RealClimate had an interesting post the other day about adaptation – specifically, how we bring together models that operate at the global-to-regional scales with an understanding of current and future impacts of climate change, which we feel at the local scale. This post was written from a climate science perspective – and so focuses on modeling capabilities and needs as related to the biophysical world.  In doing so, I think that one key uncertainty in our use of downscaled models for adaptation planning is huge – the likely pathways of human response to changes in the climate over the next several decades.  In places like sub-Saharan Africa, how people respond to climate change will have impacts on land use decisions, and therefore land cover . . . and land cover is a key component of local climate.  In other words, as we downscale climate models, we need to start adding new types of data to them – social data on adaptation decision-making, so that we might project plausible future pathways and build them into these downscaled models.

For example, many modeling exercises currently suggest that a combination of temperature increases and changes in the amount and pattern of rainfall in parts of southern Africa will make it very difficult to raise maize there over the next few decades.  This is a major problem, as maize is a staple of the region.  So, what will people do?  Will they continue to grow maize that is less hardy and takes up less CO2 and water as it grows, will they switch to a crop that takes up more CO2 than maize ever did, or will they begin to abandon the land and migrate to cities, creating pockets of fallow land and/or opening a frontier for mechanized agriculture (both outcomes likely to have significant impacts on greenhouse gas emissions and water cycling, among other things)?  Simply put, we don’t really know.  But we need to know, and we need to know with reasonably high resolution.  That is, it is not enough to simply say “they will stop planting maize and plant X.”  We need to know when this transition will take place.  We need to know if it will happen suddenly or gradually.  We need to know if that transition will itself be sustainable going forward, or if other changes will be needed in the near future.  All of this information needs to be part of iterative model runs that capture land cover changes and biogeochemical cycling changes associated with these decisions to better understand future local pathways of climate change impacts and the associated likely adaptation pathways that these populations will occupy.

The good news* is that I am on this – along with my colleague Brent McCusker at West Virginia University (see pubs here and here).  Between the two of us, we’ve developed a pretty solid understanding of adaptation and livelihoods decision-making, and have spent a good bit of time theorizing the link between land use change and livelihoods change to enable the examination of the issues I have raised above.  We have a bit of money from NSF to run a pilot this summer (Brent will manage this while I am a government employee), and I plan to spend next year working on how to integrate this research program into the global climate change programming of my current employer.

Long and short: climate modelers, you need us social scientists, now more than ever.  We’re here to work with you . . .

*Calling this good news presumes that you see me as competent, or at least that you see Brent as competent enough to make up for my incompetence.