Statistical Downscaling
STATISTICAL DOWNSCALING
Introduction
Dating back to about 2005 I began doing background work in preparation for my entry into the realm of statistical downscaling, which came to fruition during the 2011-2012 time-frame. Although I had envisioned this as a project primarily of my own doing, a convergence of factors has (happily) led me to getting swept up into a much larger effort! We now have a Statistical Downscaling Project at GFDL, with my colleague Keith Dixon as the de facto leader.Other group members are Mary Jo Nath, Carolyn Whitlock, and Dennis-Adams Smith who joined us in June 2016. Here you can meet the ESD Team!
Goals & Philosophy
Practitioners of statistical downscaling are driven to a large extent by the need for answers to practical questions regarding the potential impacts of climate change. As a result, downscaling is often carried out in an ad-hoc fashion fueled by expediency. Typically, practitioners have neither the time nor the expertise to evaluate downscaling methods or to consider the consequences of various choices made in the application of downscaling.
This dilemma was articulated nicely in a recent publication With this as motivation, we hope to contribute to filling a void in the community regarding scrutiny of statistical downscaling techniques and practices. Among our overarching goals are:
- Subject downscaling techniques to rigorous quantitative evaluation
- Test sensitivities to choices made in application of downscaling techniques
- Improve downscaling techniques
- Provide guidance to end users in the application of techniques and interpretation of results
Statistical downscaling operates by deriving relationships between climate models and observations, with the intent of producing more localized information that is free of model biases. Often, downscaling relationships derived during the recent past are applied to model projections of future climate. However, the method will be degraded if the relationships from the past are not the same as those in the future. In real-world practice it is next to impossible to validate this crucial assumption of “statistical stationarity”, because we do not have “observations from the future”! Unfortunately the assumption of statistical stationarity underpins all statistical downscaling techniques.
One of the initial thrusts of our work focuses on assessing this assumption. To circumvent the lack of observations from decades into the future we operate in a “perfect model” world. This scheme can serve as an ideal test-bed, in which GCM output from historical and future climates are used as proxies for the real world. A paper which describes this framework, applied over the CONUS, can be found here.
Initial efforts geared towards the examination of daily maximum temperature have already uncovered some unexpected quirks common in one class of statistical downscaling techniques. These involve both coastal as well as well as spring-time mountain snow effects. Further study has uncovered problems in downscaling extremes (i.e. the tails of the distribution) using one particular method. We have devised remedies and have found them to yield improvements when applied to other downscaling methods in the same class. Current work in progress is aimed at examining the performance of downscaling methods when applied to daily precipitation. We have found this to be much more challenging than for temperature, and indeed it appears that the rendering of extremes is unsatisfactory. A manuscript reporting on these results is forthcoming in 2020.
Plans
Statistical downscaling will be the main focus of my research for the foreseeable future. We have an ambitious wish list of focal points including the assessment of extremes, intercomparison of a number of different downscaling techniques — including more complex downscaling approaches, as well as examination of indices that combine more than one physical variable, and/or involve more complex relationships. In addition, I have a much longer wish list, and am open to new ideas for collaboration, which should keep me occupied for years to come!
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