Testing the daily PRISM air temperature model on semiarid mountain slopes. Issue 11 (7th June 2017)

• Record Type: Journal Article
• Authors: Strachan, Scotty; Daly, Christopher
• Is Part Of: Journal of geophysical research. Volume 122:Issue 11(2017)
• Page Start: 5697
• Page End: 5715
• Publication Date: 2017-06-07
• Subjects: gridded temperature model -- topoclimate -- downscaling -- mountain climate -- instrument bias -- sensor networks; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2016JD025920
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Studies in mountainous terrain related to ecology and hydrology often use interpolated climate products because of a lack of local observations. One data set frequently used to develop plot‐to‐watershed‐scale climatologies is PRISM (Parameter‐elevation Regression on Independent Slopes Model) temperature. Benefits of this approach include geographically weighted station observations and topographic positioning modifiers, which become important factors for predicting temperature in complex topography. Because of the paucity of long‐term climate records in mountain environments, validation of PRISM algorithms across diverse regions remains challenging, with end users instead relying on atmospheric relationships derived in sometimes distant geographic settings. Presented here are results from testing observations of daily temperature maximum ( T MAX ) and minimum ( T MIN ) on 16 sites in the Walker Basin, California‐Nevada, located on open woodland slopes ranging from 1967 to 3111 m in elevation. Individual site mean absolute error varied from 1.1 to 3.7°C with better performance observed during summertime as opposed to winter. We observed a consistent cool bias in T MIN for all seasons across all sites, with cool bias in T MAX varying with season. Model error for T MIN was associated with elevation, whereas model error for T MAX was associated with topographic radiative indices (solar exposure and heat loading). These results demonstrate that temperature conditions across mountain woodland slopes are more heterogeneous than interpolated models (such as PRISM) predict, that drivers of these differences are complex and localized in nature, and that scientific application of atmospheric/climate models in mountains requires additional attention to model assumptions and source data. Plain Language Summary: Knowledge of daily‐to‐seasonal climate (such as air temperature) in mountain areas is important for assessment of landscape conditions related to plants, animals, and resources such as water supply. Because few actual observations of climate processes exist in mountains, scientists have developed models to estimate parameters like temperature across landscapes. In this paper we test one commonly used spatial temperature model using observations and report the model error as well as influential factors. Our conclusions state that while for some science and management uses the model differences from observations are inconsequential, improper application of the model in other contexts without local verification or consideration of assumptions would lead to incorrect results. We also show that the location of long‐term monitoring stations in mountain landscapes likely impacts model accuracy more than differences in network instrumentation practices. Therefore, scientists or managers seeking to leverage such models of temperature to make decisions need to be aware of both the representation of source data and assumptions made during the modeling process. This study underscores the need for additional long‐term monitoring of climate processes in mountain areas given the importance of such regions to society in terms of resources and value. Key Points: In situ testing of a popular gridded air temperature model in a semiarid watershed revealed high accuracy but also systematic biases Model accuracy increased by moving from 4 km to 800 m grid size, but further downscaling did not improve results Biases in daily temperature extremes are linked to siting differences between in situ and model source data, and topoclimatic mechanisms