found in observations, with the dependence of SCEI on low precipitation shifting to high temperatures. Temperatures change primarily contribute to the changes in SCEI with (82.3 %) or without (57.1 %) considering the effects of long-term warming. Quantitative detection and attribution results show that during 1901–2020, 86.7 % of the observed decrease in SCEI in MLRYR can be attributed to anthropogenic warming. In a warmer future, SCEI is projected to dramatically decrease before global warming reaches 3.5 °C above the preindustrial level. After exceeding 3.5℃, the CDHE severity in MLRYR is projected to stay at a new-normal serious level which exceeds the most severe CDHEs (such as in 2019 and 2022) during the past century. This new-normal serious level could be avoided if the global warming level is limited to around 2 °C. Early action to achieve the 1.5 °C or 2 °C temperature goal can therefore markedly reduce the likelihood that MLRYR would be exposed to record-shattering CDHEs and related impacts. [ABSTRACT FROM AUTHOR]

Few coastal ecosystems remain untouched by direct human activities, and none are unimpacted by anthropogenic climate change. These drivers interact with and exacerbate each other in complex ways, yielding a mosaic of ecological consequences that range from adaptive responses, such as geographic range shifts and changes in phenology, to severe impacts, such as mass mortalities, ecological regime shifts and loss of biodiversity. Identifying the role of climate change in these phenomena requires corroborating evidence from multiple lines of evidence, including laboratory experiments, field observations, numerical models and palaeorecords. Yet few studies can confidently quantify the magnitude of the effect attributable solely to climate change, because climate change seldom acts alone in coastal ecosystems. Projections of future risk are further complicated by scenario uncertainty - that is, our lack of knowledge about the degree to which humanity will mitigate greenhouse-gas emissions, or will make changes to the other ways we impact coastal ecosystems. Irrespective, ocean warming would be impossible to reverse before the end of the century, and sea levels are likely to continue to rise for centuries and remain elevated for millennia. Therefore, future risks to coastal ecosystems from climate change are projected to mirror the impacts already observed, with severity escalating with cumulative emissions. Promising avenues for progress beyond such qualitative assessments include collaborative modelling initiatives, such as model intercomparison projects, and the use of a broader range of knowledge systems. But we can reduce risks to coastal ecosystems by rapidly reducing emissions of greenhouse gases, by restoring damaged habitats, by regulating non-climate stressors using climate-smart conservation actions, and by implementing inclusive coastalzone management approaches, especially those involving nature-based solutions. [ABSTRACT FROM AUTHOR]

This article characterizes key research gaps and opportunities for scientists across disciplines to do work that informs the rapidly growing number of climate lawsuits worldwide. It focuses on research that can be used to inform legal decisions about responsibility for greenhouse gas emissions and climate damages. Relevant lawsuits include claims filed against government and corporate defendants alleging that they have violated environmental, human rights, constitutional, tort, and consumer protection laws due to their contributions to climate change and failures to control emissions. Constructive attention has recently been given to the important role of attribution science in informing some of these cases (Burger et al., 2020, https://doi.org/10.7916/cjel.v45i1.4730; Stuart‐Smith et al., 2021, https://doi.org/10.1038/s41561-021-00686-4). Here, we draw upon both the published literature and conversations with diverse legal scholars and practitioners to characterize what further climate litigation‐relevant research is most needed. In addition to key gaps in litigation‐relevant attribution science, we identify and characterize the need and opportunity for further social science research to address the causes of climate inaction, and for further cross‐disciplinary research to inform emerging legal questions on the allocation of responsibility for emissions reductions to align with temperature limits such as those set by the Paris Climate Agreement. Our primary goal is to identify areas for researchers who are interested in contributing to climate litigation and discussions about legal responsibility for climate change. We also seek to help the research community see this as a legitimate and important domain for timely, actionable scientific research. Plain Language Summary: People are increasingly turning to courts for climate remedies due to the mounting severity of the climate crisis and the inadequacy of public and private sector responses. One key goal in litigation is to establish obligations on the part of both government and corporate actors to reduce greenhouse gas (GHG) emissions, respond to climate change‐related threats, and provide redress for climate damages. This article characterizes key research gaps and opportunities for scientists across disciplines to do work that informs the rapidly growing number of climate lawsuits cases worldwide. It focuses on research that can be used to establish government and corporate responsibility for GHG emissions and climate change‐related damages. Key Points: Courts rely on physical and social science to resolve legal claims about responsibility for greenhouse gas (GHG) emissions and climate changeConversations with legal practitioners provide insight on the types of research that would inform climate litigationKey research areas include climate change detection and attribution, climate disinformation, and GHG mitigation obligations [ABSTRACT FROM AUTHOR]

The influence of anthropogenic (ANT) activity and the other external factors on extreme temperature changes over the mid–high latitudes of Asia are analysed using the different forcing simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The optimal fingerprinting technique and the probability ratio (PR) are employed to detect and quantify the influences of the external forcings on extreme temperature changes, which include annual maximum daily maximum temperature (TXx) and annual minimum daily minimum (TNn) temperature. Results indicate that TXx and TNn have increased from 1979 to 2014, and the simulations from historical (anthropogenic plus natural; ALL), greenhouse gas (GHG), and anthropogenic (ANT) experiments reasonably reproduce the spatiotemporal characteristics of extreme temperatures. Based on the optimal fingerprinting method, the impact of ANT forcing, in which GHG forcing is critical, can be detected in the changes of warm extremes and cold extremes. ANT and NAT forcings are separately detectable for warm extremes. GHG forcing can be separated from other ANT forcings for cold extremes but not warm extremes. Furthermore, the analysis applying the PR method shows that the probability of observed warm extremes that occur once in 20 years over the mid–high latitudes of Asia has risen by approximately three times owing to the anthropogenic influence, whereas the cold extremes became once in 50 years. Briefly, the increased anthropogenic activity has exacerbated the warm extremes and soothed the cold extremes over the mid–high latitudes of Asia during the past decades. [ABSTRACT FROM AUTHOR]

Abstract This article characterizes key research gaps and opportunities for scientists across disciplines to do work that informs the rapidly growing number of climate lawsuits worldwide. It focuses on research that can be used to inform legal decisions about responsibility for greenhouse gas emissions and climate damages. Relevant lawsuits include claims filed against government and corporate defendants alleging that they have violated environmental, human rights, constitutional, tort, and consumer protection laws due to their contributions to climate change and failures to control emissions. Constructive attention has recently been given to the important role of attribution science in informing some of these cases (Burger et al., 2020, https://doi.org/10.7916/cjel.v45i1.4730; Stuart‐Smith et al., 2021, https://doi.org/10.1038/s41561-021-00686-4). Here, we draw upon both the published literature and conversations with diverse legal scholars and practitioners to characterize what further climate litigation‐relevant research is most needed. In addition to key gaps in litigation‐relevant attribution science, we identify and characterize the need and opportunity for further social science research to address the causes of climate inaction, and for further cross‐disciplinary research to inform emerging legal questions on the allocation of responsibility for emissions reductions to align with temperature limits such as those set by the Paris Climate Agreement. Our primary goal is to identify areas for researchers who are interested in contributing to climate litigation and discussions about legal responsibility for climate change. We also seek to help the research community see this as a legitimate and important domain for timely, actionable scientific research.

Few coastal ecosystems remain untouched by direct human activities, and none are unimpacted by anthropogenic climate change. These drivers interact with and exacerbate each other in complex ways, yielding a mosaic of ecological consequences that range from adaptive responses, such as geographic range shifts and changes in phenology, to severe impacts, such as mass mortalities, ecological regime shifts and loss of biodiversity. Identifying the role of climate change in these phenomena requires corroborating evidence from multiple lines of evidence, including laboratory experiments, field observations, numerical models and palaeorecords. Yet few studies can confidently quantify the magnitude of the effect attributable solely to climate change, because climate change seldom acts alone in coastal ecosystems. Projections of future risk are further complicated by scenario uncertainty – that is, our lack of knowledge about the degree to which humanity will mitigate greenhouse-gas emissions, or will make changes to the other ways we impact coastal ecosystems. Irrespective, ocean warming would be impossible to reverse before the end of the century, and sea levels are likely to continue to rise for centuries and remain elevated for millennia. Therefore, future risks to coastal ecosystems from climate change are projected to mirror the impacts already observed, with severity escalating with cumulative emissions. Promising avenues for progress beyond such qualitative assessments include collaborative modelling initiatives, such as model intercomparison projects, and the use of a broader range of knowledge systems. But we can reduce risks to coastal ecosystems by rapidly reducing emissions of greenhouse gases, by restoring damaged habitats, by regulating non-climate stressors using climate-smart conservation actions, and by implementing inclusive coastal-zone management approaches, especially those involving nature-based solutions.

Amplified warming in the Mediterranean region in summer, corresponding to strengthening temperature seasonality (Tseasonality), increases the frequency and intensity of heatwaves and endangers both human health and ecosystems. However, previous studies have not found a detectable signal of anthropogenic forcing in changes in Tseasonality in the Mediterranean and the contribution from human activities is unclear. Here, we show that signals of anthropogenic forcing and greenhouse gas forcing can be detected for the period 1950–2014, the signals of anthropogenic and natural forcing are separable, and the contribution of anthropogenic forcing is 74% using the optimal fingerprinting approach. Future projections based on attribution constraints indicated that Tseasonality will continue to strengthen by more than 2°C by 2100, which is much worse than predicted by direct model output. These results are important for understanding and projecting the impacts of human activities on Tseasonality and related summer heatwaves in the future. Plain Language Summary: The temperature seasonality, also known as the amplitude of the annual cycle of temperature, is important for human societies and ecosystems. The amplified summer warming enhances the temperature seasonality in the Mediterranean region, which is vulnerable to climate change. In this study, we detected signals of human influences on changes in the temperature seasonality in this region and further quantified the contribution of anthropogenic forcing using a formal detection and attribution technique. Future projections were then carried out based on attribution constraints and the results showed a much faster increase in the temperature seasonality than direct model output. The result highlights the importance of reducing greenhouse gas emissions for this region. Key Points: The anthropogenic forcing signal was detected in the strengthening temperature seasonality in the Mediterranean regionAnthropogenic forcing was found to play a major role in changes in the temperature seasonalityFuture projections based on attribution constraints indicate an enhancement in the temperature seasonality faster than direct model output [ABSTRACT FROM AUTHOR]

The last millennium (LM, 1000–1850 AD) is crucial for studying historical climate change on decadal to multidecadal timescales. The summer surface air temperature (SAT) evolutions on regional scales (e.g. over China) are more uncertain than the globe/Northern Hemisphere, especially in response to external forcing factors and internal climate variability. Here, we provide one‐signal (full‐forcing) fingerprints of summer SAT in China derived from three large ensemble model archives with a multi‐proxy reconstruction during the LM, Little Ice Age (LIA, 1451–1850 AD), and Medieval Climate Anomaly (MCA, 1000–1250 AD), respectively. Our results show that (a) SATs in the northeast, southeast, northwest, and Tibetan Plateau (TP) regions of China show evident decreasing trends during the LM. External forcing response from all model archives agrees with the regional SAT reconstruction but underestimates variability in northwest China at the multidecadal timescale. (b) During the LIA, the summer regional SAT exhibits a cold condition in the reconstruction and simulations, especially in the northeast and northwest regions of China. External forcing responses in most model archives are the dominant factor on multidecadal SAT evolutions in the southeast, northeast, and TP regions of China and decadal SAT evolutions in northwest China. (c) During the MCA, detection and attribution of SAT shows that internal climate variability dominates in southeast, northeast, and TP regions of China, but external forcing dominates in northwest China at decadal to multidecadal timescales. These results contribute to a better understanding of the causes and mechanisms of regional climate change. [ABSTRACT FROM AUTHOR]

The year 2021 was recorded as the 6th warmest since 1880. In addition to large-scale warming, 2021 will be remembered for its unprecedented climate extremes. Here, a review of selected high-impact climate extremes in 2021, with a focus on China, along with an extension to extreme events in North America and Europe is presented. Nine extreme events that occurred in 2021 in China are highlighted, including a rapid transition from cold to warm extremes and sandstorms in spring, consecutive drought in South China and severe thunderstorms in eastern China in the first half of the year, extremely heavy rainfall over Henan Province and Hubei Province during summer, as well as heatwaves, persistent heavy rainfall, and a cold surge during fall. Potential links of extremes in China to four global-scale climate extremes and the underlying physical mechanisms are discussed here, providing insights to understand climate extremes from a global perspective. This serves as a reference for climate event attribution, process understanding, and high-resolution modeling of extreme events. [ABSTRACT FROM AUTHOR]

The South Asian high (SAH) experienced a decadal weakening in the late 1970s under global warming. Based on an evaluation of the historical runs from CMIP6 models, we quantitatively assessed the contributions of different external forcing using "good" models that reasonably simulated the decadal decline of the SAH. All‐forcing runs yielded the weakened SAH after the late 1970s, albeit the decadal decline was underestimated by most models. Compared to the insignificant contributions of greenhouse gas and natural forcing, anthropogenic aerosol played a dominant role in the decadal decline of the SAH. The increased aerosol likely drove a cooling surface over the Tibetan Plateau (TP) and East China via its effect on radiation. Consequently, the weakened heat source over the TP and associated thermodynamic effects over East China would have driven a cooling of eddy temperature and cyclonic anomalies in the upper troposphere, respectively, thereby causing the decline of the SAH. Plain Language Summary: Under global warming, the South Asian high (SAH) measured by eddy geopotential height experienced a decadal decline in the late 1970s. However, the mechanism responsible for the decadal decline of the SAH has not been investigated. In this study, the contributions of external forcing to the decadal decline of the SAH are accessed using "good" CMIP6 models that can reasonably simulate the decadal changes of SAH. Results indicate that anthropogenic aerosol plays an important role in weakening the SAH. The increased aerosol emission over the Tibetan Plateau (TP) and East China cools the ground through the radiation effect. The weakened heat effect of TP and East China can contribute to the weakening of SAH. By contrast, the contributions of greenhouse gas and natural forcing are almost completely insignificant due to their homogenized and only very slight effects on the upper‐tropospheric eddy temperature over the SAH region, respectively. Key Points: ALL forcing can yield a weakening of the South Asian high (SAH), and anthropogenic aerosol provided the dominant contributionAnthropogenic aerosol drove the decline of the SAH by weakening the effect of the heat source over the Tibetan PlateauThe contribution of greenhouse gas and natural external forcing to the decadal decline of the SAH is insignificant [ABSTRACT FROM AUTHOR]

Observations show that summer precipitation in China has undergone pronounced changes, resulting in an enigmatic "north‐south drying‐wetting" pattern in eastern China that is of great concern for socio‐economic development. Scientific consensus on the mechanisms that are responsible for this pattern of change has not yet been achieved. We show that this complex pattern of summer total precipitation trends observed in China since the 1960s is overwhelmingly the result of changes in daily precipitation frequency, rather than being the result of changes in precipitation intensity or the frequency of synoptic circulation patterns favorable to precipitation. Changes in precipitation intensity, which are very likely due to anthropogenic greenhouse gas forcing, contribute a relatively homogeneous wetting tendency across the country while changes due to synoptic circulation change are weak. The changes in daily precipitation frequency that drive the observed patterns of change may be due to aerosols, but improved process understanding will be required to resolve that question and enable reliable projections of regional scale precipitation change in China and elsewhere. Plain Language Summary: The spatial distribution of summer precipitation has changed substantially in China since the 1960s, creating considerable concern for agriculture and other sectors dependent on summer rainfall. There is currently a lack of scientific consensus on the mechanisms that are responsible for the observed drying‐wetting pattern, in part because most studies have considered a single mechanism amongst several that may be responsible. We, therefore, partition summer precipitation trends into three components associated with changes in daily precipitation frequency, daily precipitation intensity, and synoptic circulation pattern (CP) frequency, each corresponding to a different mechanism of change. By doing so we find that China's complex pattern of summer precipitation change is mainly the result of changes in daily precipitation frequency that are layered on top of a widespread increase in precipitation intensity. We argue that the latter is consistent with the effects of human‐induced warming. Eliminating changes in precipitation intensity and CP frequency as the causes of the observed pattern of increases and decreases indicates that the primary focus of research should now shift to an understanding of the causes of observed changes in precipitation frequency. Key Points: Summer rainfall trends are decomposed into contributions from changes in synoptic pattern frequency, rainfall frequency, and intensityThere is clear evidence of intensification of precipitation, which is consistent with warmingThe enigmatic "north‐south drying‐wetting" pattern of summer rainfall in eastern China is the result of changes in rainfall frequency [ABSTRACT FROM AUTHOR]

Abstract The South Asian high (SAH) experienced a decadal weakening in the late 1970s under global warming. Based on an evaluation of the historical runs from CMIP6 models, we quantitatively assessed the contributions of different external forcing using “good” models that reasonably simulated the decadal decline of the SAH. All‐forcing runs yielded the weakened SAH after the late 1970s, albeit the decadal decline was underestimated by most models. Compared to the insignificant contributions of greenhouse gas and natural forcing, anthropogenic aerosol played a dominant role in the decadal decline of the SAH. The increased aerosol likely drove a cooling surface over the Tibetan Plateau (TP) and East China via its effect on radiation. Consequently, the weakened heat source over the TP and associated thermodynamic effects over East China would have driven a cooling of eddy temperature and cyclonic anomalies in the upper troposphere, respectively, thereby causing the decline of the SAH.

Many methods and climate/weather modelling tools have been used over the past decade for assessment of the role of anthropogenic emissions in recent specific weather events (“event attribution”). Differences in the methods and models often correspond to differences in the characterisation, or conditioning, of an observed extreme event within a model, and this might be expected to affect any attribution statement. In practice, however, it may not always be feasible or practical to use the most appropriate method for the question at hand, or to use multiple methods so as to arrive at a generic conclusion. This is especially true given the growing interest in making rapid assessments of extreme events within operational forecast centres. How transferable are conclusions across methods, hence allowing the substitution of one method or modelling tool for another? In this paper we investigate differences in event attribution conclusions across a wide range of experiment designs, running from free-running simulations of atmosphere–ocean climate models, through to weather forecasts constrained to reproduce the nature of the event quite closely. Across a number of recent extreme weather events over Aotearoa New Zealand, we find no systematic differences in conclusions across the various experiment setups. This surprising result offers hope that attribution statements may be transferable across methods, because errors that arise when transferring results across methods are overshadowed by other errors and uncertainties given current technology.

We review the field of data assimilation (DA) from a Bayesian perspective and show that, in addition to its by now common application to state estimation, DA may be used for model selection. An important special case of the latter is the discrimination between a factual model–which corresponds, to the best of the modeller's knowledge, to the situation in the actual world in which a sequence of events has occurred–and a counterfactual model, in which a particular forcing or process might be absent or just quantitatively different from the actual world. Three different ensemble-DA methods are reviewed for this purpose: the ensemble Kalman filter (EnKF), the ensemble four-dimensional variational smoother (En-4D-Var), and the iterative ensemble Kalman smoother (IEnKS). An original contextual formulation of model evidence (CME) is introduced. It is shown how to apply these three methods to compute CME, using the approximated time-dependent probability distribution functions (pdfs) each of them provide in the process of state estimation. The theoretical formulae so derived are applied to two simplified nonlinear and chaotic models: (i) the Lorenz three-variable convection model (L63), and (ii) the Lorenz 40-variable midlatitude atmospheric dynamics model (L95). The numerical results of these three DA-based methods and those of an integration based on importance sampling are compared. It is found that better CME estimates are obtained by using DA, and the IEnKS method appears to be best among the DA methods. Differences among the performance of the three DA-based methods are discussed as a function of model properties. Finally, the methodology is implemented for parameter estimation and for event attribution.