Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature

Authors

  • Jared W. Oyler,

    Corresponding author
    1. Numerical Terradynamic Simulation Group, Department of Ecosystem and Conservation Sciences, University of Montana, Missoula, MT, USA
    2. Montana Climate Office, Montana Forest and Conservation Experiment Station, University of Montana, Missoula, MT, USA
    • Correspondence to: J. W. Oyler, Numerical Terradynamic Simulation Group, Department of Ecosystem and Conservation Sciences, University of Montana, 32 Campus Drive, Missoula, MT 59812, USA. E-mail: jared.oyler@ntsg.umt.edu

    Search for more papers by this author
  • Ashley Ballantyne,

    1. Montana Climate Office, Montana Forest and Conservation Experiment Station, University of Montana, Missoula, MT, USA
    Search for more papers by this author
  • Kelsey Jencso,

    1. Montana Climate Office, Montana Forest and Conservation Experiment Station, University of Montana, Missoula, MT, USA
    Search for more papers by this author
  • Michael Sweet,

    1. Montana Climate Office, Montana Forest and Conservation Experiment Station, University of Montana, Missoula, MT, USA
    Search for more papers by this author
  • Steven W. Running

    1. Numerical Terradynamic Simulation Group, Department of Ecosystem and Conservation Sciences, University of Montana, Missoula, MT, USA
    Search for more papers by this author

ABSTRACT

Gridded topoclimatic datasets are increasingly used to drive many ecological and hydrological models and assess climate change impacts. The use of such datasets is ubiquitous, but their inherent limitations are largely unknown or overlooked particularly in regard to spatial uncertainty and climate trends. To address these limitations, we present a statistical framework for producing a 30-arcsec (∼800-m) resolution gridded dataset of daily minimum and maximum temperature and related uncertainty from 1948 to 2012 for the conterminous United States. Like other datasets, we use weather station data and elevation-based predictors of temperature, but also implement a unique spatio-temporal interpolation that incorporates remotely sensed 1-km land skin temperature. The framework is able to capture several complex topoclimatic variations, including minimum temperature inversions, and represent spatial uncertainty in interpolated normal temperatures. Overall mean absolute errors for annual normal minimum and maximum temperature are 0.78 and 0.56 °C, respectively. Homogenization of input station data also allows interpolated temperature trends to be more consistent with US Historical Climate Network trends compared to those of existing interpolated topoclimatic datasets. The framework and resulting temperature data can be an invaluable tool for spatially explicit ecological and hydrological modelling and for facilitating better end-user understanding and community-driven improvement of these widely used datasets.

Ancillary