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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]

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]

This study assesses the ability of 10 Earth System Models (ESMs) that participated in the Coupled Model Inter-comparison Project Phase 6 (CMIP6) to reproduce the present-day inhalable particles with diameters less than 2.5 micrometers (PM2.5) over Asia and discusses the uncertainty. PM2.5 accounts for more than 30% of the surface total aerosol (fine and coarse) concentration over Asia, except for central Asia. The simulated spatial distributions of PM2.5 and its components, averaged from 2005 to 2020, are consistent with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalysis. They are characterized by the high PM2.5 concentrations in eastern China and northern India where anthropogenic components such as sulfate and organic aerosol dominate, and in northwestern China where the mineral dust in PM2.5 fine particles (PM2.5DU) dominates. The present-day multi-model mean (MME) PM2.5 concentrations slightly underestimate ground-based observations in the same period of 2014–2019, although observations are affected by the limited coverage of observation sites and the urban areas. Those model biases partly come from other aerosols (such as nitrate and ammonium) not involved in our analyses, and also are contributed by large uncertainty in PM2.5 simulations on local scale among ESMs. The model uncertainties over East Asia are mainly attributed to sulfate and PM2.5DU; over South Asia, they are attributed to sulfate, organic aerosol, and PM2.5DU; over Southeast Asia, they are attributed to sea salt in PM2.5 fine particles (PM2.5SS); and over central Asia, they are attributed to PM2.5DU. They are mainly caused by the different representations of aerosols within individual ESMs including the representation of aerosol size distributions, dynamic transport, and physical and chemistry mechanisms. [ABSTRACT FROM AUTHOR]