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Biogeosciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 21 Aug 2019

Submitted as: research article | 21 Aug 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

Summarizing the state of the terrestrial biosphere in few dimensions

Guido Kraemer1,2, Gustau Camps-Valls2, Markus Reichstein1,3, and Miguel D. Mahecha1,3 Guido Kraemer et al.
  • 1Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
  • 2Image Processing Lab, Universitat de València, 46980 Paterna (València), Spain
  • 3German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany

Abstract. In times of global change, we must closely monitor the state of the planet in order to understand gradual or abrupt changes early on. 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 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 states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73 % of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions. We assume that this generic approach has great potential for the analysis of land-surface dynamics from observational or model data.

Guido Kraemer et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Guido Kraemer et al.
Guido Kraemer et al.
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Publications Copernicus
Short summary
To closely monitor the state of our planet we require systems that can monitor the observation of many different properties at the same time. We create indicators that resemble the behaviour of many different simultaneous observations. We apply the method to create two indicators representing the world's biosphere. The indicators show a productivity gradient and a water gradient. The resulting indicators can detect a large number of changes and extremes in the Earth system.
To closely monitor the state of our planet we require systems that can monitor the observation...