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Global climate model (GCM) projections of future climate are uncertain largely due to a persistent spread in cloud feedback. This is despite efforts to reduce this model uncertainty through a variety of emergent constraints (ECs); with several studies suggesting an important role for present‐day biases in clouds. Here, we use three generations of GCMs to assess the value of climatological cloud metrics for constraining uncertainty in cloud feedback. We find that shortwave cloud radiative properties across the Southern Hemisphere extratropics are most robustly correlated with tropical cloud feedback (TCF). Using this relationship in conjunction with observations, we produce an EC that yields a TCF value of 0.52 ± 0.34 W/m2/K, which equates to a 34% reduction in uncertainty. Thus, we show that climatological cloud properties can be used to reduce uncertainty in how clouds will respond to future warming. Plain Language Summary Different global climate models exhibit large variability in how clouds across the tropics will respond to future warming. This is largely due to the complexity and diversity of responses that differing cloud types may experience under warming. A long‐term goal of the community has been to narrow this disagreement between different models. Over the past 15 years, several studies have proposed ways in which the variability in future cloud changes might be related to errors in how these models represent present‐day properties. Here, we use three collections of models to show that variability in tropical cloud changes is closely tied to shortwave cloud radiative properties across the Southern Ocean. We then use this intermodel relationship along with observations to produce a best estimate of cloud feedback across the tropics. Key Points We find a relationship between tropical cloud feedback and mean‐state biases in Southern Hemisphere extratropical cloud properties This intermodel relationship is found to be present in three different ensembles of global climate models, a sign of robustness This relationship suggests a likely tropical cloud feedback value of 0.52 ± 0.34 W/m2/K, which equates to a 34% reduction in uncertainty

This study examines the West Pacific (WP) pattern, one of the primary modes of low-frequency variability during boreal winter, isolated in 11 global climate model seasonal hindcasts. WP mode separation are verified using three metrics: selected mode number, explained variance, and pattern correlation coefficient of the loading vector. When it comes to the pattern reproducibility, it turns out that the WP is tightly linked to the Atlantic jet and stationary wave. To diagnose details of mean biases in terms of WP reproducibility, atmospheric mean fields are composited for two groups: one group replicates well the key feature, a north–south dipole pattern, while the other manifests considerable displacements and magnitude disparities. The group with a lower pattern correspondence to the observation shows noticeable biases with the southeastward-shifted Atlantic dipole of the stationary wave and downstream-expanded Atlantic jet, which is attributed to the increase in meridional gradient of near surface temperature and resultant enhanced local baroclinicity. Magnified jet tail in the eastern North Atlantic (NA) can intensify the barotropic energy conversion thereby the wave energy actively passes toward both east and west. The excessive wave energy over the Arabian Peninsula and the central North Pacific can be favorable for the formation of a ridge, therefore it possibly leads to disorganized WP patterns at both ends of NA basin. A series of analysis suggests the importance of a realistic winter climatology simulation over NA for better WP representation through the interaction between different scales as well as different basins.

Global Climate Models (GCMs) offer valuable seasonal precipitation forecasts information. However, their predictive performance may be inferior to traditional climatological forecasts derived from historical precipitation data. In this study, we evaluate the spatiotemporal skill of calibrated GCMs across China to determine whether their skill surpasses climatological forecasts at various lead times. Six GCMs are statistically calibrated using a Gamma-Gaussian model and integrated via Bayesian Model Averaging. The calibrated GCMs forecast is then compared with the climate forecast for different climate zones and months, and in the formulation of actual meteorological business and academic research, integer months are often used to describe the forecast period, and the preparation time is 1 month, 2 months, and 3 months. The results indicate that for the one-month lead time, the skill of calibrated GCM forecasts outperforms climatological forecasts in 33% (322/971) of grid cells. However, the skill of calibrated GCMs declines with longer lead times, with only 24% and 20% of grid cells surpassing the climatological forecasts at two- and three-month lead times, respectively. Regionally, the calibrated GCMs forecasts exhibit stronger superiority over climatological forecasts in the Northern Subtropical Zone than in other climate zones, while showing the most limited improvement compared to climatological benchmarks in the Middle Temperate Zone. Seasonally, the skill advantages of the calibrated forecasts relative to climatological forecasts are more pronounced during the non-flood season (September to March) than during the flood season (April to August). The average proportions of grid cells during the flood season are 29%, 18%, and 18% across the three lead times, compared to 49%, 34%, and 27% during the non-flood season. Overall, this study provides a comprehensive evaluation of the skill of calibrated GCMs across China, offering a framework for assessing their effectiveness in delivering reliable seasonal precipitation forecasts in other regions.

We study the sensitivity of South Asian Summer Monsoon (SASM) precipitation to Southern Hemisphere (SH) subtropical Absorbed Solar Radiation (ASR) changes using Community Earth System Model 2 simulations. Reducing positive ASR biases over the SH subtropics impacts SASM, and is sensitive to the ocean basin where changes are imposed. Radiation changes over the SH subtropical Indian Ocean (IO) shifts rainfall over the equatorial IO northward causing 1–2 mm/day drying south of equator, changes over the SH subtropical Pacific increases precipitation over northern continental regions by 1–2 mm/day, and changes over the SH subtropical Atlantic have little effect on SASM precipitation. Radiation changes over the subtropical Pacific impacts the SASM through zonal circulation changes, while changes over the IO modify meridional circulation to bring about changes in precipitation over northern IO. Our findings suggest that reducing SH subtropical radiation biases in climate models may also reduce SASM precipitation biases. Plain Language Summary Precipitation from South Asian Summer Monsoon (SASM) is of high significance to the livelihoods of over a billion people. As the global climate continues to evolve, it is essential to have a clear understanding of the possible future changes to the SASM. However, current state‐of‐the‐art climate models have difficulties in simulating climatological mean SASM precipitations. Here we study sensitivity of SASM precipitation to subtropical southern ocean radiation as one of the possible causes of SASM precipitation bias. Our experiments indicate that SASM precipitation is sensitive to southern hemisphere (SH) subtropical radiation changes particularly to those in subtropical Pacific. These findings imply that improving SH subtropical radiation biases might improve SASM precipitation simulations in climate models. Key Points We test if biases in southern hemisphere shortwave radiation contributes to biases in South Asian summer monsoon precipitation in the CESM2 Reducing incoming shortwave radiation in the subtropical southern hemisphere reduces dry biases over continental South Asia This effect is mostly due to forcing in the South Pacific, with less impact from the Atlantic or Indian Ocean

We present a weighting strategy for use with the CMIP5 multi-model archive in the fourth National Climate Assessment, which considers both skill in the climatological performance of models over North America as well as the inter-dependency of models arising from common parameterizations or tuning practices. The method exploits information relating to the climatological mean state of a number of projection-relevant variables as well as metrics representing long-term statistics of weather extremes. The weights, once computed can be used to simply compute weighted means and significance information from an ensemble containing multiple initial condition members from potentially co-dependent models of varying skill. Two parameters in the algorithm determine the degree to which model climatological skill and model uniqueness are rewarded; these parameters are explored and final values are defended for the assessment. The influence of model weighting on projected temperature and precipitation changes is found to be moderate, partly due to a compensating effect between model skill and uniqueness. However, more aggressive skill weighting and weighting by targeted metrics is found to have a more significant effect on inferred ensemble confidence in future patterns of change for a given projection.

In this study, we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model (GCM) data to drive a regional climate model (RCM) over the Asia-western North Pacific region. Three simulations were conducted with a 25-km grid spacing for the period 1980–2014. The first simulation (WRF_ERA5) was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset and served as the validation dataset. The original GCM dataset (MPI-ESM1-2-HR model) was used to drive the second simulation (WRF_GCM), while the third simulation (WRF_GCMbc) was driven by the bias-corrected GCM dataset. The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models. Results demonstrate that the WRFGCMbc significantly reduced the root-mean-square errors (RMSEs) of the climatological mean of downscaled variables, including temperature, precipitation, snow, wind, relative humidity, and planetary boundary layer height by 50%–90% compared to the WRF_GCM. Similarly, the RMSEs of interannual-to-interdecadal variances of downscaled variables were reduced by 30%–60%. Furthermore, the WRFGCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities. The leading empirical orthogonal function (EOF) shows a monopole precipitation mode in the WRFGCM. In contrast, the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China. This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

All Earth System Models (ESMs) have climatological biases relative to the observed historical climate. The quality of a model and, more importantly, the accuracy of its predictions are often associated with the magnitude and properties of its biases. For more than a decade, new strategies have been developed to empirically reduce such biases in the model components of ESMs during their execution. The present study considers a cyclostationary class of empirical runtime bias corrections to a climate model, referred to here as empirical runtime bias corrections (ERBCs). Such ERBCs are state independent and designed to reduce biases in the climatological annual cycle of the model. We present a new procedure for deriving such ERBCs called Climatological Adaptive Bias Correction (CABCOR). CABCOR is argued to be superior to the standard relaxation approach to defining ERBCs because it requires only a climatological, rather than a multi‐year time evolving, observational reference data set. As part of this study, we perform a novel analysis of the relaxation approach in which a mapping is made between the parameter values that define the relaxation and the biases produced by ERBCs in the corrected model. This allows us to identify the optimal bias correction produced by the relaxation approach and to additionally demonstrate that the CABCOR approach can produce bias‐corrected models with smaller climatological biases. Plain Language Summary All climate models have basic‐state biases which are believed to impact their ability to make accurate predictions. We present a new method to derive fixed, repeated annual cycle, correction terms that can be added to a climate model as it runs to reduce its biases. The new method requires less observational information than existing methods and is shown to result in larger bias reductions. Key Points A new approach to derive empirical runtime bias corrections of a climate model is presented The new approach requires only climatological rather than time evolving observed reference states The approach is shown to produce larger bias reductions in an atmospheric climate model than existing relaxation‐based methods

The central role of tropical sea surface temperature (SST) variability in modulating Northern Hemisphere (NH) extratropical climate has long been known. However, the prevailing pathways of teleconnections in observations and the ability of climate models to replicate these observed linkages remain elusive. Here, we apply maximum covariance analysis between atmospheric circulation and tropical SST to reveal two coexisting tropical–extratropical teleconnections albeit with distinctive spatiotemporal characteristics. The first mode, resembling the Pacific–North American (PNA) pattern, favors a tropical–Arctic in-phase (warm Pacific–warm Arctic) teleconnection in boreal spring and winter. However, the second mode, with a slight seasonal preference of summer, is manifested as an elongated Rossby wave train emanating from the tropical eastern Pacific that features an out-of-phase relationship (cold Pacific–warm Arctic) between tropical central Pacific SSTs and temperature variability over the Arctic (referred to as the PARC mode). While climate models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) appear to successfully simulate the PNA mode and its temporal characteristics, the majority of models’ skill in reproducing the PARC mode is obstructed to some extent by biases in simulating low-frequency SST and rainfall variability over the tropical eastern Pacific and the climatological mean flow over the North Pacific during boreal summer. Considering the contribution of the PARC mode in shaping low-frequency climate variations over the past 42 years from the tropics to the Arctic, improving models’ capability to capture the PARC mode is essential to reduce uncertainties associated with decadal prediction and climate change projection over the NH.

Daily meteorological data such as temperature or precipitation from climate models are needed for many climate impact studies, e.g., in hydrology or agriculture, but direct model output can contain large systematic errors. A large variety of methods exist to adjust the bias of climate model outputs. Here we review existing statistical bias-adjustment methods and their shortcomings, and compare quantile mapping (QM), scaled distribution mapping (SDM), quantile delta mapping (QDM) and an empiric version of PresRAT (PresRATe). We then test these methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance in terms of the following demands: (1) the model data should match the climatological means of the observational data in the historical period; (2) the long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment; and (3) even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. QDM and PresRATe combined fulfill all three demands. For (2) for precipitation, PresRATe already includes an additional correction that assures that the climate change signal is conserved.

The impact of climate change on tropical cyclone (TC) activity is often assessed by various downscaling approaches, statistical–dynamical frameworks, and high-resolution global climate models using the projected changes of environmental factors. Uncertainty in simulating and projecting TC-relevant, large-scale circulation is closely linked to the projection of TC activity in a warming climate. Based on the model output in phase 6 of the Coupled Model Intercomparison Project (CMIP6), this study examines the intermodel biases in simulating the western North Pacific monsoon trough (MT), which is one of the most important large-scale circulation systems for TC activity, especially TC formation. It is found that most CMIP6 models can successfully simulate the climatological mean structure of the MT, although considerable biases remain in its exact location and its simulated historical changes. The mean latitude of the simulated MT spreads between 10° and 20°N, with noticeable differences in its orientation. The multimodel ensemble mean indicates that the MT exhibits no significant long-term zonal and poleward shifts in the future scenarios, consistent with the projection in the selected models in which the simulated MT resembles the observed spatiotemporal characteristics of the counterpart. Further analysis suggests that the intermodel bias in the simulated MT location is closely related to the east–west contrast of sea surface temperature (SST) anomalies in the tropical Pacific. More attention is required on improving the simulation of the basinwide SST distribution and its associated MT to reduce the uncertainty in predicting the future location of TC formation.