Atmospheric Research Community Land Model Research Articles

Projection of vegetation impacts on future droughts over West Africa using a coupled RegCM-CLM-CN-DV

This study investigates the projected effect of vegetation feedback on drought conditions in West Africa using a regional climate model coupled with the National Center for Atmospheric Research Community Land Model, carbon–nitrogen (CN) module, and dynamic vegetation (DV) module (RegCM4.3.4-CLM-CN-DV). The role of vegetation feedback is examined based on simulations with and without the DV module. Simulations from four different global climate models are used as lateral boundary conditions for historical and future periods (historical: 1981–2000; future: 2081–2100). Using the standardized precipitation evapotranspiration index (SPEI), we quantify the frequency, duration, and intensity of droughts over two focal regions of the Sahel and the Guinea Coast in West Africa. With the dynamic vegetation (DV) considered, future droughts are expected to become more prolonged and enhanced over the Sahel. The drought is defined when the SPEI value is lower than the threshold (− 1) in the simulations with DV than those without DV. Over the Guinea Coast, the impact of DV is opposite compared with that over the Sahel. Additionally, we show that the simulated annual leaf area index associates well with annual minimum SPEI with the correlation coefficient up to 0.91, particularly over the Sahel, which is a transition zone, where land–atmosphere feedback is relatively strong. Results signify the importance of vegetation dynamics in predicting future droughts in West Africa, where the biosphere and atmosphere interactions play a significant role in the regional climate regime.

Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4.

Several studies have shown that satellite retrievals of solar-induced chlorophyll fluorescence (SIF) provide useful information on terrestrial photosynthesis or gross primary production (GPP). Here, we have incorporated equations coupling SIF to photosynthesis in a land surface model, the National Center for Atmospheric Research Community Land Model version 4 (NCAR CLM4), and have demonstrated its use as a diagnostic tool for evaluating the calculation of photosynthesis, a key process in a land surface model that strongly influences the carbon, water, and energy cycles. By comparing forward simulations of SIF, essentially as a byproduct of photosynthesis, in CLM4 with observations of actual SIF, it is possible to check whether the model is accurately representing photosynthesis and the processes coupled to it. We provide some background on how SIF is coupled to photosynthesis, describe how SIF was incorporated into CLM4, and demonstrate that our simulated relationship between SIF and GPP values are reasonable when compared with satellite (Greenhouse gases Observing SATellite; GOSAT) and insitu flux-tower measurements. CLM4 overestimates SIF in tropical forests, and we show that this error can be corrected by adjusting the maximum carboxylation rate (Vmax ) specified for tropical forests in CLM4. Our study confirms that SIF has the potential to improve photosynthesis simulation and thereby can play a critical role in improving land surface and carbon cycle models.

The impacts of heterogeneous land surface fluxes on the diurnal cycle precipitation: A framework for improving the GCM representation of land‐atmosphere interactions

AbstractThe present study aims to investigate the modulation of the diurnal cycle precipitation by heterogeneous land surface fluxes. By combining the National Center for Atmospheric Research Community Land Model (CLM) and vector vorticity equation cloud‐resolving model (VVM), experiments are designed to understand the responses of diurnal convection to heterogeneous land surface forcings of various spatial scales and intensities. To delineate the land‐atmosphere interactions, a two‐step off‐line approach is adopted. First, the CLM is driven by observational atmospheric forcing with different characteristic length scales and magnitudes. While the surface fluxes from the CLM show no significant difference in the domain‐averaged values, their spatial distribution responds significantly to the precipitation heterogeneity. In the second step, the derived CLM surface fluxes are used to drive the VVM. Results show that the timing of precipitation accelerates with increasing magnitude of surface flux perturbation, while the mean and diurnal ranges of precipitation roughly remain the same. The larger perturbation magnitude enhances the boundary layer vertical kinetic energy, which advances the development of the boundary layer. On the other hand, the mean and diurnal ranges of precipitation increase significantly, and the timing of initial precipitation delays with increasing perturbation length scale. The inland breeze induced by the large patches of surface fluxes tends to enhance the mesoscale organization of deep convection, hence the stronger precipitation. The present results highlight the importance of taking heterogeneous land surface fluxes into consideration in future development of general circulation model convection parameterizations associated with land and atmosphere interactions.

The effect of carbon‐nitrogen coupling on the reduced land carbon sink caused by tropospheric ozone

AbstractTropospheric ozone is known to have a damaging effect on carbon uptake in the terrestrial biosphere. We show that limitations of available nitrogen for sufficient plant growth reduce the negative impact of tropospheric ozone on carbon uptake in plants, leading to a smaller indirect change in radiative forcing than previously calculated. Transient climate simulations between 1900 and 2004 where plant growth is affected by tropospheric ozone have been performed by the National Center for Atmospheric Research Community Land Model with and without a coupling to the nitrogen cycle. When the land model includes nitrogen limitation on plant growth, the negative effect from tropospheric ozone on carbon uptake in plants is reduced by up to a factor of four compared to model simulation without nitrogen limitation. Only 2–5% of the radiative forcing from CO2 between 1900 and 2004 can be attributed to the indirect effect of tropospheric ozone which is a factor of six lower than results from previous studies.

Evaluation of the CLM4 Lake Model at a Large and Shallow Freshwater Lake*

AbstractModels of lake physical processes provide the lower flux boundary conditions for numerical predictions of weather and climate in lake basins. So far, there have been few studies on evaluating lake model performance at the diurnal time scale and against flux observations. The goal of this paper is to evaluate the National Center for Atmospheric Research Community Land Model version 4–Lake, Ice, Snow and Sediment Simulator using the eddy covariance and water temperature data obtained at a subtropical freshwater lake, Lake Taihu, in China. Both observations and model simulations reveal that convective overturning was commonplace at night and timed when water switched from being statically stable to being unstable. By reducing the water thermal diffusivity to 2% of the value calculated with the Henderson–Sellers parameterization, the model was able to reproduce the observed diurnal variations in water surface temperature and in sensible and latent heat fluxes. The small diffusivity suggests that the drag force of the sediment layer in this large (2500 km2) and shallow (2-m depth) lake may be strong, preventing unresolved vertical motions and suppressing wind-induced turbulence. Model results show that a large fraction of the incoming solar radiation energy was stored in the water during the daytime, and the stored energy was diffused upward at night to sustain sensible and latent heat fluxes to the atmosphere. Such a lake–atmosphere energy exchange modulated the local climate at the daily scale in this shallow lake, which is not seen in deep lakes where dominant lake–atmosphere interactions often occur at the seasonal scale.

The role of soil ice in land-atmosphere coupling over the United States: A soil moisture–precipitation winter feedback mechanism

[1] We perform two 23 year simulations using a regional climate model coupled with the National Center for Atmospheric Research Community Land Model version 3.5 (RegCM-CLM) to investigate land-atmosphere coupling in the continental United States during the cold season (October–April) and the role of soil water phase. One simulation allows the land surface to interact freely (RunI) while the other simulation uses monthly climatological soil moisture (soil liquid plus soil ice) to represent an uncoupled land surface (RunC). A winter land-atmosphere coupling signal occurs slightly south of the freezing line, indicating that the freezing line could be regarded as a temporally varying transition zone similar to the dry-to-wet transition zone identified in prior studies during boreal summer. Warmer soil temperatures in RunI translate into additional available surface energy (sum of latent and sensible heat) producing locally elevated moist static energy in the atmosphere. Additionally, warmer temperatures and greater moist static energy increase both large-scale (6–30% increase, March) and convective precipitation (>100% increase, April). As a result of the increased liquid precipitation during transition months, soil liquid water increases thereby warming winter ground and air temperatures in the interactive run and enhancing the potential for subsequent liquid precipitation. While RunC includes more persistent soil ice than is likely present in the real land-atmosphere system, this precipitation-soil moisture feedback underscores the role of soil moisture phase and variability as an important surface parameter in the winter months.

Effect of water table dynamics on land surface hydrologic memory

The representation of groundwater dynamics in land surface models has received considerable attention in recent years. Most studies have found that soil moisture increases after adding a groundwater component because of the additional supply of water to the root zone. However, the effect of groundwater on land surface hydrologic memory (persistence) has not been explored thoroughly. In this study we investigate the effect of water table dynamics on National Center for Atmospheric Research Community Land Model hydrologic simulations in terms of land surface hydrologic memory. Unlike soil water or evapotranspiration, results show that land surface hydrologic memory does not always increase after adding a groundwater component. In regions where the water table level is intermediate, land surface hydrologic memory can even decrease, which occurs when soil moisture and capillary rise from groundwater are not in phase with each other. Further, we explore the hypothesis that in addition to atmospheric forcing, groundwater variations may also play an important role in affecting land surface hydrologic memory. Analyses show that feedbacks of groundwater on land surface hydrologic memory can be positive, negative, or neutral, depending on water table dynamics. In regions where the water table is shallow, the damping process of soil moisture variations by groundwater is not significant, and soil moisture variations are mostly controlled by random noise from atmospheric forcing. In contrast, in regions where the water table is very deep, capillary fluxes from groundwater are small, having limited potential to affect soil moisture variations. Therefore, a positive feedback of groundwater to land surface hydrologic memory is observed in a transition zone between deep and shallow water tables, where capillary fluxes act as a buffer by reducing high‐frequency soil moisture variations resulting in longer land surface hydrologic memory.

Anthropogenic land cover changes in a GCM with surface albedo changes based on MODIS data

AbstractThis study uses a global climate model (GCM) to investigate the climate response at the surface and in the atmosphere caused by land use change. The climate simulations are performed with the National Center for Atmospheric Research Community Land Model 3.5 (CLM3.5) coupled to the Community Atmosphere Model 3 (CAM3) and a slab ocean model. We use the Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo product to represent surface albedo in the CLM3.5 for both present day and to reconstruct the surface albedo for natural pre‐agriculture conditions. We compare simulations including vegetation changes and surface albedo changes to simulations including only surface albedo changes. We find that the surface albedo change is most dominant in temperate regions while the change in evapotranspiration drives the climate response in the tropics. Our results show that land cover changes contribute to an annual global warming of 0.04 K, but there are large regional differences. In North America and Europe, the surface temperatures decrease by − 0.11 and − 0.09 K, respectively, while in India the surface temperatures increase by 0.09 K. When we fix the vegetation cover in the simulations and let the climate changes be driven only by the differences in surface albedo, the annual global mean surface warming is reduced, and all three regions are now associated with surface cooling.We also show that the surface albedo value for cropland is of major importance in climate simulations of land cover change. The surface albedo effect is the main driving mechanism when the change in surface albedo between agricultural and natural vegetation is substantial. Finally, we argue that differences in the surface albedo value of cropland implemented in earlier land use change studies explain the diversity in the sign and magnitude of the climate response. Copyright © 2009 Royal Meteorological Society

A Catchment‐Based Hydrologic and Routing Modeling System with explicit river channels

In this paper, we present a macroscale hydrologic modeling system with an explicit representation of storage and movement of water in river channels and floodplains. The overall modeling system, called the Catchment‐Based Hydrologic and Routing Modeling System (CHARMS), is composed of a land surface model and a river routing model that operate on a network of hydrologic catchments (or watersheds). The land surface model in CHARMS is based on the National Center for Atmospheric Research Community Land Model. The river routing model in CHARMS generates river discharge by transporting runoff generated by the catchment‐based CLM through the river network. The routing model uses information on channel cross‐section geometry, derived from the 90 m Shuttle Radar Topography Mission digital elevation model, to simulate river discharge and the associated flow depth and inundation width. CHARMS was implemented over the Wabash River basin in the central United States (drainage area 72282 km2), and simulated streamflow was validated using daily observations. Simulated flow depth and inundation extent generally followed seasonal variations in observed flooding and droughts. Limitations of some of the assumptions and scaling factors used in this study and the issues that need to be addressed for a continental‐ or global‐scale implementation of CHARMS are discussed. This paper serves as the foundation for a catchment‐based, global land surface modeling framework that could incorporate spatiotemporal variations in surface water bodies, as well as satellite measurements of these variations.

Potential predictability of crop yield using an ensemble climate forecast by a regional circulation model

Global/Regional Circulation Models (GCM/RCM) predict the interannual climate variability better than the absolute values of meteorological variables. Statistical bias-correction methods increase the quality of daily model predictions of incoming solar radiation, maximum and minimum temperatures and rainfall frequency and amount. However, when bias-corrected forecasts/hindcasts are used by dynamic crop models, timing of dry-spell occurrences generate the largest uncertainty during the linking process. In this study, we used 20 ensemble members of an 18-year period provided by the Florida State University/Center for Ocean-Atmospheric Prediction Studies (FSU/COAPS) regional spectral model coupled to the National Center for Atmospheric Research Community Land Model (CLM2). The daily seasonal-climate hindcast was bias-corrected and used as input to the CERES-Maize model, thus producing 20 crop yield ensemble members. Using observed weather data for the same period, a time series of simulated crop yields was produced. Finally, principal component (PC) regression analysis was used to predict this time series using the crop yield ensemble members as predictors. Between 13.7 and 28.8% of the simulated corn yield interannual variability was explained using only one principal component ( p < 0.05), and estimated yields were in the correct tercile by margins of 16.7 to 38.2% beyond chance. Predictability of simulated corn yields using principal components was improved relative to the use of bias-corrected daily hindcasts. Bias-correcting all meteorological variables used by the crop model increased predictability skills compared with use of raw hindcasts, individual bias-correction of rainfall, and climatological values.

Potential future changes of the terrestrial ecosystem based on climate projections by eight general circulation models

A number of previous modeling studies have assessed the implications of projected CO2‐induced climate change for future terrestrial ecosystems. However, although current understanding of possible long‐term response of vegetation to elevated CO2 and CO2‐induced climate change in some geographical areas (e.g., the high‐latitude regions) has been strengthened by dint of accumulating evidence from these past studies, it is still weak in others. This study examines the responses of global potential natural vegetation distribution, net primary production (NPP), and fire emissions to future changes in atmospheric CO2 concentration and climate using the National Center for Atmospheric Research Community Land Model's dynamic global vegetation model. The model is run to vegetative equilibrium (i.e., with respect to leaf area index (LAI) and vegetation coverage) driven with preindustrial climate and future climate near 2100, respectively, simulated by eight general circulation models (GCMs). The simulated potential vegetation under the preindustrial control mean climate (CO2 concentration held at 275 ppm) is compared with that under the SRESA1B 2100 mean climate (CO2 concentration stabilizes at 720 ppm beyond 2100). Simulated vegetation response ranges from mild changes of the fractional coverage of different plant functional types to the rather dramatic changes of dominant plant functional types. Although such response differs significantly across different GCM climate projections, a quite consistent spatial pattern emerges, characterized by a considerable poleward spread or shift of temperate and boreal forests in the Northern Hemisphere high latitudes, and a substantial degradation of vegetation type in the tropics (e.g., increase of drought deciduous trees coverage at the expense of evergreen trees) especially in portions of West and southern Africa and South America. Despite the widespread degradation of vegetation type in the tropics, NPP, and growing season LAI are predicted to increase under most GCM scenarios over most of the globe. Carbon fluxes to the atmosphere due to fire generally increase too across the globe. Such responses of NPP and fire occurrence result from the synergistic effects of CO2 concentration changes, climate changes, and vegetation changes. In the HadCM‐driven simulation, however, extreme responses are shown in some regions: Deciduous forest is replaced by grasses in large areas in the middle latitudes, and substantial areas in northern South America and southern Africa predominantly covered by evergreen forest are replaced with grasses while NPP and fire emissions reduce drastically (by more than 100%). A future paper will examine how the biosphere response documented here influences the impact of climate change on surface hydrological conditions.

An observation‐based formulation of snow cover fraction and its evaluation over large North American river basins

Snow cover strongly interacts with climate through snow albedo feedbacks. However, global climate models still are not adequate in representing snow cover fraction (SCF), i.e., the fraction of a model grid cell covered by snow. Through an analysis of the advanced very high resolution radiometer (AVHRR) derived SCF and the Canadian Meteorological Centre (CMC) gridded snow depth and snow water equivalent (SWE), we found that the SCF–snow depth relationship varies with seasons, which may be approximated by variations in snow density. We then added snow density to an existing SCF formulation to reflect the variations in the SCF–snow depth relationship with seasons. The reconstructed SCF with the gridded snow depth and SWE by employing this snow density–dependent SCF formulation agrees better with the AVHRR‐derived SCF than other formulations. The default SCF formulation in the National Center for Atmospheric Research community land model (CLM), driven by observed near‐surface meteorological forcings, simulates a smaller SCF and a shallower snow depth than observations. Implementation of the new SCF formulation into the NCAR CLM greatly improves the simulations of SCF, snow depth, and SWE in most North American (NA) river basins. The new SCF formulation increases SCF by 20–40%, decreases net solar radiation by up to 20 W m−2, and decreases surface temperature by up to 4 K in most midlatitude regions in winter and at high latitudes in spring. The new scheme reproduces the observed SCF, snow depth, and SWE in terms of interannual variability and interbasin variability in most NA river basins except for the mountainous Columbia and Colorado River basins. It produces SCF trends similar to that of AVHRR. However, it produces greater decreasing trends in ablation seasons and smaller increasing trends in accumulation seasons than those of the CMC snow depth and SWE.

Interannual variation in biogenic emissions on a regional scale

Interannual variation in biogenic emissions is not well quantified, especially on regional scales. We use a land surface model augmented with a short‐term dynamic phenology scheme to estimate the interannual variation in the emission of biogenic volatile organic compounds (BVOCs) between 1982 and 2004. We use North American Regional Reanalysis data to drive two versions of the National Center for Atmospheric Research Community Land Model (CLM) on a 0.1° grid over eastern Texas. The first version is the standard CLM with prescribed leaf area index (LAI) (i.e., LAI varies seasonally but not interannually); the second version is the standard CLM augmented with a dynamic phenology scheme (CLM‐DP) that allows LAI to respond to environmental variation. We calibrate CLM‐DP using satellite‐derived LAI as our visual constraint. When phenology is prescribed, the domain‐mean (domain‐maximum) average absolute departure from the monthly mean BVOC flux is 11.7% (70.6%); when phenology is allowed to vary with environmental conditions, it is 22.4% (137.7%). The domain‐mean (domain‐maximum) average absolute departure from the monthly mean flux is lower during summer: using CLM‐DP, it is 15.7% (35.3%); using the standard CLM, it is 7.0% (23.0%). The domain‐average, mean‐normalized standard deviation of the June‐July‐August mean BVOC flux is 0.0619 when LAI is prescribed and 0.183 when LAI varies with environmental conditions. Our results imply that interannual variation of leaf biomass density, which is primarily driven by interannual variability of precipitation, is a significant contributor to year‐to‐year differences in BVOC flux on a regional scale, of at least equal importance to interannual variation of temperature and shortwave radiation. Phenology‐driven biogenic emission variability is most pronounced in regions with relatively low emissions: as a grid cell's mean BVOC flux decreases, the mean‐normalized standard deviation of BVOC flux tends to increase. BVOC flux is most variable between years in subhumid, sparsely wooded regions where interannual variability of precipitation is relatively large.

Improving land‐surface model hydrology: Is an explicit aquifer model better than a deeper soil profile?

We use Monte Carlo analysis to show that explicit representation of an aquifer within a land‐surface model (LSM) decreases the dependence of model performance on accurate selection of subsurface hydrologic parameters. Within the National Center for Atmospheric Research Community Land Model (CLM) we evaluate three parameterizations of vertical water flow: (1) a shallow soil profile that is characteristic of standard LSMs; (2) an extended soil profile that allows for greater variation in terrestrial water storage; and (3) a lumped, unconfined aquifer model coupled to the shallow soil profile. North American Land Data Assimilation System meteorological forcing data (1997–2005) drive the models as a single column representing Illinois, USA. The three versions of CLM are each run 22,500 times using a random sample of the parameter space for soil texture and key hydrologic parameters. Other parameters remain constant. Observation‐based monthly changes in state‐averaged terrestrial water storage (dTWS) are used to evaluate the model simulations. After single‐criteria parameter exploration, the schemes are equivalently adept at simulating dTWS. However, explicit representation of groundwater considerably decreases the sensitivity of modeled dTWS to errant parameter choices. We show that approximate knowledge of parameter values is not sufficient to guarantee realistic model performance: because interaction among parameters is significant, they must be prescribed as a congruent set.

Evaluation of soil moisture in the Florida State University climate model–National Center for Atmospheric Research community land model (FSU‐CLM) using two reanalyses (R2 and ERA40) and in situ observations

The simulated soil moisture from the Florida State University (FSU) climate model coupled to the National Center for Atmospheric Research community land model (CLM2) is evaluated using two reanalyses (the European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis (ERA40) and the National Centers for Environmental Prediction/Department of Energy Reanalysis 2 (R2)) and in situ observations in Illinois and China from the Global Soil Moisture Data Bank. While the soil moisture was prescribed in the previous FSU climate model, the implementation of the CLM2 within the FSU model (hereafter FSU‐CLM) provides a prognostic soil moisture (both soil liquid water and soil ice) in 10 vertical layers. For the first layer (∼0–10 cm) the comparison of the soil moisture annual cycle between the FSU‐CLM and the two reanalyses shows that in the Northern Hemisphere (NH) the FSU‐CLM and the ERA40 are in phase but they are out of phase with the R2. While a parameterization of the soil ice process is included in both the FSU‐CLM and ERA40, it is not included in the R2. In this study, we found that the soil ice plays a key role in determining the soil moisture variation over the NH. The two reanalyses and the FSU‐CLM are also compared with in situ observations. The FSU‐CLM turns out to follow the annual cycle of the in situ observations better than both reanalyses.

The Effects of Observed Fractional Vegetation Cover on the Land Surface Climatology of the Community Land Model

Abstract Accurate modeling of surface processes requires a specification of the amount of land covered by vegetation. The National Center for Atmospheric Research Community Land Model (CLM2) does not realistically represent sparsely vegetated regions because of a lack of bare soil in the model. In this study, the existing CLM2 surface dataset is replaced by a global 1-km fractional vegetation cover dataset. This results in a doubling of global bare soil fraction in the model. It also significantly affects the fractional coverages of shrub, grass, and crop compared with only minor changes to trees. Regional changes occur most greatly in Australia, with an increase of over 0.4 in bare soil fraction. The western United States, southern South America, and southern Africa show fractional increases of more than 0.2. Simulations of CLM2 coupled with the Community Atmosphere Model (CAM2) show several regions with statistically significant decreases of up to 2 K in 2-m air temperature and up to 10 K in ground temp...