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This study aimed to examine the spatio-temporal variations in the atmospheric boundary layer height (ABLH) over the Tarim Basin (TB). Monthly ABLH data from the ERA-Interim dataset from January 1979 to December 2018 were used. Periodicity analysis and the Mann–Kendall Abrupt Changes test were employed to identify the change cycle and abrupt change year of the boundary layer height. The Empirical Orthogonal Function (EOF) method was utilized to determine the spatial distribution of the boundary layer height, and the RF method was used to establish the relationship between the ABLH and influencing factors. The results demonstrated that the highest values of ABLH (over 1900 m) were observed in the middle parts of the study area in June, and the ABLH exhibited a significant increase over the TB throughout the study period. Abrupt changes in the ABLH were also identified in 2004, as well as in 2-, 5-, 9-, and 15-year changing cycles. The first EOF ABLH mode indicated that the middle and northeast regions are relatively high ABLH areas within the study area. Additionally, the monthly variations in ABLH show a moderately positive correlation with air temperature, while exhibiting a negative correlation with air pressure and relative humidity. [ABSTRACT FROM AUTHOR]

The Madden-Julian Oscillation (MJO) is a crucial predictability source on a sub-seasonal to seasonal (S2S) timescale. Therefore, the models participating in the WWRP/WCRP S2S prediction project focus on accurately predicting and analyzing the MJO. This study provided a detailed description of the configuration within the IAP-CAS S2S forecast system. We assessed the accuracy of the IAP-CAS model's MJO forecast using traditional RMM analysis and cluster analysis. Then, we explained the reasons behind any bias observed in the MJO forecast. Comparing the 20-year hindcast with observations, we found that the IAP-CAS ensemble mean has a skill of 24 days. However, there is still room for improvement in the ensemble spread. To examine the MJO structure in detail, we used cluster analysis to classify the MJO events during boreal winter into four types: fast-propagating, slow-propagating, standing, and jumping patterns of MJO. The model exhibits biases of overestimated amplitude and faster propagation speed in the propagating MJO events. Upon further analysis, it was found that the model forecasted a wetter background state. This leads to more intense forecasted convection and stronger coupled winds, especially in the fast MJO events. However, the horizontal moisture advection effect for eastward propagation is overestimated in IAP-CAS due to the wetter state and more substantial MJO circulations, which results in a faster MJO mode. These findings show that the IAP-CAS skilfully forecasts signals of MJO and its propagation, and they also provide valuable guidance for improving the current MJO forecast by developing the ensemble system and moisture forecast. [ABSTRACT FROM AUTHOR]

Gravity wave parameterizations contribute to uncertainties in middle atmosphere modeling. To investigate the potential for using machine learning to represent atmospheric gravity waves and the impact of implementing such schemes in a general circulation model (GCM), we train a random forest (RF) emulator on outputs from an existing complex parameterization scheme for orographic gravity wave drag (GWD). The performance of the RF emulator is then evaluated with a focus on stratospheric climatology and variability in climate simulations from the middle atmosphere resolving Beijing Climate Center Atmospheric Circulation Model. In offline tests, the predicted orographic GWD by the RF agrees remarkably well with the target GWD throughout the troposphere and the middle atmosphere. The RF emulator can reproduce the observed climatology of zonal‐mean wind and air temperature in the GCM simulation, as well as its target scheme. Compared to the target orographic GWD parameterization scheme, the RF emulator can reproduce the breakdown of the polar vortex in the Southern Hemisphere stratosphere. This study demonstrates the feasibility of using machine learning to emulate parameterized orographic GWD for modeling the stratosphere with a GCM. Plain Language Summary: Machine learning has been utilized to learn atmospheric physical parameterizations such as moist convection, cloud microphysics, radiation, nonorographic gravity waves, etc. However, it remains unknown whether it is feasible to apply machine learning algorithms to parameterize orographic gravity waves and how such schemes perform when interactively coupled in atmospheric general circulation models. Here we employ the widely used random forest algorithm to learn from an existing complex parameterization scheme for orographic gravity wave drag. Then, we implement the random forest scheme in a high‐top atmospheric general circulation model to test its performance. We find that the random forest scheme acts as well as the target parameterization scheme. This study demonstrates the potential of using machine learning to emulate parameterized orographic gravity waves for modeling the middle atmosphere. In future work, it is recommended that machine learning may be used to develop new parameterization schemes directly from observations or high‐resolution model outputs to overcome critical deficiencies in current orographic gravity wave parameterizations, helping to reduce the common biases in simulating the stratosphere with general circulation models. Key Points: Parameterized gravity wave drag from unresolved orography can be emulated with a random forestThe random forest emulator simulates vertical distributions of the zonal wind and air temperature quite well in a general circulation model (GCM)The GCM configured with the random forest scheme reproduces a reasonable breakdown of the austral polar vortex in the stratosphere [ABSTRACT FROM AUTHOR]

Sea ice is a central part of the Arctic climate system, and its changes have a significant impact on the Earth's climate. Yet, its state, especially in summer, is not fully understood and correctly predicted in dynamical forecast systems. In this study, the seasonal prediction skill of Arctic sea ice is investigated in a high‐resolution dynamical forecast system, the China Meteorological Administration Climate Prediction System (CMA‐CPSv3). A 7‐month‐long retrospective forecast is made every other month from 2001 to 2021. Employing the anomaly correlation coefficient as the metric of the prediction skill, we show that CMA‐CPSv3 can predict regional Arctic sea ice extent and sea ice thickness up to 7 lead months. The Bering Sea exhibits the highest prediction skill among the 14 Arctic subregions. CMA‐CPSv3 outperforms the anomaly persistence forecast in the Bering Sea, Sea of Okhotsk, Laptev Sea, and East Siberian Sea. The sources of the sea ice prediction skill partly come from the good performance of upper ocean temperature in CMA‐CPSv3. This holds true not only for winter sea ice in the Arctic marginal seas but also for summer sea ice in several Arctic central seas. Furthermore, CMA‐CPSv3 exhibits a strong relationship between the variability of sea ice and surface heat fluxes. This underscores the importance of enhancing the representation of air‐sea heat exchanges in dynamical forecast systems to improve the prediction skill of sea ice. Plain Language Summary: The reduction of Arctic sea ice has a significant impact on the climate and ecosystems, and accurately predicting Arctic sea ice is of broad interest. In this work, we investigate the seasonal prediction skill of sea ice in a high‐resolution climate model. Using the anomaly correlation coefficient as the skill metric, we find that the prediction skill of sea ice is good up to 7 months and varies by region and target month. Notably, the Bering Sea shows the highest prediction accuracy among the 14 Arctic subregions. Then, we explore the sources of sea ice prediction skill and find that the skill is closely related to the good performance of upper ocean temperature in the model. Furthermore, we show that the regional Arctic sea ice variability is significantly modulated by surface heat fluxes. These results suggest that improving the representation of air‐sea heat exchanges in climate models can enhance the prediction skill of sea ice. Our study contributes to an improved understanding and predicting of the Arctic sea ice variability. Key Points: The China Meteorological Administration Climate Prediction System (CMA‐CPSv3) is used for seasonal predictions of Arctic sea iceCMA‐CPSv3 has skill to predict regional Arctic sea ice up to 7 months and shows the highest skill in the Bering SeaGood performance of ocean subsurface temperature provides crucial sources of regional sea ice prediction skills [ABSTRACT FROM AUTHOR]

The surface air temperature (SAT) trend on the Tibetan Plateau (TP) was 3.45°C 100 years−1 from 1961 to 2014. The multi‐model ensemble (MME) of 33 coupled models participated in the Coupled Model Intercomparison Project phase six (CMIP6) was about 1°C 100 years−1 lower than the observation. Although MME generally shows better skill in reproducing the distribution of SAT trend over TP than most of the CMIP6 models, its performance is greatly degraded by a small group of models, about 12% on average, with large biases. In this paper, the constrained multi‐model ensemble (CMME) based on a certain observation‐based threshold is used to constrain future projections of the SAT trend over TP. Compared with the MME results, the improvements in CMME are mainly over the eastern plateau in historical simulation and are relative to the reduction of the model biases to carbon dioxide (CO2) forcing. Under the high‐emission SSP5‐8.5 scenario, SAT increases significantly over the entire TP. The constraint of CMME on the MME is mainly over the eastern plateau with a difference of 0.5°C 100 years−1, about 6% of the MME results. Under the intermediate‐emission scenario SSP2‐4.5, the effect of CMME is relatively smaller, but the corresponding spatial distribution is similar to that under the SSP5‐8.5 scenario. The CMIP6 models tend to underestimate the warming trend projections over the water source regions in the northeastern plateau and should be noticed. Key Points: The Coupled Model Intercomparison Project phase six model ensemble in surface air temperature trend reproduction on the Tibetan Plateau are greatly degraded by a small group of models with large biasesThe impact of constrained ensemble (CMME) is mainly over the eastern plateau in relative to the reduction of model biases to CO2 forcingThe CMME results indicate the underestimated warming projections in multi‐model ensemble over the northeastern plateau [ABSTRACT FROM AUTHOR]

This paper provides a systematic evaluation of the ability of 12 Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate the spatial inhomogeneity of the atmospheric carbon dioxide (CO2) concentration. The multi-model ensemble mean (MME) can reasonably simulate the increasing trend of CO2 concentration from 1850 to 2014, compared with the observation data from the Scripps CO2 Program and CMIP6 prescribed data, and improves upon the CMIP5 MME CO2 concentration (which is overestimated after 1950). The growth rate of CO2 concentration in the northern hemisphere (NH) is higher than that in the southern hemisphere (SH), with the highest growth rate in the mid-latitudes of the NH. The MME can also reasonably simulate the seasonal amplitude of CO2 concentration, which is larger in the NH than in the SH and grows in amplitude after the 1950s (especially in the NH). Although the results of the MME are reasonable, there is a large spread among ESMs, and the difference between the ESMs increases with time. The MME results show that regions with relatively large CO2 concentrations (such as northern Russia, eastern China, Southeast Asia, the eastern United States, northern South America, and southern Africa) have greater seasonal variability and also exhibit a larger inter-model spread. Compared with CMIP5, the CMIP6 MME simulates an average spatial distribution of CO2 concentration that is much closer to the site observations, but the CMIP6-inter-model spread is larger. The inter-model differences of the annual means and seasonal cycles of atmospheric CO2 concentration are both attributed to the differences in natural sources and sinks of CO2 between the simulations. [ABSTRACT FROM AUTHOR]

Using 20 quasi-biennial oscillation (QBO)-resolving models from phases 5/6 of the Coupled Model Intercomparison Project (CMIP5/6), this study examines the projected Southern Hemisphere (SH) extratropical response to the QBO. Nine of the 22 models simulate decelerated circumpolar westerlies during easterly QBO (EQBO) in the historical climate as is observed, though only ∼30% of the observed change is reproduced in the multimodel ensemble mean (MME) of these high-skill models. These high-skill models project an enhanced stratospheric QBO teleconnection for both emissions scenarios. Further, the stratospheric wind anomaly in high latitudes is projected to move to midlatitudes in future scenarios. The climatological subtropical jet is projected to strengthen, while tropical easterlies are projected to weaken. As a consequence, upward wave activity in the future appears to become more sensitive to the QBO phase. Enhanced upward propagation of waves in mid- to high latitudes during EQBO are much stronger in future scenarios than in historical simulations. The anomalous downwelling over the Antarctic, as part of the Brewer–Dobson deep branch response to EQBO, is also projected to strengthen, corresponding to increased warm anomalies. In future scenarios, the areal extent of the deep convective response to EQBO over the Maritime Continent widens and includes a sharper reduction in outgoing longwave radiation, albeit with southward expansion from Indonesia to Australia. The enhancement and spatial shift in the stratospheric polar vortex pathway and tropical convection pathway subsequently lead to changes in the tropospheric QBO signal. An annular mode–like response forms in the troposphere and near-surface in the present climate, whereas this pattern shifts farther equatorward in future projections with circulation anomalies in the tropical Indian Ocean amplifying. Significance Statement: The influence of the equatorial stratospheric quasi-biennial oscillation (QBO) has been reported to intensify in the Northern Hemisphere under climate change, even as the QBO wind magnitude weakens. It is still unknown whether the influence of the QBO on the Southern Hemisphere intensifies or weakens in a warm climate. Using simulations from multiple models, this study finds that the influence of the QBO on the Southern Hemisphere is projected to strengthen in most models even as the QBO weakens. Two QBO pathways are explored, including the stratospheric polar vortex route and the tropical convection pathway, both of which are projected to amplify in both the moderate- and high-emissions scenarios. In addition to the amplification of the QBO signals in the Southern Hemisphere extratropics, an equatorward shift of the maximum anomalies is also projected. That is, the maximum easterly anomalies in the Antarctic stratosphere during easterly QBO and the annular mode–like pattern in the troposphere or the near-surface shifts farther equatorward. [ABSTRACT FROM AUTHOR]

The probabilistic prediction skill of the weekly forecasts of extreme cold events (ECE) is illustrated and measured in the form of the Brier Skill Score (BSS) and the area under Relative Operating Characteristics (ROC) curves based on the subseasonal-to-seasonal (S2S) prediction project database. The ROC scores show that six S2S models have the good potential predictability skill required for use in ECE probabilistic forecasts, and they were more useful than climatologic probabilistic models in creating forecasts of about 3–4 weeks in length. However, the BSS results show that the actual prediction skill of six models used in ECE probabilistic forecasts are different. The ECMWF model has a good performance, and its actual probabilistic prediction skill of ECE for forecasts of about 3–4 weeks in length was higher than those of climatology, which operates close to its potential predictability. The actual probabilistic prediction skill of the NCEP model for ECE was only about 2 weeks over the extra-tropics, and no skill was recorded over the tropics given its bad reliability, especially over the tropics. BoM, JMA, and CNRM models only have a 1-week actual prediction skill over the Northern Hemisphere extra-tropics, and they have no skill over the rest of the world's land area. The CNR-ISAC model has a 1-week actual prediction skill over the extra-tropics and about 4 weeks over the tropics. There is still much room for improvement in the prediction ability of models used for ECE. MJO in tropical regions has an important influence on the probabilistic prediction skill of ECE required at middle and high latitudes. When there is an MJO in the initial conditions, the potential predictability and actual prediction skill of ECE probabilistic forecasts over North America in the 3rd week and over Europe in the 3rd–4th weeks are higher than those without MJO. [ABSTRACT FROM AUTHOR]

Using 25 quasi-biennial oscillation (QBO)-resolving models from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5/6), this study systematically explores the Southern Hemisphere (SH) extratropical response to the QBO. The observed maximum stratospheric polar vortex–weakening response to the 20-hPa easterly QBO appears in the austral spring, which is reproduced qualitatively by seven models. The Eliassen–Palm (EP) flux convergence associated with enhanced upward propagation of waves from the SH midlatitude troposphere is also reasonably simulated following easterly QBO in these seven models. The Brewer–Dobson (BD) circulation response, especially its deep branch, has a large bias among models. The anomalous upwelling of the deep branch of the BD circulation and the cold anomalies are confined to midlatitudes above 20 hPa in reanalyses, while this deep branch is simulated to expand to the Antarctic stratosphere with unrealistically cold anomalies for some models. Models with a decent stratospheric pathway tend to simulate the observed negative Southern Annular Mode (SAM)-like response in the troposphere. Further, the largest enhanced convection center is observed over the tropical Indo-Pacific Ocean and simulated by 12 models, most of which also simulate a more reasonable height anomaly pattern in the troposphere. The near-surface response to the QBO also resembles the SAM pattern with warm anomalies over eastern Antarctic, which are simulated by only five models. Seven models simulate the observed wet signals in the midlatitude southern Indo-Pacific Ocean, where an anomalous low develops. Overall, models with a better reproduction of the SH stratospheric polar vortex response exhibit a more reasonable near-surface response, though for all models the response is too weak. Significance Statement: The equatorial stratospheric quasi-biennial oscillation (QBO) has been widely reported to influence the Northern Hemisphere. This paper focuses on the possible impact of the QBO on the Southern Hemisphere (SH) using 25 models. The maximum QBO signal in the Antarctic stratosphere forms in austral spring when the climatological vortex intensity has weakened relative to austral winter. The timing and magnitude of the QBO signal is simulated with different degrees of success by the 25 models, and those with a reasonable polar vortex response tend to better simulate the near-surface response. The QBO has another pathway for the extratropical circulation change via modifying tropical convection, and models also simulate a large spread in the tropical convection change associated with the QBO. [ABSTRACT FROM AUTHOR]

Seasonal forecasting models have a harder time accurately predicting regional precipitation anomalies in East Asia than the large‐scale drivers of these precipitation anomalies. These large‐scale drivers include both sea surface temperature (SST) anomalies and conditions in the polar stratosphere. A maximum covariance analysis is used to isolate the SST and stratospheric patterns most associated with anomalous East Asian precipitation, which are employed to correct seasonal forecasts by projecting the forecasted stratospheric circulation and SST onto the observed combined modes. The nonuniform predictability of East Asian rainfall in seasonal forecast models is improved after error correction with the observed linkage between the stratosphere–tropical ocean modes and East Asian rainfall. This method should be helpful for improving rainfall forecasts in other regions as well. Plain Language Summary: Seasonal predictability of East Asian rainfall in summer is mainly associated with sea surface temperatures (SSTs) across the tropical Pacific and Indian Oceans. In contrast, East Asian rainfall in spring can be affected by both the state of tropical oceans and downward propagation of extratropical stratospheric variability. This study reports the most common combinations between the extratropical stratosphere and tropical SST in early spring, which in turn are associated with East Asian rainfall variations in spring. Rainfall in some regions like Japan and near the China‐Russia border is poorly forecasted in most seasonal models. After error correction based on the physical linkage between the most frequent stratosphere–tropical SST combinations and East Asian rainfall, an improvement especially in less predictable regions is identified in seasonal forecast models. Key Points: A novel method is proposed to improve (i.e., error correct) seasonal forecasts of East Asian spring precipitationMaximum covariance analysis is used to identify large‐scale drivers associated with rainfallThese drivers are better predicted by models than rainfall, and can be utilized to improve upon the nonuniform predictability of East Asian spring rainfall [ABSTRACT FROM AUTHOR]

Major disruptions of the winter season, high-latitude stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortex disturbances for surface predictability in subseasonal to seasonal forecast models. Based on a set of controlled, subseasonal ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models. Fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts. [ABSTRACT FROM AUTHOR]