A LAND SURFACE MODEL HIND-CAST
FOR THE TERRESTRIAL ARCTIC DRAINAGE SYSTEM
Mark C. Serreze, Martyn P. Clark and Andrew G. Slater
Cooperative Institute for Research in Environmental Sciences (CIRES),
University of Colorado, Boulder
Dennis Lettenmaier
Department of Civil Engineering, University of Washington, Seattle
Jeff Key (Steven A. Ackerman)
Department of Atmospheric and Oceanic Sciences, University of
Wisconsin, Madison
LAND SURFACE MODELS (LSMs)
LSMs address interactions between the land surface, atmosphere and
underlying surface.
Typical Application
Time series of basic variables (generally downwelling shortwave and
longwave radiation, precipitation, surface winds, humidity and air
temperature) represent model forcings. The model ingests these forcings
and generates output state variables and fluxes, including soil moisture,
soil temperature, snow water equivalent, latent and sensible heat fluxes,
and upward shortwave and longwave radiation. An attraction of LSMs
is that the derived variables typically are sparsely observed.
Key Issue
The outputs are only as good as the inputs and the model physics.
OBJECTIVES
1) Assemble the highest possible quality surface forcing data sets over
the Arctic land area (precipitation, surface air temperature, downward
solar and longwave radiation, surface wind and relative humidity)
sufficient to resolve the diurnal cycle over a 20 + year period.
Use station data (temperature precipitation), satellite-derived radiation
fluxes (from APP-x) and ERA-40 reanalysis (winds, low-level
humidity). Get the diurnal cycle using ERA-40 as a "template" to assure
the best possible physical consistency between fields.
2) Run a suite of "pre-qualified" LSMs to simulate a multi-model
ensemble of land surface fluxes and state variables. Five different LSMs
will be used, which have been tested for Arctic applications under the
PILPS Experiment 2e.
Start with a control run for each model, using the best estimates of the
forcing variables. Then develop a set of ensemble inputs (e.g., 10) by
randomly adding error to each of the LSM driving variables, and run
each model with the perturbed inputs. By using ensemble input to drive
multiple LSMs, the uncertainty in the driving fields as well as model
physics will be captured as they influence the model outputs. The
multi-model ensemble average for each output variable should be
superior to the values provided by a single run with any one LSM.
3)Route the simulated runoff over all land areas draining to the Arctic
Ocean and Hudson Bay, and diagnose interseasonal and interannual
variability in the freshwater land fluxes
4) Using the multi-model ensemble approach, test sensitivity of runoff
generation over a range of spatial scales, moisture recycling, and the
dynamics of surface and subsurface moisture storage to changes in the
climate forcings (especially precipitation and temperature), and land
cover.
"VALUE ADDED" OBJECTIVES
1) Extend the multi-model ensemble approach to include assimilation of
snow cover properties. The goal is to develop a snow cover analysis
scheme, adjusting the first guess LSM snow extent, water equivalent
and albedo fields through assimilation of station observations and
satellite data.
2) Migrate the approach to provide for real-time monitoring of the Arctic
terrestrial drainage.
LINKAGES
The project links closely with RIMS and E-RIMS (e.g., uses the same
basic land mask to foster intercomparisons). Both the model inputs and
outputs should be valuable for other studies.
SELECTED LSMs
Community Land Model (CLM): Developed for application
within the NCAR Community Climate System Model. CLM is, in some
respects, the successor to the widely used Biosphere-Atmosphere
Transfer Scheme (BATS). The physics package includes a multi layer
snow model and a 10-layer soil model with explicit frozen soil moisture
and surface heterogeneity handled via a mosaic approach. CLM did not
participate in PILPS-2e, however some PILPS-2e runs were made by
NASAs Hydrological Sciences Branch at GSFC. Dr. Zong-Liang
(University of Texas) has run CLM through the entire set of PILPS-2e
experiments.
Canadian Land Surface Scheme (CLASS): Was designed with cold
land processes in mind and was perhaps the first to incorporate a
separate snow layer. Has been the target of an extensive development
activity by the GEWEX MAGS group. It features a 3 layer soil model
that explicitly represents soil freezing and surface ponding.
VIC (Variable Infiltration Capacity Model): Developed to produce
realistic runoff and streamflow via parameterizations of subgrid soil
moisture variability and spatial variability in precipitation forcings and
land cover variability. New parameterizations have been developed to
represent processes such as sublimation and snow redistribution, lakes
and wetlands, and ephemeral and permanent soil freezing.
SELECTED LSMs (CONTINUED)
CHASM: Contains options that allow it to utilize a range of hydrologic
parameterizations and surface energy balance configurations. In it's
simplest form, CHASM collapses to the BUCKET model, while in it's
most complex mode it has a grouped mosaic structure with separate
energy balance for each tile and with explicit treatment of transpiration,
bare ground evaporation and canopy interception.
ECMWF: A tiled (mosaic) version of the land surface scheme designed
for operational use within the ECMWF forecast model (also the basis for
ERA-40). The tiled version yielded a major improvement for the
simulation of snow in boreal forest areas. We will utilize a modified
version that includes: a) parameterizations of runoff dependence on
subgrid soil moisture; b) surface roughness dependence on snow depth;
c) modifications to the aging of snow albedo; d) modification of soil
hydraulic equations.