Intermodel Spread Research Articles

Solar cycle as a distinct line of evidence constraining Earth’s transient climate response

Severity of warming predicted by climate models depends on their Transient Climate Response (TCR). Inter-model spread of TCR has persisted at ~ 100% of its mean for decades. Existing observational constraints of TCR are based on observed historical warming response to historical forcing and their uncertainty spread is just as wide, mainly due to forcing uncertainty, and especially that of aerosols. Contrary, no aerosols are involved in solar-cycle forcing, providing an independent, tighter, constraint. Here, we define a climate sensitivity metric: time-dependent response regressed against time-dependent forcing, allowing phenomena with dissimilar time variations, such as the solar cycle with 11-year cyclic forcing, to be used to constrain TCR, which has a linear time-dependent forcing. We find a theoretical linear relationship between the two. The latest coupled atmosphere-ocean climate models obey the same linear relationship statistically. The proposed observational constraint on TCR is about 1/3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{1}}/{{3}}$$\\end{document} as narrow as existing constraints. The central estimate, 2.2 oC, is at the midpoint of the spread of the latest generation of climate models, which are more sensitive than those of the previous generations.

Diverse Eurasian Temperature Responses to Arctic Sea Ice Loss in Models due to Varying Balance between Dynamic Cooling and Thermodynamic Warming

Abstract Cold winters over Eurasia often coincide with warm winters in the Arctic, which has become known as the “warm Arctic–cold Eurasia” pattern. The extent to which this observed correlation is indicative of a causal response to sea ice loss is debated. Here, using large multimodel ensembles of coordinated experiments, we find that the Eurasian temperature response to Arctic sea ice loss is weak compared to internal variability and is not robust across climate models. We show that Eurasian cooling is driven by tropospheric and stratospheric circulation changes in response to sea ice loss but is counteracted by tropospheric thermodynamical warming, as the local warming induced by sea ice loss spreads into the midlatitudes by eddy advection. Although opposing effects of thermodynamical warming and dynamical cooling are found robustly across different models or different sea ice perturbations, their net effect varies in sign and magnitude across the models, resulting in diverse model temperature responses over Eurasia. The contributions from both tropospheric dynamics and thermodynamics show substantial intermodel spread. Although some of this spread in the Eurasian winter temperature response to sea ice loss may stem from model uncertainty, even with several hundred ensemble members, it is challenging to isolate model differences in the forced response from internal variability.

Asymmetric Arctic and Antarctic Warming and Its Intermodel Spread in CMIP6

Abstract Under the background of global warming, the Arctic region has warmed faster than the Antarctic, which is referred to as asymmetric Arctic and Antarctic warming. The new generation of model simulations from the CMIP6 offers an opportunity to identify the major factors contributing to the asymmetric warming and its intermodel spread. In this study, the preindustrial and abrupt-4 × CO2 experiments from 18 CMIP6 models are examined to extract the asymmetric warming and its intermodel spread. A climate feedback-response analysis method is applied to reveal the contributions of external and internal feedback processes to the asymmetric warming and its intermodel spread, by decomposing total warming into the partial temperature changes caused by individual factors. It is found that a seasonal energy transfer mechanism (SETM) dominates in both polar warmings. The direct consequence of the sea ice declining in response to the anthropogenic forcing is an increase in the effective heat capacity of the ocean surface layer. Such an increase in the effective heat capacity temporally withholds most of the extra solar energy absorbed during summer and then releases it during winter, contributing to stronger warming in winter. However, the background oceanic circulation in the Southern Ocean, namely, the Antarctic Circumpolar Current, continually transports energy equatorward, resulting in a suppressed SETM and surface warming in the Antarctic. The key factor that accounts for intermodel spread in the asymmetric warming is the difference in their strengths of SETM. The poleward atmospheric transport and water vapor feedback also contribute to the intermodel spread. Significance Statement The asymmetric Arctic and Antarctic warming, as a response to the increase in CO2, can regulate global atmospheric and oceanic circulations via meridional temperature gradient. Previous studies have all ascribed the key role of the ocean in the asymmetric warming over the two poles with a lack of comprehensive understanding of the roles of other feedback processes. This study emphasizes that oceanic circulation is the root of the asymmetric warming via suppressing sea ice retreat and the associated SETM in the Antarctic, instead of increasing ocean heat uptake and equatorward transport only. For the intermodel spread in the asymmetric warming over two poles, the differences in the strength of all processes involving the SETM among models are the prominent issue.

State dependence of CO2 forcing and its implications for climate sensitivity.

When evaluating the effect of carbon dioxide (CO2) changes on Earth's climate, it is widely assumed that instantaneous radiative forcing from a doubling of a given CO2 concentration (IRF2×CO2) is constant and that variances in climate sensitivity arise from differences in radiative feedbacks or dependence of these feedbacks on the climatological base state. Here, we show that the IRF2×CO2 is not constant, but rather depends on the climatological base state, increasing by about 25% for every doubling of CO2, and has increased by about 10% since the preindustrial era primarily due to the cooling within the upper stratosphere, implying a proportionate increase in climate sensitivity. This base-state dependence also explains about half of the intermodel spread in IRF2×CO2, a problem that has persisted among climate models for nearly three decades.

A Semi‐Analytical Model for Water Vapor, Temperature, and Surface‐Albedo Feedbacks in Comprehensive Climate Models

AbstractRadiative feedbacks govern the Earth's climate sensitivity and elucidate the geographic patterns of climate change in response to a carbon‐dioxide forcing. We develop an analytical model for patterned radiative feedbacks that depends only on changes in local surface temperature. The analytical model combines well‐known moist adiabatic theory with the radiative‐advective equilibrium that describes the energy balance in high latitudes. Together with a classic analytical function for surface albedo, all of the non‐cloud feedbacks are represented. The kernel‐based analytical feedbacks reproduce the feedbacks diagnosed from global climate models at the global, zonal‐mean, and seasonal scales, including in the polar regions, though with less intermodel spread. The analytical model thus provides a framework for a quantitative understanding of radiative feedbacks from simple physics, independent of the detailed atmospheric and cryospheric responses simulated by comprehensive climate models.

Seasonal Changes in Atmospheric Heat Transport to the Arctic Under Increased CO2

AbstractArctic warming under increased CO2 peaks in winter, but is influenced by summer forcing via seasonal ocean heat storage. Yet changes in atmospheric heat transport into the Arctic have mainly been investigated in the annual mean or winter, with limited focus on other seasons. We investigate the full seasonal cycle of poleward heat transport modeled with increased CO2 or with individually applied Arctic sea‐ice loss and global sea‐surface warming. We find that a winter reduction in dry heat transport is driven by Arctic sea‐ice loss and warming, while a summer increase in moist heat transport is driven by sub‐Arctic warming and moistening. Intermodel spread in Arctic warming controls spread in seasonal poleward heat transport. These seasonal changes and their intermodel spread are well‐captured by down‐gradient diffusive heat transport. While changes in moist and dry heat transport compensate in the annual‐mean, their opposite seasonality may support non‐compensating effects on Arctic warming.

How atmospheric humidity drives the outgoing longwave radiation–surface temperature relationship and inter-model spread

The Earth’s global radiation budget depends critically on the relationship between outgoing longwave radiation (OLR) and surface temperature (). Using the fifth generation of European ReAnalysis dataset, we find that although OLR appears to be linearly dependent on over a wide range, there are significant deviations from the linearity in the OLR– relationship for regions warmer than 270 K , which covers 89% of the surface of Earth. While the AMIP runs of CMIP6 models largely capture the overall OLR– relationship, considerable discrepancies are found in clear-sky OLR at given ranges. In this study, we investigate physical mechanisms that control the clear-sky OLR– relationship seen in reanalysis and CMIP6 models by using accurate radiative transfer calculations. Our study identifies three key mechanisms to explain both the linearity and departure from linearity of the clear-sky OLR– relationship. The first is a surface contribution, controlled by the thermal emission of the surface and the infrared opacity of the atmosphere, accounting for 60% of the observed clear-sky OLR– linear slope. The second is changes in atmospheric emission induced by a foreign pressure effect on water vapor and other greenhouse gases, which accounts for 30% of the linear slope in a clear-sky condition. The third is changes in atmospheric emission induced by variations in relative humidity (RH), particularly in the mid-troposphere (250 to 750 hPa), which determines the non-linearity in the clear-sky OLR– relationship and adds to the remaining 10% of the slope. The inter-model spread in mid-tropospheric RH explains a large fraction of the differences in clear-sky OLR across CMIP6 models at given surface temperatures. Furthermore, the three key mechanisms outlined here apply to the OLR– relationship in all-sky conditions: clouds disguise the surface contribution but increase the atmospheric contribution, retaining a similar linear slope to the clear-sky condition while amplifying the non-linear curvature.

Overview and statistical analysis of boundary layer clouds and precipitation over the western North Atlantic Ocean

Abstract. Due to their fast evolution and large natural variability in macro- and microphysical properties, the accurate representation of boundary layer clouds in current climate models remains a challenge. One of the regions with large intermodel spread in the Coupled Model Intercomparison Project Phase 6 ensemble is the western North Atlantic Ocean. Here, statistically representative in situ measurements can help to develop and constrain the parameterization of clouds in global models. To this end, we performed comprehensive measurements of boundary layer clouds, aerosol, trace gases, and radiation in the western North Atlantic Ocean during the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. In total, 174 research flights with 574 flight hours for cloud and precipitation measurements were performed with the HU-25 Falcon during three winter (February–March 2020, January–April 2021, and November 2021–March 2022) and three summer seasons (August–September 2020, May–June 2021, and May–June 2022). Here we present a statistical evaluation of 16 140 individual cloud events probed by the fast cloud droplet probe and the two-dimensional stereo cloud probe during 155 research flights in a representative and repetitive flight strategy allowing for robust statistical data analyses. We show that the vertical profiles of distributions of the liquid water content and the cloud droplet effective diameter (ED) increase with altitude in the marine boundary layer. Due to higher updraft speeds, higher cloud droplet number concentrations (Nliquid) were measured in winter compared to summer despite lower cloud condensation nucleus abundance. Flight cloud cover derived from statistical analysis of in situ data is reduced in summer and shows large variability. This seasonal contrast in cloud coverage is consistent with a dominance of a synoptic pattern in winter that favors conditions for the formation of stratiform clouds at the western edge of cyclones (post-cyclonic). In contrast, a dominant summer anticyclone is concomitant with the occurrence of shallow cumulus clouds and lower cloud coverage. The evaluation of boundary layer clouds and precipitation in the Nliquid ED phase space sheds light on liquid, mixed-phase, and ice cloud properties and helps to categorize the cloud data. Ice and liquid precipitation, often masked in cloud statistics by a high abundance of liquid clouds, is often observed throughout the cloud. The ACTIVATE in situ cloud measurements provide a wealth of cloud information useful for assessing airborne and satellite remote-sensing products, for global climate and weather model evaluations, and for dedicated process studies that address precipitation and aerosol–cloud interactions.

Current-climate sea ice amount and seasonality as constraints for future Arctic amplification

The recent Arctic sea ice loss is a key driver of the amplified surface warming in the northern high latitudes, and simultaneously a major source of uncertainty in model projections of Arctic climate change. Previous work has shown that the spread in model predictions of future Arctic amplification (AA) can be traced back to the inter-model spread in simulated long-term sea ice loss. We demonstrate that the strength of future AA is further linked to the current climate’s, observable sea ice state across the multi-model ensemble of the 6th Coupled Model Intercomparison Project (CMIP6). The implication is that the sea-ice climatology sets the stage for long-term changes through the 21st century, which mediate the degree by which Arctic warming is amplified with respect to global warming. We determine that a lower base-climate sea ice extent and sea ice concentration (SIC) in CMIP6 models enable stronger ice melt in both future climate and during the seasonal cycle. In particular, models with lower Arctic-mean SIC project stronger future ice loss and a more intense seasonal cycle in ice melt and growth. Both processes systemically link to a larger future AA across climate models. These results are manifested by the role of climate feedbacks that have been widely identified as major drivers of AA. We show in particular that models with low base-climate SIC predict a systematically stronger warming contribution through both sea-ice albedo feedback and temperature feedbacks in the future, as compared to models with high SIC. From our derived linear regressions in conjunction with observations, we estimate a 21st-century AA over sea ice of 2.47–3.34 with respect to global warming. Lastly, from the tight relationship between base-climate SIC and the projected timing of an ice-free September, we predict a seasonally ice-free Arctic by mid-century under a high-emission scenario.

Effects of multi-observations uncertainty and models similarity on climate change projections

Climate change projections (CCPs) are based on the multimodel means of individual climate model simulations that are assumed to be independent. However, model similarity leads to projections biased toward the largest set of similar models and intermodel uncertainty underestimation. We assessed the influences of similarities in CMIP6 through CMIP3 CCPs. We ascertained model similarity from shared physics/dynamics and initial conditions by comparing simulated spatial temperature and precipitation with the corresponding observed patterns and accounting for intermodel spread relative to the observational uncertainty, which is also critical. After accounting for similarity, the information from 57 CMIP6, 47 CMIP5, and 24 CMIP3 models can be explained by just 11 independent models without significant differences in globally averaged climate change statistics. On average, independent models indicate a lower global-mean temperature rise of 0.25 °C (~0.5 °C–1 °C in some regions) relative to all models by the end of the 21st century under CMIP6’s highest emission scenario.

Present-day North Atlantic salinity constrains future warming of the Northern Hemisphere

Earth system models exhibit considerable intermodel spread in Atlantic Meridional Overturning Circulation intensity and its carbon uptake, resulting in great uncertainty in future climate. Here we show that present-day sea surface salinity (SSS) in the North Atlantic subpolar region modulates anthropogenic carbon uptake in the North Atlantic, and thus can be used to constrain future warming. Specifically, models that generate a present-day higher SSS in the North Atlantic subpolar region generate a greater uptake of anthropogenic carbon in the future, suppressing the greenhouse effect and resulting in slower warming, and vice versa in models with a present-day lower SSS. Emergent constraints based on the observed SSS greatly reduce the uncertainty of the Northern Hemisphere surface temperature warming and accumulative carbon uptake by about 30% and 53%, respectively, by the end of the twenty-first century under the Shared Socioeconomic Pathways 5–8.5 scenario.

Better-constrained climate sensitivity when accounting for dataset dependency on pattern effect estimates

Abstract. The best estimate of equilibrium climate sensitivity (ECS) constrained based on the instrumental record of historical warming becomes coherent with other lines of evidence when the dependence of radiative feedback on the pattern of surface temperature change (pattern effect) is incorporated. Pattern effect strength is usually estimated with atmosphere-only model simulations forced with observed historical sea-surface temperature (SST) and sea-ice change and constant pre-industrial forcing. However, recent studies indicate that pattern effect estimates depend on the choice of SST boundary condition dataset, due to differences in the measurement sources and the techniques used to merge and construct them. Here, we systematically explore this dataset dependency by applying seven different observed SST datasets to the MPI-ESM1.2-LR model covering 1871–2017. We find that the pattern effect ranges from -0.01±0.09 to 0.42±0.10 W m−2 K−1 (standard error), whereby the commonly used Atmospheric Model Intercomparison Project II (AMIPII) dataset produces by far the largest estimate. When accounting for the generally weaker pattern effect in MPI-ESM1.2-LR compared to other models, as well as dataset dependency and intermodel spread, we obtain a combined pattern effect estimate of 0.37 W m−2 K−1 [−0.14 to 0.88 W m−2 K−1] (5th–95th percentiles) and a resulting instrumental record ECS estimate of 3.2 K [1.8 to 11.0 K], which as a result of the weaker pattern effect is slightly lower and better constrained than in previous studies.

Contributions to regional precipitation change and its polar-amplified pattern under warming

The polar regions are predicted to experience the largest relative change in precipitation in response to increased greenhouse-gas concentrations, where a substantial absolute increase in precipitation coincides with small precipitation rates in the present-day climate. The reasons for this amplification, however, are still debated. Here, we use an atmospheric energy budget to decompose regional precipitation change from climate models under greenhouse-gas forcing into contributions from atmospheric radiative feedbacks, dry-static energy flux divergence changes, and surface sensible heat flux changes. The polar-amplified relative precipitation change is shown to be a consequence of the Planck feedback, which, when combined with larger polar warming, favors substantial atmospheric radiative cooling that balances increases in latent heat release from precipitation. Changes in the dry-static energy flux divergence contribute modestly to the polar-amplified pattern. Additional contributions to the polar-amplified response come, in the Arctic, from the cloud feedback and, in the Antarctic, from both the cloud and water vapor feedbacks. The primary contributor to the intermodel spread in the relative precipitation change in the polar region is also the Planck feedback, with the lapse rate feedback and dry-static energy flux divergence changes playing secondary roles. For all regions, there are strong covariances between radiative feedbacks and changes in the dry-static energy flux divergence that impact the intermodel spread. These results imply that constraining regional precipitation change, particularly in the polar regions, will require constraining not only individual feedbacks but also the covariances between radiative feedbacks and atmospheric energy transport.

Intermodel Spread of Historical Indian Monsoon Rainfall Change in CMIP6: The Role of the Tropical Pacific Mean State

Abstract Robust projections of the Indian summer monsoon rainfall (ISMR) are critical as it provides 80% of the annual precipitation to more than 1 billion people who are very vulnerable to climate change. However, even over the historical period, state-of-the-art climate models have difficulties in reproducing the observed ISMR trends and are affected by a large intermodel spread, which questions the reliability of ISMR projections. Such uncertainty could come from internal variability or model biases. Here, we study the impact of the latter on the historical forced change of ISMR in 34 models from CMIP6. First, we show that models’ biases over India do not significantly impact how they simulate the historical change of ISMR. However, we do find statistically significant relationships between ISMR historical forced changes and remote rainfall and temperature biases within the tropics by using a maximum covariance analysis (MCA). Our results highlight the key role of tropical Pacific sea surface temperature (SST) mean state biases as an important source of intermodel spread in the ISMR change. The physical mechanisms underlying these statistical relationships between ISMR change and the intermodel spread of Pacific SST biases are finally explored. We found that models having El Niño/La Niña–like mean SST bias in the Pacific tend to exhibit El Niño/La Niña–like changes over the historical period, impacting ISMR through a shift in the Walker circulation and Rossby wave propagation across the Pacific.

Representation of soil hydrology in permafrost regions may explain large part of inter-model spread in simulated Arctic and subarctic climate

Abstract. The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone, with differences in model structure and parametrizations being one of the main sources of uncertainty. One particularly challenging aspect in modelling is the representation of terrestrial processes in permafrost-affected regions, which are often governed by spatial heterogeneity far below the resolution of the models' land surface components. Here, we use the Max Planck Institute (MPI) Earth System Model to investigate how different plausible assumptions for the representation of permafrost hydrology modulate land–atmosphere interactions and how the resulting feedbacks affect not only the regional and global climate, but also our ability to predict whether the high latitudes will become wetter or drier in a warmer future. Focusing on two idealized setups that induce comparatively “wet” or “dry” conditions in regions that are presently affected by permafrost, we find that the parameter settings determine the direction of the 21st-century trend in the simulated soil water content and result in substantial differences in the land–atmosphere exchange of energy and moisture. The latter leads to differences in the simulated cloud cover during spring and summer and thus in the planetary energy uptake. The respective effects are so pronounced that uncertainties in the representation of the Arctic hydrological cycle can help to explain a large fraction of the inter-model spread in regional surface temperatures and precipitation. Furthermore, they affect a range of components of the Earth system as far to the south as the tropics. With both setups being similarly plausible, our findings highlight the need for more observational constraints on the permafrost hydrology to reduce the inter-model spread in Arctic climate projections.

Energetic Constraints on the Pattern of Changes to the Hydrological Cycle under Global Warming

Abstract The response of zonal-mean precipitation minus evaporation (P − E) to global warming is investigated using a moist energy balance model (MEBM) with a simple Hadley cell parameterization. The MEBM accurately emulates zonal-mean P − E change simulated by a suite of global climate models (GCMs) under greenhouse gas forcing. The MEBM also accounts for most of the intermodel differences in GCM P − E change and better emulates GCM P − E change when compared to the “wet-gets-wetter, dry-gets-drier” thermodynamic mechanism. The intermodel spread in P − E change is attributed to intermodel differences in radiative feedbacks, which account for 60%–70% of the intermodel variance, with smaller contributions from radiative forcing and ocean heat uptake. Isolating the intermodel spread of feedbacks to specific regions shows that tropical feedbacks are the primary source of intermodel spread in zonal-mean P − E change. The ability of the MEBM to emulate GCM P − E change is further investigated using idealized feedback patterns. A less negative and narrowly peaked feedback pattern near the equator results in more atmospheric heating, which strengthens the Hadley cell circulation in the deep tropics through an enhanced poleward heat flux. This pattern also increases gross moist stability, which weakens the subtropical Hadley cell circulation. These two processes in unison increase P − E in the deep tropics, decrease P − E in the subtropics, and narrow the intertropical convergence zone. Additionally, a feedback pattern that produces polar-amplified warming partially reduces the poleward moisture flux by weakening the meridional temperature gradient. It is shown that changes to the Hadley cell circulation and the poleward moisture flux are crucial for understanding the pattern of GCM P − E change under warming. Significance Statement Changes to the hydrological cycle over the twenty-first century are predicted to impact ecosystems and socioeconomic activities throughout the world. While it is broadly expected that dry regions will get drier and wet regions will get wetter, the magnitude and spatial structure of these changes remains uncertain. In this study, we use an idealized climate model, which assumes how energy is transported in the atmosphere, to understand the processes setting the pattern of precipitation and evaporation under global warming. We first use the idealized climate model to explain why comprehensive climate models predict different changes to precipitation and evaporation across a range of latitudes. We show this arises primarily from climate feedbacks, which act to amplify or dampen the amount of warming. Ocean heat uptake and radiative forcing play secondary roles but can account for a significant amount of the uncertainty in regions where ocean circulation influences the rate of warming. We further show that uncertainty in tropical feedbacks (mainly from clouds) affects changes to the hydrological cycle across a range of latitudes. We then show how the pattern of climate feedbacks affects how the patterns of precipitation and evaporation respond to climate change through a set of idealized experiments. These results show how the pattern of climate feedbacks impacts tropical hydrological changes by affecting the strength of the Hadley circulation and polar hydrological changes by affecting the transport of moisture to the high latitudes.

The Leading Intermodel Spread of the Projected Changes in the Eurasian Continent Winter Surface Air Temperature and Large‐Scale Circulations From the CMIP6 Simulations

AbstractThis study investigates the intermodel spread of changes in the Eurasian winter surface air temperature (SAT) based on Coupled Model Intercomparison Project 6 (CMIP6) and explains it from the perspective of circulation under shared socioeconomic pathway 2–4.5. Results show that the leading intermodel spread of Eurasian SAT change, derived from the intermodel empirical orthogonal function analysis (EOF), is characterized by a warming pattern of SAT change. This warming pattern is associated with the changes in the weakened Siberian High and Aleutian Low, the weakened subtropical jet, the poleward polar jet, and the tilting trough. The combination of these circulations has prevented the intrusion of the cold air to the south and therefore results in the warming SAT change pattern. Their relative contribution weights of the weakened Siberian High and Aleutian Low, the tilting East Asian trough, the weakened subtropical jet, and the poleward polar jet are 12.73%, 20.43%, 30.24%, 20.96%, and 15.64%, respectively. All these changes may be traced to the warm sea surface temperature (SST) changes in the western North Pacific and tropical Atlantic Ocean, via the meridional temperature gradient and wave trains, respectively. These warmings would be the potential metrics for reducing the intermodel uncertainties of the winter SAT projection at the end of the 21st century over the Eurasian continent.

Inter-model spreads of the climatological mean Hadley circulation in AMIP/CMIP6 simulations

Inter-model spreads of the climatological mean Hadley circulation in AMIP/CMIP6 simulations

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa— assessment of the historical simulations

This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange, and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodel spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm, respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodel spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

Future Drying Subtropical East Asia in Winter: Mechanism and Observational Constraint

Abstract Coupled climate models project robust wintertime wetting trend over midlatitude East Asia under global warming scenarios, but the projected change in precipitation shows large intermodel uncertainty over subtropical East Asia from southern China to southwestern Japan. Based on an ensemble of climate models participating in CMIP6, this study shows that the weakened southern branch westerly jet (SWJ) on the southern side of Tibetan Plateau (TP) plays a key role in suppressing subtropical East Asian precipitation. The SWJ is deflected into southwesterly wind on the southeastern side of TP, bringing ascent and precipitation to subtropical East Asia primarily through isentropic gliding. As a result of the poleward and upward shift of the planetary-scale westerly jet under global warming, the SWJ becomes weaker and it acts to suppress subtropical East Asian precipitation by weakening the southwesterly wind and ascent. The SWJ–precipitation linkage also exists on interannual time scales, but the sensitivity of precipitation to interannual SWJ variability is systematically underestimated by the models compared with observation. The combined effects of the change in SWJ strength and the sensitivity of precipitation to SWJ explain about 40% of the intermodel spread of the projected precipitation changes. Observational constraint on the SWJ–precipitation relationship amplifies the projected drying trend and narrows the intermodel spread. It shows that the regional-averaged precipitation over subtropical East Asia decreases by 3.3% per degree of warming, and the amplitude of precipitation reduction over subtropical East Asia (southern China) is about 1.4 (3.4) times the raw projection.