Empirical Statistical Downscaling Evaluations
NEW JOURNAL PUBLICATION: (Jan 2016)
Dixon KW, Lanzante JR, Nath MJ, Hayhoe K, Stoner A, Radhakrishnan A, Balaji V, Gaitán CF (2016) Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results? Climatic Change. doi:10.1007/s10584-016-1598-0 (an open access publication)
Our Statistical Downscaling Research Team’s efforts
Overview: Here at NOAA GFDL, we view our Empirical Statistical Downscaling (ESD) team’s research efforts as potentially filling a niche akin to that of a Consumer Reports® for statistical downscaling. Our primary aim is not to build a better statistical downscaling method ourselves, but rather our main goal is to do evaluations and share the results so that others can make better informed decisions about the suitability of different statistical downscaling methods for various applications relevant to decision-making and adaptation planning. We view our team’s efforts to systematically evaluate the performance characteristics of statistical downscaling methods (both using a “perfect model experimental design” and via sensitivity studies) as being relevant and valuable to three different communities interested in climate variability and change. They are…
When used in climate impacts analyses that support planning and decision-making, statistically downscaled climate projections typically are viewed as being value-added products. Our research can benefit users of ESD products in two main ways. First, our “perfect model” experimental design research and sensitivity analyses increase our knowledge about the performance of different ESD methods when applied to changing climatic conditions. Our analyses are uncovering and documenting how ESD performance varies geographically, by time of year, variable of interest, amount of climate change, and is dependent upon the ESD method used. Knowledge gained by this work can provide valuable guidance regarding some aspects of the confidence one should attribute to different statistically downscaled climate projections used in decision-support. Secondly, as outlined below, our efforts contribute to improving the GCMs and the ESD techniques used to generate the downscaled climate projection data sets that in turn are used as input to
Lacking observations of the future, it is difficult to assess whether and to what extent the skill of ESD techniques may degrade when applied to future climate projections. (Here “skill” broadly refers to the ability of an ESD method to account for GCM biases and to add meaningful fine-scale detail to the raw GCM output.) A “perfect model experimental design” developed at NOAA GFDL allows us to conduct rigorous quantitative assessments of ESD skill both for the contemporary climate and for future projections. The results of our analyses already have proven useful in isolating weakness in some ESD methods, and have led to ESD method improvements.
Empirical Statistical Downscaling can be thought of as an additional post-processing step applied to Global Climate Model (GCM) output.
In effect, an ESD method challenges GCM results with observations as it seeks to address shortcomings in the GCM-simulated climate based on information gleaned from the observational record.
Our team’s efforts can promote GCM development at NOAA GFDL by helping identify GCM weaknesses and strengths, with an emphasis on surface climate variables of particular value to adaptation planners, resource managers, and other decision-makers.