Sunday, March 9, 2014

Observing Climate Change Impacts

One can tell that David is on sabbatical. He is cracking jokes left and right and they are funny! We will be posting weekly from now on and since my last name starts with A, I start. I also get to be lead author on all papers we write together.

I have spent the last three years with a very talented group of individuals, writing a chapter on detection and attribution of climate change impacts on natural and human systems for the IPCC. The chapter will be released in a few weeks in Yokohama and I will blog live from the meeting. The fancy term detection and attribution can be casually interpreted as "what observable impact has climate change already had on [insert favorite system.outcome here] ". This is harder than I thought. Let me outline some of the issues:

1) Just because your system's changing doesn't mean climate is to blame for negative trends and can't possibly be an issue if your experiencing favorable trends. Think about crop yields for example. As has been pointed out again and again, better management practices, fertilizers, irrigation and pesticides have doubled the yields of many crops over the past 40 years. Growing yields does not mean that climate change cannot be a problem. Yields could have grown even faster in its absence. A slowdown in yield growth is possibly consistent with climate change, but could be due to worsening of other factors. So in order to blame climate change, you need to show two things. 1) A sensitivity of your system to climate change while properly controlling for all confounders. This is really hard. Wolfram is really good at this. 2) A changed climate in the region under study.

2) That opens the question, what is a changed climate? Many of the systems we are concerned with are localized systems. AR3 and AR4 in their treatment of detection and attribution focused on just "climate change", not necessarily "anthropogenic climate change". A lot of the detection and attribution literature does this. I think that this is good for now. As Marshall and Kyle have pointed out, there is plenty of local climate change happening, which they in turn use to identify a climate sensitivity.

3) The data for most sectors are not very good and weather data required are thin at best in many areas. Just because you have a gridded dataset, which provides a number, doesn't mean that number has anything to do with local temperature. There are large swaths of land and time with no reliable measure of temperature or rainfall. On the outcomes side, AR4 focused largely on detecting changes in phenologies, which was a highly publicized result. But while I love butterflies, frogs and flowers, as a social scientist I am also keenly interested in what is happening to human health, agricultural yields, fisheries and economic growth. These literatures do a decent job at characterizing sensitivities of these sectors to fluctuations in weather (and sometimes even climate), but the vast majority of them focus on projecting 100 years into the future. A very small number of papers actually turn around and take a look back.

So here is what I think we should do over the next 5-6 years until someone else gets to write the D&A paper for AR6:

1) When you are estimating sensitivities using your fancy econometric models, be clear what the sensitivities are and what they capture. Are they weather sensitivities? Climate sensitivities? What omitted variables should we worry about that you could not control for? Can we expect these sensitivities to remain stable over the next 100 years?

2) When you do your projections use multiple climate models. Relying on a single model is for suckers. OK. I was a sucker until I met Marshall Burke.

3) Don't just download the projections of climate, download the historical values too. They are freely available.

4) Simulate the changes in [insert favourite outcome here] with and without anthropogenic climate change. Daithi Stone and the gang can tell you how to do this. This essentially means you leave on Volcanoes etc. and assume away human emissions. Then you turn on the humans. The difference is anthropogenic climate change. Do this for the past 30 years and calculate impacts. Then do this to your heart's delight for the future.

5) Put the word detection and attribution in either your title, keywords or paper to make sure we can find you when we are looking for you.

I think these studies are very powerful and important. I am working on four of them now. If you are in the business of projecting climate impacts using multistep models you should do the same. It's not hard.

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