Summarizing the state of the terrestrial biosphere in few dimensions

Abstract

Abstract. In times of global change, we must closely monitor the state of the planet in order to understand the full complexity of these changes. In fact, each of the Earth's subsystems – i.e., the biosphere, atmosphere, hydrosphere, and cryosphere – can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to create global indicators for the terrestrial biosphere using principal component analysis based on a high-dimensional set of relevant global data streams. The concept was tested using 12 explanatory variables representing the biophysical state of ecosystems and land–atmosphere fluxes of water, energy, and carbon fluxes. We find that three indicators account for 82 % of the variance of the selected biosphere variables in space and time across the globe. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. The third indicator represents mostly changes in surface albedo. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heat wave (2010). The anomalies also allow us to interpret the impacts of these events. The indicators can also be used to detect and quantify changes in seasonal dynamics. Here we report, for instance, increasing seasonal amplitudes of productivity in agricultural areas and arctic regions. We assume that this generic approach has great potential for the analysis of land surface dynamics from observational or model data.

A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America

“…To our knowledge the ESDL is the first data cube implementation with an emphasis on representing interactions across the water cycle, carbon cycle, and climate system (Mahecha et al, 2020). It has been successfully used to understand biosphere-atmosphere interactions at multiple time-scales (Linscheid et al, 2020), analyzing specific variables of ecosystems to climate extremes (Flach et al, 2020), and has enabled studying the multivariate nature of land-surface dynamics globally (Kraemer et al, 2020). The RegESDL has been developed to more specifically explore biodiversity as yet another thematic domain.…”

“…Examples are the monitoring of land ecosystems with multiple sensors at different wavelengths via satellite remote sensing, e.g., from the optical to the radar domain (Joshi et al, 2016;Anaya et al, 2020;Heckel et al, 2020), the joint analysis of field measurements and remotely sensed data (Mahecha et al, 2017;Meyer et al, 2019), and productions of ensembles of multiple data sets that integrate process-based understanding (Musavi et al, 2017). In general, it is the multitude of climate data sets that allow researchers to understand the multivariate and multifaceted nature of land-dynamics in relation to climate variability (Kraemer et al, 2020;Mahecha et al, 2020). Big-data perspectives of this kind in the Earth system context are therefore highly relevant to improve our understanding of ecological processes, e.g., effects of land use and climate change, and other fundamental transformations on the functioning of land ecosystems.…”

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

“…In other words, bringing models to data, rather than the other way around, may eventually reduce artificial inconsistencies between datasets that stem from additional manipulations for making data and models match. Concomitantly, community cyberinfrastructure would facilitate [R23] interaction with a compilation of standard datasets that models need to be able to reproduce repeatedly (Anderson-Teixeira et al, 2018;Kraemer et al, 2020;Reyer et al, 2020).…”

Functional convergence of biosphere–atmosphere interactions in response to meteorological conditions

“…Distance correlation (Székely et al, 2007) is a non-linear measure to quantify the dependence between two vectors. It has been used successfully to assess the influence of variables on the low-dimensional embedding (Kraemer et al, 2020b). Székely et al (2007) details its empirical definition for a sample (X, Y) = {(X k , Y k ) : k = 1, .…”