An Assessment of High-Resolution Gridded Temperature Datasets over California

Abstract

High-resolution gridded datasets are in high demand because they are spatially complete and include important finescale details. Previous assessments have been limited to two to three gridded datasets or analyzed the datasets only at the station locations. Here, eight high-resolution gridded temperature datasets are assessed two ways: at the stations, by comparing with Global Historical Climatology Network–Daily data; and away from the stations, using physical principles. This assessment includes six station-based datasets, one interpolated reanalysis, and one dynamically downscaled reanalysis. California is used as a test domain because of its complex terrain and coastlines, features known to differentiate gridded datasets. As expected, climatologies of station-based datasets agree closely with station data. However, away from stations, spread in climatologies can exceed 6°C. Some station-based datasets are very likely biased near the coast and in complex terrain, due to inaccurate lapse rates. Many station-based datasets have large unphysical trends (>1°C decade−1) due to unhomogenized or missing station data—an issue that has been fixed in some datasets by using homogenization algorithms. Meanwhile, reanalysis-based gridded datasets have systematic biases relative to station data. Dynamically downscaled reanalysis has smaller biases than interpolated reanalysis, and has more realistic variability and trends. Dynamical downscaling also captures snow–albedo feedback, which station-based datasets miss. Overall, these results indicate that 1) gridded dataset choice can be a substantial source of uncertainty, and 2) some datasets are better suited for certain applications.

Simulating and Evaluating Atmospheric River‐Induced Precipitation Extremes Along the U.S. Pacific Coast: Case Studies From 1980–2017

“…Meanwhile, gridded data sets offer a spatially complete picture, but they rely on interpolation algorithms to determine values away from stations. Since these interpolation algorithms often incorporate simplistic assumptions of how climate elements vary in space, the reliability of gridded data sets can be especially problematic in areas with sparse station data or complex terrain—as is the case across much of the Western United States (Henn et al, ; Walton & Hall, ). Thus, gridded data are used primarily for evaluating the spatial patterns of our WRF simulations but with the understanding that they represent a plausible estimate of what the true spatially complete field might have been.…”

Disproportionate magnitude of climate change in United States national parks

“…The TopoWx dataset (Oyler et al 2015), which covers the contiguous 48 states starting in 1948, has corrected for station changes over time, but it was not available at the time of this research. PRISM has demonstrated accuracy in comparisons with weather station measurements (Bishop and Beier 2013, Behnke et al 2016, Walton and Hall 2018. Comparison of maximum temperatures from PRISM and 3855 Global Historical Climatology Network weather stations across the US found a high correlation (r > 0.95) (Behnke et al 2016).…”

“…PRISM data for the contiguous 48 states are derived from measurements at over 10 000 weather stations (Daly et al 2008), but a limitation of our methods is that PRISM does not completely control for weather station changes (Oyler et al 2015, Walton andHall 2018) and it may be less accurate in mountainous terrain (Strachan and Daly 2017), overestimating warming at higher elevations in the western US (Oyler et al 2015). The TopoWx dataset (Oyler et al 2015), which covers the contiguous 48 states starting in 1948, has corrected for station changes over time, but it was not available at the time of this research.…”

Biologically‐Relevant Trends in Springtime Temperatures Across the United States

“…We calculated cumulative GDDs for each grid cell in each year using the TopoWx gridded climatic dataset resampled from 800 m to a nominal resolution of 3 km (Oyler et al, 2014). The TopoWx dataset is well-suited for evaluating temporal trends, as it is generated using homogenized station data, thereby minimizing non-climatic trends arising from missing or erroneous station data (Oyler et al, 2015;Walton & Hall, 2018). Daily total GDDs above 10°C were calculated using the simple average method, {GDD = [(minimum temperature + maximum temperature)/2] − base temperature}; McMaster & Wilhelm, 1997).…”