Exploring the Contributing Effects of Climate and Land Surface Changes on the Variability of Pan-Arctic River Discharge and Surface Albedo Jennifer C. Adam 1, Fengge Su 1, Laura C. Bowling 2, and Dennis P. Lettenmaier 1 1.Department of Civil and Environmental Engineering, Box , University of Washington, Seattle, WA Department of Agronomy, Purdue University, West Lafayette, IN First CliC Science Conference, Beijing China, April 10 – 17, 2005 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 ( ) 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). Modeling Framework 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. Baseline Simulation: Streamflow Climatology and Trends Anthropogenic Effects Experiment #Description 1Precipitation, Temperature with Trends 2Precipitation with Trends Temperature at Climatology 3Precipitation at Climatology Temperature with Trends Designed after Hamlet et al. (2005), the following three VIC model runs were made: Photo: ObservedSimulated 2810 cells routed to 643 outlets Contributing Area: 25 million km 2 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. 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: , 1 year spin-up Run 6 – Run 5 Runoff, mm SpringSummer Seasonally Frozen SoilPermafrost Temperature at Damping Depth, °C Run 5: T damp from period Inferred Decrease in Permafrost Extent Run 6: T damp from period Indirect Climate Effects 4 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. Direct Climate Effects (Precipitation and Temperature) Monthly Mean Discharge to the Arctic Ocean (Su et al. 2005) 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) 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 Lena ( at Kusur): 2,430,000 km 2 Ob ( at Salekhard): 2,950,000 km 2 Yenisei ( at Igarka): 2,440,000 km 2 Mackenzie ( at Arctic Red River): 1,680,000 km 2 Yukon ( at Pilot Station): 831,000 km 2 Ob ( at Salekhard): 2,950,000 km 2 Exp. 2Exp. 1Exp. 3 Annual Streamflow April Albedo precipitation dominates interannual variability and long-term trends precipitation and temperature both play important roles Precipitation Mackenzie: +43 mm Lena: +3 mm Ob: +12 mm Temperature Mackenzie: °C Lena: °C Ob: °C Precipitation and temperature inputs to the model were adjusted for spurious trends according to the method of Hamlet and Lettenmaier (2005). 0% 20% 50% 75% 100% Cultivation Intensity Pre-Agriculture Vegetation 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. 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), Brown, J., O.J. Ferrians Jr., J.A. Heginbottom, and E.S. Melnikov 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, 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), 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_ 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_ pdf Matthews, E., 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: 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). 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) Results: warming soil temperatures cause a change in runoff seasonality 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)