Models

Number of papers: 16

Impacts and risks of “realistic” global warming projections for the 21st century — Geoscience Frontiers, 2024; Nicola Scafetta

“This paper examines the impacts and risks of “realistic” climate change projections for the 21st century generated by assessing the theoretical models and integrating them with the existing empirical knowledge on global warming and the various natural cycles of climate change that have been recorded by a variety of scientists and historians. … The obtained climate projections show that the expected global surface warming for the 21st-century will likely be mild, that is, no more than 2.5–3.0 °C and, on average, likely below the 2.0 °C threshold.”

Observed humidity trends in dry regions contradict climate models — Earth, Atmospheric, and Planetary Sciences, 2024; Simpson et al.

“Water vapor in the atmosphere is expected to rise with warming because a warmer atmosphere can hold more moisture. However, over the last four decades, near-surface water vapor has not increased over arid and semi-arid regions. This is contrary to all climate model simulations in which it rises at a rate close to theoretical expectations, even over dry regions. This may indicate a major model misrepresentation of hydroclimate-related processes; models increase water vapor to satisfy the increased atmospheric demand, while this has not happened in reality.”

Pervasive Warming Bias in CMIP6 Tropospheric Layers — Earth and Space Science, 2020; McKitrick and Christy

“For lower-troposphere and midtroposphere layers both globally and in the tropics, all 38 models overpredict warming in every target observational analog, in most cases significantly so, and the average differences between models and observations are statistically significant. We present evidence that consistency with observed warming would require lower model Equilibrium Climate Sensitivity (ECS) values.”

Total least squares bias in climate fingerprinting regressions with heterogeneous noise variances and correlated explanatory variables— Environmetrics, 2023; Ross McKittrick

“Regression-based “fingerprinting” methods in climate science employ total least squares (TLS) or orthogonal regression to remedy attenuation bias arising from measurement error due to reliance on climate model-generated explanatory variables. …Ordinary least squares (OLS) exhibits the expected attenuation bias which vanishes as the noise variances on the explanatory variables disappear. In some cases, TLS corrects attenuation bias but more typically imparts large and generally positive biases.”

A Mass and Energy Conservation Analysis of Drift in the CMIP6 Ensemble — Journal of Climate, 2021; Irving et al.

“Coupled climate models are prone to “drift” (long-term unforced trends in state variables) due to incomplete spinup and nonclosure of the global mass and energy budgets. ... Model drift tends to be much larger in time-integrated ocean heat and freshwater flux, net top-of-the-atmosphere radiation (netTOA) and moisture flux into the atmosphere (evaporation minus precipitation), indicating a substantial leakage of mass and energy in the simulated climate system. Most models are able to achieve approximate energy budget closure after drift is removed, but ocean mass budget closure eludes a number of models even after dedrifting and none achieve closure of the atmospheric moisture budget. The magnitude of the drift in the CMIP6 ensemble represents an improvement over CMIP5 in some cases (salinity and time-integrated netTOA) but is worse (time-integrated ocean freshwater and atmospheric moisture fluxes) or little changed (ocean heat content, ocean mass, and time-integrated ocean heat flux) for others, while closure of the ocean mass and energy budgets after drift removal has improved.” Commentary by Roy Spencer.

CMIP6 GCM Validation Based on ECS and TCR Ranking for 21st Century Temperature Projections and Risk Assessment — Atmosphere, 2023; Nicola Scafetta

This paper investigates the assumptions built into the models and concludes: “As a result, the global aggregated impact and risk estimates seem to be moderate, which implies that any negative effects of future climate change may be adequately addressed by adaptation programs. However, there are also doubts regarding the actual magnitude of global warming, which might be exaggerated because of urban heat contamination and other local non-climatic biases. ... If the global warming reported by the climate records is overestimated, the real ECS and TCR may be significantly lower than what is produced by the CMIP6 GCMs, as some independent studies have already suggested, which would invalidate all of the CMIP6 GCMs.”

The vertical profile of recent tropical temperature trends: Persistent model biases in the context of internal variability — Environmental Research Letters, 2020; Mitchell et al.

“As in earlier studies, we find considerable warming biases in the CMIP6 modeled trends, and we show that these biases are linked to biases in surface temperature. We also uncover previously undocumented biases in the lower-middle stratosphere: the CMIP6 models appear unable to capture the time evolution of stratospheric cooling, which is non-monotonic owing to the Montreal Protocol.”

Satellite Bulk Tropospheric Temperatures as a Metric for Climate
Sensitivity — Asia-Pacific Journal of Atmospheric Sciences, 2017; Christy and McNider

The authors look at satellite data from 1979–2017 and show how climate models are overly sensitive to natural variance. When properly corrected, the models can more accurately predict the very slight warming trend we have actually observed.

The vertical profile of recent tropical temperature trends: Persistent model biases in the context of internal variability — Environmental Research Letters, 2020: Mitchell, et al.

“In this study, tropical temperature trends in the CMIP6 models are examined, from 1979 to 2014, and contrasted with trends from the RICH/RAOBCORE radiosondes, and the ERA5/5.1 reanalysis. As in earlier studies, we find considerable warming biases in the CMIP6 modeled trends, and we show that these biases are linked to biases in surface temperature. We also uncover previously undocumented biases in the lower-middle stratosphere: the CMIP6 models appear unable to capture the time evolution of stratospheric cooling, which is non-monotonic owing to the Montreal Protocol.”

Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models — Nature Geoscience, 2015; Mauritsen & Stevens

Climate models build in a sensitivity to doubling of CO2 of between 2 and 4.6 degrees Celcius. The authors find that “ important feedbacks are missing from the models.” They propose that a more adaptive feedback mechanism in the models would bring their projections much closer to reality.

Checking for model consistency in optimal fingerprinting: a comment — Climate Dynamics, 2022, Ross McKitrick

In this comment on a frequently cited peer-reviewed paper that claimed to have found the “fingerprint” of human “forcing” on the earth’s climate, the author identifies mistakes in the application of the Gauss-Markov Theorem, citing six deficiencies that would have to be corrected for the paper’s conclusions to be statistically valid.

Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations — Climate Dynamics, 2021; Nicola Scafetta

The author shows the effect of urbanization that raises thermometer readings is much more prevalent than models assume. Correcting models for this bias brings model projections back down to reality.

The Art and Science of Climate Model Tuning — Bulletin of the American Meteorological Society, 2017; Hourdin et al.

Tuning is the practice of setting initial parameters to models. In an ideal model, you just give it what you know about the present and see if it can predict the future. But almost all climate models, when given data about the climate 50 years ago, fail to predict what actually happened over the next 50 years (this is called hindcasting). They fail so badly that modelers routinely set up their models to predict the alarming future the UN wants to see rather than what actually might happen. In their words: “Either reducing the number of models or overtuning, especially if an explicit or implicit consensus emerges in the community on a particular combination of metrics, would artificially reduce the dispersion of climate simulations. It would not reduce the uncertainty but only hide it.”

IPCC baseline scenarios have over-projected CO2 emissions and economic growth — Environmental Research Letters, 2021; Burgess, Ritchie, Shapland, and Pielke Jr

Even though the authors believe that CO2 has a significant (but not alarming) effect on the climate, they show that the IPCC has gone overboard in predicting too much CO2 production, which means the IPCC’s models use faulty inputs to get faulty outputs.

James Hansen’s 1988 Predictions Compared to Observations — Energy & Environment, 2009; Dale R McIntyre

From the abstract: “All James Hansen’s scenarios overestimated actual temperature change even with anthropogenic greenhouse gas assumptions that underestimated actual emissions.”

Distorting the view of our climate future: The misuse and abuse of climate pathways and scenarios — Energy Research and Social Science, 2021; Pielke, Jr, and Ritchie

“Consequently, much of the climate research community is presently off-track from scientific coherence and policy-relevance. Attempts to address scenario misuse within the community have thus far not worked. The result has been the widespread production of myopic or misleading perspectives on future climate change and climate policy. Until reform is implemented, we can expect the production of such perspectives to continue, threatening the overall credibility of the IPCC and associated climate research.”