Exploring the Contributing Effects of Climate and Land Surface Changes on the Variability of PanArct

1

• Exploring the Contributing Effects of Climate and
  Land Surface Changes on the Variability of
  Pan-Arctic River Discharge and Surface Albedo
• Jennifer C. Adam1, Fengge Su1, Laura C. Bowling2,
  and Dennis P. Lettenmaier1
• Department of Civil and Environmental
  Engineering, Box 352700, University of
  Washington, Seattle, WA 98195
• 2. Department of Agronomy, Purdue University,
  West Lafayette, IN 47907
• First CliC Science Conference, Beijing China,
  April 10 17, 2005

Photo http//gallery.maiman.net/terragen/arctic
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Indirect Climate Effects
2
Baseline Simulation Streamflow Climatology and
Trends
ABSTRACT The export of freshwater to the Arctic
Ocean plays a key role in both regional and
global climates (e.g. via effects on the strength
of the North Atlantic Deep Water (NADW) formation
that drives the thermohaline circulation). Also,
polar regions are particularly sensitive to
warming partly because of the positive feedback
response of surface albedo warming decreases
the extent of snow and ice, thus increasing the
amount of radiation absorbed by the land surface.
We report a 60-year (1930-89) run of the
Variable Infiltration Capacity (VIC) macroscale
hydrology model over the pan-arctic land domain,
designed to offer insights into the nature and
causes of observed long term trends in land
surface states and fluxes. VIC is a
semi-distributed grid-based model that
parameterizes the processes occurring at the
land-atmosphere interface. The most recent
version of the model includes several recent
improvements specific to cold regions. We
summarize a set of model runs from which we have
estimated the inflow to the Arctic Ocean from all
pan-arctic land areas (including the Canadian
Archipelago) and an assessment of the capability
of the land surface model to simulate the
observed changes in gauged streamflow. These
results utilize precipitation and temperature
fields that incorporate a method of adjustment to
reflect the best current understanding of
long-term precipitation and temperature trends
over the pan-Arctic domain. We explore the
factors controlling the variability (long-term
trends and changes in seasonality) of pan-arctic
freshwater discharge and surface albedo by
considering three groups of controls (1) direct
climate effects (precipitation and temperature)
(2) climate-induced land surface changes (such as
the northward moving tree-line, increased
bushiness, and changes in permafrost conditions)
and (3) anthropogenic-induced changes (such as
agriculture and reservoir storage).

• Possible explanations for trends mismatch
• land cover and soil specifications not static in
  reality
• missing or inaccurately parameterized physical
  processes
• biases in time-varying forcing data
• anthropogenic effects (reservoirs, irrigation,
  etc)
• observed streamflow issues (measurement biases,
  etc)
• model drift (e.g. artificial filling-up or
  drying-out of soil or lake storage)

Example permafrost degradation the following
bottom boundary soil temperatures (temperatures
at 4 m depth) are pre-specified using a Frost
Index method and the full period is run for the
two specifications (run 5 and run 6)

• Specification of bottom boundary soil
  temperature
• based on Frost Index (Nelson and Outcalt 1987)
  a function of surface temperature (beneath
  snowpack)
• uses baseline simulations (Exp. 1 in Box 3)
• calibration using the Brown et al. (1998)
  permafrost map

Run 5 Tdamp from 1931-1939 period
Inferred Decrease in Permafrost Extent
Run 6 Tdamp from 1980-1989 period
Results warming soil temperatures cause a change
in runoff seasonality

• Effects to be considered (changes to the land
  surface)
• reduction of permafrost extent and increasing
  permafrost temperatures
• northward-moving tree-line
• increased bushiness in tundra regions
• (change in vegetation due to increased fire
  frequency)

STATEMENT OF PURPOSE To evaluate the relative
importance of climatic versus non-climatic
controls on long-term trends and seasonal
variability of streamflow and surface albedo for
the major river basins that outlet to the Arctic
Ocean.
Spring
Summer
3
Direct Climate Effects (Precipitation and
Temperature)
1
Modeling Framework

• Features Specific to Cold-Land Processes
• Two-layer energy balance snow model (Storck et
  al. 1999)
• Frozen soil/permafrost algorithm (Cherkauer et
  al. 1999, 2003)
• Lakes and wetlands model (Bowling et al. 2004)
• Blowing snow algorithm (Bowling et al. 2004)
• Calibration (Su et al. 2005)
• Eleven Regions were calibrated separately (not
  including Greenland
• Calibration was focused on matching the shape of
  the monthly hydrograph.
• Parameter transfer to un-gauged basins was based
  on the hydro-climatology of the region.

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Anthropogenic Effects
Precipitation and temperature inputs to the model
were adjusted for spurious trends according to
the method of Hamlet and Lettenmaier (2005).
Designed after Hamlet et al. (2005), the
following three VIC model runs were made

• Effects to be considered
• land cover changes due to cultivation (e.g.
  southern Ob)
• (effects of water control systems, e.g. Yenisei
  reservoirs)
• Cultivation using Matthews (1983)
  pre-agriculture vegetation type and cultivation
  intensity maps, the effects of agriculture on the
  hydrologic cycle can be explored for the southern
  Ob basin.

• CONCLUDING REMARKS
• We are mainly interested in the trends and
  variability of streamflow and surface albedo,
  because both variables have potentially
  significant feedbacks to the Arctic climate
  system and also the global climate system.
• In order to use the hydrologic model (in a
  coupled model system) to predict the feedback
  effects of changing streamflow and surface
  albedo, it is important that we are able to
  reproduce the observed historical trends
  off-line. Then we can assume that we are
  capturing the most important processes.
• Simulated streamflow trends do not match
  observed trends for many basins we are working
  on removing artificial drift in storage release
  from lakes and we are applying new methods to
  remove biases in the time-varying driving data.
  Subsequently, we can begin to explore what
  important processes are missing from the modeling
  framework, e.g. time-varying land cover
  classification and soil conditions.
• Even if we are unable to capture the observed
  trends, the off-line hydrologic model can still
  be used to explore the sensitivity of streamflow
  and surface albedo to climatic and anthropogenic
  changes.

• Validation (Su et al. 2005)
• Snow Cover Extent via comparison to NOAA-NESDIS
  weekly snow charts
• Permafrost active layer depth via comparison to
  CALM network observations
• Lake algorithm validation via comparison of lake
  freeze and thaw dates to observed
• Domain
• Pan-Arctic Domain per ArcticRIMS
• 100 km by 100km EASE
• Period 1930-1989, 1 year spin-up

REFERENCES Bowling, L.C., J.W. Pomeroy and D.P.
Lettenmaier, 2004, Parameterization of blowing
snow sublimation in a macroscale hydrology model
J. Hydromet. 5(5), 745-762. Brown, J., O.J.
Ferrians Jr., J.A. Heginbottom, and E.S.
Melnikov. 1998. Circum-Arctic Map of Permafrost
and Ground-Ice Conditions. Boulder, CO National
Snow and Ice Data Center/World Data Center for
Glaciology. Digital Media. Cherkauer, K. A. and
D. P. Lettenmaier, Hydrologic effects of frozen
soils in the upper Mississippi River basin, J.
Geophys. Res., 104(D16), 19,599-19,610,
1999. Cherkauer, K. A., L. C. Bowling and D. P.
Lettenmaier, 2003, Variable Infiltration Capacity
(VIC) cold land process model updates, Global and
Planetary Change, 38(1-2), 151-159. Hamlet A.F.
and Lettenmaier D.P., 2005, Production of
temporally consistent gridded precipitation and
temperature fields for the continental U.S., J.
of Hydrometeorology, (accepted).
ftp//ftp.hydro.washington.edu/pub/hamleaf/hamlet_
met_data/hamlet_met_data_112204.pdf Hamlet A.F.,
Mote P.W, Clark M.P., Lettenmaier D.P., 2005,
Effects of temperature and precipitation
variability on snowpack trends in the western
U.S., J. of Climate (in review)
ftp//ftp.hydro.washington.edu/pub/hamleaf/hamlet_
snow_trends/hamlet_snow_trends_050504.pdf Matthews
, E., 1983. Global Vegetation, LandUse, and
Seasonal Albedo NASA Goddard Institute for Space
Studies. Digital Raster Data on a 1-degree
Geographic (lat/long) 180x360 grid. Boulder, CO
National Center for Atmospheric Research. 9 track
tape, 0.8 MB Nelson, F. E. and Outcalt, S. I.,
1987 A computational method for prediction and
regionalization of permafrost. Arctic and Alpine
Research, 19 279-288. Storck, P., L. Bowling,
P. Wetherbee and D. Lettenmaier, 1999,
Application of a GIS-based distributed hydrology
model for prediction of forest harvest effects on
peak stream flow in the Pacific Northwest, in
Hydrological Applications of GIS, A.M. Gurnell
and D.R. Montgomery (eds.), John Wiley and
Sons. Su, F., J.C. Adam, L.C. Bowling, and D.P.
Lettenmaier, 2005, Streamflow Simulations of the
Terrestrial Arctic Domain , Journal of
Geophysical Research (accepted).
2810 cells routed to 643 outlets Contributing
Area 25 million km2