Apparent limitations in the ability of CMIP5 climate models to simulate recent multi-decadal change in surface temperature: implications for global temperature projections

Power, Scott B.; Delage, François; Wang, Guomin; Smith, Ian; Kociuba, Greg

doi:10.1007/s00382-016-3326-x

Cited by 42 publications

(39 citation statements)

References 49 publications

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“…A somewhat analogous study of climate model precipitation and temperature deficiencies with respect to sub-global scales, however, over oceanic regions, was also reported earlier [35,36].…”

Section: Discussionsupporting

confidence: 58%

Observed and Projected Precipitation Changes over the Nine US Climate Regions

et al. 2017

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Abstract:We analyze the past temperature and precipitation changes in nine separate US climate regions. We find that the temperature increased in a statistically significant (95% confidence level equivalent to alpha level of 0.05) manner in all of these regions. However, the variability in the observed precipitation was much more complex. In the eastern US (east of Rocky Mountains), the precipitation increased in all five climate regions and the increase was statistically significant in three of them. In contract, in the western US, the precipitation increased in two regions and decreased in two with no statistical significance in any region. The CMIP5 climate models (an ensemble mean) were not able to capture properly either the large precipitation differences between the eastern and the western US, or the changes of precipitation between 1900 and 2015 in eastern US. The statistical regression model explains the differences between the eastern and western US precipitation as results of different significant predictors. The anthropogenic greenhouse gases and aerosol (GHGA) are the major forcing of the precipitation in the eastern part of US, while the Pacific Decadal Oscillation (PDO) has the major influence on precipitation in the western part of the US. Our analysis suggests that the precipitation over the eastern US increased at an approximate rate of 6.7%/K, in agreement with the Clausius-Clapeyron equation, while the precipitation of the western US was approximately constant, independent of the temperature. Future precipitation over the western part of the US will depend on the behavior of the PDO, and how it (PDO) may be affected by future warming. Low hydrological sensitivity (percent increase of precipitation per one K of warming) projected by the CMIP5 models for the eastern US suggests either an underestimate of future precipitation or an overestimate of future warming.

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“…A somewhat analogous study of climate model precipitation and temperature deficiencies with respect to sub-global scales, however, over oceanic regions, was also reported earlier [35,36].…”

Section: Discussionsupporting

confidence: 58%

Observed and Projected Precipitation Changes over the Nine US Climate Regions

et al. 2017

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“…Characterization of low-frequency (decadal to multidecadal) internal climate variability is crucial for understanding the timing and magnitude of changes in global mean surface temperature (Kosaka and Xie 2013;Trenberth and Fasullo 2013;England et al 2014;Dai et al 2015), regional climate (e.g., Knight et al 2006;Folland et al 1986;Power et al 1999), and climate extremes (e.g., Seager et al 2015;McCabe et al 2004). Internal climate variability is, however, very difficult to precisely quantify because of the short length of the instrumental record.…”

Section: Introductionmentioning

confidence: 99%

“…Studies based on empirical orthogonal function (EOF) analysis indicate a decadal to interdecadal (;15-30-yr period) variability in the Pacific Power et al 1999) referred to as the Pacific decadal oscillation (PDO), and a basinwide counterpart of the PDO known as the interdecadal Pacific oscillation. During a warm phase of the PDO, the sea surface temperature (SST) in the Kuroshio-Oyashio Extension and western Pacific exhibits a cool anomaly, while a warm anomaly occurs in the central and eastern equatorial Pacific extending northwestward along the North American coast (Mantua et al 1997;Mantua and Hare 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, disagreements on the definition, properties, and mechanisms of different modes of climate variability (e.g., Liu 2012) have undermined efforts to characterize internal oscillations in climate models. Several techniques that have since been shown to be inadequate, including EOF analysis (e.g., Zhang et al 1997;Power et al 1999) and simple linear detrending of SST time series (e.g., Enfield et al 2001;Goldenberg et al 2001;Zhang and Delworth 2007;Wyatt et al 2012), have been applied to identify and characterize climate modes, attribute climate extremes, and understand teleconnection patterns. These methods suffer from biases that potentially contaminate the putative signals they are designed to identify and thus have limited utility in investigations of internal climate variability (Mann and Park 1999;Knight 2009;Trenberth and Shea 2006;Mann et al 2014;Steinman et al 2015;Frankcombe et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations

et al. 2017

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Low-frequency internal climate variability (ICV) plays an important role in modulating global surface temperature, regional climate, and climate extremes. However, it has not been completely characterized in the instrumental record and in the Coupled Model Intercomparison Project phase 5 (CMIP5) model ensemble. In this study, the surface temperature ICV of the North Pacific (NP), North Atlantic (NA), and Northern Hemisphere (NH) in the instrumental record and historical CMIP5 all-forcing simulations is isolated using a semiempirical method wherein the CMIP5 ensemble mean is applied as the external forcing signal and removed from each time series. Comparison of ICV signals derived from this semiempirical method as well as from analysis of ICV in CMIP5 preindustrial control runs reveals disagreement in the spatial pattern and amplitude between models and instrumental data on multidecadal time scales (.20 yr). Analysis of the amplitude of total variability and the ICV in the models and instrumental data indicates that the models underestimate ICV amplitude on low-frequency time scales (.20 yr in the NA; .40 yr in the NP), while agreement is found in the NH variability. A multiple linear regression analysis of ICV in the instrumental record shows that variability in the NP drives decadal-to-interdecadal variability in the NH, whereas the NA drives multidecadal variability in the NH. Analysis of the CMIP5 historical simulations does not reveal such a relationship, indicating model limitations in simulating ICV. These findings demonstrate the need to better characterize low-frequency ICV, which may help improve attribution and decadal prediction.

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“…For VOLC the stratospheric aerosol optical thickness series of Sato was used. For TSI the monthly series produced by Judith Lean (Lean 2010;Kopp and Lean, 2011) van Vuuren et al 2011;IPCC 2013;Power et al 2016). For the purpose here it thus makes no difference which pathway is used to describe historical forcing and the small extrapolation out to 2016.…”

Section: Methodsmentioning

confidence: 99%

Lower Tropospheric Temperatures 1978-2016: The Role Played By Anthropogenic Global Warming

Ayers¹

2017

JSHESS

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Apparent limitations in the ability of CMIP5 climate models to simulate recent multi-decadal change in surface temperature: implications for global temperature projections

Abstract: and high greenhouse gas emission scenarios. Given that the same models are poorest in representing observed multidecadal temperature change, confidence in the highest projections is reduced.

Cited by 42 publications

References 49 publications

Observed and Projected Precipitation Changes over the Nine US Climate Regions

Observed and Projected Precipitation Changes over the Nine US Climate Regions

Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations

Lower Tropospheric Temperatures 1978-2016: The Role Played By Anthropogenic Global Warming

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