Mei Zhao

GSWP-2 Multimodel Analysis and Implications for Our Perception of the Land Surface

Abstract 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 ...

Empirical Correction of a Coupled Land-Atmosphere Model

Abstract This paper investigates empirical strategies for correcting the bias of a coupled land–atmosphere model and tests the hypothesis that a bias correction can improve the skill of such models. The correction strategies investigated include 1) relaxation methods, 2) nudging based on long-term biases, and 3) nudging based on tendency errors. The last method involves estimating the tendency errors of prognostic variables based on short forecasts—say lead times of 24 h or less—and then subtracting the climatological mean value of the tendency errors at every time step. By almost any measure, the best correction strategy is found to be nudging based on tendency errors. This method significantly reduces biases in the long-term forecasts of temperature and soil moisture, and preserves the variance of the forecast field, unlike relaxation methods. Tendency errors estimated from ten 1-day forecasts produced just as effective corrections as tendency errors estimated from all days in a month, implying that the...

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 spec...

Sensitivity of Land Surface Simulations to the Treatment of Vegetation Properties and the Implications for Seasonal Climate Prediction

Abstract A coupled land–atmosphere climate model is used to investigate the impact of vegetation parameters (leaf area index, absorbed radiation, and greenness fraction) on the simulation of surface fluxes and their potential role in improving climate forecasts. Ensemble simulations for 1986–95 have been conducted with specified observed sea surface temperatures. The vegetation impact is analyzed by comparing integrations with two different ways of specifying vegetation boundary conditions: observed interannually varying vegetation versus the climatological annual cycle. Parallel integrations are also implemented and analyzed for the land surface model in an uncoupled mode within the framework of the Second Global Soil Wetness Project (GSWP-2) for the same period. The sensitivity to vegetation anomalies in the coupled simulations appears to be relatively small. There appears to be only episodic and localized favorable impacts of vegetation variations on the skill of precipitation and temperature simulatio...

A New Method for Exploring Coupled Land–Atmosphere Dynamics

Abstract This paper proposes a new method for investigating coupled land–atmosphere interactions. The method is to apply an empirical correction technique to distinct components of a model and then examine differences between forecasts of the empirically corrected models. The correction technique is based on adding a time-dependent term to the tendency equations that subtracts the estimated tendency error at every time step. This methodology can be interpreted more generally as a series of data assimilation experiments in which only certain components of a coupled model are assimilated at a time. The correction is applied to a state-of-the-art coupled land–atmosphere model in three different ways, namely, to the atmosphere only, to the land only, and to the land and atmosphere simultaneously. The land–atmosphere interactions are inferred from monthly-mean differences between experiments. The results suggest that the land–atmosphere coupling in midlatitudes can be understood from straightforward water bala...

Limits to the Impact of Empirical Correction on Simulation of the Water Cycle

AbstractEmpirical correction is applied to wind, temperature, and soil moisture fields in a climate model to assess its impact on simulation of the water cycle during boreal summer. The empirical correction method is based on the biases in model forecasts only as a function of the time of year. Corrections are applied to the prognostic equations as an extra nudging term. Mean fields of evaporation, precipitation, moisture transport, and recycling ratio are all improved, even though humidity fields were not corrected. Simulation of the patterns of surface evaporation supplying rainfall at locations over land is also improved for most locations. There is also improvement in the simulation of evaporation and possibly rainfall, as measured by anomaly correlation coefficients and root-mean-square errors of the time series of monthly anomalies. However, monthly anomalies of other water cycle fields such as moisture transport and recycling ratio were not improved. Like any statistical adjustment, empirical corre...