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 Models are unreliable"Models solve the equations of fluid dynamics and do a very good job of describing the fluid motions of the atmosphere and the oceans. They do a very poor job of describing the clouds, the dust, the chemistry and the biology of fields, farms and forests. They do not begin to describe the real world that we live in. They are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observed data. But there is no reason to believe that the same fudge factors would give the right behaviour in a world with different chemistry, for example in a world with increased CO2 in the atmosphere," he states. (Source: Freeman Dyson) What the science says...There are two major questions in climate modelling - can they accurately reproduce the past and can they successfully predict the future? Reproducing the past A way to test the accuracy of models is through hindcasting - see whether they successfully predict what has been observed over the past century. Here is the IPCC model of surface temperature from the 1800's - both with and without anthropogenic forcings.
The key point is that all the models fail to predict recent warming without taking rising CO2 levels into account. Noone has created a general circulation model that can explain climate's behaviour over the past century without CO2 warming. Another way to look at it is multiple studies (both using models and independently) calculate a climate sensitivity of around 3°C (Hegerl 2006, Annan 2006, Tung 2007). In other words, global temperatures would warm 3°C if CO2 was doubled. To deny anthopogenic warming, you need to not only explain what's causing global warming but also explain why increasing CO2 isn't causing the expected and observed warming. More on climate sensitivity... Predicting/projecting the futureA common argument heard is "scientists can't even predict the weather next week, how can they predict the climate years from now". This betrays a misunderstanding of the difference between weather, which is chaotic and unpredictable and climate which is weather averaged out over time. While you can't predict with certainty whether a coin will land heads or tails, you can predict the statistical results of a large number of coin tosses. Or expressing that in weather terms, you can't predict the exact route a storm will take but the average temperature and precipitation will result the same for the region over a period of time. Climate projection is a difficult and ever refining art. There's the problem that future behaviour of the sun is very difficult to predict. Similarly, short term perturbations like El Nino or volcanic eruptions are difficult to model. Nevertheless, climate scientists have a handle on the major drivers of climate. Way back in 1988, James Hansen projected future temperature trends (Hansen 1988). Those initial projections show remarkable agreement with observation right to present day (Hansen 2006). Hansen even speculated on a volcanic eruption in 1995 but missed the date by a few years (we'll cut him some slack there).
Hansen's Scenario B (described as the most likely option and in hindsight, the one that most closely matched the level of CO2 emissions) shows close correlation with observed temperatures. In fact, Hansen overestimated future CO2 levels by 5 to 10% so if his model was given the correct forcing levels, the match would be even closer. There are deviations from year to year but this is to be expected. The chaotic nature of weather will add noise to the signal but the overall trend is predictable. More on predicting the future... Other results successfully predicted and reconstructed by models
Uncertainties in future projectionsThe other misconception is that climate models are biased towards exagerating CO2 effects. Uncertainty could go either way - catastrophic climate surprises are as likely to occur as smaller-than-expected changes. Many current models don't take into account positive feedback systems such as melting permafrost releasing additional greenhouse gases or warming oceans releasing more CO2. For example:
Do we know enough to act?There is a notion that we should wait till models are 100% sure and get it perfectly right before we act on reducing CO2 emissions. If we waited for that, we would never act. Models are in a constant state of improvement as they include more processes, rely on fewer approximations and increase their resolution as computer power develops. The complex and non-linear nature of climate means there will always be refinements and subtleties to be included. The main point is we know enough to act. Models have evolved to the point where they successfully predict long term trends and are always improving on predicting the more chaotic, short term changes. Multiple lines of evidence tell us global temperatures will change 3°C with a doubling of CO2. The uncertainty is ±1°C degree but this uncertainty is decreasing (and the climate sensitivity of 3°C reaffirmed) as new studies refine our understanding. Models don't need to be exact in every respect to give us an accurate overall trend and its major effects - and we have that now. If you knew there was a 10% chance you'd be in a car crash, you'd wear a seatbelt. In fact, if there was any possibility, you'd still do it. The IPCC consider it at least 90% sure humans are causing global warming. Considering the negative impacts of global warming, to wait for 100% certainty before acting is recklessly irresponsible. Further reading
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The skeptic argument...