As an experiment, I’ve created a wiki for dynamic models. I’m gradually migrating my existing model library into the wiki, in the hope that it will be easier to maintain and more useful for visitors. I’ve also created a public model library section so that users can submit new material. Check it out!
Two interesting abstracts I ran across today:
A decrease in the globally averaged low level cloud cover, deduced from the ISCCP infrared data, as the cosmic ray intensity decreased during the solar cycle 22 was observed by two groups. The groups went on to hypothesize that the decrease in ionization due to cosmic rays causes the decrease in cloud cover, thereby explaining a large part of the currently observed global warming. We have examined this hypothesis to look for evidence to corroborate it. None has been found and so our conclusions are to doubt it. From the absence of corroborative evidence, we estimate that less than 23%, at the 95% confidence level, of the 11 year cycle change in the globally averaged cloud cover observed in solar cycle 22 is due to the change in the rate of ionization from the solar modulation of cosmic rays.
Almost one-quarter of carbon dioxide released to the atmosphere is emitted in the production of internationally traded goods and services. Trade therefore represents an unrivalled, and unused, tool for reducing greenhouse gas emissions.
Roger Pielke Jr., Tom Wigley, and Christopher Green have a nice commentary in this week’s Nature. It argues that current scenarios are dangerously reliant on business-as-usual technical improvement to reduce greenhouse gas intensity:
Here we show that two-thirds or more of all the energy efficiency improvements and decarbonization of energy supply required to stabilize greenhouse gases is already built into the IPCC reference scenarios. This is because the scenarios assume a certain amount of spontaneous technological change and related decarbonization. Thus, the IPCC implicitly assumes that the bulk of the challenge of reducing future emissions will occur in the absence of climate policies. We believe that these assumptions are optimistic at best and unachievable at worst, potentially seriously underestimating the scale of the technological challenge associated with stabilizing greenhouse-gas concentrations.
They note that assumed rates of decarbonization exceed reality:
The IPCC scenarios include a wide range of possibilities for the future evolution of energy and carbon intensities. Many of the scenarios are arguably unrealistic and some are likely to be unachievable. For instance, the IPCC assumptions for decarbonization in the short term (2000’“2010) are already inconsistent with the recent evolution of the global economy (Fig. 2). All scenarios predict decreases in energy intensity, and in most cases carbon intensity, during 2000 to 2010. But in recent years, both global energy intensity and carbon intensity have risen, reversing the trend of previous decades.
In an accompanying news article, several commenters object to the notion of a trend reversal:
Energy efficiency has in the past improved without climate policy, and the same is very likely to happen in the future. Including unprompted technological change in the baseline is thus logical. It is not very helpful to discredit emission scenarios on the sole basis of their being at odds with the most recent economic trends in China. Chinese statistics are not always reliable. Moreover, the period in question is too short to signify a global trend-break. (Detlef van Vuuren)
Having seen several trend breaks evaporate, including the dot.com productivity miracle and the Chinese emissions reductions coincident with the Asian crisis, I’m inclined to agree that gloom may be premature. On the other hand, Pielke, Wigley and Green are conservative in that they don’t consider the possible pressure for recarbonization created by a transition from conventional oil and gas to coal and tar sands. A look at the long term is helpful:
Emissions intensity of GDP for 18 major emitters. Notice the convergence in intensity, with high-intensity nations falling, and low-intensity nations (generally less-developed) rising.
Corresponding decadal trends in emissions intensity. Over the long haul, there’s some indication that emissions are falling faster in developed nations – a reason for hope. But there’s also a lot of diversity, and many nations have positive trends in intensity. More importantly, even with major wars and depressions, no major emitter has achieved the kind of intensity trend (about -7%/yr) needed to achieve 80% emissions reductions by 2050 while sustaining 3%/yr GDP growth. That suggests that achieving aggressive goals may require more than technology, including – gasp – lifestyle changes.
A closer look at intensity for 6 major emitters. Notice intensity rising in China and India until recently, and that Chinese data is indeed suspect.
Pielke, Wigley, and Green wrap up:
There is no question about whether technological innovation is necessary ’” it is. The question is, to what degree should policy focus directly on motivating such innovation? The IPCC plays a risky game in assuming that spontaneous advances in technological innovation will carry most of the burden of achieving future emissions reductions, rather than focusing on creating the conditions for such innovations to occur.
There’s a second risky game afoot, which is assuming that “creating the conditions for such innovations to occur” means investing in R&D, exclusive of other measures. To achieve material reductions in emissions, “occur” must mean “be adopted” not just “be invented.” Absent market signals and institutional changes, it is unlikely that technologies like carbon sequestration will ever be adopted. Others, like vehicle and lighting efficiency, could easily see their gains eroded by increased consumption of energy services, which become cheaper as technology improves productivity.
The general objective of the challenge is to promote the proper use of science in formulating public policy. This involves such things as full disclosure of forecasting methods and data, and the proper testing of alternative methods. A specific objective is to develop useful methods to forecast global temperatures. Hopefully other competitors would join to show the value of their forecasting methods. These are objectives that we share and they can be achieved no matter who wins the challenge.
Al Gore is invited to select any currently available fully disclosed climate model to produce the forecasts (without human adjustments to the model’s forecasts). Scott Armstrong’s forecasts will be based on the naive (no-change) model; that is, for each of the ten years of the challenge, he will use the most recent year’s average temperature at each station as the forecast for each of the years in the future. The naÃ¯ve model is a commonly used benchmark in assessing forecasting methods and it is a strong competitor when uncertainty is high or when improper forecasting methods have been used.
Specifically, the challenge will involve making forecasts for ten weather stations that are reliable and geographically dispersed. An independent panel composed of experts agreeable to both parties will designate the weather stations. Data from these sites will be listed on a public web site along with daily temperature readings and, when available, error scores for each contestant.
Starting at the beginning of 2008, one-year ahead forecasts then two-year ahead forecasts, and so on up to ten-year-ahead forecasts of annual ‘mean temperature’ will be made annually for each weather station for each of the next ten years. Forecasts must be submitted by the end of the first working day in January. Each calendar year would end on December 31.
The criteria for accuracy would be the average absolute forecast error at each weather station. Averages across stations would be made for each forecast horizon (e.g., for a six-year ahead forecast). Finally, simple unweighted averages will be made of the forecast errors across all forecast horizons. For example, the average across the two-year ahead forecast errors would receive the same weight as that across the nine-year-ahead forecast errors. This unweighted average would be used as the criterion for determining the winner.
I previously noted several problems with the bet:
The Global Warming Challenge is indeed a sucker bet, with terms slanted to favor the naive forecast. It focuses on temperature at just 10 specific stations over only 10 years, thus exploiting the facts that (a) GCMs do not have local resolution (their grids are typically several degrees) (b) GCMs, unlike weather models, do not have infrastructure for realtime updating of forcings and initial conditions (c) ten stations is a pathetically small sample, and thus a low signal-to-noise ratio is expected under any circumstances (d) the decadal trend in global temperature is small compared to natural variability.
It’s actually worse than I initially thought. I assumed that Armstrong would determine the absolute error of the average across the 10 stations, rather than the average of the individual absolute errors. By the triangle inequality, the latter is always greater than or equal to the former, so this approach further worsens the signal-to-noise ratio and enhances the advantage of the naive forecast. In effect, the bet is 10 replications of a single-station test. But wait, there’s still more: the procedure involves simple, unweighted averages of errors across all horizons. But there will be only one 10-year forecast, two 9-year forecasts … , and ten 1-year forecasts. If the temperature and forecast are stationary, the errors at various horizons have the same magnitude, and the weighted average horizon is only four years. Even with other plausible assumptions, the average horizon of the experiment is much less than 10 years, further reducing the value of an accurate long-term climate model.
However, there is a silver lining. I have determined, by playing with the GHCN data, that Armstrong’s procedure can be reliably beaten by a simple extension of a physical climate model published a number of years ago. I’m busy and I have a high discount rate, so I will happily sell this procedure to the best reasonable offer (remember, you stand to make $10,000).
Update: I’m serious about this, by the way. It can be beaten.
No pun intended.
Scott Armstrong has again asserted on the JDM list that global warming forecasts are merely unscientific opinions (ignoring my prior objections to the claim). My response follows (a bit enhanced here, e.g., providing links).
Today would be an auspicious day to declare the death of climate science, but I’m afraid the announcement would be premature.
JDM researchers might be interested in the forecasts of global warming as they are based on unaided subjective forecasts (unaided by forecasting principles) entered into complex computer models.
This seems to say that climate scientists first form an opinion about the temperature in 2100, or perhaps about climate sensitivity to 2x CO2, then tweak their models to reproduce the desired result. This is a misperception about models and modeling. First, in a complex physical model, there is no direct way for opinions that represent outcomes (like climate sensitivity) to be “entered in.” Outcomes emerge from the specification and calibration process. In a complex, nonlinear, stochastic model it is rather difficult to get a desired behavior, particularly when the model must conform to data. Climate models are not just replicating the time series of global temperature; they first must replicate geographic and seasonal patterns of temperature and precipitation, vertical structure of the atmosphere, etc. With a model that takes hours or weeks to execute, it’s simply not practical to bend the results to reflect preconceived notions. Second, not all models are big and complex. Low order energy balance models can be fully estimated from data, and still yield nonzero climate sensitivity.
I presume that the backing for the statement above is to be found in Green and Armstrong (2007), on which I have already commented here and on the JDM list. Continue reading “More on Climate Predictions”
A spruce budworm outbreak here has me worried about the long-term health of our forest, given that climate change is likely to substantially alter conditions here in Montana. The nightmare scenario is for temperatures to warm up without soil moisture keeping up, so that drought-weakened trees are easily ravaged by budworm and other pests, unchecked by the good hard cold you can usually count on here at some point in January, with dead stands ultimately burning before a graceful succession of species can take place. The big questions, then, are what’s the risk, how to see it coming, and how to adapt.
To get a look at the risk, I downloaded some GCM results from the CMIP3 archive. These are huge files, and unfortunately not very informative about local conditions because the global grids simply aren’t fine enough to resolve local features. I’ve been watching for some time for a study to cover my region, and at last there are some preliminary results from Eric Salathé at University of Washington. Regional climate modeling is still an uncertain business, but the results are probably as close as one can come to a peek at the future.
The future is generally warmer. Here’s the regional temperature trend for my grid point, using the ECHAM5 model (downscaled) for the 20th century (blue) and IPCC A2 forcings (red), reported as middle-of-the-road warming:
The Saturn V used for the Apollo missions burned 203,000 gallons of RP-1 (basically kerosene) in its first stage. At 820 kg/m^3, that’s 630 metric tons of fuel. Liquid hydrocarbons tend to be close to CxH2x, or about 85% carbon by mass, so that’s 536 metric tons of carbon, which yields 1965 tons CO2 when burned, or 655 TonCO2/astronaut. Obviously that’s not personal consumption, but it is a lot of carbon in the atmosphere.
The emerging space tourism industry, on the other hand, is primarily personal consumption. I’d love to take the trip, but I’d be a little put off if the consequences of seeing the big blue marble from above were to make a major contribution to climate change. So, what are the consequences?
It’s a good idea to read things you criticize; checking your sources doesn’t hurt either. One of the most frequent targets of uninformed criticism, passed down from teacher to student with nary a reference to the actual text, must be The Limits to Growth. In writing my recent review of Green & Armstrong (2007), I ran across this tidbit:
Complex models (those involving nonlinearities and interactions) harm accuracy because their errors multiply. Ascher (1978), refers to the Club of Rome’s 1972 forecasts where, unaware of the research on forecasting, the developers proudly proclaimed, “in our model about 100,000 relationships are stored in the computer.” (page 999)
Setting aside the erroneous attributions about complexity, I found the statement that the MIT world models contained 100,000 relationships surprising, as both can be diagrammed on a single large page. I looked up electronic copies of World Dynamics and World3, which have 123 and 373 equations respectively. A third or more of those are inconsequential coefficients or switches for policy experiments. So how did Ascher, or Ascher’s source, get to 100,000? Perhaps by multiplying by the number of time steps over the 200 year simulation period – hardly a relevant measure of complexity.
Meadows et al. tried to steer the reader away from focusing on point forecasts. The introduction to the simulation results reads,
Each of these variables is plotted on a different vertical scale. We have deliberately omitted the vertical scales and we have made the horizontal time scale somewhat vague because we want to emphasize the general behavior modes of these computer outputs, not the numerical values, which are only approximately known. (page 123)
Many critics have blithely ignored such admonitions, and other comments to the effect of, “this is a choice, not a forecast” or “more study is needed.” Often, critics don’t even refer to the World3 runs, which are inconvenient in that none reaches overshoot in the 20th century, making it hard to establish that “LTG predicted the end of the world in year XXXX, and it didn’t happen.” Instead, critics choose the year XXXX from a table of resource lifetime indices in the chapter on nonrenewable resources (page 56), which were not forecasts at all. Continue reading “On Limits to Growth”
The Fed has just doled out over $300 billion in loans to bail out Bear Stearns and other bad actors in the subprime mortgage mess. It’s hard to say what fraction of that capital is really at risk, but let’s say 10%. That’s a pretty big transfer to shareholders, especially considering that there’s nothing in it for the general public other than avoidance of financial contagion effects. If this were an environmental or public health issue, skeptics would be lined up to question whether contagion in fact exists, whether fixing it does more harm than good (e.g., by creating future moral hazard), and whether there’s a better way to spend the money. Contagion would have to be proven with models, subject to infinite scrutiny and delay. Yet here, billions are doled out with no visible analysis or public process, based on policies invented ad hoc. Perhaps a little feedback control is needed here: let’s create a bailout fund, supported by taxes on firms that are deemed too big to fail by some objective criteria. Then two negative feedbacks will operate: firms that get too large will be encouraged to split themselves into manageable chunks, and the potential beneficiaries of bailouts will have to ask themselves how badly they really want insurance. Let’s try it, and see how long the precautionary principle lasts in the financial sector.
Update: Paul Krugman has a nice editorial on the problem.
And if financial players like Bear are going to receive the kind of rescue previously limited to deposit-taking banks, the implication seems obvious: they should be regulated like banks, too.
Olive Heffernan has an interesting tidbit on Climate Feedback about unintended consequences of climate policy.
It’s worth noting that most of these side-effects are not consequences of climate policy per se. They are consequences of pursuing climate policy piecemeal, from the bottom up, and seeking technological fixes in the absence of market signals. If climate policy were pursued as part of a general agenda of internalizing environmental and social externalities through market signals, some of these perverse behaviors would not occur.
The side effects of the corn ethanol boom should not be laid at the door of climate policy. Apart from hopes for cellulosic, ethanol has little to offer with respect to greenhouse gas emissions, and perhaps much to answer for. Its real motivations are oil independence and largesse to the ag sector.