Exploring the Roles of Climate and Land Surface Changes on the Variability of Pan-Arctic River Discharge 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 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). Observed changes in streamflow may be linked both to direct and indirect effects of climate change. For example, a general warming leads to earlier spring runoff, however changes in high arctic vegetation such as increased incidence of brush can lead to increased snow accumulation, and hence sustained runoff later in the summer. Also, vegetation changes can substantially change evapotranspiration. Changes in snow cover extent and the distribution of vegetation and wetlands over the pan-arctic domain affect land-atmosphere energy exchanges, and the seasonality of river flow. Furthermore, recent research has suggested that changes in permafrost extent and the active layer depth may also be affecting river flow. 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 river discharge. 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-land 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. Finally, we describe an exploratory analysis in which we use the model to evaluate the effects of changes in climate (precipitation and temperature) and active layer depth on streamflow variability and trends over the last half century. Modeling Framework CONCLUDING REMARKS Adjustment of spurious trends in precipitation and temperature (minimum and maximum daily) improves streamflow trends and variability in some basins, but not all. Is there something missing from the model, or is there still something wrong with the inputs? ….. Future work. The model shows that runoff trends are not always consistent with precipitation trends – our example shows increasing spring storage releases over the Ob. What is causing this increase? Is this realistic? Warming ground temperatures are shown to have a direct effect on streamflow response – this will also be explored further. The effects of changing land cover classification on streamflow and albedo variability will be explored, e.g. northward moving tree-line. Anthropogenic effects on streamflow and albedo variability will also be explored, e.g. agriculture and reservoir storage. Improvement of Precipitation and Temperature Inputs Effects of Permafrost Extent Changes on Runoff Variability Experiment #Description 1Precipitation, Temperature with Trends 2Precipitation with Trends Temperature at Climatology 3Precipitation at Climatology Temperature with Trends 4Precipitation, Temperature at Climatology Trend Slope, % year -1 Description of Figures: 90 Color: Trend Slope, e.g % year -1 Number: Significance Level, e.g. 90% Designed after Hamlet et al. (2004), the following four VIC model runs were made: Winter (DJF)Spring (MAM)Summer (JJA) Fall ( SON ) Annual Precipitation Observed Runoff Temperature Simulated Runoff Simulated Albedo Ob ( at Salekhard): 2,950,000 km Winter (DJF)Spring (MAM)Summer (JJA) Fall ( SON ) Annual Precipitation Observed Runoff Temperature Simulated Runoff Simulated Albedo Lena ( at Kusur): 2,430,000 km 2 Experiment # Things to explore using these figures: For each season and basin, are streamflow and albedo trends more sensitive to trends in precipitation or temperature? Do runoff trends match precipitation trends and why or why not? Do simulated runoff trends match observed runoff trends for each season? Note: Albedo is basin average (uses aging algorithm) Things learned and new questions: Precipitation : Albedo, Temperature : Albedo Precipitation : Runoff ?, Temperature : Runoff ? Ob: Runoff sensitive to precip, mostly insensitive to temp. Spring albedo sensitive to temp. Lena: Runoff sensitive to both precip and temp, e.g. winter: temp controls, spring: precip controls. Summer and Fall albedo sensitive to temp. Ob: Simulated runoff similar to observed runoff. Lena: Simulated runoff different from observed runoff. What is missing from the model? or Inaccurate forcings? Precipitation and temperature inputs to the model were adjusted for spurious trends according to the method of Hamlet et al. (2004) using temporally homogenous index stations from the GHCN ( BeforeAfter Temperature Photo: BeforeAfter Precipitation Mackenzie: +37 mm Lena: +31 mm Mackenzie: +43 mm Ob: +27 mm Lena: +3 mm Ob: +12 mm Mackenzie: °C Lena: °C Mackenzie: °C Ob: °C Lena: °C Ob: °C Before Trend AdjustmentObserved After Trend Adjustment Ob ( at Salekhard): 2,950,000 km 2 Lena ( at Kusur): 2,430,000 km 2 Adjusting the spurious trends in precipitation and temperature caused the simulated streamflow long-term trend to match observed for the Ob, but did not improve the trend over the Lena. Ob Lena Change in streamflow seasonality: peak flows during the first three decades are improved for the Lena; there is no apparent improvement for the Ob. ObservedBefore Trend Adjustment After Trend Adjustment ObservedBefore Trend Adjustment After Trend Adjustment 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 Seasonally Frozen Soil Permafrost Temperature at Damping Depth, °C Run 5: T damp from period Decrease in Permafrost Extent Over the south-eastern part of Ob where the greatest changes in ground temperatures occurred, Run 6 spring runoff is greater and summer runoff is less than Run 5, indicating an earlier melt. Because air temperatures are the same for both runs, warmer ground conditions are responsible Run 6 – Run 5 Runoff, mm DJFMAMJJASONAnnual Permafrost extent was estimated for each decade based on a method described by Nelson and Outcalt (1987) in which a frost number, F, was calculated as a predictor for the extent of permafrost in a grid cell. F is calculated as : in which DDF and DDT are the annual degree days of freezing and thawing, respectively. Rather than using surface air temperature (and adjusting for snow cover effects), we used surface temperature beneath snow pack from our baseline VIC simulations. Comparing our results to the permafrost map of Brown et al. (1998), we determined appropriate thresholds of F to estimate temperature at damping depth (shown at right for two decades). Run 6: T damp from period mm year mm year mm year mm year -1 Precipitation Evaporation Runoff Example: Ob Spring Exp. 2 Precip Runoff ΔStorage why?... future work Trend, mm year -1 Storage Release Ob Depth, mm Precipitation and Temperature Effects on Runoff and Land Surface Albedo Variability 3