Predictions and Climate Change
Scientists have called for a substantial new investment in climate modeling to increase the accuracy, precision, and reliability of climate predictions. One group of authors stresses that concepts of reductionism and emergence will aid policy makers by helping to illuminate some uncertainties. Another group of authors questions whether better predictions, if even attainable, are needed; they argue instead that resources might be better spent in helping society adapt to the likely spectrum of climate-induced outcomes.
As published in Eos:
Do We Need Better Predictions to Adapt to a Changing Climate? By S. Dessai et al.
Predicting Climate Change: Lessons From Reductionism, Emergence, and the Past By S. Harrison and D. Stainforth
April 14th, 2009 at 6:44 am
We should note that Dessai et al. distinguish predictions from projections, and they consider projections useful. So I focus on projections in the narrow sense.
Then, do we need better predictions to adapt to a changing climate?
For 50-year planning (e.g. 2010-2060), certainly “No”, for we cannot predict.
For immediate 10-year planning (e.g. 2010-2020), “No”, for we have no prediction ready yet.
For 10-year planning several years ahead (e.g. 2015-2025), maybe “Yes”, in the sense that predictions will likely be more useful than mere projections. There will still be wide range of uncertainty especially about oceanic regime shifts, but the range will likely be narrower with appropriate initial conditions.
As for emergence vs. reductionism, my conception is somewhat different from Harrison and Stainforth.
In a model (NICAM http://www.nicam.jp/) where cloud clusters and equatorial waves are possible, super cloud clusters and Madden-Julian oscillation emerged. I do not think that they are reduced to dynamics and cloud microphysics. In a model complex enough, emergence is possible. A difficult question is whether emergence in the model is analog to emergence in the real world. About weather phenomena, we can wait and see. About climate change, can we anticipate?
I think that the issue about funding to climate modelling is a case of the following problem often found in issues of technological development. We can say that “we cannot achieve X without doing Y”. In other words, Y is a necessary condition for X. But we are not sure whether Y is a sufficient condition for X. Even if we do Y, we are not sure whether we can achieve X. Then the society must decide whether it should invest on Y under irreducible uncertainty. Scientists must make sure that the decision makers know the structure of the uncertainty.
April 23rd, 2009 at 10:31 am
Enjoyed the article discussing climate models, uncertainties, and debate the need for more “accurate” predictions.
Present climate models have difficultty reverse-predicting climate data (Pliocene time for example - previous Eos issue). It is important to keep an open mind when discussing complex climate models especailly when presenting to non-technical audience; uncertainties need to be addressed as part of overall work.
Ground check of climate models with paleoclimate data is essential part of the process; uncertainties and their impact a must.
How can climate modelers claim to predict the future, within a range of uncertainties, when their tools (models) cannot “predict” that which is known data? Bad science when failures to fit observations are ignored.
April 28th, 2009 at 10:05 pm
Suraje Dessai and his co-authors raise an important issue that is central to our entire scientific approach to climate change research. The “robust decision making” approach that they espouse is well established in other areas of natural resource management, particularly fisheries research where it is usually referred to as “management strategy evaluation”. After seeing predictions in their complex systems (with high levels uncertainty) fail over many years, many fisheries scientists now see these more robust decision making processes as the only effective way forward. In Australia, they are being tested across a broad range of marine and coastal resource issues (e.g. www.cmar.csiro.au/nwsjems).
Dessai et al. provide convincing arguments for a robust decision making approach to climate change on the basis that predictive approaches face both theoretical and practical limitations. Another important reason for linking climate models more directly with management decision processes is that it allows us to identify key performance measures (that influence decision making) and thereby helps prioritize model improvements. In models with such a high level of complexity, there are almost an unlimited number of potential performance measures, and those that are currently in use may not be the most significant from a decision making perspective.
As a final comment, it needs to be recognized that the issues associated with model complexity and uncertainty raised by Dessai et al. are compounded many times over as we attempt to include other ecological and human components into climate models. If we are not to wander aimlessly in model parameter space, robust decision making needs to be one of the main drivers in the burgeoning field of Earth System Science.
Scott Condie
(scott.condie@csiro.au)
May 21st, 2009 at 5:03 pm
The thing we know with virtual certainty is that exponential growth is not sustainable. So the real issues are: 1. Is climate change the major feedback that will end growth? And if not, what is? 2. How far has the planet gone in overshooting sustainable population and economic activity? 3. What can be done to mitigate the return to sustainability? These questions need to be adressed by the AGU, other scientific specialties, and the social sciences, notably economics.
July 28th, 2009 at 7:44 am
My comment on 14 April had a typo error. In the second sentence, I wanted to focus on “predictions” rather than “projections”. Then I intended to use these words according to formal usage in the IPCC AR4.
But later I read another paper by the same group of authors:
Dessai, S., Hulme, M., Lempert, R. and Pielke, R. Jr. (2009): Climate prediction: a limit to adaptation? Chapter 5 in, Adapting to climate change: thresholds, values, governance. Adger, W.N., Lorenzoni, I. and O’Brien, K. eds., Cambridge University Press, Cambridge, 530pp. A PDF file is available at http://mikehulme.org/category/academic-publications/ .
It seems that the distinction between “predictions” and “predictions” which the authors wanted to discuss is in the attitude of the users of the results of the simulations. If the users want accurate estimates of future climate, the simulations are considered as “predictions” even IPCC does not call so. But their goal is elusive. On the ther hand, if the users want to test whether a plan of adaptation is robust, they will use a broad set of projections. Then, the range of scenarios is much more important than precision of each scenario.