Climate science from climate scientists...
Does climate sensitivity depend on the cause of the change?
Can a response to a forcing wait and then bounce up after a period of inertness?
Does the existence of an 11-year time-scale prove the existence of solar forcing?
Why does the amplitude of the secular response drop when a long-term trend is added?
[Read more…] about A phenomenological sequel
A paper on climate sensitivity today in Science will no doubt see a great deal of press in the next few weeks. In “Why is climate sensitivity so unpredictable?”, Gerard Roe and Marcia Baker explore the origin of the range of climate sensitivities typically cited in the literature. In particular they seek to explain the characteristic shape of the distribution of estimated climate sensitivities. This distribution includes a long tail towards values much higher than the standard 2-4.5 degrees C change in temperature (for a doubling of CO2) commonly referred to.
In essence, what Roe and Baker show is that this characteristic shape arises from the non-linear relationship between the strength of climate feedbacks (f) and the resulting temperature response (deltaT), which is proportional to 1/(1-f). They show that this places a strong constraint on our ability to determine a specific “true” value of climate sensitivity, S. These results could well be taken to suggest that climate sensitivity is so uncertain as to be effectively unknowable. This would be quite wrong.
Regional Climate Projections in the IPCC AR4
How does anthropogenic global warming (AGW) affect me? The answer to this question will perhaps be one of the most relevant concerns in the future, and is discussed in chapter 11 of the IPCC assessment report 4 (AR4) working group 1 (WG1) (the chapter also has some supplementary material). The problem of obtaining regional information from GCMs is not trivial, and has been discussed in a previous post here at RC and the IPCC third assessment report (TAR) also provided a good background on this topic.
The climate projections presented in the IPCC AR4 are from the latest set of coordinated GCM simulations, archived at the Program for Climate Model Diagnosis and Intercomparison (PCMDI). This is the most important new information that AR4 contains concerning the future projections. These climate model simulations (the multi-model data set, or just ‘MMD’) are often referred to as the AR4 simulations, but they are now officially being referred to as CMIP3.
One of the most challenging and uncertain aspects of present-day climate research is associated with the prediction of a regional response to a global forcing. Although the science of regional climate projections has progressed significantly since last IPCC report, slight displacement in circulation characteristics, systematic errors in energy/moisture transport, coarse representation of ocean currents/processes, crude parameterisation of sub-grid- and land surface processes, and overly simplified topography used in present-day climate models, make accurate and detailed analysis difficult.
I think that the authors of chapter 11 over-all have done a very thorough job, although there are a few points which I believe could be improved. Chapter 11 of the IPCC AR4 working group I (WGI) divides the world into different continents or types of regions (e.g. ‘Small islands’ and ‘Polar regions’), and then discusses these separately. It provides a nice overview of the key climate characteristics for each region. Each section also provides a short round up of the evaluations of the performance of the climate models, discussing their weaknesses in terms of reproducing regional and local climate characteristics.
With the blogosphere all a-flutter with discussions of hundredths of degrees adjustments to the surface temperature record, you probably missed a couple of actually interesting stories last week.
Tipping points
Oft-discussed and frequently abused, tipping points are very rarely actually defined. Tim Lenton does a good job in this recent article. A tipping ‘element’ for climate purposes is defined as
The parameters controlling the system can be transparently combined into a single control, and there exists a critical value of this control from which a small perturbation leads to a qualitative change in a crucial feature of the system, after some observation time.
and the examples that he thinks have the potential to be large scale tipping elements are: Arctic sea-ice, a reorganisation of the Atlantic thermohaline circulation, melt of the Greenland or West Antarctic Ice Sheets, dieback of the Amazon rainforest, a greening of the Sahara, Indian summer monsoon collapse, boreal forest dieback and ocean methane hydrates.
To that list, we’d probably add any number of ecosystems where small changes can have cascading effects – such as fisheries. It’s interesting to note that most of these elements include physics that modellers are least confident about – hydrology, ice sheets and vegetation dynamics.
Prediction vs. Projections
As we discussed recently in connection with climate ‘forecasting‘, the kinds of simulations used in AR4 are all ‘projections’ i.e. runs that attempt to estimate the forced response of the climate to emission changes, but that don’t attempt to estimate the trajectory of the unforced ‘weather’. As we mentioned briefly, that leads to a ‘sweet spot’ for forecasting of a couple of decades into the future where the initial condition uncertainty dies away, but the uncertainty in the emission scenario is not yet so large as to be dominating. Last week there was a paper by Smith and colleagues in Science that tried to fill in those early years, using a model that initialises the heat content from the upper ocean – with the idea that the structure of those anomalies control the ‘weather’ progression over the next few years.
They find that their initialisation makes a difference for a about a decade, but that at longer timescales the results look like the standard projections (i.e. 0.2 to 0.3ºC per decade warming). One big caveat is that they aren’t able to predict El Niño events, and since they account for a great deal of the interannual global temperature anomaly, that is a limitation. Nonetheless, this is a good step forward and people should be looking out for whether their predictions – for a plateau until 2009 and then a big ramp up – materialise over the next few years.
Model ensembles as probabilities
A rather esoteric point of discussion concerning ‘Bayesian priors’ got a mainstream outing this week in the Economist. The very narrow point in question is to what extent model ensembles are probability distributions. i.e. if only 10% of models show a particular behaviour, does this mean that the likelihood of this happening is 10%?
The answer is no. The other 90% could all be missing some key piece of physics.
However, there has been a bit of confusion generated though through the work of climateprediction.net – the multi-thousand member perturbed parameter ensembles that, notoriously, suggested that climate sensitivity could be as high as 11 ºC in a paper a couple of years back. The very specific issue is whether the histograms generated through that process could be considered a probability distribution function or not. (‘Not’ is the correct answer).
The point in the Economist article is that one can demonstrate that very clearly by changing the variables you are perturbing (in the example they use an inverse). If you evenly sample X, or evenly sample 1/X (or any other function of X) you will get a different distribution of results. Then instead of (in one case) getting 10% of models runs to show behaviour X, now maybe 30% of models will. And all this is completely independent of any change to the physics.
My only complaint about the Economist piece is the conclusion that, because of this inherent ambiguity, dealing with it becomes a ‘logistical nightmare’ – that’s is incorrect. What should happen is that people should stop trying to think that counting finite samples of model ensembles can give a probability. Nothing else changes.
There is a new critique of IPCC climate projections doing the rounds of the blogosphere from two ‘scientific forecasters’, Kesten Green and Scott Armstrong, who claim that since the IPCC projections are not ‘scientific forecasts’ they must perforce be wrong and that a naive model of no change in future is likely to be more accurate that any IPCC conclusion. This ignores the fact that IPCC projections have already proved themselves better than such a naive model, but their critique is novel enough to be worth a mention.
[Read more…] about Green and Armstrong’s scientific forecast
Global climate
Global climate statistics, such as the global mean temperature, provide good indicators as to how our global climate varies (e.g. see here). However, most people are not directly affected by global climate statistics. They care about the local climate; the temperature, rainfall and wind where they are. When you look at the impacts of a climate change or specific adaptations to a climate change, you often need to know how a global warming will affect the local climate.
Yet, whereas the global climate models (GCMs) tend to describe the global climate statistics reasonably well, they do not provide a representative description of the local climate. Regional climate models (RCMs) do a better job at representing climate on a smaller scale, but their spatial resolution is still fairly coarse compared to how the local climate may vary spatially in regions with complex terrain. This fact is not a general flaw of climate models, but just the climate models’ limitation. I will try to explain why this is below.
At Jim Hansen’s now famous congressional testimony given in the hot summer of 1988, he showed GISS model projections of continued global warming assuming further increases in human produced greenhouse gases. This was one of the earliest transient climate model experiments and so rightly gets a fair bit of attention when the reliability of model projections are discussed. There have however been an awful lot of mis-statements over the years – some based on pure dishonesty, some based on simple confusion. Hansen himself (and, for full disclosure, my boss), revisited those simulations in a paper last year, where he showed a rather impressive match between the recently observed data and the model projections. But how impressive is this really? and what can be concluded from the subsequent years of observations?
[Read more…] about Hansen’s 1988 projections
A lot of what gets discussed here in relation to the greenhouse effect is relatively simple, and yet can be confusing to the lay reader. A useful way of demonstrating that simplicity is to use a stripped down mathematical model that is complex enough to include some interesting physics, but simple enough so that you can just write down the answer. This is the staple of most textbooks on the subject, but there are questions that arise in discussions here that don’t ever get addressed in most textbooks. Yet simple models can be useful there too.
I’ll try and cover a few ‘greenhouse’ issues that come up in multiple contexts in the climate debate. Why does ‘radiative forcing’ work as method for comparing different physical impacts on the climate, and why you can’t calculate climate sensitivity just by looking at the surface energy budget. There will be mathematics, but hopefully it won’t be too painful.
[Read more…] about Learning from a simple model
The following letter from Carl Wunsch is intended to clarify his views on global warming in general, and the The Great Global Warming Swindle which misrepresented them.
Carl Wunsch 11 March 2007
I believe that climate change is real, a major threat, and almost surely has a major human-induced component. But I have tried to stay out of the `climate wars’ because all nuance tends to be lost, and the distinction between what we know firmly, as scientists, and what we suspect is happening, is so difficult to maintain in the presence of rhetorical excess. In the long run, our credibility as scientists rests on being very careful of, and protective of, our authority and expertise.
[Read more…] about Swindled: Carl Wunsch responds
by Rasmus Benestad, with contributions from Caspar & Eric
In a recent article in Climatic Change, D.G. Martinson and W.C. Pitman III discuss a new hypothesis explaining how the climate could change abruptly between ice ages and inter-glacial (warm) periods. They argue that the changes in Earth’s orbit around the Sun in isolation is not sufficient to explain the estimated high rate of change, and that there must be an amplifying feedback process kicking in. The necessity for a feedback is not new, as the Swedish Nobel Prize winner (Chemistry), Svante Arrhenius, suggested already in 1896 that CO2 could act as an amplification mechanism. In addition, there is the albedo feedback, where the amount of solar radiation that is reflected back into space, scales with the area of the ice- and snow-cover. And are clouds as well as other aspects playing a role.
por Rasmus Benestad, com contribuições de Caspar & Eric
Em um artigo recente da Climatic Change, D.G. Martinson e W.C. Pitman III discutem uma nova hipótese que explica como o clima pode mudar abruptamente entre eras glaciais e períodos interglaciais (quentes). Eles argumentam que as mudanças na órbita da Terra ao redor do Sol em isolado não são suficientes para explicar as altas taxas de mudanças estimadas, e que deve necessariamente haver a ação de um mecanismo de feedback (ou retro-alimentação) amplificando o processo. A necessidade de um feedback não é nova, pois o sueco ganhador do Prêmio Nobel (Química), Svante Arrhenius, já havia sugerido em 1896 que o CO2 deveria agir como um mecanismo de amplificação. Além do mais, existe o feedback do albedo, pelo qual a quantidade de radiação solar que é refletida de volta ao espaço é escalonável com a área de cobertura de gelo e neve. E existem nuvens bem como outros aspectos envolvidos.
A hipótese de Martinson & Pitman III formula que a entrada de água doce funciona em consonância com o ciclo de Milankovitch e o feedback de albedo. Eles concluem que os ‘maiores’ términos podem somente acontecer após um acúmulo de gelo grande o suficiente para isolar o Artico, inibindo o fluxo de entrada de água doce até um ponto em que o aumento da salinidade na camada superficial, através de um vagaroso e contínuo crescimento do gelo marinho, causa uma inversão das águas marinhas do Ártico (pelo efeito na circulação atmosférica e nas correntes oceânicas). A inversão vertical traz água quente de baixo para cima, promovendo condições mais favoráveis ao degelo. A salinidade também tem um papel, mas a hipótese não menciona variações de gases de efeito estufa (GEE). Algumas questões: Martinson e Pitman III esqueceram disso? Ou os GEE representam somente uma pequena contribuição? E, não poderiam as mudanças nos GEE explicar boa parte da variabilidade? Por outro lado, parece plausível que mudanças na salinidade e na entrada de água doce poderiam afetar a formação de gelo marinho e a convecção profunda. Contudo, até o presente, a hipótese proposta por Martinson and Pitman III é meramente uma especulação, e estamos aguardando para ver se a hipótese pode ser testada através de experimentos de modelos numéricos (o que pode requerer modelos oceânicos e de gelo marinho com maior resolução que os atualmente usados em modelos climáticos globais). Seria interessante conduzir experimentos para avaliar a significância individual da água doce, dos GEE e o efeito combinado.
Uma reação ao trabalho de Martison e Pittman é: Onde está o cálculo de energia? Gases de efeito estufa contribuem somente com alguns W/m2, em contraste com uma forçante >40 do ciclo sazonal de Milankovich. Para esta nova idéia ter mérito, teria sido melhor ter no mínimo fluxos de calor em paralelo com a forçante radioativa do CO2. Estudos de modelagem anteriores encontraram que GEE produzem aproximadamente 50% de todo Último Máximo Glacial (inglês, LGM) para a resposta da temperatura atual (veja por exemplo Broccoli & Manabe), a outra parte sendo o albedo, etc., que respondem ao ciclo sazonal de irradiância. É muito difícil isolar completamente as causas individuais pois as mudanças nos GEE podem produzir alterações na distribuição de nuvens e gelo marinho. Mas a grosso modo, se você rodar um LGM e somente somente reduzir o nível do mar, introduzir as calotas de gelo, mudar a vegetação, adicionar alguma poeria (embora esta ainda seja grosseira), então você alcançaria ao redor de 50% do caminho que você quer ir. Mude a concentração de GEE e você chegaria mais próximo. Isso é mais ou menos o que Manabe e Stouffer mostraram há quinze anos atrás. A questão é se realmente precisamos de algo mais, e se esse ‘algo mais’ tem força suficiente.
traduzido por Ivan B. T. Lima e Fernando M. Ramos.