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…

Leave a Reply

Your email address will not be published. Required fields are marked *