Climate Model Lowdown

Global warming is just one aspect of the man-made major mass extinction event underway but is the facet upon which the most attention has been paid. The approach used has consistently underestimated the dynamic of demise. Here is a summary of the modeling effort.

“Climate scientists have a surprising habit: they often underplay the climate threat,” says history of science professor Naomi Oreskes. “These underestimates represent a bias. Scientists tended toward lower projections because they did not want to be accused of making dramatic and exaggerated claims.”

Mathematical modeling of physical dynamics is a nebulous craft of creating equations which capture complex, often entangled, gyres. Climate modelers face difficulties in both modeling technique and data acquisition.

Wind is a significant factor in climate. But climate models have failed to capture atmospheric circulation despite wind patterns being predictable. As such, all current climate models are “wrong,” and are “probably making pretty profound mistakes in climate change assessment,” figures American atmospheric scientist Benjamin Kirtman.

Another major aspect of climate is cloud cover. The most vexing aspect of modeling clouds is convection: the process where warm air rises from Earth’s surface, cooling and condensing into clouds in the upper troposphere. As with wind, models have yet to get convection and cloud formation correct.

Climate models to date have failed to capture the rapid, accelerating changes in recent years. In Antarctica, models have not expressed the degree of ice melt from below by warming waters. In Greenland, the effects of warming air and melt flow were underestimated as furthering glacial melting.

Global warming is quite uneven. Already, with Earth’s global surface air temperature averaging 0.8°C above 1880, it has been over 3°C warmer in parts of the Arctic. Climate models have consistently underestimated the rapid pace of polar warming and ice loss.

The warming experienced now is the result of greenhouse gas pollution decades ago. Because the oceans absorb so much thermal energy, a mid-point estimate of the lag between emissions and atmospheric warming is 40 years.

Sophisticated models sometimes project what climatologist Gavin Schmidt called “emergent properties”: confluence effects which are not specifically coded into the model. These emergent properties do not always enhance accuracy.

When models don’t simulate known conditions, modelers fiddle with their equations. “Bias correction – sometimes called ‘calibration’ – is the process of accounting for biases in the climate model simulations to provide projections which are more consistent with the available observations,” said English climate scientist Ed Hawkins. Because models are a system of interrelated equations, bias correction often has knock-on effects, including emergent properties which may subtly skew outputs.

Climate sensitivity – how intensely climate changes from incremental warming – is an intermediate figure in predicting temperatures. If climate sensitivity is off, temperature forecasts amount to a roll of the dice – which is about as good as climate models have gotten.

“Equilibrium climate sensitivity, the global surface temperature response to CO2 doubling, has been persistently uncertain,” wrote American atmospheric scientist Mark Zelinka and colleagues. Recent estimates vary by 300%: a very wide range. Modelers are increasingly appreciating that climate sensitivity is higher than previously thought.

Current climate models vary significantly in atmospheric temperature based upon differing assumptions. Model estimates for average global surface air temperature in 2019 ranged over 0.8°C: a stunningly wide variance.

Climate modelers themselves tend to be defensive and conservative. This is understandable in being under pressure to create a clear crystal ball upon which public policy may be reliably made. John Fyfe perhaps typifies climate scientists when he said in 2020 that “it’s a bit too early to get wound up.”

The foregoing analysis only casts climate models and their creators into doubt. The relevant itch that still needs scratching is what level of atmospheric warming is likely to happen.

The simulation scenario that “most closely – within 1% for 2005 to 2020 -” corresponds “with total cumulative CO2 emissions” is RCP8.5, reports atmospheric scientists Christopher Schwalm, Spencer Glendon, & Philip Duffy. Other RCP scenarios were  further off: on the cooler, conservative end. (RCP is an esoteric acronym for Representative Concentration Pathways.)

Schwalm, Glendon & Duffy work at Woods Hole Research Center, an esteemed research institute in Massachusetts, USA. The quotes that follow are theirs.

RCP8.5 has been “characterized as extreme, alarmist, and misleading” by those with a vested interest in the status quo, for whom such a scenario wounds, as it is a cogent argument for radical change.

“We note that the usefulness of RCP8.5 is not changed due the ongoing coronavirus pandemic.”

Current climate models – including the one that spit out RCP8.5 – likely understate climate sensitivity, and thereby the hotting up to come. Further, even if greenhouse gases lower in the next few decades – a prospect for which there is no indication – the warming forecast does not change; for there is a lag of some 4 decades before emissions might alter the atmospheric warming vector.

“End-of-century warming outcomes in RCP8.5” have “a median of 4.5°C.” Under that scenario, humans are extinct before the turn of the next century, as we simply can’t adapt. As Naomi Oreskes observes, “Low estimates create the false impression that we have more time to fix the problem than we actually do.”

The root problem of current climate models is that they employ a problematic approach to forecast global warming. Current models use geophysical modeling. Such simulation is fiendishly intricate. A specific problem with this approach is oversized reliance on certain guesses, such as the pivotal “climate sensitivity.”

For forecasting global warming, a much simpler statistical analysis would be superior precisely because of its relative simplicity. Statistical forecasting by the oil company Exxon in 1970s and 1980s was more accurate for the next 30 years than the geophysical climate models used now. “ExxonMobil predicted global warming correctly and skillfully using established statistical techniques,” concluded Naomi Oreskes and colleagues in a study.

Updated 27 November 2022 from 3 August, 2020.


Ishi Nobu, “Climate change,” in The Fruits of Civilization (2019).

Ishi Nobu, “Climate models,” (13 December 2019).

Ishi Nobu, “Climate model accuracy,” (15 December 2019).

Ishi Nobu, “Climate model conservatism,” (21 December 2019).

Ishi Nobu, “Cloud of doubt,” (5 January 2020).

Ishi Nobu, “Climate model ill wind,” (31 July 2020).

Kay McMonigal et al, “Historical changes in wind-driven ocean circulation can accelerate global warming,” Geophysical Research Letters (22 February 2023).

G. Supran, et al, “Assessing ExxonMobil’s global warming projections,” Science (13 January 2023).

Naomi Oreskes, “Why scientists got the fast pace of arctic warming wrong,” Scientific American (November 2022).

Christopher R. Schwalm, Spencer Glendon, & Philip B. Duffy, “RCP8.5 tracks cumulative CO2 emissions,” PNAS (3 August 2020).