Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

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.

Evaluation of the New Dynamic Global Vegetation Model in CAS-ESM

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.

Evaluating Common Land Model Energy Fluxes Using FLUXNET Data

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 rain） 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.

Predicting climate anomalies:A real challenge

In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predict climatic variations,there are still several major problems for improving climate prediction.In 2020,the Center for Climate System Prediction Research(CCSP) was established with support from the National Natural Science Foundation of China.CCSP aims to tackle three scientific problems related to climate prediction—namely,El Ni?o-Southern Oscillation(ENSO) prediction,extended-range weather forecasting,and interannual-to-decadal climate prediction—and hence provide a solid scientific basis for more reliable climate predictions and disaster prevention.In this paper,the major objectives and scientific challenges of CCSP are reported,along with related achievements of its research groups in monsoon dynamics,land-atmosphere interaction and model development,ENSO variability,intraseasonal oscillation,and climate prediction.CCSP will endeavor to tackle key scientific problems in these areas.

A 3D Nonhydrostatic Compressible Atmospheric Dynamic Core by Multi-moment Constrained Finite Volume Method

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 terrainfollowing 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.

Changes in Global Vegetation Distribution and Carbon Fluxes in Response to Global Warming:Simulated Results from IAP-DGVM in CAS-ESM2

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.CO_(2)concentration is kept constant in all simulations to isolate CO_(2)-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 CO_(2)-fertilization effects,this application is still beneficial for adding to our understanding of vegetation processes and to further improve upon model parameterizations.

Assessment of global meteorological,hydrological and agricultural drought under future warming based on CMIP6

Quantifying the changes and propagation of drought is of great importance for regional eco-environmental safety and water-related disaster management under global warming.In this study,phase 6 of the Coupled Model Intercomparison Project was employed to examine future meteorological(Standardized Precipitation Index,SPI,and Standardized Precipitation-Evapotranspiration Index,SPEI),hydrological(Standardized Runoff Index,SRI),and agricultural(Standardized Soil moisture Index,SSI) drought under two warming scenarios(SSP2-4.5 and SSP5-8.5).The results show that,across the globe,different types of drought events generally exhibit a larger spatial extent,longer duration,and greater severity from 1901 to 2100,with SPEI drought experiencing the greatest increases.Although SRI and SSI drought are expected to be more intensifying than SPI drought,the models show higher consistency in projections of SPI changes.Regions with robust drying trends include the southwestern United States,Amazon Basin,Mediterranean,southern Africa,southern Asia,and Australia.It is also found that meteorological drought shows a higher correlation with hydrological drought than with agricultural drought,especially in warm and humid regions.Additionally,the maximum correlation between meteorological and hydrological drought tends to be achieved at a short time scale.These findings have important implications for drought monitoring and policy interventions for water resource management under a changing climate.

Plant drought tolerance trait is the key parameter in improving the modeling of terrestrial transpiration in arid and semi-arid regions

The prediction of precipitation depends on accurate modeling of terrestrial transpiration.In recent decades,the trait-based plant hydraulic stress scheme has been developed in land surface models,in order to better predict the hydraulic constraint on terrestrial transpiration.However,the role that each plant functional trait plays in the modeling of transpiration remains unknown.The importance of different plant functional traits for modeled transpiration needs to be addressed.Here,the Morris sensitivity analysis method was implemented in the Common Land Model with the plant hydraulic stress scheme(CoLM-P_(50)HS).Traits related to drought tolerance(P_(50);),stomata,and photosynthesis were screened as the most critical from all 17 plant traits.Among 12 FLUXNET sites,the importance of P_(50);,measured by normalized sensitivity scores,increased towards lower precipitation,whereas the importance of stomatal traits and photosynthetic traits decreased towards drier climate conditions.P_(50);was more important than stomatal traits and photosynthetic traits in arid or semi-arid sites,which implies that hydraulic safety strategies are more crucial than plant growth strategies when plants frequently experience drought.Large variation in drought tolerance traits further proved the coexistence of multiple plant strategies of hydraulic safety.Ignoring the variation in drought tolerance traits may potentially bias the modeling of transpiration.More measurements of drought tolerance traits are therefore necessary to help better represent the diversity of plant hydraulic functions.

Global and regional soil organic carbon estimates:Magnitudes and uncertainties

Globally,soil is the largest terrestrial carbon(C)reservoir.Robust quantification of soil organic C(SOC)stocks in existing global observation-based estimates avails accurate predictions in carbon-climate feedbacks and future climate trends.We investigated the magnitudes and distributions of global and regional SOC estimates(i.e.,density and stocks)based on five widely used global gridded SOC datasets,a regional permafrost dataset developed in 2021(UM2021),and a global-scale soil profile database(World Soil Information Service)reporting measurements of a series of physical and chemical edaphic attributes.The five global gridded SOC datasets were the Harmonized World Soil Database(HWSD),World Inventory of Soil Emission Potentials at 30 arc-second resolution(WISE30sec),Global Soil Dataset for Earth System Models(GSDE),Global Gridded Soil Information at 250-m resolution(SoilGrids250m),and Global Soil Organic Carbon Map(GSOCmap).Our analyses showed that the magnitude and distribution of SOC varied widely among datasets,with certain datasets showing region-specific robustness.At the global scale,SOC stocks at the top 30 and 100 cm were estimated to be 828(range:577–1171)and 1873(range:1086–2678)Pg C,respectively.The estimates from GSDE,GSOCmap,and WISE30sec were comparable,and those of SoilGrids250m and HWSD were at the upper and lower ends.The spatial SOC distribution varied greatly among datasets,especially in the northern circumpolar and Tibetan Plateau permafrost regions.Regionally,UM2021 and WISE30sec performed well in the northern circumpolar permafrost regions,and GSDE performed well in China.The estimates of SOC by different datasets also showed large variabilities across different soil layers and biomes.The discrepancies were generally smaller for the 0–30 cm soil than the 0–100 cm soil.The datasets demonstrated relatively higher agreement in grasslands,croplands,and shrublands/savannas than in other biomes(e.g.,wetlands).The users should be mindful of the gaps between regions and biomes while choosing the most appropriate SOC dataset for specific uses.Large uncertainties in existing global gridded SOC estimates were generally derived from soil sampling density,different sources,and various mapping methods for soil datasets.We call for future efforts for standardizing soil sampling efforts,cross-dataset comparison,proper validation,and overall global collaboration to improve SOC estimates.

Monitoring dynamic changes of global land cover types:fluctuations of major lakes in China every 8 days during 2000–2010

Remote sensing images can be used to delineate variations in the area of lakes and to assess the influence of environmental changes and human activities.However,because lakes are dynamic,results obtained from individual images acquired on a single date are not representative and do not accurately reflect ongoing changes.In this study,we used 8-day moderate resolution imaging spectroradiometer(MODIS)composite data from 2000 to2010 to map water surface changes over 629 lakes in China.We combined automatic extraction of training data and support vector machine classification to derive the spatial distribution of these large water bodies.The producer’s and user’s accuracies for MODIS images were91.06%and 89.81%,respectively,when compared with interpretation results from 30 m resolution Landsat images taken on similar days.Area changes,variability,inundation intensity,and rainy seasons of the 629 lakes were analyzed based on this multi-temporal lake database.The total area of the 629 lakes increased over the study period,primarily as a result of the expansion of lake areas on the Tibetan Plateau.There were 12 lakes with a maximum area[1,000 km2,and six of these decreased in area from 2000to 2010.The shrinkages of Poyang Lake and Dongting Lake were-54.76 and-25.08 km2/a,respectively.The area of lakes on Tibetan Plateau,in northern Xinjiang,northeastern Inner Mongolia,and northeastern China varied little,while lakes on the Yangtze Plain,in southern Inner Mongolia,and central Xinjiang fluctuated considerably.Inundation intensity increased for lakes on the Tibetan Plateau,in northern Xinjiang,Heilongjiang,and Jilin,while inundation extent decreased in central Xinjiang,southern Tibet,southern Inner Mongolia,Sichuan,and on the Yangtze Plain.This study is an attempt to develop high-frequency specific land cover maps to improve applicability of general land cover maps.The lake products serve as an important supplement to hydrologic data.The lake database enables the generation of new land surface process models,which could improve the precision of simulations,based on more accurate observations of dynamic lake systems.

Coupling the Common Land Model to ECHAM5 Atmospheric General Circulation Model

The ECHAM5 model is coupled with the widely used Common Land Model(CoLM). ECHAM5 is a state-of-theart atmospheric general circulation model incorporated into the integrated weather and climate model of the Chinese Academy of Meteorological Sciences(CAMS-CSM). Land surface schemes in ECHAM5 are simple and do not provide an adequate representation of the vegetation canopy and snow/frozen soil processes. Two AMIP(Atmospheric Model Intercomparison Project)-type experiments using ECHAM5 and ECHAM5-CoLM are run over 30 yr and the results are compared with reanalysis and observational data. It is found that the pattern of land surface temperature simulated by ECHAM5-CoLM is significantly improved relative to ECHAM5. Specifically, the cold bias over Eurasia is removed and the root-mean-square error is reduced in most regions. The seasonal variation in the zonal mean land surface temperature and the in situ soil temperature at 20-and 80-cm depths are both better simulated by ECHAM5-CoLM. ECHAM5-CoLM produces a more reasonable spatial pattern in the soil moisture content, whereas ECHAM5 predicts much drier soils. The seasonal cycle of soil moisture content from ECHAM5-CoLM is a better match to the observational data in six specific regions. ECHAM5-CoLM reproduces the observed spatial patterns of both sensible and latent heat fluxes. The strong positive bias in precipitation over land is reduced in ECHAM5-CoLM, especially over the southern Tibetan Plateau and middle–lower reaches of the Yangtze River during the summer monsoon rainy season.