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Popp, A., Humpenöder, F., Weindl, I., Bodirsky, B. L., Bonsch, M., Lotze-Campen, H., et al. (2014). Land-use protection for climate change mitigation. Nat. Clim. Change, 4(12), 1095–1098.
Abstract: Land-use change, mainly the conversion of tropical forests to agricultural land, is a massive source of carbon emissions and contributes substantially to global warming(1-3). Therefore, mechanisms that aim to reduce carbon emissions from deforestation are widely discussed, A central challenge is the avoidance of international carbon leakage if forest conservation is not implemented globally’’, Here, We show that forest conservation schemes, even if implemented globally, could lead to another type of carbon leakage by driving cropland expansion in non-forested areas that are not subject to forest conservation schemes (non-forest leakage). These areas have a smaller. but still considerable potential to store carbon(5,6). We show that a global forest policy could reduce carbon emissions by 77 Gt CO2, but would still allow for decreases in carbon stocks of non-forest land by 96 Gt CO2, until 2100 due to non-forest leakage effects. Furthermore; abandonment of agricultural hand and associated carbon uptake through vegetation regrowth is hampered. Effective mitigation measures thus require financing structures and conservation investments that cover the full range of carbon-rich ecosystems. However, our analysis indicates that greater agricultural productivity increases would be needed to compensate for such restrictions on agricultural expansion.
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Rolinski, S., & Sætnan, E. (2013). Uncertainties in climate change prediction and modelling (Vol. 1).
Abstract: As models become increasingly complex and integrated, uncertainty among model parameters, variables and processes become critical for evaluating model outcomes and predictions. A framework for understanding uncertainty in climate modelling has been developed by the IPCC and EEA which provides a framework for discussion of uncertainty in models in general. Here we report on a review of this framework along with the results of a survey of sources of uncertainty in livestock and grassland models. Along with the identification of key sources of uncertainty in livestock and grassland modelling, the survey highlighted the need for a development of a common typology for uncertainty. When collaborating across traditionally separate research fields, or when communicating with stakeholders, differences in understanding, interpretation or emphasis can cause confusion. Further work in MACSUR should focus on improving model intercomparison methods to better understand model uncertainties, and improve availability of high quality datasets which can reduce model uncertainties. No Label
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Rolinski, S., Heinke, J., & Weindl, I. (2014). Grazing effects on grassland productivity – Linking livestock production to grass yields..
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Rolinski, S., Heinke, J., & I., W. (2014). Modelling the effects of grassland management on the carbon cycle..
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Sándor, R., Ma, S., Acutis, M., Barcza, Z., Ben, T., H., Doro, L., et al. (2014). Uncertainty in simulating biomass yield and carbon-water fluxes from Euro-Mediterranean grasslands under climate changes..
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