Monckton’s Fundamentally Flawed Simple Climate Model

Guest article written by Rose Andreatta.

moncktonBy the time a team of five climate experts finished responding to the serious errors in a paper led by climate contrarian Christopher Monckton, they had more than a quick critique on their hands. In fact, the team—made up of Mark Richardson, Zeke Hausfather, Dana Nuccitelli, Ken Rice and John Abraham—had so much upon which to comment, they wound up publishing their thorough debunking in the same journal where Monckton and his co-authors published their original paper.

For those who may have missed it, here’s the backstory. On January 8, Christopher Monckton, Willie Soon, David Legates and William Briggs—a group of four prominent climate change deniers—published a paper in the Chinese journal, Science Bulletin, titled, “Why models run hot: results from an irreducibly simple climate model.”

The title alone raises some serious red flags, mainly the suggestion that climate model estimates are generally flawed and that something as complex as the global climate is best understood through a model that admits to being “irreducibly simple.”

Yet this is the position assumed by Monckton and his co-authors. They purport to resolve past errors in the model projections of the Intergovernmental Panel on Climate Change (IPCC)—which is the leading scientific body for the assessment of climate change—by using a model so simple it was “designed to empower even non-specialists to research the question of how much global warming [humans] may cause.” This is diametrically opposed to the well-established approach of the IPCC and the 97 percent of climate scientists who agree that climate-warming trends over the past century are due to human activities. In contrast to Monckton’s simplified model, the IPCC describes its models as extremely sophisticated computer programs that encapsulate scientists’ best understanding of the climate system with as much fidelity and detail as possible.

After sympathetic media and blogs unquestioningly trumpeted Monckton’s findings, the blowback from the scientific community was swift and strong. According to Gavin Schmidt, the head of NASA’s Goddard Institute for Space Studies, the paper is “complete trash.” Grantham Institute Co-Director Joanna Haigh noted the “inappropriate” and “judicious choices” of parameters and others had equally stinging criticisms.

Adding another wrinkle to the story is the fact that not long after the paper was published, the New York Times revealed that one of the authors, Willie Soon, had been receiving funding from fossil fuel sources and had failed to disclose that funding. In fact, one of the conditions was that he keep hidden the source of his funding, in direct opposition to the standard scientific practice of disclosing potential conflicts of interest. Some in the denier blogosphere rushed to Soon’s defense, in part claiming that because scientists couldn’t rebut Soon’s study, they instead had to smear the man. Fortunately, with Richardson’s paper, there is now a solid peer-reviewed rebuttal, proving that Soon’s funders weren’t paying for high quality work.

At this point, it should come as no surprise that Richardson and his co-authors had a lot of ground to cover in their response to Monckton’s paper, given its biased authorship and claims to improve the modeling of a complex system through simplification.

Richardson’s rebuttal exposes, point-by-point, the errors of Monckton’s simplified model approach and how it is disproven by the wider body of scientific research, with each successive revelation of inaccuracy adding another nail in the coffin of Monckton and his co-author’s credibility as researchers.

Richardson and his team investigate three main issues with the simplified model analysis. First, the simple model’s results were not validated using the observational temperature record and in fact the model performs poorly against observed data. During 1970-2010, Monckton’s model consistently underestimated global temperatures. Then, in a performance test from 2000-2010 comparing different models’ projections against the values actually observed, Richardson and his co-authors found the simplified model has a bias of approximately 350 percent, or 150 percent larger than that of the IPCC’s CMIP5 models.

Monckton model comparison

Comparison between modelled and observed temperature anomaly from 1850. Solid lines show temperature series HadCRUT4, Berkeley Earth (BEST) and Cowtan and Way (CW14). The 5 %–95 % CMIP5 range and Monckton 2015 range are shown as shaded areas as labelled in the caption. Top graphic shows how the CMIP5 and Monckton performs to temperature measurements. Bottom graphic shows performance of Monckton model with corrected input values.

The second issue Richardson and his team address concerns the faulty assumptions Monckton’s group built into their model. To accurately program climate models requires capturing the complex interactions between the atmosphere, land surface, snow and ice, the global ecosystem and a variety of chemical and biological processes. It requires mathematical analysis and continuous adjustment as our knowledge of the climate system improves.

Monckton et al. can only claim that “models run hot” by stacking the deck, that is, by using faulty semantic arguments instead of mathematical analysis to justify how they construct their model. Monckton and his co-authors assume (in defiance of the vast body of evidence suggesting otherwise) that temperature responds immediately and very little to additional greenhouse gases in the atmosphere—what is referred to as low “climate sensitivity.” They claim that because temperatures in the 810,000-year ice core record varied little that temperature is generally very stable. They do not, however, acknowledge research testifying to past climate events in which temperatures spiked significantly aided by greenhouse gas increases. They also do not test their assumed climate sensitivity against the recent observational record, because as Richardson et al. show it performs poorly. In contrast, the well-established IPCC climate models have demonstrated their ability to accurately capture complex climate interactions at increasingly smaller scales in both space and time.

Finally, Richardson and his team reveal how Monckton’s paper fails to consider many relevant studies and that many of the studies Monckton’s group considered were misrepresented. In addition to ignoring studies that contradict their assumptions about temperature change and climate sensitivity, Monckton et al. ignore ocean heat measurements that show there is a growing imbalance between how much heat is coming into the climate system versus how much gets out.

NOAA Global Ocean Heat Content March 2015

Global ocean heat content up to March 2015 from the NOAA Global Ocean Heat and Salt Content page.

They also ignore studies that resolve discrepancies between short-term IPCC predictions and measured temperatures. Actual scientists, as Richardson’s paper concludes, spend a great deal of time and are making excellent progress improving global measurements, quantifying energy flows, increasing resolution and capturing the influencing physics.

Richardson and his co-authors demonstrate that Monckton’s simplified model performs poorly, with a bias of 350% for 2000-2010; that it’s based on faulty reasoning rather than physical reality; and that it contradicts the existing body of related research. To pretend a simplified climate model could outperform the IPCC models that capture scientists’ best understanding of the climate system is disingenuous.

Rose Andreatta is a Communications Associate at Climate Nexus and specializes in the research and development of media products communicating the latest in climate change science and impacts. Rose holds a Master of Public Administration in Environmental Science and Policy from Columbia University.

Collin Maessen is the founder and editor of Real Skeptic and a proponent of scientific skepticism. For his content he uses the most up to date and best research as possible. Where necessary consulting or collaborating with scientists.