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The first of our new workshop series on leading-edge, actionable global change research focuses on water -- an essential ingredient for the prosperity, health, and sustainability of a continually changing, complex, and globally-linked society.

2017 Water Workshop

There are considerable forces shaping the future of agriculture. Identifying research needs on sector-specific climate effects and risks is both urgent and essential.

Researchers at the MIT Joint Program on the Science and Policy of Global Change have many years of experience identifying challenges for the agricultural sector, and can provide expert guidance to assess risk and create new opportunities.

2018 Agriculture Workshop
Statistical Emulators of Irrigated Crop Yields and Irrigation Water Requirements

Statistical emulators of globally gridded crop models are designed to provide decision-makers with a far less computationally intensive way to assess the impact of climate change on crop yields. In a previous paper (Blanc, 2017) focused on four major rain-fed breadbasket crops—maize, rice, soybean and wheat—the author developed a new set of crop yield emulators and showed that they could produce results comparable to those generated by an ensemble of globally gridded crop model simulations upon which they were trained. This study advances statistical emulators to provide an accessible tool to assess the impact of climate change on irrigated crop yields and irrigation water withdrawals, while accounting for crop modeling uncertainty. Together with the 2017 study, this research enables decision-makers to estimate the impact of climate change on, separately, rain-fed and irrigated crops, resulting in a more comprehensive assessment of the impact of climate change on agriculture.  

Baseline evaluation of the impact of updates to the MIT Earth System Model on its model parameter estimates

Authors' summary: We present a transparent method for evaluating how changes to the MIT Earth System Model impact its response to anthropogenic and natural forcings. We tested the effects that changes to both model components and forcings have on the estimates of model parameters that agree with historical observations. Overall, changes to model forcings are more important than the new components, while the long-term model response is unchanged. The methodology serves as a guide for documenting model development.

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