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Climate Models Disagree On Why Temperature 'Wiggles' Occur

26.01.2015 17:06 Age: 3 yrs

Most computer climate models underestimate the magnitude of unforced variability at the longest periods, according to research. Inconsistencies may undermine a model's reliability for projecting decade-to-decade warming and lead to misinterpretation of data, say the researchers.

Click to enlarge. From the paper's supplemental information. Power spectral density of global mean surface air temperature (GMT) for unforced models runs (black lines) and estimates of the unforced component from observations (Method 1: blue line, Method 2: red line). The green dashed line marks the 10-year period and thus separates thesubdecadal from the interdecadal timescale. Despite the differences in phasing between the unforced variability obtained from Method 1 and Method 2, both methods indicate that most models underestimate the magnitude of unforced variability at the longest periods (lowest frequencies). Courtesy: authors


From Duke University

A new Duke University-led study finds that most climate models likely underestimate the degree of decade-to-decade variability occurring in mean surface temperatures as Earth's atmosphere warms. The models also provide inconsistent explanations of why this variability occurs in the first place.

These discrepancies may undermine the models' reliability for projecting the short-term pace as well as the extent of future warming, the study's authors warn. As such, we shouldn't over-interpret recent temperature trends.

"The inconsistencies we found among the models are a reality check showing we may not know as much as we thought we did," said lead author Patrick T. Brown, a Ph.D. student in climatology at Duke's Nicholas School of the Environment.

"This doesn't mean greenhouse gases aren't causing Earth's atmosphere to warm up in the long run," Brown emphasized. "It just means the road to a warmer world may be bumpier and less predictable, with more decade-to-decade temperature wiggles than expected. If you're worried about climate change in 2100, don't over-interpret short-term trends. Don't assume that the reduced rate of global warming over the last 10 years foreshadows what the climate will be like in 50 or 100 years."

Brown and his colleagues published their findings this month in the peer-reviewed Journal of Geophysical Research, at

To conduct their study, they analyzed 34 climate models used by the Intergovernmental Panel on Climate Change (IPCC) in its fifth and most recent assessment report, finalized last November.

The analysis found good consistency among the 34 models explaining the causes of year-to-year temperature wiggles, Brown noted. The inconsistencies existed only in terms of the model's ability to explain decade-to-decade variability, such as why global mean surface temperatures warmed quickly during the 1980s and 1990s, but have remained relatively stable since then.

"When you look at the 34 models used in the IPCC report, many give different answers about what is causing this decade-to-decade variability," he said. "Some models point to the Pacific Decadal Oscillation as the cause. Other models point to other causes. It's hard to know which is right and which is wrong."

Hopefully, as the models become more sophisticated, they will coalesce around one answer, Brown said.


We document the geographic regions where local variability is most associated with unforced global mean surface air temperature (GMT) variability in Coupled Model Intercomparison Project Phase 5 coupled global climate models (GCMs) at both the subdecadal and interdecadal timescales. For this purpose, Regions of Significant Influence on GMT are defined as locations that have a statistically significant correlation between local surface air temperature (SAT) and GMT (with a regression slope greater than 1), and where local SAT variation leads GMT variation in time. In both GCMs and observations, subdecadal timescale GMT variability is most associated with SAT variation over the eastern equatorial Pacific. At the interdecadal timescale, GMT variability is also linked with SAT variation over the Pacific in many GCMs, but the particular spatial patterns are GCM dependent, and several GCMs indicate a primary association between GMT and SAT over the Southern Ocean. We find that it is difficult to validate GCM behavior at the interdecadal timescale because the pattern derived from observations is highly depended on the method used to remove the forced variability from the record. The magnitude of observed GMT variability is near the ensemble median at the subdecadal timescale but well above the median at the interdecadal timescale. GCMs with a stronger subdecadal relationship between GMT and SAT over the Pacific tend to have more variable subdecadal GMT while GCMs with a stronger interdecadal relationship between GMT and SAT over parts of the Southern Ocean tend to have more variable GMT.


Regions of significant influence on unforced global mean surface air temperature variability in climate models by Patrick T. Brown, Wenhong Li and Shang-Ping Xie published in the Journal of Geophysical Research Atmospheres Atmospheres, 22 January 2015, DOI: 10.1002/2014JD022576

Read the abstract and get the paper here.


Duke University news release issued via EurekAlerts! here.