New Study: Climate Models Are ‘Prone To Errors’

A longwave image from Clouds and the Earth’s Radiant Energy System Flight Model 6 (CERES FM6) shows the heat energy radiating from Earth. The hottest regions—radiating the most energy out to space—are represented in yellow. Cooler areas emitting less energy are shown in dark blue and white (representing clouds). Credit: NASA

By Aaron Sidder

Various climate feedbacks shape Earth’s climate; these feedbacks manifest in oceans, on land, and in the atmosphere and are critical components of the climate system. Radiative feedbacks are the atmospheric mechanisms that balance Earth’s energy budget, the balance between incoming solar radiation and heat released from Earth’s surface as infrared radiation.

Researchers typically estimate radiative feedback by running regressions of top-of-atmosphere radiation against near-surface air temperature. However, observations of these variables, particularly top-of-atmosphere measurements, are prone to errors that undermine the estimates. Furthermore, regression-based approaches do not adequately explain the year-to-year variability of the observations.

Proistosescu et al. set out to improve our understanding of radiative feedback by looking at how different ocean and atmospheric factors drive interannual variability in surface temperature and top-of-atmosphere energy balance. The team developed a framework to more accurately single out the influence of random atmospheric and ocean processes, like the El Niño–Southern Oscillation, that affect Earth’s radiative balance. The framework builds on the Hasselmann model, which was initially developed to link weather variability to slower climate fluctuations.

The team validated the framework using three climate model simulations that each applied different ocean dynamics. The simulations revealed that rather than a single driving force, many ocean and atmospheric mechanisms influence regression-based radiative feedback estimates. In fact, the study showed that the relationship between radiative imbalance and surface temperature is best explained through these three processes with distinct forcing sources, feedbacks, and timescales. When these processes are combined, as they are currently in feedback analyses, the estimates may incur significant biases that skew results relative to the feedback governing Earth’s long-term warming.

The novel application of the Hasselmann model provides researchers with a new approach to explain the relationship between top-of-atmosphere fluxes and surface temperatures and offers useful insight into the natural variability of radiative feedbacks. The framework also improves estimates of radiative feedback that may currently be inaccurate and biased. (Geophysical Research Letters, 2018)

—Aaron Sidder, Freelance Writer


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