Understanding Downscaled Climate Scenarios over Idaho Brandon Moore and Von P. Walden University of Idaho (with lots of input from Eric Salathe, UW CIG) Outline • Purpose for downscaling climate for Idaho • Description of the downscaling method – U of I (where differs from CIG [denoted by *]) – Discussion of the choices involved • • • • • • • • A priori assumption of stationarity of variability Length of the historical record Method of detrending Interpretation of GCM grids - Interpolation? What downscaled output looks like for Idaho Data availability - web service Current work (precipitation, snow cover extent) Future work Purpose for Downscaling Climate Data for Idaho • Universities – Increasing demand for data • State and Federal Agencies – Current project with IDWR – DEQ - Governor’s Climate “Initiative” – USGS, USFS, Bureau of Rec, USDA, etc … Purpose for Downscaling Climate Data for Idaho • Examples – M.S. Student looking at changes in fire risk in upper Great Basin (A. Kuchy, S. Bunting) – Faculty interested in modeling changes in hydrology in the Palouse Region (C. Harris) – Hydrologic changes in the upper Snake due to climate change (R. Qualls) – Interest from foresty faculty, … Description of Downscaling Method 1. Account for differences between model and obs. • • Determine Bias Correction between climate and observational data (1950-1999). Apply Bias Correction to entire Climate dataset (1900-2100). • • Apply T across entire grid cell. (CIG) Interpolate T between centers of grid cells. (UI) 2. Account for sub-grid topography in climate data. • • Determine Anomaly Grids using PRISM data. Downscale to finer spatial resolution. Bias Correction • Aggregate PRISM to Climate resolution raw PRISM grid (1950-1999) 4-km grid aggregated PRISM grid (1950-1999) 2.5-degree grid Bias Correction • For each climate grid cell: – Determine and remove long-term trend in full time-series (1900-2100) of climate data by applying a 2nd degree polynomial fit to the data. * Bias Correction • For each climate grid cell: – Compute T between de-trended climate data and observational data • Determine mean difference between de-trended climate data and observation data for 1950-1999 T * Bias Correction • For each climate grid cell: – Add T back onto de-trended data to shift the climate data to location as raw climate data but without the trend Bias Correction Anomaly Grids • Compute Anomaly Grid (“perturbation factor”)* – Interpolate aggregated PRISM data to PRISM resolution using same schema as climate interpolation – Difference raw PRISM grid and interpolated PRISM (Difference grids as anomalies for 50 years) raw PRISM grid interpolated PRISM grid aggregated PRISM grid * anomaly grids (50) Anomaly Grids Anomaly Grids anomaly grid Regionally averaged PRISM cdf Probability 1 Interpolated climate grid De-trended Regionally averaged climate cdf 0 -15 Temperature 25 Downscaled climate grid Downscaled Data for Idaho • Using three models selected by CIG as spanning the range of potential change: – low (GISS) – medium (ECHAM) – high (IPSL) • Two climate change scenarios: – A2 - aggressive use of fossil fuels – B1 - more ecologically friendly http://www.ipcc.ch/SPM2feb07.pdf Differences in April (1990/99 - 2090/99) ECHAM5 A2 Tmax ECHAM5 B1 Tmax ECHAM5 A2 Tmin ECHAM5 B1 Tmin IPSL A2 Tmin IPSL A2 Tmin U of I Climate Data Website U of I Climate Data Website Current Research • Temperature downscaling is nearly completed. • Precipitation downscaling is in progress. • Predicting future snow cover extent over Idaho based on relationship between historical snow images and past climate model output. Then impose future climate change. – Visualizations • Animations of snow cover forecasted to 2100 with Snow Water Equivalence (SWE) and Thermometer indicators • Spatial depiction of trends of temperature and precipitation in Idaho • Applying climate change scenarios to hydrologic models in small to medium-sized watersheds. Future Research • New EPSCoR Research Infrastructure Improvement (RII) proposal, “Water in a Changing Climate” – Connect with CIG – Focus on Snake River Basin • Connection between surface and ground water – Interactions of hydrology with biology and economics/policy – If funded, $2M / per year for 5 years • Develop junior faculty and make strategic new hires.