Rapid Assessment of Agriculture in a + 1.5 Scenario: An AgMIP Coordinated Global and Regional Assess
- Lead PI: Carolyn Mutter
-
Unit Affiliation: Center for Climate Systems Research (CCSR)
- September 2016 - September 2017
- Inactive
- Global ; New York City, NY ; New York
- Project Type: Research
DESCRIPTION: This project provided support to the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments (CGRA) of the agricultural future in a world where global mean temperature warming is limited to +1.5 oC above pre-industrial conditions. This target was specifically requested by policymakers from parties in the United Nations Framework Convention on Climate Change and is the topic of a special report by the Intergovernmental Panel on Climate Change (IPCC SR1.5). Of particular interest is a comparison between this future +1.5 oC world and present-day conditions (which helps us understand the adaptation and mitigation challenges) as well as a comparison between a +1.5 oC world and a +2.0 oC world (which elucidates the degree to which mitigation efforts benefit society).
OUTCOMES: The AgMIP Coordinated Global and Regional Assessments (CGRA) of the impacts of 1.5 and 2.0 oC Warming on the global agricultural sector has resulted in cutting-edge, policy-relevant assessments that broke new ground in multi-discipline, multi-scale, multi-model assessment of future challenges to agricultural production, food prices, and food security. The CGRA team, coordinated at Columbia University, facilitated direct connection of models via common protocols and consistent scenario assumptions, linking in parallel global efforts and facilitating a wide range of leveraged research that together constitute a substantial contribution of relevance to the IPCC Special Report on 1.5 oC Warming and future planning for a number of stakeholders. Key products include production, price, and cropped area projections for 1.5 and 2.0 oC Worlds, a comparison of direct climate impacts and the ramifications of carbon price mitigation on agricultural systems, and an unprecedented tracking of uncertainty from various model and data sources.