Earth System Modelling: Biogeochemical Cycles (F. Joos)
The insight that anthropogenic emissions of carbon dioxide (CO2 ) perturb climate with far-reaching implications for natural and socio-economic systems continues to stimulate research to improve our understanding of the carbon cycle and climate. Earth System Models simulate climate and the flow of carbon and related elements through the atmosphere, land, ocean and ocean sediments. This permits us to quantify sources and sink of carbon dioxide and other greenhouse gases such as methane (CH4) and nitrous oxide (N2O). Earth System Models are also key to study climate- biogeochemical feedbacks and processes, to simulate past and future changes in the Earth System and to quantify allowable anthropogenic carbon emissions to meet policy-relevant climate targets.
At Climate and Environmental Physics (CEP), we develop and apply a hierarchy of Earth System Models. We investigate, by combining observational data and models, recent to glacial-interglacial periods for improved projections of greenhouse gas concentrations, ocean acidification, and climate change and impacts over the 21st century and beyond. We study with our models natural and human-caused greenhouse gas sources and climate feedbacks and consider human land use and land use change, vegetation, peat and permafrost evolution, as well as changes in ocean circulation, marine biology and productivity, and marine nutrient and oxygen cycling. Isotopes of carbon and other elements are applied in a range of diverse analyses such as the reconstruction of solar activity over the Holocene, the quantification of CO2 and N2O fluxes between the ocean, land and atmosphere, the study of water use efficiency of plants on land or of glacial water mass and circulation changes in the ocean. We also investigate natural forced and internal variability of the coupled carbon-climate system. The observational evidence from ice cores, other climate archives and instrumental records is integrated by diagnostic approaches and sophisticated Bayesian Monte Carlo methods for probabilistic, process-based model projections.