Will GCMs really tell us everything we need to know about climate change?

In a previous blog, I discussed General Circulation Models (GCMs) at varying resolutions.

Here, I’ll highlight a few limitations, especially when looking at tropical cyclones.

Even though GCMs are able to capture tropical cyclone tracks and storm formation to provide hugely valuable forecasts for public safety concerns, we should be aware of the limitations in looking at climate scale variability and change. For example, looking seasons or years ahead into a climate projection, GCMs have less ability to say how many and how intense the storms might be. Hurricane season forecasts are put together using a variety of statistical and GCM-based techniques and we can get a lot of value from both approaches. But there is only so much that we can say.

However, papers by Deser et al 2012 and Done et al 2014 are useful in determining what can be explained on a seasonal or decadal time-scale. James Done found that based on one season, his regional climate model experiments shows that around 40% of the variability in tropical cyclone frequency in the North Atlantic is simply natural variability, and not associated with forcing from greenhouse gases, volcanoes, aerosols or solar variability (external forcing). He notes that from Deser et al. 2012, regional scales can see internal variability becoming greater than externally forced variability. This also highlights the difficulty in assigning a single regional event to changes in climate on a global scale.

To sum up, GCMs

• as numerical weather prediction models, offer great ability to provide operational forecasts and warnings on a day-to-day basis, 
• as global/regional climate models, to experiment with the atmosphere and explore sensitivities in the processes that bring about extremes of climate, global climate variability or climate change. 

When looking at seasonal or longer timescales, GCMs run at lower resolution and so lose the ability to capture small scale features that drive tropical cyclones, and so we have to model the large scale influences to look at more general shifts in probabilities of single or seasonal phenomena (e.g. hurricanes or droughts).

Deser et al. 2012 also calls for greater dialogue between science and policy/decision-makers to improve communication and avoid raising expectations of regional climate predictions. I totally agree. Better communication between scientists and stakeholders is important because talking about storms and climate change is highly political. Poor communication can lead to gross misrepresentations by those aiming to mitigate and adapt to climate change, as well as those who do not accept that climate change is a concern.

Future for GCMs?

I can see how GCMs have great ability in helping us understand the sensitivities of the climate system, and as they improve and as computing power increases (along with big data solutions), then so too should our understanding of various climate processes. In fact, growth of the GCM capabilities may well increase the level of uncertainty as we start to model more and more complexity. I do wonder where the next big step will be though. Between CMIP3 and CMIP5 (two rounds of climate model comparison projects – see previous blog) Bellenger et al. (2015) showed some progress, but also commented that overall, there were limited improvements of how ENSO (a dominant mode of climate variability)  is characterised.

An interesting article here by Shackley et al. back in 1998 called; “Uncertainty, Complexity and Concepts of Good Science in Climate ChangeModeling: Are GCMs the Best Tools?”, shows a range of interesting discussion points asking whether GCM-based climate science is actually the best approach from a number of perspectives. Are there alternative types of models that could allow us to better engage with the public, with policy makers or with the private sector? There are certainly alternatives that show promise as discussed on Judith Curry’s blog, who is of the opinion that climate modelling is in a “big expensive rut.” I hope I can find time to expand on this interesting topic in my blog here.

Personally, I am a big fan of GCMs. It's amazing that they can represent the atmosphere with such high fidelity, but it's good to ask these questions and not to forget alternative approaches which may be much more practical and 'fit-for-purpose' in particular situations.. 

In a future blog, I’ll discuss a little about how we talk about probability of future events, and then follow on with a blog on how we currently stand on tropical cyclones and climate change.