Ecosystem Greenhouse Gas Fluxes Respond Directly to Weather Not Climate: A Case Study on the Relationship of Global Atmospheric Circulation, Foehn Frequency, and Winter Weather to Northern Alps Regional Grassland Phenology and Carbon Cycling

Abstract:

The impact of climate change on regional ecosystem structure and biogeochemical cycling has two important aspects that require better elaboration to improve projections of these effects. The first is that ecosystems don't respond directly to climate, but indirectly via frequency and occurrence of weather systems, which are driven by climatic shifts in global circulation and radiative processes. The second is that many responses of ecosystems to these weather patterns and extremes are lagged in time. Here, we examine these aspects for northern Alpine grasslands. Long-term eddy covariance flux tower and phenology observations in Austria and Germany and biophysical models reveal a strong influence of winter air temperature, snowfall, and snowmelt frequency on winter grass mortality and spring grassland carbon uptake. Further, the mode of climate variability that drives winter air temperature and snow depth patterns is primarily the frequency of strong regional southerly Foehn flow that promotes warm, dry conditions in winter. Finally, we demonstrate that much of the interannual variance in Foehn frequency and southerly flow is driven by statistics and climatic trends of 500 hPa pressure patterns in Greenland, part of the Arctic Oscillation. However, a few years, including the unusually warm and dry winter of 2013-2014 appear to have secondary, possibly local thermotopographic circulation factors that promoted its weather conditions regionally, which also included primarily cool and wet conditions in northern Europe and the southern Alps. These findings demonstrate that the regional response of ecosystems to climate change is modulated by how large-scale circulation patterns influence local meteorology and topographic flows both during and outside the growing season and provides a framework for future assessment and climate model improvements of linkages of climate change, weather patterns, and ecosystem responses.