Climate Model Credibility Gap
Marine and terrestrial proxy records suggest that there was a peak in global warming between 10,000 and 6,000 years ago, following the end of the last glacial period. Since the Holocene Thermal Maximum, Earth has undergone global cooling. The physical mechanism responsible for this global cooling has remained unknown and doesn't fit in with the current CO2 based climate models. Those climate models generate a robust global annual mean warming throughout the Holocene, mainly in response to rising CO2 levels and albedo changes due to retreating of ice sheets. In other words, the models disagree with reality, and when models disagree with nature the models have a credibility gap. A new paper in the Proceedings of the National Academy of Sciences (PNAS) says this model-data inconsistency indicates a critical reexamination of both proxy data and models is called for.
The fact that all the world's complex and expensive climate models can't explain climate change since the last glacial period ended is one of the little talked about embarrassments of climate science. In a new study, soon to be published in PNAS, a team led by Zhengyu Liu, a researcher at the Nelson Center for Climatic Research and Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, has come out of the modeling closet to examine the model-data inconsistency. They took as their baseline a study by Shaun Marcott et al. previously published in Science. The composite proxy record generated by those researchers is shown below.
As can be clearly seen, there is a sharp temperature rise at the end of the last ice age, more correctly called the last glacial period (we are still in an ice age). But then something interesting happens, the temperatures peak, and after a period of several thousand years begins to decline. The squiggly bit at the end is just the expected and obligatory bow to current climate alarmist orthodoxy, was not part of the reconstruction, and has no impact on the newer paper. Here an explanation of the conundrum as stated in the first paragraph of the PNAS paper, published online:
In the latest reconstruction of the global surface temperature throughout the Holocene (hereafter M13), the most striking feature is a pronounced cooling trend of ∼0.5 °C following the Holocene Thermal Maximum (HTM) (∼10–6 ka) toward the late Holocene, with the Neoglacial cooling culminating in the Little Ice Age (LIA; ∼1,800 common era) (Fig. 1, blue). Numerous previous reconstructions have shown cooling trends in the Holocene, but most of these studies attribute the cooling trend to regional and/or seasonal climate changes (2–6). The distinct feature of the M13 reconstruction is that it arguably infers the cooling trend in the global mean and annual mean temperature. This inferred global annual cooling in the Holocene is puzzling: With no direct net contribution from the orbital insolation, the global annual mean radiative forcing in the Holocene should be dominated by the retreating ice sheets and rising atmospheric greenhouse gases (GHGs), with both favoring a globally averaged warming. Therefore, how can the global annual temperature exhibit a cooling trend in response to global warming forcing? This inconsistency between the reconstructed cooling and the inferred warming forced by GHGs and ice sheet poses the so-called Holocene temperature conundrum and will be the subject of this study. Here, we study the global annual temperature trend in the Holocene and its physical mechanism by comparing the temperature reconstruction with three different transient climate model simulations. Our analysis shows a robust warming trend in current climate models, opposite from the cooling in the M13 reconstruction. This model-data discrepancy suggests potentially significant biases in both the reconstructions and current climate models, and calls for a major reexamination of global climate evolution in the Holocene.
Stated more plainly, the historical temperature record does not agree with what computer models say should have happened. The upshot is that, if the models get the past 10 thousand years or so wrong, how can we trust them to predict the future? The answer, of course, is that we can not. The situation is made clearer in the graphic from the paper referenced in the quote above.
Figure 1 from Liu et al., PNAS.
All three models generated robust annual mean warming (∼0.5 °C) throughout the Holocene (Fig. 1, black and yellow), resulting in a model-data inconsistency in global annual temperature of ∼1 °C. Discrepancies were different during last millennium and the start of the deglaciation. Over the last thousand years, climate models generate a global cooling toward the Little Ice Age (LIA)consistent with the M13 reconstruction only after adding realistic volcanic aerosols and solar variability (Fig. 1, Inset, gray line). During the early deglaciation both the proxy data and model show a large deglacial warming (3–4 °C) overwhelming the data-model inconsistency.
It all comes down to uncertainty in proxy records vs uncertainty in climate model output. Liu et al. dive into both in great detail and conclude that there are probable errors in both. They further note that the errors in the climate history data might propagate as errors through the models, since those data are used as inputs to drive the models. Even so, the authors conclude: “Although the potential biases in the reconstruction may contribute to the data-model discrepancy, it is also important to recognize that the data-model discrepancy can be caused by potential biases in current models. Indeed, even after considering the seasonal bias effect, the models still fail to produce some important features in the reconstruction.”
No matter how you adjust and tweak, the models just don't get it right. This caused the researchers to ask some fundamental questions about what drives the climate models—the “forcings” in climate speak. They focus on the usual suspects: rising greenhouse gases and the change in Earth's albedo caused by retreating northern hemisphere ice sheets.:
Regardless of any climate model, it is useful to ask the following question first: What is the forcing mechanism to the coupled ocean–atmosphere system that can generate a global cooling in the Holocene? Physically, either rising GHGs or retreating ice sheets will lead to a global warming. Therefore, a global annual cooling, if it had occurred, can only be generated by other forcing, such as the orbital insolation and meltwater; furthermore, the cooling thus generated has to be strong enough to overwhelm the combined warming by the GHGs and ice sheets.
So they turn to changes in orbital forcings, cloud effects, or other, unidentified feedbacks. Liu et al. also consider other, less probable forcings, casting about for a good reason for the models to be so wrong. In the end they return to Earth's orbit and those pesky feedbacks.
Summarizing, neither meltwater forcing nor volcanic forcing is likely an able candidate to generate a significant global cooling trend. The biases in current models, if they exist, are more likely to be related to their sensitivity to the orbital forcing and additional feedbacks in climate models. Whatever the biases, the model biases have to exhibit a common warming bias across all of the current models with a total magnitude of at least ∼1 °C, such that removal of this model bias can generate a global cooling of ∼1 °C, which overcomes the 0.5 °C warming by GHGs and ice sheets to leave a net cooling of 0.5 °C as in the M13 reconstruction.
In the end the authors brand this unsolved mystery the “Holocene temperature conundrum.” We have heard this song before, usually from climate modelers asking for more grant money to “improve” their wonky programs. There may be some wiggle room in the interpretation of the proxy data but the new study, M13, is supposed to be the latest and most accurate temperature history available. This points directly back at the models as the weak link in the story. “If the M13 reconstruction is correct, it will imply major biases across the current generation of climate models,” Liu et al conclude. This is more than just a conundrum for climate scientists—those computer models are the embodiment of climate science's understanding of how climate works here on planet Earth. If the models do not work, climate science has gotten it wrong.
Even many climate scientists realize something's wrong with their central theory, that global temperature is primarily driven by atmospheric CO2 levels. They recognize that their predictions rest on faulty models, erroneous data, or a combination of both. As a result, climate science has a credibility gap—none of the dire predictions spread by the climate alarmists is based on sound science.
Yet green activists, grant seeking researchers, and self-serving politicians continue to spread climate alarmist lies. Lies based on fiction, not fact. To believe in such unsubstantiated tripe substitutes wishful thinking for logic, and hubris for the humility that science demands of all who study nature. This forces even the worst scientist to the ultimate realization, that nature is always right and science is most often wrong.
Be safe, enjoy the interglacial and stay skeptical.