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In order to assess the potential impact of climate change on terrestrial equilib- rium net primary production (NPP), information about the sensitivity of terrestrial NPP to climate change is needed. In this paper, a novel approach to the deni- tion of climate sensitivity is introduced, which does not depend on specic (and uncertain) scenarios, but rather describes the worst-case climate sensitivity of NPP, as measured by the magnitude of the gradient of NPP, as a function of the cli- mate variables. The metric in climate change necessary for the determination of the gradient is calculated on the foundation of the existing spatial variation of the cli- mate variables as a measure for the potential to climate change, taking into account the unchanging determinants of latitude and altitude of the climate. The current correlations between the climate variables are preserved using principal component analysis. The resulting map of global NPP sensitivity shows especially high sensitivity, e. g., in the midwestern, in southern Africa, Australia and the Caucasus as well as in the Maghreb and in Spain. The sensitivity is aggregated to the country level excluding the eects of the very insensitive deserts to make these results applicable for policy analysis. The Neural Net NPP model NNN, predicts annual equilibrium terrestrial net primary production, NPP, of potential natural vegetation on the basis of a clima- tology including monthly values for temperature, precipitation and light. Due to its very fast numerical evaluation, it enables this (and other future) extensive climate sensitivity studies. To construct NNN, the average of the global NPP results of

Climate sensitivity of global terrestrial net primary production (NPP) calculated using the reduced-form model NNN