English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Summarizing the state of the terrestrial biosphere in few dimensions

MPS-Authors
/persons/resource/persons209162

Kraemer,  Guido
Empirical Inference of the Earth System, Dr. Miguel D. Mahecha, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62524

Reichstein,  Markus
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62472

Mahecha,  Miguel D.
Empirical Inference of the Earth System, Dr. Miguel D. Mahecha, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

Locator
Fulltext (public)

BGC3121D.pdf
(Preprint), 6MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Kraemer, G., Camps-Valls, G., Reichstein, M., & Mahecha, M. D. (2019). Summarizing the state of the terrestrial biosphere in few dimensions. Biogeosciences Discussions. doi:10.5194/bg-2019-307.


Cite as: http://hdl.handle.net/21.11116/0000-0004-8323-0
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.