How can climate modelling help in achieving food security?

Earlier this month, Grantham Scholar Monica Ortiz attend a training course in Italy based around climate modelling. Here, she explains why this technique is useful and how it will help her make her own contribution to global food security.

monica-ortizAchieving food security in a changing environment will be one of the greatest challenges of our generation and future generations of food scientists.

In a new report leading up to the Conference of the Parties of the United Nations Framework Convention on Climate Change (COP, UNFCCC) in Lima, Peru, the global research partnership for food security CGIAR reports that without urgent action for mitigation of emissions and adaptation, the world faces more loss and damage due to climate change, which will affect our farmers’ capacity to produce food.

But one of the biggest challenges in planning for climate change is to understand how warming temperatures and unpredictable rainfall will affect agriculture. We need to understand how climate change will affect some of the globally important crops like wheat, corn, rice and soya, which according to Stanford University scientist David Lobell, constitute around 75% of all the calories we consume through direct or indirect consumption.

One of the ways to understand the impacts of future climate is through the use of climate models. Climate models are complex equations that represent the interactions and dynamics of our atmosphere. Climate models are extremely useful in conducting experiments that would otherwise take a lot of time and resources and thus are useful in planning adaptation scenarios, although they do have uncertainties and limitations.

Using climate models and future scenarios of how much carbon dioxide there will be, and using these together with information on how key crops grow, will be the focus of my studies as a Grantham Scholar.

Climate models are incredibly complex and getting them at the appropriate scale for studying impacts on agriculture is not an easy task, because our atmosphere is so complex. Supercomputers are needed to run global-scale models. One way to localize these huge amounts of data is through the use of a mathematical process called “downscaling”.

You “R” so beautiful to me – a sample of generated weather data using Rglimclim. The dark black lines are real observations and the grey lines represent what the weather generator has simulated. (Software by Rglimclim. Data source: European Climate Assessment and Dataset)
You “R” so beautiful to me – a sample of generated weather data using Rglimclim. The dark black lines are real observations and the grey lines represent what the weather generator has simulated. (Software by Rglimclim. Data source: European Climate Assessment and Dataset)

Earlier this month, I was able to participate in a training course at the International Centre for Theoretical Physics (ICTP) in Trieste, Italy, to learn more about downscaling at the Third VALUE Training School: ‘Spatial and Temporal Variability in Statistical and Dynamical Downscaling’ which is part of the European Cooperation on Science and Technology’s Validating and Integrating Downscaling Methods for Climate Change Research project (COST-VALUE).

Together with participants from all over the world studying climate, we learned several key processes and concepts necessary to begin downscaling in our own respective projects. One of the most interesting aspects to me was learning how to use Weather Generators. Using the open-source statistical software R and a package called Rglimclim created by Professor Richard Chandler of the Department of Statistical Science at University College London, we were able to create multiple years of simulated weather data (pictured) and compare them with real weather observations.

In my project, I will potentially use this new knowledge to simulate future weather in locations in Europe that I can later use to feed into crop models, in order to see how much yields will increase or decrease. It was a great learning experience, however, it was also a stark reminder that the work of international policy – as the UN Lima climate change conference draws closer – is a key shaper in what our future carbon dioxide emissions, and thus what our common future climate, will be like.

References

CGIAR (2014). 6 Food security issues COP-20 must address. ccafs.cgiar.org/blog/6-food-security-issues-cop20-must-address

Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop production since 1980. Science (New York, N.Y.), 333(6042), 616–20. doi:10.1126/science.1204531

ICTP: www.ictp.it

Third VALUE school: http://www.value-cost.eu/node/1143