Chill out.

If it comes to prediction future malaria numbers, development comes first, and medicine and climate come second and third.

As all these things are uncertain, I will not enter into prediction games.

I hope you are write, but the Horn of Africa is descending into war, and West Africa is very shaky. I can easily imagine a scenario with lots of misery and more malaria deaths.

I actually have more trouble imaging a golden future for Africa (although my latest model predicts just that), even with the help of Saints Bill, Bob, Bono, and Jeff.

You write, “I do not know how many malaria cases there will be in the future.”

Well, when you write things like:

“He’s making fun of models, but is not known to have contributed to the field he is mocking,” and “Please read the epidemiological literature”…you give the impression you’re pretty sure of yourself.

My point, and what I think is Paul Reiter’s point (who I can assure you HAS read the epidemiological literature), and what I think was Ryan Meyer’s point, is that if one was constructing a model to try to answer the question, “What will the number of malaria deaths worldwide be 20, 30, 50, or 100 years in the future?” the model would NOT start with the input, “What will the temperatures and humidities be in the future, relative to today?”

Do you agree?

There is little malaria in the East and South African highlands because it is too cold. Very few things are monocausal.

I do not know how many malaria cases there will be in the future. Parts of Africa are growing rapidly, and other parts are descending into chaos. Previous global programs to eradicate malaria had a limited and transient effect only. Drug resistance is an issue.

Richard: Please grow up.

Perhaps even better, please simply either agree or disagree with this scientific prediction of mine:

The total number of worldwide deaths from malaria will be less in 2020 than in 2006; and less in 2040 than 2020; and less in 2060 than 2040; and less in 2080 than 2060; and less in 2100 than 2080.

Do you agree or disagree?

“Infectious diseases are first and foremost diseases of poverty and underdevelopment. However, they are also sensitive to weather and climate as bugs are more active under hot and wet conditions.”

The effects of weather and climate are completely overwhelmed by the effects of wealth and technological development. There is no malaria in Southern Florida because everyone there has air conditioning and houses with window screens (not to mention the local mosquito control programs!). Similarly, Puerto Rico eradicated malaria in 1962:

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=9312420&dopt=Abstract

There still IS malaria in Haiti, because they have no wealth and no technological development.

The difference in malaria incidence between Southern Florida and Puerto Rico (on the one hand) and Haiti (on the other hand) is NOT due to differences in climate.

Do you agree or disagree?

Ryan: Thanks. Einstein said that you should make things as simple as possible, but not simpler. Leamer wondered whether any number is better than no number. There are indeed areas where I throw up my hands in dispair and refuse to model them. At the same time, other people argue that I often model the unmodelable. This is a fine line, and more art than science. If a simple change of parameters, or the addition of a new process drastically changes the results, then the initial model was clearly not good. Does not mean that the extended model is better, though.

If that was indeed Ryan Meyer’s point, it was not what I understood his point to be.

I thought his point was that global models that attempt to work only from the basis of “What will global warming do to this problem?” tend to produce results that are later clearly shown to be wrong…because other factors are far more important to the problem of interest than global warming.

Models that determine the effects of global warming on malaria are a perfect case in point. No less prestigious a researcher than Paul Reiter (of the U.S. Centers for Disease Control) has pointed out that malaria is a disease of *poverty*, not of climate:

http://www.cdc.gov/ncidod/eid/vol6no4/reiter_letter.htm

http://linkinghub.elsevier.com/retrieve/pii/S1473309904010382

Do you really think the global malarial burden (sickness and death) will be greater in 2050, 2070, or 2100, than in 2006?

I think any reasonable analysis of the likely trajectory of the global economy (most certainly including the rise of global private charity initiatives, such as those of the Gates Foundation) and medical technology would indicate that it is extremely unlikely that the global malarial burden will be greater in the last half of this century than at present…REGARDLESS of how warm the earth gets.

To begin with, let me state that indeed, I have made no contribution to the field of modeling, and thus can only apply my limited understanding to the work you and others have already done. Furthermore, I have the utmost respect for the modeling work carried out by you (in particular) and your colleagues working to understand the impacts of climate change. Many of my own ideas on the subject are informed by papers you have written, and comments you have made on this site. So, be assured that any inference of a mocking tone in my Ogmius article was unintended.

For now, let me make a few points, drawing from your own statements, in the hope of clearing up my own ideas, and eliciting your feedback.

Some of the statements you have made on this very site seem to back up certain points in my short piece. For example, from the extended debate over the Stern Review: “In an economic system, the first order effect is dominant in the short run. In the long run, second, third, and even seventeenth order effects take over.” (http://sciencepolicy.colorado.edu/prometheus/archives/climate_change/000974the_stern_review_on_.html). This is exactly the point I was hoping to demonstrate with the figure in the Ogmius piece – we cannot know what the important drivers of change will be during the time when significant climate change impacts might begin to be felt. You mention the same problem in your 2002 paper (p.3), which I quote here:

“food entitlements, on the other hand, depend on social structures, which are hardly generalisable, let alone modelable. Political instabilities, perhaps triggered by a redistribution of crucial matters such as water and food or by streams of migrants and refugees…, depend on socio-political structures. Environmental hardship may be a root cause or trigger for instability and (armed) conflict, but it is always confounded in a web of other causes and triggers. Hence, such issues are excluded from this assessment.”

This leads to my primary question for you, which is also implied in the Ogmius piece: how can anyone know which factors will be “confounded in a web of other causes and triggers,” and which ones can be defensibly isolated from such effects? Curable diseases seem like perfect candidates for this problem, not to mention the number of environmental refugees one might expect due to climate change, but one commonly sees them included in estimates of climate impacts.

Secondly, I do not mean to dismiss modeling entirely, and do not believe that what I have written does so. My central point is that the exercise tells us more about today than it can about the future. This is simply meant as a warning about the misuse of model results, not a wholesale indictment of climate impact modelers. In fact, I find your approach in the above-mentioned paper particularly appealing for exactly this reason. Another quotation (with my emphasis):

“the impact climate change would have on the PRESENT situation is investigated. Although counterfactual, this approach has the advantage that only one parameter (climate) is varied, so that findings can readily be interpreted. The results are indications of potential pressure points and relative vulnerabilities. The results are not useful as predictors or as input to decision analysis.”

I feel that an approach such as this is quite useful, because its results are unlikely to be misconstrued as predictive, objective, neutral, or comprehensive, as was the Stern Review, which has been so roundly criticized on this site by yourself and others.

As you demonstrate quite effectively, both in your criticism of the Stern Review and in your review of discount rates (2005), any model of climate impacts is heavily reliant on value-based decisions. Cost-benefit analysis is no exception. As Stanford’s Stephen Schneider recently pointed out at the AGU conference, in CBA $1 = 1 vote. There may be many arguments in defense of this approach, but do they hold any particular scientific or moral authority?

I will allow that any individual decision could be said to turn on some sort of tacit cost-benefit analysis, but it is simply incorrect to claim that every policy must be dictated by a formal one. Just for example, I doubt very much that the policies creating the US Climate Change Science Program or the Climate Change Technology Program – policies with ostensible relevance to climate policy – were implemented based on the results of such an analysis. Why should we expect the decision about emissions cuts to be any different? Can we point to a cost-benefit analysis that definitively supports the Kyoto Protocol?

Thanks again, for your input!

Ryan

Tol, R. 2002 Estimates of the damage costs of climate change. 1. Benchmark estimates. Environ. Resource Econ. 21, 47–73.

Tol, R. S. J. 2005. The marginal damage costs of carbon dioxide emissions: an assessment of the uncertainties. Energy Policy 33:2064-2074

Bemoaning the fact that they don’t consider local or other issue-specific phenomena in the same way that a regional model, or a malaria-specific model does is akin to throwing out a hammer because it’s not good at cutting down trees. They’re different tools, designed for different purposes.

Now if Ryan’s criticism really is about inappropriate use of global climate change models in formulating regional/local/issue-specific policy analyses then his paper would benefit by including at least a few examples where this has in fact happened, otherwise the whole thing comes off as a strawman IMO…

You write, “Yes, we added diarrhoea to our model. It kills 2-3 million children each year. It is very sensitive to climate, and very sensitive to development.”

Diarrhoea is “very sensitive to climate”? That’s very surprising to me.

The U.S. has a wide range of climates within its borders. Are you saying that the incidence of diarrhoea in the U.S. can be closely tied to the various climates within the U.S. (e.g. presumably hotter areas have more diarrhoea)? Can you draw a map of the U.S. incidence of diarrhoea that looks similar to a map of climate in the U.S.? I’d be very surprised by that!

Even within the U.S., if hotter areas have more diarrhoea, have you normalized data for “development”? For example, I would not be at all surprised that New Orleans has more diarrhoea than Boston…but the per-capita income in New Orleans is much lower than in Boston. (Plus, hurricane Katrina didn’t help much.) However, if one compares diarrhoea in Boston versus Palm Beach, FL (a very rich city in Florida), are you saying there would be a signficant difference (with Palm Beach having a higher incidence of diarrhoea)?

Best wishes,
Mark

Ryan essentially says that all global models are nonsense and all research should be local and bottom-up. That is silly. For local problems, local studies are needed. For global problems, global studies are needed. As local and global problems interacts, so should local and global studies. Ryan correctly points out that global studies cannot address local issues, but he forgets that local studies cannot address global issues either. And he was writing about climate change, which is global problem.

On diarrhoea: Tom Schelling was the first to point out the complex relationships between development, vulnerability, and climate change. Hadi Dowlatabadi and I were the first to model this (for malaria). The diarrhoea model works the same, in principle, but the numbers are of course different.

Diarrhoea goes up with warming, and it goes down with development. As emission reduction slows warming, but also slows and redirects development, one cannot say a priori whether emission reduction would increase or decrease diarrhoea. However, if you put realistic parameters in the model, emission reduction is more likely to increase diarrhoea than it is to decrease it.

Furthermore, this effect is much more pronounced for diarrhoea than it is for malaria.

And yes, this is utterly irrelevant for a farmer in Zimbabwe who is trying to survive this idiot president.