Arctic CHAMP Freshwater Initiative Conference (June 1-3 2005) A Land Surface Model Hind Cast for the Arctic Terrestrial Drainage Basin Theodore J. Bohn1, Andrew G. Slater2, James McCreight2, Dennis P. Lettenmaier1, and Mark C. Serreze2 1Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle, WA 98195 2Cooperative Institute for Research in Environmental Sciences, 216 UCB, University of Colorado, Boulder, CO 80309-0216 Arctic CHAMP Freshwater Initiative Conference (June 1-3 2005) 4 Comparison of ERA-40-based and NCEP-based Forcings ABSTRACT River runoff from the Arctic terrestrial drainage system is thought to exert a significant influence over global climate, contributing to the global thermohaline circulation via its effects on salinity, sea ice, and surface freshening in the North Atlantic. Changes in these freshwater fluxes, as well as other components of the Arctic terrestrial hydrologic cycle such as snow cover and albedo, have the potential to amplify the Arctic’s response to global climate change. However, the extent to which the Arctic terrestrial hydrological cycle is changing or may contribute to change through feedback processes is still not well understood, in part due to the sparseness of observations of such variables as stream flow, snow water equivalent, and energy fluxes. The objective of this project is to assemble the best possible time series (covering a 20+ year period) of these and other prognostic variables for the Arctic terrestrial drainage basin. While these variables can be estimated with a single land surface model (LSM), the predictions are often subject to biases and errors in the input atmospheric forcings and limited by the accuracy of the model physics. To reduce these errors, we have followed a two-pronged approach: on one hand, we are assembling an optimum set of atmospheric forcings by blending the output of the ERA-40 reanalysis project with gridded in-situ and satellite observations; on the other hand, we have created an ensemble of five LSMs: VIC1, CLM2, ECMWF3, NOAH4 and CHASM5, all of which have been used previously to simulate Arctic hydrology under the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Experiment 2e. Model predictions of land surface state variables (snow water content, soil moisture, permafrost active layer depth) and fluxes (latent, sensible, and ground heat fluxes; runoff) are averaged both across the ensemble and over multiple runs, using our optimum atmospheric forcing data with and without added random perturbations. Here we demonstrate the results from an individual model using our ERA-40-derived forcings, and evaluate the performance of the multi-model ensemble averages in comparison with individual model simulations of variables including snow water equivalent and total runoff over the pan-arctic domain (using forcings derived from Adam and Lettenmaier (2003) and NCEP reanalysis). 1Variable Infiltration Capacity macroscale model (Liang et al., 1994) 4NCEP, OSU, Air Force, and NWS Hydrologic Research Lab collaborative model 2Community Land Model (NCAR & UCAR) 5CHAmeleon Surface Model 3European Center for Medium-range Weather Forecasting, land component of Integrated Forecast System model Given the influence of forcing data, we are compiling an ensemble of forcings which includes assimilated station based data, satellite radiances and samples from the NCEP reanalysis. Figure 2 shows examples of differences in mean monthly incident shortwave and longwave radiation. The month shown is June, thus differences will be near a maximum. We have fields that are based on ERA-40, but include satellite estimates of cloud cover (Fig 2a). The suggested bias in the longwave flux is clearly shown. An alternative set of radiation fields is based on NCEP reanalysis (Fig 2b) and shows a different spatial distribution of differences. A 1 2 Typical LSM Structure Ensemble Process Flow The LSMs in our ensemble all share the same basic structure, consisting of grid cells containing a multi-layer soil column overlain by one or more “tiles” of different land covers, including vegetation with and without canopy, bare soil, and in some cases, lakes, wetlands, or glaciers. Water and energy fluxes are tracked vertically throughout the column from the atmosphere through the land cover to the bottom soil layer. The figure to the right illustrates these features as implemented in the VIC (Variable Infiltration Capacity) macroscale land surface model (Liang et al., 1994). Process flow in the multi-model ensemble. Forcing data consist of ALMA variables stored in NetCDF format files. These are translated into each model’s native variables and format. After the model simulations finish, the results are translated back into ALMA-standard variables and stored in NetCDF files. These standardized results are then analyzed over a “training period” and combined to form the ensemble’s aggregate result. Forcing Data (NetCDF, ALMA) VIC CLM ECMWF NOAH CHASM Translate (VIC format) Translate (CLM format) Translate (ECMWF format) Translate (NOAH format) Translate (CHASM format) Translate Results (NetCDF, ALMA) Combine Results B Figure 1: Simulated latent heat fluxes for the CHASM model using ERA-40 based forcings. Figure 1 shows simulated latent heat fluxes for the CHASM model using ERA-40 based forcings. Two versions of the model are shown. The 1 Tile version (upper panel) computes a single energy balance for the grid box and lumps bare soil and vegetation together to form an effective parameter model. In contrast, the 2 Tile version (mid panel) computes a separate energy balance for each of the vegetated and bare soil portions. Notably more evaporation occurs in the mid-latitude regions with the 1 Tile model. Overall, the models produce similar results to those taken directly from ERA-40 (lower panel) and suggests that the forcings play a major role in determining the resultant fluxes. Figure 2: Differences in shortwave and longwave radiation fields between ERA-40 and (a) AVHRR cloud-cover-corrected and (b) NCEP-based forcings. 3 Ensemble Results using Adam/NCEP Forcings CONCLUDING REMARKS While this study is still in its preliminary stages, evidence so far suggests that: An ensemble of land surface models can make more accurate predictions of hydrological variables than individual models. An ensemble trained against one variable (in this case fractional snow cover) can make plausible predictions of other variables (e.g. annual discharge). This may not be true in general, however, and deserves further investigation. The outputs of individual models can be sensitive to choice of forcing dataset. A multi-model ensemble may be more stable than individual models in the face of different forcings, due to the compensatory effects of the model weights. However, any systematic responses across all models would remain. Ensembles of land surface models may be able to help us more accurately estimate hydrological variables in regions where there are few observations. Future Work: The ensemble’s sensitivity to choice of training variable remains to be determined. Implementation of multiple constraints (e.g. snow cover and stream flow simultaneously). Testing of weights that vary in time and space. Investigation of the effects of data assimilation on ensemble performance. Note: See the author for a list of references. Using the coefficients derived from the linear regression against fractional snow cover, we have combined the models’ results for annual discharge in the Lena, Mackenzie, Ob, and Yenisei river basins, shown at right. For three of the four rivers, the ensemble does at least as well or better than the individual models at predicting annual discharge. In the case of the Ob, the ensemble is clearly influenced by extreme results from two of its members. We have not yet investigated the cause of this large discrepancy, but a possible cause is over-prediction of condensation in at least one model. It should be noted that the relative performance of the individual models for prediction of annual stream flow is not necessarily the same as for fractional snow cover. For example, our implementation of CHASM predicts snow cover relatively well but predicts discharge relatively poorly. However, the relative performance of the individual models seems to vary little from basin to basin. The main exception is CHASM’s performance in the Mackenzie basin, which agrees with observations much better than elsewhere. Whether this is part of a systematic difference between North America and Eurasia remains to be seen. Ensemble results are a linear combination of the results of the individual LSMs, whose weights are the coefficients from a multiple linear regression of observations against the model predictions. In this example, we trained the ensemble against weekly MODIS snow cover extent, running the models with the NCEP-based forcings of Adam and Lettenmaier (2003). The rms error between the ensemble results and observations, as well as the rms errors of the individual models, are shown below. In general, the ensemble rms error tends to be less than or equal to the lowest of the rms errors of the individual models. Fractional Snow Cover, 1983 A B C D Representative snapshots of each season (labeled “A ”, “B”, “C”, and “D”) are displayed to the right, for observations and ensemble predictions of fractional snow cover, and the error (predicted minus observed). High rms error tends to occur in spring and fall at the edges of the snow-covered region. This is due in part to the fact that the model results used here are 3-hourly data averaged to weekly intervals, which tends to blur the boundary between snow-covered and snow-free regions, while the observations are weekly snapshots. A more fair comparison would use sub-sampled model results to mimic the observations.