Comparing statistical downscaling methods: From simple to complex Anne Stoner, Katharine Hayhoe Texas Tech University Keith Dixon, John Lanzante, Aparna Radhakrishnan GFDL

approach Goal: Evaluate and compare multiple statistical downscaling methods using the same framework Monthly and daily versions of Delta, Quantile Mapping, and Asynchronous Regional Regression Model Variables – Minimum, maximum daily 2m temperature Daily accumulative precipitation Input: GFDL-HiRES experimental model as both model and observations OBS: 25km GFDL-HiRES (1979-2008) Model: 200km coarsened GFDL-HiRES (1979-2008, 2086-2095) Output: Daily 25km downscaled Tmin, Tmax, Prcp (2086-2095)

Method 1: Delta Change Calculates average difference between present and future GCM simulations, then adds that difference to the observed time series for the point of interest Here: individually for each high-resolution grid cell Assumptions – GCMs are more successful at simulating changes in climate rather than actual local values Stationarity in local climate variability

Method 2: quantile mapping (e.g. bcsd) Projects PDFs for monthly or daily simulated GCM variables onto historical observations Changes the shape of the simulated PDF to appear more like the observed PDF, but allowing the mean and variance of the GCM to change in accordance with GCM future simulations

Method 3: quantile regression (e.g. ARRM) Asynchronous Regional Regression Model Daily quantile regression using piecewise linear segments to improve fit for the training period Individual monthly models allows for different distributions throughout the year

Colorado National Monument, CO Delta Quantile Mapping ARRM Colorado National Monument, CO Comparison The shape of the resulting downscaled distribution depends highly on the downscaling method used

Maximum temperature

Minimum temperature

precipitation

precipitation

Maximum temperature

Minimum temperature

precipitation

precipitation

Daily downscaled tmax

Monthly downscaled tmax

conclusions Comparing multiple downscaling methods in a standardized framework gives us useful information If someone has already used a certain downscaling method they can correctly interpret the biases If someone is trying to decide which method to use, this can help their decision, because there’s no perfect method Simple methods can be fine for studying monthly/annual means, daily output for low latitudes More complex methods are required when studying climate extremes and high latitudes

Next steps Downscale relative humidity Figure out physical causes of the biases we’re seeing Explore the influence of different predictors Incorporate more downscaling techniques