Over the last two years, evidence has accumulated that the IPCC reports released just two years ago underestimate the pace of climate change. Nature provides this summary. See also this article in Science Daily; and there are plenty more like it;

Emissions from fossil fuels growing faster than any of the scenarios included in the IPCC reports (news article ; original paper here). And recent studies indicate the effects are irreversible., at least for the next 1000 years.

Arctic Sea Ice, which is probably the most obvious “canary in the coal mine” is melting faster than the models predicted, and will likely never recover (Story from IPY here)

Greenland and Antarctic ice sheets melting 100 years ahead of schedule (news report; original papers here and here). Meanwhile new studies show the effect on the coastlines will be worse than previously thought, especially in North America and around the Indian Ocean (press release here; original paper here).

Sea level rise following the worst case scenario given in the IPCC reports (news report; original paper here and here).

Oceans soaking up less CO2, and hence losing their role as a carbon sink. (news report; original paper here

And finally some emerging evidence of massive methane releases as the permafrost melts (news report; no peer-reviewed paper yet).

Okay, some of the tools produced by the Essence project are pretty cool:

http://globalsensemaking.wik.is/ESSENCE/Tools

I especially like Debategraph, but mainly for its aesthetic properties when you pull the graph around, and the ability to zoom in to nodes by clicking, rather than how it’s actually being used (mapping debates about whether climate change is happening? That’s so last century). Could we use a tool like this for playing with i* models?

I originally wrote this as a response to a post on RealClimate on hypothesis testing

I think one of the major challenges with public understanding of climate change is that most people have no idea of what climate scientists actually do. In the study I did last summer of the software development practices at the Hadley Centre, my original goal was to look just at the “software engineering” of climate simulation models -i.e. how the code is developed and tested. But the more time I spend with climate scientists, the more I’m fascinated by the kind of science they do, and the role of computational models within it.

The most striking observation I have is that climate scientists have a deep understanding of the fact that climate models are only approximations of earth system processes, and that most of their effort is devoted to improving our understanding of these processes (“All models are wrong, but some are useful” – George Box). They also intuitively understand the core ideas from general systems theory – that you can get good models of system-level processes even when many of the sub-systems are poorly understood, as long as you’re smart about choices of which approximations to use. The computational models have an interesting status in this endeavour: they seem to be used primarily for hypothesis testing, rather than for forecasting. A large part of the time, climate scientists are “tinkering” with the models, probing their weaknesses, measuring uncertainty, identifying which components contribute to errors, looking for ways to improve them, etc. But the public generally only sees the bit where the models are used to make long term IPCC-style predictions.

I never saw a scientist doing a single run of a model and comparing it against observations. The simplest use of models is to construct a “controlled experiment” by making a small change to the model (e.g. a potential improvement to how it implements some piece of the physics), comparing this against a control run (typically the previous run without the latest change), and comparing both runs against the observational data. In other words, there is a 3-way comparison: old model vs. new model vs. observational data, where it is explicitly acknowledged that there may be errors in any of the three. I also see more and more effort put into “ensembles” of various kinds: model intercomparison projects, perturbed physics ensembles, varied initial conditions, and so on. In this respect, the science seems to have changed (matured) a lot in the last few years, but that’s hard for me to verify.

It’s a pretty sophisticated science. I would suggest that the general public might be much better served by good explanations of how this science works, rather than with explanations of the physics and mathematics of climate systems.