Meeting the radiative forcing targets of the representative concentration pathways in a world with agricultural climate impacts. (24th February 2014)

• Record Type: Journal Article
• Authors: Kyle, Page; Müller, Christoph; Calvin, Katherine; Thomson, Allison
• Is Part Of: Earth's future. Volume 2:Part 2(2014)
• Page Start: 83
• Page End: 98
• Publication Date: 2014-02-24
• Subjects: Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1002/2013EF000199
• Languages: English
• ISSNs: 2328-4277

Abstract

<abstract abstract-type="main" id="eft220-abs-0001"> <title>Abstract</title> <p id="eft220-para-0002">This study assesses how climate impacts on agriculture may change the evolution of the agricultural and energy systems in meeting the end‐of‐century radiative forcing targets of the representative concentration pathways (RCPs). We build on the recently completed Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP) exercise that has produced global gridded estimates of future crop yields for major agricultural crops using climate model projections of the RCPs from the Coupled Model Intercomparison Project Phase 5 (CMIP5). For this study we use the bias‐corrected outputs of the HadGEM2‐ES climate model as inputs to the LPJmL crop growth model, and the outputs of LPJmL to modify inputs to the GCAM integrated assessment model. Our results indicate that agricultural climate impacts generally lead to an increase in global cropland, as compared with corresponding emissions scenarios that do not consider climate impacts on agricultural productivity. This is driven mostly by negative impacts on wheat, rice, other grains, and oil crops. Still, including agricultural climate impacts does not significantly increase the costs or change the technological strategies of global, whole‐system emissions mitigation. In fact, to meet the most aggressive climate change mitigation target (2.6 W/m<sup>2</sup> in 2100), the net mitigation costs are slightly lower when agricultural climate impacts are considered. Key contributing factors to these results are (a) low levels of climate change in the low‐forcing scenarios, (b) adaptation to climate impacts simulated in GCAM through inter‐regional shifting in the production of agricultural goods, and (c) positive average climate impacts on bioenergy crop yields.</p> </abstract>