CLiMA!TE - backgroundThe concentration of CO2 in the atmosphere is increasing, globaltemperatures are increasing, and local precipitation patterns arechanging with increases in the intensity of rain events and droughtperiods. This is expected to affect the structure and functioning ofterrestrial ecosystems (IPCC, 2013) with major impacts on naturalenvironments as well as ecosystems used for agriculture or forestry. Over the past three decades, major efforts have been devoted to understandand predict such impacts of climate change on ecosystemprocesses and functioning in order to understand the urgency of the changes as well as the possibilities for ecosystem adaptation or climatechange mitigation. These efforts have included observationsof past changes, monitoring of ongoing changes, observations acrossenvironmental gradients (space for time substitution), ecosystem manipulationexperiments mimicking future climate changes, and dynamicecosystem modelling (Beier, 2004; Rustad, 2008). Each of theseapproaches has their forces and drawbacks, but across all a generallimitation is that observations and experiments have focused on onesingle climate factor. For example, observations across gradients canhardly combine simultaneous and ideal differences in two or eventhree climate factors at the same time to provide a multi factor responsepicture. Ecosystem experiments, which could do it, often limits themselves to one factor for practical reasons or because of lackof resources, since inclusion of one extra factor doubles the numberof experimental units and the demand for resources in a classic experimentaldesign. Therefore, very few multi factor climate change experiments exist. Instead the underlying assumption has been thatif the individual responses are known based on single factor experiments,then dynamic ecosystem or global models can predict theresponses of the combined factors. This approach may seem reasonablebut is constrained by at least two problems, which CLiMA!TE specifically aimed to overcome:1. When several factors act together, they may interact, and theseinteractions among the different climate change factors may not belinear and/or predictable. Computer models may predict some ofthese interactions relatively well (e.g. resource limitations due toincreased growth), while other interactions may be unpredictable.8 beier, c., et al.The assumption that the impact of the "climate change cocktail"may be predicted from an understanding of the individual factorsmay therefore be erroneous.2. Even when models do predict the interactions, we still need multifactor experiments to train and test the models in order to knowif the predictions are correct. Another inherent problem related to climate change and experimentationis the time scale. Climate change acts over decades, meaningthat climate change experiments running for 2-4 years only highlightshort term and transient effects on the ecosystems, while lackingthe ability to inform about long term and more stable effects.The "long term" perspective of climate change was therefore anotherimportant rationale for the CLiMA!TE experiment.The "long term" perspective of climate change calls for long termexperiments, which for decades has been argued from the scientificcommunity, was therefore another important rationale for the CLiMA!TE experiment. The VILLUM FOUNDATION provided avery rare opportunity to pursue this in reality.In summary, the CLiMA!TE experiment was driven by two major rationales:1) a need for realistic experiments involving combinations of the mainclimate change drivers and 2) the long term perspective of climate change.