Understanding the impacts of land surface processes on summer extreme precipitation is crucial for accurate climate predictions. This study investigated these impacts across three subregions of eastern China (North China, Central China, and South China) using the regional Climate-Weather Research and Forecasting model with two land surface parameterization schemes: the Conjunctive Surface-Subsurface Process (CSSP) scheme and the NOAH Land Surface Model (NOAH). When compared with observational and reanalysis data, both schemes were found to successfully reproduce the spatial distribution of extreme precipitation, with the CSSP scheme showing distinct advantages in simulating evapotranspiration. The influence of land surface processes on summer extreme precipitation varies among the three subregions, largely depending on soil moisture conditions. In North China, a transitional zone between arid and humid regions, soil moisture primarily influences extreme precipitation, with biases arising from difference between the lifting condensation level and the planetary boundary layer height. In Central China, where soil moisture is moderate, soil moisture and net radiation jointly influence extreme precipitation, with biases linked to the planetary boundary layer height. In South China, where soil moisture is mostly saturated during summer, net radiation dominates the variability of land surface variables, with latent heat bias leading to extreme precipitation bias. Overall, soil moisture affects extreme precipitation by altering the energy and stability of the planetary boundary layer and the lifting condensation level. These findings could inform the assessment and future improvement of models, and support the monitoring and predicting of extreme precipitation events.

Plants can alter their physiology through modifying root growth and architecture to adapt to water-limited environments. However, current climate models do not fully incorporate these physiological regulatory processes, leading to uncertainties and inaccuracies in climate projections. Here, we integrate a novel root water uptake scheme into the Beijing Climate Center Atmosphere-Vegetation Interaction Model (BCC_AVIM), enabling dynamic root water uptake through hydrotropic growth to replenish plant water storage. We conduct global offline simulations during 1981–2014 using three meteorological forcing datasets and assess the model performance in simulating soil moisture (SM), gross primary productivity (GPP), latent heat flux (LE), and total runoff (RF). Our findings reveal that the dynamic root scheme significantly enhances SM and RF simulations across diverse geographical regions, while also improving GPP and LE estimates in tropical forests such as the Amazon. Moreover, incorporating the dynamic root process into BCC_AVIM benefits longstanding challenges in land-surface/climate modelling such as the underestimation of transpiration-to-evapotranspiration ratio and rain use efficiency under water stress. However, uncertainties persist, stemming from reference data, meteorological forcing, and parameter constraints. We recommend parameter optimization using time series of root traits as a foundational step toward enhancing the robustness and portability of the dynamic root scheme. Additionally, incorporating optimal stomatal conductance and plant hydraulic schemes into future models is essential to refine the representation of the hydrological cycle between land surface and atmosphere.

The forest loss impacts the land surface temperature (Ts) through the biophysical effects. The impacts of deforestation on Ts could be better understood through the investigation of the magnitude and sign of changes in Ts varying with the fraction loss of different forest types in different seasons, which is expected to be beneficial to further model improvement. In this study, the biophysical effect on Ts due to forest loss of different tree types and changes of Ts with the forest loss were investigated based on the Coupled Model Intercomparison Project Phase 6 simulations from the BCC-CSM-MR, CESM2, CMCC-ESM2, and IPSL-CM6A-LR model. The results indicated that the deforestation at the global scale had a cooling effect (varied from -0.4 K to -0.1 K across models). The deforestation had a cooling effect in the boreal region for almost all the season and thus the whole year but controversial effect in the temperate and tropical region depending on the models, seasons, and forest types. The changes in Ts were different across tree types in different seasons (especially in March–April-May and December-January–February in boreal and temperate regions) and different models (especially CESM2 and CMCC-ESM2 in tropical regions). The sign of Ts changes in the boreal region was more geography-depended but more forest-type-depended and model-depended in the temperate and tropical region. The trend of the relationship between cooling effects and forest loss fraction generally presented an increased trend. The relationship between the warming effect due to deforestation and forest loss fraction primarily depended on tree types (increased trend for temperate and tropical broadleaf evergreen tree vs. decreased trend for tropical broadleaf deciduous tree).

Land surface temperature (Ts) is crucial for understanding surface energy balance and climate change. This study investigates the sources of Ts biases in CoLMv2024 and Noah-MP land surface model (LSM) using identical forcing data over China by decomposing the surface energy balance equation using ERA5-Land reanalysis (ERA5L), MODIS, and ground-based reference data sets. Both LSMs show significant seasonal and regional Ts biases, characterized by summer warm biases and pronounced winter cold biases. Uncertainty in energy fluxes across reference data sets limits single-dataset evaluations. Energy fluxes in both LSMs are generally within uncertainty ranges of reference data, except that Noah-MP consistently underestimates winter net radiation and sensible heat flux. Compared with ERA5L, the largest Ts bias across regions is caused by radiation forcing, that is, downward solar radiation or longwave radiation (DLW), except that the largest winter cold bias is caused by upward solar radiation (USW) in Noah-MP in Northern China. Compared with MODIS, DLW probably contributes most to Ts biases for Noah-MP in autumn and for CoLMv2024 in autumn and winter, except that USW contributes most to the winter cold bias in Noah-MP. In March and April, the largest contributing term is ground heat flux in both LSMs. Despite large discrepancies in simulated snow properties, Ts simulations in two LSMs over the snow-covered regions are similar and underestimated during the cold season. This study emphasizes the importance of radiation forcing data and the parameterization of processes other than snow physics in complex regions in cold seasons.

Gross primary production (GPP) is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively, yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and simulations. This study evaluates the performance of the second version of the Beijing Climate Center Atmosphere−Vegetation Interaction Model (BCC_AVIM2.0) in simulating GPP on multiple spatial and temporal scales in the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments. Model simulations driven by two meteorological datasets were compared with two observation-based GPP products covering 1982–2008. Spatial patterns of annual GPP show a significant latitudinal gradient in each dataset, increasing from cold (tundra) and dry (desert) biomes to warm (temperate) and humid (tropical rainforest) biomes. BCC_AVIM2.0 overestimates GPP in most parts of the globe, especially in boreal forest regions and Southeast China, while underestimating GPP in subhumid regions in eastern South America and tropical Africa. The four datasets broadly agree on the GPP seasonal cycle, but BCC_AVIM2.0 predicts an earlier beginning of spring growth and a larger amplitude of seasonal variations than those in the observations. The observation-based datasets exhibit slight interannual variability (IAV) and weak GPP linear trends, while the BCC_AVIM2.0 simulations demonstrate relatively large year-to-year variability and significant trends in the low-latitudes and temperate monsoon regions in North America and East Asia. Regarding the possible relationships between annual means of GPP and climate factors, BCC_AVIM2.0 predicts more extensive regions of the globe where the IAV of annual GPP is dominated by precipitation, especially in mid-to-high latitudes of the Northern Hemisphere and tropical Africa, while the observed GPP in the above regions is temperature- or radiation-dominant. The positive GPP biases due to earlier spring growth in boreal forest regions and negative GPP biases in off-equator tropical areas in the BCC_AVIM2.0 simulations imply that cold stress on biomes in boreal mid-to-high latitudes should be strengthened to restrain plant growth, while drought stress in low-latitude regions might be eased to enhance plant production in the future version of BCC_AVIM.

Terrains strongly affect the surface solar radiation (SSR) and energy balance, and further greatly modulate the weather and climate in rugged areas. In this study, we have developed a clear‐sky 3‐dimensional sub‐grid terrain solar radiative effect (3DSTSRE) parameterization scheme based on the mountain radiation theory with full consideration of the influences of 3‐dimensional configuration of terrains. Results show that the 3DSTSRE scheme achieves the equivalent effect of the downward SSR flux at the model grids derived from those explicitly calculated at the sub‐grids without reducing the calculating efficiency of numerical models. It performs well at model grids with different horizontal resolutions. The instant downward SSR flux calculated by the 3DSTSRE scheme at 76.8%, 84.8%, 88.7%, 91.6%, 93.0%, and 87.1% model grids with the horizontal resolution of 0.025°, 0.05°, 0.1°, 0.2°, 0.4°, and 0.8° in the areas featured by complex terrains shows relative errors within ±1.0% against those derived from the explicit calculations at sub‐grids, respectively. The normalized mean absolute errors of the instant downward SSR flux calculated by the 3DSTSRE scheme are below 1% (2%) throughout the day and the year for the model grids with resolutions ranging from 0.05° to 0.8° (of 0.025°). Although the performance of 3DSTSRE scheme decreases slightly under the conditions with much lower solar zenith angle and finer model horizontal resolution, the 3DSTSRE scheme developed in current study shows broad application prospects in various numerical models with the advantages of a solid physical foundation, high accuracy, strong portability and flexibility.

Abstract Surface solar radiation (SSR), as a primary component of heat budget between land and atmosphere, controls both water and energy exchanges. However, the sub‐grid terrain radiative effect (STRE) which exerts critical influences on SSR simulation is usually extremely simplified or even ignored in most current land surface models (LSMs) due to the heavy computational burden. In this study, we developed a physically realistic and computationally efficient three dimensional (3D) STRE scheme and implemented it into the Common Land Model (CoLM) to indicate its quantitative influences on surface energy budget, land surface temperature (LST), soil temperature, and moisture simulations over the Heihe River Basin, Tibetan Plateau. Results show that the CoLM coupled with 3D‐STRE scheme shows more realistic description of SSR and improves the simulation of soil thermal and moist features at both single‐point and regional scales. Compared to the results without 3D‐STRE, the inclusion of 3D‐STRE scheme efficiently diminishes the overestimation of SSR, which leads to the root mean square error (RMSE) of LST simulation reduced by 17.1% due to significant improvements in valley areas. Adopting 3D‐STRE scheme also improves the pattern and amplitude of temporal variability of simulated soil temperature (moisture) at 37 sites with the mean Taylor score increased by 3.6–3.7% (14.0–14.3%). These results emphasize the importance of considering the 3D‐STRE scheme in LSMs and are significantly helpful to deepen our understanding of surface heat exchanges and improve the representations of land surface processes over complex terrain.

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.

Abstract. Sub-seasonal to seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging but has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called Impact of initialized Land Surface temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P), as the first international grass-root effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land/atmosphere interactions. LS4P focuses on process understanding and predictability, hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than forty groups worldwide have participated in this effort, including 21 Earth System Models, 9 regional climate models, and 7 data groups. This paper overviews the history and objectives of LS4P, provides the first phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST in the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation and its S2S prediction. LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations which both tend to limit the soil memory; and ii) reanalysis data, that are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.

Center Climate System Model 2 3 4 Tongwen Wu, Rucong Yu, Yixiong Lu, Weihua Jie, Yongjie Fang, Jie Zhang, 5 Li Zhang, Xiaoge Xin, Laurent Li, Zaizhi Wang, Yiming Liu, Fang Zhang, 6 Fanghua Wu, Min Chu, Jianglong Li, Weiping Li, Yanwu Zhang, 7 Xueli Shi, Wenyan Zhou, Junchen Yao, Xiangwen Liu, He Zhao, Jinghui Yan, 8 Min Wei, Wei Xue, Anning Huang, Yaocun Zhang, Yu Zhang, Qi Shu 9 10 1.Beijing Climate Center, China Meteorological Administration, Beijing, China 11

本文总结了中国国家自然科学基金委重点项目"青藏高原调控区域能量过程及其影响机理"的研究进展。着重阐明了春夏季伊朗高原和青藏高原（TIP）地表热通量特征及变化原因、TIP上空独特的水汽、云宏观和微观垂直结构,以及降水和云辐射效应;在夏季两个高原地区的感热加热存在相互影响和反馈,形成观测到的加热与大气垂直环流之间的准平衡耦合系统,由此提出了TIP系统（TIPS）的概念;项目还从天文和水文的角度佐证了TIPS对亚洲夏季风的影响,揭示TIPS导致上对流层暖、下平流层冷的南亚高压的形成机理及TIPS影响北半球环流和印度洋海气相互作用的物理过程;揭示TIPS系统对南亚高压年际变化的影响,提出高原位涡强迫激发中国东部激烈天气过程的一种新机制。此外还揭示了CMIP5模式对高原表面温度模拟存在冷偏差的原因和其中的物理过程,这是大气环流与冰雪反照率的动力耦合的结果。

To improve the performance of the second generation of Beijing Climate Center Atmosphere-Vegetation Interaction Model (BCC_AVIM2.0) with a fine resolution (45 km) over lake-rich areas, the default lake scheme in BCC_AVIM2.0 is replaced by the Common Land Surface Model (CoLM)-Lake scheme with much more realistic treatments of the energy exchanges in the snow-ice-water-sediment system relative to the default lake scheme. Results show that the lake surface temperature (LST) biases produced by BCC_AVIM2.0 with the default lake scheme can be largely reduced by adopting the CoLM-Lake scheme in winter due to much more realistically simulated vertical water temperature profiles over the Great Lakes region. The spatial distributions and seasonal variations of the LST simulations can also be significantly improved by the CoLM-Lake scheme within BCC_AVIM2.0. The performances of BCC_AVIM2.0 in simulating the lake ice in winter can be largely improved by replacing the default lake scheme with the CoLM-Lake scheme. The improvements in the LST simulated by BCC_AVIM2.0 with the CoLM-Lake scheme further lead to reduced biases in the simulated ground surface temperature. The simulations of air temperature and precipitation in the coupled model are also improved by adopting the CoLM-Lake scheme over the Great Lakes region, which indicates the improvements in simulating the energy and water exchange between the atmosphere and lakes. This study highlights the importance of a more realistic lake scheme in simulating the ground surface temperature and the energy exchanges between the atmosphere and lakes.

Abstract. Main progresses of Beijing Climate Center (BCC) climate system model from the phase five of the Coupled Model Intercomparison Project (CMIP5) to its phase six (CMIP6) are presented, in terms of physical parameterizations and models performance. BCC-CSM1.1 and BCC-CSM1.1m are the two models involved in CMIP5, and 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 (a) energy budget at top of the atmosphere, (b) surface air temperature, precipitation, and atmospheric circulation for global and East Asia regions, (c) sea ice extent and thickness and Atlantic Meridional Overturning Circulation (AMOC), and (d) climate variations at different time scales such as global warming trend in the 20th century, stratospheric quasi-biennial oscillation (QBO), Madden-Julian Oscillation (MJO) and diurnal cycle of precipitation. Compared to BCC CMIP5 models, BCC CMIP6 models show significant improvements in many aspects including: tropospheric air temperature and circulation at global and regional scale in East Asia, climate variability at different time scales such as QBO, MJO, diurnal cycle of precipitation, and long-term trend of surface air temperature.

The improvements and validation of several parameterization schemes in the second version of the Beijing Climate Center Atmosphere-Vegetation Interaction Model (BCC_AVIM2.0) are introduced in this study. The main updates include a replacement of the water-only lake module by the common land model lake module (CoLM-lake) with a more realistic snow-ice-water-soil framework, a parameterization scheme for rice paddies added in the vegetation module, renewed parameterizations of snow cover fraction and snow surface albedo to accommodate the varied snow aging effect during different stages of a snow season, a revised parameterization to calculate the threshold temperature to initiate freeze (thaw) of soil water (ice) rather than being fixed at 0°C in BCC_AVIM1.0, a prognostic phenology scheme for vegetation growth instead of empirically prescribed dates for leaf onset/fall, and a renewed scheme to depict solar radiation transfer through the vegetation canopy. The above updates have been implemented in BCC_AVIM2.0 to serve as the land component of the BCC Climate System Model (BCC_CSM). Preliminary results of BCC_AVIM in the ongoing Land Surface, Snow, and Soil Moisture Model Intercomparison Project (LS3MIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) show that the overall performance of BCC_AVIM2.0 is better than that of BCC_AVIM1.0 in the simulation of surface energy budgets at the seasonal timescale. Comparing the simulations of annual global land average before and after the updates in BCC_AVIM2.0 reveals that the bias of net surface radiation is reduced from −12.0 to −11.7 W m−2 and the root mean square error (RMSE) is reduced from 20.6 to 19.0 W m−2; the bias and RMSE of latent heat flux are reduced from 2.3 to −0.1 W m−2 and from 15.4 to 14.3 W m−2, respectively; the bias of sensible heat flux is increased from 2.5 to 5.1 W m−2 but the RMSE is reduced from 18.4 to 17.0 W m−2.

地表覆盖是陆面和气候模式中的一个重要基础数据.以陆面过程模式BCC_AVIM为例,介绍模式中的地表覆盖数据变量、数据分辨率、不同类型数据的来源,重点比较分类方法差异巨大且类型众多的植被覆盖.综述比较了国际和国内常用的几套全球地表覆盖数据的来源、分类系统和分类方法以及空间分辨率,根据陆面过程模式的地表覆盖数据需求,确定不同全球土地覆盖数据在模式中的应用方法,讨论分析了全球地表覆盖产品在模式应用中存在的差距,提出不同遥感数据产品之间一致性较差的可能解决方案,探讨遥感数据产品在模式中应用的可能方式,以期更好地发挥全球地表覆盖数据产品的作用.

Variation in the location of the South Asian High (SAH) in early boreal summer is strongly influenced by elevated surface heating from the Tibetan Plateau (TP) and the Iranian Plateau (IP). Based on observational and ERA-Interim data, diagnostic analyses reveal that the interannual northwestward–southeastward (NW–SE) shift of the SAH in June is more closely correlated with the synergistic effect of concurrent surface thermal anomalies over the TP and IP than with each single surface thermal anomaly over either plateau from the preceding May. Concurrent surface thermal anomalies over these two plateaus in May are characterized by a negative correlation between sensible heat flux over most parts of the TP (TPSH) and IP (IPSH). This anomaly pattern can persist till June and influences the NW–SE shift of the SAH in June through the release of latent heat (LH) over northeastern India. When the IPSH is stronger (weaker) and the TPSH is weaker (stronger) than normal in May, an anomalous cyclone (anticyclone) appears over northern India at 850 hPa, which is accompanied by the ascent (descent) of air and anomalous convergence (divergence) of moisture flux in May and June. Therefore, the LH release over northeastern India is strengthened (weakened) and the vertical gradient of apparent heat source is decreased (increased) in the upper troposphere, which is responsible for the northwestward (southeastward) shift of the SAH in June.

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local-and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).

Water and carbon fluxes simulated by 12 Earth system models (ESMs) that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) over several recent decades were evaluated using three functional constraints that are derived from both model simulations, or four global datasets, and 736 site-year measurements. Three functional constraints are ecosystem water-use efficiency (WUE), light-use efficiency (LUE), and the partitioning of precipitation P into evapotranspiration (ET) and runoff based on the Budyko framework. Although values of these three constraints varied significantly with time scale and should be quite conservative if being averaged over multiple decades, the results showed that both WUE and LUE simulated by the ensemble mean of 12 ESMs were generally lower than the site measurements. Simulations by the ESMs were generally consistent with the broad pattern of energy-controlled ET under wet conditions and soil water-controlled ET under dry conditions, as described by the Budyko framework. However, the value of the parameter in the Budyko framework , obtained from fitting the Budyko curve to the ensemble model simulation (1.74), was larger than the best-fit value of to the observed data (1.28). Globally, the ensemble mean of multiple models, although performing better than any individual model simulations, still underestimated the observed WUE and LUE, and overestimated the ratio of ET to P, as a result of overestimation in ET and underestimation in gross primary production (GPP). The results suggest that future model development should focus on improving the algorithms of the partitioning of precipitation into ecosystem ET and runoff, and the coupling of water and carbon cycles for different land-use types.