Monday, November 16, 2015

Climatology and Predictive Modeling

A notice about this paper just arrived.

Climate Engineering Economics

Garth HeutelJuan Moreno-CruzKatharine Ricke

NBER Working Paper No. 21711
Issued in November 2015
NBER Program(s):   EEE 

Very cool, I thought. So I clicked on the EEE above, to see more systematically what the NBER's Environmental and Energy Economics group is doing these days. In general it has a very interesting list, and in particular it has an interesting list from a predictive modeling viewpoint. Check this, for example:

Modeling Uncertainty in Climate Change: A Multi-Model Comparison

Kenneth GillinghamWilliam D. NordhausDavid AnthoffGeoffrey BlanfordValentina BosettiPeter ChristensenHaewon McJeonJohn ReillyPaul Sztorc

NBER Working Paper No. 21637
Issued in October 2015
NBER Program(s):   EEE 
The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events.

There's lots of great stuff in GNABBCMRS. (Sorry for the tediously-long acronym.) Among other things, it is correct in noting that "It is conceptually clear that the ensemble approach is an inappropriate measure of uncertainty of outcomes," and it takes a much broader approach. [The "ensemble approach" means different things in different meteorological / climatological contexts, but in this paper's context it means equating forecast error uncertainty with the dispersion of point forecasts across models.] The fact is that point forecast dispersion and forecast uncertainty are very different things. History is replete with examples of tight consensuses that turned out to be wildly wrong.

Unfortunately, however, the "ensemble approach" remains standard in meteorology / climatology. The standard econometric/statistical taxonomy, in contrast, includes not only model uncertainty, but also parameter uncertainty and innovation (stochastic shock) uncertainty. GNABBCMRS focus mostly on parameter uncertainty vs. model uncertainty and find that parameter uncertainty is much more important. That's a major advance.

But more focus is still needed on the third component of forecast error uncertainty, innovation uncertainty. The deterministic Newton / Lorenz approach embodied in much of meteorology / climatology needs thorough exorcising. I have long believed that traditional time-series econometric methods have much to offer in that regard.