Abstract
Extreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which downscaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the overall performance of LOCA-derived changes in extreme daily precipitation. We show that the low (negative) bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.
Highlights
Large infrastructure decisions have in the past been based on average temperature and precipitation, their variability, and various measures of extreme events
To assess the performance of statistical downscaling solutions for location-specific analysis of extreme precipitation, we use, as a case study, the subset of United States Department of Defense (DoD) installations within the CONUS that serve as foci for environmental research supported by the Strategic Environmental Research and Development Program (SERDP) and Environmental Security Technology Certification Program (ESTCP)
These specific locations are chosen for our analysis because practitioners who are assessing the risks at each site from climate-changed temperature and precipitation patterns would benefit from high-confidence, localized projections of statistical distributions of these quantities (Moss 2017)
Summary
Large infrastructure decisions have in the past been based on average temperature and precipitation, their variability, and various measures of extreme events. Practitioners using downscaled projections to describe and understand atmospheric and land-surface impacts at finer scales than those produced by the parent model (e.g., GCM) should consider multiple questions in tandem (e.g., Barsugli et al 2013; Vano et al 2018), including their tolerance for parent model spread across variables of interest, the level of confidence in, and fidelity of, the parent model to known and relevant processes at the parent model scale, and whether the downscaling solution introduces additional process information to fine-scale projections that is absent from the parent model but which materially improves the downscaled projection (e.g., Walton et al 2020; Hall et al 2020, which highlights that errors in under-resolved precipitation and snow albedo processes in parent model projections for California can be addressed, at least in part, through dynamical downscaling) These questions arise irrespective of whether practitioners directly apply downscaled fields to specific locations or indirectly apply downscaled outputs through approaches such as decision-scaling, in which a stochastic assessment of risk is combined with insights derived from climate projections at some scale to determine the likelihood of changes in events of particular interest at a given location (e.g., Brown et al 2012).
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