1. American Geophysical Union Fall Meeting (December 2004)
Estimation of Arctic Land Surface Conditions via a Suite of Land Surface Models
Theodore J. Bohn1, Andrew G. Slater2, Dennis P. Lettenmaier1, and Mark C. Serreze2
1Department of Civil and Environmental Engineering, Box , University of Washington, Seattle, WA
2Cooperative Institute for Research in Environmental Sciences, 216 UCB, University of Colorado, Boulder, CO
American Geophysical Union Fall Meeting (December 2004) 5
Ensemble Results vs. Objective Variable
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, soil moisture, soil temperature, 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 implemented 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 the best available atmospheric forcing data with and without added random perturbations. Here we evaluate the multi-model ensemble averages in comparison with individual model simulations of variables including snow water equivalent, evaporation, total runoff, and soil thaw depth over the pan-arctic domain, and attempt to evaluate the hypothesis that the ensemble-averaged results are superior to those from any single LSM. In addition, we evaluate individual and multi-model performance in comparison with observations of stream flow, snow areal extent, and other variables as available.
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
We constructed a linear combination of predicted fractional snow cover from the four models using these coefficients. 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 1 3
Four main regimes are evident: low rms errors for all models in winter and summer, and high rms errors in spring and fall. This is not unexpected, since snow cover fraction saturates at high and low values of snow water equivalent and thus varies the most at the onset of snow accumulation or the end of snow melt. The individual models show the greatest spread in rms errors in the spring. Representative snapshots of each of these four regimes (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).
One explanation for differences between predicted and observed snow cover is that the models are operating at a higher temporal resolution than the observations. The observations were made by weekly satellite passes, while the individual models’ predictions are weekly averages based on daily meteorological forcings, which may contain storm events that were missed by the weekly satellite passes. A more fair comparison might be to low-pass filter predicted and observed time series before performing the linear regression.
Ensemble Process Flow
Model Inputs and Outputs
Forcing
100km EASE grid,
Precipitation from Adam and Lettenmaier (2003)
Other variables from NCEP/NCAR reanalysis
Interpolated to 3-hourly time step
Initialization
Soil temperature initialized to 273 K
Soil moisture initialized to saturation
First year (1979) treated as spin-up
Internal time step
VIC: 3 hours; others: 30 minutes
Output
3-hourly, ALMA NetCDF
Land Surface Parameters
Different models have different requirements
Soil: FAO or Zobler soil types
Land cover: from University of Maryland 1-km Global Land Cover
Some models handle glaciers, lakes, and wetlands while others do not
Number of land cover tiles per grid cell varies from 1 (NOAH) to 4 or more (VIC and CLM)
Number of soil and snow layers varies from model to model
Models were not rigorously calibrated
Forcing Data (NetCDF, ALMA)
Translate (VIC format)
Translate (CLM format)
Translate (ECMWF format)
Translate (NOAH format)
Translate (CHASM format)
VIC
CLM
ECMWF
NOAH
CHASM
Translate
Translate
Translate
Translate
Translate 4
Method
Following Krishnamurti et al (2000), our goal was to find a time-invariant linear combination F(t) of model results Xi(t):
F(t) = Σ aiXi(t)
that minimizes the mean-square error between F(t) and observations Y(t) of a particular variable. To find the coefficients ai, one can run a multiple linear regression of observations Y against predicted results Xi from the models in the ensemble, over a suitable “training” period.
We chose to use NSIDC weekly snow cover observations (aggregated to fractional snow cover at 100km resolution) as the objective variable, and ran a multiple linear regression against the weekly average fractional snow cover predicted by VIC, ECMWF, NOAH, and CHASM (CLM’s results are not yet ready for analysis) over a training period from This training period was chosen to avoid the influence of model spin-up during the first year of simulation (1979), and to allow comparison of ensemble results to observations during a forecast period from 6
Ensemble Results vs. Other Observations
Results (NetCDF, ALMA)
Results (NetCDF, ALMA)
Results (NetCDF, ALMA)
Results (NetCDF, ALMA)
Results (NetCDF, ALMA)
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.
While model coefficients vary substantially on short time scales, their long-term average values appear stable. What effect this has on the behavior of the ensemble is not yet known.
The ensemble’s sensitivity to choice of training variable remains to be determined.
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.
At first glance, relative performance of individual models seems somewhat uniform from region to region, and implies that model coefficients derived in one region may be applicable elsewhere. This also warrants further investigation.
Ensembles of land surface models may be able to help us more accurately estimate hydrological variables in regions where there are few observations.
Note: See the author for a list of references.
Using the coefficients derived from the linear regression against fractional snow cover, we combined the models’ results for annual discharge in the Lena, Mackenzie, Ob, and Yenisei river basins.
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 relatively constant 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.
Combine Results
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. 2
General LSM Structure
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).
The resulting coefficients from this period are shown above. Average coefficient values over the training period appear in parentheses. While these coefficients vary significantly through time, exhibiting a strong seasonal component, the relative goodness of fit of the models is consistent from year to year. A more detailed analysis is under way of the behavior of these coefficients over time, and the effect this has on ensemble predictions.