The main advancements of the Beijing Climate Center (BCC) climate system model from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to phase 6 (CMIP6) are presented, in terms of physical parameterizations and model performance. BCC-CSM1.1 and BCC-CSM1.1m are the two models involved in CMIP5, whereas BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models configured for CMIP6. Historical simulations from 1851 to 2014 from BCC-CSM2-MR (CMIP6) and from 1851 to 2005 from BCC-CSM1.1m (CMIP5) are used for models assessment. The evaluation matrices include the following: (a) the energy budget at top-of-atmosphere; (b) surface air temperature, precipitation, and atmospheric circulation for the global and East Asia regions; (c) the sea surface temperature (SST) in the tropical Pacific; (d) sea-ice extent and thickness and Atlantic Meridional Overturning Circulation (AMOC); and (e) climate variations at different timescales, such as the global warming trend in the 20th century, the stratospheric quasi-biennial oscillation (QBO), the Madden–Julian Oscillation (MJO), and the diurnal cycle of precipitation. Compared with BCC-CSM1.1m, BCC-CSM2-MR shows significant improvements in many aspects including the tropospheric air temperature and circulation at global and regional scales in East Asia and climate variability at different timescales, such as the QBO, the MJO, the diurnal cycle of precipitation, interannual variations of SST in the equatorial Pacific, and the long-term trend of surface air temperature.

BCC-CSM2-HR is a high-resolution version of the Beijing Climate Center (BCC) Climate System Model (T266 in the atmosphere and $\mathrm{1}/\mathrm{4}$ ^∘ latitude × $\mathrm{1}/\mathrm{4}$ ^∘ longitude in the ocean). Its development is on the basis of the medium-resolution version BCC-CSM2-MR (T106 in the atmosphere and 1^∘ latitude × 1^∘ longitude in the ocean) which is the baseline for BCC participation in the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study documents the high-resolution model, highlights major improvements in the representation of atmospheric dynamical core and physical processes. BCC-CSM2-HR is evaluated for historical climate simulations from 1950 to 2014, performed under CMIP6-prescribed historical forcing, in comparison with its previous medium-resolution version BCC-CSM2-MR. Observed global warming trends of surface air temperature from 1950 to 2014 are well captured by both BCC-CSM2-MR and BCC-CSM2-HR. Present-day basic atmospheric mean states during the period from 1995 to 2014 are then evaluated at global scale, followed by an assessment on climate variabilities in the tropics including the tropical cyclones (TCs), the El Niño–Southern Oscillation (ENSO), the Madden–Julian Oscillation (MJO), and the quasi-biennial oscillation (QBO) in the stratosphere. It is shown that BCC-CSM2-HR represents the global energy balance well and can realistically reproduce the main patterns of atmospheric temperature and wind, precipitation, land surface air temperature, and sea surface temperature (SST). It also improves the spatial patterns of sea ice and associated seasonal variations in both hemispheres. The bias of the double intertropical convergence zone (ITCZ), obvious in BCC-CSM2-MR, almost disappears in BCC-CSM2-HR. TC activity in the tropics is increased with resolution enhanced. The cycle of ENSO, the eastward propagative feature and convection intensity of MJO, and the downward propagation of QBO in BCC-CSM2-HR are all in a better agreement with observations than their counterparts in BCC-CSM2-MR. Some imperfections are, however, noted in BCC-CSM2-HR, such as the excessive cloudiness in the eastern basin of the tropical Pacific with cold SST biases and the insufficient number of tropical cyclones in the North Atlantic.

The Quasi‐Biennial Oscillation (QBO) is the dominant mode of interannual variability in the tropical stratosphere, with easterly and westerly zonal wind regimes alternating over a period of about 28 months. It appears to influence the Northern Hemisphere winter stratospheric polar vortex and atmospheric circulation near the Earth's surface. However, the short observational record makes unequivocal identification of these surface connections challenging. To overcome this, we use a multicentury control simulation of a climate model with a realistic, spontaneously generated QBO to examine teleconnections with extratropical winter surface pressure patterns. Using a 30‐hPa index of the QBO, we demonstrate that the observed teleconnection with the Arctic Oscillation (AO) is likely to be real, and a teleconnection with the North Atlantic Oscillation (NAO) is probable, but not certain. Simulated QBO‐AO teleconnections are robust, but appear weaker than in observations. Despite this, inconsistency with the observational record cannot be formally demonstrated. To assess the robustness of our results, we use an alternative measure of the QBO, which selects QBO phases with westerly or easterly winds extending over a wider range of altitudes than phases selected by the single‐level index. We find increased strength and significance for both the AO and NAO responses, and better reproduction of the observed surface teleconnection patterns. Further, this QBO metric reveals that the simulated AO response is indeed likely to be weaker than observed. We conclude that the QBO can potentially provide another source of skill for Northern Hemisphere winter prediction, if its surface teleconnections can be accurately simulated.

Using reanalysis, observations, and subseasonal to seasonal (S2S) forecasts, the most recent sudden stratospheric warming (SSW) in January 2021 and its predictability are explored. Previous work has shown that SSWs can be forecasted at relatively long lead time if favorable conditions are present. However, favorable conditions were not present for this most recent SSW, which occurred under the tropical westerly quasi‐biennial oscillation (QBO) and weak convection over tropical Pacific. In mid‐December 2020 and early January 2021, the Ural ridge and East Asian trough were anomalously strong, corresponding to enhanced climatological wavenumber 2. In late December 2020 and mid‐January 2021, negative (positive) height anomalies over the North Pacific (Atlantic) enhanced the climatological wavenumber 1. Alternate wave pulses by the wavenumber 1 and 2 finally led to the SSW onset and the long lifetime of the January 2021 SSW. As the composite for QBO westerlies displays a strengthened stratospheric polar vortex, the weak vortex in January 2020 was not attributed to this tropical forcing. Therefore, the predictability for the occurrence of the January 2021 SSW is not beyond two weeks with the required hit ratio >50%. Splitting of the vortex adds further difficulty to the prediction of this event. The cold anomalies over North Eurasia initiated one to two weeks before the SSW onset were likely due to the SSW precursors, which then persisted until mid‐January 2021 as the SSW signal began to propagate downward. However, the persistent cold anomalies can only be forecasted in SSW‐hit members and SSW‐hit S2S models. The observed Arctic sea ice loss is unlikely to extend the predictability of this event in S2S models.

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.