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

Summarizing the state of the terrestrial biosphere in few dimensions