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Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy-level is usually not well-represented. Here, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf-level fluorescence yield was simulated by a parametric simplification of the Soil Canopy-Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R 2 > 0.91, root-mean-square error  0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite-observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (SM), soil temperature, soil salinity, and vegetation, using L-band microwave brightness temperature (TB) observations and retrievals from the soil moisture ocean salinity (SMOS) and soil moisture active passive satellite missions, Catchment land surface model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based SM outperformed CLSM SM when evaluated against field data, and the forward L-band TB simulations derived from in situ SM and soil temperature performed better than those derived from CLSM estimates when evaluated against SMOS TB observations. The surface salinity for the sampled pixels was on average only 4 mg/g and only locally influenced the TB simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize TB simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multiangular SMOS TB data and constraints of CLSM SM and soil temperature. However, the latter modeled SM was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo, and surface roughness resulted in realistic values.

The accuracy of snow density in land surface model (LSM) simulations impacts the accuracy of simulated terrestrial water and energy budgets. However, there has been little research that has focused on enhancing snow compaction in operationally used LSMs. A baseline snow simulation with the widely used Noah‐MP LSM systematically overestimates snow depth by 55 mm even after removing daily snow water equivalent (SWE) biases. To reduce uncertainties associated with snow compaction, we enhance the most sensitive Noah‐MP snow compaction parameter—the empirical parameter for compaction due to overburden (Cbd)—such that Cbd is calculated as a function of surface air temperature as opposed to a fixed value in the baseline simulation. This enhancement improves accuracy in simulated snow compaction across the majority of western U.S. (WUS) SNOTEL test sites (biases reduced at 88% of test sites), with modest bias reductions in cooler accumulation periods (biases reduced at 70% of test sites) and substantial improvements during warmer ablation periods (biases reduced at 99% of test sites). Relatively larger improvements during warm conditions are attributable to the default Cbd value being reasonable for cold temperatures (≤−5°C). Improvements in simulated snow depth and density with observations outside of the training sites and optimization periods support that the snow compaction enhancement is transferable in space and time. Differences between enhanced and baseline gridded simulations across the total WUS support that the enhancement can have important impacts on snowpack evolution, snow albedo feedback, and snow hydrology. Plain Language Summary: Snow density, the ratio of water in the snowpack to the snow depth, is a fundamental property of snow that is important to accurately represent in snow simulations. In this research, we develop and evaluate a model enhancement designed to more accurately simulate the snow densification process in a widely used land surface model, Noah‐MP. Evaluations of the baseline snow compaction scheme, without model enhancements, reveals reasonable performance during cold conditions but substantial errors during warm conditions. We update the Noah‐MP snow compaction scheme to calculate the most sensitive snow compaction parameter as a function of surface air temperature. This enhancement provides substantially more accurate representations of snow compaction during warm conditions and provides similar performance relative to the baseline simulation during cold conditions. Improved accuracy in simulating snow densification is consistent between training sites the enhancement was optimized over and sites that were only used for validation both within and outside of the time period of optimization, supporting that improvements associated with using our model update can be expected at places and times distinct from the optimization locations and time periods. Key Points: Noah‐MP snow compaction parameterization is enhanced using SNOTEL snow observationsThe snow compaction enhancement provides substantial improvements in snow density and depth particularly during warm conditionsThe enhanced snow compaction scheme can have notable effects on snowpack evolution and snow albedo feedback [ABSTRACT FROM AUTHOR]

Global heat stress is a phenomenon that impacts the livelihood of humans worldwide. Due to climate change, heatwaves are already increasing negatively impact outdoor laborers and activities. However, calculating heat stress on a global scale is disparaged due to the interplay and treatment of temperature, humidity, and radiation. To help resolve this issue, the Wet Bulb Globe Temperature (WBGT), a standardized heat stress metric, is implemented into the Community Land Model (CLM5), the land surface component of the Community Earth System Model (CESM2). This resolves a long lasting, complex issue within global heat stress: the treatment of solar and thermal radiation. A default configuration of CLM5 is executed and shows the advantages of simulating the WBGT within multiple environments. Additionally, two commonly used WBGT approximations are implemented for solar exposed (sWBGT) and shaded (FiWBGT) conditions. The 1995 Chicago Heatwave is examined as a case study, focusing on the rural regions impacted by the heatwave. Derivative functions of labor capacity show that assumptions about calculating a non‐linear algorithm generate non‐negligible biases that can grossly over or underestimate the impact of heat stress on future climate change projections. For example, a difference of 0.5°C from WBGT can result in >10% change in labor capacity. Using a conservative difference of ±0.3°C, 100% of land surface extreme sWBGT values and >77% extreme shaded conditions (FiWBGT) differ from WBGT. Therefore, to accurately assess the direct exposure, risk, and damage from climate change on people, it is critical to implement diagnostics directly into Earth system models. Plain Language Summary: Heat stress is a global phenomenon that impacts people of all backgrounds. However, calculating heat stress on a global scale is difficult due to the interplay between temperature, humidity, and radiation. Unfortunately, because there are no global observation data sets that include radiation to accurately calculate heat stress, this leaves climate models and theory to determine heat stress. To that end, metrics that include radiation, like the Wet Bulb Globe Temperature, are sensitive to small changes in environmental weather, such as cloud variability. Furthermore, impacts derived from heat stress are likewise sensitive to small changes in metrics. Implementing representations of radiation‐based heat stress metrics within climate models produces accurate heat stress calculations, as compared to results produced from using offline climate output, improving weather forecasts for today's climate and projections into future damages from climate change. Key Points: Heat stress derived from offline global output introduces large biases in their results, which in turn produce errors in impact assessmentsRadiation‐based heat stress metrics implemented into Earth system model frameworks is recommended to reduce biases in results and outcomes [ABSTRACT FROM AUTHOR]

Under the context of global climate change and growing population, irrigation and fertilization have become important ways to ensure food production, with consequences on water cycling, energy flow, and materials cycling in terrestrial ecosystems. In the land surface model (LSM), coupling irrigation and fertilization schemes are of great importance for clearly understanding the land-atmosphere interactions to ensure food security. We reviewed the expression methods of three key parameters, namely, the applied method, usage, and time in the parameterization process of irrigation and fertilization (nitrogen fertilizer) in LSM. We found that the ways to irrigate and fertilize in LSM are different from the ways used in actual practice due to the limitation of the high resolution of spatio-temporal data, which makes it difficult to understand the actual influences of irrigation and fertilization on grain yield, environment, and local climate. Finally, we proposed future works: 1) taking the differences of crop water demand into account and making the different irrigation thresholds for different crops to properly evaluate the total and intensity of water consumption of different crops; 2) using the field records and the regional grid data of fertilization and irrigation developed in recent years to develop parameterized schemes that are more in line with actual agricultural operations, which can accurately reveal their economic, ecological, and climatic effects; 3) developing fertilization diagnosis scheme considering crop type, phenological stage, and soil basic fertility as the supplementary scheme in LSM, to improve the applicability and simulation accuracy of LSM in the areas without nitrogen fertilizer data. [ABSTRACT FROM AUTHOR]

This paper optimized the hydrological postprocessing of CLM5 using CaMa-Flood, combining multi-source meteorological forcing datasets and a dynamically changing surface dataset containing 16 PFTs (plant functional types) to simulate the high-resolution runoff process in the SRB from 1996 to 2014, specifically by integrating discharge with flooded area. Additionally, we evaluated the spatiotemporal variations of precipitation data from meteorological forcing datasets and discharge to validate the accuracy of model improvements. Both the discharge and the flooded area simulated by the coupled model exhibit pronounced seasonality, accurately capturing the discharge increase during the warm season and the river recession process in the cold season, along with corresponding changes in the flooded area. This highlights the model's capability for hydrological process monitoring. The simulated discharge shows a high correlation coefficient (0.65–0.80) with the observed discharge in the SRB, reaching a significance level of 0.01, and the Nash–Sutcliffe efficiency ranges from 0.66 to 0.78. Leveraging the offline coupling of CLM and CaMa-Flood, we present a method with a robust physical mechanism for monitoring and providing a more intuitive representation of hydrological events in the SRB. [ABSTRACT FROM AUTHOR]

Terrestrial Water Storage (TWS) changes have been estimated at basin to continental scales from gravity variations using data from the Gravity Recovery and Climate Experiment (GRACE) satellites since 2002. The relatively low spatial resolution (∼300 km) of GRACE observations has been a main limitation in such studies. Various data processing strategies, including mascons, forward modeling, and constrained linear deconvolution (CLD), have been employed to address this limitation. Here we develop a revised CLD method to obtain a TWS estimate that combines GRACE observations with much higher spatial resolution land surface models. The revised CLD constrains model estimates to agree with GRACE TWS when smoothed. As an example, we apply the method to obtain a high spatial resolution TWS estimate in Australia. We assess the accuracy of the approach using synthetic GRACE data. Plain Language Summary: The estimation of terrestrial water storage (TWS) changes using gravity recovery and climate experiment (GRACE) satellites suffers from low spatial resolution, making it challenging to interpret local‐scale mass changes. In this study, we improved the sparse resolution of GRACE observations by incorporating high‐resolution land surface models (LSM) that provides detailed hydrological information. Through synthetic experiments, we confirmed the accuracy of our estimations in regional‐ and local‐scale. When applied to real GRACE data, our new TWS estimations show better spatial resolution compared to conventional GRACE products. Further, our estimations consistently yield reliable results although different LSM were used. Key Points: High‐resolution terrestrial water storage was estimated by combining gravity recovery and climate experiment and land surface modelsOur new estimates reduced both land‐ocean and inter‐basin leakages simultaneously [ABSTRACT FROM AUTHOR]

Summary: Leaf dark respiration (Rd) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models.We hypothesized that Rd and Rubisco carboxylation capacity (Vcmax) at 25°C (Rd,25, Vcmax,25) are coordinated so that Rd,25 variations support Vcmax,25 at a level allowing full light use, with Vcmax,25 reflecting daytime conditions (for photosynthesis), and Rd,25/Vcmax,25 reflecting night‐time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5‐yr warming experiment, and spatially using an extensive field‐measurement data set. We compared the results to three published alternatives: Rd,25 declines linearly with daily average prior temperature; Rd at average prior night temperatures tends towards a constant value; and Rd,25/Vcmax,25 is constant.Our hypothesis accounted for more variation in observed Rd,25 over time (R2 = 0.74) and space (R2 = 0.68) than the alternatives. Night‐time temperature dominated the seasonal time‐course of Rd, with an apparent response time scale of c. 2 wk. Vcmax dominated the spatial patterns.Our acclimation hypothesis results in a smaller increase in global Rd in response to rising CO2 and warming than is projected by the two of three alternative hypotheses, and by current models. [ABSTRACT FROM AUTHOR]

Existing land surface models still have large error in the simulation of surface albedo. At present, introducing the influence of meteorological factors into the albedo parameterization scheme is an effective way to improve the albedo simulation. However, the improvement of the vegetation canopy surface albedo parameterization scheme is still insufficient. Therefore, based on the radiation and meteorological observation data during 1 November 2015–30 April 2018 from a land‐atmosphere interaction observation tower in a typical secondary evergreen broadleaved forest in Southern China, this study firstly analyzed the influencing factors of canopy surface albedo. It was found that the solar elevation angle and air relative humidity are two key factors influencing the canopy surface albedo, and the impact of the solar elevation angle on canopy surface albedo is not exactly the same on diurnal and seasonal scales. Then, two new canopy surface albedo parameterization schemes of additive form and multiplicative form were proposed and introduced into CLM5 model for single point simulation. Results show that the adoption of newly developed canopy surface albedo parameterization schemes can improve the simulation of diurnal and seasonal variations of albedo and reduce the overestimation of the simulation of near‐infrared radiation albedo and visible radiation albedo in the CLM5 model, then significantly reduce the root mean square errors of reflected solar radiation and net radiation simulation. Plain Language Summary: Surface albedo is an important factor affecting the heat exchange between land and atmosphere, and is critical for weather forecasting and regional climate modeling. At present, even in some complex land surface models, the canopy surface albedo parameterization scheme only considers the variation of leaf and stem index and the cosine of the solar zenith angle over time but does not take into account the effect of meteorological factors. In this study, two newly developed canopy albedo schemes that take into account the effects of solar elevation angle and air relative humidity were developed, and then introduced into the CLM5 model for single point simulation. The results show that the new canopy albedo scheme can indeed improve the simulation skills of the reflected solar radiation. The results emphasize the important influence of meteorological factors on the surface albedo, and the findings may provide a base to further develop and improve the CLM5 model. Key Points: Two new canopy surface albedo schemes considering the influence of solar elevation angle and air relative humidity are proposedThe new canopy surface albedo schemes can reduce the canopy surface albedo of evergreen broad‐leaved forest overestimated by CLM5 model [ABSTRACT FROM AUTHOR]

This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS's ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model's ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales. [ABSTRACT FROM AUTHOR]

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The Tibetan Plateau (TP) is distributed with large areas of permafrost, which have received increasing attention as the climate warms. Accurately modeling the extent of permafrost and permafrost changes is now an important challenge for climate change research and climate modeling in this region. Uncertainty in land use and land cover (LULC), which is important information characterizing surface conditions, directly affects the accuracy of the simulation of permafrost changes in land surface models. In order to investigate the effect of LULC uncertainty on permafrost simulation, we conducted simulation experiments on the TP using the Community Land Model, version 5 (CLM5) with five high-resolution LULC products in this study. Firstly, we evaluated the simulation results using shallow soil temperature data and deep borehole data at several sites. The results show that the model performs well in simulating shallow soil temperatures and deep soil temperature profiles. The effect of different land use products on the shallow soil temperature and deep soil temperature contours is not obvious due to the small differences in land use products at these sites. Although there is little difference in the simulating results of different land use products when compared to the permafrost distribution map, the differences are noticeable for the simulation of the active layer. Land cover had a greater impact on soil temperature simulations in regions with greater land use inconsistency, such as at the junction of bare soil and grassland in the northwestern part of the TP, as well as in the southeast region with complex topography. The main way in which this effect occurs is that land cover affects the net surface radiation, which in turn causes differences in soil temperature simulations. In addition, we discuss other factors affecting permafrost simulation results and point out that increasing the model plant function types as well as carefully selecting LULC products is one of the most important ways to improve the simulation performance of land-surface models in permafrost regions. [ABSTRACT FROM AUTHOR]

The persistence or memory of soil moisture (θ) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple U.K. soil moisture products, including satellite-merged (i.e., TCM), in situ (i.e., COSMOS-UK), hydrological model [i.e., Grid-to-Grid (G2G)], statistical model [i.e., Soil Moisture U.K. (SMUK)], and land surface model (LSM) [i.e., Climate Hydrology and Ecology research Support System (CHESS)] data. The drydown decay time scale (τ) for all gridded products is computed at an unprecedented resolution of 1–2 km, a scale relevant to weather and climate models. While their range of τ differs (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyze the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted τ based on COSMOS and LSM are well correlated, with a bias of lower τ for COSMOS. Our findings contribute to a growing body of literature to characterize τ, with the aim of developing a method to systematically validate model soil moisture products at a range of scales. Significance Statement: While important for many aspects of the environment, the evaluation of modeled soil moisture has remained incredibly challenging. Sensors work at different space and time scales to the models, the definitions of soil moisture vary between applications, and the soil moisture itself is subject to the soil properties while the impact of the soil moisture on evaporation or river flow is more dependent on its variation in time and space than its absolute value. What we need is a method that allows us to compare the important features of soil moisture rather than its value. In this study, we choose to study drydown as a way to capture and compare the behavior of different soil moisture data products. [ABSTRACT FROM AUTHOR]

Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments. Significance Statement: The purpose of this study is to improve streamflow estimation by integrating soil moisture information from satellite observations into a hydrological modeling framework. This is important preparatory work for operational centers that are responsible for producing the most accurate flood forecasts for the society. Our results provide new insights into how and where streamflow forecasting could benefit from high-spatial-resolution Sentinel-1 radar backscatter observations. [ABSTRACT FROM AUTHOR]

Understanding contributions of advanced land surface models and high‐resolution model inputs (e.g., meteorological forcings and soil parameters) to root‐zone soil moisture (RSM) simulations provides critical implications for both model and data development. Previous works have investigated influences of these factors separately, without considering the interdependence between multiple factors (e.g., positive impacts of high‐resolution forcings may be reduced by coarse‐resolution parameters). To date, how to quantify this interdependence and its relative importance remain to be investigated. Here, we propose a framework to quantify independent and interdependent/synergistic effects of forcings, parameters and models on high‐resolution RSM modeling using ensemble simulations. Forty‐eight RSM simulations with different high‐resolution factors superior in both spatial resolution and data accuracy are performed over China during 2013–2017, and observations from 1,553 stations across different climate zones are used to conduct evaluation. Results show that, the increase in Kling‐Gupta efficiencies (KGEs) after combining different high‐resolution factors are larger than the sum of that using individual factors. Such synergistic effects dominate the improvement of high‐resolution modeling at national and regional scales, and contribute to consistent improvements of simulations of RSM's mean state and variability. At station scale, although independent effect increases over western China, synergistic effect contributes 42%–60% to the improved KGEs over eastern China. The positive effects of an individual high‐resolution factor on RSM modeling could be reduced by 25%–80% without synergistic effects, indicating that the synergistic developments of models, meteorological forcings and soil parameters can facilitate high‐resolution RSM modeling more efficiently than only focusing on a single factor. Plain Language Summary: Facilitated by high‐resolution meteorological forcings, soil parameters and numerical models, land surface modeling is an efficient way to provide locally relevant root‐zone soil moisture (RSM) for the agriculture management and drought monitoring. Previous works used to quantify the influences of different high‐resolution factors by using sensitivity experiments with an independent assumption (e.g., the added value of high‐resolution forcing keeps constant no matter high‐resolution parameter is used or not), so as to find the factor that can improve RSM modeling efficiently. However, whether the independent assumption is appropriate remains to be investigated. This study develops a new framework to separate the interdependent influences among multiple high‐resolution factors and the independent effects of individual factors through ensemble simulations. We show that the impacts of different high‐resolution factors are strongly interdependent. Large part of the positive effect of individual high‐resolution factors on RSM modeling cannot be achieved when other factors have coarse‐resolution and large uncertainties. Such an interdependence is identified as the synergistic effect, and dominates the improvement of high‐resolution RSM modeling over East China. Therefore, efforts are needed to the collaborative development of high‐resolution models, forcings and parameters for high‐resolution RSM simulations. Key Points: Independent and synergistic effects of model, forcing and parameter on high‐resolution soil moisture (SM) simulations are estimatedUsing only one high‐resolution factor has limited improvement in SM simulations due to uncertainties from other coarse‐resolution factorsSynergistic effect of multiple high‐resolution factors interprets 25%–80% of the improvement in high‐resolution SM modeling over China [ABSTRACT FROM AUTHOR]

Soil thermal conductivity (STC) is an essential parameter for soil temperature and soil heat flux prediction in the land surface model. In recent years, various STC schemes have been developed and evaluated based on direct laboratory measurements. Appropriate selection of STC scheme is the key to applying land surface model in permafrost regions. In this study, we compared the estimation from nine typical STC schemes with laboratory measurements obtained on undisturbed soil samples, and then incorporated these schemes into the latest version of the Community Land Model (CLM5.0) to evaluate their performance in simulating soil temperature and permafrost extent on the Tibetan Plateau. Statistical analysis shows that among the nine schemes, Lu (L2014) and Nikoosokhan (N2015) schemes provide good STC estimation for undisturbed soil samples with varying soil saturation. The performance of the default scheme of CLM5.0 ranked after the two schemes with a root mean squared error of 0.33 W m−1 K−1. The single-point simulation results show that L2014 scheme is significantly better than the other schemes for incorporating into CLM to simulate soil temperature. The scheme that performs best in soil samples STC estimation do not necessarily perform best in soil temperature simulation due to bias in soil moisture simulation. The permafrost simulation results show that the L2014 scheme with higher simulated temperatures gives the smallest overestimated fraction of the simulated permafrost extent, while the N2015 scheme gives the smallest underestimated fraction. According to the evaluation analysis, we found that different STC schemes have important effects on the simulation of permafrost dynamic. In addition, uncertainties in land surface model such as atmospheric forcing, soil moisture and soil texture also have non-negligible effects on the accuracy of permafrost simulations.

Study region: North China Plain (NCP), China, a semi-arid region with intense groundwater withdrawals. Study focus: This paper developed a framework using meteorological data, model-simulated terrestrial water storage anomalies (TWSA), and additional in-situ (groundwater level, GL) data to improve the unsatisfactory GRACE-TWSA reconstruction in arid and semi-arid regions due to the intense anthropogenic influence on groundwater. The inconsistency between point-scale data (GL) and grid-scale data (GRACE-TWSA and predictors other than GL) is handled by feature extraction techniques. Moreover, to deal with temporal non-stationarity, the time series are separated into trend and detrended components, the patterns of which are further learned by linear and nonlinear machine learning models, respectively.New hydrological insights for the region: Multi-site GL observations in NCP can not only serve as validation data but also as predictors providing invaluable information on human effects for the reconstructed TWSA improvement (from 6.51 to 3.86 cm for Root Mean Square Error and from 0.56 to 0.82 for Nash-Sutcliffe Efficiency). Our results show that multi-site GL data in NCP are highly inter-correlated and can be represented by several principal components, demonstrating the strong hydraulic connectivity in NCP. We also find a significant one-month lag and linear relationship between the trends of GRACE-TWSA and GL changes in NCP. These deeper understandings of hydrologic processes have implications for enhancing the GRACE-TWSA estimations in other similar regions.

This study applies three widely used land models (SSiB, CLM, and Noah-MP) coupled in a regional climate model to quantitatively assess their skill in preserving the imposed ± 5 °C anomalies on the initial land surface and subsurface temperature (LST/SUBT) and generating the 2-m air temperature (T2m) anomalies over Tibetan Plateau (TP) during May–August. The memory of the LST/SUBT initial anomalies (surface/soil memory) is defined as the first time when time series of the differences in daily LST/SUBT cross the zero line during the simulation, with the unit of days. The memory of the T2m anomalies (T2m memory) is defined in the same way. The ensemble results indicate that the simulated soil memory generally increases with soil depth, which is consistent with the results based on the observations with statistic methods. And the soil memory is found to change rapidly with depth above ~ 0.6–0.7 m and vary slowly below it. The land models have fairly long soil memories, with the regional mean 1.0-m soil memory generally longer than 60 days. However, they have short T2m memory, with the regional means generally below 20 days. This may bring a big challenge to use the LST/SUBT approach on the sub-seasonal to seasonal (S2S) prediction. Comparison between the three land models shows that CLM and Noah-MP have longer soil memory at the deeper layers (> ~ 0.05 m) while SSiB has longer T2m/surface memories and near-surface (≤ ~ 0.05 m) soil memory. As a result, it is difficult to say which land model is optimal for the application of the LST/SUBT approach on the S2S prediction. The T2m/surface/soil memories are various over TP, distinct among the land models, and different between the + 5 °C and − 5 °C experiment, which can be explained by both changes in the surface heat fluxes and variances in the hydrological processes over the plateau.

This study is dedicated to the detection and attribution of changes in annual streamflow, maximum and mean winter snow water equivalent (SWE), start and end dates of seasonal snow cover, and its duration in three Arctic river basins (the Northern Dvina, Taz, and Indigirka) located in the European part of Russia, West, and East Siberia in different natural conditions. The observations of the above characteristics are rather scarce to detect statistically significant trends. At the same time, the available observations make it possible to calibrate the key parameters of the SWAP model, apply for hydrological simulations, and validate the model. Then, following the approach suggested within the framework of the international ISIMIP3a project, long-term simulations are performed for each basin using observational (factual) climate data, characterized by long-term changes, and counterfactual de-trended climate data. A comparison of factual and counterfactual simulations allows us to attribute the detected changes (in terms of trends) in the analyzed variables to climatic drivers. Statistically significant positive trends in streamflow are attributed to changes in annual precipitation for the Northern Dvina and Indigirka, and to the joint impact of increasing precipitation and warming, which resulted in permafrost thawing, for the Taz River. Negative trends in the basin-averaged end dates of snow cover and its duration as well as positive trends in winter and maximum SWE are detected for all basins and attributed to joint influence of changes in seasonal precipitation, air temperature, and solar radiation. The results highlight the vulnerability of Arctic river basins to climate change. [ABSTRACT FROM AUTHOR]

Groundwater and climate interact in a two-way manner. Precipitation ultimately controls groundwater recharge and, conversely, groundwater may influence climate through evapotranspiration. Yet very few global climate models or Earth system models actually simulate groundwater flows. And while the expected impacts of climate change on groundwater resources are the subject of a growing concern, global-scale groundwater–climate feedbacks have received very little attention so far. Here we show that the integration of unconfined aquifers in a global climate model can regionally affect the climate change signal on temperatures and precipitation. We assess the impact of groundwater under preindustrial and 4xCO2 conditions (after climate stabilization). In both cases, we find that groundwater has a cooling and a wetting effect in certain regions of the world. In eastern Europe, both these impacts are stronger in the warmer climate (4xCO2 forcing) where the presence of groundwater reduces the frequency of summer heatwaves by 40%, compared to a 15% reduction in the preindustrial world. This work constitutes one of the very first global assessments of the potential feedbacks of groundwater on climate change. Our results support the idea that groundwater should be represented in global climate models and Earth system models, as it does indeed play an active role in the climate system. [ABSTRACT FROM AUTHOR]

Horizontal convective rolls (HCR) are sub-mesoscale motions that influence the transport of heat, momentum, and pollutants within the boundary layer. In this work, a high-resolution (0.666 km) Weather Research and Forecasting (WRF) model is employed to simulate the structure of the HCRs over Kalpakkam along the southeast coast of India. The sensitivity of HCR simulation to model land surface physics is studied with two land surface models (LSM), (i) Noah and (ii) Noah multi-parameterization (NMP), for three selected days (15 April 2013, 07 May 2015, 28 March 2018) during summer synoptic conditions. On all three selected days, the boundary layer rolls formed over a period of about 2–3 h in the morning under moderate winds (4–5.0 m/s), moderate vertical wind shear (2.4–3.5 m/s) in the lower atmosphere, and slightly unstable conditions [gradient Richardson number (RiG) −4.5 to −5.0] in both simulations and observations, indicating that thermal instability is the chief mechanism in their development. Simulated mean surface meteorological parameters by NMP were found to be in better agreement with observations than Noah. Results suggest that the LSMs mainly affected the simulated turbulent roll structure in terms of updraft cores and their horizontal and vertical extent by variation in simulated surface energy fluxes, boundary layer structure, wind shear, and stability. The structure of simulated HCRs is better represented by NMP due to the improvements in the flux distribution and surface properties. Simulations using the FLEXPART dispersion model for a hypothetical case of tracer release indicated an uneven spatial concentration pattern due to upward and downward motions in the region of HCRs. The stronger winds and stronger flow convergence in Noah and higher heat flux and more unstable conditions in NMP led to differences in the simulated tracer concentrations in the two cases. [ABSTRACT FROM AUTHOR]

Realistic initialization of the land surface is important to produce accurate NWP forecasts. Therefore, making use of available observations is essential when estimating the surface state. In this work, sequential land surface data assimilation of soil variables is replaced with an offline cycling method. To obtain the best possible initial state for the lower boundary of the NWP system, the land surface model is rerun between forecasts with an analyzed atmospheric forcing. We found a relative reduction of 2-m temperature root-mean-square errors and mean errors of 6% and 12%, respectively, and 4.5% and 11% for 2-m specific humidity. During a convective event, the system was able to produce useful (fractions skill score greater than the uniform forecast) forecasts [above 30 mm (12 h)−1] down to a 100-km length scale where the reference failed to do so below 200 km. The different precipitation forcing caused differences in soil moisture fields that persisted for several weeks and consequently impacted the surface fluxes of heat and moisture and the forecasts of screen level parameters. The experiments also indicate diurnal- and weather-dependent variations of the forecast errors that give valuable insight on the role of initial land surface conditions and the land–atmosphere interactions in southern Scandinavia. [ABSTRACT FROM AUTHOR]

The article discusses the First International Noah-MP Annual Users' Workshop, which brought together over 200 participants from 16 countries to discuss advancements in the Noah-MP land surface model. The workshop focused on enhancing the model's capabilities, applicability, and interoperability in Earth system applications. The article highlights the various applications of the Noah-MP model, including weather prediction, climate projection, hydrology, agriculture, and urban heat island studies. It also identifies current challenges and limitations of the model, such as uncertainties in certain processes and a lack of communication and coordination within the Noah-MP community. The article concludes with recommendations for future model development and the establishment of a Noah-MP Academia Collaboratory to support community collaborations and enhance research-to-operation activities. [Extracted from the article]

Vegetation water content (VWC) plays a key role in transpiration, plant mortality, and wildfire risk. Although land surface models now often contain plant hydraulics schemes, there are few direct VWC measurements to constrain these models at global scale. One proposed solution to this data gap is passive microwave remote sensing, which is sensitive to temporal changes in VWC. Here, we test that approach by using synthetic microwave observations to constrain VWC and surface soil moisture within the Climate Modeling Alliance Land model. We further investigate the possible utility of sub‐daily observations of VWC, which could be obtained through a satellite in geostationary orbit or combinations of multiple satellites. These high‐temporal‐resolution observations could allow for improved determination of ecosystem parameters, carbon and water fluxes, and subsurface hydraulics, relative to the currently available twice‐daily sun‐synchronous observational patterns. We find that incorporating observations at four different times in the diurnal cycle (such as could be available from two sun‐synchronous satellites) provides a significantly better constraint on water and carbon fluxes than twice‐daily observations do. For example, the root mean square error of projected evapotranspiration and gross primary productivity during drought periods was reduced by approximately 40%, when using four‐times‐daily relative to twice‐daily observations. Adding hourly observations of the entire diurnal cycle did not further improve the inferred parameters and fluxes. Our comparison of observational strategies may be informative in the design of future satellite missions to study plant hydraulics, as well as when using existing remotely sensed data to study vegetation water stress response. Plain Language Summary: The amount of water contained within the tissues of plants influences how much water plants transpire from the soil to the atmosphere and how much carbon they take in. However, it is difficult to estimate how much water is in plants around the world at any given time, due to the diversity of plants storing and releasing water in different ways. Certain earth‐orbiting satellites carry sensors that can indicate plant water content, but they provide only snapshots of data at two points in time per day due to their orbit shapes. Here, we simulated what information could be gleaned from different combinations of satellites in different orbits when they are combined with computer models of water flow in plants. We found that using data from two satellites in different orbits, instead of just one, could greatly increase the accuracy of plant water content estimates, almost as much as if we had a large fleet of satellites observing around the clock. Our work should be useful to scientists studying plant water content with existing data sets as well as those planning future satellite missions. Key Points: We demonstrate that ecohydrological parameters and variables can be inferred from microwave radiometry via model‐data fusionWe compare scenarios that use synthetic observations at different times of day, corresponding to current and proposed satellite orbitsFor inferring land surface variables, using observations from just four times of day proves to be as useful as using data from every hour [ABSTRACT FROM AUTHOR]

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement: Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia. [ABSTRACT FROM AUTHOR]

Ensemble data assimilation (DA) is an efficient approach to reduce snow simulation errors by combining observation and land surface modeling. However, there is a small spread between ensemble members of simulated snowpack, which typically occurs for a long time with 100% snow cover fraction (SCF) or snow-free conditions. Here, we apply a hybrid DA method, in which direct insertion (DI) is a supplement of the ensemble square root filter (EnSRF), to assimilate the spaceborne SCF into a land surface model, driven by China Meteorological Administration Land Data Assimilation System high-resolution climate forcings over northern China during the snow season in 2021/22. Compared to the open-loop experiment (without SCF assimilation), the root-mean-square error (RMSE) of SCF is reduced by 6% through the original EnSRF and is even lower (by 14%) in the combined DI and EnSRF (EnSRFDI) experiment. The results reveal the ability of both EnSRF and EnSRFDI to improve the SCF estimation over regions where the snow cover is low, while only EnSRFDI is able to efficiently reduce the RMSE over areas with high SCF. Moreover, the SCF assimilation is also observed to improve the snow depth and soil temperature simulations, with the Kling–Gupta efficiency (KGE) increasing at 60% and 56%–70% stations, respectively, particularly under conditions with near-freezing temperature, in which reliable simulations are typically challenging. Our results demonstrate that the EnSRFDI hybrid method can be applied for the assimilation of spaceborne observational snow cover to improve land surface simulations and snow-related operational products. Significance Statement: Due to the small spread between the seasonal snowpack of ensemble simulations, ensemble snow cover fraction (SCF) data assimilation (DA) proves to be ineffective. Therefore, we apply a hybrid method that combines the direct insertion (DI) and ensemble square root filter (EnSRF) to assimilate the spaceborne SCF into a land surface model (LSM) driven by high-resolution climate forcings. Our results reveal the applicability of the EnSRFDI to further improve snow cover simulations over regions with high SCF. Furthermore, the DA experiments were validated through a large number of in situ observations from the China Meteorological Administration. The uncertainties of snow depth and soil temperature simulations are also slightly reduced by the SCF DAs, particularly over regions with a poor LSM performance. [ABSTRACT FROM AUTHOR]

The land surface hydrology of the North American Great Lakes region regulates ecosystem water availability, lake levels, vegetation dynamics, and agricultural practices. In this study, we analyze the Great Lakes terrestrial water budget using the Noah‐MP land surface model to characterize the catchment hydrological regimes and identify the dominant quantities contributing to the variability in the land surface hydrology. We show that the Great Lakes domain is not hydrologically uniform and strong spatiotemporal differences exist in the regulators of the hydrological budget at daily, monthly, and annual timescales. Subseasonally, precipitation and soil moisture explain nearly all the terrestrial water budget variability in the southern basins, while the northern latitudes are snow‐dominated regimes. Seasonal assessments reveal greater differences among the basins. Precipitation, evaporation, and runoff are the dominant sources of variability at lower latitudes, while at higher latitudes, terrestrial water storage in the form of ground snowpack and soil moisture has the leading role. Differences in land cover categorizations, for example, croplands, forests, or urban zones, further induce spatial differences in the hydrological characteristics. This quantification of variability in the terrestrial water cycle embedded at different temporal scales is important to assess the impacts of changes in climate and land cover on catchment sensitivities across the diverse hydroclimate of the Great Lakes region. Key Points: The Laurentian Great Lakes domain is not hydrologically uniform, with different regulators of the water budget in the five subbasinsDominant quantities characterizing the subregional terrestrial hydrology vary for daily, monthly, and annual timescalesClimate change impact studies on regional hydrology need to account for basin‐scale differences in the terrestrial hydroclimatic dynamics [ABSTRACT FROM AUTHOR]

Downward solar radiation (DSR) and air temperature (Ta) have significant influences on the thermal state of frozen ground. These parameters are also important forcing terms for physically based land surface models (LSMs). However, the quantitative influences of inaccuracies in DSR and Ta products on simulated frozen ground temperatures remain unclear. In this study, three DSR products (CMFD‐SR, Tang‐SR, and GLDAS‐SR) and two Ta products (CMFD‐Ta and GLDAS‐Ta) were used to force an LSM model in an alpine watershed in Northwest China, to investigate the sensitivity of simulated ground temperatures to different DSR and Ta products. Compared to a control model (CTRL) forced by in situ observed DSR, ground temperatures simulated by the experimental model forced by GLDAS‐SR are obviously decreased because GLDAS‐SR is much lower than in situ observations. Instead, simulation results in models forced by CMFD‐SR and Tang‐SR are much closer to those of CTRL. Ta products led to significant errors in simulated ground temperatures. In conclusion, both CMFD‐SR and Tang‐SR could be used as good alternatives to in situ observed DSR for forcing a model, with acceptable errors in simulation results. However, more care need to be paid for models forced by Ta products instead of Ta observations, and conclusions should be carefully drawn. [ABSTRACT FROM AUTHOR]

AbstractLand-use and land-cover change (LULCC) is one of the most important forcings affecting climate in the past century. This study evaluates the global and regional LULCC impacts in 1950–2015 by employing an annually updated LULCC map in a coupled land–atmosphere–ocean model. The difference between LULCC and control experiments shows an overall land surface temperature (LST) increase by 0.48 K in the LULCC regions and a widespread LST decrease by 0.18 K outside the LULCC regions. A decomposed temperature metric (DTM) is applied to quantify the relative contribution of surface processes to temperature changes. Furthermore, while precipitation in the LULCC areas is reduced in agreement with declined evaporation, LULCC causes a southward displacement of the intertropical convergence zone (ITCZ) with a narrowing by 0.5°, leading to a tripole anomalous precipitation pattern over the warm pool. The DTM shows that the temperature response in LULCC regions results from the competing effect between increased albedo (cooling) and reduced evaporation (warming). The reduced evaporation indicates less atmospheric latent heat release in convective processes and thus a drier and cooler troposphere, resulting in a reduction in surface cooling outside the LULCC regions. The southward shift of the ITCZ implies a northward cross-equatorial energy transport anomaly in response to reduced latent/sensible heat of the atmosphere in the Northern Hemisphere, where LULCC is more intensive. Tropospheric cooling results in the equatorward shift of the upper-tropospheric westerly jet in both hemispheres, which, in turn, leads to an equatorward narrowing of the Hadley circulation and ITCZ.

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

Climate change impact on the water balance components (including river runoff, evapotranspiration and precipitation) of five Arctic river basins (the Northern Dvina, Taz, Lena, Indigirka, and MacKenzie), located in different natural conditions, was investigated using a physically-based land surface model SWAP and meteorological projections simulated at half-degree spatial resolution by five Global Climate Models (GCM) for four Representative Concentration Pathways (RCP) scenarios from 2005 to 2100. After the SWAP model calibration and validation, 20 projections of changes in climatic values of the water balance components were obtained for each river basin. The projected changes in climatic river runoff were analyzed with climatic precipitation and evapotranspiration changes. On average, all rivers’ water balance components will increase by the end of the 21st century: precipitation by 12-30%, runoff by 10–30%, and evapotranspiration by 6-47% depending on the river basin. The partitioning of increment in precipitation between runoff and evapotranspiration differs for the selected river basins due to differences in their natural conditions. The Northern Dvina and Taz river runoff will experience the most negligible impact of climate change under the RCP scenarios. This impact will increase towards eastern Siberia and reach a maximum in the Indigirka basin. Analysis of the obtained hydrological projections made it possible to estimate their uncertainties by applying different GCMs and RCP scenarios. On average, the contribution of GCMs to the uncertainty of hydrological projections is nearly twice more significant than the contribution of scenarios in 2006–2036 and decreases over time to 1.1-1.2 in 2068–2099.

This study evaluates historical simulation of solar photovoltaic potential (PVpot) during 1989–2008 over China against the ERA5 reanalysis, using the Weather Research and Forecasting (WRF) model and Regional Climate Model version 4 (RegCM4) within the framework of the Coordinated Regional Downscaling Experiment-East Asia second phase (CORDEX-EA-II). The impacts of spectral nudging technique and land surface model on the simulated PVpot are investigated as well. Results indicate that the observed PVpot is abundant over western China, which can reach up to 26% in summer particularly. The WRF simulations significantly overestimate the PVpot over most areas of China, with the bias about 9% over southeastern China, while the utilization of spectral nudging method and CLM4 land surface model can greatly reduce the deviation. The RegCM4 simulations generate underestimation of PVpot over Northwest China and overestimation over Southeast China. As for the interannual variation, the observed PVpot features an increase of 0.9%/decade over southeastern China. The WRF and RegCM4 simulations can reproduce the rising trend of PVpot, while the magnitude is much lower than ERA5. The WRF simulations can properly portray the characteristics of seasonal cycle of PVpot, with the peaks in May over entire China and most subregions, while RegCM4 exhibits poor skill in reproducing the intra-annual variation. Moreover, the simulated bias in the clear-sky solar radiation (RSDSCS), low-level cloud fraction, and light rain during the daylight may contribute to the deficiency of PVpot. [ABSTRACT FROM AUTHOR]

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is one of the world's four major bay areas. Groundwater is indispensable in ensuring water supply for human production and living, as well as social and economic development. Studying the spatial–temporal variation in groundwater storage (GWS) and exploring the impact of climate change on GWS is of great significance for water resource management in the GBA. In this work, we conducted a simulation using the Community Land Model version 5.0 (CLM5.0) and combined it with Gravity Recovery and Climate Experiment (GRACE) data to calculate GWS in the GBA. In addition, based on the multiple linear regression model, we quantitatively assessed the effects of different climate factors on the change in GWS in the GBA. Comparisons with groundwater wells, automatic weather stations, and satellite observations demonstrated reasonable results. Our results showed that precipitation and evapotranspiration are the main factors affecting the change in GWS in the GBA. Precipitation dominates GWS anomaly changes in areas where wetting and precipitation vary drastically, such as the northern part of Foshan. GWS is closely related to evapotranspiration, in which water and heat changes are significant. [ABSTRACT FROM AUTHOR]

In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas. [ABSTRACT FROM AUTHOR]

Hydrology and land surface and models (HM and LSM) are essential tools for estimating global terrestrial water storage (TWS), an important component of the global water budget for assessing the accessibility and long-term variability of water supplies. With the expansion of open-source and open-data policies, the community can now perform model TWS simulation from source codes as well as directly exploit end-user hydrologic products for water resource applications. Regardless of the model effectiveness and usability, an accuracy assessment is necessary to quantify the model's global and regional strengths, weaknesses, and reliability. This paper compares the most recent global TWS estimates from six models, namely the PCRaster Global Water Balance (PCR-GLOBWB), Noah, Noah-Multiparameterization (Noah-MP), Catchment LSM, and Variable Infiltration Capacity (VIC), and Community Atmosphere Biosphere Land Exchange (CABLE)—the latter of which is cross validated for the first time. TWS observations from the Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions between 2002 and 2021 are used to validate the model. The analyses show that Noah-MP outperforms other models in terms of global average correlations and root mean square errors. PCR-GLOBWB performance is superior in irrigated regions because of the inclusion of human intervention components in the model. CABLE, a core LSM of the Australian climate model, significantly outperforms all others in Australia. CLSM performs reasonably well, but the TWS long-term trend appears to be incorrect due to an overestimated groundwater component. Noah performs similarly (but inferiorly) to Noah-MP, most likely due to model physics sharing. VIC has the least agreement with GRACE and GRACE-FO. The evaluation also sheds some light on the role of forcing data in model performance, particularly for ready-to-use products such as GLDAS, where incorporating MERRA-2 or ERA5 data into GLDAS Noah simulations may potentially improve its TWS accuracy, which has previously been overlooked due to limited modeling capacity. Despite each model's unique strength, the ensemble mean TWS, particularly when Noah-MP and PCR-GLOBWB are included, yields better TWS estimates than an individual model result. The findings of this study could serve as a benchmark for future model development and the data published in this paper could aid in the scientific advancement and discoveries of the hydrology community. [ABSTRACT FROM AUTHOR]

Due to an imbalance between incoming and outgoing radiation at the top of the atmosphere, excess heat has accumulated in Earth's climate system in recent decades, driving global warming and climatic changes. To date, it has not been quantified how much of this excess heat is used to melt ground ice in permafrost. Here, we diagnose changes in sensible and latent ground heat contents in the northern terrestrial permafrost region from ensemble‐simulations of a tailored land surface model. We find that between 1980 and 2018, about 3.9+1.4−1.6 $3.9\genfrac{}{}{0pt}{}{+1.4}{-1.6}$ ZJ of heat, of which 1.7+1.3−1.4 $1.7\genfrac{}{}{0pt}{}{+1.3}{-1.4}$ ZJ (44%) were used to melt ground ice, were absorbed by permafrost. Our estimate, which does not yet account for the potentially increased heat uptake due to thermokarst processes in ice‐rich terrain, suggests that permafrost is a persistent heat sink comparable in magnitude to other components of the cryosphere and must be explicitly considered when assessing Earth's energy imbalance. Plain Language Summary: In recent decades, planet Earth has received more energy from the sun than it has radiated back into space. This has led to an excess of energy that is causing global warming and climate change. While most of this excess energy is absorbed by Earth's oceans, some of it is used to melt ice in perennially frozen ground called permafrost. However, we do not know how much. In this study, we use a computer model to calculate how much energy the permafrost in the Arctic has absorbed over the past four decades. We find that permafrost has absorbed about 3.9 sextillion Joules of energy between 1980 and 2018. About 44% of this energy was used to melt ice contained in the ground, while the remaining energy was used to warm the ground. Our results suggest that permafrost absorbs a similar amount of energy as other large bodies of ice on Earth, such as ice sheets, glaciers, or sea ice. Our study implies that the energy taken up by permafrost needs to be considered in global assessments of Earth's energy budget, which has not been the case in the past. Key Points: We provide the first estimate of the heat uptake through both warming and thawing of Arctic terrestrial permafrostFrom 1980 to 2018, the northern terrestrial permafrost region absorbed 3.9+1.4−1.6 $3.9\genfrac{}{}{0pt}{}{+1.4}{-1.6}$ ZJ of heat, about 44% of it from melting of ground iceThawing permafrost acts as a heat sink in the Earth's climate system, similar in magnitude to other components of the cryosphere [ABSTRACT FROM AUTHOR]

Tropical forests hold large stocks of carbon in biomass and face pressures from changing climate and anthropogenic disturbance. Forests' capacity to store biomass under future conditions and accumulate biomass during regrowth after clearance are major knowledge gaps. Here we use chronosequence data, satellite observations and a C-cycle model to diagnose woody C dynamics in two dry forest ecotypes (semi-deciduous and semi-evergreen) in Yucatán, Mexico. Woody biomass differences between mature semi-deciduous (90 MgC ha−1) and semi-evergreen (175 MgC ha−1) forest landscapes are mostly explained by differences in climate (c. 60%), particularly temperature, humidity and soil moisture effects on production. Functional variation in foliar phenology, woody allocation, and wood turnover rate explained c. 40% of biomass differences between ecotypes. Modeling experiments explored varied forest clearance and regrowth cycles, under a range of climate and CO2 change scenarios to 2100. Production and steady state biomass in both ecotypes were reduced by forecast warming and drying (mean biomass 2021–2100 reduced 16–19% compared to 2001–2020), but compensated by fertilisation from rising CO2. Functional analysis indicates that trait adjustments amplify biomass losses by 70%. Experiments with disturbance and recovery across historically reported levels indicate reductions to mean forest biomass stocks over 2021–2100 similar in magnitude to climate impacts (10–19% reductions for disturbance with recovery). Forest disturbance without regrowth amplifies biomass loss by three- or four-fold. We conclude that vegetation functional differences across the Yucatán climate gradient have developed to limit climate risks. Climate change will therefore lead to functional adjustments for all forest types. These adjustments are likely to magnify biomass reductions caused directly by climate change over the coming century. However, the range of impacts of land use and land use change are as, or more, substantive than the totality of direct and indirect climate impacts. Thus the carbon storage of Yucatan's forests is highly vulnerable both to climate and land use and land use change. Our results here should be used to test and enhance land surface models use for dry forest carbon cycle assessment regionally and globally. A single plant functional type approach for modeling Yucatán's forests is not justified.

Terrestrial water storage (TWS) is pivotal in understanding environmental dynamics, climate change, and human impacts. Despite the utility of land surface models, uncertainties persist in their parameterization schemes. This study employs GRACE (Gravity Recovery and Climate Experiment) satellite data to optimize the runoff parameterization scheme within the Common Land Model by a data assimilation and parameter optimization method. The optimization algorithm sets an adjustment factor that varies with time and space for runoff simulation and updates it along with the running of the land surface model. The evaluation reveals that there are improved correlation coefficients and reduced root mean square errors compared to GRACE observations. Independent assessments by using in situ river discharge observations demonstrate enhanced model performance, particularly in mountainous regions such as western North America. This study underscores the efficacy of integrating GRACE data to improve land surface model parameterization, offering more accurate predictions of TWS changes.

Abstract The spring dry season occurring in an arid region of the southwestern United States, which receives both winter storm track and summer monsoon precipitation, is investigated. Bimodal precipitation and vegetation growth provide an opportunity to assess multiple climate mechanisms and their impact on hydroclimate and ecosystems. We detect multiple shifts from wet to drier conditions in the observational record and land surface model output. Focusing on the recent dry period, a shift in the late 1990s resulted in earlier and greater spring soil moisture draw down, and later and reduced spring vegetation green-up, compared to a prior wet period (1979–97). A simple soil moisture balance model shows this shift is driven by changes in winter precipitation. The recent post-1999 dry period and an earlier one from 1948 to 1966 are both related to the cool tropics phase of Pacific decadal variability, which influences winter precipitation. In agreement with other studies for the southwestern United States, we find the recent drought cannot be explained in terms of precipitation alone, but also is due to the rising influence of temperature, thus highlighting the sensitivity of this region to warming temperatures. Future changes in the spring dry season will therefore be affected by how tropical decadal variability evolves, and also by emerging trends due to human-driven warming.

Runoff generated by land surface models (LSMs) is extensively used to predict future river discharge under global warming. However, the structural bias of LSMs, the precipitation bias of the climate model, and other factors could cause the runoff to be biased. A model intercomparison study can help understand LSM behavior. Traditional model intercomparison can discover output variation and evaluate performance, but explaining the reason for the difference is challenging. This study developed a novel method to identify the reasons for disparities and suggest improvements. Consequently, we investigated the impacts of model settings by adopting the settings of another model in one model until it can mimic similar features in its output. Hence, we developed a process called the "emulation model." We employed two LSMs [Simple Biosphere with Urban Canopy (SiBUC) and Meteorological Research Institute Simple Biosphere model (MRI-SiB)] in the Thai River basin. SiBUC produced a higher surface runoff than MRI-SiB, and the development of the MRI-SiB emulation revealed the cause of this variation. The differences in runoff characteristics affected streamflow estimation. For instance, the SiBUC peak discharge was faster and higher than observed in the dry year. Conversely, there was a tendency to underestimate the flow estimated by MRI-SiB runoff during the transition from dry to wet seasons. Incorporating other model settings can alleviate the shortcomings of each model. Overall, the proposed method can identify the strengths and weaknesses of a model and enhance the reproducibility of the hydrological characteristics of the observed discharge in the basin. Significance Statement: This study aims to develop a new methodology for model intercomparison to identify the reasons for model output variation. Understanding why models behave differently is important to enhancing the reliability of model prediction. Our findings guide what affects disparities in land surface model runoff-based streamflow estimation, which will help reduce the uncertainty of future flood and drought predictions. [ABSTRACT FROM AUTHOR]

The study evaluates the performance of the Conformal Cubic Atmospheric Model (CCAM) when simulating an urban heat island (UHI) over the city of eThekwini, located along the southeast coast of South Africa. The CCAM is applied at a grid length of 1 km on the panel with eThekwini, in a stretched-grid mode. The CCAM is coupled to the urban climate model called the Australian Town Energy Budget (ATEB). The ATEB incorporates measured urban parameters including building characteristics, emissions, and albedo. The ATEB incorporates the land-cover boundary conditions obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The CCAM configuration applied realistically captured the orientation of the city and land-cover types. Simulations of meteorological variables such as temperatures and longwave radiation reproduced the spatial distribution and intensity of the UHI. Results show that the UHI is stronger during summer and weaker in all other seasons. The UHI developed because of natural factors (e.g., distribution of longwave radiation) and human factors (e.g., urban expansion, an increase in anthropogenic emissions, and additional heating). Because of the city's location along the coast, the UHI simulation could be weakened by atmospheric circulations resulting from land and sea breezes. Mitigation methods such as applying reflective paints and revegetation of the city may increase albedo and latent heat fluxes but reduce the sensible heat fluxes and weaken the UHI. However, the UHI may not be completely eliminated since natural factors and emissions constantly influence its development. Significance Statement: The outcome of this study could be particularly valuable for municipalities in their disaster management planning since the occurrence of UHIs can cause heat-related diseases such as heatstrokes and even fatalities, especially for the elderly, in cities. Increases in temperatures also lead to higher demand for air conditioners, which in the long term lead to higher demand and pressure on the electricity grid system as well as increased costs for the individual. As higher temperatures increase heatwave events, increases in anthropogenic emissions also result in degraded air quality that impacts health. UHIs impact human lives and can cause deterioration in health when individuals experience high temperatures in summer. Warmer temperatures also reduce energy demand (and in the long term assist with global environmental restoration). [ABSTRACT FROM AUTHOR]

Plant phenology regulates the carbon cycle and land‐atmosphere coupling. Currently, climate models often disagree with observations on the seasonal cycle of vegetation growth, partially due to how spring onset is measured and simulated. Here we use both thermal and leaf area index (LAI) based indicators to characterize spring onset in CMIP6 models. Although the historical timing varies considerably across models, most agree that spring has advanced in recent decades and will continue to arrive earlier with future warming. Across the Northern Hemisphere for the periods 1950–2014, 1981–2014, and 2015–2099 in the historical and SSP5‐8.5 simulations, thermal‐based indicators estimate spring advances of −0.7 ± 0.2, −1.4 ± 0.4, and −2.4 ± 0.7 days/decade, while LAI‐based indicators estimate −0.4 ± 0.3, −0.1 ± 0.3, and −1±1.1 days/decade. Thereby, LAI‐based indicators exhibit weaker trends toward earlier onset, leading to uncertainties from different indices being as large or larger than model uncertainty. Reconciling these discrepancies is critical for understanding future changes in spring onset. Plain Language Summary: The timing of spring onset as indicated by green‐up affects plants, bird and insect populations, rivers, and agriculture. However, state‐of‐the‐art land surface models disagree with satellite‐derived records on the seasonal cycles of vegetation growth, making it difficult to accurately predict green‐up, its response to climate, and the ecological consequences. Here we calculate two sets of spring onset indicators using climate model outputs to characterize spring onset variations and trends in the recent past and future. We find spring has been advancing in recent decades and will continue to arrive earlier with future warming. Thermal‐based indicators show that spring onset advances by −0.7, −1.4, and −2.4 days/decade in the Northern Hemisphere during 1950–2014, 1981–2014, and 2015–2099, respectively. This result suggests that spring onset today is on average four days earlier than spring onset 30 years ago and this rate will nearly double in the future. However, compared to meteorological‐based indicators, vegetation growth‐based indicators exhibit weaker trends toward earlier onset. Therefore, how we define and measure spring onset, as well as the models we use to predict changes in the environmental factors, influence future changes in the start of spring. Key Points: Divergence between thermal‐ and growth‐based spring onset indicators grows with time as global temperatures increaseThermal‐based indicators estimate spring advances of −0.7, −1.4, and −2.4 days/decade in 1950–2014, 1981–2014, and 2015–2099Vegetation growth‐based indicators exhibit weaker trends toward earlier spring onset and larger disagreements among models [ABSTRACT FROM AUTHOR]

Canada's boreal forests and tundra ecosystems are responding to unprecedented climate change with implications for the global carbon (C) cycle and global climate. However, our ability to model the response of Canada's terrestrial ecosystems to climate change is limited and there has been no comprehensive, process‐based assessment of Canada's terrestrial C cycle. We tailor the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to Canada and evaluate its C cycling performance against independent reference data. We utilize skill scores to assess model performance against reference data alongside benchmark scores that quantify the level of agreement between the reference data sets to aid in interpretation. Our results demonstrate CLASSIC's sensitivity to prescribed vegetation cover. They also show that the addition of five region‐specific Plant functional types (PFTs) improves CLASSIC's skill at simulating the Canadian C cycle. CLASSIC performs well when tailored to Canada, falls within the range of the reference data sets, and meets or exceeds the benchmark scores for most C cycling processes. New region‐specific land cover products, well‐informed PFT parameterizations, and more detailed reference data sets will facilitate improvements to the representation of the terrestrial C cycle in regional and global land surface models. Incorporating a parameterization for boreal disturbance processes and explicitly representing peatlands and permafrost soils will improve CLASSIC's future performance in Canada and other boreal regions. This is an important step toward a comprehensive process‐based assessment of Canada's terrestrial C cycle and evaluating Canada's net C balance under climate change. Plain Language Summary: Canada plays an important role in the global carbon cycle. Its boreal forests and tundra are responding to climate change. There has not been a comprehensive modeling assessment of Canada's land carbon cycle. We modify our model to better represent the distribution of plants in Canada and to include five new plant‐type representations. We then compare results from our model and other independent observation‐based data sets. Our modifications produced model results that agreed better with the independent data sets. This is an important step toward a comprehensive modeling assessment of Canada's land carbon cycle. Key Points: Using region‐specific prescribed vegetation cover and adding five region‐specific Plant functional types reduced model biases against reference dataCanadian Land Surface Scheme Including Biogeochemical Cycle's performance when tailored to the Canada domain is similar to that for comparisons between independent reference data setsFuture work should focus on boreal disturbance (i.e., fire, insect damage, and harvest), peatlands, and permafrost in Canada and other boreal regions [ABSTRACT FROM AUTHOR]

The two-resistance mechanism (TRM) attribution method, which was designed to analyze the urban–rural contrast of temperature, is improved to study the urban–rural contrast of heat stress. The improved method can be applied to diagnosing any heat stress index that is a function of temperature and humidity. As an example, in this study we use it to analyze the summertime urban–rural contrast of simplified wet bulb globe temperature (SWBGT) simulated by the Geophysical Fluid Dynamics Laboratory land model coupled with an urban canopy model. We find that the urban–rural contrast of SWBGT is primarily caused by the lack of evapotranspiration in urban areas during the daytime and the release of heat storage during the nighttime, with the urban–rural differences in aerodynamic features playing either positive or negative roles depending on the background climate. Compared to the magnitude of the urban–rural contrast of temperature, the magnitude of the urban–rural contrast of SWBGT is damped due to the moisture deficits in urban areas. We further find that the urban–rural contrast of 2-m air temperature/SWBGT is fundamentally different from that of canopy air temperature/SWBGT. Turbulent mixing in the surface layer leads to much smaller urban–rural contrasts of 2-m air temperature/SWBGT than their canopy air counterparts. Significance Statement: Heat leads to serious public health concerns, but urban and rural areas have different levels of heat stress. Our study explains the magnitude and pattern of the simulated urban–rural contrast in heat stress at the global scale and improves an attribution method to quantify which biophysical processes are mostly responsible for the simulated urban–rural contrast in heat stress. We highlight two well-known causes of higher heat stress in cities: the lack of evapotranspiration and the stronger release of heat storage. Meanwhile, we draw attention to the vegetation types in rural areas, which determine the urban–rural difference in surface roughness and significantly affect the urban–rural difference in heat stress. Last, we find the urban–rural contrasts of 2-m air temperature/SWBGT are largely reduced relative to their canopy air counterparts due to the turbulent mixing effect. [ABSTRACT FROM AUTHOR]

Land-use and land-cover changes (hereafter simply "land use") alter climates biogeophysically by affecting surface fluxes of energy and water. Yet, near-surface temperature responses to land use across observational versus model-based studies and spatial-temporal scales can be inconsistent. Here we assess the prevalence of the historical land use signal of daily maximum temperatures averaged over the warmest month of the year (tLU) using regularized optimal fingerprinting for detection and attribution. We use observations from the Climatic Research Unit and Berkeley Earth alongside historical simulations with and without land use from phase 6 of the Coupled Model Intercomparison Project to reconstruct an experiment representing the effects of land use on climate. To assess the signal of land use at spatially resolved continental and global scales, we aggregate all input data across reference regions and continents, respectively. At both scales, land use does not comprise a significantly detectable set of forcings for two of four Earth system models and their multimodel mean. Furthermore, using a principal component analysis, we find that tLU is mostly composed of the nonlocal effects of land use rather than its local effects. These findings show that, at scales relevant for climate attribution, uncertainties in Earth system model representations of land use are too high relative to the effects of internal variability to confidently assess land use. [ABSTRACT FROM AUTHOR]