climate modelling food security

How can climate modelling help food security? By Monica Ortiz

Grantham Scholar Monica Ortiz explains why climate modelling is useful to her research in global food security.

Food security and climate change

Grantham Scholar Monica Ortiz.
Grantham Scholar Monica Ortiz.

Achieving food security in a changing environment will be one of the greatest challenges for current and future food scientists.

In a report leading up to the COP20 in Lima, CGIAR report that without urgent action for mitigation of emissions and adaptation, the world faces more loss and damage due to climate change. As a result, our farmers’ capacity to produce food will be reduced.

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. According to Stanford University scientist David Lobell, these 4 crops constitute around 75% of all the calories we consume through direct or indirect consumption.

Climate models

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. Thus they 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.

Downscaling climate models for food security

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”.

Earlier this month, I went to the International Centre for Theoretical Physics (ICTP) in Trieste. There, I took part in Third VALUE Training School: ‘Spatial and Temporal Variability in Statistical and Dynamical Downscaling’. This is part of the European Cooperation on Science and Technology’s Validating and Integrating Downscaling Methods for Climate Change Research project.

Weather Generators

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 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.

Main image: 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).

Edited by Claire Moran.