Land Model Research Articles

Development of a multi-layer canopy model for E3SM Land Model with support for heterogeneous computing

The vertical structure of vegetation canopies creates micro-climates. However, the land components of most Earth System Models, including the Energy Exascale Earth System Model (E3SM), typically neglect vertical canopy structure by using a single layer big-leaf representation to simulate water, CO2, and energy exchanges between the land and the atmosphere. In this study, we developed a Multi-Layer Canopy Model for the E3SM Land Model to resolve the micro-climate created by vegetation canopies. The model developed in this study re-implements the CLM-ml_v1 to support heterogeneous computing architectures consisting of CPUs and GPUs and includes three additional optimization-based stomatal conductance models. The use of Portable, Extensible Toolkit for Scientific Computation provides a speedup of 25–50 times on a GPU relative to a CPU. The numerical implementation of the model was verified against CLM-ml_v1 for a month-long simulation using data from the Ameriflux US-University of Michigan Biological Station site. Model structural uncertainty was explored by performing control simulations for five stomatal conductance models that exclude and include the control of plant hydrodynamics (PHD) on photosynthesis. The bias in simulated sensible and latent heat fluxes was lower when PHD was accounted for in the model. Additionally, six idealized simulations were performed to study the impact of three environmental variables (i.e. air temperature, atmospheric CO2, and soil moisture) on canopy processes (i.e. net CO2 assimilation, leaf temperature, and leaf water potential). Increasing air temperature reduced net CO2 assimilation and increased air temperature. Net CO2 assimilation increased at higher atmospheric CO2, while decreasing soil moisture resulted in lower leaf water potential.

Embedding a novel phenology model into the Common Land Model for improving the modeling of land-atmosphere fluxes

Land surface models (LSMs) are essential tools for simulating the energy and momentum exchange between the land surface and the atmosphere. The vegetation phenology module, a pivotal component of LSMs, reflects the feedback of vegetation to climate variations. However, the vegetation phenology module in several LSMs, such as the Common Land Model (CoLM), face challenges concerning its rationality. Currently, the original satellite phenology mode in CoLM, which uses satellite-based leaf area index (LAI) data directly and statically, constrains the model's ability to accurately represent complex and dynamic ecosystem processes. In this study, we embedded a dynamic vegetation phenology model into CoLM, serving as a novel phenology module. We conducted an extensive comparison of the effects of various phenology modules on CoLM, focusing on their performance in the modeling of gross primary productivity (GPP) and evapotranspiration (ET) at both site and global scales. Results demonstrate an improvement in CoLM for modeling GPP and ET following the integration of the novel phenology module. Specifically, the Pearson's correlation coefficient for GPP increased by 1.29 % to 33.74 %, and for ET by 0.32 % to 7.47 % on a site scale. Furthermore, on a global scale, when compared with global reference datasets, the CoLM embedded with the novel phenology module successfully captures the spatiotemporal distribution of GPP and ET, and its performance is closer to the reference data than that of the CoLM with the original satellite phenology mode. This study highlights the importance of vegetation phenology in LSMs and its potential to improve the modeling of dynamic interactions between vegetation processes and environmental changes within LSMs.

Estimates of net primary productivity and actual evapotranspiration over the Tibetan Plateau from the Community Land Model version 4.5 with four atmospheric forcing datasets

Abstract 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 (R2) 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.

Simulating Global Terrestrial Carbon and Nitrogen Biogeochemical Cycles With Implicit and Explicit Representations of Soil Microbial Activity

AbstractNutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C‐only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit versus implicit representation of soil microbial activity, as represented in the MIcrobial‐MIneral Carbon Stabilization (MIMICS) and Carnegie‐Ames‐Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity, and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS‐CN model, which also increases the inferred SOM turnover time. Over the historical period, however, warming‐induced acceleration of SOM decomposition over high latitude ecosystems increases rates of N mineralization in MIMICS‐CN. This reduces N limitation and results in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS‐CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios.

Variational Assimilation of the SMAP Surface Soil Moisture Retrievals into an Integrated Urban Land Model

Variational Assimilation of the SMAP Surface Soil Moisture Retrievals into an Integrated Urban Land Model

Underestimated Land Heat Uptake Alters the Global Energy Distribution in CMIP6 Climate Models

AbstractCurrent global warming results in an uptake of heat by the Earth system, which is distributed among the different components of the climate system. However, current‐generation climate models deliver heat inventory and partitioning estimates of Earth system components that differ from recent observations. Here we investigate the global heat distribution under warming by using fully‐coupled CMIP6 Earth system model experiments, including a version of the MPI‐ESM with a deep land model component, accommodating the required space for more realistic terrestrial heat storage. The results show that sufficiently deep land models exert increased subsurface land heat uptake, leading to a heat uptake partitioning among the Earth system components that is closer to observational estimates. The results are relevant for the understanding of Earth's heat partitioning and highlight the importance of the land heat sink in the Earth heat inventory.

Modeling the carbon dynamics of ecosystem in a typical permafrost area

Climate change poses mounting threats to fragile alpine ecosystem worldwide. Quantifying changes in carbon stocks in response to the shifting climate was important for developing climate change mitigation and adaptation strategies. This study utilized a process-based land model (Community Land Model 5.0) to analyze spatiotemporal variations in vegetation carbon stock (VCS) and soil organic carbon stock (SOCS) across a typical permafrost area - Qinghai Province, China, from 2000 to 2018. Multiple potential factors influencing carbon stocks dynamics were analyzed, including climate, vegetation, soil hydrothermal status, and soil properties. The results indicated that provincial vegetation carbon storage was 0.22 PgC (0.32 kg/m2) and soil organic carbon pool was 9.12 PgC (13.03 kg/m2). VCS showed a mild increase while SOCS exhibited fluctuating uptrends during this period. Higher carbon stocks were observed in forest (21.74 kg/m2) and alpine meadow (18.08 kg/m2) compared to alpine steppes (9.63 kg/m2). Over 90 % of the carbon was stored in the 0-30 cm topsoil layer. The contribution rates of soil carbon in the 30-60 cm and 60-100 cm soil layers were significantly small, despite increasing stocks across all depths. Solar radiation, temperature, and NDVI emerged as primary influential factors for overall carbon stocks, exhibiting noticeable spatial variability. For SOCS at different depths, the normalized differential vegetation index (NDVI) was the foremost predictor of landscape-level carbon distributions, which explained 52.8 % of SOCS variability in shallow layers (0-30 cm) but dropped to just 12.97 % at the depth of 30-60 cm. However, the dominance of NDVI diminished along the soil depth gradients, superseded by radiation and precipitation. Additionally, with an increase in soil depth, the influence of inherent soil properties also increased. This simulation provided crucial insights for landscape-scale carbon responses to climate change, and offered valuable reference for other climate change-sensitive areas in terms of ecosystem carbon management.

Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Tibetan Plateau from 1979 to 2018

The Net Primary Productivity (NPP) of the Tibetan Plateau (TP) has undergone significant changes since the 1980s. The investigation of the spatiotemporal changes of NPP and its driving factors is of significant importance. Here, we analyze the spatial and temporal trends of Net Primary Production (NPP) and the effects of meteorological factors on the NPP change on the Tibetan Plateau (TP) using version 5.0 of the Community Land Model. The results showed that the average NPP was 256 (g C·m2·yr−1) over the past 40 years, with a continuously increasing trend of 2.38 (g C·m2·yr−1). Precipitation was the main factor affecting NPP changes, temperature had no significant effect on NPP changes, while radiation showed a negative trend. Changes in precipitation, temperature and radiation account for approximately 91%, 5.3%, and 3.8% of NPP variation, respectively. Based on grass coverage, we categorized alpine grasslands into three types: high, medium, and low coverage. Our findings indicate the NPP change of the high-coverage grasslands was mainly affected by precipitation, and then the temperature and radiation. Comparatively, the precipitation change is the driving factor of the increased NPP of low-coverage grasslands, but the temperature increase is the negative factor. Our studies have implications for assessing and predicting vegetation responses to future climate change.

Biophysical Drivers of Seasonal Hysteresis of Urban Heat Islands Across Climates and Urban Landscapes

AbstractThe phenomenon of seasonal hysteresis between urban heat islands (UHIs) and background surface temperature was reported in many studies, but a unifying explanation for the causes of its magnitude, shape and looping direction remained less clear, and the impacts of urban landscapes and background climate on such hysteresis were also the subject of enquiry. Here we performed a systematic study based on a long‐term offline simulation for surface energy budgets, surface air and skin temperature of the urban‐biosphere system in East China. It was carried out with an up‐to‐date land surface model — the CLM5‐LCZs (the Community Land Model version 5 (CLM5) coupled with a newly developed urban canopy model representing diverse urban landscapes with the local climate zones classification scheme). To isolate the causes of UHIs hysteresis, a simplified framework was constructed by combining a mathematical representation of grid/subgrid model outputs, the two‐resistance mechanism method and the time‐lagged cross correlation analysis. The results exhibited distinct hysteresis patterns of UHIs such as twisted, concave‐down, concave‐up and convex‐up curve across diverse background climates and urban landscapes. The basic shapes of hysteresis depict a gradual transition from concave/convex‐up to concave/convex‐down by characterized by a larger winter UHIs from a wetter and warmer climate to a drier and colder climate. Magnitude and seasonality of surface UHIs are separately dominated by the urban‐rural contrast of evapotranspiration and heat storage at daytime and nighttime, whereas across‐climate variations of daytime and nighttime surface UHIs hysteresis are regulated by the phase shift of convection efficiency and anthropogenic heat. Urban landscapes affect the hysteresis mainly by altering the amplitude rather than the shape of looping curves. Our work could provide a fuller picture of how to mitigate urban warming considering inter‐seasonal tradeoffs over a broader region.

Analysis of landing gear simplified modeling and simulation based on amesim

Aiming at the theoretical research of aircraft take-off and landing taxiing control characteristics and real-time interactive application scenarios, the simplified model modeling method of landing gear is studied. Through the reasonable simplification hypothesis of the landing gear pillar, a benchmarked, general, and pillar-type simplified model of the landing gear is formed. In the process of ground motion, based on the force analysis of aircraft taxiing, a six-degree-of-freedom mathematical model of ground taxiing is established. Based on the Amesim six-degree-of-freedom model, the aircraft is built, and the simulation test of aircraft taxiing turning and landing brake is carried out in combination with the simplified take-off and landing model. The results show that the general benchmarked pillar landing gear model formed in this paper is reasonable and feasible, and can quickly construct the aircraft landing gear model, which is convenient for subsequent optimization and design.

On the Sensitivity of the Potential Evapotranspiration of Egypt to Different Initial Conditions of the Soil Moisture Using a High-resolution Regional Climate Model

The present study investigated the sensitivity of the Potential Evapotranspiration (PET) of Egypt to different initial conditions of soil moisture using a high-resolution Regional Climate Model (RegCM4). To address this issue, two 32-year simulations were conducted from 1979 to 2010. The first two years were considered as spin-up. In the first simulation, the RegCM4 was initialized with the satellite product ESACCI (ESA) and the RegCM4 was initialized with version 3 of the Century Reanalysis Product (CEN) in the second simulation. The PET was computed using a calibrated version of the Hargreaves–Samani (HS) equation. In the two simulations, the RegCM4 was configured with the Community Land Model version 4.5 (CLM45). Era-Interim Reanalysis of 1.5 degrees (EIN15) was used to downscale the RegCM4 with 25 km grid spacing. The ERA5-based product (ERA5) was used as the observational dataset. The results showed that switching between ESA and CEN didn’t induce a notable influence on the daily maximum air temperature. Regarding the daily minimum air temperature, the CEN outperforms the ESA concerning the Climate Research Unit (CRU). In comparison with the ERA5 product, the CEN was able to reduce the PET negative bias (0.8 to 1.8 mm day-1) better than the one noted in the ESA (0.8 to 3 mm day-1). On a point scale, the performance of the ESA/CEN varies with the location and month. Such a point was confirmed using different statistical metrics such as mean bias, Pearson correlation coefficient and standard deviation ratio. In conclusion, the CEN can be recommended to initialize the RegCM4 to ensure a reliable estimation of the PET of Egypt either in the present climate or under different future scenarios.

Development and preliminary validation of a land surface image assimilation system based on the Common Land Model

Abstract. Data assimilation is an essential approach to improve the predictions of land surface models. Due to the characteristics of single-column models, assimilation of land surface information has mostly focused on improving the assimilation of single-point variables. However, land surface variables affect short-term climate more through large-scale anomalous forcing, so it is indispensable to pay attention to the accuracy of the anomalous spatial structure of land surface variables. In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The fifth-generation ECMWF Reanalysis – Land (ERA5-Land) soil moisture reanalysis data are used as ideal observation for the preliminary effectiveness validation of the image assimilation system. The results show that the new image assimilation system is able to absorb the spatial-structure information of the observed data well and has a remarkable ability to adjust the spatial structure of soil moisture in the land model. The spatial correlation coefficient between the model surface soil moisture and observation increased from 0.39 to about 0.67 after assimilation. By assimilating the surface soil moisture data and combining these with the model physical processes, the image assimilation system can also gradually improve the spatial structure of soil moisture content at a depth of 7–28 cm, with the spatial correlation coefficient between the model soil moisture and observation increased from 0.35 to about 0.57. The forecast results show that the positive assimilation effect could be maintained for more than 30 d. The results of this study adequately demonstrate the application potential of image assimilation system in short-term climate prediction.

The Role of Regional Revenue Agency in Increasing Land and Building Tax Revenue (Pbb) Case Study nn Maulafa District, Kupang City

This research aims to determine the role of the Regional Revenue Agency of Kupang City in increasing land and building tax revenue in Maulafa District, Kupang City, and to identify factors that are obstacles to increasing PBB revenue. The type of approach used in this research is descriptive research using a qualitative approach. The research informants were nine people, and purposive sampling was used to take informants. In this research, there are four focuses to look at the role of the Regional Revenue Agency of Kupang City in increasing Land and Building Tax Revenue in Maulafa District, Kupang City, namely planning, determining targets for determining Land and Building Tax at the Regional Revenue Agency of Kupang City does not involve lower-level employees in this case officers in the field. First, distributing and billing are carried out in 9 sub-districts for three months starting from the issuance of the PBB SPPT. Second, the PBB and BPHTB sectors formed collection groups for several sub-districts to collect receivables and PBB for the current year. Third, the implementation of Land and Building Tax Services Role Model Week. Fourth, Bapenda collaborates with Law Enforcement Officials (APH), namely Satpol PP and the Prosecutor's Office. Several inhibiting factors that are often faced in efforts to increase Land and Building Tax revenues in Maulafa District, Kupang City, are Double-printing of PBB Tax Payable Letters, Unclear Taxpayer Addresses who are domiciled outside the region and data in Payable Tax Returns that are not up to date.

The Land Component LM4.1 of the GFDL Earth System Model ESM4.1: Model Description and Characteristics of Land Surface Climate and Carbon Cycling in the Historical Simulation

AbstractWe 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.

Responses of vegetation photosynthetic processes to aerosol-induced direct radiative effect in China

Aerosols play an essential role in the energy cycle of land-atmosphere systems through interactions with radiation and clouds. Aerosol-induced direct radiative effect (ADRE) has a large effect on the photosynthetic processes of vegetation in terrestrial ecosystems. We used the Fu-Liou radiation transfer model integrated with the Community Land Model (CLM) to quantify ADRE and the responses of vegetation photosynthetic processes to ADRE in China from 2001 to 2014. The aerosols exhibited a positive effect on the diffuse fraction and it increased at first and decreased later. Positive impacts of ADRE on the leaf area index (LAI) of both sun (LAISUN) and shaded leaves (LAISHA) were captured in most areas of China, with increases of 0.01 and 0.09, respectively. The changes in LAI caused by ADRE were consistent with aerosol loading in northern and eastern China. In addition, due to the large decrease in total radiation caused by aerosols, ADRE reduced the photosynthetic rate of sun leaves. However, when the aerosol optical depth (AOD) ranged from 0.50 to 0.57, ADRE had positive effects on the photosynthetic rate of shaded leaves, and the AOD interval for positive impacts on the photosynthetic rate was different in each subregion of China. Mechanistic analysis found that ADRE could reduce the ribulose 1,5-bisphosphate (RuBP) carboxylase-limited rate of carboxylation and enhance the light-limited rate. When the AOD was around 0.47, the positive impacts of ADRE on the light-limited rate peaked throughout China (∼0.149 μmol m−2 s−1).

Predicting post-fire hydrological and erosive catchment response during rainfall events. A comparison of OpenLISEM and MOHID Land models

Wildfires are a source of instability for the natural water cycle in forested watersheds, endangering the water quantity and quality reaching downstream water bodies. The faster hydrological response of a burned area leads to increased runoff and transport of sediment and ash particles during and after rainfall events. Therefore, the use of an adequate spatiotemporal resolution in hydrological models is necessary to properly estimate post-fire impacts. Especially when addressing hydrological events such as flash floods and debris flows, which are highly unpredictable and are characterized by short duration and high impact outside the burned area.This study aims to compare the ability of two hydrological models to simulate the hydrological response and sediment transport during the first year after a fire to ultimately understand which one would best serve as a post-fire hydrological predicting tool at event scale. To achieve this goal, OpenLISEM, an event-based hydrological model, and MOHID Land, a continuous model with variable timestep, were compared. Driven by several limitations identified in previous modeling exercises at this scale during the calibration phase, this work performed a parametrization through the variation in boundary conditions characterizing each event.OpenLISEM and MOHID Land models exhibited similar capabilities in simulating runoff during the first post-fire year. However, the larger erosion input parameters required by MOHID Land increase the complexity of erosion prediction and increase equifinality. In addition, MOHID Land limited capacity to perform sensitivity and uncertainty analyses emerged as a major disadvantage, hindering the assessment of the reliability of the model's predictions. Despite its limitations for not integrating subsurface flow and base flow, OpenLISEM is the most suitable model for assessing post-fire impacts on runoff and sediment production at the event scale, because of its ease of implementation and its reduced computational requirements.

Global 1 km land surface parameters for kilometer-scale Earth system modeling

Abstract. Earth system models (ESMs) are progressively advancing towards the kilometer scale (“k-scale”). However, the surface parameters for land surface models (LSMs) within ESMs running at the k-scale are typically derived from coarse-resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1 km land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31 % to 54 % of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSM and ESM modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The 1 km land surface parameters are publicly available at https://doi.org/10.5281/zenodo.10815170 (Li et al., 2024).

Evaluating the impact of peat soils and snow schemes on simulated active layer thickness at pan-Arctic permafrost sites

Abstract Permafrost stability is significantly influenced by the thermal buffering effects of snow and active-layer peat soils. In the warm season, peat soils act as a barrier to downward heat transfer mainly due to their low thermal conductivity. In the cold season, the snowpack serves as a thermal insulator, retarding the release of heat from the soil to the atmosphere. Currently, many global land models overestimate permafrost soil temperature and active layer thickness (ALT), partially due to inaccurate representations of soil organic matter (SOM) density profiles and snow thermal insulation. In this study, we evaluated the impacts of SOM and snow schemes on ALT simulations at pan-Arctic permafrost sites using the Energy Exascale Earth System Model (E3SM) land model (ELM). We conducted simulations at the Circumpolar Active Layer Monitoring (CALM) sites across the pan-Arctic domain. We improved ELM-simulated site-level ALT using a knowledge-based hierarchical optimization procedure and examined the effects of precipitation-phase partitioning methods (PPMs), snow compaction schemes, and snow thermal conductivity schemes on simulated snow depth, soil temperature, ALT, and CO2 fluxes. Results showed that the optimized ELM significantly improved agreement with observed ALT (e.g. RMSE decreased from 0.83 m to 0.15 m). Our sensitivity analysis revealed that snow-related schemes significantly impact simulated snow thermal insulation levels, soil temperature, and ALT. For example, one of the commonly used snow thermal conductivity schemes (quadratic Sturm or SturmQua) generally produced warmer soil temperatures and larger ALT compared to the other two tested schemes. The SturmQua scheme also amplified the model’s sensitivity to PPMs and predicted deeper ALTs than the other two snow schemes under both current and future climates. The study highlights the importance of accurately representing snow-related processes and peat soils in land models to enhance permafrost dynamics simulations.

Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)

Abstract. Understanding carbon exchange processes between land reservoirs and the atmosphere is essential for predicting carbon–climate feedbacks. Still, considerable uncertainty remains in the representation of the terrestrial carbon cycle in Earth system models. An emerging strategy to constrain these uncertainties is to include the role of different microbial groups explicitly. Following this approach, we extend the framework of the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with additional mycorrhizal groups and a nitrogen cycle that includes a novel representation of inorganic nitrogen sorption to particles via a Langmuir isotherm. MIMICS+ v1.0 is designed to capture and quantify relationships between soil microorganisms and their environment, with a particular emphasis on boreal ecosystems. We evaluated MIMICS+ against podzolic soil profiles in Norwegian forests as well as the conventional Community Land Model (CLM). MIMICS+ matched observed carbon stocks better than CLM and gave a broader range of C:N ratios, more in line with observations. This is mainly explained by a higher directly plant-derived fraction into the soil organic matter (SOM) pools. The model produces microbial biomass estimates in line with numbers reported in the literature. MIMICS+ also showed better representation of climate gradients than CLM, especially in terms of temperature. To investigate responses to changes in nutrient availability, we performed an N enrichment experiment and found that nitrogen sorbed to particles through the sorption algorithm served as a long-term storage of nutrients for the microbes. Furthermore, although the microbial groups responded considerably to the nitrogen enrichment, we only saw minor responses for carbon storage and respiration. Together, our results present MIMICS+ as an attractive tool for further investigations of interactions between microbial functioning and their (changing) environment.

Optimizing Parameters in the Common Land Model by Using Gravity Recovery and Climate Experiment Satellite Observations

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.