In 2015 Grantham Scholar Monica Ortiz attended the National Centre for Atmospheric Science (NCAS) climate modelling summer school. Here, she reflects on the challenges of climate modelling and its implications for the fight to save our planet.
One of the most challenging aspects of being a Grantham Scholar, and for any researcher working on a ‘wicked problem’ like climate change, is our need to be well-versed in 2 or more disciplinary fields. My project will rely on knowledge from both climate science and agriculture (plant biology) in order to use climate models and crop models to predict the future of European wheat production.
As Grantham Scholars, we have the opportunity to apply and attend training, workshops, seminars and other opportunities related to our field of study. I was delighted to get a place as the NCAS summer school.
Because I trained primarily as an ecologist, the world of climate models always seemed very complex to me. So I was fortunate to secure a place in the National Centre for Atmospheric Science climate modelling summer school. This is an intensive 11-day course on the science behind climate models and practical sessions on how run them for experiments.
The term ‘black box’ often crops up in science, computing and engineering. A black box refers to a complex device, system or object whose internal workings are not readily understood. Summer school at NCAS helped to demystify some of the complexity of climate models, which are frequently treated as black boxes.
Climate models are the primary tools for simulating future climates at different timescales. They are physics and mathematics-based representations of how our climate system works. Climate models also represent our collective scientific understanding of our ocean-atmosphere systems. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change reports current projections of our future climate. Projections which have become the basis for decisions and planning for adaptation by policymakers and various stakeholders.
We need to be confident that climate models can represent the climate because of their importance in planning how to adapt to climate change. However, because our atmosphere is so chaotic (one only needs to experience English summertime), it is a difficult task for research centres to perfectly capture our atmosphere and ocean in terms of equations. Thus, climate models can only describe a limited number of processes compared to the nearly unlimited range of processes that affect the climate in reality. In addition, there are some climate processes that cannot be completely physically represented. And so climate models contain a lot of uncertainties and simplifications.
Today, there are global gains in the accuracy of climate models, as our computing power increases and as scientists improve climate models based on modern research and collaboration. Access to data has also improved in many parts of the world.
However, a new problem has emerged. Decision-makers and adaptation planners now have a multitude of datasets, models, and projections to choose from. In turn this can lead to adaptation decisions potentially being based on availability and convenience rather than scientific requirements. A greater challenge now exists in how adaptation planners can choose an appropriate dataset, assess its credibility, and use it wisely amidst varying local resources, capacity and infrastructure for adaptation (Harris et al., 2014; Ekström et al., 2015).
There is a more pressing need, therefore, for science to communicate and elucidate these uncertainties of climate models to decision-makers and stakeholders (Ramirez-Villegas et al., 2013; Vermeulen et al., 2013).
My time at the NCAS climate modelling summer deepened my understanding of the need to communicate uncertainty. It is a crucial point that needs to be prioritised by the climate science community. After all, climate model projections are what we currently have as basis of adaptation for communities that may be vulnerable to climate change, or are already experiencing changing precipitation patterns or increased temperatures.
Previously I worked as a disaster risk reduction and climate change adaptation advocate in the climate-vulnerable Philippines. I learned there how important the reliability of climate projections are for decision-makers. They need them to plan for our survival from the next super typhoon, El Niño event, or other slow-onset climate changes.
Should scientists be the only ones to communicate climate model uncertainties to the greater community? I believe not, there needs to be increased collaboration between climate scientists, the impacts research community, decision-makers, and the affected communities. And these different groups need to work together before we get to a point of no return. As Jeremy Grantham famously said, climate change is not only the crisis of our lives – it is also the crisis of our species’ existence (Grantham, 2012). We all, and not just scientists, need to be brave.
Ekström, M., Grose, M. R., & Whetton, P. H. (2015). An appraisal of downscaling methods used in climate change research. Wiley Interdisciplinary Reviews: Climate Change, 6(3), 301–319. doi:10.1002/wcc.339
Grantham, J. (2012). Be persuasive. Be brave. Be arrested (if necessary). Nature, 491, 303. http://doi.org/10.1038/491497a
Harris, R. M. B., Grose, M. R., Lee, G., Bindoff, N. L., Porfirio, L. L., & Fox-Hughes, P. (2014). Climate projections for ecologists. Wiley Interdisciplinary Reviews: Climate Change, 5(October), n/a–n/a. doi:10.1002/wcc.291
Ramirez-Villegas, J., Challinor, A. J., Thornton, P. K., & Jarvis, A. (2013). Implications of regional improvement in global climate models for agricultural impact research. Environmental Research Letters, 8(2), 024018. doi:10.1088/1748-9326/8/2/024018
Vermeulen, S. J., Challinor, A. J., Thornton, P. K., Campbell, B. M., Eriyagama, N., Vervoort, J. M., … Smith, D. R. (2013). Addressing uncertainty in adaptation planning for agriculture. Proceedings of the National Academy of Sciences of the United States of America, 110(21), 8357–62. doi:10.1073/pnas.1219441110