– The direction of the (expected) changes is clear
– Globe is warming
– It’s due to us
– It’s bad news
– Carbon is forever; Aerosols are not
– Uncertainty + Inertia = Danger
That is the short version of what scientists know about climate change.
And a normative statement:
– Science should inform policy measures. We are used to that regarding human health; we should also get used to it regarding climate change.
update: See here for a more elaborate description of the scientific consensus on climate change.
Tags: climate policy, Climate science, inertia, uncertainty
My view on climate change 
October 26, 2009 at 19:16
Yoú did well in your presentation today and I think it was deservedly very well received indeed.
October 28, 2009 at 11:00
My summary of the greenhouse issue is of course quite different from yours:
– Climate change is still barely perceptible against the background of natural variability
– CO2 also has the important co-benefit of being a fertiliser
– The balance of the evidence indicates that CO2 emissions have been midly beneficial, especially so for industrialised nations
– Sometime in the future there may be substantial net negative effects, especially if CO2 concentrations are allowed to run up,
but this also needs to be seen in the light of the possibility that there may be substantive net positive effects[Retracted in later comment: “It was wrong to write this and I want to retract it. It’s clear that at some stage we’ll be well beyond optimum.”]– The best approach to deal with this now is to put money into developing technology
– Getting education and infrastructure development policy in India and China right matters a lot more to the wellbeing of future generations than minor errors in climate policy now
Some specific quibbles I had:
I have an issue with the too ready use of the words weather and climate. Local weather depends on wind direction among other things. Winters in the Netherlands are warm when there is predominantly southwesterly winds. It’s cold when the wind comes from the East. Over the globe as a whole and for the year from what I know this sort of stuff is not a big factor, and you’d think it averages out. After all southwesterly winds over Europe will drive the Arctic air over Russia somewhere else, which will then be colder than average. Now you just pinpoint the sun as relevant over longer time scales, but changes in ocean currents and changes in the reflectivity of clouds (which could for example be related to ocean currents) also are candidates.
What I am saying here is that you can’t readily say that “weather noise” is limited to a time scale of a year, ie could be modelled in a spreadsheet by say the normal distribution with dice being thrown every year. Why can you exclude a random component with the dice being thrown every thirty years? Or semi random cycles of that sort of time scale or even longer time scales? Just throwing out the words weather and climate is a bit too easy. I think this needs to be argued on the underlying causes, ie what exactly causes the shortterm randomness, and why is it just short term? Why is the short term a year long? Why not say a week or a month, in which case the yearly volatility would be much less?
I also have an issue with the graph comparing models with and without forcing for the 20th century. I find this unbelievably weak. The models are tweaked to give the right output for the twentieth century assuming all forcings. I know enough from my CFD past that this tweaking is solely responsible for the shown result, and that there is big uncertainty about some key forcings and that it is therefore an utterly meaningless graph. There is a reason we don’t try to divine climate sensitivity primarily from 20th century data for forcings and temperature. Having done CFD I also see how this sort of graphic will get past professors and peer reviewers, when in my opinion it should not, or at least then should not be used in the way it widely is.
I very much liked what you said about sea level change, comparisons with past climates, aerosols, carbon capture and solar radiation management. As I said in my earlier brief comment it was a very good presentation.
October 28, 2009 at 14:11
Heiko,
I think your summary is slightly at odds with the science.
– Climate change has been clearly perceptible against the background natural variability since the mid-nineties or so, though of course this depends on what confidence interval you’re inclined to accept. The latest IPCC report claims the CI for that statement is 95%.
– The fertilizing effect of CO2 is only one factor. Others are direct and indirect temperature and humidity effects. Net global effects are deemed to be negative, though in temperature regions they could start out positive for many crops. Beyond 2 degrees warming or so, it’s mostly negative. To even make a chance of avoiding 2 degrees warming, we’d have to dramatically step up our actions.
– Already now negative effects of climate change dominate positive effects, globally.
– Besides R&D we also need much more deployment of current technologies I think (as well as a carbon pricing structure). This however is a policy-statement; not science.
– I agree with your point about India and China. (also policy).
I don’t think changes in ocean currents and cloudiness are serious candidates in explaining the current warming trend. They lack a strong enough trend to be able to explain the observed changes in climate, and even if so, they would still need to be caused by something. What is that something? It sounds a bit hand wavy; I’m not aware of any serious analysis showing this.
I think your description of modeling is a bit of a caricature. See eg http://www.giss.nasa.gov/research/briefs/schmidt_04/ about what climate modeling is all about (written by a modeler) and http://www.yaleclimatemediaforum.org/2008/01/common-climate-misconceptions-modeling-the-climate/ or http://initforthegold.blogspot.com/2007/07/how-do-we-know-climate-models-are.html. Some model successes are listed here: http://bartonpaullevenson.com/ModelsReliable.html. The impact of major volcanoes was predicted in the correct ballpark before it was observed. Models have major uncertainties, but they are more useful than you make them out to be.
I have done some atmospheric transport modeling, and tweaking only gets you a little bit of leeway. Not nearly as much as is needed to be able to get such a good match as is shown between observed and modeled temperatures. They are still physically based models after all; you can’t abandon the physics and just play with knobs.
Thanks for the compliment… :-)
October 28, 2009 at 14:13
Heiko,
Since you use the common argument that CO2 is a fertilizer, you’ll probably be able to tell us
1. Where on earth it is the limiting fertilizer (along with H2O), rather than other compounds that plants need to grow
2. For which plants it is a good fertilizer (all, some?)
3. Whether it will only have beneficial influence on the food quality of plants
You will find, in looking for information, that
1. in most places of the world CO2 and water are not the most important limiting factors. Thus, additional growth merely results in more cellulose, which means animals would need to eat more plants to get the same total nutritional value. For some animals that actually also means more methane emissions.
2. that not all plants benefit from more CO2, and that one can significantly alter the prevalence of certain plants. For example, poison ivy really likes more CO2, thus outgrowing other, more beneficial, plants.
3. having mentioned poison ivy, it also produces more toxic compounds with more CO2. Clover, same story. You can imagine yourself that this is not really a good thing.
In short, the “fertilizer” argument is one fraught with problems.
October 28, 2009 at 16:30
Hi Bart,
I think we still need to look pretty hard to see the signal. We would not have to, if climate related disasters had really intrinsically moved up by a factor 10, or grain yield had halved over the 20th century due to drought, or weather in London now was like weather in Rome a hundred years ago.
The impacts of climate change are awfully difficult to discern against other factors, eg grain yields have risen massively, coastal populations are up hugely, people are no longer dying in heat waves due to air conditioner use, and you need to add up some very different things, from the amenity role of climate and cold deaths through to agricultural output in Ethiopia and the value of polar bear habitat.
Looking at the economic models I see that fairly little needs to be done to get a positive outcome, especially for the present amount of warming, and consequently what’s published in the literature does indeed range from a net negative impact now to a positive one (for future impacts the range, especially towards the net negative impact side of what’s been published is a lot wider, look for Tol’s review paper on the matter).
I am not saying that ocean currents are a serious candidate to explain all the warming, what I am saying is that you can’t just declare there is “weather” and “climate” to explain that random noise is only present on the 1 year scale. It might very well be so that random noise is not important at the 30 year scale, but then I want that explained, not plain stated as fact with some vague analogy with local climate (where of course a twenty year winter average is a lot less volatile than a one year winter average). Much of what’s being averaged away in the local climate analogy is not really applicable for world temperatures.
On the models. Of course there is an awful lot to play with, climate sensitivity is somewhere between 1.5 and 4.5C, aerosols even in the here and now are somewhere between 0.5 and 2 W/m2K or so and data on how aerosol forcing has evolved over the 20th century is awfully patchy. How can you then make reality fit the models with such a narrow error span when the model inputs are so uncertain? From what I know this works by hindcasting and then throwing any model runs out that don’t get a reasonable hindcast. It’s hardly surprising that if you then change the assumptions for that ensemble that gives a reasonable hindcast, and do so primarily towards the end of the hindcast period, that you’ll see a divergence.
October 28, 2009 at 17:22
I think you’re right that the impacts are not yet confidently descernible and attributable to climate change. I had the temperature record in mind, and from that, it’s clear that the past 30 years started diverging away from what can reasonably expected to be natural variability.
My climate vs weather argument was primarily meant to point out the silliness in only looking at a relatively small perido of data and leave all the rest out of the picture. there’s certainly more to be said than just that, but within that context, I think I made a perfectly valid point: short term temperature variability (on the scale of a few to ten years) is not useful to diagnose any changes in climate. That was my main point, and I guess you’re not contesting that?
Re modeling, climate sensitivity is not a knob you can just turn at will in any climate model. It is an emergent property, ie it is the result of all the physical dependencies in the model, aggregated.
October 28, 2009 at 19:44
As I said the climate versus weather thing is very much a quibble.
On the modeling, how on Earth can you get a narrow output range for the models when you can reasonably have forcing and climate sensitivity inputs of respectively 1 to 2 W/m2 and 2 to 4C? These are not at the edges of what is reasonable and still should give an output range of a factor 4, yet the envelope indicating the range of model outputs on the graphic is ridiculously small. From what I’ve read about it, this has been achieved by throwing combinations of forcing and climate sensitivity that don’t narrowly reflect the 20th century out. Then I can see how you get a narrow range, but it of course makes the comparison between the anthropogenic forcing on/off cases ridiculous.
October 28, 2009 at 21:46
Heiko,
I think you’re expressing yourself a tad too strong here, though you do make some good points. I’ve read (Myles Allan presentation) that models that happen to have a high sensitivity indeed use a strong aerosol forcing (and hence a smaller net positive forcing), so as to still be able to get a decent match to the observations. That explains that the envelop of model outcomes is not as wide as you would initially expect. The good match by itself is no proof, but still, to be able to match different time periods and different processes with a physically based model is not nothing and nor is it ridiculous.
October 28, 2009 at 22:08
Allan’s presentation is here:
http://climateprediction.net/content/public-presentations-talks-and-posters
(Myles Allen, What can be said about future climate? Quantifying uncertainty in multi-decade climate forecasting, Harvard University, February 2008.) slide 7. He cites Kiehl, 2007.
Another relevant paper: “Should we believe model predictions of future climate change?” by Reto Knutti http://www.iac.ethz.ch/people/knuttir/papers/knutti08ptrs.pdf
Now, while attempts are obviously made to make the model fit the observations, you are limited by the physics that’s in the model. Try to tweak as hard as you can, nobody has yet produced a physics based model that can reproduce both the last deglaciation and the current warming, as well as response to large volcanoes, if you ask me. If you know of one, I’m all ears.
Tweaking only gets you that far, but not far enough.
October 29, 2009 at 09:50
Thanks for the interesting reference to the presentation by Myles Allen. Maybe it’s a tad strong language, but I do strongly dislike this particular graphic and think it’s quite misleading. I put it under quibbles, because there is a lot more to the notion that recent warming is hard to explain without the contribution of greenhouse gases than this graphic, but it implies something about the models and our knowledge of forcings that just plain is not true.
November 1, 2009 at 02:53
Bart, could I ask a favor? Could you link to the survey I’m running at examiner.com? I would like to get your readers’ views, if at all possible. It’s at http://www.examiner.com/x-9111-SF-Environmental-Policy-Examiner~y2009m10d30-Examinercoms-First-Annual-Survey-on-Global-Warming?#comments
Many thanks
Tom
November 1, 2009 at 16:33
Hi Tom,
My readers views… all five of them? ;-)
I’ll post it up.
November 5, 2009 at 01:45
Six readers now :) Although I arrived too late for the survey. I was impressed with your exchange with Roger Pielke. One quibble. There is danger in scientists failing to launch broadsides at the popularizer exaggerators on both sides of what has become (and probably had to become) an acrimonious debate, dealing as it does with enormous interests shared at least in some measure by anyone who wants to read under a hundred watt bulb. I can’t be the only person who reacts to an apparently one sided debater / critiquer by turning him off – even if his core arguments seem reasonable.
I used to go, for instance to the warming side links at climate debate daily until I noticed that even reasonable sounding “denier” and cost effectiveness commenters were handled abusively, shut down, etc. while semi and sometimes complete loonies on the warming side were more tolerated. The managers of a lot of those sites “discuss” things like priests rather than scientists.
November 5, 2009 at 15:11
Sully,
Without specifics it’s hard to respond to what you’re claiming. There are bound to be places and people where an honest exchange of ideas is not welcomed, and that may indeed also happen at the ‘warm’ side of the fence. But all too often, such claims are made regarding people and sites that are open to such honest exchange of ideas, but that are not open to the same old nonsense that keeps resurfacing. Without specifics I have no clue of you’re talking about the former or the latter. RealClimate is a prime example of the latter, though plenty of people disagree of course.
January 19, 2010 at 20:26
“but this also needs to be seen in the light of the possibility that there may be substantive net positive effects”
It was wrong to write this and I want to retract it. It’s clear that at some stage we’ll be well beyond optimum.