Constrained dynamical downscaling for assessment of climate impacts

Abstract

[1] To assess climate change impacts on hydrology, conservation biology, and air quality, impact studies typically require future climate data with spatial resolution high enough to resolve urban-rural gradients, complex topography, and sub-synoptic atmospheric phenomena. We present here an approach to dynamical downscaling using analysis nudging, where the entire domain is constrained to coarser-resolution parent data. Here meteorology from the North American Regional Reanalysis and the North American Regional Climate Change Assessment Program data archive are used as parent data and downscaled with the Advanced Research version of the Weather Research and Forecasting model to a 12 km  12 km horizontal resolution over the Eastern U.S. Our results show when analysis nudging is applied to all variables at all levels, mean fractional errors relative to parent data are less than 2% for maximum 2 m temperatures, less than 15% for minimum 2 m temperatures, and less than 18% for10 m wind speeds. However, the skill of representing fields that are not nudged, such as boundary layer height and precipitation, is less clear. Our results indicate that though nudging can be a useful tool for consistent, comparable studies of downscaling climate for regional and local impacts, which variables are nudged and at what levels should be carefully considered based on the climate impact(s) of study.Citation: Harkey, M., and T. Holloway (2013), Constrained dynamical downscaling for assessment of climate impacts,

Climate Change and Heat-Related Excess Mortality in the Eastern USA

“…WRF simulations employed dynamical downscaling to maximize agreement with the input data (NARR for 2007; NARCCAP for 2069) at 36 km and 12 km resolution. This downscaling technique, in which the entire study domain is constrained to coarser resolution parent climate data, offers more consistent and readily comparable results (see Harkey and Holloway 2013). Although we focus on a single future year, by constraining to the NARCCAP projections, we can evaluate results in the context of a multi-year, multi-model climate ensemble.…”

“…From the future WRF-CCSM simulations, we selected a year (2069) representing the highest average summer (June-August) temperature for the region. This scenario demonstrates a potential upper bound for health risks associated with exposure to higher summer temperatures (Harkey and Holloway 2013). We refer to this future year as a mid-century estimate, because 2069 reflects sampling from model data spanning 2041–2070.…”

“…Calculating the effects of long-term temperature change on health requires the selection of a baseline scenario for comparison. We chose the summer of 2007 as the baseline scenario because it adequately simulates presentday summer temperature and air pollution exposure patterns across the study region and corresponds to a parallel climate impacts downscaling study (Harkey and Holloway 2013). Present-day summer temperature patterns across the study region were simulated using North American Regional Reanalysis (NARR) data (Mesinger et al 2006).…”

“…NARR data comprise long term datasets that simulate land–atmosphere interaction on a 36 km grid, are output every three hours from January 1979 to the present, and provide a dynamically consistent meteorology based on observations, with no discontinuities or gaps as might exist in an observational data set. In 2007, summer temperatures were near normal in the northeast, and above normal in the southeast and midwest (Harkey and Holloway 2013). …”

“…Basu 2002; Zanobetti and Schwartz 2008; Kovats and Hajat 2008). There is a high likelihood that climate change will amplify the severity and duration of future heat waves, but disagreement among climate models as to the mid-century temperature profile of the study region (Harkey and Holloway 2013; Abel et al 2018). …”