Projecting grassland sensitivity to climate change from an ensemble of models

Abstract

The grassland biome covers about one-quarter of the earth’s land area and contributes to the livelihoods of ca. 800 million people. Increased aridity and persistent droughts are projected in the twenty-first century for most of Africa, southern Europe and the Middle East, most of the Americas, Australia and South East Asia. A number of these regions have a large fraction of their land use covered by grasslands and rangelands. Grasslands are the ecosystems that respond most rapidly to precipitation variability. However, global projections of climate change impacts on grasslands are still lacking in the scientific literature. Within AgMIP, based on the C3MP protocol initially developed for crops, we have explored the sensitivity of temperate grasslands to climate change drivers with an ensemble of models. Site calibrated models are used to provide projections under probabilistic climate change scenarios, which are defined by a combination of air temperature, precipitation and atmospheric CO2 changes resulting in 99 runs for each model times site combination. This design provides a test of grassland production, GHG (N2O and CH4) emissions and soil carbon sensitivity to climate change drivers. This integrated approach has been tested for 12 grassland simulation models applied to 19 sites over three continents. We show here that a single polynomial emulator can be fitted with high significance to the results of all models and sites, when these are expressed as relative changes from the optimal combination of climate drivers. This polynomial emulator shows that elevated atmospheric CO2 expands the thermal and hydric range which allows for the development of temperate grasslands. Moreover, we calculate the climatic response surface of GHG emissions per unit grassland production and we show that this surface varies with elevated CO2. From these results we provide first estimates of the impacts of climate change on temperate grasslands based on a range of climate scenarios

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