Global Mean Warming Research Articles

Land-atmosphere coupling amplified the record-breaking heatwave at altitudes above 5000 meters on the Tibetan Plateau in July 2022

In July 2022, regions with elevations exceeding 5 000 m on the inner Tibetan Plateau (TP) witnessed a record-breaking heatwave. But how the atmospheric circulation and soil moisture play roles in the occurrence and maintenance of the heatwave in such high elevation climate sensitive region remains unknown. Here, by using the flow analogue method, we find that negative soil moisture anomalies explain more than half of the extreme high temperature during the heatwave, while atmospheric circulation explains less than half. The high soil moisture-temperature coupling metric and the increased correlation between latent heat flux and soil moisture during heatwave revealed strong land-atmosphere feedback in the Qiangtang Plateau which has amplified the heatwave. Analyses of numerical experiments confirm that the presence of interaction between soil moisture and the atmosphere has increased the intensity of hot extreme event under the same atmospheric circulation conditions. Under the warming background, land-atmosphere coupling leads to a faster increase in extreme high temperatures compared to the global mean warming rate, and it is twice as fast as the increase in extreme high temperatures without coupling. We highlight the increased probability of extreme high temperature over the TP in the future due to soil moisture feedback and the results are hoped to inform policymakers in making decisions for climate adaptation activities.

Accounting for Pacific climate variability increases projected global warming

Observational constraint methods based on the relationship between the past global warming trend and projected warming across climate models were used to reduce uncertainties in projected warming by the Intergovernmental Panel on Climate Change. Internal climate variability in the eastern tropical Pacific associated with the so-called pattern effect weakens this relationship and has reduced the observed warming trend over recent decades. Here we show that regressing out this variability before applying the observed global mean warming trend as a constraint results in higher and narrower twenty-first century warming ranges than other methods. Whereas the Intergovernmental Panel on Climate Change assessed that warming is unlikely to exceed 2 °C under a low-emissions scenario, our results indicate that warming is likely to exceed 2 °C under the same scenario, and hence, limiting global warming to well below 2 °C will be harder than previously anticipated. However, the reduced uncertainties in these projections could benefit adaptation planning.

Why Does the Ensemble Mean of CMIP6 Models Simulate Arctic Temperature More Accurately Than Global Temperature?

An accurate simulation and projection of future warming are needed for a proper policy response to expected climate change. We examine the simulations of the mean global and Arctic surface air temperatures by the CMIP6 (Climate Models Intercomparison Project phase 6) climate models. Most models overestimate the observed mean global warming. Only seven out of 19 models considered simulate global warming that is within ±15% of the observed warming between the average of the 2014–2023 and 1961–1990 reference period. Ten models overestimate global warming by more than 15% and only one of the models underestimates it by more than 15%. Arctic warming is simulated by the CMIP6 climate models much better than the mean global warming. The reason is an equal spread of over and underestimates of Arctic warming by the models, while most of the models overestimate the mean global warming. Eight models are within ±15% of the observed Arctic warming. Only three models are accurate within ±15% for both mean global and Arctic temperature simulations.

Exacerbated summer European warming not captured by climate models neglecting long-term aerosol changes

AbstractIn much of western-central Europe, summer temperatures have surged three times faster than the global mean warming since 1980, yet this is not captured by most climate model simulations. Here we disentangle this warming into thermodynamic and circulation-induced contributions, and show that the latter is the main reason why numerically simulated warming is weaker than observed. Crucially, regional climate models from the Coordinated Regional Downscaling Experiment with constant aerosol forcings systematically show the strongest discrepancies from observations: in these simulations, the regional brightening and associated thermodynamic warming due to aerosol reductions is not represented. We estimate an effect of ~0.5 °C over western-central Europe for our model ensemble, and the discrepancy to climate models with evolving aerosols increases in future projections. To better reap the benefits of regional high-resolution simulations, it is thus imperative to represent the relevant external forcings and associated responses across the entire climate model chain.

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO[Formula: see text] forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

Understanding the Diversity of CMIP6 Models in the Projection of Precipitation Over Tibetan Plateau

AbstractA reliable projection of precipitation over the Tibetan Plateau (TP) is crucial for climate adaptation activities in this climate‐sensitive region, but existing studies show a large spread in magnitude. Based on Coupled Model Intercomparison Project Phase 6 models, we investigate the TP summer precipitation projection and understand the sources of uncertainty. The results show that the TP exhibits a profound wetting trend throughout the 21st century, with precipitation increasing by 0.64 ± 0.06 mm day−1 during 2050–2099 under the SSP5–8.5 scenario. The moisture budget analysis indicates that the thermodynamical response to global warming determines the precipitation increase. However, both the thermodynamical and dynamical components contribute to the uncertainty of precipitation projection. The inter‐model spread of the thermodynamic term arises from divergent global mean warming, which is closely related to model climate sensitivity. The uncertainty of the dynamic component is driven by model‐dependent circulation changes induced by different equatorial Pacific warming rates.

Robust changes in global subtropical circulation under greenhouse warming

The lower tropospheric subtropical circulation (SC) is characterized by monsoons and subtropical highs, playing an important role in global teleconnections and climate variability. The SC changes in a warmer climate are influenced by complex and region-specific mechanisms, resulting in uneven projections worldwide. Here, we present a method to quantify the overall intensity change in global SC, revealing a robust weakening across CMIP6 models. The weakening is primarily caused by global-mean surface warming, and partly counteracted by the direct CO2 effect. The direct CO2 effect is apparent in the transient response but is eventually dominated by the surface warming effect in a slow response. The distinct response timescales to global-mean warming and direct CO2 radiative forcing can well explain the time-varying SC changes in other CO2 emission scenarios. The declined SC implies a contracted monsoon range and drying at its boundary with arid regions under CO2-induced global warming.

Observationally Constrained Projections of Twenty-First-Century Regional Warming in the Extratropical Northern Hemisphere

Abstract Physically based observational constraint methods can effectively reduce uncertainty in global warming projections but have not been widely applied at regional scales. We first develop and apply multivariate linear regression models for constraining projections of surface air temperature averaged over subcontinental regions in the extratropical Northern Hemisphere, based on a set of potential constraints including climatological metrics derived from tropical and subtropical low-level cloud and global average past warming trend, as well as a set of regional climate metrics previously used in the literature. We evaluate the performance of the multivariate linear regression models based on cross-validated tests using output from phases 5 and 6 of the Coupled Model Intercomparison Projects (CMIP). We find that linear regression models using global-scale low-cloud metrics alone perform more robustly than linear regression models using the past global mean warming trend or regional climate metrics as constraints. These results, while favoring global constraints over the set of regional constraints considered, do not preclude the existence of even better regional constraints for particular regions. Through model-based cross-validation, the projections constrained using low-level cloud metrics exhibit more accurate best estimate projections, narrower uncertainty ranges, and more reliable uncertainty estimates in most Northern Hemisphere regions when compared with unconstrained projections. Application of the approach to climate projections based on both Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 using observed low-cloud metrics results in considerably narrower 5%–95% uncertainty ranges of twenty-first-century warming over subcontinental Northern Hemisphere land regions compared to unconstrained projections.

Arctic warming trends and their uncertainties based on surface temperature reconstruction under different sea ice extent scenarios

IPCC AR6 states that the neglect of recent rapid warming in the Arctic generally leads to an underestimate of global warming trends. In this study, by reconstructing the surface temperature (ST) datasets in the Arctic under different sea ice extent scenarios (Imax and Imin), we respectively evaluated the annual and seasonal warming trends and their uncertainties from 1900 to 2020. The results show that the reconstructed datasets have a good representation of the ST change trends in the Arctic. In 1900–2020, the annual warming trends in the Arctic (0.17 ± 0.031 and 0.14 ± 0.025 °C per decade under the Imax and Imin reconstruction, respectively) are roughly 1.6–1.8 times the global mean warming trends (0.10 ± 0.008 and 0.09 ± 0.008 °C per decade). While in 1979–2020, the Arctic warming trends (0.66 ± 0.100 and 0.55 ± 0.080 °C per decade) increase to 3.1–3.5 times of the global warming trend (0.19 ± 0.023 and 0.18 ± 0.023 °C per decade) for Imax and Imin, respectively, indicating that the Arctic amplification effect has been significantly enhancing in recent decades. Although the seasonal warming trends are closely related to cloud feedback mechanisms, atmospheric circulation, and ocean circulation, they are not sensitive to the different reconstruction scenarios.

Montreal Protocol's impact on the ozone layer and climate

Abstract. It is now recognized and confirmed that the ozone layer shields the biosphere from dangerous solar UV radiation and is also important for the global atmosphere and climate. The observed massive ozone depletion forced the introduction of limitations on the production of halogen-containing ozone-depleting substances (hODSs) by the Montreal Protocol and its amendments and adjustments (MPA). Previous research has demonstrated the success of the Montreal Protocol and increased public awareness of its necessity. In this study, we evaluate the benefits of the Montreal Protocol on climate and ozone evolution using the Earth system model (ESM) SOCOLv4.0 (modeling tools for studies of SOlar Climate Ozone Links) which includes dynamic modules for the ocean, sea ice, interactive ozone, and stratospheric aerosol. Here, we analyze the results of the numerical experiments performed with and without limitations on the ozone-depleting substance (ODS) emissions. In the experiments, we have used CMIP6 (Coupled Model Intercomparison Project) SSP2-4.5 and SSP5-8.5 (Shared Socioeconomic Pathway) scenarios for future forcing behavior. We confirm previous results regarding catastrophic ozone layer depletion and substantial climate warming in the case without MPA limitations. We show that the climate effects of MPA consist of additional global-mean warming by up to 2.5 K in 2100 caused by the direct radiative effect of the hODSs, which is comparable to large climate warming obtained with the SSP5-8.5 scenario. For the first time, we reveal the dramatic effects of MPA on chemical species and cloud cover. The response of surface temperature, precipitation, and sea-ice fields was demonstrated for the first time with the model that has interactive tropospheric and stratospheric chemistry. We have found some differences in the climate response compared to the model with prescribed ozone, which should be further addressed. Our research updates and complements previous modeling studies on the quantifying of MPA benefits for the terrestrial atmosphere and climate.

Performance-based sub-selection of CMIP6 models for impact assessments in Europe

Abstract. We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to a multi-model ensemble of CMIP6 models to (a) filter the ensemble for performance against these climatological and processed-based criteria and (b) create a smaller subset of models based on performance that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least-realistic models leads to higher-sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted towards greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast, this shifts the distribution towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

Future changes in wind energy resources in Egypt under Paris climate agreements’ goals

The Paris climate agreements’ goals ambitiously aim to hold mean global warming below 2.0°C and to pursue efforts to limit the warming to 1.5°C. One of the effective strategies for achieving these goals and reducing greenhouse gas emissions in the energy sector is using wind power. As Egypt is heavily investing in wind farm projects and planning to depend more on wind energy resources in its energy mix, it is important to assess the impact of climate change on its future wind energy production. This study employed eight global climate models of CMIP6 to project the wind power density (WPD) changes under the shared socioeconomic pathways (SSPs) 1–1.9 and 1–2.6 that inform Paris climate agreements and SSP5–8.5 that present the extreme warming scenario. The results showed that the WPD would increase in most Egypt, except in the far southeast. Increases would be pronounced over the far western desert and in Winter compared to other seasons. Nevertheless, Summer and Fall shall have the highest WPD by the end of the century compared to the present. This is favorable because the seasonal WPD pattern is sufficient to meet the local energy need. Unlike the intra-annual variability, few changes were projected in the inter-annual variability of WPD. Furthermore, a shift towards stronger WPDs compared to the historical period was observed. This study’s results can be useful for energy policymakers and planners in managing wind energy production under climate change scenarios.

Drivers of intermodel uncertainty in land carbon sink projections

Abstract. Over the past decades, land ecosystems removed from the atmosphere approximately one-third of anthropogenic carbon emissions, highlighting the importance of the evolution of the land carbon sink for projected climate change. Nevertheless, the latest cumulative land carbon sink projections from 11 Earth system models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) show an intermodel spread of 150 Pg C (i.e., ∼ 15 years of current anthropogenic emissions) for a policy-relevant scenario, with mean global warming by the end of the century below 2 ∘C relative to preindustrial conditions. We hypothesize that this intermodel uncertainty originates from model differences in the sensitivities of net biome production (NBP) to atmospheric CO2 concentration (i), to air temperature (ii), and to soil moisture (iii), as well as model differences in average conditions of air temperature (iv) and soil moisture (v). Using multiple linear regression and a resampling technique, we quantify the individual contributions of these five drivers for explaining the cumulative NBP anomaly of each model relative to the multi-model mean. We find that the intermodel variability of the contributions of each driver relative to the total NBP intermodel variability is 52.4 % for the sensitivity to temperature, 44.2 % for the sensitivity to soil moisture, 44 % for the sensitivity to CO2, 26.2 % for the average temperature, and 21.9 % for the average soil moisture. Furthermore, the sensitivities of NBP to temperature and soil moisture, particularly at tropical regions, contribute to explain 34 % to 65 % of the cumulative NBP deviations from the ensemble mean of the two models with the lowest carbon sink (ACCESS-ESM1-5 and UKESM1-0-LL) and of the two models with the highest sink (CESM2 and NorESM2-LM), highlighting the primary role of the response of NBP to interannual climate variability. Overall, this study provides insights on why each Earth system model projects either a low or high land carbon sink globally and across regions relative to the ensemble mean, which can focalize efforts to identify the representation of processes that lead to intermodel uncertainty.

Potential impacts of climate extremes on snow under global warming conditions in the Mongolian Plateau

PurposeThe paper aims to investigate the possible changes in mean temperature in the Mongolian Plateau associated with the 1.5 and 2°C global warming targets and how snow changes in the Mongolian Plateau when the mean global warming is well below 2°C or limited to 1.5°C.Design/methodology/approachIn total, 30 model simulations of consecutive temperature and precipitation days from Coupled Model Inter-comparison Project Phase 5 (CMIP5) are assessed in comparison with the 111 meteorological monitoring stations from 1961–2005. Multi-model ensemble and model relative error were used to evaluate the performance of CMIP5 models. Slope and the Mann–Kendall test were used to analyze the magnitude of the trends and evaluate the significance of trends of snow depth (SD) from 1981 to 2014 in the Mongolian Plateau.FindingsSome models perform well, even better than the majority (80%) of the models over the Mongolian Plateau, particularly HadGEM2-CC, CMCC-CM, BNU-ESM and GFDL-ESM2M, which simulate best in consecutive dry days (CDD), consecutive wet days (CWD), cold spell duration indicator (CSDI) and warm spell duration indicator (WSDI), respectively. Emphasis zones of WSDI on SD were deeply analysed in the 1.5 and 2 °C global warming period above pre-industrial conditions, because it alone has a significant negative relation with SD among the four indices. It is warmer than before in the Mongolian Plateau, particularly in the southern part of the Mongolian Plateau, indicating less SD.Originality/valueProviding climate extremes and SD data sets with different spatial-temporal scales over the Mongolian Plateau. Zoning SD potential risk areas and proposing adaptations to promote regional sustainable development.

Two-Way Teleconnections between the Southern Ocean and the Tropical Pacific via a Dynamic Feedback

Abstract Despite substantial global mean warming, surface cooling has occurred in both the tropical eastern Pacific Ocean and the Southern Ocean over the past 40 years, influencing both regional climates and estimates of Earth’s climate sensitivity to rising greenhouse gases. While a tropical influence on the extratropics has been extensively studied in the literature, here we demonstrate that the teleconnection works in the other direction as well, with the southeast Pacific sector of the Southern Ocean exerting a strong influence on the tropical eastern Pacific. Using a slab ocean model, we find that the tropical Pacific sea surface temperature (SST) response to an imposed Southern Ocean surface heat flux forcing is sensitive to the longitudinal location of that forcing, suggesting an atmospheric pathway associated with regional dynamics rather than reflecting a zonal-mean energetic constraint. The transient response shows that an imposed Southern Ocean cooling in the southeast Pacific sector first propagates into the tropics by mean-wind advection. Once tropical Pacific SSTs are perturbed, they then drive remote changes to atmospheric circulation in the extratropics that further enhance both Southern Ocean and tropical cooling. These results suggest a mutually interactive two-way teleconnection between the Southern Ocean and tropical Pacific through atmospheric circulations, and highlight potential impacts on the tropics from the extratropical climate changes over the instrumental record and in the future.

CMIP6 GCM ensemble members versus global surface temperatures

The Coupled Model Intercomparison Project (phase 6) (CMIP6) global circulation models (GCMs) predict equilibrium climate sensitivity (ECS) values ranging between 1.8 and 5.7 {^circ }C. To narrow this range, we group 38 GCMs into low, medium and high ECS subgroups and test their accuracy and precision in hindcasting the mean global surface warming observed from 1980–1990 to 2011–2021 in the ERA5-T2m, HadCRUT5, GISTEMP v4, and NOAAGlobTemp v5 global surface temperature records. We also compare the GCM hindcasts to the satellite-based UAH-MSU v6 lower troposphere global temperature record. We use 143 GCM ensemble averaged simulations under four slightly different forcing conditions, 688 GCM member simulations, and Monte Carlo modeling of the internal variability of the GCMs under three different model accuracy requirements. We found that the medium and high-ECS GCMs run too hot up to over 95% and 97% of cases, respectively. The low ECS GCM group agrees best with the warming values obtained from the surface temperature records, ranging between 0.52 and 0.58 {^circ }C. However, when comparing the observed and GCM hindcasted warming on land and ocean regions, the surface-based temperature records appear to exhibit a significant warming bias. Furthermore, if the satellite-based UAH-MSU-lt record is accurate, actual surface warming from 1980 to 2021 may have been around 0.40 {^circ }C (or less), that is up to about 30% less than what is reported by the surface-based temperature records. The latter situation implies that even the low-ECS models would have produced excessive warming from 1980 to 2021. These results suggest that the actual ECS may be relatively low, i.e. lower than 3 {^circ }C or even less than 2 {^circ }C if the 1980–2021 global surface temperature records contain spurious warming, as some alternative studies have already suggested. Therefore, the projected global climate warming over the next few decades could be moderate and probably not particularly alarming.

Future Climate Change Under SSP Emission Scenarios With GISS‐E2.1

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

A compound event-oriented framework to tropical fire risk assessment in a changing climate

Tropical fire activity closely follows the co-occurrence of multiple climate stressors. Yet, it remains challenging to quantify how changes in climate alter the likelihood of fire risks associated with compound events. Recent abrupt changes in fire regimes in iconic landscapes in Brazil (namely the Pantanal and Xingu) provide a key opportunity to evaluate how extremely dry and hot conditions, both together and individually, have influenced the probability of large fires. Here we quantify the relationships between climate and fire across these regions and provide evidence on the extent to which fire risk and the associated impacts could be constrained if anthropogenic global warming is limited. We investigate the burned area, differentiating between fire types according to land use (forest fires, savanna fires, farming fires and grassland and wetland fires), and derive present and future fire risks linked to multiple climate variables. We show that concurrent air dryness (high vapour-pressure deficit (VPD)) and low precipitation have driven fire occurrence in both Xingu and the Pantanal, with VPD playing a dominant role. Historical climatic change has already increased compound event-related (CE-related) fire risks of all fire types (5%–10%), and these risks are likely to increase in the future due to global warming. The likelihood of CE-related increase in fire risk may be reduced by up to 14.4% if global warming is constrained to +1.5 °C instead of +3 °C. Nevertheless, substantially increased CE-related fire risks are still expected even if restricting global mean warming to 1.5 °C, particularly in the Pantanal. We thus conclude that climate action should be coordinated with environmental protection to reduce ignition sources and promote effective conservation measures to preserve these biomes.

Emergent Constraints on CMIP6 Climate Warming Projections: Contrasting Cloud- and Surface Temperature–Based Constraints

AbstractThe latest Coupled Model Intercomparison Project (CMIP6) multimodel ensemble shows a broader range of projected warming than the previous-generation CMIP5 ensemble. We show that the projected warming is well correlated with tropical and subtropical low-level cloud properties. These physically meaningful relations enable us to use observed cloud properties to constrain future climate warming. We develop multivariate linear regression models with metrics selected from a set of potential constraints based on a stepwise selection approach. The resulting linear regression model using two low-cloud metrics shows better cross-validated results than regression models that use single metrics as constraints. Application of a regression model using the low-cloud metrics to climate projections results in similar estimates of the mean, but substantially narrower uncertainty ranges, of projected twenty-first-century warming when compared with unconstrained simulations. The resulting projected global-mean warming in 2081–2100 relative to 1995–2014 is 2.84–5.12 K (5%–95% range) for Shared Socioeconomic Pathway (SSP) 5–8.5 compared with a range of 2.34–5.81 K for unconstrained projections, and 0.60–1.70 K for SSP1–2.6 compared to an unconstrained range of 0.38–2.04 K. We provide evidence for a higher lower bound of the projected warming range than that obtained from constrained projections based on the past global-mean temperature trend. Consideration of the impact of the sea surface temperature pattern effect on the recent observed warming trend, which is not well captured in the CMIP6 ensemble, indicates that the relatively low projected warming resulting from the global-mean temperature trend constraint may not be reliable and provides further justification for the use of climatologically based cloud metrics to constrain projections.

Physical processes of summer extreme rainfall interannual variability in Eastern China—part II: evaluation of CMIP6 models

Eastern China is regularly exposed to extreme precipitation with significant socio-economical consequences. Following an observational analysis in a first part of this study, here the ability of the Coupled Model Intercomparison Project Phase 6 models to reproduce the main modes of interannual variability of 5-day summer extreme precipitation over eastern China is evaluated, using an empirical orthogonal teleconnection (EOT) method. These models capture the main patterns and magnitudes of the different EOT patterns, although the North China mode is less well represented. Models also reproduce the dynamical features associated with each mode. There is no systematic improvement in the ability of models to simulate either the pattern or the 5-day intensity when using higher resolution models compared to coarser resolution ones. Instead, multi-member or multi-model ensembles lead to results closer to observations. Using a low mitigation projection pathway (SSP-370), it is shown that the risk of the most extreme 5-day precipitation events by about 40%, 80% and more than 150% for global-mean warming levels, relative to 1850–1900, of + 1.5, + 2 and + 3 ^{circ }C respectively. This increase is found to be more significant for 5-days events than for seasonal scale precipitation, consistent with previous studies.