Community Land Model Version Research Articles

Emulation of Community Land Model Version 5 (CLM5) to Quantify Sensitivity of Soil Moisture to Uncertain Parameters

AbstractLand surface models (LSMs) are limited in their ability to reproduce observed soil moisture partially due to uncertainties in model parameters. Here we conduct a variance-based sensitivity analysis to quantify the relative contribution of different model parameters and their interactions to the uncertainty in the surface and root-zone soil moisture in the Community Land Model 5.0 (CLM5). We focus on soil-texture-related parameters (porosity, saturated matric potential, saturated hydraulic conductivity, shape parameter of soil-water retention model) and organic matter fraction. A Gaussian process emulator is constructed based on CLM5 simulations and used to estimate soil moisture across the five-dimensional parameter space for sensitivity analysis. The procedure is demonstrated for four seasons across various U.S. sites of distinct soil and vegetation types. We find that the emulator captures well the CLM5 behavior across the parameter space for different soil textures and seasons. The uncertainties of surface and root-zone soil moisture are dominated by the uncertainties in porosity and shape parameter with negligible parametric interactions. However, relative importance of porosity versus shape parameter varies with soil textures (sites), depths (surface versus root zone), and seasons. At most of the sites, surface soil moisture uncertainty is attributed largely to shape parameter uncertainty, while porosity uncertainty is more important for the root-zone soil moisture uncertainty. All individual parameter and interaction effects demonstrate less variability across different soil textures and seasons for root zone than for surface soil moisture. These results provide scientific guidance to prioritize reducing the uncertainty of sensitive parameters for improving soil moisture modeling with CLM.

Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0

Abstract. The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy, and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site-specific field data focusing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields, as well as water, energy, and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines following Lu et al. (2017) in CLM5; (2) implementing plant-specific parameters for sugar beet, potatoes, and winter wheat, thereby adding the two crop functional types (CFTs) for sugar beet and potatoes to the list of actively managed crops in CLM5; and (3) introducing a cover-cropping subroutine that allows multiple crop types on the same column within 1 year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity as it reduces erosion and improves soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop-specific parameterization and the winter wheat subroutines led to a significant simulation improvement in terms of energy fluxes (root-mean-square error, RMSE, reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange, and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover-cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes, seasonal cycle of LAI, and latent heat flux (reduction of wintertime RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.

Role of remotely sensed leaf area index assimilation in eco-hydrologic processes in different ecosystems over East Asia with Community Land Model version 4.5 – Biogeochemistry

The leaf area index (LAI) is a key variable for representing vegetation state, and it is closely related to simulating carbon and water exchanges between land and the overlying atmosphere in land surface and terrestrial ecosystem models. Model simulations are still limited in their representation of vegetation phenological processes and capturing the resulting LAI seasonality. Therefore, this study demonstrated how LAI assimilation into the model improved carbon and water fluxes in different ecosystems over East Asia. We assimilated LAI derived from Moderate Resolution Imaging Spectroradiometer data for seven years (2004–2010) over East Asia into the Community Land Model version 4.5 with a biogeochemistry module (CLM4.5-BGC) by employing the ensemble adjustment Kalman filter method. Results showed that LAI assimilation remarkably improved estimated gross primary production (GPP). In particular, the root mean square error decreased from 97.12 to 48.63 gC/m2/month across the region for June–August. Additionally, while evapotranspiration (ET) was less sensitive to LAI than GPP, the ET components of ground evaporation, canopy evaporation, and canopy transpiration significantly changed after assimilation. The analysis of plant-available soil water showed that LAI assimilation has unique effects on soil moisture depending on the soil layer, climate, and ecosystems. In general, the improvement in ecological prediction skill by LAI assimilation was particularly evident in temperate needleleaf forests where LAI was overestimated most distinctly. This study improves our understanding of the role of LAI assimilation in eco-hydrological processes in different ecosystems with CLM4.5-BGC, which allows for the improvement of model forecasting and more accurate simulation of the effects of LAI state evolution.

Representing Intercell Lateral Groundwater Flow and Aquifer Pumping in the Community Land Model

AbstractRepresentation of subsurface hydrology in global land surface models has been advanced but outstanding challenges and opportunities remain, especially in better simulating lateral groundwater flow and aquifer pumping for irrigation. This study improves the representation of groundwater in the latest version of the Community Land Model (version 5) by implementing a prognostic groundwater module that accounts for lateral groundwater flow, aquifer pumping, and conjunctive use of groundwater and surface water for irrigation. In particular, we introduce—for the first time—explicit representation of steady‐state well equation in large‐scale hydrological modeling. Simulations are conducted at ∼5 km resolution over the conterminous US. Groundwater level, terrestrial water storage (TWS), and river discharge are evaluated over the major US aquifers and river basins using in situ observations of groundwater and river discharge and the TWS inferred from the Gravity Recovery and Climate Experiment (GRACE) satellites. The implementation of groundwater pumping results in significant improvements in simulation of spatiotemporal water‐level change over the US High Plains and Central Valley Aquifer (CVA) and that of TWS over the CVA. Improvements in river discharge over the major US river basins are also achieved but are not substantial. Furthermore, the representation of pumping leads to certain overestimation in TWS trend compared to GRACE data in regions with high groundwater withdrawals. The consideration of lateral groundwater flow provides improvements in the subsurface response to pumping and natural wet and dry cycles, particularly at the grid cell level. These results provide a basis for improved groundwater modeling over large scales.

Plant Hydraulic Stress Strategy Improves Model Predictions of the Response of Gross Primary Productivity to Drought Across China

AbstractThe response of the terrestrial carbon cycle to droughts, which have recently increased in incidence and severity, is a hot topic in research, but the role played by vegetation water use strategies remains unclear. Both the soil moisture stress (SMS) and plant hydraulic stress (PHS) strategies are used in land surface models, but their performance in simulating the terrestrial carbon cycle and its response to droughts in various climatic zones remains uncertain. In this study, we used Community Land Model Version 5 to simulate gross primary productivity (GPP) across four different climatic subregions of China during 2000–2015 to investigate the roles played by PHS and SMS in the response of GPP to dry events and dry or rainy years in various climate zones. Simulated GPP with the two strategies were overestimated by 2.0–2.3% for the entire China compared to Moderate Resolution Imaging Spectroradiometer (MODIS) data. GPP simulated using PHS configuration fit eddy covariance and MODIS data better than that obtained using SMS across China, except for the Qinghai‐Tibet Plateau. PHS increased simulated GPP by 0.10–0.63 g C m−2 day−1 during dry events across different climatic regions because of root hydraulic lift but decreased GPP by 0.37–0.45 g C m−2 day−1 during growing season in the humid southeastern region because of high atmospheric vapor pressure deficit and root hydraulic descent. Simulations forced by data collected locally in China performed better than those forced by CRUNCEP7 global data at the site but worse at the regional scale, in particular for the Qinghai‐Tibet Plateau.

Full Implementation of Matrix Approach to Biogeochemistry Module of CLM5

AbstractEarth system models (ESMs) have been rapidly developed in recent decades to advance our understanding of climate change‐carbon cycle feedback. However, those models are massive in coding, require expensive computational resources, and have difficulty in diagnosing their performance. It is highly desirable to develop ESMs with modularity and effective diagnostics. Toward these goals, we implemented a matrix approach to the Community Land Model version 5 (CLM5) to represent carbon and nitrogen cycles. Specifically, we reorganized 18 balance equations each for carbon and nitrogen cycles among the 18 vegetation pools in the original CLM5 into two matrix equations. Similarly, 140 balance equations each for carbon and nitrogen cycles among the 140 soil pools were reorganized into two additional matrix equations. The vegetation carbon and nitrogen matrix equations are connected to soil matrix equations via litterfall. The matrix equations fully reproduce simulations of carbon and nitrogen dynamics by the original model. The computational cost for forwarding simulation of the CLM5 matrix model was 26% more expensive than the original model, largely due to calculation of additional diagnostic variables, but the spin‐up computational cost was significantly saved. We showed a case study on modeled soil carbon storage under two forcing data sets to illustrate the diagnostic capability that the matrix approach uniquely offers to understand simulation results of global carbon and nitrogen dynamics. The successful implementation of the matrix approach to CLM5, one of the most complex land models, demonstrates that most, if not all, the biogeochemical models can be reorganized into the matrix form to gain high modularity, effective diagnostics, and accelerated spin‐up.

Effects of Irrigation on Water, Carbon, and Nitrogen Budgets in a Semiarid Watershed in the Pacific Northwest: A Modeling Study

AbstractIn this study, we use the Community Land Model Version 5 (CLM5) to investigate how irrigation modulates hydrologic and biogeochemical dynamics in the Upper Columbia‐Priest Rapids (UCPR) watershed, a typical semiarid watershed located in the northwestern United States dominated by cropland. To our knowledge, this constitutes the first application of CLM5 with landscape heterogeneity fully resolved over a watershed. The model is calibrated and evaluated against flux measurements from an AmeriFlux site and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Two numerical experiments (i.e., irrigated and rainfed) are performed at hyperresolution (~1 km) over the period of 2010–2018, accounting for realistic crop types and management practices. Our results show that irrigation fundamentally alters hydrologic and biogeochemical dynamics of the watershed. By adding 79.6 mm year−1 water in addition to the mean annual precipitation of 204.0 mm year−1, irrigation leads to increases in evapotranspiration and runoff, accompanied by shallower groundwater table depths. Increases in crop productivity in response to irrigation result in more carbon storage in the watershed, and drastically large seasonal fluctuations in soil organic carbon in response to changes in soil temperature and moisture. Irrigation also intensifies the rate of denitrification and mineralization during the growing season, enhancing the interactions between soil mineral nitrogen, the atmosphere, and freshwater systems. Our study demonstrates the potential of CLM5 as an effective tool for understanding hydrological and biogeochemical dynamics in highly managed semiarid watersheds.

The impacts of late spring soil moisture in the Tibetan Plateau on summer precipitation in eastern China

AbstractThe Community Land Model version 4.5(CLM4.5), driven by the atmospheric forcing dataset CRUNCEP, was used to simulate the spatial and temporal characteristics of soil moisture across the Tibetan Plateau from 1981 to 2016. Compared with station data and two reanalysis products (i.e., the ERA‐Interim and GLDAS‐CLM), it is found that CLM4.5 can well reproduce the spatial and temporal variations of soil moisture in the Tibetan Plateau. This study reveals the relationship between soil moisture on the Tibetan Plateau in May and summer precipitation in eastern China and the physical mechanism for the impact of soil moisture on summer precipitation. Singular value decomposition (SVD)analysis shows that the surface soil moisture in the south‐central Tibetan Plateau was positively related to the summer precipitation in South China and negatively related to that in the middle and lower reaches of the Yangtze River and Northeast China. The soil moisture in the western Tibetan Plateau was the opposite of that in the south‐central region. The wetter the surface soil moisture in the south‐central Tibetan Plateau in May was, the lower the surface temperature, sensible heat flux and net longwave radiation flux, and the higher the latent heat flux and net shortwave radiation flux and leading to weaker surface heating. In contrast, lower surface soil moisture in the western Tibetan Plateau led to stronger surface heating. This led to a weaker western Pacific subtropical high and a more northerly rain belt. The weather in South China was controlled by large cyclonic circulations forming convergences and updrafts that led to more rainfall in South China. The precipitation in the middle and lower reaches of the Yangtze River and Northeast China was less under the control of the anomalous anticyclone than that in South China, and vice versa.

Simulating Agriculture in the Community Land Model Version 5

AbstractAgricultural expansion and management have greatly increased global food production and altered Earth's climate by changing physical and biogeochemical properties of terrestrial ecosystems. Few Earth system models represent agricultural management practices due to the complexity of the interactions between human decisions and biological processes on global scales. We describe the new capabilities of representing crop distributions and management in the Community Land Model (CLM) Version 5, which includes time‐varying spatial distributions of major crop types and their management through fertilization and irrigation, and temperature‐based phenological triggers. Including active crop management increases peak growing season gross primary productivity (GPP), increases the amplitude of Northern Hemisphere net ecosystem exchange, and changes seasonal and annual patterns of latent and sensible heat fluxes. The CLM5 crop model simulates the global observed historical trend of crop yields with relative fidelity from 1850 to 1990. Cropland expansion was important for increasing crop production, especially during the first century of the simulations, while fertilization and irrigation were important for increasing yields from 1950 onward. From 1990 to present day, observed crop production continued to increase while CLM5 production levels off, likely because intensification practices are not represented in the model. Specifically, CLM does not currently include increasing planting density, crop breeding and genetic modification, representations of tillage, or other management practices that may also affect crop‐climate and crop‐carbon cycle interactions and alter trends in yields. These results highlight the importance of including crop management in Earth system models, particularly as global data sets for parameterization and evaluation become more readily available.

Assessment of Seasonal Winter Temperature Forecast Errors in the RegCM Model over Northern Vietnam

This study verified the seasonal six-month forecasts for winter temperatures for northern Vietnam in 1998–2018 using a regional climate model (RegCM4) with the boundary conditions of the climate forecast system Version 2 (CFSv2) from the National Centers for Environmental Prediction (NCEP). First, different physical schemes (land-surface process, cumulus, and radiation parameterizations) in RegCM4 were applied to generate 12 single forecasts. Second, the simple ensemble forecasts were generated through the combinations of those different physical formulations. Three subclimate regions (R1, R2, R3) of northern Vietnam were separately tested with surface observations and a reanalysis dataset (Japanese 55-year reanalysis (JRA55)). The highest sensitivity to the mean monthly temperature forecasts was shown by the land-surface parameterizations (the biosphere−atmosphere transfer scheme (BATS) and community land model version 4.5 (CLM)). The BATS forecast groups tended to provide forecasts with lower temperatures than the actual observations, while the CLM forecast groups tended to overestimate the temperatures. The forecast errors from single forecasts could be clearly reduced with ensemble mean forecasts, but ensemble spreads were less than those root-mean-square errors (RMSEs). This indicated that the ensemble forecast was underdispersed and that the direct forecast from RegCM4 needed more postprocessing.

Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States.

Soil organic carbon (SOC) is a key component of the global carbon cycle, yet it is not well-represented in Earth system models to accurately predict global carbon dynamics in response to climate change. This novel study integrated deep learning, data assimilation, 25,444 vertical soil profiles, and the Community Land Model version 5 (CLM5) to optimize the model representation of SOC over the conterminous United States. We firstly constrained parameters in CLM5 using observations of vertical profiles of SOC in both a batch mode (using all individual soil layers in one batch) and at individual sites (site-by-site). The estimated parameter values from the site-by-site data assimilation were then either randomly sampled (random-sampling) to generate continentally homogeneous (constant) parameter values or maximally preserved for their spatially heterogeneous distributions (varying parameter values to match the spatial patterns from the site-by-site data assimilation) so as to optimize spatial representation of SOC in CLM5 through a deep learning technique (neural networking) over the conterminous United States. Comparing modeled spatial distributions of SOC by CLM5 to observations yielded increasing predictive accuracy from default CLM5 settings (R2 = 0.32) to randomly sampled (0.36), one-batch estimated (0.43), and deep learning optimized (0.62) parameter values. While CLM5 with parameter values derived from random-sampling and one-batch methods substantially corrected the overestimated SOC storage by that with default model parameters, there were still considerable geographical biases. CLM5 with the spatially heterogeneous parameter values optimized from the neural networking method had the least estimation error and less geographical biases across the conterminous United States. Our study indicated that deep learning in combination with data assimilation can significantly improve the representation of SOC by complex land biogeochemical models.

Influence of the dry event induced hydraulic redistribution on water and carbon cycles at five AsiaFlux forest sites: A site study combining measurements and modeling

Recent studies have incorporated hydraulic redistribution (HR) schemes into Earth system models to explore its effects on the hydrological, biogeochemical, and ecological processes of terrestrial ecosystems. However, the influence of HR on ecosystems during increasingly frequent extreme drought events, and the performance of different HR schemes at sites with contrasting ecosystem types and climate regimes, remains unclear, especially among ecologically fragile forest ecosystems in Asia. In this study, we incorporated two different HR schemes into Community Land Model version 4.5 (CLM4.5). The model generally improved predictions of soil moisture, evapotranspiration (ET), and carbon (C) fluxes at five AsiaFlux sites with contrasting climate regimes. Upward HR stimulated surface soil moisture, and increased ET by 0.29–0.68 mm H2O day−1, and net ecosystem C uptake by 1.03 × 10–6 to 1.64 × 10–5 g C m−2 s−1, at the five forest sites during dry events. Remarkable downward HR was found only in a semi-arid ecosystem located at the northeastern margin of the Qinghai–Tibet Plateau. Incorporating of a simple empirical HR scheme into CLM4.5 yielded better performance than incorporating a physically based scheme into the model for ecosystems with seasonal frozen soil layers. Combined with previous studies at AmeriFlux sites, it can be concluded that climate zones with overlapped dry season and growing season showed clear hydraulic lift during dry season; climate zones with overlapped wet season and growing season showed clear hydraulic descent if precipitation was limited (e.g. ≤ 400 mm a−1), and did not showed clear influences of HR if the precipitation was unlimited (e.g. ≥ 800 mm a−1).

Lower boundary conditions in land surface models – effects on the permafrost and the carbon pools: a case study with CLM4.5

Abstract. Earth system models (ESMs) use bottom boundaries for their land surface model (LSM) components which are shallower than the depth reached by surface temperature changes in the centennial timescale associated with recent climate change. Shallow bottom boundaries reflect energy to the surface, which along with the lack of geothermal heat flux in current land surface models, alter the surface energy balance and therefore affect some feedback processes between the ground surface and the atmosphere, such as permafrost and soil carbon stability. To evaluate these impacts, we modified the subsurface model in the Community Land Model version 4.5 (CLM4.5) by setting a non-zero crustal heat flux bottom boundary condition uniformly across the model and by increasing the depth of the lower boundary from 42.1 to 342.1 m. The modified and original land models were run during the period 1901–2005 under the historical forcing and between 2005 and 2300 under forcings for two future scenarios of moderate (Representative Concentration Pathway 4.5; RCP4.5) and high (RCP8.5) emissions. Increasing the thickness of the subsurface by 300 m increases the heat stored in the subsurface by 72 ZJ (1 ZJ = 1021 J) by the year 2300 for the RCP4.5 scenario and 201 ZJ for the RCP8.5 scenario (respective increases of 260 % and 217 % relative to the shallow model), reduces the loss of near-surface permafrost area in the Northern Hemisphere between 1901 and 2300 by 1.6 %–1.9 %, reduces the loss of intermediate-depth permafrost area (above 42.1 m depth) by a factor of 3–5.5 and reduces the loss of soil carbon by 1.6 %–3.6 %. Each increase of 20 mW m−2 of the crustal heat flux increases the temperature at 3.8 m (the soil–bedrock interface) by 0.04±0.01 K. This decreases near-surface permafrost area slightly (0.3 %–0.8 %) and produces local differences in initial stable size of the soil carbon pool across the permafrost region, which reduces the loss of soil carbon across the region by as much as 1.1 %–5.6 % for the two scenarios. Reducing subsurface thickness from 42.1 to 3.8 m, used by many LSMs, produces a larger effect than increasing it to 342.1 m, because 3.8 m is not enough to damp the annual signal and the subsurface closely follows the air temperature. We determine the optimal subsurface thickness to be 100 m for a 100-year simulation and 200 m for a simulation of 400 years. We recommend short-term simulations to use a subsurface of at least 40 m, to avoid the perturbation of seasonal temperature propagation.

Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method

Land surface evapotranspiration (ET) is important in land-atmosphere interactions of water and energy cycles. However, regional ET simulation has a great uncertainty. In this study, a highly-efficient parameter optimization framework was applied to improve ET simulations of the Community Land Model version 4.0 (CLM4) in China. The CLM4 is a model at land scale, and therefore, the monthly ET observation was used to evaluate the simulation results. The optimization framework consisted of a parameter sensitivity analysis (also called parameter screening) by the multivariate adaptive regression spline (MARS) method and sensitivity parameter optimization by the adaptive surrogate modeling-based optimization (ASMO) method. The results show that seven sensitive parameters were screened from 38 adjustable parameters in CLM4 using the MARS sensitivity analysis method. Then, using only 133 model runs, the optimal values of the seven parameters were found by the ASMO method, demonstrating the high efficiency of the method. For the optimal parameters, the ET simulations of CLM4 were improved by 7.27%. The most significant improvement occurred in the Tibetan Plateau region. Additional ET simulations from the validation years were also improved by 5.34%, demonstrating the robustness of the optimal parameters. Overall, the ASMO method was found to be efficient for conducting parameter optimization for CLM4, and the optimal parameters effectively improved ET simulation of CLM4 in China.

Plant Growth Nullifies the Effect of Increased Water‐Use Efficiency on Streamflow Under Elevated CO2 in the Southeastern United States

AbstractPlant response to elevated CO2 concentration is known to increase leaf‐level water‐use efficiency through a reduction in stomatal opening. Recent studies have emphasized that increased plant water‐use efficiency can ameliorate the impact of drought due to climate change. However, there is a potentially counterbalancing impact due to the increased leaf area. We investigate long‐term trends (1951 to 2015) of observed streamflow in the Southeastern United States (SE US) and quantify the contribution of major drivers of streamflow changes using single factor climate modeling experiments from Community Land Model Version 5 (CLM5). The SE US streamflow observations do not exhibit a trend, which is in agreement with the CLM5 control experiment. Using the factorial set of CLM5 experiments, we find that increased leaf area under elevated CO2 leads to decreased runoff and completely counteracts increased runoff due to water‐use efficiency gains under elevated CO2 and land‐use change.

Exposure to cold temperature affects the spring phenology of Alaskan deciduous vegetation types

Temperature is a dominant factor driving arctic and boreal ecosystem phenology, including leaf budburst and gross primary production (GPP) onset in Alaskan spring. Previous studies hypothesized that both accumulated growing degree day (GDD) and cold temperature (chilling) exposure are important to leaf budburst. We test this hypothesis by combining both satellite and aircraft vegetation measurements with the Community Land Model Version 4.5 (CLM), in which the end of plant dormancy depends on thermal conditions (i.e. GDD). We study the sensitivity of GPP onset of different Alaskan deciduous vegetation types to a GDD model with chilling requirement (GC model) included. The default CLM simulations have a 1–12 d earlier day of year GPP onset over Alaska vegetated regions compared to satellite constrained estimates from the Polar Vegetation Photosynthesis and Respiration Model. Integrating a GC model into CLM shifts the phase and amplitude of GPP. During 2007–2016, mean GPP onset is postponed by 5 ± 7, 4 ± 8, and 1 ± 6 d over Alaskan northern tundra, shrub, and forest, respectively. The GC model has the greatest impact during warm springs, which is critical for predicting phenology response to future warming. Overall, spring GPP high bias is reduced by 10%. Thus, including chilling requirement in thermal forcing models improves northern high-latitude phenology, but leads to other impacts during the growing season which require further investigation.

Parameterizing Perennial Bioenergy Crops in Version 5 of the Community Land Model Based on Site‐Level Observations in the Central Midwestern United States

AbstractWith projected expansion of biofuel production at a global scale, there is a pressing need to develop adequate representation of bioenergy crops in land surface models to help effectively quantify the biogeophysical and biogeochemical effects of its associated land use changes. This study implements two new perennial bioenergy crops, Miscanthus and switchgrass, into the Community Land Model Version 5 based on site‐level observations from the midwestern United States by modifying parameters associated with photosynthesis, phenology, allocation, decomposition, and carbon cost of nitrogen uptake and integrating concomitantly land management practices. Sensitivity analyses indicate that carbon and energy fluxes of the perennial crops are most sensitive to photosynthesis and phenology parameters. Validation of simulated fluxes against site‐level measurements demonstrates that the model is capable of capturing the overall patterns of energy and carbon fluxes, as well as physiological transitions from leaf emergence to senescence. Compared to annual crops, perennial crops feature longer growing season, greater leaf areas, and higher productivity, leading to increased transpiration, lower annual runoff, and larger carbon uptake. The model simulations suggest that with higher CO2 assimilation rates and lower demands for nutrients and water, high‐yielding perennial crops are promising alternatives of bioenergy feedstocks compared to traditional annual crops not only for mitigating climate change but also for environmental conservation purposes by reducing fertilizer application and therefore alleviating surface‐ and ground‐water contaminations. Although the local‐scale simulations shed light on potential benefits of using perennial grasses as bioenergy feedstocks, quantifying consequences of their plantations at larger scales warrants additional investigation.

Improving lake mixing process simulations in the Community Land Model by using <i>K</i> profile parameterization

Abstract. We improved lake mixing process simulations by applying a vertical mixing scheme, K profile parameterization (KPP), in the Community Land Model (CLM) version 4.5, developed by the National Center for Atmospheric Research. Vertical mixing of the lake water column can significantly affect heat transfer and vertical temperature profiles. However, the current vertical mixing scheme in CLM requires an arbitrarily enlarged eddy diffusivity to enhance water mixing. The coupled CLM-KPP considers a boundary layer for eddy development, and in the lake interior water mixing is associated with internal wave activity and shear instability. We chose a lake in Arctic Alaska and a lake on the Tibetan Plateau to evaluate this improved lake model. Results demonstrated that CLM-KPP reproduced the observed lake mixing and significantly improved lake temperature simulations when compared to the original CLM. Our newly improved model better represents the transition between stratification and turnover. This improved lake model has great potential for reliable physical lake process predictions and better ecosystem services.

The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

AbstractThe Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time‐evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

A Soil Moisture Data Assimilation System for Pakistan Using PODEn4DVar and CLM4.5

Soil moisture is an important state variable for land-atmosphere interactions. It is a vital land surface variable for research on hydrology, agriculture, climate, and drought monitoring. In current study, a soil moisture data assimilation framework has been developed by using the Community Land Model version 4.5 (CLM4.5) and the proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation (PODEn4DVar) algorithm. Assimilation experiments were conducted at four agricultural sites in Pakistan by assimilating in-situ soil moisture observations. The results showed that it was a reliable system. To quantify further the feasibility of the data assimilation (DA) system, soil moisture observations from the top four soil-depths (0–5, 5–10, 10–20, and 20–30 cm) were assimilated. The evaluation results indicated that the DA system improved soil moisture estimation. In addition, updating the soil moisture in the upper soil layers of CLM4.5 could improve soil moisture estimation in deeper soil layers [layer 7 (L7, 62.0 cm) and layer 8 (L8, 103.8 cm)]. To further evaluate the DA system, observing system simulation experiments (OSSEs) were designed for Pakistan by assimilating daily observations. These idealized experiments produced statistical results that had higher correlation coefficients, reduced root mean square errors, and lower biases for assimilation, which showed that the DA system is able to produce and improve soil moisture estimation in Pakistan.