Development of new metrics to assess and quantify climatic drivers of Extreme event driven Arctic browning

Abstract

Rapid climate change in Arctic regions is resulting in more frequent extreme climatic events. These can cause large-scale vegetation damage, and are therefore among key drivers of declines in biomass and productivity (or “browning”) observed across Arctic regions in recent years. Extreme events which cause browning are driven by multiple interacting climatic variables, and are defined by their ecological impact – most commonly plant mortality. Quantifying the climatic causes of these multivariate, ecologically defined events is challenging, and so existing work has typically determined the climatic causes of browning events on a case-by-case basis in a descriptive, unsystematic manner. While this has allowed development of important qualitative understanding of the mechanisms underlying extreme event driven browning, it cannot definitively link browning to specific climatic variables, or predict how changes in these variables will influence browning severity. It is therefore not yet possible to determine how extreme events will influence ecosystem responses to climate change across Arctic regions. To address this, novel, process-based climate metrics that can be used to quantify the conditions and interactions that drive the ecological responses defining common extreme events were developed using publicly available snow depth and air temperature data (two of the main climate variables implicated in browning). These process-based metrics explained up to 63% of variation in plot-level Normalised Difference Vegetation Index (NDVI) at sites within areas affected by extreme events across boreal and sub-Arctic Norway. This demonstrates potential to use simple metrics to assess the contribution of extreme events to changes in Arctic biomass and productivity at regional scales. In addition, scaling up these metrics across the Norwegian Arctic region resulted in significant correlations with remotely-sensed NDVI, and provided much-needed insights into how climatic variables interact to determine the severity of browning across Arctic regions