Well, I had a fabulous week at the EGU. I tried to take in many different aspects of climate research, but inevitably ended up at lots of sessions on earth systems informatics (to satisfy my techie streak), and sessions looking at current cutting edge research on earth systems models, such as integrating weather forecast and climate models, model ensembles, and probabilistic predictions. Lots of interesting things going on in this space. 

Here’s what I would regard as the major themes of the conference from my perspective:

  • Ocean Acidification. It’s pretty easy to predict because it’s linear in the concentration of CO2 in the atmosphere – i.e. there’s no uncertainty at all. When we kill off life in the seas we also lose a major carbon sink.
  • Feedbacks. I learned at least nine different definitions of the word feedback, and also that there are a huge number of feedbacks that we might want to put into an earth system model, so someone’s got to work out which ones are most likely to be important.
  • Abrupt Climate Change. I learned that the paleontological record tells us that the earth is quite likely to be twitchy, and we still don’t know anywhere near enough about the triggers. Oh, and lots of climate scientists think we’ve already hit some of those triggers.
  • Probabilistic forecasting. I learned a lot about the use of model ensembles (both multi-models, and perturbed physics experiments with single models) to quantify our uncertainties. There’s a strong move in the climate community to replace single predictions of climate change with probabilistic forecasts. The simplest exposition of this idea is MIT’s wheels of fortune.
  • Simpler targets for policy makers. I’m very taken with the analysis from Chris Jones and colleagues that show that if we want to stay below the 2°C temperature rise, we have a total budget of One Trillion Tonnes of Carbon to emit, and since the dawn of industrialization, we used up more than half of it. 
  • Geo-Engineering. Suddenly it’s okay for climate scientists to start talking about geo-engineering. For years, this has been anathema, on the basis that even just talking about this possibility can undermine the efforts to reduce carbon emissions (which is always the most sensible way to tackle the problem). But now it appears that many scientists have concluded that it’s too late anyway to do the right thing, and now we have to start thinking the unthinkable.

Plus some things that I missed that I wish I’d seen (based on what others told me afterwards):

Okay, I finally got some of the webstreaming working (I needed an updated plugin). I managed to watch some of the medal award lectures after the fact from the EGU webstream page. Turns out the medal award lectures are not so interesting (although Leonard Bengtsson’s lecture on extra-tropical cyclones is worth it for his observations about the current state of the art in modeling cyclones).

However, the press conferences are far more interesting:

  • The press conference on uncertainties in climate change is definitely worth it to hear scientists separate what we know from what we don’t know, along with a basic introduction to the principles of climate modeling. Make sure you watch at least until the “wheel of fortune” bit (about halfway through). Bottom line: quantifying uncertainty is crucial. Over dinner this evening we were wishing that other fields (e.g. economics) would be anywhere near this willing to quantify their uncertainties…
  • The press conference on improving outreach and education in the cryosphere is great for lots of facts and figures about the frightening rate at which glaciers and sea ice are melting, and the wide ranging implications (it’s a little slow to get going, but worth it once the panelists start).
  • The press conference on ocean acidification packs a powerful punch. It starts with the screening of a film about how absorption of CO2 by the oceans leads to dramatic change, told from the perspective of how it will affect the generation of kids growing up today.

I missed out on liveblogging the last session on Tuesday on Seamless approaches in weather and climate, because the room had no power outlets at all, and my battery died. Which is a shame, as it was very interesting. The aim of seamless assessment is to be able to move back and forth between weather forecast models and climate models.

The last speaker in the session, Randall Dole, give a good explanation for the reasons why this is an emerging priority, with his three challenges:

  • Understanding and modeling organized tropical convection and its global impacts. This is a key problem in predictability of weather beyond about a week, and a major factor in the regional differences in climate variations within the overall climate change trends.
  • Predicting weather and climate extremes in a changing climate (e.g. tropical cyclones, floods, droughts, coastal inundation, etc)
  • Integrating earth system models and observations. Or: how to build a scientifically-based, internally consistent record of how the earth system is evolving over time.

Randall also identified an opportunity to provide better information for energy and climate policy, for example to assess the likely unintended consequences of major new energy projects, geo-engineering proposals, etc.

David Williamson from NCAR described the Transpose-AMIP project, which takes a set of models (both numerical weather prediction (NWP) and climate models) and runs them against a benchmark of 2 months worth of real weather observations. The aim is to analyze the primary errors, and is especially useful for comparing parameterization schemes with the field data, to track down which parameterizations cause which forecast errors. The NWP models did dramatically better than the climate models, but probably because they are highly tuned to give high quality forecasts of things like precipitation.

Keith Williams from the UK Met Office Hadley Centre talked about progress on a new initiative there on seamless assessment. The aim is to get all of the Met Office models to use the same physics schemes, from the 2-day weather forecast model all the way to the centennial and regional climate models. (Except in cases where it is scientifically justifiable to use an alternative scheme). Work by Rodwell and Palmer paved the way for this. One of the big challenges is to predict extreme events (e.g. heavy storms and flash floods) under climate change. Keith demonstrated why this is hard, with an example of a particular flood in North Cornwall, which is only predicted by high resolution weather forecast models on a 1.5km grid, and not by climate models working on a 20km grid). The problem is we can’t say anything about the frequency of such events under future climate change scenarios if the models don’t capture them.

Frank Selten gave a talk on EC-Earth, a project aimed at extending the ECMWF weather forecast model, currently rated as the best in the world for medium range weather forecasts, and creating a longer range climate model. Interestingly, the plan is to synchronize this effort with the ECMWF’s seasonal model, rather than forking the code. [Note: In conversations after the talk, we speculated on what software engineering problems they might encounter with this, given that the two will be developed at different sites in different countries. My work at the Met Office suggested that a major factor in their success at keeping the weather and climate models integrated is that everything is done in a single building at a single site. EC-Earth might make a good case study for me]. Oh, and they’ll be using the climate prediction index introduced by Murphy et al to assess progress.

Finally, Prashant Sardeshmukh blew my mind, with his description of the twentieth century reanalysis project. The aim of this project is to recreate an entire record of 6-hour measurements of near-surface and tropospheric temperatures, extending back to the start of the 20th century, using all the available observational data and a 56-model ensemble of climate models. Once they’ve done that they plan to go all the way back to 1871. They do this by iteratively improving the estimates until the models and the available field data converge. I amused myself by speculating whether it would be easier to invent a time machine and send a satellite back in time to take the measurements instead…

Not much to report from this morning, but here’s a few interesting talks from this afternoon:

15:30: Dick Schaap, speaking about SeaDataNet. Another big European project: 49 partners and 40 data centres. Most of the effort focusses on establishing standard data formats and metadata descriptions. The aim is to collect all the data providers into a federated system, with single portal, with a shopping basket for users to search for data they need, and secure access to them through a single sign-on. Oh, and they use Ocean Data View (ODV) for interactive exploration and visualization.

15:45: Roy Lowry, of the British Oceanographic Data Centre, whose talk is A RESTful way to manage ontologies. He covered some of the recent history of the NERC Datagrid, and some of the current challenges. 100,000 concepts, organised into about 100 collections. Key idea was to give each concept its own URN throughout the data and metadata, with a resolving service to get URLs from URNs. URLs instantiated as SKOS documents. Key issues:

  • Versioning – if you embed version numbers in the URNs, you have many URNs per concept. So the lesson is to define the URN syntax so that it doesn’t include anything that varies over time. 
  • Deprecation – you can deprecate conecpts by moving the collection, so that the URN now refers to the replacement. But that means the URN of the deprecated concept changes. Lesson: deprecation implemented by change of status, rather than change of address.
  • WSDL structure – RDF triples are implemented as complex types in WSDL. So adding new relationships requires a change in the WSDL, and changing the WSDL during operation breaks the system. 

Oh, and this project supports several climate science initiatives: the Climate Science Modeling Language, and, of course, Metafor.

16:05: Massimo Santoro, on SeaDataNet interoperability, but I’m still too busy exploring the NDG website to pay much attention. Oh, this one’s interesting: Data Mashups based on Google Earth.

16:45: Oh, darn, I’ve missed Fred Spilhaus’s lecture on Boundless Science. Fred was executive director of the AGU for 39 years, until he retired last year. I’m in the wrong room, and tried the webstreaming, but of course it didn’t work. Curse this technology…

17:30: Now for something completely different: Geoengineering. Jason Blackstock, talking on Climate Engineering Responses to Climate Emergencies. Given that climate emergencies are possible, we need to know as much as possible about possible “plan B”s. Jason’s talk is about the outcome of a workshop last year, to investigate what research would be needed to understand the effects of geoengineering. They ignored the basic “should we” question, along with questions on whether consideration of geoengineering approaches might undercut efforts to reduce GHG emissions.

Here’s the premise: we cannot rule out the possibility that the planet is “twitchy“, and might respond suddenly and irreversibly to tipping points. In which case we might need some emergency responses to cool the planet again. Two basic categories of geogengineering – remove CO2 (which is likely to be very slow), or increase the albedo of the earth just a little bit (which could be very fast). The latter options are the most plausible. The most realistic of these are cloud whitening and stratospheric aerosols, so that’s what the workshop focussed on. We know aerosols can work fast because of the data from the eruption of Mt Pinatubo. Ken Caldeira and Lowell Wood did some initial modeling that demonstrated how geoengineering through aerosols might work.

But there are major uncertainties: transient vs. equilibrium response. Controllability and reversability; ocean acidification continues unaffected; plus we don’t know about regional effects, and effects on weather systems. Cost is not really an issue: $10B – $100B per year. But how do we minimize the potential for unanticipated consequences? 

  • Engineering questions: which aerosols? Most likely sulphates. How and where to deploy them? Lots of options.
  • Climate science questions: What climate parameters will be affected by the intervention? What would we need to monitor? We need a ‘red team’ of scientists on hand to calculate the effects, and assess different options. 
  • Climate monitoring: what to we need to measure, with what precision, coverage, and duration, to keep track of how the deployment proceeding?

If we need to be ready with good answers in a ten-year timeframe, what research needs to be done to get there? Phase 1: Non-intervention research. Big issues: hard to learn much without intervention. Phase II: field experiments. Big issues: can’t learn much from a small ‘poke'; need to understand scaling. Phase III: Monitored deployment.

Non-technical issues: What are sensible trigger conditions? Who should decide whether to even undertake this research? Ethics of field tests? Dealing with winners and losers from deployment. And of course the risk of ‘rogue’ geoengineering efforts.

Takehome messages: research into geoengineering responses is no longer “all or nothing” – there are incremental efforts that can be undertaken now. Development of an ‘on the shelf’ plan B option requires a comprehensive and integrated research program – this is a 10-year research program at least.

Some questions: How would this affect acid rain? Not much, because we’re talking about something of the order of 1% of our global output of sulphurous aerosols, plus problems of acid rain are reducing steadily anyway. A more worrying concern would be effect on the tropospheric ozone.

Who decides? There are some scientists saying already we’ve reached a climate emergency. If the aim is to avoid dangerous tipping points (e.g. melting of the poles, destruction of the rainforests), at what point do we pull the trigger? No good answer to this one.

Read more: Journal special issue on geo-engineering.

Chris Jones, from the UK Met Office Hadley Centre, presented a paper at EGU 2009 yesterday on The Trillionth Tonne. The analysis shows that the key driver of temperature change is the total cumulative amount of carbon emissions. To keep below the 2°C global average temperature rise generally regarded as the threshold for preventing dangerous warming, we need to keep total cumulative emissions below a trillion tonnes. And the world is already halfway there.

Which is why the latest news about Canada’s carbon emissions are so embarrassing. Canada is now top among the G8 nations for emissions growth. Let’s look at the numbers: 747 megatonnes in 2007, up from 592 megatonnes in 1990. Using the figures in the Environment Canada report, I calculated the Canada has emitted over 12 gigatonnes since 1990. That’s 12 billion tonnes. So, in 17 years we burnt though more than 1.2% of the entire world’s total budget of carbon emissions. A total budget that has to last from the dawn of industrialization to the point at which the whole world become carbon-neutral. Oh, and Canada has 0.5% of the world’s population.

Disclaimer: I have to check whether the Hadley Centre’s target is 1 trillion tonnes of CO2-equivalent, or 1 trillion tonnes of Carbon (they are different!). The EnvCanada report numbers refer to the former.

Update: I checked with Chris, and as I feared, I got the wrong units – it’s a trillion tonnes of carbon. The conversion factor is about 3.66, so that gives us about 3.66 trillion tonnes of carbon dioxide to play with. [Note: Emissions targets are usually phrased in terms of "Carbon dioxide equivalent", which is a bit hard to calculate as different greenhouse gases have both different molecular weights and different warming factors].

So my revised figures are that Canada burnt through only about 0.33% of the world’s total budget in the last 17 years. Which looks a little better, until you consider:

  • by population, that’s 2/3 of Canada’s entire share. 
  • Using the cumulative totals from 1900-2002. plus the figures for the more recent years from the Environment Canada report (and assuming 2008 was similar to 2007) we’ve emitted 27 gigatonnes of CO2 since 1900. Which is about 0.73% of the world’s budget, or about 147% of our fair share per head. 
  • By population, our fair share of the world’s budget is about 18 gigatonnes CO2 (=5 gigatonnes Carbon). We’d burnt through that by 1997. Everything since then is someone else’s share.

Google tells me I’m not the only one blogging from the EGU meeting this week:

And some others who might blog this week:

But by and large, Google also seems to be telling me that this community doesn’t blog very much.

Update: 

  • RealClimate blogs about the token skeptic;
  • Liz Kalaugher does some excellent summaries of the threat to Canada’s ice fields, MIT’s wheel of fortune; and Chris Jones’ trillionth tonne;

A leisurely breakfast this morning, chatting with Tim, so we didn’t make it to the conference until the coffee break. 

10:30: From climate predictability to end user applications: on the route to more reliable seasonal ensemble forecasts, by Andreas Weigel, who is also a Young Scientist award winner. Most of the analysis is based on the ECMWF model. Uses probabilistic forecasts for seasonal predictions – e.g. 41 runs, with perturbed physics, and use probability density functions to create the forecasts. Uses RPSS (Ranked Probabilistic Skill Score) to compare model predictions with observations. Interesting point: if you get lots of random models, and do ensemble forecasts with them, they approach 0 on this skill scale. This kinds of analysis helps to identify bias in the skill score metric, so that this bias can be removed. Multi-models ensembles have been shown to outperform individual models, but this is a bit of a paradox, because the multi-models include less skillful models. Which implies you can improve your forecasts by adding lower skill models to the ensemble. The answer is to do with reducing overconfidence in the forecasts. Last topic for the talk: how can prediction skill be communicated to the public? Introduce an intuitive skill score that makes sense to the public. Rather than just adding up the accuracy of a series of forecasts, look at two different specific observations, and test whether the forecasts correctly distinguish them (this is known as 2AFC). Then add up the skill as the sum of these tests (okay, I’m not sure I’ve got my head around how this works – I’ll need to read the paper…). Oh, and I like the cartoon on the second slide of this talk.

11:15: change of sessions, and I’ve come in partway through Chris Jones’ talk – “Impact of cumulative emissions of carbon dioxide: the trillionth tonne” (Chris is from the UK Met Office, and I had lots of interesting discussions with him last summer). He’s talking about modeling experiments to determine what reduction in emissions is needed to meet the target of stabilizing climate to the 2°C target. Here’s an interesting emergent result from the modeling: peak warming is related strongly to the total cumulative emissions, rather than the specific pathway (i.e. when the emissions occur). This leads to an observation that you should set a total emissions budget as a policy, without constraining when these emissions should occur. Best answer: total emissions should be no more that 1 trillion tonnes of carbon. We’re halfway there right now! So over the next 40 years or so, we mustn’t emit more than 1/2 trillion tonnes. But the longer we leave it before peak emissions, the more dramatic the cuts after that will have to be. The bottom line is that this analysis greatly simplifies climate negotiations, because it makes the target very clear.

11:30: “Marine oxygen holes as a consequence of oceanic acidification“, presented by Matthias Hofmann. It’s well known that higher CO2 levels leads to ocean acidification, which reduces the ability of shellfish and coral to grow, because it inhibits calcification. But how quickly does this occur under different emissions scenarios? There is one bit of good news: there’s a negative feedback – reduced biogenic calcification has a negative effect on atmospheric CO2. But there are also some other effects that are more worrying: the massive growth of oxygen holes, because of oxidation of organic matter in shallow water. This has very worrying implications for marine life. (Here’s the paper).

11:45: last talk before lunch: Quantifying DMS-cloud-climate interactions using the ECHAM5-HAMMOZ model. The CLAW hypothesis suggests there is a negative feedback loop between the ocean and atmosphere, because warmer oceans enhance the growth of phytoplankton leads to increased So2 and hence more clouds (here’s a nice diagram that explains the feedback). Not sure I can summarize the results of the study presented here, except that they showed the effect is seasonal in nature.

Note to self: get to the sessions earlier and find a seat near a power outlet.

Lunch: I managed to visit the exhibition and pick up a couple of books:

13:30: Ray Bates, giving a talk entitled Climate Feedbacks: Some Conceptual and Physical Issues. Ray is receiving the Vilhelm Bjerknes Medal, and this is the lecture associated with the medal. Standing room only (but I got here first and nabbed one of the only power outlets). Ray started off by giving a little restrospective on his career, starting with his PhD with Charney. Likes the idea of being an Irishman studying tropical dynamics!

Here’s the key idea: most dynamical systems are characterized by negative feedbacks – which keep the system stable. Climate scientists appear to be an exception – they assume climate systems are subject to positive feedbacks that lead to runaway warming. So scientists outside of climate science are often skeptical. To understand this, you first have to understand what the zero feedback case is, and then figure out what we mean by positive/negative feedback. Ray presents four different definitions of “feedback”, F1 from control theory, F2 from electronics, and then two from climate science: F3: a stability altering feedback, and F4, a sensitivity-altering feedback. Ray then points out that any pair of these can give the opposite sign when applied in a particular way to the same system. He then goes on to give several more definitions of different types of feedback in the climate literature. (here’s the paper). Bottom line: an urgent need for a common definition (or set of definitions), so that readers of the climate literature know what we’re talking about.

Ray then gives a long account of Lindzen’s BAMS paper on cloud feedback effects – the paper that causes Lindzen to argue that climate scientists are being alarmist about global warming, because their model (the LCH model) gives a much lower figure for climate sensitivity. Several problems with the LCH model: e.g. it doesn’t include explicit heat transport between the tropics and extra-tropics. Adding these in explicitly gives a very different set of dynamics. With an extended LCH model (with these heat transports) it’s possible to choose parameters that give the opposite feedback effects than when those same parameters are used in the LCH model. (alright, this is a gross simplification of the analysis…) Bottom line: unless we’re much clearer about what we mean by feedback, a lot of the confusion will remain.

14:15: Martin Claussen, giving a talk entitled Is the Sahara a Tipping Element? This work looked at periods in prehistory when the Sahara region was green – covered with grassland. From both the models and the marine sediment cores, it appears that the Sahara flips readily between a ‘green’ state and the desert state, and it only takes a small increase in rainfall to reach this tipping point. As the general circulation models suggest such an increased rainfall as a result of global warming, it’s possible that the Sahara could change dramatically in the coming decades. However, it’s not clear whether it’s a single tipping point, or multiple swings (e.g. different swings for the Eastern vs. Western Sahara). Here’s a summary of the work.

14:30Peter Brockhaus, giving a talk on soil-moisture feedback effects. Here’s another dilemma about feedbacks. Two different runs of a model (the CCLM) at different resolutions (2.2km and 25km) give soil-moisture feedback effects that are opposite in sign. (Here’s the paper)

14:45: Hezi Gildor, on Lightning-biota feedback effects. This one is fascinating: increased temperature leads to increased incidence of lightning, which generates nitrogen compounds that stimulate plant growth. It also makes the grass greener! The analysis indicates this feedback effect is small, but not necessarily insignificant, so it might need to be investigated in earth system models. [my thought: this begs the question - how many of these different feedback effects do we need to track down and incorporate into the general models? Each new effect that we add increases the complexity of the model, and increases the complexity of the coupling...] Now it gets complicated: one of the questioners points out that lightning also causes forest fires, which burns vegetation (in the short term) but which also stimulate more forest growth (in the long term). More feedback effects to account for!

Time for a break, and some ice cream in the hot Austrian sun.

15:30: Larry Hinzman, talking about Hydrological Changes in the Polar Regions: An Analysis of Linkages and Feedbacks. It’s already getting noticeably drier in many polar regions (many lakes are shrinking), but as the permafrost melts, it generally subsides and significantly increases groundwater, which makes these regions wetter. The connections between different processes here are complex, and Larry indicated they are making progress on sorting them out an quantifying them. [He mentioned a new paper (in submission) that has some nice graphics indicating the linkages]. I did find this recent paper, which summarizes many of the changes in Actic hydrology that have already been seen.

16:45: Emma StoneCould vegetation feedbacks determine whether the Greenland ice sheet regrows after deglaciation?  This is a long-term question – if we lose the Greenland ice-sheet, will it eventually re-grow once greenhouse gas concentrations stabilize? Two previous studies offer conflicting answers: Lunt’s work suggested it might regrow in 20,000 years, while Toniazzo’s study indicated that it would not happen at all. Emma is running a series of experiments using HadCM3 from the UK Met Office to investigate. She initializes the model with bare soil (for one treatment) and needle leaf (for another treatment), tested under a return to pre-industrial CO2 concentrations. She found that in some runs, some glaciation reappears on the Greenland’s eastern coast, but it depends on assumptions about vegetation. In other words, vegetation feedback effects are critical here for answering the question. Of course, this all pre-supposes that we ever do return to pre-industrial CO2 concentrations…

20. April 2009 · 4 comments · Categories: climate science, EGU 2009 · Tags:

Okay, here’s my first attempt to do liveblogging from the EGU General Assembly in Vienna. I’ve arrived and registered, but now I face my first problem: the sheer scale of the thing. The printed program runs to 140 pages, and it doesn’t even tell you the titles and authors of any of the papers – it mainly consists of table after table mapping session titles to rooms. Yikes! 

And the next problem is that the wireless internet is swamped at 10,000 geoscientists all try to check their email at once. There’s this neat tool on the conference website that allows you to click on any talk or session while you’re browsing the online program, to add them to your personal program. But as I can’t get the website to load now, I can’t see what I put on my personal program. Ho hum. Ah, it’s loaded.

Looks like I got here too late for Stefan Rahmsdorf’s review of the stability of the ocean circulation under climate change scenarios. I wanted to see it because Stefan has been consistently warning of higher sea level rise than the numbers in the IPCC reports.

Okay, next interesting paper is in the Earth Systems Informatics session, entitled “Semantic metadata application for information resources systematization in water spectroscopy“. Let’s see if I can find the room before the talk is over….

14:56: Found the room, but talk is nearly over. Next up is more promising anyway: Peter Fox, talking about Semantic Provenance Management for Large Scale Scientific Datasets…

15:06: Peter Fox is up. I should point out that this is the last talk in the session (which is on semantic interoperability, knowledge and ontologies). He’s showing some data flow analysis of the current way in which scientific images get passed around – processed images (e.g. gifs) get forwarded without their metadata. He’s characterized the problem as one of combining information from two different streams – the raw images from the instruments (which then get processed in various ways), and comments from observers (both human and tools) who are adding information about images. Another use case: two different graphs from the same data – a daily time series and a monthly time series (for Aerosol Optical Thickness), but the two averagings are produced by different tools, and one tool inverts the Y axis. The scientist working with the two graphs spent two days trying to figure out why the two series seemed to contradict each other! The key idea in this work seems to be the use of a semantic markup language, PML, for capturing additional information on datasets.

15:30: Next session is on International Informatics Collaborations and first speaker is Bryan Lawrence, (who incidentally, I’ve been meaning to get in contact with since he commented on Jon’s blog back in December about our brainstorming sessions). He’s talking about the European Contribution to a Global Solution For Access to Climate Model Simulations. Much of the data is available at WDCC: the World Data Centre on Climate. From this summer, they are expecting to collect a petabyte of data per year. Main task right now: CMIP5, which is the collection of model runs ready for comparison and analysis for the next IPCC assessment. Overall impression of Bryan’s talk: lots of technology problems, just shipping large streams of data around, authentication, and not enough bandwidth! Oh, and there’s a new European framework 7 project just starting up, IS-ENES, which is supposed to increase the collaboration around earth system modeling tools and frameworks.

15:45: Talk on EuroGEOSS, another big European project, just about to kick off next month. It’s hard to get my head around these big interoperability projects: to the outsider, it’s hard to tell what the project will actually focus on.

16:00: I like the title of the next one: Herding Cats: The Challenges of Collaboratively Defining the Geology of Europe, presented by Kristine Asch.  Here’s her motivation: Rich data exists, it’s critical for society, but it’s hard to find, access, share, understand. There is an EU directive on this, called INSPIRE: Infrastructure for Spatial Information in Europe. Looks like the directive involves lots of working groups. Kristine is talking about OneGeology, a project to create an interoperable geological map of Europe. The challenge is that everyone currently maps their geological survey data in different (incompatible) ways. Here’s an example of how deep the problem goes: 20 people sitting around a table trying to reach agreement on what terms like “bedrock”  and “surface geology” mean. Lots of cultural challenges: 27 countries (each with their own geological survey organisation), 27 different standards, national pride, and everyone speaks different variants of English.

Question Session: here’s a good question – have they thought of bringing in any social scientists or anthropologists to study the communities involved in this, to ensure we learn appropriate lessons from the experience. Okay, I’m off on a tangent now, because this question reminded me of an old colleague, Susan Leigh Star, and I just discovered she has a book out that I somehow missed: Sorting Things Out: Classification and it’s consequences

16:30: A talk on Siberia. Actually, on the Siberian Integrated Regional Study, important because of the role of melting permafrost in Siberia and its effect as a climate feedback. The disappointing thing about this talk is that he’s talking a lot about creating web portals, and not much about the real challenges.

17:30: Okay, change of scenery. There’s a session on Education, Computational Methods and Complex Systems in Nonlinear Proceses in Geophysics. First talk by Jeffrey Johnson (prof of complexity science) is about eToile, a Social Intelligent ICT-System for very large scale education in complex systems. Jeff was instrumental a few years ago in setting up the Complex Systems Society. The idea behind eToile (see also related project: Assyst) is to change how we design a curriculum. Normally: define curriculum & learning outcomes, create course materials, deliver them, examine the students, mark exam scripts, and pass/fail/grade the students. This is enormously expensive, especially the bits that are proportional to the number of students (e.g. marking is linear in # of students). Can set up a web portal to provide curriculum and assessment, with semi-automated marking. But how do we provide appropriate learning resources for very large numbers of students to use? Solution: create a “resource ecology”, initially populated with some junk URLs. When students submit work, they also have to submit the web resources they used; these become part of the ecology, and links that many students find useful rise in the ecology. Also, stuff that gets outdated falls in value as fewer students use it, or if the curriculum changes, you get the same effect. Jeff argues that this is a much cheaper, more scaleable way of providing education. In the question session, he clarified: it’s only intended for grad students (who are sufficiently capable of self-study), and probably wouldn’t work for undergrads. Reminds me of some of the experiments we’ve played with on my grad courses with the students contributing web resources to our growing collection.

17:45: Next up: a talk on education around flood management. Big paradigm change, from fighting floods, where engineers are the dominant stakeholder, to “living with floods” when it becomes much more of a shared issue among many different kinds of stakeholder. Set up displays, with lots of cylinders of water, to help people visualize different flood levels. Lots of hands on learning to get stakeholders to take ownership of the problem.

Okay, jetlag kicking in seriously. Off to get some air…

When my children grow up, the world they live in is likely be very different from ours. There’s a small chance that humanity will rapidly come to its senses, start massive program of emissions reductions, and avoid the worst climate change scenarios. The Hadley Centre gives us about a 50/50 chance if carbon emissions peak by 2015, and then fall steadily at a rate of 3% per year (They are currently rising by nearly 3% per year). If we manage to pull this off, and also win the 50/50 bet, our children and grandchildren will ask us how the hell we managed it.

If we can’t stop emissions growth in the next five years, things look much more grim. Perhaps the simplest way to explain it is the picture painted by the New Scientist: How to survive the coming century: a world that is 4°C warmer, 90% of the human population wiped out, the rest relocated to dense cities in Canada, Scandinavia and Siberia. Uninhabitable deserts across the subtropics. Virtually no life in the oceans. And that’s the good part. The New Scientist article glosses over the climate wars that are almost certain if large parts of the world become uninhabitable. If they survive, our children will demand to know what the hell we were doing: we knew it was coming, we knew how bad it would be, and still we did almost nothing to prevent it.

What did you do in the war?When my kids ask me these questions in decades to come, I need to be ready with an answer. I’d like to say that I did everything I could possibly do. I’d like to say that what I did was effective. And I’d like to be able to say that I made a difference.

Having talked with some of our graduate students about how to get a more inter-disciplinary education while they are in grad school, I’ve been collecting links to collaborative grad programs at U of T:

The Dynamics of Global Change Doctoral Program, housed in the Munk Centre. The core course, DGC1000H is very interesting – it starts with Malcolm Gladwell’s Tipping Point book, and then tours through money, religion, pandemics, climate change, the internet and ICTs, and development. What a wonderful journey.

The Centre for the Environment runs a Collaborative Graduate Program (MSc and PhD) in which students take some environmental science courses in addition to satisfying the degree requirements of their home department. The core course for this program is ENV1001, Environmental Decision Making, and it also include an internship to get hands on experience with environmental problem solving.

The Knowledge Media Design Institute (KMDI) also has a collaborative doctoral program, perfect for those interested in design and evaluation of new knowledge media, with a strong focus on knowledge creation, social change, and community

Finally, the Centre for Global Change Science has a set of graduate student awards, to help fund grad students interested in global change science. Oh, and they have a fascinating seminar series, mainly focussed on climate science (all done for this year, but get on their mailing list for next years seminars).

Are there any more I missed?

Had an interesting conversation this afternoon with Brad Bass. Brad is a prof in the Centre for Environment at U of T, and was one of the pioneers of the use of models to explore adaptations to climate change. His agent based simulations explore how systems react to environmental change, e.g. exploring population balance among animals, insects, the growth of vector-borne diseases, and even entire cities. One of his models is Cobweb, an open-source platform for agent-based simulations. 

He’s also involved in the Canadian Climate Change Scenarios Network, which takes outputs from the major climate simulation models around the world, and extracts information on the regional effects on Canada, particularly relevant for scientists who want to know about variability and extremes on a regional scale.

We also talked a lot about educating kids, and kicked around some ideas for how you could give kids simplified simulation models to play with (along the line that Jon was exploring as a possible project), to get them doing hands on experimentation with the effects of climate change. We might get one of our summer students to explore this idea, and Brad has promised to come talk to them in May once they start with us.

Oh, and Brad is also an expert on green roofs, and will be demonstrating them to grade 5 kids at the Kids World of Energy Festival.

Computer Science, as an undergraduate degree, is in trouble. Enrollments have dropped steadily throughout this decade: for example at U of T, our enrollment is about half what it was at the peak. The same is true across the whole of North America. There is some encouraging news: enrollments picked up a little this year (after a serious recruitment drive, ours is up about 20% from it’s nadir, while across the US it’s up 6.2%). But it’s way to early to assume they will climb back up to where they were. Oh, and percentage of women students in CS now averages 12% – the lowest ever.

What happened? One explanation is career expectations. In the 80’s, its was common wisdom that a career in computers was an excellent move, for anyone showing an aptitude for maths. In the 90’s, with the birth of the web, computer science even became cool for a while, and enrollments grew dramatically, with a steady improvement in gender balance too. Then came the dotcom boom and bust, and suddenly a computer science degree was no longer a sure bet. I’m told by our high school liaison team that parents of high school students haven’t got the message that the computer industry is short of graduates to recruit (although with the current recession that’s changing again anyway).

A more likely explanation is perceived relevance. In the 80’s, with the birth of the PC, and in the 90’s with the growth of the web, computer science seemed like the heart of an exciting revolution. But now computers are ubiquitous, they’re no longer particularly interesting. Kids take them for granted, and a only a few über-geeks are truly interested in what’s inside the box. But computer science departments continue to draw boundaries around computer science and its subfields in a way that just encourages the fragmentation of knowledge that is so endemic of modern universities.

Which is why an experiment at Georgia Tech is particularly interesting. The College of Computing at Georgia Tech has managed to buck the enrollment trend, with enrollment numbers holding steady throughout this decade. The explanation appears to be a radical re-design of their undergraduate degree, into a set of eight threads. For a detailed explanation, there’s a white paper, but the basic aim is to get students to take more ownership of their degree programs (as opposed to waiting to be spoonfed), and to re-describe computer science in terms that make sense to the rest of the world (computer scientists often forget the the field is impenetrable to the outsider). The eight threads are: Modeling and simulation; Devices (embedded in the physical world); Theory; Information internetworks; Intelligence; Media (use of computers for more creative expression); People (human-centred design); and Platforms (computer architectures, etc). Students pick any two threads, and the program is designed so that any combination covers most of what you would expect to see in a traditional CS degree.

At first sight, it seems this is just a re-labeling effort, with the traditional subfields of CS (e.g. OS, networks, DB, HCI, AI, etc) mapping on to individual threads. But actually, it’s far more interesting than that. The threads are designed to re-contextualize knowledge. Instead of students picking from a buffet of CS courses, each thread is designed so that students see how the knowledge and skills they are developing can be applied in interesting ways. Most importantly, the threads cross many traditional disciplinary boundaries, weaving a diverse set of courses into a coherent theme, showing the students how their developing CS skills combine in intellectually stimulating ways, and preparing them for the connected thinking needed for inter-disciplinary problem solving.

For example the People thread brings in psychology and sociology, examining the role of computers in the human activity systems that give them purpose. It explore the perceptual and cognitive abilities of people as well as design practices for practical socio-technical systems. The Modeling and Simluation thread explores how computational tools are used in a wide variety of sciences to help understand the world. Following this thread will require consideration of epistemology of scientific knowledge, as well as mastery of the technical machinery by which we create models and simulations, and the underlying mathematics. The thread includes in a big dose of both continuous and discrete math, data mining, and high performance computing. Just imagine what graduates of these two threads would be able to do for our research on SE and the climate crisis! The other thing I hope it will do is to help students to know their own strengths and passions, and be able to communicate effectively with others.

The good news is that our department decided this week to explore our own version of threads. Our aims is to learn from the experience at Georgia Tech and avoid some of the problems they have experienced (for example, by allowing every possible combination of 8 threads, it appears they have created too many constraints on timetabling and provisioning individual courses). I’ll blog this initiative as it unfolds.

Okay, here’s a slightly different modeling challenge. It might be more of a visualization challenge. Whatever. In part 1, I suggested we use requirements analysis techniques to identify stakeholders, and stakeholder goals, and link them to the various suggested “wedges“.

Here, I want to suggest something different. There are several excellent books that attempt to address the “how will we do it?” challenge. They each set out a set of suggested solutions, add up the contribution of each solution to reducing emissions, assess the feasibility of each solution, add up all the numbers, and attempt to make some strategic recommendations. But each book makes different input assumptions, focusses on slightly different kinds of solutions, and ends up with different recommendations (but they also agree on many things).

Here are the four books:

Cover image for Monbiots Heat
George Monbiot, Heat: How to Stop the Planet from Burning. This is probably the best book I have ever read on global warming. It’s brilliantly researched, passionate, and doesn’t pull it’s punches. Plus it’s furiously upbeat – Monbiot takes on the challenge of how we get to 90% emissions reduction, and shows that it is possible (although you kind of have to imagine a world in which politicians are willing to do the right thing).

Joseph Romm, Hell and High Water: Global Warming–the Solution and the Politics–and What We Should Do. While lacking Monbiot’s compelling writing style, Romm makes up by being an insider – he was an energy policy wonk in the Clinton administration. The other contrast is Monbiot is British, and focusses mainly on British examples, Romm is American and focusses on US example. The cultural contrasts are interesting.

David MacKay, Sustainable Energy – Without the Hot Air. Okay, so I haven’t read this one yet, but it got a glowing write-up on Boing Boing . Oh, and it’s available as a free download.

Lester Brown, Plan B 3.0L Mobilizing to Save Civilization. This one’s been on my reading list for a while, will read it soon. It has a much broader remit than the others: Brown wants to solve world poverty, cure disease, feed the world, and solve the climate crisis. I’m looking forward to this one. And it’s also available as a free download.

Okay, so what’s the challenge? Model the set of solutions in each of these books so that it’s possible to compare and contrast their solutions, compare their assumptions, and easily identify areas of agreement and disagreement. I’ve no idea yet how to do this, but a related challenge would be to come up with compelling visualizations that explain to a much broader audience what these solutions look like, and why it’s perfectly feasible. Something like this (my current favourite graphic):

Graph of cost/benefit of climate mitigation strategies

Graph of cost/benefit of climate mitigation strategies