New analysis attempts to reconcile differences between satellites and climate models

An academic attempt to gloss over some glaring discrepancies between results from theory-based climate models and observed data. The research paper says: ‘Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979’. Over forty years of being so wrong, by their own admission, takes a lot of explaining.
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Satellite observations and computer simulations are important tools for understanding past changes in Earth’s climate and for projecting future changes, says Lawrence Livermore National Laboratory (via Phys.org).

However, satellite observations consistently show less warming than climate model simulations from 1979 to the present, especially in the tropical troposphere (the lowest ~15 km of Earth’s atmosphere).

This difference has raised concerns that models may overstate future temperature changes.

Rather than being an indicator of fundamental model errors, the model-satellite difference can largely be explained by natural fluctuations in Earth’s climate and imperfections in climate-model forcing agents, according to new research by Lawrence Livermore National Laboratory (LLNL) scientists.

“Natural climate variability appears to have partly masked warming over the satellite era,” said Stephen Po-Chedley, a LLNL climate scientist and lead author of a paper appearing in the Proceedings of the National Academy of Sciences.

The results of the study provide an improved understanding of the causes of historical changes in climate and increase confidence in model simulations of continued global warming over the 21st century.

“Although the Earth is warming as a result of human emissions of carbon dioxide, natural variations in the Earth’s climate can temporarily accelerate or diminish this overall warming trend,” noted Zachary Labe, a co-author from Princeton University and the National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamics Laboratory.

In addition to modulating the rate of warming, natural fluctuations in climate such as the Interdecadal Pacific Oscillation also produce unique patterns of regional surface temperature change.

These surface temperature patterns were key in quantifying the influence of natural variability on satellite-era warming. The research team considered thousands of surface-warming maps from climate-model simulations. The team then trained machine-learning algorithms to relate the pattern of surface warming to the overall magnitude of warming or cooling attributable to natural climate oscillations.

The machine-learning approach was successful in disentangling the component of atmospheric warming due to natural climate oscillations versus warming from other causes, such as human-induced increases in the levels of heat-trapping greenhouse gases.

When this approach was applied to the observed pattern of warming, the prediction from machine learning methods indicated that natural oscillations reduced the real-world tropical tropospheric warming trend by about 25% over the satellite era.

Although climate models simulate such natural decadal oscillations in climate, the timing and sequence of these fluctuations differs in each simulation and will only match the observations by chance.

This partial “offsetting” of warming by natural variability helps to explain why climate model simulations tend to simulate more warming than satellite observations of tropical tropospheric temperature during the last few decades.

Full article here.
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Study: Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming (Nov. 2022)