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Accurate soil moisture (SM) prediction is critical for understanding hydrological processes. Physics-based (PB) models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes. In addition to PB models, deep learning (DL) models have been widely used in SM predictions recently. However, few pure DL models have notably high success rates due to lacking physical information. Thus, we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions. To this end, we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale (attention model). We further built an ensemble model that combined the advantages of different hybrid schemes (ensemble model). We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory (ConvLSTM) model for 1–16 days of SM predictions. The performances of the proposed hybrid models were investigated and compared with two existing hybrid models. The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models. Moreover, the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions. It is highlighted that the ensemble model outperformed the pure DL model over 79.5% of in situ stations for 16-day predictions. These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions. [ABSTRACT FROM AUTHOR]

Accurate prediction of hydrological variables (HVs) is critical for understanding hydrological processes. Deep learning (DL) models have shown excellent forecasting abilities for different HVs. However, most DL models typically predicted HVs independently, without satisfying the principle of water balance. This missed the interactions between different HVs in the hydrological system and the underlying physical rules. In this study, we developed a DL model based on multitask learning and hybrid physically constrained schemes to simultaneously forecast soil moisture, evapotranspiration, and runoff. The models were trained using ERA5-Land data, which have water budget closure. We thoroughly assessed the advantages of the multitask framework and the proposed constrained schemes. Results showed that multitask models with different loss-weighted strategies produced comparable or better performance compared to the single-task model. The multitask model with a scaling factor of 5 achieved the best among all multitask models and performed better than the single-task model over 70.5% of grids. In addition, the hybrid constrained scheme took advantage of both soft and hard constrained models, providing physically consistent predictions with better model performance. The hybrid constrained models performed the best among different constrained models in terms of both general and extreme performance. Moreover, the hybrid model was affected the least as the training data were artificially reduced, and provided better spatiotemporal extrapolation ability under different artificial prediction challenges. These findings suggest that the hybrid model provides better performance compared to previously reported constrained models when facing limited training data and extrapolation challenges. [ABSTRACT FROM AUTHOR]

The accurate prediction of surface soil moisture (SM) is crucial for understanding hydrological processes. Deep learning (DL) models such as the long short-term memory model (LSTM) provide a powerful method and have been widely used in SM prediction. However, few studies have notably high success rates due to lacking prior knowledge in forms such as causality. Here we present a new causality-structure-based LSTM model (CLSTM), which could learn time interdependency and causality information for hydrometeorological applications. We applied and compared LSTM and CLSTM methods for forecasting SM across 64 FLUXNET sites globally. The results showed that CLSTM dramatically increased the predictive performance compared with LSTM. The Nash–Sutcliffe efficiency (NSE) suggested that more than 67% of sites witnessed an improvement of SM simulation larger than 10%. It is highlighted that CLSTM had a much better generalization ability that can adapt to extreme soil conditions, such as SM response to drought and precipitation events. By incorporating causal relations, CLSTM increased predictive ability across different lead times compared to LSTM. We also highlighted the critical role of physical information in the form of causality structure to improve drought prediction. At the same time, CLSTM has the potential to improve predictions of other hydrometeorological variables. [ABSTRACT FROM AUTHOR]

Accurate spatiotemporal predictions of surface soil moisture (SM) are important for many critical applications. Machine learning models provide a powerful method for building an accurate and reliable predictive model of SM. However, the models used in recent studies have some limitations, including lack of spatial autocorrelation (SAC), vague representation of important features, and primarily focused on the one-step forecast. Thus, we proposed an attention-based convolutional long short-term memory model (AttConvLSTM) for multistep forecasting. The model includes three layers, spatial compression, axial attention, and encoder–decoder prediction, which are used for compressing spatial information, feature extraction, and multistep prediction, respectively. The model was trained using surface SM from the Soil Moisture Active Passive L4 product at 18-km spatial resolution over the United States. The results show that AttConvLSTM predicts 24 h ahead SM with mean R2 and RMSE is equal to 0.82 and 0.02, respectively. Compared with LSTM, AttConvLSTM improves the model performance over 73.6% of regions, with an improvement of 8.4% and 17.4% in R2 and RMSE, respectively. The performance of the model is mainly influenced by temporal autocorrelation (TAC). Moreover, we also highlight the importance of SAC on model performance, especially over regions with high SAC and low TAC. Our model is also competent for SM predictions from several hours to several days, which could be a useful tool for predicting all meteorological variables and forecasting extremes. [ABSTRACT FROM AUTHOR]

In Earth system modeling, the land surface is coupled with the atmosphere through surface turbulent fluxes. These fluxes are computed using mean meteorological variables between the surface and a reference height in the atmosphere. However, the dependence of flux computation on the reference height, which is usually set as the lowest level in the atmosphere in Earth system models, has not received much attention. Based on high-resolution large-eddy simulation (LES) data under unstable conditions, we find the setting of reference height is not trivial within the framework of current surface layer theory. With a reasonable prescription of aerodynamic roughness length (following the setting in LESs), reference heights near the top of the surface layer tend to provide the best estimate of surface fluxes, especially for the momentum flux. Furthermore, this conclusion for the sensible heat flux is insensitive to the ratio of roughness length for momentum versus heat. These results are robust, whether using the classical or revised surface layer theory. They provide a potential guide for setting the proper reference heights for Earth system modeling and can be further tested in the near future using observational data from land–atmosphere feedback observatories. [ABSTRACT FROM AUTHOR]

Soil thermal conductivity (STC) is an important physical parameter in modeling land surface processes. Previous studies on evaluations of STC schemes are mostly based on direct measurements of local conditions, and their recommendations cannot be used as a reference in selecting STC schemes for land modeling use. In this work, seven typical STC schemes are incorporated into the Common Land Model to evaluate their applications in land surface modeling. Statistical analyses show that the Johansen (1975) scheme and its three derivatives, Côté and Konrad (2005, https://doi.org/10.1139/t04‐106), Balland and Arp (2005, https://doi.org/10.1139/s05‐007), and Lu et al. (2007, https://doi.org/10.2136/sssaj2006.0041), are significantly superior to the other schemes with respect to both STC estimations and their applications in modeling soil temperature and the partitioning of surface available energy in the Common Land Model. The Balland and Arp (2005, https://doi.org/10.1139/s05‐007) scheme ranks at the top among the selected schemes. Uncertainty analyses based on single‐point and global simulations both show that the differences in STC estimations can induce significant differences in simulated soil temperature in arid/semiarid and seasonally frozen regions, especially at deep layers. The hydrology‐related variables are slightly affected by STC variations, but slight changes in these variables can induce notable changes in soil temperature by altering soil thermal properties. These results emphasize the important role of STC in modeling soil thermodynamics and suggest the necessity of further developing STC schemes based on land modeling applications. According to the evaluation analyses, we recommend the Balland and Arp (2005, https://doi.org/10.1139/s05‐007) scheme as a more suitable selection for use in land surface models. Plain Language Summary: Soil thermal conductivity (STC) is essential for simulating soil temperature and heat flux in land surface models. Previous studies have proposed quite a few parameterization schemes to estimate STC. Although many works have tried to determine which scheme performed best, most of them were based on direct measurements from specific experimental conditions or local soil samples, and few studies have considered the performances of STC schemes in land surface modeling. Here, we present an evaluation of seven typical STC schemes based on their land modeling applications. The results show that four of the schemes, the Johansen (1975) scheme, Côté and Konrad (2005, https://doi.org/10.1139/t04‐106) scheme, Balland and Arp (2005, https://doi.org/10.1139/s05‐007) scheme, and Lu et al. (2007, https://doi.org/10.2136/sssaj2006.0041) scheme, perform comparatively better than the other schemes and that the Balland and Arp (2005, https://doi.org/10.1139/s05‐007) scheme ranks at the top. Uncertainty analyses demonstrate that soil thermodynamics are strongly sensitive to STC estimates, especially in dry and partly frozen regions. This relationship highlights the important role of STC in modeling land surface processes. Currently, we recommend the Balland and Arp (2005, https://doi.org/10.1139/s05‐007) scheme as a good selection for use in land surface models. Key Points: Seven typical soil thermal conductivity schemes are evaluated by incorporation into the Common Land ModelThe Johansen (1975) scheme and its three derivatives perform better than the others, with the Balland and Arp (2005, https://doi.org/10.1139/s05‐007) scheme ranking at the topUncertainty analyses show strong effects of soil thermal conductivity on soil thermodynamics in arid/semiarid regions and seasonally frozen regions [ABSTRACT FROM AUTHOR]

Modeling land surface processes requires complete and reliable soil property information to understand soil hydraulic and heat dynamics and related processes, but currently, there is no data set of soil hydraulic and thermal parameters that can meet this demand for global use. In this study, we propose a fitting approach to obtain the optimal soil water retention parameters from ensemble pedotransfer functions (PTFs), which are evaluated using the global coverage National Cooperative Soil Survey Characterization Database and show better performance for global applications than our original ensemble estimations (median values of PTFs) as done in Dai et al. (2013, https://doi.org/10.1175/JHM‐D‐12‐0149.1). Soil hydraulic conductivities are still estimated as the median values of multiple PTFs, and the results are shown to perform comparably to the estimates from the existing precision‐advanced models. Soil thermal properties are estimated following the schemes identified by Dai et al. (2019a, http://arxiv.org/abs/1908.04579), which evaluated several highly recommended schemes based on their land modeling applications. Using these approaches, we develop two global high‐resolution data sets of soil hydraulic and thermal parameters based on Global Soil Dataset for Earth System Models (GSDE) and SoilGrids soil composition databases. The delivered variables include six basic soil properties, four soil hydraulic parameters in the Campbell (1974, https://doi.org/10.1097/00010694‐197406000‐00001) model, five soil hydraulic parameters in the van Genuchten (1980, https://doi.org/10.2136/sssaj1980.03615995004400050002x) model, and four soil thermal properties. The delivered data sets are available at a 30″ × 30″ geographical spatial resolution and provide four sets of vertical profiles following the resolutions of SoilGrids, Noah‐Land Surface Models (LSM), Joint UK Land Environment Simulator (JULES), and Common Land Model/Community Land Model (CoLM/CLM). The data sets can be used in both regional and global applications. Plain Language Summary: The parameters of soil hydraulic and thermal properties are essential to the modeling of land surface processes, but no data set is currently available for global use. Using some newly proposed and existing widely used schemes, we developed two global high‐resolution data sets of soil hydraulic and thermal parameters based on two commonly used soil composition databases (Global Soil Dataset for Earth System Models [GSDE] and SoilGrids). The delivered variables include multiple basic soil properties and all the required parameters for solving soil hydraulic and heat dynamics and related processes. The data sets follow the vertical profiles of SoilGrids data set and four land surface models (i.e., Noah‐Land Surface Models [LSM], Joint UK Land Environment Simulator [JULES], Common Land Model [CoLM], and Community Land Model [CLM]). The data sets can be used in both regional and global applications. Key Points: A new fitting approach is proposed to obtain the optimal soil water retention parameters from ensemble pedotransfer functionsThe adopted approaches for soil hydraulic parameter estimations are evaluated to be superior or comparative to other approachesGlobal maps of soil hydraulic and thermal parameters for use in land models are produced based on two soil composition data sets [ABSTRACT FROM AUTHOR]

Highlights • Different representations of canopy structure have large differences in land modeling. • Sensitivity analysis confirms such differences among different canopy representations. • These differences are suggested to be a large source of uncertainty for land modeling. Abstract Land surface models generally use a "mosaic" approach to represent subgrid variation in canopy structure within a given land cell in two ways: one is based on vegetation cover types (VCT) (e.g., biomes or land cover classes); the other uses plant functional types (PFT)—a further breakdown of VCTs. The two representations largely differ in their representations of vegetation composition and canopy geometries, but both are one-dimensional (1D) treatments. To isolate the effects arising from the canopy structures, idealized experiments in a simple three-dimensional (3D) canopy scenario are conducted to analyze the effects of different representations (VCT, PFT and a newly developed 3D representation) on the simulated surface fluxes. The results show that the conventional VCT and PFT representations may have large discrepancies in terms of radiation transfer, resistance and surface flux calculations, whereas the 3D representation usually has values in between or closer to one of the other representations. The discrepancies in surface flux can be attributed to differences in the radiation absorptions and resistance values of different canopy representations. The resistance values of the 3D representation are more similar to those of the VCT case. For shortwave radiation absorption at small sun zenith angles, the 3D representation shows values closer to those of the PFT case but approaches the values of the VCT case for large sun zenith angles. Sensitivity analysis identified such a large source of uncertainty resulting from different representations of canopy structure in land surface modeling, especially for the VCT and PFT representations currently being used. We recommend that proper canopy structures (at least at the crown level), as well as the corresponding physical processes, need to be incorporated in the future development of land surface models. [ABSTRACT FROM AUTHOR]

As leaves are the basic elements of plants that conduct photosynthesis and transpiration, vegetation leaf dynamics controls canopy physical and biogeochemical processes and hence largely influences the interactive exchanges of energy and materials between the land surface and the atmosphere. Given that the processes of plant leaf allocation is highly sensitive to climatological and environmental conditions, developing robust models that simulate leaf dynamics via climate variables contributes a key component to land surface models and coupled land-atmosphere models. Here we propose a new method to simulate seasonal leaf dynamics based on the idea of applying vegetation productivity as a synthesized metric to track and assess the climate suitability to plant growth over time. The method first solves two closed simultaneous equations of leaf phenology and canopy photosynthesis as modeled using the Growing Production-Day model iteratively for deriving the time series of steady-state leaf area index (LAI), and then applies the method of simple moving average to account for the time lagging of leaf allocation behind steady-state LAI. The seasonal LAI simulated using the developed method agree with field measurements from a selection of AmeriFlux sites as indicated by high coefficient of determination (R 2 = 0.801) and low root mean square error (RMSE = 0.924 m 2 /m 2 ) and with satellite-derived data (R 2 = 0.929 and RMSE = 0.650 m 2 /m 2 ) for the studied flux tower sites. Moreover, the proposed method is able to simulate seasonal LAI of deciduous broadleaf forests that match with satellite-derived LAI time series across the entire eastern United States. Comparative modeling studies suggest that the proposed method produces more accurate results than the method based on Growing Season Index in terms of correlation coefficients and error metrics. The developed method provides a complete solution to modeling seasonal leaf dynamics as well as canopy productivity solely using climate variables. [ABSTRACT FROM AUTHOR]

Given the crucial role of land surface processes in global and regional climates, there is a pressing need to test and verify the performance of land surface models via comparisons to observations. In this study, the eddy covariance measurements from 20 FLUXNET sites spanning more than 100 site-years were utilized to evaluate the performance of the Common Land Model (CoLM) over different vegetation types in various climate zones. A decomposition method was employed to separate both the observed and simulated energy fluxes, i.e., the sensible heat flux, latent heat flux, net radiation, and ground heat flux, at three timescales ranging from stepwise (30 min) to monthly. A comparison between the simulations and observations indicated that CoLM produced satisfactory simulations of all four energy fluxes, although the different indexes did not exhibit consistent results among the different fluxes. A strong agreement between the simulations and observations was found for the seasonal cycles at the 20 sites, whereas CoLM underestimated the latent heat flux at the sites with distinct dry and wet seasons, which might be associated with its weakness in simulating soil water during the dry season. CoLM cannot explicitly simulate the midday depression of leaf gas exchange, which may explain why CoLM also has a maximum diurnal bias at noon in the summer. Of the eight selected vegetation types analyzed, CoLM performs best for evergreen broadleaf forests and worst for croplands and wetlands. [ABSTRACT FROM AUTHOR]

Four representative two-stream canopy radiative transfer models were examined and intercompared using the same configuration. Based on the comparison results, two modifications were introduced to the widely used Dickinson-Sellers model and then incorporated into the Community Land Model (CLM4.5). The modified model was tested against Monte-Carlo simulations and produced significant improvements in the simulated canopy transmittance and albedo values. In direct comparison with MODIS albedo data, the modified model shows good performance over most snow/ice-free vegetated areas, especially for regions that are covered by dense canopy. The modified model shows seasonally dependent behavior mainly in the near-infrared band. Thus, the improvements are not present in all seasons. Large biases are still noticeable in sparsely vegetated areas, in particular for the snow/ice covered regions, that is possibly related to the model, the land surface input data, or even the observations themselves. Further studies focusing on the impact of the seasonal changes in leaf optical properties, the parameterizations for snow/ice covered regions and the case of sparsely vegetated areas, are recommended. [ABSTRACT FROM AUTHOR]

Precipitation-induced sensible heat (HPR) which is transferred between the land surface and rainwater can be extremely large during a heavy precipitation event. Thus, the local surface temperature can be sharply altered on an hourly to daily timescale. However, HPR is commonly neglected in current land surface models because of its small magnitude on long timescales. As a consequence, the simulated land-surface air temperature (TLSA) may be biased. In this study, we use satellite and reanalysis data sets to estimate HPR on the global scale, and we use CESM1 (with CAM4 physics and the prescribed sea surface temperature) to investigate the effect of HPR on TLSA simulations over the second half of the 20th century. Our results show that the reanalysis-estimated HPR is largest over Intertropical Convergence Zone (ITCZ) regions, with seasonal mean values of −0.22 W m−2 (−2.31 to 0.29 W m−2) in DJF and −0.28 W m−2 (−2.89 to 0.24 W m−2) in JJA. These values are consistent with the model-simulated HPR patterns. In DJF, the HPR leads to a weaker equator-pole surface heating difference and therefore, weakens northern stationary waves. The consequent changes of the heat and moisture advection result in noticeable TLSA warming effects (for example, +0.53 K over northern Eurasia and +0.46 K over central North America) and cooling effects (for example, −0.62 K over Alaska and −0.42 K over central Asia) at regional scales. These temperature changes help to reduce the TLSA biases in the model. In JJA, the TLSA changes slightly because of the weak stationary wave responses during the northern summer. [ABSTRACT FROM AUTHOR]

We developed a comprehensive, gridded Global Soil Dataset for use in Earth System Models (GSDE) and other applications. The GSDE provides soil information, such as soil particle-size distribution, organic carbon, and nutrients, and quality control information in terms of confidence level at 30″ × 30″ horizontal resolution and for eight vertical layers to a depth of 2.3 m. The GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. We used a standardized data structure and data processing procedures to harmonize the data collected from various sources. We then used a soil type linkage method (i.e., taxotransfer rules) and a polygon linkage method to derive the spatial distribution of the soil properties. To aggregate the attributes of different compositions of a mapping unit, we used three mapping approaches: the area-weighting method, the dominant soil type method, and the dominant binned soil attribute method. The data set can also be aggregated to a lower resolution. In this paper, we only show the vertical and horizontal variations of sand, silt and clay contents, bulk density, and soil organic carbon as examples of the GSDE. The GSDE estimates of global soil organic carbon stock to the depths of 2.3, 1, and 0.3 m are 1922.7, 1455.4, and 720.1 Gt, respectively. This newly developed data set provides more accurate soil information and represents a step forward to advance earth system modeling. [ABSTRACT FROM AUTHOR]

A comprehensive 30×30 arc-second resolution gridded soil characteristics data set of China has been developed for use in the land surface modeling. It includes physical and chemical attributes of soils derived from 8979 soil profiles and the Soil Map of China (1:1,000,000). We used the polygon linkage method to derive the spatial distribution of soil properties. The profile attribute database and soil map are linked under the framework of the Genetic Soil Classification of China which avoids uncertainty in taxon referencing. Quality control information (i.e., sample size, soil classification level, linkage level, search radius and texture) is included to provide 'confidence' information for the derived soil parameters. The data set includes 28 attributes for 8 vertical layers at the spatial resolution of 30×30 arc-seconds. Based on this data set, the estimated storage of soil organic carbon in the upper 1 m of soil is 72.5 Pg, total N is 6.6 Pg, total P is 4.5 Pg, total K is 169.9 Pg, alkali-hydrolysable N is 0.55 Pg, available P is 0.03 Pg, and available K is 0.61 Pg. These estimates are reasonable compared with previous studies. The distributions of soil properties are consistent with common knowledge of Chinese soil scientists and the spatial variations over large areas are well represented. The data set can be incorporated into land models to better represent the role of soils in hydrological and biogeochemical cycles in China. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop a dataset of the soil hydraulic parameters associated with two empirical soil functions (i.e., a water retention curve and hydraulic conductivity) using multiple pedotransfer functions (PTFs). The dataset is designed specifically for regional land surface modeling for China. The authors selected 5 PTFs to derive the parameters in the Clapp and Hornberger functions and the van Genuchten and Mualem functions and 10 PTFs for soil water contents at capillary pressures of 33 and 1500 kPa. The inputs into the PTFs include soil particle size distribution, bulk density, and soil organic matter. The dataset provides 12 estimated parameters and their associated statistical values. The dataset is available at a 30 × 30 arc second geographical spatial resolution and with seven vertical layers to the depth of 1.38 m. The dataset has several distinct advantages even though the accuracy is unknown for lack of in situ and regional measurements. First, this dataset utilizes the best available soil characteristics dataset for China. The Chinese soil characteristics dataset was derived by using the 1:1 000 000 Soil Map of China and 8595 representative soil profiles. Second, this dataset represents the first attempt to estimate soil hydraulic parameters using PTFs directly for continental China at a high spatial resolution. Therefore, this dataset should capture spatial heterogeneity better than existing estimates based on lookup tables according to soil texture classes. Third, the authors derived soil hydraulic parameters using multiple PTFs to allow flexibility for data users to use the soil hydraulic parameters most preferable to or suitable for their applications. [ABSTRACT FROM AUTHOR]

Surface flux estimation is essential to land surface modeling in earth system models. In practice, parameterizations of surface turbulent fluxes are almost all based on the similarity theory. That is, the grid or subgrid mean surface‐layer flow is assumed at equilibrium with the underlying earth surface, and therefore some empirical relations can be used to estimate surface fluxes. In this paper, scale‐dependent estimability of turbulent flux in the unstable surface layer is systematically investigated based on high‐resolution large‐eddy simulation data over a flat and homogeneous domain, representing a typical land surface modeling grid. It is found that turbulent flow in the unstable surface layer inherently fluctuates over a wide range of scales. This kind of fluctuation affects the steady‐state relations between mean atmospheric quantities and underlying earth surface, and hence affects the estimability of surface fluxes. Sensitivity tests show that the relative root mean square error of the estimated surface friction velocity for a subdomain generally increases as the subdomain becomes smaller. The error can be as high as 35% as the subdomain size decreases to the order of the surface layer height. To achieve an error of 10% for all cases, the subdomain size should be at least on the order of the boundary layer height. These findings imply that estimability‐based strategies may be needed for representing subgrid heterogeneity for surface flux estimation in land surface modeling. Plain Language Summary: Flux‐related land surface modeling involves much the representation of subgrid land surface. Land Cover Type (LCT) and Plant Functional Type (PFT) are the two main subgrid land surface representation approaches applied by modern land surface models. The two approaches both use some sort of subgrid "tiling" system. Tiles are defined as spatially aggregation of the area belonging to a land surface type (or category). Applying a novel analysis approach developed in this study and based on high‐resolution large‐eddy simulation data over an ideal domain, representing a typical land surface modeling grid, we find that the multiscale fluctuation of turbulent flow in the unstable surface layer makes the estimability of surface fluxes scale dependent. A subdomain with larger size or sparser distribution tends to achieve higher estimability. It is indicated that to reduce the estimation error to 10% for land surface modeling, the subgrid size should be at least on the order of the boundary layer height. This implies that aggregating small patches into a larger subgrid tile may introduce errors and traditional strategies, such as the LCT and PFT mosaic approaches, are probably not applicable to surface flux estimation. Estimability‐based strategies may need to be developed. Key Points: Multiscale turbulent fluctuations affect local equilibrium in the unstable surface layerFluctuation‐related nonequilibrium in the surface layer yields scale‐dependent surface turbulent fluxUncertainty of surface flux is quantified based on equilibrium‐based classical theory [ABSTRACT FROM AUTHOR]

Abstract: We developed a multi-layer soil particle-size distribution dataset (sand, silt and clay content), based on USDA (United States Department of Agriculture) standard for regional land and climate modelling in China. The 1:1,000,000 scale soil map of China and 8595 soil profiles from the Second National Soil Survey served as the starting point for this work. We reclassified the inconsistent soil profiles into the proper soil type of the map as much as possible because the soil classification names of the map units and profiles were not quite the same. The sand, silt and clay maps were derived using the polygon linkage method, which linked soil profiles and map polygons considering the distance between them, the sample sizes of the profiles, and soil classification information. For comparison, a soil type linkage was also generated by linking the map units and soil profiles with the same soil type. The quality of the derived soil fractions was reliable. Overall, the map polygon linkage offered better results than the soil type linkage or the Harmonized World Soil Database. The dataset, with a 1-km resolution, can be applied to land and climate modelling at a regional scale. [Copyright &y& Elsevier]

Abstract: Land surface and climate modelling requires continuous and consistent Leaf Area Index (LAI). High spatiotemporal resolution and long-time record data are more in demand nowadays and will continue to be in the future. MODIS LAI products meet these requirements to some degree. However, due to the presence of cloud and seasonal snow cover, the instrument problems and the uncertainties of retrieval algorithm, the current MODIS LAI products are spatially and temporally discontinuous and inconsistent, which limits their application in land surface and climate modelling. To improve the MODIS LAI products on a global scale, we considered the characteristics of the MODIS LAI data and made the best use of quality control (QC) information, and developed an integrated two-step method to derive the improved MODIS LAI products effectively and efficiently on a global scale. First, we used the modified temporal spatial filter (mTSF) method taking advantage of background values and QC information at each pixel to do a simple data assimilation for relatively low quality data. Then we applied the post processing-TIMESAT (A software package to analyze time-series of satellite sensor data) Savitzky–Golay (SG) filter to get the final result. We implemented the method to 10years of the MODIS Collection 5 LAI data. In comparison with the LAI reference maps and the MODIS LAI data, our results showed that the improved MODIS LAI data are closer to the LAI reference maps in magnitude and also more continuous and consistent in both time-series and spatial domains. In addition, simple statistics were used to evaluate the differences between the MODIS LAI and the improved MODIS LAI. [Copyright &y& Elsevier]

We propose a surface flux estimation formula to account for large eddy effects by keeping the boundary layer depth the only added parameter. We develop a novel way to evaluate the estimation formula by using a set of independent large eddy simulations, in which the vertical extents of largest eddies affecting surface layer are confined in a wide range of depths. It is found that these confined vertical extents hold a significant linear relationship (with slope of ∼1) with the inversely estimated vertical extents of affecting large eddies. This close relationship between the two vertical extents verifies the hypothesis that the boundary layer depth is linearly relevant for accounting for large eddy effects. Moreover, the two vertical extents are found to be comparable in value. This implies that the estimation formula we propose here is likely to be valid and applicable to unstable boundary layer in general. Plain Language Summary: Surface flux estimation is crucial to land surface modeling and numerical weather prediction and climate modeling. We recently revealed that large eddies can greatly affect surface fluxes under unstable conditions. But the large eddy effects are usually neglected in traditional surface flux estimation schemes. Here, we propose a surface flux estimation scheme to account for large eddy effects for land surface modeling. We develop a novel way to evaluate the estimation scheme by using a set of independent large eddy simulations, in which the vertical extents of largest eddies affecting surface layer are confined in a wide range of depths. It is found that these confined vertical extents hold a significant linear relationship (with slope of ∼1) with the inversely estimated vertical extents of large eddies that affect the surface layer. This close relationship between the two vertical extents verifies the hypothesis that the boundary layer depth is linearly relevant to account for large eddy effects. Moreover, the two vertical extents are found to be comparable in value. This implies that the estimation formula we propose here is likely to be valid and applicable to land surface modeling in general. Key Points: Surface momentum flux estimation formula considering large eddy effects in unstable boundary layer is further simplifiedThe simplified surface momentum flux estimation formula reflects the varying vertical extent of largest eddiesThese results are obtained by confining the vertical extents of largest eddies in a wide range of depths in large eddy simulations [ABSTRACT FROM AUTHOR]

Understanding urban vertical structures, particularly building heights, is essential for examining the intricate interaction between humans and their environment. Such datasets are indispensable for a variety of applications, including climate modeling, energy consumption analysis, and socioeconomic activities. Despite the importance of this information, previous studies have primarily focused on estimating building heights regionally at the grid scale, often resulting in datasets with limited coverage or spatial resolution. This limitation hampers comprehensive global analysis and the ability to generate actionable insights at finer scales. In this study, we developed a global building height map at the building footprint scale by leveraging Earth Observation (EO) datasets and advanced machine learning techniques. Our approach integrated multisource remote-sensing features and building morphology features to develop height estimation models using the extreme gradient boosting (XGBoost) regression method across diverse global regions. This methodology allowed us to estimate the heights of individual buildings worldwide, culminating in the creation of the three-dimensional (3D) Global Building Footprints (3D-GloBFP) dataset for the year 2020. Our evaluation results show that the height estimation models perform exceptionally well at a global scale, with R2 values ranging from 0.66 to 0.96 and root-mean-square errors (RMSEs) ranging from 1.9 to 14.6 m across 33 subregions. Comparisons with other datasets demonstrate that 3D-GloBFP closely matches the distribution and spatial pattern of reference heights. Our derived 3D global building footprint map shows a distinct spatial pattern of building heights across regions, countries, and cities, with building heights gradually decreasing from the city center to the surrounding rural areas. Furthermore, our findings indicate disparities in built-up infrastructure (i.e., building volume) across different countries and cities. China is the country with the most intensive total built-up infrastructure (5.28×1011 m3 , accounting for 23.9 % of the global total), followed by the USA (3.90×1011 m3 , accounting for 17.6 % of the global total). Shanghai has the largest volume of built-up infrastructure (2.1×1010 m3) of all representative cities. The derived building-footprint-scale height map (3D-GloBFP) reveals the significant heterogeneity in urban built-up environments, providing valuable insights for studies on urban socioeconomic dynamics and climatology. The 3D-GloBFP dataset is available at 10.5281/zenodo.11319912 (Building height of the Americas, Africa, and Oceania in 3D-GloBFP; Che et al., 2024c), 10.5281/zenodo.11397014 (Building height of Asia in 3D-GloBFP; Che et al., 2024a), and 10.5281/zenodo.11391076 (Building height of Europe in 3D-GloBFP; Che et al., 2024b). [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Topography exerts remarkable influences on the land surface solar radiation (SSR) over complex terrain, which controls both water and energy exchanges between land and atmosphere. However, the parameterization of sub‐grid topographical effects on SSR are usually extremely simplified or even ignored in most current numerical models due to the heavy computational burden. In this study, we developed a parameterization with much more real physical processes, simple parameters and high computational efficiency to consider the impact of sub‐grid topography on SSR, it tends to show widely potential application in land process and climate modeling. Taking the Heihe River Basin in China as an example, we found that the land surface model coupled with this scheme shows more realistic description of SSR and significantly improves the simulations of soil thermal and moist features. These results highlight the necessity of considering the sub‐grid topographical effects on SSR simulation in land surface modeling and are obviously helpful to advance our understanding of the surface heat exchanges and improve the representations of land surface processes over complex terrain areas. Key Points: A 3D sub‐grid terrain radiative effect (STRE) scheme is developedThe 3D STRE scheme can show more realistic description of the land surface solar radiationThe CoLM coupled with the 3D STRE scheme significantly improved the simulation of soil thermal and moist features over complex terrain areas [ABSTRACT FROM AUTHOR]

Aim: Vegetation phenology that characters the periodic life cycles of plants is indicative of the interactions between the biosphere and the atmosphere. Robust modelling of vegetation phenology metrics that correspond to canopy development events is essential to our understanding of how plants and ecosystems respond to a changing climate. Given considerable uncertainties associated with vegetation phenology modelling using numerical models, we explore the deep learning approach to predicting the timing of global vegetation phenology metrics. Location: Global. Time period: 2001–2015. Major taxa studied: Deciduous vegetation (DV), stressed deciduous vegetation (SDV), evergreen vegetation (EV). Methods: We developed a one‐dimensional convolutional neural network regression (1D‐CNNR) model with 10 hierarchical structures to model global vegetation phenology using meteorological variables as inputs. The developed deep learning model was evaluated using satellite‐derived phenology metrics (i.e., green‐up, maturity, senescence, and dormancy) and compared with the terrestrial ecosystem model Biome‐BGC (BioGeochemical Cycles). Results: Our experimental results show that the 1D‐CNNR model well captures both the spatial pattern and inter‐annual variation of satellite‐derived multiyear vegetation phenology metrics on a global scale. The median root‐mean‐square errors (RMSEs) and standard deviations between phenology metrics derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data and predicted by the 1D‐CNNR model on a global scale from 2001 to 2015 are 4.1 ± 5.9, 4.2 ± 12.1, 3.0 ± 6.8, and 3.4 ± 4.3 days for green‐up, maturation, senescence, and dormancy, respectively, for the DV type; 13.3 ± 29.6, 8.4 ± 29.1, 8.1 ± 21.3, and 9.1 ± 21.6 days for green‐up, maturation, senescence, and dormancy, respectively, for the SDV type; and 13.9 ± 17.4, 17.7 ± 34.6, 18.8 ± 42.9, and 12.1 ± 17.7 days for green‐up, maturation, senescence, and dormancy, respectively, for the EV type. Main conclusions: This research demonstrates that the 1D‐CNNR model has the potential for large‐scale modelling of vegetation phenology. Results from the deep learning model suggest that there is room to improve numerical vegetation phenology models for use in land surface models. [ABSTRACT FROM AUTHOR]

Accurate and high-resolution spatial soil information is crucial for efficient and sustainable land use, management, and conservation. Since the establishment of digital soil mapping (DSM) and the GlobalSoilMap working group, significant advances have been made in spatial soil information globally. However, accurately predicting soil variation over large and complex areas with limited samples remains a challenge, especially for China, which has diverse soil landscapes. To address this challenge, we utilized 11,209 representative multi-source legacy soil profiles (including the Second National Soil Survey of China, World Soil Information Service, First National Soil Survey of China, and regional databases) and high-resolution soil-forming environment characterization. Using advanced Quantile Regression Forest algorithms and a high-performance parallel computing strategy, we developed comprehensive maps of 23 soil physical, chemical and fertility properties at six standard depth layers from 0 to 2 meters in China with a 90 m spatial resolution (China dataset of soil properties for land surface modeling version 2, CSDLv2). Data-splitting and independent samples validation strategies were employed to evaluate the accuracy of the predicted maps quality. The results showed that the predicted maps were significantly more accurate and detailed compared to traditional soil type linkage methods (i.e., CSDLv1, the first version of the dataset), SoilGrids 2.0, and HWSD 2.0 products, effectively representing the spatial variation of soil properties across China. The prediction accuracy of most soil properties at the 0–5 cm depth interval ranged from good to moderate, with Model Efficiency Coefficients for most soil properties ranging from 0.75 to 0.32 during data-splitting validation and from 0.88 to 0.25 during independent sample validation. The wide range between the 5 % lower and 95 % upper prediction limits may indicate substantial room for improvement in current predictions. The relative importance of environmental covariates in predictions varied with soil properties and depth, indicating the complexity of interactions among multiple factors in the soil formation processes. As the soil profiles used in this study mainly originate from the Second National Soil Survey of China during 1970s and 1980s, they could provide new perspectives of soil changes together with existing maps based on 2010s soil profiles. The findings make important contributions to the GlobalSoilMap project and can also be used for regional Earth system modeling and land surface modeling to better represent the role of soil in hydrological and biogeochemical cycles in China. This dataset is freely available and can be accessed at https://doi.org/10.11888/Terre.tpdc.301235 (Shi et al, 2024). [ABSTRACT FROM AUTHOR]

Understanding urban vertical structures, particularly building heights, is essential for examining the intricate interaction between humans and their environment. Such datasets are indispensable for a variety of applications, including climate modeling, energy consumption analysis, and socioeconomic activities. Despite the importance of this information, previous studies have primarily focused on estimating building heights regionally on a grid scale, often resulting in datasets with limited coverage or spatial resolution. This limitation hampers comprehensive global analyses and the ability to generate actionable insights on finer scales. In this study, we developed a global building height map (3D-GloBFP) at a building footprint scale by leveraging Earth Observation (EO) datasets and advanced machine learning techniques. Our approach integrated multisource remote sensing features and building morphology features to develop height estimation models using the eXtreme Gradient Boosting (XGBoost) regression method across diverse global regions. This methodology allowed us to estimate the heights of individual buildings worldwide, culminating in the creation of the first global three-dimensional (3-D) building footprints (3D-GloBFP). Our evaluation results show that the height estimation models perform exceptionally well on a worldwide scale, with R 2 ranging from 0.66 to 0.96 and root mean square errors (RMSEs) ranging from 1.9 m to 14.6 m across 33 subregions. Comparisons with other datasets demonstrate that our 3D-GloBFP closely matches the distribution and spatial pattern of reference heights. Our derived 3-D global building footprint map shows a distinct spatial pattern of building heights across regions, countries, and cities, with building heights gradually decreasing from the city center to the surrounding rural areas. Furthermore, our findings indicate the disparities in built-up infrastructure (i.e., building volume) across different countries and cities. China is the country with the most intensive total built-up infrastructure (5.28×1011 m3, accounting for 23.9 % of the global total), followed by the United States (3.90×1011 m3, accounting for 17.6 % of the global total). Shanghai has the largest volume of built-up infrastructure (2.1×1010 m3) of all representative cities. The derived building-footprint scale height map (3D-GloBFP) reveals the significant heterogeneity of urban built-up environments, providing valuable insights for studies in urban socioeconomic dynamics and climatology. The 3D-GloBFP dataset is available at https://doi.org/10.5281/zenodo.11319913 (Building height of the Americas, Africa, and Oceania in 3D-GloBFP) (Che et al., 2024a), https://doi.org/10.5281/zenodo.11397015 (Building height of Asia in 3D-GloBFP) (Che et al., 2024b), and https://doi.org/10.5281/zenodo.11391077 (Building height of Europe in 3D-GloBFP) (Che et al., 2024c). [ABSTRACT FROM AUTHOR]

The root water uptake (RWU) rate of plants is influenced by various environmental factors. Low soil temperature stress can reduce RWU rate by inhibiting the growth of plant roots and increasing the hydraulic resistance of water transport among the soil‐plant‐atmosphere continuum. Given that low soil temperature stress is not accounted for in current land surface models (LSMs); in this study, we introduce three functions to represent low soil temperature stress, and modify the RWU scheme of the Common Land Model to quantify the role of low soil temperature stress on water and energy exchange between land and atmosphere. The simulated water and energy fluxes are evaluated using both in situ and global observational data sets. The results from in situ simulations show that ignoring effects of low soil temperature stress, latent, and sensible heat fluxes in spring are overestimated and underestimated, respectively, with the root mean square error up to 40 W/m2. By incorporating the low soil temperature stress functions into the RWU scheme, nearly 40% of the simulated errors are reduced. The global simulated results also highlight the importance of accounting for low soil temperature stress on increasing the accuracy of the modeled latent heat flux over high latitude areas. While uncertainties from related physical processes and parameters warrant further investigations, our results indicate that consideration of low soil temperature stress significantly affects water and energy transport from land to atmosphere by restricting RWU rate, emphasizing the need to integrate it in LSMs to increase the model reliability, especially over cold regions. Plain Language Summary: Plants obtain water from the soil through their roots, but the process of obtaining water is affected by a variety of factors. The low temperature in the soil is one of the important influencing factors, which usually reduces the rate of water absorption by plant roots. However, this influence factor is not considered in current land surface process model. Here, we propose three empirical functions that can represent the effects of low soil temperature, incorporate them into the Common Land Model (CoLM), and validate the impact of these functions in the model by using the local and global observation data. The results of numerical experiments show that considering the effect of low soil temperature on root water uptake in CoLM can improve the model performance on simulating water and energy exchange between land and atmosphere over many areas. Key Points: Three functions to characterize the low soil temperature stress were introduced in the Common Land Model (CoLM)By considering the low soil temperature stress in the CoLM, the bias in energy flux simulation is significantly decreasedThe low soil temperature stress functions were evaluated in both site and global scale [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) should have reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are considered the benchmarking data for LSMs. However, currently available flux tower datasets often require multiple aspects of processing to ensure data quality before application to LSMs. More importantly, these datasets lack site-observed attribute data, limiting their use as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, external disturbances, and energy balance closure (EBC), leading to 90 high-quality sites. For these sites, we collected vegetation, soil, topography information, and wind speed measurement height from literature, regional networks, and Biological, Ancillary, Disturbance, and Metadata (BADM) files. Then we obtained the final flux tower attribute dataset by global data product complement and plant functional types (PFTs) classification. This dataset is provided in NetCDF format complete with necessary descriptions and reference sources. Model simulations revealed substantial disparities in output between the attribute data observed at the site and the defaults of the model, underscoring the critical role of site-observed attribute data and increasing the emphasis on flux tower attribute data in the LSM community. The dataset addresses the lack of site attribute data to some extent, reduces uncertainty in LSMs data source, and aids in diagnosing parameter as well as process deficiencies. The dataset is available at https://doi.org/10.5281/zenodo.10939725 (Shi et al., 2024). [ABSTRACT FROM AUTHOR]

This study develops an unstructured mesh generation tool for land surface modeling using a multi‐scale hexagon discrete mesh. The tool can automatically determine the required mesh resolution for different regions based on multi‐objective criteria such as elevation, slope, land cover, and land use. The refined unstructured meshes demonstrate significant enhancement in the representation of spatial heterogeneity. The tool is coupled with the Common Land Model (CoLM) to enable land surface simulations using unstructured meshes. Evaluations focused on runoff, river discharge, and inundation indicate improved model performance compared to traditional structured mesh‐based CoLM simulations under the same computational cost constraints. This tool provides new capabilities for more efficiently capturing localized land surface processes and extreme events. Plain Language Summary: The traditional square mesh cells used in land surface models (LSMs) have limitations in capturing the hydrological processes in regions with spatial solid heterogeneity, for example, mountains and forest‐grass transition zones. This study develops a new tool to generate unstructured mesh with flexible cell shapes and sizes. The tool can automatically adjust the mesh resolution based on the various land surface features. Thus, it can represent better spatial differences in elevation, vegetation, soil, etc. For example, it can use finer meshes for mountains and coarser meshes for desert areas. The new unstructured meshes are coupled with an existing LSM called Common Land Model. Model testing shows improved runoff, river discharge, and flooding simulation compared to traditional square mesh models under the same computation cost. Overall, the new unstructured mesh modeling approach enables more efficient and accurate simulation of localized land processes and extreme events, for example, droughts and floods. It has significance for disaster prevention and environmental studies. Key Points: An unstructured mesh generation tool for land surface models can automatically refine resolution based on multi‐objective landscape criteriaCoupling refined unstructured meshes with the Common Land Model (CoLM) model improves local hydrological simulation compared to traditional mesh‐based CoLMThe tool enables more efficient and accurate modeling of localized land surface processes and extreme events such as droughts and floods [ABSTRACT FROM AUTHOR]

Using an objective feature-tracking algorithm and the fifth major global reanalysis produced by ECMWF data (ERA5), the seasonal behaviors of cyclonic transient eddies (cyclones) at different levels around the Tibetan Plateau (TP) were examined to understand the effects of the TP on cyclones. Results show that the TP tends to change the moving directions of the remote cyclones when they are close to the TP, with only 2% of the 250-hPa eastward-moving cyclones directly passing over the TP. The sudden reductions of their moving speeds and relative vorticity intensities around the TP suggest a suppression effect of the plateau. Over 70% of these cyclones perish over the TP regardless of the altitude. This percentage decreases to around 65% during summertime, exhibiting a weaker summer suppression effect. On the other hand, the TP has a stimulation effect on local cyclones through its dynamic forcing in winter, thermodynamic forcing in summer, and both forcings in the transitional seasons. The numbers of locally generated cyclones, especially at 500 hPa, just above the TP, are significantly larger than those of the remote cyclones during all seasons. Although about one-half of the local cyclones dissipate over the TP, the cyclones moving off the plateau significantly outnumber the moving-in cyclones, with the differences ranging from 0 to 6 cyclones per month. Only the 250-hPa wintertime moving-off cyclones are fewer than the cyclones entering the TP, which may be caused by the weaker stimulation effect and stronger suppression effect of the TP on the wintertime upper-level cyclones. Significance Statement: Cyclonic transient eddies (cyclones), steered by westerly jet streams, can influence climate and induce extreme weather processes under certain conditions. The Tibetan Plateau (TP), the highest and largest obstacle embedded in the westerly jet streams, suppresses remote cyclones entering the TP region, destroying over 70% of these cyclones. However, because of the excitation effect of the TP on local cyclones, the numbers of cyclones moving off the TP are still larger than or equal to those of the moving-in cyclones, except at the upper levels in winter. This feature suggests that the TP cannot significantly decrease the total cyclone numbers in most cases, but it indeed weakens the mean intensity and moving speed of the cyclones. [ABSTRACT FROM AUTHOR]

This study examines the effects of modeled planetary boundary layer (PBL) mixing on the simulated temperature diurnal cycle climatology over land in 20 CMIP5 models with AMIP simulations. When compared with observations, the magnitude of diurnal temperature range (DTR) is systematically underestimated over almost all land areas due to a widespread warm bias of daily minimum temperature (T) and mostly a cold bias of daily maximum temperature (T). Analyses of the CMIP5 multi-model ensemble means suggest that the biases of the simulated PBL mixing could very likely contribute to the temperature biases. For the regions with the cold bias in T, the daytime PBL mixing is generally underestimated. The consequent more dry air entrainment from the free atmosphere could help maintain the surface humidity gradient, and thus produce more surface evaporation and potentially lower the T. The opposite situation holds true for the regions with the warm bias of T. This mechanism could be particularly applicable to the regions with moderate and wet climate conditions where surface evaporation depends more on the surface humidity gradient, but less on the available soil moisture. For the widespread warm bias of T, the widely-recognized overestimated PBL mixing at nighttime should play a dominant role by transferring more heat from the atmosphere to the near-surface to warm the T. Further analyses using the high resolution CFMIP2 output also support the CMIP5 results about the connections of the biases between the simulated turbulent mixing and the temperature diurnal cycle. The large inter-model variations of the simulated temperature diurnal cycle primarily appear over the arid and semi-arid regions and boreal arctic regions where the model differences in the PBL turbulence mixing could make equally significant contributions to the inter-model variations of DTR, T and T compared to the model differences in surface radiative processes. These results highlight the importance and need for accurate descriptions of the PBL processes with respect to the turbulent mixing in order to improve the temperature diurnal cycle simulations in climate models. [ABSTRACT FROM AUTHOR]

Reservoirs are an essential part of modern society, serving a variety of critical functions while exerting an uncertain and debatable impact on the surrounding climate and environment. The precipitation‐temperature coupling relationship and the associated compound extreme events are critical climatic factors for humans and ecosystems, but how reservoirs would affect these factors is unknown. Here, we found that sites near reservoirs had higher sensitivity of precipitation extremes to temperature increases, as well as an increased frequency of compound precipitation‐temperature extreme events, based on a global‐scale analysis of reservoir and meteorological observations and a paired comparison approach. Additionally, these reservoir climate effects are closely correlated with reservoir size and are more pronounced in warm‐dry climates. It highlights the potential climate risks associated with reservoirs which have important implications for strengthening a community's resilience in the face of these challenges. Plain Language Summary: The interactive effects of reservoirs on changes in meteorological variables have a great impact on the local community, including water security, energy production, and agriculture. Previous studies have focused on changes in individual variables such as precipitation or temperature, which however are interconnected; these individual variables do not fully represent the many changes that can occur as a result of reservoir construction. This study conducted a global scale analysis based on observations, for the first time, quantifying the changes in the precipitation‐temperature coupling features caused by reservoirs. The results show significant differences between sites near and far from reservoirs in the sensitivity of precipitation extremes to temperature increases, as well as the frequency of compound hot‐wet and hot‐dry events. The effects of large reservoirs in warm and dry regions were most pronounced, leading to increased compound precipitation‐temperature extreme events. Given that the construction of additional dams is regarded as one of the best available options for meeting future increases in water and energy demands, these findings aid in anticipating and mitigating any adverse effects of reservoirs on local climate and have important implications for regional climate prediction under the influence of human activities. Key Points: This study presents the first global‐scale analysis of changes in precipitation‐temperature coupling relationships caused by reservoirsReservoirs intensify the sensitivity of precipitation extremes to temperature and frequency of compound precipitation‐temperature extremesReservoirs have the most pronounced climate impacts in warm and arid regions [ABSTRACT FROM AUTHOR]

Satellite-based leaf area index (LAI) products, such as the MODIS LAI, play an essential role in land surface and climate modeling research, from regional to global scales. However, data gaps and high-level noise can exist, thus limiting their applications to a broader scope. Our previous work has reprocessed the MODIS LAI Collection 5 (C5) product, and the reprocessed data have been widely used these years. In this study, the MODIS C6.1 LAI data were reprocessed to broaden its application as a successor. We updated the integrated two-step method that is used for MODIS C5 LAI and implemented it into the MODIS C6.1 LAI product. Comprehensive evaluations for the original and reprocessed products were conducted. The results showed that the reprocessed LAI data had better performance in validation against reference maps. In addition, the site scale time series of reprocessed data was much smoother and more consistent with adjacent values. The global scale comparison showed that, though the MODIS C6.1 LAI does have improvements in ground validation with LAI reference maps, its spatial continuity, temporal continuity, and consistency showed little improvement when compared to C5. In contrast, the reprocessed data were more spatiotemporally continuous and consistent. Based on this evaluation, some suggestions for using various MODIS LAI products were given. This study assessed the quality of these different versions of MODIS LAI products and demonstrated the improvement of the reprocessed C6.1 data, which we recommended for use as a substitute for the reprocessed C5 data in land surface and climate modeling. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) serve as vital tools for comprehensively simulating the intricate interplay of physical, chemical, and biological processes across the Earth system's diverse components. Here, we provide a brief overview of the historical development of ESMs and highlight key challenges posed by the intricate feedback mechanisms in the cryosphere, the nonlinear and long-term effects of the lithosphere, and the growing impacts of human activities for modeling Earth system. We then focus on the current opportunities in Earth system modeling, driven by the growing capacity for data-driven approaches such as machine learning (ML) and Artificial Intelligence (AI). The next generation of ESMs should embrace dynamic frameworks that enable more precise representations of physical processes across a range of spatiotemporal scales. Multi-resolution models are pivotal in bridging the gap between global and regional scales, fostering a deeper understanding of local and remote influences. Data-driven methodologies including ML/AI offer promising avenues for advancing ESMs by harnessing a wide array of data sources and surmounting limitations inherent in traditional parameterization techniques. However, the integration of ML/AI into ESMs presents its own set of challenges, including the identification of suitable data sources, the seamless incorporation of ML/AI algorithms into existing modeling infrastructures, and the resolution of issues related to model interpretability and robustness. A harmonious amalgamation of physics-based and data-driven methodologies have the potential to produce ESMs that achieve greater precision and computational efficiency, better capturing the intricate dynamics of Earth system processes. Although ESMs have made substantial progress in simulating the complex dynamics of Earth system's subsystems, there is still considerable work to be done. Prospects in the development of ESMs entail a deepened comprehension of pivotal subsystems, including the anthroposphere, lithosphere, and cryosphere. Adopting innovative technologies and methodologies, such as ML/AI and multi-resolution modeling, holds immense potential to substantially enhance our capability to anticipate and mitigate the consequences of human activities on the Earth system. [ABSTRACT FROM AUTHOR]

Atmospheric circulations bring moisture from above the ocean to the high mountains of the western Tibet Plateau (TP), thus wind variability is of great importance to the water cycle centered on the western TP. This study therefore examines the leading modes of the wind fields over the western TP. We use the multivariate empirical orthogonal function (MV-EOF) analysis method to detect the dominant wind patterns above the western TP. This method extracts the leading modes of combined meridional and zonal wind variability at 200-hPa in the region of 22° N–50° N, 50° E–92° E. We find the first leading mode of the combined zonal and meridional wind field in the annual mean and in most seasons (spring, summer and autumn) over the western TP shows high similarity to the Western Tibetan Vortex (WTV), a large-scale atmospheric vortex-like pattern recently recognized over the western TP. In winter, the WTV, however, is closer to the second leading mode. We estimate the sensitivity of our results by changing the domain of the MV-EOF analysis region surrounding the western. We find the influence of the WTV is less sensitive to analysis location around the western TP. In short, the WTV generally represents the first leading mode of the wind field in most seasons over the western TP. This study augments our knowledge on wind variability over the western TP. [ABSTRACT FROM AUTHOR]

High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, land cover type, topography and soil properties as predictors. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000–2020. Using in situ soil moisture as the benchmark, two independent experiments were conducted to evaluate the estimation accuracy of SMCI1.0: year-to-year (ubRMSE ranges from 0.041 to 0.052 and R ranges from 0.883 to 0.919) and station-to-station experiments (ubRMSE ranges from 0.045 to 0.051 and R ranges from 0.866 to 0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4, and SoMo.ml. However, the high errors of soil moisture are often located in the North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to study the spatial–temporal patterns. As SMCI1.0 is based on in situ data, it can be a useful complement to existing model-based and satellite-based soil moisture datasets for various hydrological, meteorological, and ecological analyses and models. The DOI link for the dataset is http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022). [ABSTRACT FROM AUTHOR]

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal resolution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km×1 km. The approach comprises two steps: (1) fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; (2) apply a simple kriging procedure to the residual for trend surface correction. The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets. [ABSTRACT FROM AUTHOR]

• Utilizing variables such as Soil Water Content, Slope Terrain, Surface Slope Difference, Soil Carbon, Surface Temperature, and Digital Elevation Model as clustering criteria significantly enhances global soil moisture prediction accuracy, surpassing LSTM-based models. • Our sampling strategy improves various deep learning models, including LSTM, Encoder-Decoder LSTM (EDLSTM), and Attention LSTM (AttLSTM). • Significant performance enhancements are observed in challenging environments, such as arid, cold, and polar regions, with R2 improvements of up to 50 %. Understanding and predicting global soil moisture (SM) is crucial for water resource management and agricultural production. While deep learning methods (DL) have shown strong performance in SM prediction, imbalances in training samples with different characteristics pose a significant challenge. We propose that improving the diversity and balance of batch training samples during gradient descent can help address this issue. To test this hypothesis, we developed a Cluster-Averaged Sampling (CAS) strategy utilizing unsupervised learning techniques. This approach involves training the model with evenly sampled data from different clusters, ensuring both sample diversity and numerical consistency within each cluster. This approach prevents the model from overemphasizing specific sample characteristics, leading to more balanced feature learning. Experiments using the LandBench1.0 dataset with five different seeds for 1-day lead-time global predictions reveal that CAS outperforms several Long Short-Term Memory (LSTM)-based models that do not employ this strategy. The median Coefficient of Determination (R2) improved by 2.36 % to 4.31 %, while Kling-Gupta Efficiency (KGE) improved by 1.95 % to 3.16 %. In high-latitude areas, R2 improvements exceeded 40 % in specific regions. To further validate CAS under realistic conditions, we tested it using the Soil Moisture Active and Passive Level 3 (SMAP-L3) satellite data for 1 to 3-day lead-time global predictions, confirming its efficacy. The study substantiates the CAS strategy and introduces a novel training method for enhancing the generalization of DL models. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems are an important part of Earth systems, and they are undergoing remarkable changes in response to global warming. This study investigates the response of the terrestrial vegetation distribution and carbon fluxes to global warming by using the new dynamic global vegetation model in the second version of the Chinese Academy of Sciences (CAS) Earth System Model (CAS-ESM2). We conducted two sets of simulations, a present-day simulation and a future simulation, which were forced by the present-day climate during 1981–2000 and the future climate during 2081–2100, respectively, as derived from RCP8.5 outputs in CMIP5. CO2 concentration is kept constant in all simulations to isolate CO2-fertilization effects. The results show an overall increase in vegetation coverage in response to global warming, which is the net result of the greening in the mid-high latitudes and the browning in the tropics. The results also show an enhancement in carbon fluxes in response to global warming, including gross primary productivity, net primary productivity, and autotrophic respiration. We found that the changes in vegetation coverage were significantly correlated with changes in surface air temperature, reflecting the dominant role of temperature, while the changes in carbon fluxes were caused by the combined effects of leaf area index, temperature, and precipitation. This study applies the CAS-ESM2 to investigate the response of terrestrial ecosystems to climate warming. Even though the interpretation of the results is limited by isolating CO2-fertilization effects, this application is still beneficial for adding to our understanding of vegetation processes and to further improve upon model parameterizations. [ABSTRACT FROM AUTHOR]

The process of solar radiative transfer at the land surface is important to energy, water, and carbon balance, especially for vegetated areas. Currently the most commonly used two-stream model considers the plant functional types (PFTs) within a grid to be independent of each other and their leaves to be horizontally homogeneous. This assumption is unrealistic in most cases. To consider canopy three-dimensional (3D) structural effects, a new framework of 3D canopy radiative transfer model was developed and validated by numerical simulations and shows a good agreement. A comparison with the two-stream model in the offline Community Land Model (CLM4.0) shows that an increase of canopy absorption mainly happens with sparse vegetation or with multilayer canopies with a large sun zenith angle θsun and is due to increases of the ground and sky shadows and of the optical pathlength because of the shadow overlapping between bushes and canopy layers. A decrease of canopy absorption occurs in densely vegetated areas with small θsun. For a one-layer canopy, these decreases are due to crown shape effects that enhance the transmission through the canopy edge. For a multilayer canopy, aside from these shape effects, transmission is also increased by the decreased ground shadow due to the shadow overlapping between layers. Ground absorption usually changes with opposite sign as that of the canopy absorption. Somewhat lower albedos are found over most vegetated areas throughout the year. The 3D model also affects the calculation of the fraction of sunlit leaves and their corresponding absorption. [ABSTRACT FROM AUTHOR]

Abstract: Most land surface models (LSMs) used in climate models do not perform well in modeling the permafrost processes. Due to the complex permafrost distribution characteristics and landscapes of the Qinghai–Tibet Plateau (QTP), the LSMs simulations over QTP are even worse. In this study, we revised the permafrost scheme in the original Common Land Model (CoLM) to improve its capability of simulating permafrost processes. We adopted a new frozen soil parameterization scheme, in which maximum unfrozen water content is defined as a function of soil matric potential. In addition, we extended the model''s bottom to a depth below that without annual variations in temperature and replaced the zero-flux lower boundary condition with a constant geothermal heat flux based on literature and temperature gradient measurements in a 34.5-m-deep borehole. What''s more, we revised the original snow cover fraction parameterization scheme of CoLM according to the special snow cover distribution characteristics over QTP. We calibrated and validated the modified model against observations from 2005 to 2008. The results indicate that the modified model produced more reasonable simulations of radiation balance components and significantly improved the simulation of soil liquid water content. It also shows an improved capability of reproducing soil temperatures from the top to the bottom of soil layers. The modified CoLM provides a useful tool for understanding and predicting the fate of permafrost in QTP under a warming climate. [Copyright &y& Elsevier]

urate simulations of terrestrial carbon assimilation and canopy transpiration are needed for both climate modeling and vegetation dynamics. Coupled stomatal conductance and carbon assimilation ( A − g) models have been widely used as part of land surface parameterizations in climate models to describe the biogeophysical and biogeochemical roles of terrestrial vegetation. Differences in various A − g schemes produce substantial differences in the estimation of carbon assimilation and canopy transpiration, as well as in other land-atmosphere fluxes. The terrestrial carbon assimilation and canopy transpiration simulated by two different representative A - g schemes, a simple A- g scheme adopted from the treatments of the NCAR model (Scheme I) and a two-big-leaf A - g scheme newly developed by Dai et al. (J Clim 17:2281-2299, ) (Scheme II), are compared via some sensitivity experiments to investigate impacts of different A − g schemes on the simulations. Major differences are found in the estimate of canopy carbon assimilation rate, canopy conductance and canopy transpiration between the two schemes, primarily due to differences in (a) functional forms used to estimate parameters for carbon assimilation sub-models, (b) co-limitation methods used to estimate carbon assimilation rate from the three limiting rates, and (c) leaf-to-canopy scaling schemes. On the whole, the differences in the scaling approach are the largest contributor to the simulation discrepancies, but the different methods of co-limitation of assimilation rate also impact the results. Except for a few biomes, the residual effects caused by the different parameter estimations in assimilation sub-models are relatively small. It is also noted that the two-leaf temperature scheme produces distinctly different sunlit and shaded leaf temperatures but has negligible impacts on the simulation of the carbon assimilation. [ABSTRACT FROM AUTHOR]

The Common Land Model (CLM), which results from a 3-yr joint effort among seven land modeling groups, has been coupled with the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3). Two 15-yr simulations of CCM3 coupled with CLM and the NCAR Land Surface Model (LSM), respectively, are used to document the relative impact of CLM versus LSM on land surface climate. It is found that CLM significantly reduces the summer cold bias of surface air temperature in LSM, which is associated with higher sensible heat fluxes and lower latent heat fluxes in CLM, and the winter warm bias over seasonally snow-covered regions, especially in Eurasia. CLM also significantly improves the simulation of the annual cycle of runoff in LSM. In addition, CLM simulates the snow mass better than LSM during the snow accumulation stage. These improvements are primarily caused by the improved parameterizations in runoff, snow, and other processes (e.g., turbulence) in CLM. The new land boundary data (e.g., leaf-area index, fractional vegetation cover, albedo) also contribute to the improvement in surface air temperature simulation over some regions. Overall, CLM has little impact on precipitation and surface net radiative fluxes. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5 ∘ resolution (available at 10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from - 0.044 to 0.033 m 3 m -3 , root mean square errors from 0.076 to 0.104 m 3 m -3 , Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management. [ABSTRACT FROM AUTHOR]

The advancements in deep learning methods have presented new opportunities and challenges for predicting land surface variables (LSVs) due to their similarity with computer sciences tasks. However, few researchers focus on the benchmark datasets for LSVs predictions that hampers fair comparisons of different data-driven deep learning models. Hence, we propose a LSVs benchmark dataset and prediction toolbox to boost research in data-driven LSVs modeling and improve the consistency of data-driven deep learning models for LSVs. LSVs benchmark dataset contains a large number of hydrology-related variables, such as global soil moisture, runoff, etc., which can verify the simulation of hydrological processes. Various global data from European Centre for Medium-Range Weather Forecasts reanalysis 5 (ERA5), ERA5-land, global gridded soil information (SoilGrid), soil moisture storage capacity (SMSC), and moderate-resolution imaging spectroradiometer (MODIS) datasets have been pre-processed into daily data at 0.5-, 1-, 2-, and 4-degree resolutions to facilitate their use in data-driven models. Simple statistical metrics, i.e., the root mean squared error and correlation coefficient, are chosen to evaluate the performance of different deep learning (DL) models, including convolutional neural network, long short-term memory and convolution long short-term memory models, with lead times of 1 and 5 days. A processed-based model serves as a physic baseline, soil moisture and surface sensible heat fluxes are taken as the target variables. The developed benchmark dataset and evaluation metrics for predicting LSVs using data-driven approaches, named as the LandBench toolbox, were implemented using Pytorch. This toolbox facilitates the reimplementation of existing methods, the development of novel predictive models, and the utilization of unified evaluation metrics. Additionally, the toolbox incorporates address mapping technology to enable high-resolution global predictions with constrained computing resources. We hope LandBench will not only serves as a standardized framework, fostering equitable model comparisons, but also provides indispensable data and a robust scientific foundation essential for advancing climate change research, disaster management, and sustainable development initiatives. [ABSTRACT FROM AUTHOR]

Vegetation leaf phenology, often reflected by the dynamics in leaf area index (LAI), influences a variety of land surface processes. Robust models of vegetation phenology are pivot components in both land surface models and dynamic global vegetation models but remain challenging in terms of the model accuracy. This study develops a semiprognostic phenology model that is suitable for simulating time series of vegetation LAI. This method establishes a linear relationship between the steady‐state LAI (i.e., the LAI when the environment conditions remain unchanging) and gross primary productivity, meaning that the LAI an unchanging environment can carry is proportional to the photosynthetic products produced by plant leaves and implements with a simple light use efficiency algorithm of MOD17 to form a closed set of equations. We derive an analytical solution based on the Lambert W function to the closed equations and then apply a simple restricted growth process model to simulate the time series of actual LAI. The results modeled using global climate data demonstrate that the model is able to capture both the spatial pattern and intra‐annual and interannual variation of LAI derived from the satellite‐based product on a global scale. The results modeled using the flux tower data suggest that the developed model is able to explain over 70% variation in daily LAI for each plant functional type except evergreen broadleaf forest. The developed semiprognostic approach provides a simple solution to modeling the spatiotemporal variation in vegetation LAI across plant functional types on the global scale. Plain Language Summary: Modeling vegetation leaf phenology remains challenging in terms of the model accuracy. We develop a semiprognostic vegetation phenology method that is suitable for simulating the variation in the time series of leaf area index. This approach establishes a linear relationship between LAI and vegetation productivity and implements with a simple light use efficiency algorithm of MOD17. The results modeled using global climate data demonstrate that the model is able to capture both the spatial pattern and intra‐annual and interannual variation of LAI derived from the remote sensing data on the global scale. Key Points: We developed a semiprognostic phenology model for simulating the dynamics of vegetation leaf area index on the global scaleThe novel model is able to capture the spatiotemporal variation of global vegetation leaf area index as compared with observationsThe developed approach provides a simple solution to modeling multidecadal variation of global vegetation leaf area index [ABSTRACT FROM AUTHOR]

Soil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. The results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes. [ABSTRACT FROM AUTHOR]

A 3D compressible nonhydrostatic dynamic core based on a three-point multi-moment constrained finite-volume (MCV) method is developed by extending the previous 2D nonhydrostatic atmospheric dynamics to 3D on a terrain-following grid. The MCV algorithm defines two types of moments: the point-wise value (PV) and the volume-integrated average (VIA). The unknowns (PV values) are defined at the solution points within each cell and are updated through the time evolution formulations derived from the governing equations. Rigorous numerical conservation is ensured by a constraint on the VIA moment through the flux form formulation. The 3D atmospheric dynamic core reported in this paper is based on a three-point MCV method and has some advantages in comparison with other existing methods, such as uniform third-order accuracy, a compact stencil, and algorithmic simplicity. To check the performance of the 3D nonhydrostatic dynamic core, various benchmark test cases are performed. All the numerical results show that the present dynamic core is very competitive when compared to other existing advanced models, and thus lays the foundation for further developing global atmospheric models in the near future. [ABSTRACT FROM AUTHOR]

The performance of the regional Climate-Weather Research and Forecasting model (CWRF) for downscaling China climate characteristics is evaluated using a 1980-2015 simulation at 30 km grid spacing driven by the ECMWF Interim reanalysis (ERI). It is shown that CWRF outperforms the popular Regional Climate Modeling system (RegCM4.6) in key features including monsoon rain bands, diurnal temperature ranges, surface winds, interannual precipitation and temperature anomalies, humidity couplings, and 95th percentile daily precipitation. Even compared with ERI, which assimilates surface observations, CWRF better represents the geographic distributions of seasonal mean climate and extreme precipitation. These results indicate that CWRF may significantly enhance China climate modeling capabilities. [ABSTRACT FROM AUTHOR]

In the past several decades, dynamic global vegetation models (DGVMs) have been the most widely used and appropriate tool at the global scale to investigate vegetation-climate interactions. At the Institute of Atmospheric Physics, a new version of DGVM (IAP-DGVM) has been developed and coupled to the Common Land Model (CoLM) within the framework of the Chinese Academy of Sciences’ Earth System Model (CAS-ESM). This work reports the performance of IAP-DGVM through comparisons with that of the default DGVM of CoLM (CoLM-DGVM) and observations. With respect to CoLMDGVM, IAP-DGVM simulated fewer tropical trees, more “needleleaf evergreen boreal tree” and “broadleaf deciduous boreal shrub”, and a better representation of grasses. These contributed to a more realistic vegetation distribution in IAP-DGVM, including spatial patterns, total areas, and compositions. Moreover, IAP-DGVM also produced more accurate carbon fluxes than CoLM-DGVM when compared with observational estimates. Gross primary productivity and net primary production in IAP-DGVM were in better agreement with observations than those of CoLM-DGVM, and the tropical pattern of fire carbon emissions in IAP-DGVM was much more consistent with the observation than that in CoLM-DGVM. The leaf area index simulated by IAP-DGVM was closer to the observation than that of CoLM-DGVM; however, both simulated values about twice as large as in the observation. This evaluation provides valuable information for the application of CAS-ESM, as well as for other model communities in terms of a comparative benchmark. [ABSTRACT FROM AUTHOR]