Downscaling Climate over Complex Terrain: High Finescale (<1000 m) Spatial Variation of Near-Ground Temperatures in a Montane Forested Landscape (Great Smoky Mountains)*

Abstract

Landscape-driven microclimates in mountainous terrain pose significant obstacles to predicting the response of organisms to atmospheric warming, but few if any studies have documented the extent of such finescale variation over large regions. This paper demonstrates that ground-level temperature regimes in Great Smoky Mountains National Park (Tennessee and North Carolina) vary considerably over fine spatial scales and are only partially linked to synoptic weather patterns and environmental lapse rates. A 120-sensor network deployed across two watersheds in 2005-06 exhibited finescale (,1000-m extent) temperature differences of over 28C for daily minima and over 48C for daily maxima. Landscape controls over minimum temperatures were associated with finescale patterns of soil moisture content, and maximum temperatures were associated with finescale insolation differences caused by topographic exposure and vegetation cover. By linking the sensor array data to 10 regional weather stations and topographic variables describing site radiation load and moisture content, multilevel spatial models of 30-m resolution were constructed to map daily temperatures across the 2090-km 2 park, validated with an independent 50-sensor network. Maps reveal that different landscape positions do not maintain relative differences in temperature regimes across seasons. Near-stream locations are warmer in the winter and cooler in the summer, and sites of low elevation more closely track synoptic weather patterns than do wetter high-elevation sites. This study suggests a strong interplay between near-ground heat and water balances and indicates that the influence of past and future shifts in regional temperatures on the park's biota may be buffered by soil moisture surfeits from high regional rainfall.

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

“…To generalize the analysis, we chose error ranges for the field uncertainty that enveloped the reported uncertainty of different methods for acquiring forcing data. T air error ranges spanned errors in measurements (Huwald et al, 2009) and commonly used models, such as lapse rates and statistical methods (Bolstad et al, 1998;Chuanyan et al, 2005;Fridley, 2009;Hasenauer et al, 2003;Phillips and Marks, 1996). U error ranges spanned errors in topographic drift models (Liston and Elder, 2006;Winstral et al, 2009) and numerical weather prediction (NWP) models (Cheng and Georgakakos, 2011).…”

Strengths and Weaknesses of Quantitative Climate Reconstructions Based on Late-Quaternary Biological Proxies

“…pollen percentages) into ordinal classes reflecting absence, presence, and abundance (Fauquette et al 1998(Fauquette et al , 1999 should be used more frequently. The appropriateness of the modern climate data used is very critical (Daly 2006;Ashcroft et al 2008Ashcroft et al , 2009Fridley 2009;Randin et al 2009a;Kitricos & Leriche 2010;Roubicek et al 2010;Tabor & Williams 2010). In speciesclimate modelling it is assumed that modern climate data used are the actual climate values where the species occurs in its known localities.…”

Monthly microclimate models in a managed boreal forest landscape

“…high-resolution surface mapping with LiDAR producing digital elevation models or canopy maps, see overview in He et al, 2015), the growing computational power and the development of new statistical techniques (Keppel et al, 2012;Lenoir et al, 2017). Up to now, the majority of the predictive models have been done in montane landscapes or other types of complex terrain, accounting for local physiography Frey et al, 2016;Fridley, 2009;Lookingbill and Urban, 2003;Vanwalleghem and Meentemeyer, 2009). Only a few studies have included also vegetation features, despite its recognized importance in e.g.…”

“…While many studies have modelled minimum and maximum temperatures (e.g. Fridley, 2009;Geiger et al, 2012;Lookingbill and Urban, 2003;Meineri et al, 2015), only few have considered also seasonal changes in climate-forcing factors and their relative influence on near-ground temperatures (e.g. Ashcroft and Gollan, 2013a).…”