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Abstract

Climate extremes have the potential to cause extreme responses of terrestrial ecosystem functioning. However, it is neither straightforward to quantify and predict extreme ecosystem
responses, nor to attribute these responses to specific climate drivers. Here, we construct a
factorial experiment based on a large ensemble of process-oriented ecosystem model simulations
driven by a regional climate model (12 500 model years in 1985–2010) in six European regions.
Our aims are to (1) attribute changes in the intensity and frequency of simulated ecosystem
productivity extremes (EPEs) to recent changes in climate extremes, CO2 concentration, and land
use, and to (2) assess the effect of timing and seasonal interaction on the intensity of EPEs.
Evaluating the ensemble simulations reveals that (1) recent trends in EPEs are seasonally
contrasting: spring EPEs show consistent trends towards increased carbon uptake, while trends in
summer EPEs are predominantly negative in net ecosystem productivity (i.e. higher net carbon
release under drought and heat in summer) and close-to-neutral in gross productivity. While
changes in climate and its extremes (mainly warming) and changes in CO2 increase spring
productivity, changes in climate extremes decrease summer productivity neutralizing positive
effects of CO2. Furthermore, we find that (2) drought or heat wave induced carbon losses in
summer (i.e. negative EPEs) can be partly compensated by a higher uptake in the preceding
spring in temperate regions. Conversely, however, carry-over effects from spring to summer that
arise from depleted soil moisture exacerbate the carbon losses caused by climate extremes in
summer, and are thus undoing spring compensatory effects. While the spring-compensation
effect is increasing over time, the carry-over effect shows no trend between 1985–2010. The
ensemble ecosystem model simulations provide a process-based interpretation and generalization
for spring-summer interacting carbon cycle effects caused by climate extremes (i.e. compensatory
and carry-over effects). In summary, the ensemble ecosystem modelling approach presented in
this paper offers a novel route to scrutinize ecosystem responses to changing climate extremes in
a probabilistic framework, and to pinpoint the underlying eco-physiological mechanisms.