Land Surface Model Intercomparison Project Research Articles

Multi-model assessment of trends, variability and drivers of terrestrial carbon uptake in India

A multi-model-based assessment is made to assess the trends and variability in the land carbon uptake in India during the period 1901–2010. Data from nine models, used in a recent land surface model inter-comparison project called TRENDY, are used for this purpose. Our analysis is focused on the trends and variability in the net primary productivity (NPP), net ecosystem productivity (NEP) and net ecosystem exchange (NEE). The multi-model mean NPP shows a positive trend of 2.03% per decade during this period. The NEP, which is the difference between NPP and heterotrophic respiration, has a mean value of $$-\,0.138\,\pm \,0.086\,\hbox {Pg}\,\hbox {C}\,\hbox {yr}^{-1}$$ over this region. According to our analysis of TRENDY multi-models, the inter-annual variation in NPP and NEP is strongly driven by precipitation, but remote drivers such as El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) do not have a strong influence. The water use efficiency (WUE) shows an increase of about 25% over the 110-yr period. Our multi-model-based estimate of the cumulative NEE is $$0.613\,\pm \,0.1\,\hbox {Pg} \,\hbox {C}$$ during 1901–2010, indicating that the Indian terrestrial ecosystem was neither a strong source nor a significant sink during this period. However, we caution that our conclusion is based on a limited set of offline land models, and the results presented here have large uncertainties due to model resolution and parameterisation of various land surface processes, the inadequate validation procedures and the use of limited number of models and land use change data sets. Further investigations using more observational data, rigorous validation using extensive observational data sets and a set of comprehensive coupled models that include several feedbacks between land, atmosphere, ocean and the cryosphere are needed to assess the robustness of our results.

Evaluation of Groundwater Simulations in Benin from the ALMIP2 Project

AbstractA comprehensive estimation of water budget components, particularly groundwater storage (GWS) and fluxes, is crucial. In this study, we evaluate the terrestrial water budget of the Donga basin (Benin, West Africa), as simulated by three land surface models (LSMs) used in the African Monsoon Multidisciplinary Analysis Land Surface Model Intercomparison Project, phase 2 (ALMIP2): CLM4, Catchment LSM (CLSM), and Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO). All three models include an unconfined groundwater component and are driven by the same ALMIP2 atmospheric forcing from 2005 to 2008. Results show that all three models simulate substantially shallower water table depth (WTD) with smaller seasonal variations, approximately 1–1.5 m compared to the observed values that range between 4 and 9.6 m, while the seasonal variations of GWS are overestimated by all the models. These seemingly contradictory simulation results can be explained by the overly high specific yield prescribed in all models. All models achieve similar GWS simulations but with different fractions of precipitation partitioning into surface runoff, base flow, and evapotranspiration (ET), suggesting high uncertainty and errors in the terrestrial and groundwater budgets among models. The poor performances of models can be attributed to bias in the hydrological partitioning (base flow vs surface runoff) and sparse subsurface data. This analysis confirms the importance of subsurface hydrological processes in the current generation of LSMs and calls for substantial improvement in both surface water budget (which controls groundwater recharge) and the groundwater system (hydrodynamic parameters, vertical geometry).

Modeling Surface Runoff and Water Fluxes over Contrasted Soils in the Pastoral Sahel: Evaluation of the ALMIP2 Land Surface Models over the Gourma Region in Mali

Abstract Land surface processes play an important role in the West African monsoon variability. In addition, the evolution of hydrological systems in this region, and particularly the increase of surface water and runoff coefficients observed since the 1950s, has had a strong impact on water resources and on the occurrence of floods events. This study addresses results from phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2), carried out to evaluate the capability of different state-of-the-art land surface models to reproduce surface processes at the mesoscale. Evaluation of runoff and water fluxes over the Mali site is carried out through comparison with runoff estimations over endorheic watersheds as well as evapotranspiration (ET) measurements. Three remote-sensing-based ET products [ALEXI, MODIS, and Global Land Evaporation Amsterdam Model (GLEAM)] are also analyzed. It is found that, over deep sandy soils, surface runoff is generally overestimated, but the ALMIP2 multimodel mean reproduces in situ measurements of ET and water stress events rather well. However, ALMIP2 models are generally unable to distinguish among the two contrasted hydrological systems typical of the study area. Employing as input a soil map that explicitly represents shallow soils improves the representation of water fluxes for the models that can account for their representation. Shallow soils are shown to be also quite challenging for remote-sensing-based ET products, even if their effect on evaporative loss was captured by the diagnostic thermal-based ALEXI. A better representation of these soils, in soil databases, model parameterizations, and remote sensing algorithms, is fundamental to improve the estimation of water fluxes in this part of the Sahel.

Streamflows over a West African Basin from the ALMIP2 Model Ensemble

Abstract Comparing streamflow simulations against observations has become a straightforward way to evaluate a land surface model’s (LSM) ability in simulating water budget within a catchment. Using a mesoscale river routing scheme (RRS), this study evaluates simulated streamflows over the upper Ouémé River basin resulting from 14 LSMs within the framework of phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2). The ALMIP2 RRS (ARTS) has been used to route LSM outputs. ARTS is based on the nonlinear Muskingum–Cunge method and a simple deep water infiltration formulation representing water-table recharge as previously observed in that region. Simulations are performed for the 2005–08 period during which ground observations are largely available. Experiments are designed using different ground-based rainfall datasets derived from two interpolation methods: the Thiessen technique and a combined kriging–Lagrangian methodology. LSM-based total runoff (TR) averages vary from 0.07 to 1.97 mm day−1, while optimal TR was estimated as ~0.65 mm day−1. This highly affected the RRS parameterization and streamflow simulations. Optimal Nash–Sutcliffe coefficients for LSM-averaged streamflows varied from 0.66 to 0.92, depending on the gauge station. However, individual LSM performances show a wider range. A more detailed rainfall distribution provided by the kriging–Lagrangian methodology resulted in overall better streamflow simulations. The early runoff generation related to reduced infiltration rates during early rainfall events features as one of the main reasons for poor LSM performances.

Evaluating LSM-Based Water Budgets over a West African Basin Assisted with a River Routing Scheme

AbstractWithin the framework of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project phase 2 (ALMIP-2), this study evaluates the water balance simulated by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) over the upper Ouémé River basin, in Benin, using a mesoscale river routing scheme (RRS). The RRS is based on the nonlinear Muskingum–Cunge method coupled with two linear reservoirs that simulate the time delay of both surface runoff and base flow that are produced by land surface models. On the basis of the evidence of a deep water-table recharge in that region, a reservoir representing the deep-water infiltration (DWI) is introduced. The hydrological processes of the basin are simulated for the 2005–08 AMMA field campaign period during which rainfall and streamflow data were intensively collected over the study area. Optimal RRS parameter sets were determined for three optimization experiments that were performed using daily streamflow at five gauges within the basin. Results demonstrate that the RRS simulates streamflow at all gauges with relative errors varying from −20% to 3% and Nash–Sutcliffe coefficients varying from 0.62 to 0.90. DWI varies from 24% to 67% of the base flow as a function of the subbasin. The relatively simple reservoir DWI approach is quite robust, and further improvements would likely necessitate more complex solutions (e.g., considering seasonality and soil type in ISBA); thus, such modifications are recommended for future studies. Although the evaluation shows that the simulated streamflows are generally satisfactory, further field investigations are necessary to confirm some of the model assumptions.

Review of Recent Developments and the Future Prospective in West African Atmosphere/Land Interaction Studies

This paper reviews West African land/atmosphere interaction studies during the past decade. Four issues are addressed in this paper: land data development, land/atmosphere interactions at seasonal-interannual scales, mesoscale studies, and the future prospective. The development of the AMMA Land Surface Model Intercomparison Project has produced a valuable analysis of the land surface state and fluxes which have been applied in a number of large-scale African regional studies. In seasonal-interannual West African climate studies, the latest evidence from satellite data analyses and modeling studies confirm that the West African region has a climate which is particularly sensitive to land surface processes and there is a strong coupling between land surface processes and regional climate at intraseasonal/seasonal scales. These studies indicate that proper land surface process representations and land status initialization would substantially improve predictions and enhance the predictability of West African climate. Mesoscale studies have revealed new understanding of how soil moisture heterogeneity influences the development of convective storms over the course of the diurnal cycle. Finally, several important issues regarding the future prospective are briefly addressed.

Evaluation of soil moisture ensemble runs to estimate precipitation variability in convection-permitting model simulations for West Africa

In this investigation, the impact of soil moisture on the precipitation amount and distribution in model simulations for West Africa was assessed. This included the examination of the dependence of precipitation on soil moisture, boundary layer parameters or convective conditions. For this purpose, different soil moisture fields from the rainy season of the year 2006 in West Africa were collected. These included three analysis fields, satellite measurements, and model output from the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project. The different soil moisture fields were analyzed and compared first; large differences in the mean values, the north–south gradients, and the heterogeneity were revealed. The distributions were taken to initialize five convection-permitting model runs performed with the limited-area model COSMO (Consortium for Small-scale Modeling), forced by reanalyses from the European Centre for Medium-Range Weather Forecasts. A case study of a period with intense convective activity in the Sudanian and Sahelian zone was selected for the simulations. The simulated precipitation was compared with data from two satellite-based precipitation analyses and with forecasts from two global models of lower resolution. The model spread encompassed the precipitation analyses in the southern subdomain, while the latter lay far above the model results in the northern subdomain. The different soil moisture fields also influenced the turbulent fluxes at the surface, the boundary layer state and height as well as convective instability. Standard deviation of the simulations was highest for convective available potential energy, while the impact on convective inhibition was less pronounced. Only a weak negative relation of precipitation to soil moisture or conditional instability could be discerned. In this case, dry soil favored a higher boundary layer with better developed convective cells contributing to larger precipitation sums. The spread between the five precipitation forecasts suggests that a high-resolution limited-area ensemble, built by applying atmospheric variations as well as variations of the initial land surface conditions, will be a proper method to improve the prediction of mesoscale convective systems and convective precipitation for West Africa.

Wetland inundation dynamics in a model of land surface climate: Evaluation in the Niger inland delta region

Observed river gauging data show significant evaporative losses from the land and water surface in the Niger inland delta. These losses indicate an important potential feedback between the land surface and atmosphere. Moreover, the reduction in river flow downstream of the wetland has clear implications for water management in the region and beyond. Here we have modeled the evaporative losses that occur over the Niger inland delta by adding an overbank flow parameterization to the Joint UK Land‐Environment Simulator (JULES) land surface model. The hydrological component of this model comprises a probability‐distributed model of soil moisture and runoff production coupled with a discrete approximation to the one‐dimensional kinematic wave equation to route river water downslope. We use subgrid‐resolution topographic data to derive a two‐parameter frequency distribution of inundated areas for each grid box which we then employ to represent overbank inundation in the model. The model was driven using data from the ALMIP experiment (ALMIP stands for AMMA Land surface Model Intercomparison Project, wherein AMMA stands for African Monsoon Multidisciplinary Analyses). The model reproduces the salient features of the observed river flow and inundation patterns; these include significant evaporative losses from the inundated region accounting for doubling of the total land‐atmosphere water flux during periods of greatest flooding. Our predictions of inundated area are in good agreement with observed estimates of the extent of inundation obtained using satellite infrared and microwave remote sensing.

Intercomparison and analyses of the climatology of the West African Monsoon in the West African Monsoon Modeling and Evaluation project (WAMME) first model intercomparison experiment

This paper briefly presents the West African Monsoon (WAM) Modeling and Evaluation Project (WAMME) and evaluates WAMME general circulation models’ (GCM) performances in simulating variability of WAM precipitation, surface temperature, and major circulation features at seasonal and intraseasonal scales in the first WAMME experiment. The analyses indicate that models with specified sea surface temperature generally have reasonable simulations of the pattern of spatial distribution of WAM seasonal mean precipitation and surface temperature as well as the averaged zonal wind in latitude-height cross-section and low level circulation. But there are large differences among models in simulating spatial correlation, intensity, and variance of precipitation compared with observations. Furthermore, the majority of models fail to produce proper intensities of the African Easterly Jet (AEJ) and the tropical easterly jet. AMMA Land Surface Model Intercomparison Project (ALMIP) data are used to analyze the association between simulated surface processes and the WAM and to investigate the WAM mechanism. It has been identified that the spatial distributions of surface sensible heat flux, surface temperature, and moisture convergence are closely associated with the simulated spatial distribution of precipitation; while surface latent heat flux is closely associated with the AEJ and contributes to divergence in AEJ simulation. Common empirical orthogonal functions (CEOF) analysis is applied to characterize the WAM precipitation evolution and has identified a major WAM precipitation mode and two temperature modes (Sahara mode and Sahel mode). Results indicate that the WAMME models produce reasonable temporal evolutions of major CEOF modes but have deficiencies/uncertainties in producing variances explained by major modes. Furthermore, the CEOF analysis shows that WAM precipitation evolution is closely related to the enhanced Sahara mode and the weakened Sahel mode, supporting the evidence revealed in the analysis using ALMIP data. An analysis of variability of CEOF modes suggests that the Sahara mode leads the WAM evolution, and divergence in simulating this mode contributes to discrepancies in the precipitation simulation.

A regional climate model simulation over West Africa: parameterization tests and analysis of land-surface fields

The West African Monsoon has been simulated with the regional climate model PROMES, coupled to the land-surface model ORCHIDEE and nested in ECMWF analysis, within AMMA-EU project. Three different runs are presented to address the influence of changes in two parameterizations (moist convection and radiation) on the simulated West African Monsoon. Another aim of the study is to get an insight into the relationship of simulated precipitation and 2-m temperature with land-surface fluxes. To this effect, data from the AMMA land-surface model intercomparison project (ALMIP) have been used. In ALMIP, offline simulations have been made using the same land-surface model than in the coupled simulation presented here, which makes ALMIP data particularly relevant for the present study, as it enables us to analyse the simulated soil and land-surface fields. The simulation of the monsoon depends clearly on the two analysed parameterizations. The inclusion of shallow convection parametrization affects the intensity of the simulated monsoon precipitation and modifies some dynamical aspects of the monsoon. The use of a fractional cloud-cover parameterization and a more complex radiation scheme is important for better reproducing the amplitude of the latitudinal displacement of the precipitation band. This is associated to an improved simulation of the surface temperature field and the easterly jets. However, the parameterization changes do not affect the timing of the main rainy and break periods of the monsoon. A better representation of downward solar radiation is associated with a smaller bias in the surface heat fluxes. The comparison with ALMIP land-surface and soil fields shows that precipitation and temperature biases in the regional climate model simulation are associated to certain biases in land-surface fluxes. The biases in soil moisture seem to be driven by atmospheric biases as they are strongly affected by the parameterization changes in atmospheric processes.

The AMMA Land Surface Model Intercomparison Project (ALMIP)

The rainfall over West Africa has been characterized by extreme variability in the last half-century, with prolonged droughts resulting in humanitarian crises. There is, therefore, an urgent need to better understand and predict the West African monsoon (WAM), because social stability in this region depends to a large degree on water resources. The economies are primarily agrarian, and there are issues related to food security and health. In particular, there is a need to better understand land-atmosphere and hydrological processes over West Africa because of their potential feedbacks with the WAM. This is being addressed through a multiscale modeling approach using an ensemble of land surface models that rely on dedicated satellite-based forcing and land surface parameter products, and data from the African Multidisciplinary Monsoon Analysis (AMMA) observational field campaigns. The AMMA land surface model (LSM) Intercomparison Project (ALMIP) offline, multimodel simulations comprise the equivalent of a multimodel reanalysis product. They currently represent the best estimate of the land surface processes over West Africa from 2004 to 2007. An overview of model intercomparison and evaluation is presented. The far-reaching goal of this effort is to obtain better understanding and prediction of the WAM and the feedbacks with the surface. This can be used to improve water management and agricultural practices over this region.

Evaluation of the WAMME model surface fluxes using results from the AMMA land-surface model intercomparison project

The West African monsoon (WAM) circulation and intensity have been shown to be influenced by the land surface in numerous numerical studies using regional scale and global scale atmospheric climate models (RCMs and GCMs, respectively) over the last several decades. The atmosphere–land surface interactions are modulated by the magnitude of the north–south gradient of the low level moist static energy, which is highly correlated with the steep latitudinal gradients of the vegetation characteristics and coverage, land use, and soil properties over this zone. The African Multidisciplinary Monsoon Analysis (AMMA) has organised comprehensive activities in data collection and modelling to further investigate the significance land–atmosphere feedbacks. Surface energy fluxes simulated by an ensemble of land surface models from AMMA Land-surface Model Intercomparison Project (ALMIP) have been used as a proxy for the best estimate of the “real world” values in order to evaluate GCM and RCM simulations under the auspices of the West African Monsoon Modelling Experiment (WAMME) project, since such large-scale observations do not exist. The ALMIP models have been forced in off-line mode using forcing based on a mixture of satellite, observational, and numerical weather prediction data. The ALMIP models were found to agree well over the region where land–atmosphere coupling is deemed to be most important (notably the Sahel), with a high signal to noise ratio (generally from 0.7 to 0.9) in the ensemble and a inter-model coefficient of variation between 5 and 15%. Most of the WAMME models simulated spatially averaged net radiation values over West Africa which were consistent with the ALMIP estimates, however, the partitioning of this energy between sensible and latent heat fluxes was significantly different: WAMME models tended to simulate larger (by nearly a factor of two) monthly latent heat fluxes than ALMIP. This results due to a positive precipitation bias in the WAMME models and a northward displacement of the monsoon in most of the GCMs and RCMs. Another key feature not found in the WAMME models is peak seasonal latent heat fluxes during the monsoon retreat (approximately a month after the peak precipitation rates) from soil water stores. This is likely related to the WAMME northward bias of the latent heat flux gradient during the WAM onset.