Climate Center Research Articles

Retraction Note to: Numerical simulation of ocean sea temperature based on feature prediction and monitoring of coastal running health data

The condition of ocean underlying surface is an important factor to determine the predictability of short-term climate. Different forms of ocean boundary conditions are often used in dynamic forecasting models for forecasting objects with different characteristic scales. Many institutions are still using a forced general circulation model (GCM) based on given SST to predict climate, but there are still differences in the degree of short-term climate prediction affected by different SST frequencies. Considering this, this paper uses the BCC model of the National Climate Center_ Agcm3 which is used to develop and conduct multiple sets of numerical simulations and experiments to predict the impact of SST at different frequencies of each month, week, and day. The results show that the frequency of SST forcing field is similar to that of Asian summer monsoon. As we all know, sprint and middle- and long-distance race belong to speed race group or endurance race group. The movement speed of two types of sports depends on their physiological mechanism, the transformation speed of cerebral cortex motor center, and the performance of nervous system. Although there are many studies on the speed of short-distance running and medium- and long-distance running, there are relatively few studies on the characteristics of middle- and long-distance runners in various sports. The system uses STM32 + MTK dual processor system and uses mpu9250 nine axis inertial sensor and max30102 heart rate sensor to develop acquisition module, positioning module, low-power consumption module, motion data detection module, and heart rate software and hardware system. Finally, this paper designs the results of SST numerical simulation and prediction experiment.

Assessment of Lead-Lag and Spatial Changes in simulating different epochs of the Indian summer monsoon using RegCM4

The present study examines the efficacy of the regional climate model RegCM4 in depicting the onset, active, and break phases of the Indian summer monsoon (ISM). The model is driven by National Centers for Environmental Prediction (NCEP)/NCAR Reanalysis 1 project (NNRP1) data of 28 years (1981–2008) for each monsoon separately. High-resolution gridded rainfall analysis dataset of India Meteorological Department (IMD) and the monsoon epochal report from National Climate Center (NCC) are used for verification. Besides assessment of model performance using qualitative methods, several standard statistical techniques are used to understand the capability of the model in representing monsoonal epochs.A relationship between the SST changes over the Niño-3.4 region and the ISM over all India and the monsoon core region (MCR) is established where a strong dependency in the rainfall variation over the MCR with the SST variation is found than all India rainfall. The performance of the model is satisfactory in portraying the spatial and temporal intraseasonal variability (ISV) of monsoon over MCR and all India regions. It is found that the model performance is higher in simulating the onset and break phases (dry days) than the active phases. The limited skill of the RegCM4 model in representing the active phase is due to the model simulated low temperature and weak pressure gradient over the MCR which prevents the convection and given rise to small and weak active phases over the region. The mechanism from the pre-onset to onset of monsoon, and the monsoon advance between the onset and the monsoon ISV is also well captured by the RegCM4. Weak tropospheric temperature gradient over the Indian subcontinent during the pre-onset period causes delay onset over the Indian subcontinent and the subsequent high tropospheric temperature gradient during the mid of June causes a faster progression of the active/break phases of monsoon. At the same time, simulation of the monsoon phase by the model is heavily regulated by the changes of SST over the Niño-3.4 region. The signal of ENSO variability, forces to the model through the ICBC, modulates the capability of the regional model in identifying the interannual and ISV of monsoon over the MCR region.

The Effect of the Arctic Oscillation on the Predictability of Mid-High Latitude Circulation in December

The impact of the Arctic Oscillation (AO) on the predictability of mid-high latitude circulation in December is analysed using a full set of hindcasts generated form the Beijing Climate Center Atmospheric General Circulation Model version 2.2 (BCC_AGCM2.2). The results showed that there is a relationship between the predictability of the model on the Eurasian mid-high latitude circulation and the phase of AO, with the highest predictability in the negative AO phase and the lowest predictability in the normal AO phase. Moreover, the difference of predictability exists at different lead times. The potential sources of the high predictability in the negative AO phase in the BCC_AGCM2.2 model were further diagnosed. It was found that the differences of predictability on the Eurasian mid-high latitude circulation also exist in different Arctic sea ice anomalies, and the model performs well in reproducing the response of Arctic sea ice on the AO. The predictability is higher when sudden stratospheric warming (SSW) events occur, and strong SSW events tend to form a negative AO phase distribution in the Eurasian mid-high latitudes both in the observation and model. In addition, the model captured the blocking over the mid-high latitudes well, it may be related to the relatively long duration of the blocking. Changes in the AO will affect the blocking circulations over the mid-high latitudes, which partly explains the high predictability of the model in negative AO phases from the aspect of the internal atmospheric dynamics.

Retrieval of Outgoing Longwave Radiation from the Fengyun-3D Satellite and Its Climate Applications

The Fengyun-3D (FY-3D) satellite is a Chinese Earth observation satellite with high spectral resolution that can provide multi-spectral observations under all weather conditions. Outgoing longwave radiation (OLR) is an important parameter in the earth radiation energy balance and can reflect changes in atmospheric circulation and convective activity in response to incoming solar radiation. To apply the OLR data of the FY-3D satellite (F_OLR) to weather and climate analyses, the traditional single-channel OLR inversion algorithm for the NOAA (National Oceanic and Atmospheric Administration) satellite was used to calculate F_OLR, and the difference between F_OLR and the OLR data of the NOAA 18 satellite (N_OLR) was analyzed. A correction algorithm was proposed to correct F_OLR to match N_OLR; the spatiotemporal consistency of the corrected F_OLR and N_OLR was evaluated, and the two types of OLR data were used to analyze the onset of the South China Sea Summer Monsoon (SCSSM) and typhoon precipitation in China. The results showed that the corrected F_OLR and N_OLR were consistent in both temporal variation and spatial distribution and that the monitoring of the SCSSM and typhoon precipitation by the two types of OLR data was also in agreement, showing their equivalent quality. Finally, the N_OLR (2006–2019) and the corrected F_OLR (2020-present) were combined to form a long time series OLR dataset that was used in the Beijing Climate Center climate monitoring system in China to monitor abnormal changes in the global convective activity. This study can provide a reference method for future weather and climate applications of Chinese satellites.

Improving the Spring Air Temperature Forecast Skills of BCC_CSM1.1 (m) by Spatial Disaggregation and Bias Correction: Importance of Trend Correction

In this study, an improved method named spatial disaggregation and detrended bias correction (SDDBC) based on spatial disaggregation and bias correction (SDBC) combined with trend correction was proposed. Using data from meteorological stations over China from 1991 to 2020 and the seasonal hindcast data from the Beijing Climate Center Climate System Model (BCC_CSM1.1 (m)), the performances of the model, SDBC, and SDDBC in spring temperature forecasts were evaluated. The results showed that the observed spring temperature exhibits a significant increasing trend in most of China, but the warming trend simulated by the model was obviously smaller. SDBC performed poorly in temperature trend correction. With SDDBC, the model’s deviation in temperature trend was corrected, and consequently, the temporal correlation between the model’s simulation and the observation as well as the forecasting skill on the phase of temperature were improved, thus improving the MSSS and the ACC. From the perspective of probabilistic prediction, the relative operating characteristic skill score (ROCSS) and the Brier skill score (BSS) of the SDDBC for three categorical forecasts were higher than those of the model and SDBC. The SDDBC’s BSS increased as the effect of the increasing resolution component was greater than that of the decreasing reliability component. Therefore, it is necessary to correct the predicted temperature trend in post-processing for the output of numerical prediction models.

Examining patterns and drivers of variability in playa water status on the High Plains of western Kansas, 2016–2019

Playa wetlands are widely distributed across the High Plains of the central United States, providing a range of ecosystem services, such as groundwater recharge, surface water storage, and wetland habitat. Although playas are essential resources, few studies have examined the variability and controls on playa water storage. The purpose of this project is to determine how playa and watershed morphology, watershed land cover, and precipitation patterns affect timing and duration of water storage in playas. This project focuses on 92 playas distributed throughout a 10-county region in western Kansas.
 Playa and watershed morphology were calculated in a GIS environment and classified into quartiles based on playa and watershed surface area. Watershed tilled index (i.e., percent cropland versus grassland) was determined using 2016, 2017, 2018, and 2019 Cropland Data Layers available from the National Agricultural Statistics Service and classified as either cropland (more than 75% cropland), grassland (more than 75% grassland), or mixed. Monthly precipitation data for 2016–2019 were compiled from the Oakley 22S High Plains Regional Climate Center weather station. Playa water status for 2016–2019 was classified monthly as either standing water or dry (i.e., no visible standing water) by visually examining four-band satellite imagery with 3.7 m resolution available from Planet Explorer (www.planet.com).
 Playa water status is influenced by a combination of factors, including playa and watershed morphology, watershed land cover, and precipitation patterns. Larger playas have larger watersheds and standing water more frequently and for longer periods than smaller playas. Playas in cropland watersheds store water more frequently and for longer periods than playas in grassland watersheds, though differences are not statistically significant. Standing water within playas is positively correlated with monthly precipitation and reflects a short-term response to precipitation patterns, regardless of playa size or watershed land cover. The strongest controls on playa water status are playa area, monthly precipitation, and watershed area.
 Playas are critical resources for the High Plains, providing a range of ecosystem services that are dependent upon the playa’s ability to store water. Playa functions are under continued threat from cropland expansion, climate change, and playa and watershed modifications. To sustain playa functions in Kansas, efforts should focus on conserving larger grassland playas and reducing sediment inputs to playas in cropland watersheds.

Mitigation of the double ITCZ syndrome in BCC-CSM2-MR through improving parameterizations of boundary-layer turbulence and shallow convection

Abstract. The spurious double Intertropical Convergence Zone (ITCZ) is one of the most prominent systematic biases in coupled atmosphere–ocean general circulation models (CGCMs), and the underestimated marine stratus over eastern subtropical oceans has been recognized as a possible contributor. Rather than modifying the cloud scheme itself, this study significantly ameliorates the marine stratus simulation through improving parameterizations of boundary-layer turbulence and shallow convection in the medium-resolution Beijing Climate Center Climate System Model version 2 (BCC-CSM2-MR). The University of Washington moist turbulence scheme is implemented in BCC-CSM2-MR to better represent the stratocumulus, and a decoupling criterion is also introduced to the shallow convection scheme for improving the simulation of the stratocumulus-to-cumulus transition. Results show that the simulated precipitation in the eastern Pacific south of the Equator is largely reduced, alleviating the double ITCZ problem. The tropical precipitation asymmetry index increases from −0.024 in the original BCC-CSM2-MR to 0.147 in the revised BCC-CSM2-MR, which is much closer to the observation. The study suggests that improving parameterizations of boundary-layer turbulence and shallow convection is effective for mitigating the double ITCZ syndrome in CGCMs.

RETRACTED ARTICLE: Numerical simulation of ocean sea temperature based on feature prediction and monitoring of coastal running health data

The condition of ocean underlying surface is an important factor to determine the predictability of short-term climate. Different forms of ocean boundary conditions are often used in dynamic forecasting models for forecasting objects with different characteristic scales. Many institutions are still using a forced general circulation model (GCM) based on given SST to predict climate, but there are still differences in the degree of short-term climate prediction affected by different SST frequencies. Considering this, this paper uses the BCC model of the National Climate Center_ Agcm3 which is used to develop and conduct multiple sets of numerical simulations and experiments to predict the impact of SST at different frequencies of each month, week, and day. The results show that the frequency of SST forcing field is similar to that of Asian summer monsoon. As we all know, sprint and middle- and long-distance race belong to speed race group or endurance race group. The movement speed of two types of sports depends on their physiological mechanism, the transformation speed of cerebral cortex motor center, and the performance of nervous system. Although there are many studies on the speed of short-distance running and medium- and long-distance running, there are relatively few studies on the characteristics of middle- and long-distance runners in various sports. The system uses STM32 + MTK dual processor system and uses mpu9250 nine axis inertial sensor and max30102 heart rate sensor to develop acquisition module, positioning module, low-power consumption module, motion data detection module, and heart rate software and hardware system. Finally, this paper designs the results of SST numerical simulation and prediction experiment.

RETRACTED ARTICLE: Numerical simulation of sea surface temperature based on big data and calculation of economic effect of import trade

The Arctic Oscillation and the North Atlantic Oscillation are the dominant patterns of the atmospheric circulation except the tropics in the northern region. In winter, the Arctic Oscillation and the North Atlantic Oscillation have a great influence on the climate of most regions except the tropics, and it has an organic integration with the North Atlantic sea surface temperature. It is still used by many departments and institutions to judge and predict atmospheric circulation according to the set sea surface temperature. However, the short-term climate judgment is greatly affected by different sea surface temperatures, and the understanding of the otherness is still lacking. Therefore, it is the main way for China to predict the climate of relevant regions through the prediction experiments of several different sea surface temperatures for atmospheric circulation climate carried out by the National Climate Center and the analysis of relevant data information through the experiments. It is still the main way to strengthen the development of trade, especially the development of import and export trade, by reducing tariffs. But it is inevitable that the reduction of tariffs will also reduce the country’s government revenue. By studying the impact of tariff reduction on finance on the basis of theory, to enhance the level of fiscal revenue of each country. Import trade is the main content of international trade, and its scale and form have a significant impact on the development of a country. Research shows that we should strengthen the import of high-tech products. Improving the import structure of general products and paying attention to the import of consumer goods play a very important role in stabilizing the country’s economic growth level and improving the country’s economic income.

How is adaptive potential distributed within species ranges?

Quantitative genetic variation (QGV) represents a major component of adaptive potential and, if reduced toward range-edge populations, could prevent a species' expansion or adaptive response to rapid ecological change. It has been hypothesized that QGV will be lower at the range edge due to small populations-often the result of poor habitat quality-and potentially decreased gene flow. However, whether central populations are higher in QGV is unknown. We used a meta-analytic approach to test for a general QGV-range position relationship, including geographic and climatic distance from range centers. We identified 35 studies meeting our criteria, yielding nearly 1000 estimates of QGV (including broad-sense heritability, narrow-sense heritability, and evolvability) from 34 species. The relationship between QGV and distance from the geographic range or climatic niche center depended on the focal trait and how QGV was estimated. We found some evidence that QGV declines from geographic centers but that it increases toward niche edges; niche and geographic distances were uncorrelated. Nevertheless, few studies have compared QGV in both central and marginal regions or environments within the same species. We call for more research in this area and discuss potential research avenues related to adaptive potential in the context of global change.

K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China

The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast.

Климатические особенности зимнего сезона 2020/2021 гг. и прогностические оценки температурно-влажностного режима на лето 2021 г. по территории Северной Евразии

Climatic features of the 2020/2021 winter season and the air temperature and precipitation outlook for the summer of 2021 over Northern Eurasia / Khan V.M., Vilfand R.M., Emelina E.V., Kaverina E.S., Kulikova I.A., Sumerova K.A., Tischenko V.A. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 6-19. The main features of the Northern Hemisphere large-scale atmospheric circulation are analyzed for the past 2020/2021 winter. The accuracy of consensus forecasts of air temperature and precipitation compiled during the work of the 19th session of the North Eurasian Climate Outlook Forum (NEACOF-19) is presented, with the skill scores of consensus forecasts for Northern Eurasia. The main features of the thermal state of the ocean and large-scale atmospheric circulation for the coming summer of 2021 are considered and analyzed. A forecast of surface air temperature and precipitation anomalies for the summer of 2021 agreed with the NEACOF-20 experts is formulated. Keywords: North Eurasian Climate Outlook Forum, North Eurasian Climate Center, consensus forecast, air temperature, precipitation, large-scale atmospheric circulation, hydrodynamic models, sea surface temperature

Seasonal Associations Between Foodborne Campylobacter Infections and Ambient Temperature in US, 2010–2019

Abstract Objectives In order to effectively mitigate the effects of climate change on human health, spatiotemporal relationships between Campylobacter infections and environmental drivers have to be systematically examined to determine whether common seasonal summer peaks observed in Campylobacter infections are well aligned with peaks in raising ambient temperatures. This study aims to evaluate the seasonal relationship between monthly ambient temperature and the monthly Campylobacter infections routinely collected by the CDC Foodborne Diseases Active Surveillance Network (FoodNet) in the United States in 2010–2019. Methods We created time series of monthly Campylobacter infection rates from the FoodNet Fast platform for ten participating states from January 2010 to December 2019 (120 months). We estimated average monthly temperatures for the ten states for the study period using the National Climate and Data Center's Global Summary of the Day database. To assess the seasonal synchronization and determine the lag effect, we used serial cross-correlation analysis. To examine the associations between Campylobacter rates with ambient temperature and adjust for seasonality and trend, we applied a harmonic negative binomial mixed-effects regression model. We also estimated peak timing and amplitude for infections and temperature using the δ-method. Results Serial synchronization between monthly Campylobacter infection rates and ambient temperature was observed in all FoodNet Surveillance states except for California. A one-month delay in peak infection after the peak in temperature was detected in Colorado, Connecticut, Maryland, Minnesota, New Mexico, New York, and Oregon. After adjusting for seasonality and trend across ten states, the relative risk of infection rates increased by 5% with the increase in monthly average temperature by 5.45°C equivalent to a shift from 75th to 95th percentile (95%CI: 1.02–1.09, p < 0.002). Conclusions An increase in monthly average temperature is associated with an increased risk of Campylobacter infection after adjusting for common summer seasonal patterns. Knowledge of the relationship will potentially improve the reliability and accuracy of integrated early warning outbreak forecasts and could guide climate mitigation strategies. Funding Sources None.

Evaluating the 2019 NARO-APCC Joint Crop Forecasting Service Yield Forecasts for Northern Hemisphere Countries

AbstractForecasting global food production is of growing importance in the context of globalizing food supply chains and observed increases in the frequency of climate extremes. The National Agriculture and Food Research Organization–Asia-Pacific Economic Cooperation Climate Center (NARO-APCC) Crop Forecasting Service provides yield forecasts for global cropland on a monthly basis using seasonal temperature and precipitation forecasts as the main inputs, and 1 year of testing the operation of the service was recently completed. Here we evaluate the forecasts for the 2019 yields of major commodity crops by comparing with the reported yields and forecasts from the European Commission’s Joint Research Centre (JRC) and the U.S. Department of Agriculture (USDA). Forecasts for maize, wheat, soybean, and rice were evaluated for 20 countries located in the Northern Hemisphere, including 39 crop-producing states in the United States, for which 2019 reported yields were already publicly available. The NARO-APCC forecasts are available several months earlier than the JRC and USDA forecasts. The skill of the NARO-APCC forecasts was good in absolute terms, but the forecast errors in the NARO-APCC forecasts were almost always larger than those of the JRC and USDA forecasts. The forecast errors in the JRC and USDA forecasts decreased as the harvest approached, whereas those in the NARO-APCC forecasts were rather stable over the season, with some exceptions. Although this feature seems to be a disadvantage, it may turn into an advantage if skillful forecasts are achievable in the earlier stages of a season. We conclude by discussing relative advantages and disadvantages and potential ways to improve global yield forecasting.

Air quality services on climate time-scales for decision making: An empirical study of China

The provision of climate services for assessing and governing environmental problems such as poor air quality requires interactions between scientists and decision-makers. Air quality information services in China mainly focus on the coming days to weeks. However, users may benefit from air quality information on climate time-scales—from months to decades; hereafter air quality climate services. We focused on key decision-makers and stakeholders that are users of air quality climate services and conducted five workshops with these identified users to ascertain their priorities for air quality climate services, and the reasoning behind these priorities. We also conducted a choice-based conjoint experiment via an online survey distributed amongst regional and local Climate Centres and Environmental Monitoring Centres to assess quantitatively the decision-makers’ needs. The results from the workshops and the survey showed that the needs for air quality climate services by users in China mainly relate to seasonal forecasting of winter haze events (PM2.5 levels and/or the meteorological conditions conducive to the dispersion of the air pollution); there is also some interest in long-term projections of haze under climate change and a growing interest in ozone pollution in summer. Spatial relevance is perceived to be important to regional and city-level stakeholders who prefer information on the city-level, whilst national-wide information is important for national government agencies. A high level of reliability of forecasts was needed for uptake. The findings on the needs for air quality climate services by potential users can support researchers and policy-makers in developing the scientific capacity and providing tailored and effective air quality climate services in China.

Interdecadal Strengthening in the Independent Relationship Between the East Asian Summer Monsoon and the Indian Ocean Basin Mode around the Early 1990s

AbstractThis study conducts correlation and regression analyses of the JRA-55 reanalysis data and observational rainfall datasets from China’s National Climate Center. The analyses reveal that interdecadal enhancement in the relationship between the East Asian summer monsoon (EASM) and the Indian Ocean Basin mode (IOBM) after the early 1990s, and the diminished correlation between the EASM and the Niño-3 index. The analyses also reveal that the relationship between EASM-related rainfall/circulation with IOBM also experienced an interdecadal shift at the same time. During the first epoch (1977–1989), EASM-related rainfall was correlated significantly with the Niño-3 index, and accompanied by a Pacific–Japan-like anomaly pattern of horizontal winds. In a subsequent epoch (1994–2014), EASM-related rainfall was correlated significantly with IOBM, and accompanied by a meridional dipole pattern in the horizontal winds. After the 1990s, IOBM exerted influence on EASM through land–sea thermal contrast, and the critical land area was the region 33°–47°N, 110°–140°E. The interdecadal strengthening in the EASM–IOBM linkage around the early 1990s may be attributable to a faster rate of decay of El Niño after the 1990s.

Evaluation and Bias Correction on the Subseasonal Forecast of Summer Precipitation over Southwestern China Forecasted by the Beijing Climate Center Sub-seasonal to Seasonal Predication System

Evaluation and Bias Correction on the Subseasonal Forecast of Summer Precipitation over Southwestern China Forecasted by the Beijing Climate Center Sub-seasonal to Seasonal Predication System

BCC-CSM2-HR: a high-resolution version of the Beijing Climate Center Climate System Model

Abstract. BCC-CSM2-HR is a high-resolution version of the Beijing Climate Center (BCC) Climate System Model (T266 in the atmosphere and 1/4∘ latitude × 1/4∘ 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.

Intraseasonal variability of Indian Summer Monsoon Rainfall in CMIP6 models simulation

The reliability of the projection of Indian Summer Monsoon Rainfall (ISMR) and associated wind circulation in the simulation of the Coupled Climate Models (CCMs) is based on their ability to reproduce themselves in the control experiment. The performances of CCMs, namely BCC-CSM2-MR and BCC-ESM1 of the Beijing Climate Center, China, and MPI-ESM1-2-HR and MPI-ESM1-2-LR of Max Planck Institute (MPI) Germany, are evaluated under control experiment of CMIP6, since they had better performed in Coupled Model Intercomparison Project phase 5 (CMIP5). Under the historical experiment (control experiment) in the period from 1979 to 2014, the simulated wind circulation, relative humidity, and rainfall are evaluated on the seasonal and sub-seasonal scales during the Indian summer monsoon season (i.e. June-July-August-September). The simulated wind at pressure levels of 1000, 850, 700 and 200 hPa and relative humidity at similar pressure levels are considered for the evaluation. The India Meteorological Department (IMD) observed rainfall (0.25° x 0.25°) is taken to validate the model’s simulated rainfall. Further, to validate the zonal wind (u component), meridional wind (v component) and relative humidity, the reanalysed data (0.25° × 0.25°) at the pressure levels of 1000, 850, 700 and 200 hPa are taken from ERA5 of the European Centre for Medium-Range Weather Forecasting (ECMWF). The seasonal and monthly mean wind and relative humidity are vertically averaged from the levels of 1000 to 700 hPa, while monthly mean wind at the level of 200 hPa is considered for upper-level analysis. The models BCC-CSM2-MR, BCC-ESM1, MPI-ESM1-2-HR and MPI-ESM1-2-LR simulated wind, humidity and rainfall on the monthly and seasonal scales are validated against the respective observed/reanalysed data. The evaluations show that the CMIP6 model BCC-CSM2-MR performs well in reproducing relative humidity over the Arabian Sea and the Bay of Bengal. The model BCC-CSM2-MR and BCC-ESM1 perform well in simulating JJAS rainfall in comparison to observed rainfall of IMD.

The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model

The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model