Empirical Statistical Downscaling Evaluations

Two Postdoctoral Researcher Position Announcements

• [♦AUG 2023♦]  A Princeton CIMES position for our ongoing Downscaled Climate Projections for Heat & Health Applications has been announced.
  See https://www.princeton.edu/acad-positions/position/31521

• [♦AUG 2023♦] A UCAR CPAESS position associated with a multi-institutional research project assessing the extent to which climate downscaling methods are ‘fit for purpose’ for some infrastructure planning applications.
  See https://ucar.wd5.myworkdayjobs.com/en-US/UCAR_Careers/details/CPAESS-Postdoctoral-Researcher_REQ-2023-288-2

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Our Statistical Downscaling Research Team’s efforts

Overview:

Here at NOAA GFDL, we view our Empirical Statistical Downscaling (ESD) team’s research efforts as occupying a niche akin to that of a Consumer Reports^® for statistical downscaling and bias correction techniques that are used to refine climate forecasts and projections. Our primary aim is not to build the most advanced, state-of-the-art statistical downscaling method ourselves. Instead, our main goal is to perform 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 resiliency planning. Our team’s efforts to systematically evaluate the performance characteristics of bias correction and statistical downscaling methods are relevant and potentially valuable to three different communities interested in climate variability and change. They are…

Applied Science Researchers & Decision-Makers

For many climate impacts studies, large-scale dynamical climate model output is viewed as having inadequate spatial resolution and/or unacceptably large biases that make it unsuitable for direct use in applications of interest. When used in climate impacts analyses that support planning and decision-making, statistically downscaled climate projections typically are viewed as being value-added products that address climate model biases and provide finer spatial scale detail. Our research can benefit users of ESD products in two main ways. First, information gleaned from our various sensitivity analyses, inter-comparisons, and “perfect model” experimental design research efforts increase our knowledge about the performance of different ESD methods when applied to changing climatic conditions. Our analyses uncover 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 from our studies can be used to help applied researchers who employ ESD data products as input to their studies to better match their applications’ needs and sensitivities.  Also, as outlined below, our efforts can 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 impacts analyses.

Empirical-Statistical Downscaling Community

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.

Global Climate Modeling Community

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 by helping to identify GCM weaknesses and strengths, with an emphasis on surface climate variables of particular value to adaptation planners, resource managers, and other decision-makers.

SELECT JOURNAL PUBLICATIONS :

Wootten AM, KW Dixon, DJ Adams‐Smith, RA McPherson, (2021) Statistically Downscaled Precipitation Sensitivity to Gridded Observation Data and Downscaling Technique, International Journal of Climatology, doi:10.1002/joc.6716

Lanzante JR, KW Dixon, MJ Nath, CE Whitlock, D Adams-Smith, (2018) Some Pitfalls in Statistical Downscaling of Future Climate. Bulletin of the American Meteorological Society, doi:10.1175/BAMS-D-17-0046.1

Dixon KW, JR Lanzante, MJ Nath, K Hayhoe, A Stoner, A Radhakrishnan, V Balaji, CF Gaitán (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

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PEOPLE
Team Members & Collaborators

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SELECT JOURNAL PUBLICATIONS
3 Papers of Interest

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PROJECTS & LINKAGES
Past & Present

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NEWS & PRESENTATIONS

SELECT CONFERENCE PRESENTATIONS: