Extreme events in gross primary production: a characterization across continents

Abstract

Abstract. Climate extremes can affect the functioning of terrestrial ecosystems, for instance via a reduction of the photosynthetic capacity or alterations of respiratory processes. Yet the dominant regional and seasonal effects of hydrometeorological extremes are still not well documented and in the focus of this paper. Specifically, we quantify and characterize the role of large spatiotemporal extreme events in gross primary production (GPP) as triggers of continental anomalies. We also investigate seasonal dynamics of extreme impacts on continental GPP anomalies. We find that the 50 largest positive extremes (i.e., statistically unusual increases in carbon uptake rates) and negative extremes (i.e., statistically unusual decreases in carbon uptake rates) on each continent can explain most of the continental variation in GPP, which is in line with previous results obtained at the global scale. We show that negative extremes are larger than positive ones and demonstrate that this asymmetry is particularly strong in South America and Europe. Our analysis indicates that the overall impacts and the spatial extents of GPP extremes are power-law distributed with exponents that vary little across continents. Moreover, we show that on all continents and for all data sets the spatial extents play a more important role for the overall impact of GPP extremes compared to the durations or maximal GPP. An analysis of possible causes across continents indicates that most negative extremes in GPP can be attributed clearly to water scarcity, whereas extreme temperatures play a secondary role. However, for Europe, South America and Oceania we also identify fire as an important driver. Our findings are consistent with remote sensing products. An independent validation against a literature survey on specific extreme events supports our results to a large extent.

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

“…However, some information might only be inferred when taking the multivariate combination of several data streams into account (Vicente-Serrano et al, 2010;Seneviratne et al, 2012;Fischer, 2013;Zscheischler et al, 2015). For instance, a significant fraction of events of carbon extremes in Europe is not associated with univariate climate extremes (Zscheischler et al, 2014b). Earth observations (EOs) are multivariate and naturally characterized by strong dependencies and correlations in space, time, and across dimensions (Leonard et al, 2013).…”

Detecting impacts of extreme events with ecological in situ monitoring networks

“…This contribution could be relevant to other studies beyond the present application. This method has advantages over using a global threshold, which fundamentally changes the obtained picture and leads to a few hotspots of extremes in regions where the data have high variability (for the case of GPP, see Zscheischler et al, 2014b). The effect of building on regional thresholds to delineate which anomalies should be considered "extreme" (recall Fig.…”

Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

“…Climate extremes often refer to well-defined weather or climate events that are quantified using measurable physical quantities such as temperature, precipitation, or wind speed and that are rare (i.e., occurring at the tails of the distribution) relative to current climate states (Zwiers et al, 2013). The definition of climate extremes, in general, varies with the nature of the phenomena and may be based on their impacts.…”

“…While phenomena like heat waves under climate change are better understood than most other climate-related extremes (Coumou and Rahmstorf, 2012;Field et al, 2012), their very definitions may depend on the impact sector of interest (Ebi and Meehl, 2007). Quantitative research relating climate extremes and anomalies to impacts, for example terrestrial ecology Zscheischler et al, , 2014 and agricultural production (Lobell et al, 2006(Lobell et al, , 2012, often examine climate indices derived from extremes with disciplinary specificity. Figure 3a shows that different definitions of hot extremes can significantly impact the final insights; however each definition remains useful for its specific context, such as energy demand (Christenson et al, 2006) or public health (Kovats and Kristie, 2006).…”