Advancing Water and Food Sustainability through Improved Understanding of Uncertainties in Climate Change and Climate Variability

Advancing Water and Food Sustainability through Improved Understanding of Uncertainties in Climate Change and Climate Variability
Susan Solomon, Department of Earth, Atmospheric, and Planetary Sciences and Department of Chemistry
Kenneth Strzepek, MIT Joint Program on the Science and Policy of Global Change

Period of performance: 

September 2015 to August 2017
food, water, sustainability, climate change, agriculture, africa, Nile Basin, crops, modeling

Abstract: 

Understanding the future evolution of water needs, food production, availability and adaptation challenges due to climate change requires an understanding not only of average climate changes, but also of their inter-annual and decadal variability and associated uncertainties. In recent years, two different types of large ensemble runs of climate projections have become available, those from more than twenty different climate models (“between-model” ensembles), and those from repeated runs of several individual models (“within-model ensembles”). Climate model ensemble information provides a rich new basis for improved quantification of climate factors contributing to water and crop stresses across a range of applications and sectors, and a greatly improved basis for future projections of where adaptation measures that “climate-proof” against past experience of variability should be bolstered or changed.

We are linking a broad range of available multi- and individual climate model ensembles to both crop production models and water supply/demand models in order to (1) project climate-related changes in crop production and (2) estimate the benefits and costs of proposed irrigation expansion projects in sub-Saharan Africa across the 21st century. The risks of climate change for the agricultural sector in this region are a particularly immediate and important problem because the majority of the rural population depends either directly or indirectly on agriculture for their livelihoods. Further, the new climate ensemble information shows that they will experience or are already experiencing climates that are statistically different from the past. As part of this work, we are identifying a set of measures at the national and agricultural region level that, if enacted, have the potential to dramatically increase the resilience of this vulnerable region’s agriculture to climate change.

The first part of this work, an exploration of uncertainty in global climate models and its impact on the predicted impacts of climate change on maize (corn) production in sub-Saharan Africa, was recently published in Earth’s Future [Dale et al., 2017].  We predict several robust regional and sub-regional trends in maize production due to climate change, including widespread yield losses in the Sahel region (south of the Sahara desert) and Southern Africa, resilience in Central Africa, and sub-regional increases in East Africa and at the southern tip of the continent (Figure 1).  Yield losses corresponded with widespread increases in aridity.  Projected climate change impacts on maize production in different regions and nations ranged from near-zero or positive to substantially negative, highlighting a need for risk management strategies that are adaptive and robust to uncertainty.  Internal variability (chaos) was a major source of uncertainty and explained most of the spatial distribution of uncertainty in our predictions.

Figure 1. Median percent change in maize yield from 2010 to 2050 for a crop model (FAO AquaCrop-OS) driven by 122 climate futures predicted by five global climate models ensembles, three “between-model” ensembles (top row) and two “within-model” ensembles (bottom row).  White cells indicate regions in which the median percent change in crop yields is predicted to exceed 5%, yet less than two thirds of the ensemble members agree on the direction of change.  Modified with permission from Dale et al. (2017).

Dale, A., C. Fant, K. Strzepek, M. Lickley, and S. Solomon (2017), “Climate model uncertainty in impact assessments for agriculture: A multi-ensemble case study on maize in sub-Saharan Africa,” Earth’s Future, 4, doi: 10.1002/2017EF000539.

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