Archive for January, 2012

Climate change and extreme events on Nature Soapbox Science

January 10, 2012

I wrote a post for the Nature Soapbox Science community blog on climate change and extreme events. If you want to take a look, it’s here. UPDATE (11/1/2012): well, I’d may as well just put the post here as well…

As I type, I have a massive chapter for the next full Assessment Report (due to be published in 2014) sitting on my desk to review and a couple of analysis routines churning their way through terabytes of climate model data. There’ll be hundreds of other people around the world focussed on similar things. The aim is to produce the 5th series of Assessment Reports since the IPCC was formed in 1988 to help decision makers, well, make decisions.

But the IPCC has been up to other things recently as well. In November 2011 it published a Special Report Summary for Policymakers on “Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation” (or SREX, the full report will be published in February 2012). Understanding how extreme events might change in the future is really important as it’s these things that will really impact people: heat waves, flash floods, hurricanes, droughts and sea level rise related inundation. This is far more useful to know than the quite abstract concept of global mean temperature change. This report looks like an advance in the IPCC procedure as it involved a far more integrated approach than usual IPCC outputs, having authors from climate science, impacts and adaptation backgrounds as well as disaster risk management experts.

Although it sounds obvious, one of the key conclusions of SREX was that the impact of extreme climatic events is greatest where vulnerability is highest. On the ground, this has manifested itself as higher fatality levels in developing nations and higher economic losses in developed countries. There’s a lot to think about here in terms of how developing nations move forward and how developed nations approach things sustainably to reduce exposure. That’s not really my area though.

From a scientific point of view, they also point out that analysing extremes is relatively difficult as they are rare and data from around the world are not always up to the job. That said, this depends a lot on the particular “extreme” being investigated – this has always struck me as slightly odd about the climate extremes community in that the only common theme is the statistics and not the science behind the phenomena.

Looking to projections, the IPCC SREX assign their highest confidence assessment (“virtually certain”) to increases in temperature extremes by 2100. This is because this is pretty much a direct response to the radiation changes forced by atmospheric greenhouse gas emissions. Everything else is a slightly more messy consequence of the temperature changes and these other fields vary much more amongst the 12 different models used in this analysis making their projections uncertain. However, it also looks likely that heavy precipitation events will increase in certain regions and that the maximum winds associated with tropical cyclones will increase whilst their total number will likely decrease.

Oddly enough, the emissions pathway that we take in the future (the IPCC analyses different sets of projections based on different socioeconomic and technological development assumptions) has little impact on extreme events in the next 30 years or so – they don’t appear to have an impact until the latter half of the 21st Century when inter-model variability masks most of the climate signal anyway. This highlights how making projections of extreme events is a difficult game. In that spirit, here are two of the key problems as I see them relating to my area of research on severe storms in Europe:

Loading the dice or getting new dice?

If we assume that climatic quantities have a normal distribution (which isn’t always the case, especially with precipitation) then you can view the extremes as the tails at either end of the distribution e.g. hot or cold. So climate change could be viewed as like loading dice – you start rolling more sixes (or getting more hot days). However, when the climate regime changes this analogy breaks down as, instead of just rolling more sixes, you start needing to roll sevens as climate records are broken (see the figure below). This poses a problem for climate models as, like a six sided die isn’t designed to roll a seven, climate models haven’t been designed (or at least haven’t been verified against) conditions that have never been observed.

The green curve represents the distribution of Swiss summer temperatures from 1864 to 2002. Clearly, 2003 does not align well with that distribution and is an example of an extreme breaking a previous record. This figure has been taken from the IPCC AR4, for more details click the image.

We’re gonna need a smaller box.

The second problem is that some important things – like severe storms, tornados and regional and local changes such as river catchment area precipitation changes – are too small for climate models to represent or resolve. The reason for this is that these computer models split the atmosphere (and oceans) into a 3D array of boxes. The important equations are solved in each box and then they pass information to neighbouring boxes as appropriate at each model time step. These boxes usually have horizontal dimensions of around 100-400 km to allow for a convenient computational time. However, storms and tornados work on scales of significantly less than 100 km so there’s no way that the models can tell us anything about these things. This problem is particularly acute in relation to the IPCC SREX as this analysis used a suite of climate model data from a project called CMIP3, which was completed in 2006 for the last IPCC assessment and, therefore, does not use the most up-to-date and highest resolution model data. (The data currently being prepared for the next IPCC Assessment Report called CMIP5 is, however, not yet complete so perhaps this criticism is a bit unfair.)

Is this good enough?

So does this mean that analyses using these model data are not useful or reliable? When faced with this question I struggle to get past the fact that, however much they can improve in the future, these models are still the best and only tool we have for making climate projections. Beyond that, we can take comfort in the fact that the very basic physics of climate science is really well understood – even very simple energy balance models can tell us useful things about the effects of increasing atmospheric greenhouse gas changes. What we’re talking about here are the details, albeit very important details, and in that respect our current analyses are consistent with the things that we’re pretty sure of.

Advertisements