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This paper presents the INM RAS–MSU land surface scheme, extracted from the INM RAS Earth system model into an independent software complex and supplemented with several modules to reproduce new components and processes of the Earth system. The resulting software product is referred to as TerM (Terrestrial Model). The physical and mathematical foundations of the model, the main features of the software implementation, and examples of applications in reproducing components of the terrestrial hydrological and carbon cycles are briefly outlined. Separating the land surface block into a standalone software complex significantly saves computational resources when assessing the impact of global and regional climate changes on natural resources (including hydrological ones), ecosystem dynamics, and emissions of climate-relevant substances with high spatial detalization. Within the TerM modelling complex, the development, validation, and calibration of new parameterizations of physical and biogeochemical processes are being conducted in an autonomous mode for subsequent implementation into the full INM RAS Earth system model. [ABSTRACT FROM AUTHOR]

In this paper, we present the current stage of development of the INM-CM Earth system model family by the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences. The major change from the previous model version INM-CM5 is a new computational platform for the Earth System modelling. We describe the main parts of this digital platform, such as ocean-atmosphere coupling, version control, compilation/configuration, and automated testing subsystems. We also discuss major modifications of the physical parts of the climate model whereby the model simulations of observed climate were significantly improved as well as the model computational performance. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) play a crucial role in elucidating water and carbon cycles by simulating processes such as plant transpiration and evaporation from bare soil, yet calibration often relies on comparing LSM outputs of landscape total evapotranspiration (ET) and discharge with measured bulk fluxes. Discrepancies in partitioning into component fluxes predicted by various LSMs have been noted, prompting the need for improved evaluation methods. Stable water isotopes serve as effective tracers of component hydrologic fluxes, but data and model integration challenges have hindered their widespread application. Leveraging National Ecological Observation Network measurements of water isotope ratios at 16 US sites over 3 years combined with LSM‐modeled fluxes, we employed an isotope‐enabled mass balance framework to simulate ET isotope values (δET) within three operational LSMs (Mosaic, Noah, and VIC) to evaluate their partitioning. Models simulating δET values consistent with observations were deemed more reflective of water cycling in these ecosystems. Mosaic exhibited the best overall performance (Kling‐Gupta Efficiency of 0.28). For both Mosaic and Noah there were robust correlations between bare soil evaporation fraction and error (negative) as well as transpiration fraction and error (positive). We found the point at which errors are smallest (x‐intercept of the multi‐site regression) is at a higher transpiration fraction than is currently specified in the models. Which means that transpiration fraction is underestimated on average. Stable isotope tracers offer an additional tool for model evaluation and identifying areas for improvement, potentially enhancing LSM simulations and our understanding of land‐surface hydrologic processes. Plain Language Summary: Models help us understand where and how much water moves in our environment. For example, how much water moves through plants (transpiration) and how much evaporates from the soil. We usually check how correct these models are by comparing the combined evaporation and transpiration (ET) and water discharge, with field measurements. This approach can lead to errors, as models often disagree on how to split ET into plant transpiration and soil evaporation. Water isotopes (water molecules with different atomic weights) can help identify the right split in ET, but the lack of data and the difficulty in using this data in models has hindered their implementation. We used newly available water isotope data from the National Ecological Observation Network from 16 sites across the U.S. We followed this water through the models and compared their predicted isotope values of ET with observations. Models with good predictions will most likely have a correct split of ET. Analysis showed that for Noah and Mosaic models, the split of transpiration is too small on average. By following stable isotopes as a new tool for model evaluation, researchers can better identify areas for improvement, leading to more accurate simulations of water movement. Key Points: An isotope mass balance was applied to operational land surface models to evaluate their hydrologic partitioning of evapotranspirationIsotopic composition of water vapor in evapotranspiration from 16 National Ecological Observatory Network sites were compared to simulationsEvaporation and transpiration fraction were strongly correlated with simulation error; elucidating their over‐ and underestimation [ABSTRACT FROM AUTHOR]

A marked decrease in land surface snow cover within the Northern Hemisphere under global warming will warm the atmosphere near the land surface. However, minimal information exists regarding the contribution of snowpack variation to interannual surface air temperature variability. The current study investigates the effects caused by snow water equivalent (SWE) change on surface air temperature (SAT). To this end, an SWE pacemaker experiment of land–atmosphere coupling (AMIP-type) is undertaken for the period 1901–2010, during which the Model for Interdisciplinary Research on Climate, version 6 (MIROC6), atmospheric general circulation model's SWE is continuously nudged toward SWE derived from an land-only (LMIP-type) experiment. Compared to a reference MIROC6 simulation without SWE nudging, the spatial correlation of 1980–2010 interannual SWE trends in our experiment with those in GlobSnow observation data increased by 0.31 over the Northern Hemisphere. Similarly, the linear correlation of interannual SAT with reanalysis data is greater by 0.04, 0.04, 0.08, and 0.07 for autumn, winter, spring, and summer over the region from the reference experiment, respectively. It is shown that due to this SWE nudging, the modeled interannual SAT change in the Northern Hemisphere becomes more accurate. Through a surface energy budget analysis, changes in SAT are attributed to changes in surface albedo, soil evaporation, and soil temperature. Areas of greater contribution of SWE variability to SAT variability appear to shift from south to north areas as snow melts. These results highlight surface albedo, snow hydrological, and land heat storage effects through which the SWE interannual trend on the land surface significantly controls atmospheric air temperature variability near the land surface. Significance Statement: This study investigates the contribution of land snowpack to surface air temperature in the Northern Hemisphere using a climate model updated with observation-based estimates of snow water equivalent. A marked decrease in snowpack under global warming is expected to warm the atmosphere near the land surface. Variations in surface albedo, soil evaporation, and soil temperature through the snow–soil–atmosphere processes during the early snow-melting season are crucial for accurately simulating surface air temperature in the Northern Hemisphere. Moreover, terrestrial snowpack is a significant factor in global warming rates. [ABSTRACT FROM AUTHOR]

Determining the distribution and dynamics of water on land at any given moment poses a significant challenge due to the constraints of observation. Consequently, as advancements in land surface models (LSMs) have been made, numerical simulation has emerged as an increasingly accurate and effective method for hydrological research. Nonetheless, systems that represent multiple land surface parameters in a near‐real‐time manner are scarce. In this study, we present an innovative land surface and river simulation system, termed Today's Earth (TE), which generates state and flux values for the near‐surface environment with multiple outputs in near‐real‐time. There are currently three versions of TE, distinguished by the forcing data utilized: JRA‐55 version, employing the Japanese 55‐year Reanalysis (JRA‐55, from 1958 to the present); GSMaP version, utilizing, the Global Satellite Mapping of Precipitation (GSMaP, from 2001 to the present), and MODIS version, utilizing the Moderate Resolution Imaging Spectroradiometer (MODIS, from 2003 to the present). These long‐term forcing data set allow for outputs of the JRA‐55 version from 1958, the GSMaP version from 2001, and the MODIS version from 2003. Aiming to provide water and energy values on a global scale in real‐time, the TE system utilizes the LSM Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) (Takata et al., 2003, https://doi.org/10.1016/s0921‐8181(03)00030‐4; Yamazaki et al., 2011, https://doi.org/10.1029/2010wr009726) at a horizontal resolution of 0.5°, along with the river routing model CaMa‐Flood (Yamazaki et al., 2011, https://doi.org/10.1029/2010wr009726) at a horizontal resolution of 0.25°. Both land surface and river products are available in 3‐hourly, daily, and monthly intervals across all three versions. A notable feature of TE is its ability to release both state and flux parameters in near‐real‐time, offering convenience for various aspects of hydrological research. In addition to presenting the general features of TE‐Global, this study examines the performance of snow depth, soil moisture, and river discharge data in daily intervals from 2003 to 2021, with validation spanning 2003 to 2016. When comparing snow depth results, the correlation coefficient ranged between 0.644 and 0.658, while for soil moisture it ranged between 0.471 and 0.494. These findings suggest that the LSM yields comparable results when utilizing JRA‐55, MODIS, or GSMaP. Interestingly, river output from the three products exhibited distinct characteristics varying from GSMaP to JRA‐55 and MODIS. For river discharge, the correlation coefficient ranged from 0.494 to 0.519, the root mean square error ranged from 3,730 m3/s to 6,330 m3/s, and the mean absolute error ranged from 3,000 m3/s to 5,160 m3/s among the different forcing versions. The overall bias in river discharge from GSMaP was 1,570 m3/s, in contrast to −589 m3/s for JRA‐55 and −200 m3/s for MODIS. These metrics demonstrate that the TE system is capable of generating practical land surface and river products, highlighting differences arising from the use of various types of forcing data. This comprehensive system would be valuable for monitoring water‐related movements, predicting disasters, and contributing to sophisticated water resource management. Regarding its application, the TE system has been included in the World Meteorological Organization as a Global Hydrological Modelling System. All TE‐Global products can be freely accessed through File Transfer Protocol. Plain Language Summary: Understanding the distribution and movement of water on land in real‐time is difficult due to observation limitations. However, recent advancements in land surface models (LSMs) have allowed numerical simulations to become more accurate and effective for hydrological research. This study introduces a new system named Today's Earth (TE), which provides near‐real‐time data on the state of the Earth's surface and water movement. The TE system uses three different versions, using JRA55, GSMaP, and MODIS to generate outputs from the past to the present. It combines two models, LSM Minimal Advanced Treatments of Surface Interaction and Runoff, and CaMa‐Flood, for land surface and river simulation, respectively. The TE system offers state and flux values at different time intervals and has been tested for its performance in estimating snow depth, soil moisture, and river discharge data. The results show that the TE system produces comparable outcomes across different data sources. The river discharge, however, varied among the different versions. Overall, the TE system is valuable for monitoring water‐related movements, predicting disasters, and managing water resources effectively. It is recognized by the World Meteorological Organization as a Global Hydrological Modelling System, and the TE‐Global products are freely accessible through FTP. Key Points: We present an innovative land surface and river simulation system, Today's Earth (TE)TE releases state and flux parameters in near‐real‐time, offering convenience for various aspects of hydrological researchThe TE system can generate practical land surface and river products, highlighting differences arising from various types of forcing data [ABSTRACT FROM AUTHOR]

High-resolution urban climate projections are needed for local decision-making on climate change adaptation. Regional climate models have resolutions that are too coarse to simulate the urban climate at such resolutions. A novel statistical–dynamical downscaling (SDD) approach is used here to downscale the EURO-CORDEX ensemble to a resolution of 1 km while adding the effect of the city of Paris (France) on air temperature. The downscaled atmospheric fields are then used to drive the Town Energy Balance urban canopy model to produce high-resolution temperature maps over the period 1970–2099, while maintaining the city's land cover in its present state. The different steps of the SDD are evaluated for the summer season. The regional climate models simulate minimum (maximum) temperatures (TN/TX) that are too high (low). After correction and downscaling, the urban simulations inherit some of these biases but give satisfactory results for summer urban heat islands (UHIs), with average biases of −0.6 K at night and +0.3 K during the day. Changes in future summer temperatures are then studied for two greenhouse gas emission scenarios, RCP4.5 and RCP8.5. Outside the city, the simulations project average increases of 4.1 and 4.8 K for TN and TX for RCP8.5, respectively. In the city, warming is lower, resulting in a decrease in UHIs of −0.19 K at night (from 2.1 to 1.9 K) and −0.16 K during the day. The changes in UHIs are explained by higher rates of warming in rural temperatures due to lower summer precipitation and soil water content and are partially offset by increased ground heat storage in the city. [ABSTRACT FROM AUTHOR]

Wildfire activity in the western United States (WUS) is increasingly impacting water supply, and land surface models (LSMs) that do not explicitly account for fire disturbances can have critical uncertainties in burned areas. This study quantified responses from the Weather Research and Forecasting Hydrological modelling system (WRF‐Hydro) to a suite of fire‐related perturbations to hydrologic soil and runoff parameters, vegetation area, land cover classifications and associated vegetation properties, and snow albedo across the heavily burned Feather River Basin in California. These experiments were used to quantify the impacts of fire‐related perturbations in model simulations under the observed meteorological conditions during the 2000–2022 water years and determine whether applying these fire‐related perturbations enhanced post‐fire model accuracy across the 11–12 post‐fire months evaluated herein. The most comprehensive fire‐aware simulation consistently modelled enhanced annual catchment streamflow (by 8%–37%), subsurface flow (by 72%–116%), and soil moisture (by 4%–9%), relative to the baseline simulation which neglected fire impacts. Simulated fire‐enhanced streamflow was predominately attributable to fire‐induced vegetation area reductions that reduced transpiration. Simulated streamflow enhancements occurred throughout the water year, excluding early‐summer (e.g., May–June) when the baseline simulation modelled relatively more snowmelt and streamflow because fire perturbations caused earlier model snow depletion. Vegetation area reductions favoured increased model ground snow accumulation and enhanced snow ablation while imposed snow albedo darkening enhanced ablation, ultimately resulting in similar peak SWE and earlier snow disappearance (on average by 8‐days) from the most comprehensive fire‐aware simulation relative to the baseline simulation. The baseline simulation had large degradations in streamflow accuracy following major fire events that were likely partially attributable to neglecting fire disturbances. Applying fire‐related perturbations reduced post‐fire streamflow anomaly biases across the three study catchments. However, remaining large post‐fire streamflow uncertainties in the fire‐perturbed simulation underscores the importance of additional observationally constrained fire‐disturbance model developments. [ABSTRACT FROM AUTHOR]

Climate change is associated with increasingly frequent disasters such as floods and droughts, and/or changes in the timing and duration of seasons. Forest ecosystems play an important role in mitigating greenhouse gas emissions to the atmosphere with their large storage of carbon (carbon sequestration). Understanding the energy balance, heat flux, and net ecosystem exchange in forests is important for developing approaches to cope with the effects of climate change via management, mitigation, and adaptation. This study utilized the Advanced Canopy--Atmosphere--Soil Algorithm (ACASA), a land surface model (LSM) which is used here to examine a dry dipterocarp tropical forest in Thailand's Phayao province. Half-hourly averaged data from 2015 were used to calibrate the model, while data from 2014 and 2016 were used to validate the model. The results showed that the ACASA model can simulate net radiation, incoming and outgoing shortwave radiation, and outgoing longwave radiation parameters very accurately, with R² values greater than 0.9 and root mean square errors of 4.49-38.02 W/m². In addition, the model can achieve reasonable estimates of sensible heat flux and latent heat flux, with R² values of 0.57-0.68. The results from this LSM have potential implications for developing and validating climate models as well as analyzing forest sensitivity and adaptation under a changing climate. [ABSTRACT FROM AUTHOR]

Ecosystem respiration (Reco) arises from interacting autotrophic and heterotrophic processes constrained by distinct drivers. Here, we evaluated up‐scaling of observed components of Reco in a mature eucalypt forest in southeast Australia and assessed whether a land surface model adequately represented all the fluxes and their seasonal temperature responses. We measured respiration from soil (Rsoil), heterotrophic soil microbes (Rh), roots (Rroot), and stems (Rstem) in 2018–2019. Reco and its components were simulated using the CABLE–POP (Community Atmosphere‐Biosphere Land Exchange–Population Orders Physiology) land surface model, constrained by eddy covariance and chamber measurements and enabled with a newly implemented Dual Arrhenius and Michaelis‐Menten (DAMM) module for soil organic matter decomposition. Eddy‐covariance based Reco (Reco.eddy, 1,439 g C m−2 y−1) was slightly higher than the sum of the respiration components (Reco.sum, 1,295 g C m−2 y−1) and simulated Reco (1,297 g C m−2 y−1). The largest mean contribution to Reco was from Rsoil (64%) across seasons. The measured contributions of Rh (49%), Rroot (15%), and Rstem (22%) to Reco.sum were very close to model outputs of 46%, 11%, and 22%, respectively. The modeled Rh was highly correlated with measured Rh (R2 = 0.66, RMSE = 0.61), empirically validating the DAMM module. The apparent temperature sensitivities (Q10) of Reco were 2.22 for Reco.sum, 2.15 for Reco.eddy, and 1.57 for CABLE‐POP. This research demonstrated that bottom‐up respiration component measurements can be successfully scaled to eddy covariance‐based Reco and help to better constrain the magnitude of individual respiration components as well as their temperature sensitivities in land surface models. Plain Language Summary: Ecosystem respiration (Reco) represents losses of carbon from the land to the atmosphere and consists of aboveground plant respiration and belowground root and microbial respiration. Because respiration processes increase exponentially with temperature, understanding their contributions to Reco is critical to predicting carbon cycle responses to warming. We used field observations to test and improve the modeling of respiration components of an evergreen eucalypt forest in Australia. Field measurements indicated that the model adequately captured the quantitative contributions of respiration components to Reco. In particular, the improved microbial version of the model was in good agreement with measurements. However, improvements are needed for modeling and measuring the autotrophic components from roots, stems, and forest canopy. This study highlights that scaling up individual respiratory sources and their temperature responses provides insights to understanding ecosystem scale carbon cycle‐climate feedbacks. Key Points: Concurrent scaled chamber measurements matched flux tower observations to within 10%Implementing a substrate function into a land surface model improved representation of heterotrophic respirationDiscrepancies between observations and simulations were largest for temperature sensitivity of canopy and root respiration [ABSTRACT FROM AUTHOR]

Accurate simulation of plant phenology is important in Earth system models as phenology modulates land‐atmosphere coupling and the carbon cycle. Evaluations based on grid cell average leaf area index (LAI) can be misleading because multiple plant functional types (PFTs) may be present in one model grid cell and PFTs with different phenology schemes have different LAI seasonal cycles. Here we examined PFT‐specific LAI magnitudes and seasonal cycles in the Community Land Model versions 5.0 and 4.5 (CLM5.0 and CLM4.5) and their relationship with the onset of growing season triggers in the Northern Hemisphere. LAI seasonal cycle and spring onset in CLM show the best agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) for temperature‐dominated deciduous PFTs. Although the agreement in LAI magnitude between CLM5.0 and MODIS is better than CLM4.5, the agreement in seasonal cycles is worse in CLM5.0. Agreements between CLM and MODIS leaf phenology are primarily determined by the PFT and phenology scheme. While productivity depends on the environmental factors to which the plant is exposed during any given growing season, differences in phenology sensitivity to its environment necessitate a decoupling between the seasonality of LAI and GPP, which in turn could lead to biases in the carbon cycle as well as surface energy balance and hence land‐atmosphere interactions. Because the discrepancy not only depends on parameterizing phenology but phenology‐environment relationship, future improvements to other model components (e.g., soil moisture) could better align the seasonal cycle of LAI and GPP. Plain Language Summary: The timing of leaf growth and senescence modifies the exchange of water, carbon, and energy between the land and the atmosphere. However, discrepancies exist between how land surface models simulate leaf area and what remote sensing products show. In this study, we examined how different types of plants (like evergreen and deciduous trees) vary in leaf area and seasonality in two versions of a land surface model—the Community Land Model versions 5.0 and 4.5 (CLM5.0 and CLM4.5). In the Northern Hemisphere, the timing of spring leaf growth and growing season length match satellite data best for temperature‐sensitive deciduous plants. The newer model version (CLM5.0) is more accurate in representing the magnitude of leaf area but is less accurate in seasonal timing. We also observed that the timing of leaf changes is mainly determined by plant type, while plant productivity is more affected by environmental factors. This misalignment between seasonal leaf area and productivity can lead to errors in understanding the carbon cycle and interactions between the land and the atmosphere. Improving other parts of the model, like soil moisture, could help better align leaf area with productivity in future models. Key Points: CLM LAI exhibits the best agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) in seasonal deciduous PFTs and deciduous broadleaf treesAgreements in LAI magnitudes and seasonal cycles between CLM and MODIS are primarily determined by the PFT and phenology schemeDiscrepancies in LAI result in biases in GPP, but improvements in one variable may not lead to better results in the other [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) are used to simulate the terrestrial component of water, energy, and biogeochemical cycles. These simulations are useful for water resources management, drought and flood prediction, and numerical climate/weather prediction. However, the usefulness of LSMs are dependent by their ability to reproduce states and fluxes realistically. Accurate measurements of water storage are useful to calibrate and validate LSMs outputs. Geological weighing lysimeters (GWLs) are instruments that can provide field‐scale estimates of integrated total water storage within a soil profile. We use field estimates of total water storage and subsurface storage to critically evaluate two different land surface models: the Modélisation Environnementale communautaire—Surface Hydrology (MESH) which uses the Canadian Land Surface Scheme (CLASS), and the Structure for Unifying Multiple Modeling Alternatives: (SUMMA). These models have differences in how the processes and properties of the land surface are represented. We attempted to parameterize each model in an equivalent manner, to minimize model differences. Both models were able to reproduce observations of total water storage and subsurface storage reasonably well. However, there were inconsistencies in the simulated timing of snowmelt; depth of soil freezing; total evapotranspiration; partitioning of evaporation between soil evaporation and evaporation of intercepted water; and soil drainage. No one model emerged as better overall, though each model had specific strengths and weaknesses that we describe. Insights from this study can be used to improve model physics and performance. [ABSTRACT FROM AUTHOR]

Understanding soil erosion and sediment transport from the hillslope scale to the regional scale is crucial for studies on water quality, soil‐water conservation, the lateral carbon cycle, environmental zoning and vulnerability. However, most existing erosion and sediment transport models are only applicable at the hillslope scale or for small watersheds with fine spatial resolutions (typically much less than 1 km). This study presents a process‐based soil erosion and sediment transport model for model applications designed for applications with coarse spatial (e.g., ≥10 km) and temporal (e.g., from hourly to daily) resolutions. This new model, referred to as VIC‐SED, effectively accounts for interactions between erosion and hydrological processes. This is achieved by tightly coupling the erosion processes with a hydrologically based Three‐layer Variable Infiltration Capacity (VIC‐3L) land surface model (LSM) and to a multi‐scale routing (MSR) model. VIC‐SED considers the impacts of (a) the spatio‐temporal variability of rainfall intensity on erosion processes and (b) soil moisture on the soil detachment process. VIC‐SED is evaluated in two watersheds. Results demonstrate that VIC‐SED is capable of reproducing water and suspended sediment discharges at coarse spatial resolutions and varying temporal scales varying from 15‐min to daily intervals. Our study indicates that the VIC‐SED model is a promising tool for studying and assessing the impacts of climate and land cover changes on suspended sediment yields over large regions using coarse spatial and temporal resolutions. Plain Language Summary: Soil erosion is a global issue impacting soil‐water conservation, making the qualification of sediment transport crucial for evaluating water quality and landscape evolution. Existing models predominantly focus on hillslopes or small watersheds, with limited applicability to larger spatial scales. This limitation is partly due to the complexity of erosion processes and the empirical formulations involved in the erosion and sediment transport. To address this gap, we developed a novel process‐based soil erosion and sediment transport model for large‐scale land surface modeling. This model is integrated with the sophisticated Three‐layer Variable Infiltration Capacity (VIC‐3L) hydrological model and a multi‐scale routing (MSR) scheme for water and sediment transport. It explicitly accounts for the spatial and temporal variability of rainfall intensity and formulates the impact of soil moisture on the soil detachment process. Results from two applications demonstrate the model's capability to predict soil erosion and sediment dynamics at large spatial and temporal scales. Key Points: A novel process‐based SED model is developed for applications with coarse spatial and temporal resolutions of precipitationThe SED model is coupled with VIC accounting for subgrid‐scale variability of rainfall intensity, soil, vegetation, and flow path lengthThe coupled VIC‐SED model is evaluated with two case studies and its model parameters are insensitive to spatial and temporal resolutions [ABSTRACT FROM AUTHOR]

For modern land surface models (LSMs) representing a singular bulk vegetation layer, the longwave optical properties (i.e., emissivity, reflectivity, and transmittivity) of vegetation layer are derived with a simplified constraint of assuming zero leaf reflectivity. This constraint is necessary, for instance, to the Beer–Lambert (B–L) law to establish a relationship between the optical properties and leaf area index. However, the simplified constraint leads to an overestimation of land surface emissivity in the vegetated regions. In this study, we introduce a new scheme considering realistic leaf reflectivity values rather than assuming zero. This new scheme is based on the relationship derived from the B–L law, but it is statistically augmented to consider the effects of leaf reflections. It is designed to emulate a multi‐vegetation‐layer numerical model known as the Norman model, which is capable of numerical calculations of multi‐reflections among leaves. This new method consists of only a couple of simple equations; despite its simplicity, it very closely mimics the Norman model; The discrepancy of the results between the new method and the Norman model is less than measurement uncertainties for any combination of input parameters. When the new scheme is implemented in the Community Land Model version 5 (CLM5), the land surface emissivity values are simulated much more consistently with global measurements, resulting in significant alterations of land surface energy budget. The enhanced realism through our new scheme is poised to contribute to more accurate numerical weather and climate simulations. Plain Language Summary: In many state‐of‐the‐art land surface models (LSMs), vegetation is represented as a single layer overlying the ground. In calculations for land surface processes, it is essential to determine the emissivity, reflectivity, and transmittivity of a vegetation layer; these properties for longwave radiation are, typically, either calculated using a simple function that relates them to the leaf area index or are prescribed to be constants. All these methods utilized a simplified constraint of zero leaf reflectivity, resulting in an apparent overestimation of the land surface emissivity over vegetated surface. In this study, a new method is developed to eliminate the simplified constraint and to consider leaf reflections. The new method is designed to be simple, having only a couple of simple equations, but involving the effect of multiple reflections of radiative beams among leaves. When the new method is applied to an LSM with realistic values of leaf reflectivity, the LSM yields much more accurate global patterns of land surface emissivity, accompanied by significant changes in land surface energy balance. Owing to its simplicity, this method is expected to be applied readily to various LSMs, potentially contributing to enhanced realism of weather and climate simulations. Key Points: A new scheme considers leaf reflection when deriving vegetation layer longwave optical properties for land surface modelDespite its simplicity, the new scheme demonstrates accuracy comparable to expensive numerical multi‐layer radiative transfer modelThe new scheme generates land surface emissivity values exhibiting greater agreements with satellite retrievals compared to previous schemes [ABSTRACT FROM AUTHOR]

Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts. Significance Statement: Flash droughts are a subset of droughts with rapid onset and intensification leading to devastating losses to crops. Rapid soil moisture decline is one way to detect flash droughts. Because there is a lack of widespread observational data, we often rely on model outputs of soil moisture. Here, we explore how the representation of vegetation within land surface models influences the U.S. flash drought characteristics covering 1981–2017. We show that the misrepresentation of vegetation status propagates soil moisture biases into flash drought monitoring, impacting our understanding of the onset, magnitude, duration, and trends in flash droughts. Our results suggest that the assimilation of near-real-time vegetation into land surface models could improve the detection, monitoring, and prediction of flash droughts. [ABSTRACT FROM AUTHOR]

Study region: Chesapeake Bay Watershed Study focus: Climate plays a critical role in regulating N loading from terrestrial ecosystems to coastal waters and further affecting the health and functioning of coastal ecosystems. However, the sensitivities of riverine exports of different N species to climate change have rarely been investigated. This study examines the response of riverine exports of ammonia (NH4+), nitrate (NO3–), dissolved organic nitrogen (DON), and particulate organic nitrogen (PON) to future changes in precipitation and temperature. A suite of climate forcings was used to drive a process-based terrestrial–aquatic model, DLEM (Dynamic Land Ecosystem Model), to project changes in N loading to the Chesapeake Bay in the mid-21st century, relative to the 1990s. New hydrological insights for the region: Our simulations show that, despite a relatively small average change in freshwater discharge driven by future climate change, annual average NH4+, NO3–, DON, PON, and total nitrogen exports are likely to change by –12 %, +13 %, +2 %, –9 %, and +9 %, respectively. Driven by rising temperature, NH4+ decreases as a result of enhanced volatilization and nitrification, but NO3– export may increase due to high mineralization and nitrification. The change in DON export is mainly regulated by discharge, and the PON change is highly uncertain due to its high sensitivity to extreme precipitation events. This study highlights the importance of considering different responses of N species to climate change when designing nutrient reduction strategies to mitigate estuarine hypoxia.

Ecosystem process modeling is vital for a broad range of applications. It requires a key metric to characterize vegetation canopies: the leaf area index (LAI). Many land surface models utilize satellite-derived LAI for modeling ecosystem processes. The use of satellite-derived LAI constrains the models for predicting ecosystem processes when observational data are unavailable. Enriching prognostic models for predicting the LAI is favorable to land surface studies for forecasting vegetation-related processes. An attention-enhanced long and short memory (AELSTM) model was developed to predict the vegetation LAI time series based on climatic data. The AELSTM outperformed comparative machine learning models when assessed using satellite and flux tower data. The LAI predicted by AELSTM are temporally and spatially consistent with satellite-derived LAI. The R2 values attained 0.86–0.93 across biomes. AELSTM can be coupled with the land surface models to predict gross primary productivity. Our proposed model was demonstrated to be effective in predicting the LAI time series under different Shared Socioeconomic Pathways in CMIP6. Our study demonstrated that deep learning approaches are capable of modeling and characterizing the spatial and temporal patterns of key vegetation variables such as LAI. Moreover, the study has provided references for vegetation processes in land surface studies.

AbstractUnderstanding the roles of land surface conditions and atmospheric circulation on continental daily temperature variance is key to improving predictions of temperature extremes. Evaporative resistance (rs, hereafter), a function of the land cover type, reflects the ease with which water can be evaporated or transpired and is a strong control on land–atmosphere interactions. This study explores the effects of rs perturbations on summer daily temperature variance using the Simple Land Interface Model (SLIM) by mimicking, for rs only, a global land cover conversion from forest to crop/grassland. Decreasing rs causes a global cooling. The cooling is larger in wetter areas and weaker in drier areas, and primarily results from perturbations in shortwave radiation (SW) and latent heat flux (LH). Decreasing rs enhances cloud cover due to greater land surface evaporation and thus reduces incoming SW over most land areas. When rs decreases, wetter areas experience strong evaporative cooling, while drier areas become more moisture-limited and thus experience less cooling. Thermal advection further shapes the temperature response by damping the combined impacts of SW and LH. Temperature variance increases in drier areas and decreases in wetter areas as rs decreases. The temperature variance changes can be largely explained from changes in the combined variance of SW and LH, including an important contribution of changes in the covariance of SW and LH. In contrast, the effects of changes in thermal advection variance mainly affect the Northern Hemisphere midlatitudes.Significance StatementThis study aims to better understand processes governing daily near-surface air temperature variance over land. We use an idealized modeling framework to explore the effects of land surface evaporative resistance (a parameter that controls how hard it is to evaporate water from the surface) on summer daily temperature variance. We find that a uniform decrease of evaporative resistance across the global land surface causes changes in the temperature variance that can be predicted from changes in the combined variance of shortwave radiation and latent heat flux. The variance of horizontal advection is important in altering the temperature variance in the Northern Hemisphere midlatitudes. Our findings shed light on predicting the characteristics of temperature variability as a function of surface conditions.

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400–2,500 nm for reflectance, 640–850 nm for fluorescence) at hourly time step and 1° spatial resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The spatial patterns of modeled GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R2 = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, for example, providing insights into when GPP, SIF, and NIRv diverge.

This paper has investigated the impact of convective parameterization schemes (CPS) and land surface models (LSM) on the simulation of summer climate over the Democratic People's Republic of Korea (DPR Korea) using the regional climate model (RegCM 4.7). The sensitivity experiments with two LSMs [Biosphere Atmosphere Transfer Scheme (BATS) and Community Land Model (CLM 3.5)] and four CPSs (Grell, Emanuel, Grell over land and Emanuel over ocean (GL_EO), Emanuel over land and Grell over ocean (EL_GO)) at 30 km horizontal resolution are carried out in summer (from June to August) for 10 years (2001–2010) for this purpose. The simulation results are compared with the available observation data provided from the State Hydro-Meteorological Administration of the DPR Korea (SHMAK). The results show that summer mean circulation patterns (SMCP) and summer averaged surface temperature (SAST) is well captured for most of the simulations, but summer rainfall is not well represented by RegCM 4.7. The performance of the CLM3.5 scheme is better in all the simulations than the BATS scheme. Among the CPSs, the EL_GO scheme shows the smallest biases in the simulation of SAST and summer rainfall. The simulations using EL_GO with CLM3.5 shows the best performance in simulating the SAST and summer rainfall over the study region among the considered CPSs and LSMs. These results will be helpful to improve the prediction of climate change over the DPR Korea. [ABSTRACT FROM AUTHOR]

Recent European heatwaves have significantly impacted forest ecosystems, leading to increased plant water stress. Advances in land surface models aim to improve the representation of vegetation drought responses by incorporating plant hydraulics into the plant functional type (PFT) classification system. However, reliance on PFTs may inadequately capture the diverse plant hydraulic traits (PHTs), potentially biasing transpiration and vegetation water stress representations. The detection of vegetation drought stress is further complicated by the mixing of different tree species and forest patches. This study uses the Community Land Model version 5.0 to simulate an experimental mixed‐forest catchment with configurations representing standalone, patched mixed, and fully‐mixed forests. Biome‐generic, PFT‐specific, or species‐specific PHTs are employed. Results emphasize the crucial role of accurately representing mixed forests in reproducing observed vegetation water stress and transpiration fluxes for both broadleaf and needleleaf tree species. The dominant vegetation fraction is a key determinant, influencing aggregated vegetation response patterns. Segregation level in PHT parameterizations shapes differences between observed and simulated transpiration fluxes. Simulated root water potential emerges as a potential metric for detecting vegetation stress periods. However, the model's plant hydraulic system has limitations in reproducing the long‐term effects of extreme weather events on needleleaf tree species. These findings highlight the complexity of modeling mixed forests and underscore the need for improved representation of plant diversity in land surface models to enhance the understanding of vegetation water stress under changing climate conditions. Plain Language Summary: Numerical simulation models for large‐scale ecosystems often oversimplify mixed forests, neglecting the diversity of species and structural complexity. This oversight impacts the accuracy of simulated plant water use, especially during droughts and heatwaves. This study focused on the specific traits of key tree species at a Luxembourg site, aiming to enhance the model's ability to represent the vegetation response to extreme conditions. By incorporating detailed plant traits, the model improved in replicating observed tree water use and identifying periods of water deficit. The findings highlight the importance of considering the functional diversity of mixed forest ecosystems for accurate simulations. Moreover, the study introduces a simple metric using the model's structure to pinpoint periods when different species experience severe water deficit. The proposed metric provides a practical tool for identifying critical periods of water stress for various species within mixed forests. This approach enhances our understanding of mixed forest dynamics under extreme conditions, emphasizing the need for nuanced representations in large‐scale models. Key Points: We show that the model's dominant fraction of a mixed ecosystem masks the water status of smaller fractions within a grid cellWe demonstrate that refining the plant hydraulic traits based on species presence improves the representation of mixed forests in CLM5We evidence the limitations of CLM5 in reproducing the needleleaf water stress using tree water deficit measurements [ABSTRACT FROM AUTHOR]

The accuracy of the simulation of carbon and water processes largely relies on the selection of atmospheric forcing datasets when driving land surface models (LSM). Particularly in high-altitude regions, choosing appropriate atmospheric forcing datasets can effectively reduce uncertainties in the LSM simulations. Therefore, this study conducted four offline LSM simulations over the Tibetan Plateau (TP) using the Community Land Model version 4.5 (CLM4.5) driven by four state-of-the-art atmospheric forcing datasets. The performances of CRUNCEP (CLM4.5 model default) and three other reanalysis-based atmospheric forcing datasets (i.e. ITPCAS, GSWP3 and WFDEI) in simulating the net primary productivity (NPP) and actual evapotranspiration (ET) were evaluated based on in situ and gridded reference datasets. Compared with in situ observations, simulated results exhibited determination coefficients (R 2) ranging from 0.58 to 0.84 and 0.59 to 0.87 for observed NPP and ET, respectively, among which GSWP3 and ITPCAS showed superior performance. At the plateau level, CRUNCEP-based simulations displayed the largest bias compared with the reference NPP and ET. GSWP3-based simulations demonstrated the best performance when comprehensively considering both the magnitudes and change trends of TP-averaged NPP and ET. The simulated ET increase over the TP during 1982–2010 based on ITPCAS was significantly greater than in the other three simulations and reference ET, suggesting that ITPCAS may not be appropriate for studying long-term ET changes over the TP. These results suggest that GSWP3 is recommended for driving CLM4.5 in conducting long-term carbon and water processes simulations over the TP. This study contributes to enhancing the accuracy of LSM in water–carbon simulations over alpine regions. [ABSTRACT FROM AUTHOR]

The influence of biogeochemical cycles, particularly the nitrogen cycle, on near‐surface meteorological fields is a critical yet understudied aspect of regional climate modeling. Neglecting such interactions may compromise the accurate representation of vegetation growth and hydrological processes in climate models, consequently affecting the simulated regional near‐surface climate conditions. In order to quantify such effects, we coupled the nitrogen‐augmented Noah‐MP land surface model with the Weather Research and Forecasting (WRF) model v4.1.2 (hereafter WRF‐CN) for regional climate modeling. Compared to the default WRF simulation without nitrogen dynamics, the WRF‐CN simulated net primary productivity, gross primary productivity (GPP), and leaf area index (LAI) were all higher in the study region. Because WRF underestimated the observed GPP and LAI due to the fixed nitrogen limitation of plant growth, these higher estimations improved WRF‐CN's performance in modeling GPP and LAI, which translated into improved simulations of near‐surface climate. Specifically, for the 2‐m air temperature, compared to WRF, WRF‐CN reduced the mean absolute error and root mean square error by 14.45% and 14.19%, respectively, while increased the Nash‐Sutcliffe efficiency coefficient by 7.23%, with the most pronounced improvements in the regions dominated by croplands. Our findings shed light on the crucial interactions between biogeochemical processes and near‐surface meteorological conditions, emphasizing the significance of incorporating terrestrial nitrogen dynamics in regional climate models. These insights contribute to advancing our understanding of climate system dynamics and improving the accuracy of climate predictions at the mesoscale. Plain Language Summary: Nitrogen is an indispensable nutrient for all forms of life. The terrestrial nitrogen availability modulates plant growth, regulates canopy transpiration, and affects soil moisture and land‐atmosphere interactions. Nitrogen dynamics has been included in most, if not all, global Earth system models. However, previous studies have rarely considered it in regional climate models. In this study, we coupled a nitrogen dynamics component with a regional climate model to study the effects of nitrogen cycling on regional near‐surface climate. Adding the nitrogen cycle affected vegetation productivity, the water cycle, and air temperature. The new model performed better than the original model, which indicates that nitrogen dynamics play an important role in regional climate simulations. Key Points: Integrate Noah‐MP‐CN into Weather Research and Forecasting for mesoscale climate modelingTerrestrial nitrogen dynamics largely improve the WRF performance in modeling the carbon cycle and near‐surface meteorological variablesThe nitrogen dynamics' influences are most noteworthy over croplands [ABSTRACT FROM AUTHOR]

This study examines the effects of land-use (LU) change on regional climate, comparing historical and future scenarios using seven climate models from phase 6 of Coupled Model Intercomparison Project–Land Use Model Intercomparison Project experiments. LU changes are evaluated relative to land-use conditions during the preindustrial climate. Using the Community Earth System Model, version 2–Large Ensemble (CESM2-LE) experiment, we distinguish LU impacts from natural climate variability. We assess LU impact locally by comparing the impacts of climate change in neighboring areas with and without LU changes. Further, we conduct CESM2 experiments with and without LU changes to investigate LU-related climate processes. A multimodel analysis reveals a shift in LU-induced climate impacts, from cooling in the past to warming in the future climate across midlatitude regions. For instance, in North America, LU's effect on air temperature changes from −0.24° ± 0.18°C historically to 0.62° ± 0.27°C in the future during the boreal summer. The CESM2-LE shows a decrease in LU-driven cooling from −0.92° ± 0.09°C in the past to −0.09° ± 0.09°C in future boreal summers in North America. A hydroclimatic perspective linking LU and climate feedback indicates LU changes causing soil moisture drying in the midlatitude regions. This contrasts with hydrology-only views showing wetter soil conditions due to LU changes. Furthermore, global warming causes widespread drying of soil moisture across various regions. Midlatitude regions shift from a historically wet regime to a water-limited transitional regime in the future climate. This results in reduced evapotranspiration, weakening LU-driven cooling in future climate projections. A strong linear relationship exists between soil moisture and evaporative fraction in midlatitudes. Significance Statement: Land–atmosphere feedback involving soil moisture can increase local temperature and affect how land-use (LU) change impacts manifest in a warming climate. Conversely, an increased surface reflectance due to LU change can decrease local temperature in the midlatitude regions. Further, the LU change signal is often mixed with the internal climate variability, making it harder to separate. This study uses a novel technique to separate LU change impact from other climate forcing in the latest generation of climate and Earth system models. In the future climate, soil moisture drying lessens the cooling impact. A large-ensemble climate experiment analysis confirms a significant weakening of the LU-driven cooling impact in the midlatitudes. Both LU and climate changes exacerbate soil moisture drying, leading to a shift toward a water-limited system where hydrological feedback becomes more influential than radiative feedback. [ABSTRACT FROM AUTHOR]

The Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth system models (ESMs). Historical hydrological variables from six ESMs driven by four meteorological forcing data sets (GSWP, WFDEI, CRU‐NCEP, and Princeton) in Land Model Intercomparison Project (LMIP) have been extensively evaluated with various high‐quality reference data sets over Chinese mainland. Compared with the reference data sets, the multi‐model ensemble means (MMEs) of most hydrological variables are underestimated, while their annual trends show high spatial consistency, with sign consistency over 56%–85% of land area. After computing and ranking four statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root‐mean‐square biases) between simulations and references, it is found that the CLM5 has the best performance, while the GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) is then used to trace sources (model, atmospheric forcing data sets and their interactions) of the uncertainty of those modeling hydrological variables for 1900–2012 (1948–2012 for runoff) over China. The results indicate that the total uncertainty and its composition vary with time and decrease significantly in recent decades, reflecting the enhanced forcing data quality. Larger forcing uncertainty existed during the early twentieth century because less available observation data sets have been adopted to constrain climate variables. For all modeling hydrological variables, the model uncertainty plays the dominant role, suggesting that the quality of LMIP products largely relies on Land surface models. Plain Language Summary: Land surface models (LSMs) have served as essential tools for simulating the response of land surface processes under changing climate. This study focuses on the performance and uncertainty of hydrological variables from historical (1900–2012) simulations in the Land Model Intercomparison Project (LMIP), which is a part of the Land Surface, Snow, and Soil Moisture Model Intercomparison Project (LS3MIP). Using various reference data sets over Chinese mainland, we evaluated precipitation, evapotranspiration, soil moisture, total runoff and snow cover fraction products from six LSMs driven by four meteorological forcing data sets. Our findings reveal that, on average, all hydrological variables are underestimated, but they exhibit a high spatial consistency of trend signs with reference data sets. Among all simulations, CLM5 stands out for its superior performance and GSWP3 forcing demonstrates the highest quality. Additionally, the Analysis of Variance (ANOVA) method is adopted to separate the simulation uncertainties into three sources from the model, the meteorological forcing data set and their interactions. It is indicated that the total uncertainty has substantially decreased in recent decades, and the model uncertainty is the dominant factor for these hydrological variables. This study may serve as some valuable references in selecting LSMs and forcing data sets in the future. Key Points: The precipitation, evapotranspiration, soil moisture, total runoff, and snow cover fraction in LS3MIP are extensively evaluated in ChinaFor LS3MIP historical hydrological variables over China, model uncertainty is the dominant factor overall [ABSTRACT FROM AUTHOR]

The hydrologic flows across the river–aquifer interface play an important role in groundwater dynamics and biogeochemical reactions within the subsurface; however, little is known about the effects of river–aquifer interactions on land surface processes. In this study, we developed a fully coupled three‐dimensional (3D) land surface and subsurface model at a high resolution (~1 km) that accounts for high‐frequency hydrologic exchange flow conditions to investigate how river–aquifer interactions modulate surface water budgets in the Upper Columbia‐Priest Rapids watershed, a typical semiarid watershed located in the northwestern United States where river stage fluctuates in response to reservoir releases changing. Our results show that the spatiotemporal dynamics of river–aquifer interactions are highly heterogeneous, driven mainly by river‐stage fluctuations. Adding 6.64 × 106 m3 year−1 of water over the watershed from the river to groundwater owing to the lateral flow, river–aquifer interactions led to an increase in soil evaporation and transpiration supplied by higher soil moisture content, particularly in deeper subsurface. In a hypothetic future scenarios where a 5‐m rise in river stage was assumed, the hydrologic flow exchange rates were intensified, resulting in higher surface water over the entire watershed. Overall, lateral flow induced by river–aquifer exchanges leads to an increase in evapotranspiration of ~75% in the historical period and of ~83% in the hypothetical future scenario. Our study demonstrates the potential of coupled model as an effective tool for understanding river–aquifer–land surface interactions, and indicates that river–aquifer interactions fundamentally alter the water balance of the riparian zone for the semiarid watershed and will likely become more frequent and intense in the future under the effects of climate change. [ABSTRACT FROM AUTHOR]

Many land surface models (LSMs) assume a steady‐state assumption (SS) for forest growth, leading to an overestimation of biomass in young forests. Parameters inversion under SS will potentially result in biased carbon fluxes and stocks in a transient simulation. Incorporating age‐dependent biomass into LSMs can simulate real disequilibrium states, enabling the model to simulate forest growth from planting to its current age, and improving the biased post‐calibration parameters. In this study, we developed a stepwise optimization framework that first calibrates "fast" light‐controlled CO2 fluxes (gross primary productivity, GPP), then leaf area index (LAI), and finally "slow" growth‐controlled biomass using the Global LAnd Surface Satellite (GLASS) GPP and LAI products, and age‐dependent biomass curves for the 25 forests. To reduce the computation time, we used a machine learning‐based model to surrogate the complex integrated biosphere simulator LSM during calibration. Our calibrated model led to an error reduction in GPP, LAI, and biomass by 28.5%, 35.3% and 74.6%, respectively. When compared with net biome productivity (NBP) using no‐age‐calibrated parameters, our age‐calibrated parameters increased NBP by an average of 50 gC m−2 yr−1 across all forests, especially in the boreal needleleaf evergreen forests, the NBP increased by 118 gC m−2 yr−1 on average, increasing the estimate of the carbon sink in young forests. Our work highlights the importance of including forest age in LSMs, and provides a novel framework for better calibrating LSMs using constraints from multiple satellite products at a global scale. Plain Language Summary: Physical and biological process‐based models always overestimate the biomass of young forests, with an assumption that they usually hold maximum carbon stocks like old‐growth stands. Such an assumption can lead to biased carbon fluxes and stocks in further simulation. Considering stand age in LSMs improves their ability to simulate real forest growth. In this study, we develop a stepwise method to account for stand age effects in model simulations by assimilating remotely sensed information on vegetation productivity, leaf area, biomass, and age. To reduce the computational cost of the complex original code, we use a substitute model constructed using a machine learning method for calculations. The improved model successfully reproduces the changes in ecosystem biomass and fluxes as forest age varies. Our research provides a novel approach to improving other land surface models for predicting age‐dependent ecosystem properties. Key Points: We presented a stepwise calibration framework for better integrating age‐dependent biomass into the integrated biosphere simulator modelWe utilized a machine learning‐based model as an alternative to the physical model, accelerating the calibration processThe calibration noticeably improved the simulation of gross primary productivity, leaf area index, and biomass [ABSTRACT FROM AUTHOR]

Terrestrial hydrology is altered by fires, particularly in snow‐dominated catchments. However, fire impacts on catchment hydrology are often neglected from land surface model (LSM) simulations. Western U.S. wildfire activity has been increasing in recent decades and is projected to continue increasing over at least the next three decades, and thus it is important to evaluate if neglecting fire impacts in operational land surface models (LSMs) is a significant error source that has a noticeable signal among other sources of uncertainty. We evaluate a widely used state‐of‐the‐art LSM (Noah‐MP) in runoff and snowpack simulations at two representative fire‐affected snow‐dominated catchments in the Pacific Northwest: Andrew's Creek in Washington and Johnson Creek in Idaho. These two catchments are selected across all western U.S. fire‐affected catchments because they are snow‐dominated and experienced more than 50% burning in a single fire event with minimal burning outside of this event, which allows analyses of distinct pre‐ and post‐fire periods. There are statistically significant shifts in model skills from pre‐to post‐fire years in simulating runoff and snowpack. At both study catchments, simulations miss enhancements in early‐spring runoff and annual runoff efficiency during post‐fire years, resulting in persistent underestimates of annual runoff anomalies throughout the 12‐year post‐fire analysis periods. Enhanced post‐fire snow accumulation and melt contributes to observed but unmodeled increases of spring runoff and annual runoff efficiency at these catchments. Informing simulations with satellite observed land cover classifications, leaf area index, and green fraction do not consistently improve the model ability to simulate hydrologic responses to fire disturbances. Plain Language Summary: Western U.S. fire activity has been increasing in recent decades and is expected to continue to increase in coming decades. Fires remove vegetation and alter soils which in turn alters terrestrial hydrology. Fire effects on hydrology are particularly significant over snowy catchments that serve as natural water towers for major western U.S. rivers. Sophisticated models often neglect or underrepresent fire effects on land surface properties, and thus are susceptible to larger errors after fires. This study compares runoff and snow simulations from a widely used land surface model (LSM) with observations to quantify fire‐induced changes in model accuracy over two snow dominated catchments in the Pacific Northwest. Simulations persistently underestimate enhanced spring runoff and annual runoff anomalies in post‐fire years. These underestimates are consistent with observed enhancements in post‐fire snow accumulation and melt, which the model mostly failed to capture. The finding that post‐fire model errors are consistent with previously published fire impacts on hydrology supports that fire is an important error source in LSM simulations that should be accounted for. Key Points: Noah‐MP underestimates annual runoff anomalies relative to observations following fires in the Pacific‐NorthwestIn post‐fire years, Noah‐MP fails to simulate deeper snowpacks that ablate faster and the associated enhanced spring runoffInforming the model with satellite‐observed vegetation characteristics (including fire effect) did not resolve post‐fire model deficiencies [ABSTRACT FROM AUTHOR]

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400–700 nm. However, ultra‐violet (UV) radiation within 280–400 nm and far‐red (FR) radiation within 700–750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. We explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next‐generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy‐covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year−1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs. [ABSTRACT FROM AUTHOR]

We describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Land Model version 4.1 (LM4.1), which builds on component and coupled model developments over 2013–2019 for the coupled carbon‐chemistry‐climate Earth System Model Version 4.1 (ESM4.1) simulation as part of the sixth phase of the Coupled Model Intercomparison Project. Analysis of ESM4.1/LM4.1 is focused on biophysical and biogeochemical processes and interactions with climate. Key features include advanced vegetation dynamics and multi‐layer canopy energy and moisture exchanges, daily fire, land use representation, and dynamic atmospheric dust coupling. We compare LM4.1 performance in the GFDL Earth System Model (ESM) configuration ESM4.1 to the previous generation component LM3.0 in the ESM2G configuration. ESM4.1/LM4.1 provides significant improvement in the treatment of ecological processes from GFDL's previous generation models. However, ESM4.1/LM4.1 likely overestimates the influence of land use and land cover change on vegetation characteristics, particularly on pasturelands, as it overestimates the competitiveness of grasses versus trees in the tropics, and as a result, underestimates present‐day biomass and carbon uptake in comparison to observations. Plain Language Summary: The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a new Land Model (LM4.1) as part of its 4th generation coupled model development. This model includes advances from the previous generation and introduces a new vegetation demography model, multi‐layer canopy, plant hydraulics, fire, and land use representation as well as dynamic atmospheric dust coupling. Coupled within an Earth System Model (ESM4.1), LM4.1 features an improved representation of many ecological processes from the previous generation of GFDL ESMs. Key Points: A new land model LM4.1 is developed at the Geophysical Fluid Dynamics Laboratory (GFDL) for the next‐generation Earth System Model (ESM) ESM4.1LM4.1 integrates age‐height structured vegetation dynamics, multi‐layer canopy‐soil‐snow energy exchanges, and prognostic fires and mineral dustESM4.1/LM4.1 improves patterns of land surface climate and carbon cycle compared to the previous generation GFDL model ESM2G/LM3.0 [ABSTRACT FROM AUTHOR]

Hydrological predictions at subseasonal-to-seasonal (S2S) time scales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the generalized analog regression downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for 3-month hydrological forecasts for the austral autumn season (March–May) using ensemble hindcasts for 2002–17. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to 1-month lead, evapotranspiration up to 2 months lead, and soil moisture content up to 3 months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: El Niño–Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at 1-month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world. [ABSTRACT FROM AUTHOR]

The forest–atmosphere exchange of carbon and water is regulated by meteorological conditions as well as canopy properties such as leaf area index (LAI, m2 m−2), photosynthetic capacity (PC μmol m−2 s−1), or surface conductance in optimal conditions (Gs,opt, mmol m−2 s−1), which can vary seasonally and inter-annually. This variability is well understood for deciduous species but is poorly characterized in evergreen forests. Here, we quantify the seasonal dynamics of a temperate evergreen eucalypt forest with estimates of LAI, litterfall, carbon and water fluxes, and meteorological conditions from measurements and model simulations. We merged MODIS Enhanced Vegetation Index (EVI) values with site-based LAI measurements to establish a 17-year sequence of monthly LAI. We ran the Community Atmosphere Biosphere Land Exchange model (CABLE-POP (version r5046)) with constant and varying LAI for our site to quantify the influence of seasonal canopy dynamics on carbon and water fluxes. We observed that the peak of LAI occurred in late summer–early autumn, with a higher and earlier peak occurring in years when summer rainfall was greater. Seasonality in litterfall and allocation of net primary productivity (FNPP) to leaf growth (af, 0–1) drove this pattern, suggesting a complete renewal of the canopy before the timing of peak LAI. Litterfall peaked in spring, followed by a high af in summer, at the end of which LAI peaked, and PC and Gs,opt reached their maximum values in autumn, resulting from a combination of high LAI and efficient mature leaves. These canopy dynamics helped explain observations of maximum gross ecosystem production (FGEP) in spring and autumn and net ecosystem carbon loss in summer at our site. Inter-annual variability in LAI was positively correlated with Net Ecosystem Production (FNEP). It would be valuable to apply a similar approach to other temperate evergreen forests to identify broad patterns of seasonality in leaf growth and turnover. Because incorporating dynamic LAI was insufficient to fully capture the dynamics of FGEP, observations of seasonal variation in photosynthetic capacity, such as from solar-induced fluorescence, should be incorporated in land surface models to improve ecosystem flux estimates in evergreen forests. [ABSTRACT FROM AUTHOR]

Snow plays a vital role in the interaction between land and atmosphere in the state-of-the-art land surface models (LSMs) and the real world. While snow plays a crucial role as a boundary condition in meteorological applications and serves as a vital water resource in certain regions, the acquisition of its observational data poses significant challenges. An effective alternative lies in utilizing simulation data generated by Land Surface Models (LSMs), which accurately calculate the snow-related physical processes. The LSMs show significant differences in the complexities of the snow parameterizations in terms of variables and processes considered. In this regard, the synthetic intercomparisons of the snow physics in the LSMs can give insight for further improvement of each LSM. This study revealed and discussed the differences in the parameterizations among LSMs related to snow cover fraction, albedo, and snow density. We selected the most popular and well-documented LSMs embedded in the earth system models or operational forecasting systems. We examined single-layer schemes, including the Unified Noah Land Surface Model (Noah LSM), the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the Biosphere-Atmosphere Transfer Scheme (BATS), the Canadian Land Surface Scheme (CLASS), the University of Torino land surface Process Interaction model in Atmosphere (UTOPIA), and multilayer schemes of intermediate complexity including the Community Noah Land Surface Model with Multi-Parameterization Options (Noah-MP), the Community Land Model version 5 (CLM5), the Joint UK Land Environment Simulator (JULES), and the Interaction Soil-Biosphere-Atmosphere (ISBA). Through the comparison analysis, we emphasized that inclusion of geomorphic and vegetation-related variables such as elevation, slope, time-varying roughness length, and vegetation indexes as well as optimized parameters for specific regions, in the snow-related physical processes, are crucial for further improvement of the LSMs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Modeling leaf photosynthesis is essential for quantifying the carbon, water, and energy fluxes of the terrestrial biosphere. However, due to the lack of simultaneous measurements of leaf light absorption and gas exchange, canopy radiative transfer (RT) and photosynthesis modeling often rely on simplified assumptions about light absorption and electron transport. These assumptions ignore variations in leaf biophysical traits and environmental conditions. In this study, we utilized a next‐generation land surface model (LSM)—CliMA Land, which incorporates hyperspectral canopy RT and provides a more accurate representation of trait variations. We evaluated the potential bias in electron transport estimates introduced by the broadband RT schemes used in traditional LSMs. Additionally, we explored the impact of different leaf electron transport parameterization schemes on global‐scale photosynthesis and fluorescence modeling. We showed that (a) traditional LSMs that disregard the impacts of leaf temperature and leaf traits on electron transport tend to overestimate electron transport rates. (b) Photosynthesis and fluorescence within a grid can exhibit biases exceeding 20%, with these biases demonstrating contrasting seasonality. (c) Global estimates of integrated photosynthesis and fluorescence differ by 8.1% and 8.8%, respectively. These results underscore the importance of adopting more sophisticated and accurate modeling schemes, such as hyperspectral canopy RT, in future LSMs and Earth system modeling to enhance the reliability of modeling outcomes. Plain Language Summary: The way sunlight interacts within the forest canopy is often simulated using just two broad channels: one for light that helps plants grow (photosynthetically active radiation) and one for near‐infrared light. Unfortunately, these simulations don't take into account key things about leaves, like their color (determined by chlorophyll). These simplifications mean that the models ignore differences in how different leaves respond to light. For instance, green light is more common in the lower canopy, but the models treat it the same as red and blue light. The problem is that plants can use red and blue light more effectively for photosynthesis. So, while these simplified models are faster, they can lead to big mistakes when estimating how much light leaves can absorb and how much they can photosynthesize. To address this issue, we used a more detailed model that considers many different wavelengths of light. We looked at how much the simplified models might mess up estimates of photosynthesis and fluorescence. Our findings show that these errors can be larger than 20% for specific locations. To help make the simplified models more accurate, we've provided data and formulas that consider differences in leaf traits and light conditions throughout the canopy. Key Points: Hyperspectral canopy radiative transfer model is used to assess the biases in electron transport, photosynthesis, and fluorescenceVegetation gross primary productivity and solar‐induced fluorescence may be substantially biased in broadband radiative transfer modelsApproaches are provided for broadband radiative transfer models [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Integrating numerical weather forecasts that provide ensemble precipitation forecasts, land surface hydrological modeling that resolves surface and subsurface hydrological processes, and artificial intelligence techniques that correct the forecast bias, known as the "meteo-hydro-AI" approach, has emerged as a popular flood forecast method. However, its performance during extreme flood events across different interval basins has received less attention. Here, we evaluated the meteo-hydro-AI approach for forecasting extreme flood events from headwater to downstream sub-basins in the Luo River basin during 2010–2017, with forecast lead times up to 7 days. The proposed meteo-hydro approach based on ECMWF weather forecasts and the Conjunctive Surface-Subsurface Process version 2 land surface model with a spatial resolution of 1 km captured the flood hydrographs quite well. Compared with the ensemble streamflow prediction (ESP) approach based on initial conditions, the meteo-hydro approach increased the Nash-Sutcliffe efficiency of streamflow forecasts at the three outlet stations by 0.27–0.82, decreased the root-mean-squared-error by 22–49%, and performed better in reliability and discrimination. The meteo-hydro-AI approach showed marginal improvement, which suggested further evaluations with larger samples of extreme flood events should be carried out. This study demonstrated the potential of the integrated meteo-hydro-AI approach for ensemble forecasting of extreme flood events. [ABSTRACT FROM AUTHOR]

Irrigation is an important component of the terrestrial water cycle, but it is often poorly accounted for in models. Recent studies have attempted to integrate satellite data and land surface models via data assimilation (DA) to (a) detect and quantify irrigation, and (b) better estimate the related land surface variables such as soil moisture, vegetation, and evapotranspiration. In this study, different synthetic DA experiments are tested to advance satellite DA for the estimation of irrigation. We assimilate synthetic Sentinel‐1 backscatter observations into the Noah‐MP model coupled with an irrigation scheme. When updating soil moisture, we found that the DA sets better initial conditions to trigger irrigation in the model. However, DA updates to wetter conditions can inhibit irrigation simulation. Building on this limitation, we propose an improved DA algorithm using a buddy check approach. The method still updates the land surface, but now the irrigation trigger is not primarily based on the evolution of soil moisture, but on an adaptive innovation (observation minus forecast) outlier detection. The new method was found to be optimal for more temperate climates where irrigation events are less frequent and characterized by higher application rates. It was found that the DA outperforms the model‐only 14‐day irrigation estimates by about 20% in terms of root‐mean‐squared differences, when frequent (daily or every other day) observations are available. With fewer observations or high levels of noise, the system strongly underestimates the irrigation amounts. The method is flexible and can be expanded to other DA systems, also real‐world cases. Plain Language Summary: Irrigation has an important impact on the terrestrial water cycle. However, it remains poorly simulated by models and it is hard to quantify through satellite observations alone. The combination of models and satellite observations to detect and quantify irrigation has been explored in the last few years. Recently, Sentinel‐1 radar (microwave) observations have been assimilated into the Noah‐MP land surface model in order to quantify irrigation, and better estimate the related land surface variables, such as soil moisture and vegetation. This system has shown benefits but also limitations, which are highlighted and addressed in our study using synthetic experiments. We propose an improved data assimilation algorithm and test it for different sites and levels of model and observation error. For temperate regions, the new method estimates irrigation more accurately (20%) than the model alone, provided that frequent (daily or every other day) observations are available. With further developments, the new methodology could be used in a real‐world experiment. Key Points: Model‐driven irrigation estimation has limitations, also when based on improved soil moisture conditions obtained via data assimilationA new method based on an adaptive outlier detection improves the estimated irrigation in a synthetic backscatter data assimilation setupThe method performs best with frequent data assimilation in a temperate climate, reducing irrigation errors by 20% [ABSTRACT FROM AUTHOR]

Estimates of soil moisture from two National Oceanic and Atmospheric Administration (NOAA) models are compared to in situ observations. The estimates are from a high-resolution atmospheric model with a land surface model [High-Resolution Rapid Refresh (HRRR) model] and a hydrologic model from the NOAA Climate Prediction Center (CPC). Both models produce wetter soils in dry regions and drier soils in wet regions, as compared to the in situ observations. These soil moisture differences occur at most soil depths but are larger at the deeper depths below the surface (100 cm). Comparisons of soil moisture variability are also assessed as a function of soil moisture regime. Both models have lower standard deviations as compared to the in situ observations for all soil moisture regimes. The HRRR model's soil moisture is better correlated with in situ observations for drier soils as compared to wetter soils—a trend that was not present in the CPC model comparisons. In terms of seasonality, soil moisture comparisons vary depending on the metric, time of year, and soil moisture regime. Therefore, consideration of both the seasonality and soil moisture regime is needed to accurately determine model biases. These NOAA soil moisture estimates are used for a variety of forecasting and societal applications, and understanding their differences provides important context for their applications and can lead to model improvements. Significance Statement: Soil moisture is an essential variable coupling the land surface to the atmosphere. Accurate estimates of soil moisture are important for forecasting near-surface temperature and moisture, predicting where clouds will form, and assessing drought and fire risks. There are multiple estimates of soil moisture available, and in this study, we compare soil moisture estimates from two different National Oceanic and Atmospheric Administration (NOAA) models to in situ observations. These comparisons include both soil moisture amount and variability and are conducted at several soil depths, in different soil moisture regimes, and for different seasons and years. This comprehensive assessment allows for an accurate assessment of biases within these models that would be missed when conducting analyses more broadly. [ABSTRACT FROM AUTHOR]

Macroscale hydrological/land surface models are important tools for assessing historical and predicting future characteristics of extreme hydrological events, yet quantitative understandings of how these large-scale models perform in simulating extreme hydrological characteristics remain limited. Here we evaluate simulated high and low flows from 23 macroscale models within three modeling experiments (i.e., 14 climate models from CMIP6, 6 global hydrological models from ISIMIP2a, and 3 land surface models from GLDAS) against observation in 633 unimpaired catchments globally over 1971–2010. Our findings reveal limitations in simulating extreme flow characteristics by these models. Specifically, we find that (i) most models overestimate high-flow magnitudes (bias range: from +15% to +70%) and underestimate low-flow magnitudes (bias range: from −80% to −20%); (ii) interannual variability in high and low flows is reasonably reproduced by ISIMIP2a and GLDAS models but poorly reproduced by CMIP6 models; (iii) no model consistently replicates the observed trend direction in high and low flows in over two-thirds of the catchments, and most models overestimate high-flow trends and underestimate low-flow trends; and (iv) CMIP6 and GLDAS models show timing biases, with early high flows and late low flows, while ISIMIP2a models exhibit the opposite pattern. Furthermore, all models performed better in more humid environments and noncold regions, with model structure and parameterization contributing more to uncertainties than climatic forcings. Overall, our results demonstrate that extreme flow characteristics simulated from current state-of-the-art macroscale models still contain large uncertainties and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs. Significance Statement: Macroscale hydrological and land surface models represent crucial tools for assessing historical trends and making predictions about future hydrological changes. Nevertheless, our current understanding of the quantitative performance of these large-scale models in simulating extreme hydrological characteristics remains limited. Here, we evaluate simulated high and low flows from 23 state-of-the-art macroscale models against observation in 633 unimpaired catchments globally over 1971–2010. Our results reveal important limitations in the extreme flow characteristics simulated from these models and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs. The model evaluation performed herein serves as a pivotal, offering valuable insights to inform the development of the next generation of macroscale hydrological and land surface models. [ABSTRACT FROM AUTHOR]

Although urbanization fundamentally alters water and energy cycles, contemporary land surface models (LSMs) often do not include key urban vegetation processes that serve to transfer water and energy laterally across heterogeneous urban land types. Urban water/energy transfers occur when rainfall landing on rooftops, sidewalks, and driveways is redirected to lawns or pervious pavement and when transpiration occurs from branches overhanging impervious surfaces with the corresponding root water uptake takes place in nearby portions of yards. We introduce Noah‐MP for Heterogenous Urban Environments (Noah‐MP HUE), which adds sub‐grid water transfers to the widely used Noah‐MP LSM. We examine how sub‐grid water transfers change surface water and energy balances by systematically increasing the amount of simulated water transfer for four scenarios: tree canopy expanding over pavement (Urban Tree Expansion), tree canopy shifting over pavement (Urban Tree Shift), and directing impermeable runoff onto surrounding vegetation (Downspout Disconnection) or into an engineered pavement (Permeable Pavement). Even small percentages of sub‐grid water transfer can reduce runoff and enhance evapotranspiration and deep drainage. Event‐scale runoff reduction depends on storm depth, rainfall intensity, and antecedent soil moisture. Sub‐grid water transfers also tend to enhance (reduce) latent (sensible) heat. Results highlight the importance not only of fine‐scale heterogeneity on larger scale surface processes, but also the importance of urban management practices that enhance lateral water transfers and water storage–so‐called green infrastructure–as they change land surface fluxes and, potentially, atmospheric processes. This work opens a pathway to directly integrate those practices in regional climate simulations. Key Points: We develop an urban land surface model representation of impervious to pervious runon and canopy overhanging impervious surfacesUsing idealized land use, we systematically examine the effects of lateral transfers on water and energy budgets over warm seasonsWe found large changes in runoff generation, water balances, and energy partitioning when lateral transfers are simulated [ABSTRACT FROM AUTHOR]

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]