Experimental climatology for kids

13. January 2010 · 1 comment · Categories: climate modeling, research ideas

I picked up Stephen Schneider’s “Science as a Contact Sport” to read on travel this week. I’m not that far into it yet (it’s been a busy trip), but was struck by a comment in chapter 1 about how he got involved in climate modeling. In the late 1960’s, he was working on his PhD thesis in plasma physics, and (in his words) “knew how to calculate magneto-hydro-dynamic shocks at 20,000 times the speed of sound”, with “one-and-a-half dimensional models of ionized gases” (Okay, I admit it, I have no idea what that means, but it sounds impressive)…

…Anyway, along comes Joe Smagorinsky from Princeton, to give a talk on the challenges of modeling the atmosphere as a three-dimensional fluid flow problem on a rotating sphere, and Schneider is immediately fascinated by both the mathematical challenges and the potential of this as important and useful research. He goes on to talk about the early modeling work and the mis-steps made in the early 1970’s on figuring out whether the global cooling from aerosols would be stronger than the global warming from greenhouse gases, and getting the relative magnitudes wrong by running the model without including the stratosphere. And how global warming denialists today like to repeat the line about “first you predicted global cooling, then you predicted global warming…” without understanding that this is exactly how science proceeds, by trying stuff, making mistakes, and learning from them. Or as Ms. Frizzle would say, “Take chances! Make Mistakes! Get Messy!” (No, Schneider doesn’t mention Magic School Bus in the book. He’s too old for that).

Anyway, I didn’t get much further reading the chapter, because my brain decided to have fun with the evocative phrase “modeling the atmosphere as a three-dimensional fluid flow problem on a rotating sphere”, which is perhaps the most succinct description I’ve heard yet of what a climate model is. And what would happen if Ms. Frizzle got hold of this model and encouraged her kids to “get messy” with it. What would they do?

Let’s assume the kids can run the model, and play around with its settings. Let’s assume that they have some wonderfully evocative ways of viewing the outputs too, such as these incredible animations of precipitation from a model (my favourite is “August“) from NCAR, and where greenhouse gases go after we emit them (okay, the latter was real data, rather than a model, but you get the idea).

What experiments might the kids try with the model? How about:

  1. Stop the rotation of the earth. What happens to the storms? Why? (we’ll continue to ask “why?” for each one…)
  2. Remove the land-masses. What happens to the gulf stream?
  3. Remove the ice at the poles. What happens to polar temperatures? Why? (we’ll switch to a different visualization for this one)
  4. Remove all CO2 from the atmosphere. How much colder is the earth? Why? What happens if you leave it running?
  5. Erupt a whole bunch of volcanoes all at once. What happens? Why? How long does the effect last? Does it depend on how many volcanoes you use?
  6. Remove all human activity (i.e. GHG emissions drop to zero instantly). How long does it take for the greenhouse gases to return to the levels they were at before the industrial revolution? Why?
  7. Change the tilt of the earth’s axis a bit. What happens to seasonal variability? Why? Can you induce an ice age? If so, why?
  8. Move the earth a little closer to the sun. What happens to temperatures? How long do they take to stabilize? Why that long?
  9. Burn all the remaining (estimated) reserves of fossil fuels all at once. What happens to temperatures? Sea levels? Polar ice?
  10. Set up the earth as it was in the last ice age. How much colder are global temperatures? How much colder are the poles? Why the difference? How much colder is it where you live?
  11. Melt all the ice at the poles (by whatever means you can). What happens to the coastlines near where you live? Over the rest of your continent? Which country loses the most land area?
  12. Keep CO2 levels constant at the level they were at in 1900, and run a century-long simulation. What happens to temperatures? Now try keeping aerosols constant at 1900 levels instead. What happens? How do these two results compare to what actually happened?

Now compare your answers with what the rest of the class got. And discuss what we’ve learned. [And finally, for the advanced students – look at the model software code, and point to the bits that are responsible for each outcome… Okay, I’m just kidding about that bit. We’d need literate code for that].

Okay, this seems like a worthwhile project. We’d need to wrap a desktop-runnable model in a simple user interface with the appropriate switches and dials. But is there any model out there that would come anywhere close to being useable in a classroom situation for this kind of exercise?

(feel free to suggest more experiments in the comments…)

Documenting climate models

13. January 2010 · 7 comments · Categories: climate modeling

This week I’m visiting the Max Planck Institute for Meteorology (MPI-M) in Hamburg. I gave my talk yesterday on the Hadley study, and it led to some fascinating discussions about software practices used for model building. One of the topics that came up in the discussion afterwards was how this kind of software development compares with agile software practices, and in particular the reliance on face-to-face communication, rather than documentation. Like many software projects, climate modellers struggle to keep good, up-to-date documentation, but generally feel they should be doing better. The problem of course, is that traditional forms of documentation (e.g. large, stand-alone descriptions of design and implementation details) are expensive to maintain, and of questionable value – the typical experience is that you wade through the documentation and discover that despite all the details, it never quite answers your question. Such documents are often produced in a huge burst of enthusiasm for the first release of the software, but then never touched again through subsequent releases. And as the code in the climate models evolves steadily over decades, the chances of any stand-alone documentation keeping up are remote.

An obvious response is that the code itself should be self-documenting. I’ve looked at a lot of climate model code, and readability is somewhat variable (to put it politely). This could be partially addressed with more attention to coding standards, although it’s not clear how familiar you would have to be with the model already to be able to read the code, even with very good coding standards. Initiatives like Clear Climate Code intend to address this problem, by re-implementing climate tools as open source projects in Python, with a strong focus on making the code as understandable as possible. Michael Tobis and I have speculated recently about how we’d scale up this kind of initiative to the development of coupled GCMs.

But readable code won’t fill the need for a higher level explanation of the physical equations and their numerical approximations used in the model, along with rationale for algorithm choices. These are often written up in various forms of (short) white papers when the numerical routines are first developed, and as these core routines rarely change, this form of documentation tends to remain useful. The problem is that these white papers tend to have no official status (or perhaps at best, they appear as technical reports), and are not linked in any usable way to distributions of the source code. The idea of literate programming was meant to solve this problem, but it never took off, probably because it demands that programmers must tear themselves away from using programming languages as their main form of expression, and start thinking about how to express themselves to other human beings. Given that most programmers define themselves in terms of the programming languages they are fluent in, the tyranny of the source code is unlikely to disappear anytime soon. In this respect, climate modelers have a very different culture from most other kinds of software development teams, so perhaps this is an area where the ideas of literate programming could take root.

Lack of access to these white papers could also be solved by publishing them as journal papers (thus instantly making them citeable objects). However, scientific journals tend not to publish descriptions of the designs of climate models, unless they are accompanied with new scientific results from the models. There are occasional exceptions (e.g. see the special issue of the Journal of Climate devoted to the MPI-M models). But things are changing, with the recent appearance of two new journals:

  • Geoscientific Model Development, an open access journal that accepts technical descriptions of the development and evaluation of the models;
  • Earth Science Informatics, a Springer Journal with a broader remit than GMD, but which does cover descriptions of the development of computational tools for climate science.

The problem is related to another dilemma in climate modeling groups: acknowledgement for the contributions of those who devote themselves more to model development rather than doing “publishable science”. Most of the code development is done by scientists whose performance is assessed by their publication record. Some modeling centres have created job positions such as “programmers” or “systems staff”, although most people hired into these roles have a very strong geosciences background. A growing recognition of the importance of their contributions represents a major culture change in the climate modeling community over the last decade.

AGU Day 3 part C: How good are predictions from climate models?

07. January 2010 · 6 comments · Categories: AGU fall meeting 2009

The highlight of the whole conference for me was the Wednesday afternoon session on Methodologies of Climate Model Confirmation and Interpretation, and the poster session the following morning on the same topic, at which we presented Jon’s poster.  Here’s my notes from the Wednesday session.

Before I dive in, I will offer a preamble for people unfamiliar with recent advances in climate models (or more specifically, GCMs) and how they are used in climate science. Essentially, these are massive chunks of software that simulate the flow of mass and energy in the atmosphere and oceans (using a small set of physical equations), and then couple these to simulations of biological and chemical processes, as well as human activity. The climate modellers I’ve spoken to are generally very reluctant to have their models used to generate predictions of future climate – the models are built to help improve our understanding of climate processes, rather than to make forecasts for planning purposes. I was rather struck by the attitude of the modellers at the Hadley centre at the meetings I sat in on last summer in the early planning stages for the next IPCC reports – basically, it was “how can we get the requested runs out of the way quickly so that we can get back to doing our science”. Fundamentally, there is a significant gap between the needs of planners and policymakers for detailed climate forecasts (preferably with the uncertainties quantified), and the kinds of science that the climate models support.

Climate models do play a major role in climate science, but sometimes that role is over-emphasized. Hansen lists climate models third in his sources of understanding of climate change, after (1) paleoclimate and (2) observations of changes in the present and recent past. This seems about right – the models help to refine our understanding and ask “what if…” questions, but are certainly only one of many sources of evidence for AGW.

Two trends in climate modeling over the past decade or so are particularly interesting: the push towards higher and higher resolution models (which thrash the hell out of supercomputers), and the use of ensembles:

  • Higher resolution models (i.e. resolving the physical processes over a finer grid) offer the potential for more detailed analysis of impacts on particular regions (whereas older models focussed on global averages). The difficulty is that higher resolution requires much more computing power, and the higher resolution doesn’t necessarily lead to better models, as we shall see…
  • Ensembles (i.e. many runs of either a single model, or of a collection of different models) allow us to do probabilistic analysis, for example to explore the range of probabilities of future projections. The difficulty, which came up a number of times in this session, is that such probabilities have to be interpreted very carefully, and don’t necessarily mean what they appear to mean.

Much of the concern is over the potential for “big surprises” – the chance that actual changes in the future will lie well outside the confidence intervals of these probabilistic forecasts (to understand why this is likely, you’ll have to read on to the detailed notes). And much of the concern is with the potential for surprises where the models dramatically under-estimate climate change and its impacts. Climate models work well at simulating 20th Century climate. But the more the climate changes in the future, the less certain we can be that the models capture the relevant processes accurately. Which is ironic, really: if the climate wasn’t changing so dramatically, climate models could give very confident predictions of 21st century climate. It’s at the upper end of projected climate changes where the most uncertainty lies, and this is the scary stuff. It worries the heck out of many climatologists.

Much of the question is to do with adequacy for answering particular questions about climate change. Climate models are very detailed hypotheses about climate processes. They don’t reproduce past climate precisely (because of many simplifications). But they do simulate past climate reasonably well, and hence are scientifically useful. It turns out that investigating areas of divergence (either from observations, or from other models) leads to interesting new insights (and potential model improvements).

Okay, with that as an introduction, on to my detailed notes from the session (be warned: it’s a long post). More »

Fun at the AGU

03. January 2010 · Write a comment · Categories: AGU fall meeting 2009, blogging

I’m still only halfway through getting my notes from the AGU meeting turned into blog posts. But the Christmas vacation intervened. I’m hoping to get the second half of the conference blogged over the next week or so, but in the meantime, I thought I’d share these tidbits:

  1. A kid’s-eye view of the AGU. My kids (well, 2/3 of them) accompanied me to the AGU meeting and had great fun interviewing climate scientists. The interviews they videoed were great, but unfortunately the sound quality sucks (you know what noisy conference venues are like). I’ll see if we can edit them into something usable…
  2. I’m a geoblogger! On the Wednesday lunchtime, the AGU hosted a lunch for “geobloggers“, complete with a show-and-tell by each blogger. There’s a nice write up on the AGU blog of the lunch, including a snippet of Julie’s graphical summary with Serendipity right at the centre:

geoblogging lunch

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