Downscaling climate in complex terrain with temperature sensor networks: deployment and data analysis challenges
Abstract
The spatial complexities of mountain climate provide ecological resiliency in the face of rapid climate change. Species may not have to move far to track macroclimatic change, as the range of topoclimates over hundreds of meters distance often exceed the degree of macroclimatic changes. Two key topoclimatic gradients - insolation differences producing variability in maximum temperatures, and cold-air pooling producing variability in minimum temperatures - are readily measured using stratified deployments of small temperature sensors. By holding the microclimatic conditions - radiation shelters, shade, and height above ground - as constant as possible, the temperature differentials across terrain features can be quantified. Those temperature differentials can be projected across complex terrain in GIS using multiple regression and machine-learning models. Weather conditions measured at a base station provide a temporal stratification, so that radiation differences are muted under cloudy skies, and cold air pools are disrupted by wind, clouds, and precipitation, and these can be modeled on daily or even hourly time-steps using machine learning.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMGC0260011W
- Keywords:
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- 3305 Climate change and variability;
- ATMOSPHERIC PROCESSES;
- 0799 General or miscellaneous;
- CRYOSPHERE;
- 1616 Climate variability;
- GLOBAL CHANGE;
- 1630 Impacts of global change;
- GLOBAL CHANGE