Sunday, August 3, 2014

Let's Talk About Climate Models

One of the most common statements made in an attempt to support claims that AGW is not real concerns climate models. People have made all sorts of statements about how global warming is not real because all of the models have failed. This is a false statement in many ways. But, it has been made so many times that I want to address it in some depth.


First False Argument:

The first way this is a false argument is that climate science is not about modeling. Models, in the modern sense of the word, are mathematical representations of something to help us understand things. In the case of climate models, they help us to understand an extremely complex system involving a multitude of different processes. But, those processes will continue to do what they want with, or without, the models.

Nature is what it is and is not sitting around waiting for a model to tell it what to do!

If is it raining outside and I have a model that predicts rainfall, is the actual rain outside going to change depending on what my model says? No! Of course, not! Then, why in the world would you think that the study of possibly the most complex process on the entire planet is all about a model?

We have many tools and models are important ones. But, they are not the only tools we use. There are satellites, sonobuoys, various kinds of thermometers, ice cores, sea floor cores, lacustrine cores, coral cores, weather balloons, tree rings and many more tools. The science is not dependent on just one of them.

Climate science is the study of all of these processes involved in making our climate. But, the climate will do what it does, and our study or understanding of that doesn't change the reality. Global warming is all about the real world stuff going on with our planet's climate. It is not about models or papers or discussions at a conference. Those are things we do in the study of the science. Global warming is the reality of nature independent of anything we do or say.


Second False Argument

The second way the argument is false is that it is assumed that if the models are not 'accurate' (without any definition of what that means), then the models are invalid and climate science is invalid. This is so preposterous that it truly shows the mindset of anyone saying it, and that mindset is a desperate attempt to reject science.

What about weather models? Is meteorology invalid because weather models are not accurate? Do you ever bother to check the weather forecast? Do you check to see if you need to take an umbrella with you today? Do you check to see if its going to be cold or hot? Do you go to the supermarket and get some food when they say a big snowstorm is coming in? Have you ever made a single decision based on the weather forecast?

Why?

Weather forecasts come from meteorology models and we all know that the weather forecast is not very accurate. Does that mean meteorology is fake? Does that mean there are a bunch of meteorologists promoting a false science in order to keep their government grants coming? Does that mean there are a bunch of people that have deluded themselves and are following meteorologists like sheep? Of course, not!

So, if this line of reasoning is false with regard to meteorology and weather models, why is it valid for climate science and climate models?



Third False Argument

I have had people actually pull out model results from 20, even 30 years ago and point to them as evidence that man made global warming isn't real. First, review the first and second false arguments above to see why this is not even a valid argument to begin with. But, this is its own brand of false argument all by itself.

Let's go back to the weather forecast. Which would you rather have, the weather forecasting of today, or the weather forecasting of 1980? What's that you said? You said you would prefer the weather forecasting of today?

Why?

Could it be that you recognize that there have been advances in the weather modeling over the last 30 years? That meteorologists have tools and data bases they didn't have 30 years ago? That the science of meteorology has advanced and we understand the weather better today than we did 30 years ago? Those would all be correct conclusions and, yes, the weather models of today are much better than the models of 1980, along with the forecasts we get from them.

Then, why would anyone suppose the climate science models would be any different? Why would anyone suppose that the climate models of 2014 are not any better than the climate models of 1980, or 1990, or even 2000? The science is advancing. We are getting new tools and the data base is growing. Our understanding of the science is improving. Just as with the meteorology models, the climate models are getting better all the time.

The fact that models improve with time does not invalidate models, it serves as validation. It shows we are increasing our understanding and that we are making progress. And, it certainly, in no way, is any kind of evidence that AGW is not real.


Fourth False Argument

The fourth false argument centers around the conclusion that a climate model that does not give an accurate forecast on the global average temperature is 'wrong.' This is not the case. There are many different models and they do different things. Read this statement from the IPCC AR5 report on modeling:

The models used in climate research range from simple energy balance models to complex Earth System Models (ESMs) requiring state of the art high-performance computing. The choice of model depends directly on the scientific question being addressed (Held, 2005; Collins et al., 2006d). Applications include simulating palaeo or historical climate, sensitivity and process studies for attribution and physical understanding, predicting near-term climate variability and change on seasonal to decadal time scales, making projections of future climate change over the coming century or more and downscaling such projections to provide more detail at the regional and local scale. Computational cost is a factor in all of these, and so simplified models (with reduced complexity or spatial resolution) can be used when larger ensembles or longer integrations are required. Examples include exploration of parameter sensitivity or simulations of climate change on the millennial or longer time scale. Here, we provide a brief overview of the climate models evaluated in this chapter.

 IPCC AR5, Chapter 9 - Evaluation of Models


As you can see, there are lots of different kinds of models and you can't evaluate them all the same way.


Besides, a model that gives a result that does not conform with observed results can still be very valuable. Models are built on our understanding of physics. If we understand the situation correctly, then they should reflect the reality. When they don't reflect reality as well as we would like that tells us there is something we are missing. This can be extremely valuable.

The important point to remember is that there are lots of different models and they do lots of different things. You cannot judge all of them by the same standard.

I will have more to say on that topic below.



Fifth False Argument

The fifth way this is a false argument is that contrarians and deniers criticize the models and cite them as proof that we shouldn't do anything about global warming, but don't develop any models of their own to support their claims.

Why is that? Why is it the people criticizing models can't produce any models to support their claims? The only thing they can do is criticize, but they can't produce anything of their own. If, as they claim, you can get models to do anything, why have they not done so?  Where is the Heartland Institute's model? Where is Craig Idzo's model? Where is Richard Lindzen's model?

Mr. Roy Spencer has, in fact, produced a model he claims shows global warming is nothing more than a naturally occurring event. Unfortunately, the only way Mr. Spencer could get it to work is to use false inputs. The results are very different when real data is used. Here is a nice review of his work that really shows how he keeps manipulating things until he gets the desired result.  

So, why don't we see forecasts from denier models that accurately forecast the climate?

And, more importantly, why have they avoided this question? What are they trying to hide from the public?

The answer is tragically simple - Because they can't!

It is easy to sit there and say the models are no good when you can't do it yourself. The last thing any denier ever wants to do is to try and develop a model that ends up giving results counter to their claims. That really would be a case of Frankenstein's monster. The denier model that turned on its creator.

So, the next time you here some contrarian or denier going on about climate models, ask them one question - Where are the alternative models that support the contrarian claims? Be prepared for the silence.


Sixth False Argument

The sixth way this is a false argument is because contrarians and deniers are actually lying about model errors. How many times have you seen this plot? It even states right on the graphic, "Over 95% of Climate Models Agree: The Observations Must be Wrong"
 
http://thefederalist.com/wp-content/uploads/2014/05/Climate-Model-Comparison.png
Source: The Federalist
To be clear, this plot shows the results of 90 different models (all of the colored lines) with the average plotted as the black, dotted line. The green dotted line is the global average surface temperature measured using surface instruments. The blue dotted line is the global average surface temperature measured using satellite born instruments.

You may play with the climate model outputs for yourself at this site here.

One particular contrarian site states, "Unfortunately, climate models — ones that can accurately and consistently predict global temperatures in the not-so-distant future — simply don’t exist in the present."  The message is certainly being spread. But, is it a valid message? Let's check into this plot and find out.

I had a serious question about this plot the moment I saw it - the IPCC data page only lists 59 models but this plot has results from 90 models, so where did these other 31 models plotted here come from? It is true that there might be other models that are not listed on the IPCC page, but why doesn't this graphic list them or give a link to a list of them? This made me curious about this plot. Where did it come from and what are these plots it shows? Ultimately, I have to wonder if it was falsified. It would not be the first time denier organizations promulgated false statements.

And, you know what I found? It was falsified!

To no surprise, I found the chart originated with Roy Spencer, a denier with a record of falsifying his research. His original chart can be found here. In his posting he states he plotted the results of 90 climate models, but I cannot find a list of those models anywhere. But, analysis of the plot has shown he falsified the data by misaligning it. And, the evidence indicates it was done deliberately.

This is how Mr. Spencer falsified the graph. The data is plotted versus some baseline. Normally, we use a baseline based on some average to smooth out the large amount of variability observed from year to year. Picking a large number of years as the baseline average prevents one weird year from skewing the average. Using a small number of years allows one particular year to throw off the data. We normally use a 30-year average. Mr. Spencer used a 5-year average. Why would he do that when he is well-versed in this methodology? And, why did he use the particular baseline he selected: 1979 - 1983? Well, one result of using a five-year average based on the 1979-1983 period is that it resulted in a misalignment of the data. You can read the analysis here.

Here is what happens when the alignment is done incorrectly and then redone correctly:

Source: HotWhopper

Quite a change. And, isn't it amazing that the selection of the five-year baseline served to support the claims of the denier organizations?

But, there is still more problems with Mr. Spencer's work. Take a look at his original plot of the 90 models above and you can see the UAH data (the blue line) is consistently significantly lower than the HadCRUT4 data (the green line). But, look at the plot just above this paragraph graphing both of these data sets. The UAH data is not consistently higher, the reality is they are actually very close, especially when aligned using a proper baseline. One more indication that Mr. Spencer deliberately falsified the graph.

This plot here shows the two temperature plots the same as above, but adds the results of the CMIP5 model. The first shows the results of  Mr. Spencer's improper alignment, while the second shows what happens when you use a proper alignment. The difference is pretty dramatic. Clearly, the model results are MUCH better than Mr. Spencer would like you to believe.

http://2.bp.blogspot.com/-JYiRB5vrdC8/Uvkmn7d_OjI/AAAAAAAAFFI/hfHnInQMmcA/s1600/SpencerDeception.gif
Source: HotWhopper


It is amazing to see how many mistakes this guy makes and how each and every one of those mistakes works to confirm his desired conclusion. You would think, by the law of averages, that at least some of those errors would work against his desired conclusion. This all leads me to the conclusion that Mr. Spencer intentionally and deliberately falsified this plot in order to undermine climate science and support the conclusion he wants.

Ultimately, the question has to be, why is Mr. Spencer falsifying his results? If the science really supported what he claimed, it would not be necessary to falsify his data. There can be one, and only one, answer to this question - Spencer has a desired outcome that is not supported by science, so he will do whatever is necessary to obtain that conclusion with his work rather than change his beliefs.

And, that is the penultimate definition of a denier.

If the models are as wrong as the deniers claim, why do they have to lie about them?


Seventh False Argument:

The seventh way this is a false argument is that the models are actually much more accurate than contrarians would like you to know. As we have seen, when someone tells you the models have all failed they are selling you a bad bill of goods. But, the accuracy of the models is really the heart of the whole issue, isn't it? We know the deniers are lying about the inaccuracies, but just how accurate are they?Take a look at this plot of the AR4 models and the actual recorded data. The models look pretty good to me.


Source: Open Mind

To no surprise, there is plenty of literature out there on this subject. Some of it in the form of refereed papers in scientific journals, some of it in more popular forms. I'll be using both. Let's start with some of the scientific papers because they make some points that I want to use later. The link to the paper is provided as well as each paper's abstract.

Performance metrics for climate models, by P. J. Gleckler, K. E. Taylor and C. Doutriaux, published in the Journal of Geophysical Research - Atmospheres, Volume 113, Issue D6, 27 March 2008

Abstract

[1] Objective measures of climate model performance are proposed and used to assess simulations of the 20th century, which are available from the Coupled Model Intercomparison Project (CMIP3) archive. The primary focus of this analysis is on the climatology of atmospheric fields. For each variable considered, the models are ranked according to a measure of relative error. Based on an average of the relative errors over all fields considered, some models appear to perform substantially better than others. Forming a single index of model performance, however, can be misleading in that it hides a more complex picture of the relative merits of different models. This is demonstrated by examining individual variables and showing that the relative ranking of models varies considerably from one variable to the next. A remarkable exception to this finding is that the so-called “mean model” consistently outperforms all other models in nearly every respect. The usefulness, limitations and robustness of the metrics defined here are evaluated 1) by examining whether the information provided by each metric is correlated in any way with the others, and 2) by determining how sensitive the metrics are to such factors as observational uncertainty, spatial scale, and the domain considered (e.g., tropics versus extra-tropics). An index that gauges the fidelity of model variability on interannual time-scales is found to be only weakly correlated with an index of the mean climate performance. This illustrates the importance of evaluating a broad spectrum of climate processes and phenomena since accurate simulation of one aspect of climate does not guarantee accurate representation of other aspects. Once a broad suite of metrics has been developed to characterize model performance it may become possible to identify optimal subsets for various applications.

What they are saying:

This paper was published in 2008, so it is examining the CMIP3 (Coupled Model Intercomparison Project 3. A coupled model is one that combines more than one model to get a single result), instead of the newer CMIP5, but it is still a valid paper. What they are doing is trying to examine the forecasts of the model to see how accurate it is. This is much more difficult with climate models than with weather models. With a weather model you are getting a solid feedback every single day. It takes a lot longer to get performance feedback with climate models. And, what does that feedback mean? How do you evaluate it.

So, they came up with a grading system for a number of different variables and they graded the models accordingly. They made a very interesting statement:
"Forming a single index of model performance, however, can be misleading in that it hides a more complex picture of the relative merits of different models." 

This is consistent with what I said above under Fourth False Argument.

Note this statement from the paper:
Although the value of climate model metrics has been recognized for some time [e.g., Williamson, 1995], there are reasons why climate modelers have yet to follow the lead of the NWP community. First, a limited set of observables (e.g., surface pressure anomalies) have proven to be reliable proxies for assessing overall NWP forecast skill, whereas for climate models, examination of a small set of variables may not be sufficient. Because climate models are utilized for such a broad range of research purposes, it seems likely that a more comprehensive evaluation will be required to characterize a host of variables and phenomena on diurnal, intraseasonal, annual, and longer times scales. To date, a succinct set of measures that assess what is important to climate has yet to be identified.
Later, they state,
"We note that even the “better” models score below average in the simulation of some fields, while the “poorer” models score above average in some respects (especially in the tropics)"

The overall conclusion is that it is not easy to evaluate models, and they state,
"Finally, in spite of the increasing use of metrics in the evaluation of models, it is not yet possible to answer the question often posed to climate modelers: “What is the best model?” The answer almost certainly will depend on the intended application."

But, if it is so difficult, how is it possible for the deniers to conclusively say they all fail? Where are the evaluation metrics they use to reach that conclusion? That should be a gigantic red flag for anyone listening the denier claims about climate models - scientists have difficulty coming up with evaluations of models, but contrarians don't. Hmmm.

Let's try another paper.


How reliable are climate models?, by JOUNI RÄISÄNEN in Tellus A, Volume 59, Issue 1, pages 2–29, January 2007
ABSTRACT


How much can we trust model-based projections of future anthropogenic climate change? This review attempts to give an overview of this important but difficult topic by using three main lines of evidence: the skill of models in simulating present-day climate, intermodel agreement on future climate changes, and the ability of models to simulate climate changes that have already occurred. A comparison of simulated and observed present-day climates shows good agreement for many basic variables, particularly at large horizontal scales, and a tendency for biases to vary in sign between different models, but there is a risk that these features might be partly a result of tuning. Overall, the connection between model skill in simulating present-day climate and the skill in simulating future climate changes is poorly known. An intercomparison of future climate changes between models shows a better agreement for changes in temperature than that for precipitation and sea level pressure, but some aspects of change in the latter two variables are also quite consistent between models. A comparison of simulations with observed climate changes is, in principle, a good test for the models, but there are several complications. Nonetheless, models have skilfully simulated many large-scale aspects of observed climate changes, including but not limited to the evolution of the global mean surface air temperature in the 20th century. Furthermore, although there is no detailed agreement between the simulated and observed geographical patterns of change, the grid box scale temperature, precipitation and pressure changes observed during the past half-century generally fall within the range of model results. Considering the difficulties associated with other sources of information, the variation of climate changes between different models is probably the most meaningful measure of uncertainty that is presently available. In general, however, this measure is more likely to underestimate than overestimate the actual uncertainty.

What he is saying:

This one is similar to the previous one we looked at in that it tries to find a way to evaluate models. The thing I found pertinent about this paper was the effort to evaluate models based on, among other things, the ability to simulate climate change that has already occurred. What that means is to go back in the data and use it to model the climate at a later date that has already occurred so we can compare the model results to the reality. The author states,
A comparison of simulated and observed present-day climates shows good agreement for many basic variables, particularly at large horizontal scales, and a tendency for biases to vary in sign between different models, but there is a risk that these features might be partly a result of tuning.
And,
Nonetheless, models have skilfully simulated many large-scale aspects of observed climate changes, including but not limited to the evolution of the global mean surface air temperature in the 20th century. 
But, he still reaches the conclusion,
Overall, the connection between model skill in simulating present-day climate and the skill in simulating future climate changes is poorly known.
In other words, what he sees is pretty good, but he could not determine if that means it will be accurate going into the future.

And,
...the reliability of long-term climate change projections is much harder to estimate than that of weather forecasts. The latter can be quickly verified against the weather evolution that actually happened and, although the accuracy of the forecasts varies from time to time, their typical quality can be quantified by collecting verification statistics over a sufficient number of cases. For climate change projections, this approach is not practical, particularly as there are no earlier well-observed analogies of the type of primarily greenhouse-gas-induced climate change that is expected in the future. The reliability of these projections can therefore only be estimated by indirect methods.
In his conclusion, he states,
Although there are many reasons to believe that climate models can give useful information on future climate, the question on model reliability has no simple quantitative answer. Below, I first list some key arguments that suggest that models do give reliable projections of climate change or, at least, that the uncertainty is reasonably well captured by the variation between different models:
  • 1. Models are built on well-known physical principles. Despite the approximations needed in the description of some processes, this gives a priori reason to expect that models should be able to provide useful information on climate changes.
  • 2. Many large-scale aspects of present-day climate are simulated quite well by the models. In addition, biases in the simulated climate tend to be unsystematic, so that observational estimates of present-day climate fall within the variation of model results.
  • 3. When compared with each other, different climate models agree qualitatively or semi-quantitatively on several aspects of climate change. Moreover, many large-scale aspects of simulated greenhouse-gas-induced climate change are understood well in physical terms – one example of this is the general increase in high-latitude precipitation allowed by a larger moisture transport capacity of a warmer atmosphere.
  • 4. Models have successfully simulated several large-scale aspects of climate change observed during the instrumental period. Although there is no detailed agreement between observed and simulated changes on smaller horizontal scales, this is largely as expected from the internal variability in the climate system. In most parts of the world, the temperature, precipitation and pressure changes observed during the past half-century fall within the range of model-simulated changes. Exceptions do occur, but not much more frequently than would be expected in the case that the simulated and observed changes belonged to the same statistical population.
  • 5.Observation-based estimates of global climate sensitivity are, although uncertain, consistent with model results.
On the other hand, there are a number of issues that weaken the arguments given above and complicate their interpretation:
  • 1. Many small-scale processes that cannot be simulated explicitly in current climate models are important for the feedback effects that regulate the response of climate to changes in external forcing. Cloud processes are the most important example.
  • 2. The good agreement between simulated and observed present-day climates, and the tendency of the biases to vary in sign between different models, might arise partly because observations of present-day climate are used in tuning the models.
  • 3. Models do not agree on all aspects of future climate change, particularly not on small horizontal scales. Overall, the agreement on changes in precipitation and atmospheric circulation is worse than the agreement on temperature changes.
  • 4. A comparison between simulated and observed climate changes is complicated by uncertainty in the forcing factors (particularly the magnitude of aerosol forcing) that have affected 20th century climate. In addition, the climate changes projected for the rest of the 21st century are much larger than those observed this far. The impact of possible common model errors on the simulated climate changes will therefore also be larger for the future than for the past.
  • 5. Because of uncertainties associated with forcing, observations and internal climate variability, key properties of the climate system such as the equilibrium climate sensitivity are still difficult to estimate from observations with a useful accuracy. Regional aspects of greenhouse-gas-induced climate change are even more difficult to constrain by observations.
  • 6. Although climate models have been run for different emission scenarios, other aspects of forcing uncertainty are not covered well by existing multimodel ensemble simulations of future climate.
We can see a trend is developing - It is difficult to evaluate models, but they do well when the effort is made.


BTW, if you want to obtain some background knowledge on how models are built and operate, this paper goes into quite a bit of detail on the subject.

So, we are learning that models aren't nearly as bad as contrarians claim. Nor is it as easy to evaluate them as contrarians claim.

There are lots and lots of papers on climate models and I'm not going to burden you with too many of them, but let's do one more before moving on to other sources of review.

How Well Do Coupled Models Simulate Today's Climate?, by Thomas Reichler and Junsu Kim in the Bulletin of the American Meteorological Society, Volume 89, Issue 3 (March 2008)
Abstract
Information about climate and how it responds to increased greenhouse gas concentrations depends heavily on insight gained from numerical simulations by coupled climate models. The confidence placed in quantitative estimates of the rate and magnitude of future climate change is therefore strongly related to the quality of these models. In this study, we test the realism of several generations of coupled climate models, including those used for the 1995, 2001, and 2007 reports of the Intergovernmental Panel on Climate Change (IPCC). By validating against observations of present climate, we show that the coupled models have been steadily improving over time and that the best models are converging toward a level of accuracy that is similar to observation-based analyses of the atmosphere.
The abstract pretty much says it all - models have been steadily improving and are getting pretty good.

This figure below is an example of how we can use models to help us understand what is going on. The first graph shows model results using only naturally occurring factors with the global average surface temperature data plotted on top. The model forecast is in grey (the band area represents the plus or minus confidence of the forecast) and the temperature data is in red. The second plot uses only man made effects. The third plot uses both man made and naturally occurring effects.

As you can see, the third result fits the observed data pretty well, much better than the other two. In this way, we can see the effect on the global average surface temperature from both the naturally occurring effects as well as the man made and the only way we can get reasonable results is if we include both. This the kind of thing models can help us to do.

And, we can see the model result is pretty good. Where are the contrarian claims about the model being a failure?



http://www.grida.no/climate/ipcc_tar/wg1/images/figspm-4.gif
Source: IPCC Third Assessment Report

Here is a plot of the temperature data plotted with the results of CMIP5 and CMIP3. You can see the models have a very nice correlation with the temperature data, especially CMIP5. Is the correlation perfect? Of course, not. There are some gaps and there is the very noticeable discrepancy near the end that contrarians love to point at with the claim that models have failed. As you can see, that claim is not valid. The models have, in fact, produced very good results.

So, how about that business at the end? It just means we have more to learn. Actually, continue down below and you will see that it is more a reflection of these models and these particular forecasts alone. Some models gave accurate forecasts for the period after the year 2000. In any event, it does not mean the models have failed.

Source: HotWhopper

OK, how about some more common source of information. Let's begin with this one. It is a very nice summary of how the contrarians make false claims about the data. He reviews the claims concerning heating before and after 1950 and shows the contrarian claims are not valid. Then, he discusses the trends of the IPCC reports. What he finds is that when the data and the forecasts are plotted correctly, the observed data goes right down the middle of the model projections. It really is a nice piece of work and pretty conclusive.

This article here discusses a paper published in Nature Geoscience about a year and half ago. Unfortunately, the paper is not available for free online, but you can purchase it or go to the library to read it. It is a very good paper and the article does a good job of summarizing it. The paper examined model projections that started in the late 1990s and found that they gave a good projection of the global temperature after the year 2000. What is really interesting is that the warming went faster than the models predicted at first, not slower. But, again, we see the reality of the models does not reflect the claims of the contrarians.

Here is another paper, this one from the Union of Concerned Scientists. This one also states the models have been greatly improving, but we see the familiar statement again,
There remain some uncertainties with climate model performance, but that is to be expected from any system that aims to approximate conditions in the real world. Scientists are still trying to nail down cloud processes, aerosol distribution, ocean models, and sea ice changes. But model developers and climate physicists are addressing these issues by using large numbers of model simulations as well as a variety of statistical methods based on current and past observational data.
Climate models are not easy and it is difficult to evaluate them fully. But, they are good and getting better.

Just to make sure I really beat this one to death (in my dreams, I know contrarians will be out there making the same claims), let me cite one more article. This one is from the American Meteorological Society. This paper is a little more complicated, but is a very nice piece of work. Unfortunately, it is from 2008 and does not include the most recent models, but that does not invalidate their evaluation. Perhaps they will repeat their work some day. And, their conclusion? Models are good and getting better.


Current models are certainly not perfect, but we found that they are much more realistic than their predecessors. This is mostly related to the enormous progress in model development that took place over the last decade,
So, when we actually look at model evaluations we find a) it is very difficult to properly evaluate model performance; 2) within that limitation, we find the models are actually doing pretty well; and 3) models are getting better and doing so at a pretty rapid pace.

What is crucial in this is that every one of those points is contrary to the claims contrarians make about models. What is that? Why are they out there saying things about models that simply are not true? The reason is because they think they can get away with it.

Conclusion

So, what we have seen is that the claims by contrarians about models is not true. In fact, some of the claims being made are actually faked in order to produce the results desired by contrarians. The evidence shows that models are doing a good job and have been doing so for quite some time.

Yes, there are problems. But, keep in mind they are trying to model what may be the single most complicated system on the entire planet - the climate. Do you really think that it is going to go smoothly and easily? The thing I find amazing is that they are actually doing as well as they are. What we consistently see is the actual data is going right down the middle of the model projections.

And, that isn't bad at all.
 






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