Probabilistic Spatial Meteorological Estimates for Alaska and the Yukon. Issue 22 (16th November 2020)

• Record Type: Journal Article
• Authors: Newman, Andrew J.; Clark, Martyn P.; Wood, Andrew W.; Arnold, Jeffrey R.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 22(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-16
• Subjects: precipitation -- Arctic -- hydrometeorology -- temperature -- spatial interpolation -- climatology; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2020JD032696
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: It is challenging to develop observationally based spatial estimates of meteorology in Alaska and the Yukon. Complex topography, frozen precipitation undercatch, and extremely sparse in situ observations all limit our capability to produce accurate spatial estimates of meteorological conditions. In this Arctic environment, it is necessary to develop probabilistic estimates of precipitation and temperature that explicitly incorporate spatiotemporally varying uncertainty and bias corrections. In this paper we exploit the recently developed ensemble Climatologically Aided Interpolation (eCAI) system to produce daily historical estimates of precipitation and temperature across Alaska and the Yukon Territory at a 2 km grid spacing for the time period 1980–2013. We extend the previous eCAI method to address precipitation gauge undercatch and wetting loss, which is of high importance for this high‐latitude region where much of the precipitation falls as snow. Leave‐one‐out cross‐validation shows our ensemble has little bias in daily precipitation and mean temperature at the station locations, with an overestimate in the daily standard deviation of precipitation. The ensemble is statistically reliable compared to climatology and can discriminate precipitation events across different precipitation thresholds. Long‐term mean loss adjusted precipitation is up to 36% greater than the unadjusted estimate in windy areas that receive a large fraction of frozen precipitation, primarily due to wind induced undercatch. Comparing the ensemble mean climatology of precipitation and temperature to PRISM and Daymet v3 shows large interproduct differences, particularly in precipitation across the complex terrain of southeast and northern Alaska. Plain Language Summary: It is challenging to create spatial maps of precipitation and temperature in Alaska and the Yukon. The rugged terrain and extreme conditions, particularly snow and wind, along with the extremely sparse station networks, limit our ability to estimate historical conditions. Thus, it is critical to understand the uncertainty in our spatial estimates of meteorological conditions. Here we develop a new estimate of uncertainty for historical meteorology and include corrections to precipitation measurements for errors due to snowfall and wind. We show that there are large differences between several independent mapping efforts. We then demonstrate that our ensemble product skillfully predicts the observed probability of occurrence of multiple precipitation events sizes (the ensemble is statistically reliable). Finally, our precipitation corrections increase precipitation estimates by more than 30% in some portions of Alaska. Key Points: It is difficult to estimate the spatial variability in meteorology across the Arctic due to complex topography, sparse observations, and frozen precipitation We developed a probabilistic approach to estimate precipitation and temperature, including precipitation undercatch There are large uncertainties in our precipitation and temperature estimates over much of Alaska