Land Surface State Variables Research Articles

Land–Snow Data Assimilation Including a Moderately Coupled Initialization Method Applied to NWP

Abstract Initialization methods are needed for geophysical components of Earth system prediction models. These methods are needed from medium-range to decadal predictions and also for short-range Earth system forecasts in support of safety (e.g., severe weather), economic (e.g., energy), and other applications. Strongly coupled land–atmosphere data assimilation (SCDA), producing balanced initial conditions across the land–atmosphere components, has not yet been introduced to operational numerical weather prediction (NWP) systems. Most NWP systems have evolved separate data assimilation (DA) procedures for the atmosphere versus land/snow system components. This separated method has been classified as a weakly coupled DA system (WCDA). In the NOAA operational short-range weather models, a moderately coupled land–snow–atmosphere assimilation method (MCLDA) has been implemented, a step forward from WCDA toward SCDA. The atmosphere and land (including snow) variables are both updated within the DA using the same set of observations (aircraft, radiosonde, satellite radiances, surface, etc.). Using this assimilation method, land surface state variables have cycled continuously for 6 years since 2015 for the 3-km NOAA HRRR model and with CONUS cycling since 1997. Month-long experiments were conducted with and without MCLDA for both winter and summer seasons using the 13-km Rapid Refresh model with atmosphere (50 levels), soil (9 levels), and snow (up to 2 layers if present) on the same horizontal grid. Improvements were evident for 2-m temperature for all times of day out to 6–12 h for both seasons but stronger in winter. Better temperature forecasts were also shown in the 1000–900-hPa layer corresponding roughly to the boundary layer. Significance Statement Accuracy of weather models depends on accurate initial conditions for soil temperature and moisture as well as for the atmosphere itself. This paper describes a moderately coupled data assimilation method that modifies soil conditions based on forecast error corrections indicated by atmospheric observations. This method has been tested for a month-long period in summer and winter and shown to consistently improve short-range forecasts of 2-m temperature and moisture. This coupled data assimilation method is used already in NOAA operational short-range models to improve its prediction skill for clouds, convective storms, and general weather conditions.

Coupling a New Version of the Common Land Model (CoLM) to the Global/Regional Assimilation and Prediction System (GRAPES): Implementation, Experiment, and Preliminary Evaluation

Land surface processes can significantly influence weather and climate. The Common Land Model version 2005 (CoLM2005) has been coupled to the Global Forecast System of the Global/Regional Assimilation and Prediction System (GRAPES_GFS), which is independently developed by the China Meteorological Administration. Since a new version of CoLM has been developed (CoLM2014) with updated soil basic data and parts of hydrological processes, we coupled CoLM2014 with GRAPES_GFS to investigate whether the land surface model can help to improve the prediction skill of the weather forecast model. The forecast results were evaluated against global validation datasets at different forecasting lengths and over various regions. The results demonstrate that GRAPES_GFS coupled with CoLM2005 and CoLM2014 can both well reproduce the spatial patterns and magnitude of atmospheric variables, and the effective predictable lengths of time are up to 3 days on the global scale and even up to 6 days on regional scales. Moreover, the GRAPES_GFS coupled with CoLM2014 outperforms the original one in predicting atmospheric variables. In addition, GRAPES_GFS coupled with both versions of CoLM reproduce acceptably accurate spatial distribution and magnitude of land variables. GRAPES_GFS coupled with CoLM2014 significantly improves the forecast of land surface state variables compared to the one coupled with CoLM2005, and the improvement signal is more notable than that in atmospheric variables. Overall, this study shows that CoLM is suitable for coupling with GRAPES_GFS, and the improvement of the land surface model in a weather forecast model can significantly improve the prediction skill of both atmospheric and land variables.

Temporal interpolation of land surface fluxes derived from remote sensing – results with an unmanned aerial system

Abstract. Remote sensing imagery can provide snapshots of rapidly changing land surface variables, e.g. evapotranspiration (ET), land surface temperature (Ts), net radiation (Rn), soil moisture (θ), and gross primary productivity (GPP), for the time of sensor overpass. However, discontinuous data acquisitions limit the applicability of remote sensing for water resources and ecosystem management. Methods to interpolate between remote sensing snapshot data and to upscale them from an instantaneous to a daily timescale are needed. We developed a dynamic soil–vegetation–atmosphere transfer model to interpolate land surface state variables that change rapidly between remote sensing observations. The “Soil–Vegetation, Energy, water, and CO2 traNsfer” (SVEN) model, which combines the snapshot version of the remote sensing Priestley–Taylor Jet Propulsion Laboratory ET model and light use efficiency GPP models, now incorporates a dynamic component for the ground heat flux based on the “force-restore” method and a water balance “bucket” model to estimate θ and canopy wetness at a half-hourly time step. A case study was conducted to demonstrate the method using optical and thermal data from an unmanned aerial system at a willow plantation flux site (Risoe, Denmark). Based on model parameter calibration with the snapshots of land surface variables at the time of flight, SVEN interpolated UAS-based snapshots to continuous records of Ts, Rn, θ, ET, and GPP for the 2016 growing season with forcing from continuous climatic data and the normalized difference vegetation index (NDVI). Validation with eddy covariance and other in situ observations indicates that SVEN can estimate daily land surface fluxes between remote sensing acquisitions with normalized root mean square deviations of the simulated daily Ts, Rn, θ, LE, and GPP of 11.77 %, 6.65 %, 19.53 %, 14.77 %, and 12.97 % respectively. In this deciduous tree plantation, this study demonstrates that temporally sparse optical and thermal remote sensing observations can be used to calibrate soil and vegetation parameters of a simple land surface modelling scheme to estimate “low-persistence” or rapidly changing land surface variables with the use of few forcing variables. This approach can also be applied with remotely-sensed data from other platforms to fill temporal gaps, e.g. cloud-induced data gaps in satellite observations.

Evaluation of State and Bias Estimates for Assimilation of SMOS Retrievals Into Conceptual Rainfall-Runoff Models

For an accurate estimation of land surface state variables through remote sensing data assimilation, it is important to estimate the forecast and observation biases as well. This study focuses on the evaluation of a methodology to estimate land surface state variables, together with model forecast and observation biases. Two conceptual rainfall-runoff models (HBV and GRKAL) are used for this purpose. Soil moisture data, retrieved by the Soil Moisture Ocean Salinity (SMOS) mission, are assimilated into these models for 59 unregulated sub-basins of the Murray-Darling basin in Australia. When both models simulate similar soil moisture values, the methodology results in similar forecast and observation bias estimates for both models. The same behavior is obtained when the temporal evolution of the soil moisture simulations is different, but with a similar long-term mean climatology. However, when the long-term mean climatology of both models is different, but with a similar temporal evolution, the bias estimates from both models have a different climatology as well, but with a high temporal correlation. The overall conclusion from this paper is that observation bias estimation is of key importance when updating internal state variables in a conceptual rainfall-runoff system that is calibrated to produce realistic discharge output for possibly biased internal state variables, and that the relative partitioning of bias into forecast and observation bias remains a model-dependent challenge.

Estimation of surface heat fluxes via variational assimilation of land surface temperature, air temperature and specific humidity into a coupled land surface-atmospheric boundary layer model

Numerous studies have estimated surface heat fluxes by assimilating land surface temperature (LST) observations (as the state variable of land surface). A number of other studies have focused on the estimation of surface energy balance components by assimilating air temperature and specific humidity (as the state variables of atmosphere) into an atmospheric boundary layer model. This study advances these existing data assimilation approaches by the synergistic assimilation of LST, air temperature, and specific humidity into a coupled land surface-atmospheric boundary layer model. The unknown parameters are the neutral bulk heat transfer coefficient (CHN) and evaporative fraction (EF). CHN scales the sum of turbulent heat fluxes, and EF represents their partitioning. The developed approach is tested at the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site in the summer of 1987 and 1988. The results indicate that the new approach performs well in both wet and dry periods because it uses the implicit information in both the land surface and atmospheric state variables (i.e., LST, air temperature, and specific humidity). The root-mean-square-errors (RMSEs) of estimated daily sensible and latent heat fluxes are 21.80 Wm−2 (22.10 Wm−2) and 39.32 Wm−2 (36.89 Wm−2) for FIFE 87 (88). The corresponding mean-absolute-percentage-errors (MAPEs) are 22.16% (18.64%) and 13.98% (13.44%) for FIFE 87 (88). The new variational data assimilation (VDA) system outperforms the previous studies that assimilated either LST or air temperature/specific humidity. For FIFE 87, this study decreases the RMSEs of daily sensible and latent heat fluxes estimates by 12.5% and 24.4% compared to assimilating only LST, and by 15.2% and 26.7% compared to assimilating only air temperature and specific humidity. A similar improvement is obtained for FIFE 88. The atmospheric boundary layer height, potential temperature, and specific humidity estimates from the VDA approach are also in good agreement with the corresponding radiosonde observations, and can capture their variations during the course of the day.

Spatial and Temporal Variations of Terrestrial Water Storage in Upper Indus Basin Using GRACE and Altimetry Data

Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry are suitable for the precise measurement of terrestrial water storage (TWS) and lake water level variations from space. In this study, two GRACE solutions, namely, spherical harmonics (SH) and mascon (MSC), are utilized with the Global Land Data Assimilation System (GLDAS) model to estimate the spatial and temporal variations of TWS in the Upper Indus Basin (UIB) for the study period of January 2003 to December 2016. The TWS estimated by SH, MSC, and the GLDAS model are consistent and generally show negative trends of -4.47 ± 0.38 mm/year, -4.81 ± 0.49 mm/year, and -3.77 ± 0.46 mm/year, respectively. Moreover, we use the GLDAS model data to understand the roles of variations in land surface state variables (snow water equivalent (SWE), soil moisture, and canopy water storage) in enhancing or dissipating the TWS in the region. Results indicate that SWE, which has a significant contribution to GRACE TWS variability, is an important parameter. Spearman's rank correlations are calculated to demonstrate the relationship of the GLDAS land surface state variables and the GRACE signals. A highly positive correlation between SWE with TWS is estimated by SH and MSC as 0.691 and 0.649, respectively, indicating that the TWS signal is mainly reliant on snow water in the study region. The ground water storages estimated by SH and MSC solutions are nearly stable with slight increasing trends of 0.63 ± 0.48 mm/year and 0.29 ± 0.51 mm/year, respectively. We also take advantage of the potential of satellite altimetry in measuring lake water level variations, and our result indicates that Cryosat-2 SARin mode altimetry data can be used in estimating small water bodies accurately in the high mountainous region of the UIB. Moreover, the climate indices data of El-Niño Southern Oscillation and Pacific Decadal Oscillation are analyzed to determine the influence of pacific climatic variability on TWS.

Response of Groundwater Storage and Recharge in the Qaidam Basin (Tibetan Plateau) to Climate Variations From 2002 to 2016

AbstractGroundwater (GW) and recharge as the main drivers of the water budget are challenging to quantify due to the complexity of hydrological processes and limited observations. Understanding these processes in relation to climate is crucial for evaluating future water availability of Tibetan Plateau. By computing storage changes in Gravity Recovery and Climate Experiment terrestrial water storage and Global Land Surface Data Assimilation System land surface state variables and water balance approach, we calculated GW storage changes and recharges. In the Qaidam Basin (northern Tibetan Plateau), terrestrial water storage from the Gravity Recovery and Climate Experiment revealed a significant increasing trend of 25.5 mm/year during 2002–2012. However, an obviously turning point was found around 2012 and terrestrial water storage revealed a significant decreasing rate of 37.9 mm/year during 2013–2016. Similarly, GW (recharge) had a significant increasing trend of 21.2 (4.5) mm/year before 2012 and a decreasing rate of 32.1 (10.9) mm/year after 2012. Domain‐averaged difference (P‐ET) between precipitation (P) and evapotranspiration (ET) also exhibited an increasing trend of 4.4 mm/year during 2002–2012 and a decreasing rate of 9.0 mm/year during 2013–2016. Precipitation followed dissimilar pattern with an increasing rate of 5.3 mm/year during 2002–2012 while no significant trend during 2013–2016. However, ET had a consistent increasing trend over the basin during the past 15 years (0.9 mm/year before 2012 and 9.0 mm/year thereafter). This study concluded that GW amount and distribution is mainly controlled by precipitation and ET. Decrease in precipitation at high elevations and increase in ET may impact future groundwater availability in this region.

The water resources of tropical West Africa: problems, progress, and prospects

West Africa plays key roles in global climate and shows one of the strongest variations in hydro-climatic conditions. As it turns out, the region appears to be underrepresented in the existing compendium of Earth science and hydrology-focused journal papers when it comes to significant discussion on terrestrial hydrology and freshwater science. This prominent gap is largely precipitated by increasing number of constraints that include lack of considerable and robust investments in gauge measurements for meteorological and hydrological applications, poor funding of research institutions and other disincentives, among other factors. In this manuscript, the challenges and problems in large-scale terrestrial hydrology-focused investigation in West Africa are reviewed. Using a dossier of some recent contributions in the field of remote sensing hydrology, this review also highlights some of the progress in terrestrial hydrology and the opportunities that exist for hydro-geodetic research in West Africa that leverage on sustained investments in satellite geodetic missions. It is noted that West Africa is still a pristine environment for hydrology-focused research and can benefit from recent advancements in sophisticated space agency programs such as the Gravity Recovery and Climate Experiment, which undoubtedly has revolutionized terrestrial hydrology research around the world for nearly two decades. Given the poor density of gauge stations and limited ground observations, hydrological research in West Africa is expected to benefit more from independent space observations and multi-resolution data. This is because the lack of sufficient in-situ data for the parameterizations and adequate initialization of outputs from hydrological models and reanalysis data for hydrological applications results in poor representation of the West African land surface and hydrological state variables. To further improve our contemporary understanding of West Africa’s terrestrial hydrology, the continued evaluation/validation of these observations and space-borne measurements is advocated.

Monitoring groundwater fluctuations over India during Indian Summer Monsoon (ISM) and Northeast monsoon using GRACE satellite: Impact on agriculture

Since 2002, Gravity Recovery and Climate Experiment (GRACE) satellite has provided monthly estimates of land water storage variations by monitoring time-variable components of earth's gravity field. We present GRACE satellite RL05 gravity data at 1° × 1° resolution to estimate terrestrial water storage change (surface water + groundwater) over India during Indian Summer Monsoon (ISM) and Northeast (NE) monsoon periods for the year 2015. As the NE monsoon was above normal in 2015, groundwater storage has increased significantly. By using GLDAS2.1 (1° × 1° resolution data) land surface state variables, we deduced as to how Terrestrial Water Storage (TWS) is partitioned into canopy water and soil moisture components. By computing storage changes in GRACE TWS, Global Land Data Assimilation System (GLDAS) land surface state variables and by using a terrestrial-based water balance approach, we calculated changes in the groundwater storage. The GRACE data indicate the variation of 15 cm in the TWS during the ISM period over India and a gain of 2.35 cm NE season over Peninsular India. After the ISM season, the groundwater storage was increased by 13 cm over India and 6.45 cm during the NE monsoon over Peninsular India. We also observed a high run-off (sub-surface + surface) rate in both the monsoon periods. The accumulated rainfall over India was estimated as 76.58 cm out of which 33.8 cm was computed as run-off (ISM season). Similarly, the mean rainfall in Peninsular India was calculated as 8 cm out of which 3.86 cm was approximated as run-off (NE monsoon season).

A theoretical approach to assess soil moisture–climate coupling across CMIP5 and GLACE-CMIP5 experiments

Abstract. Terrestrial climate is influenced by various land–atmosphere interactions that involve numerous land surface state variables. In several regions on Earth, soil moisture plays an important role for climate via its control on the partitioning of net radiation into sensible and latent heat fluxes; consequently, soil moisture also impacts on temperature and precipitation. The Global Land–Atmosphere Coupling Experiment–Coupled Model Intercomparison Project phase 5 (GLACE-CMIP5) aims to quantify the impact of soil moisture on these important climate variables and to trace the individual coupling mechanisms. GLACE-CMIP5 provides experiments with different soil moisture prescriptions that can be used to isolate the effect of soil moisture on climate. Using a theoretical framework that relies on the distinct relation of soil moisture with evaporative fraction (the ratio of latent heat flux over net radiation) in different soil moisture regimes, the climate impact of the soil moisture prescriptions in the GLACE-CMIP5 experiments can be emulated and quantified. The framework-based estimation of the soil moisture effect on the evaporative fraction agrees very well with estimations obtained directly from the GLACE-CMIP5 experiments (pattern correlation of 0.85). Moreover, the soil moisture effect on the daily maximum temperature is well captured in regions where soil moisture exerts a strong control on latent heat fluxes. The theoretical approach is further applied to quantify the soil moisture contribution to the projected change of the temperature on the hottest day of the year, confirming recent estimations by other studies. Finally, GLACE-style soil moisture prescriptions are emulated in an extended set of CMIP5 models. The results indicate consistency between the soil moisture–climate coupling strength estimated with the GLACE-CMIP5 and the CMIP5 models. Although the theoretical approach is only designed to capture the local soil moisture–climate coupling strength, it can also help to distinguish non-local from local soil moisture–atmosphere feedbacks where sensitivity experiments (such as GLACE-CMIP5) are available. Overall, the theoretical framework-based approach presented here constitutes a simple and powerful tool to quantify local soil moisture–climate coupling in both the GLACE-CMIP5 and CMIP5 models that can be applied in the absence of dedicated sensitivity experiments.

Estimation of quantitative measures of total water storage variation from GRACE and GLDAS-NOAH satellites using geospatial technology

This study represents the first attempt to examine spatial and seasonal variations of the surface water budget by using the Gravity Recovery and Climate Experiment (GRACE) by measuring gravity anomalies on earth to estimate changes in Total Water Storage (TWS) content over the north-western region of the India including New Delhi and states of Rajasthan, Uttar Pradesh and Haryana, covering an area of 676,917 km2. The TWS (surface plus ground) and its changes were estimated from 2003 to 2012. Additionally, Global Land Data Assimilation System (GLDAS) variables were used to infer as to how TWS was partitioned into canopy water and soil moisture components. To evaluate monthly accumulated rainfall, Tropical Rainfall Measuring Mission (TRMM) data, processed by the Global Precipitation Climatology Center (GPCC) were used. By computing storage changes in GRACE, TWS, GLDAS land surface state variables and terrestrial-based water balance approach, we calculated groundwater storage changes. The time-series comparisons show good agreement between the GRACE satellite data, GLDAS model data and computed groundwater data. The change in soil moisture storage is less than that in saturated storage. Both the GRACE and calculated groundwater storage changes indicate storage loss in the range of 86.43 km3/y ± average of 10 years data (in terms of equivalent water thickness). The average groundwater loss for was calculated as 9.7 ± km3/y, states of Haryana as 9.7 ± km3/y, Rajasthan as 33.199 ± km3/y and Uttar Pradesh as 44.4827 ± km3/y. Our results are convincing of a credible GRACE hydrology data which can be handy in monitoring storage dynamics and water availability at regional scale. As GRACE data are available for virtually every region of the world, their application in conjunction with hydrological models will improve applications of hydrological studies which may lead not only to water balance closures, but also to sustainable water resource management at regional scale.

Impacts of snow cover fraction data assimilation on modeled energy and moisture budgets

Two data assimilation (DA) methods, a simple rule‐based direct insertion (DI) approach and a one‐dimensional ensemble Kalman filter (EnKF) method, are evaluated by assimilating snow cover fraction observations into the Community Land surface Model. The ensemble perturbation needed for the EnKF resulted in negative snowpack biases. Therefore, a correction is made to the ensemble bias using an approach that constrains the ensemble forecasts with a single unperturbed deterministic LSM run. This is shown to improve the final snow state analyses. The EnKF method produces slightly better results in higher elevation locations, whereas results indicate that the DI method has a performance advantage in lower elevation regions. In addition, the two DA methods are evaluated in terms of their overall impacts on the other land surface state variables (e.g., soil moisture) and fluxes (e.g., latent heat flux). The EnKF method is shown to have less impact overall than the DI method and causes less distortion of the hydrological budget. However, the land surface model adjusts more slowly to the smaller EnKF increments, which leads to smaller but slightly more persistent moisture budget errors than found with the DI updates. The DI method can remove almost instantly much of the modeled snowpack, but this also allows the model system to quickly revert to hydrological balance for nonsnowpack conditions.

Calibration of aerodynamic roughness over the Tibetan Plateau with Ensemble Kalman Filter analysed heat flux

Abstract. Aerodynamic roughness height (Zom) is a key parameter required in several land surface hydrological models, since errors in heat flux estimation are largely dependent on optimization of this input. Despite its significance, it remains an uncertain parameter which is not readily determined. This is mostly because of non-linear relationship in Monin-Obukhov similarity (MOS) equations and uncertainty of vertical characteristic of vegetation in a large scale. Previous studies often determined aerodynamic roughness using a minimization of cost function over MOS relationship or linear regression over it, traditional wind profile method, or remotely sensed vegetation index. However, these are complicated procedures that require a high accuracy for several other related parameters embedded in serveral equations including MOS. In order to simplify this procedure and reduce the number of parameters in need, this study suggests a new approach to extract aerodynamic roughness parameter from single or two heat flux measurements analyzed via Ensemble Kalman Filter (EnKF) that affords non-linearity. So far, to our knowledge, no previous study has applied EnKF to aerodynamic roughness estimation, while the majority of data assimilation study have paid attention to updates of other land surface state variables such as soil moisture or land surface temperature. The approach of this study was applied to grassland in semi-arid Tibetan Plateau and maize on moderately wet condition in Italy. It was demonstrated that aerodynamic roughness parameter can be inversely tracked from heat flux EnKF final analysis. The aerodynamic roughness height estimated in this approach was consistent with eddy covariance method and literature value. Through a calibration of this parameter, this adjusted the sensible heat previously overestimated and latent heat flux previously underestimated by the original Surface Energy Balance System (SEBS) model. It was considered that this improved heat flux estimation especially during the summer Monsoon period, based upon a comparison with precipitation and soil moisture field measurement. For an advantage of this approach over other previous methodologies, this approach is useful even when eddy covariance data are absent at a large scale and is time-variant over vegetation growth, as well as is not directly affected by saturation problem of remotely sensed vegetation index.

A History and Review of the Global Soil Wetness Project (GSWP)

AbstractThe Global Soil Wetness Project (GSWP) is an international land surface modeling research effort involving dataset production, validation, model comparison, and scientific investigation in the areas of land surface hydrology and climatology. GSWP is characterized by the integration of multiple land surface models on a latitude–longitude grid in a stand-alone uncoupled mode, driven by meteorological forcing data constructed by combining atmospheric analyses and gridded observed data products. The models produce time series of gridded estimates of land surface fluxes and state variables that are then studied and compared. Defining characteristics that have distinguished GSWP include its global scale, application of land surface models in the same gridded structure as they are used in weather and climate models, and the multimodel approach, which included production of a multimodel analysis in its second phase. This paper gives an overview of the history of GSWP beginning with its inception within the International Satellite Land Surface Climatology Project. Various phases of the project are described, and a review of scientific results stemming from the project is presented. Musings on future directions of research are also discussed.

Remote sensing: hydrology

Satellite remote sensing is a viable source of observations of land surface hydrologic fluxes and state variables, particularly in regions where in situ networks are sparse. Over the last 10 years, the study of land surface hydrology using remote sensing techniques has advanced greatly with the launch of NASA’s Earth Observing System (EOS) and other research satellite platforms, and with the development of more sophisticated retrieval algorithms. Most of the constituent variables in the land surface water balance (eg, precipitation, evapotranspiration, snow and ice, soil moisture, and terrestrial water storage variations) are now observable at varying spatial and temporal resolutions and accuracy via remote sensing. We evaluate the current status of estimates of each of these variables, as well as river discharge, the direct estimation of which is not yet possible. Although most of the constituent variables are observable by remote sensing, attempts to close the surface water budget from remote sensing alone have generally been unsuccessful, suggesting that current generation sensors and platforms are not yet able to provide hydrologically consistent observations of the land surface water budget at any spatial scale.

The CarboEurope Regional Experiment Strategy

The Second Global Soil Wetness Project (GSWP-2) is an initiative to compare and evaluate 10-year simulations by a broad range of land surface models under controlled conditions. A major product of GSWP-2 is the first global gridded multimodel analysis of land surface state variables and fluxes for use by meteorologists, hydrologists, engineers, biogeochemists, agronomists, botanists, ecologists, geographers, climatologists, and educators. Simulations by 13 land models from five nations have gone into production of the analysis. The models are driven by forcing data derived from a combination of gridded atmospheric reanalyses and observations. The resulting analysis consists of multimodel means and standard deviations on the monthly time scale, including profiles of soil moisture and temperature at six levels, as well as daily and climatological (mean annual cycle) fields for over 50 land surface variables. The monthly standard deviations provide a measure of model agreement that may be used as a quality metric. An overview of key characteristics of the analysis is presented here, along with information on obtaining the data.

Flux Replacement as a Method to Diagnose Coupled Land–Atmosphere Model Feedback

Abstract The potential role of the land surface state in improving predictions of seasonal climate is investigated with a coupled land–atmosphere climate model. Climate simulations for 18 boreal-summer seasons (1982–99) have been conducted with specified observed sea surface temperature (SST). The impact on prediction skill of the initial land surface state (interannually varying versus climatological soil wetness) and the effect of errors in downward surface fluxes (precipitation and longwave/shortwave radiation) over land are investigated with a number of parallel experiments. Flux errors are addressed by replacing the downward fluxes with observed values in various combinations to ascertain the separate roles of water and energy flux errors on land surface state variables, upward water and energy fluxes from the land surface, and the important climate variables of precipitation and near-surface air temperature. Large systematic errors are found in the model, which are only mildly alleviated by the specification of realistic initial soil wetness. The model shows little skill in simulating seasonal anomalies of precipitation, but it does have skill in simulating temperature variations. Replacement of the downward surface fluxes has a clear positive impact on systematic errors, suggesting that the land–atmosphere feedback is helping to exacerbate climate drift. Improvement in the simulation of year-to-year variations in climate is even more evident. With flux replacement, the climate model simulates temperature anomalies with considerable skill over nearly all land areas, and a large fraction of the globe shows significant skill in the simulation of precipitation anomalies. This suggests that the land surface can communicate climate anomalies back to the atmosphere, given proper meteorological forcing. Flux substitution appears to have the largest benefit to improving precipitation skill over the Northern Hemisphere midlatitudes, whereas use of realistic land surface initial conditions improves skill to significant levels over regions of the Southern Hemisphere. Correlations between sets of integrations show that the model has a robust and systematic global response to SST anomalies.

An Evaluation of the Strength of Land–Atmosphere Coupling

Abstract Two ensembles of 1-month integrations of a coupled land–atmosphere climate model that differ only in their treatment of land surface boundary conditions have been generated from initial conditions chosen from the July states taken from each year of a 17-yr integration from the second Atmospheric Model Intercomparison Project (AMIP2). Both ensembles have specified sea surface temperature from one randomly chosen year, but one ensemble has the land surface state variables specified in each member at each time step to be identical to those from a single member of the other ensemble. Comparisons with the 17-yr AMIP2 integration provide an estimate of the role of interannually varying SST in affecting climate variability. Comparison between the two ensembles helps to quantify the role of land surface variability on the variance of surface fluxes and the climate. In this model system, the impacts of suppressed ocean variability on intra-ensemble spread are generally stronger than for suppressed land su...

On the representation of land surface fluxes for atmospheric modelling

Numerical Weather Prediction (NWP)-models require accurate simulation of land surface fluxes. Land surface models increasingly use complex descriptions of the physical mechanisms governing land surface processes. Consequently, these models may require large numbers of soil and land surface parameters controlling the vertical fluxes. Uncertainty in land surface parameterisation and the heterogeneous nature of landscape hydrological controls may lead to considerable uncertainty in predicted land surface fluxes and state variables such as soil moisture. In this paper, the predictive uncertainty associated with possible model parameterisations, and the role of calibration data, are reviewed. It is noted that spatial variability may lead to significant uncertainty when represented by one-dimensional models. As heterogeneity is an essential property of the land surface, surface parameterisation needs to adequately describe this heterogeneity. This paper discusses the use of remotely sensed thermal imagery with surface energy budget models for prediction of land surface fluxes. Particular emphasis is given to the role of remote sensing in assessing spatial variability in land surface processes, obtaining landscape fluxes and predicting soil moisture status. Recent studies have shown that remote sensing in combination with land surface modelling has significant potential in improving the estimation of fluxes and derived variables such as surface wetness. Remotely derived fluxes and surface wetness information may be assimilated in NWP-models providing better land surface boundary conditions as well as contributing to improved land surface models. This paper concludes with a brief discussion of recent work on the assimilation of satellite derived heating rates in regional atmospheric mesoscale models.