An overview of results from the Coupled Model Intercomparison Project

Covey, Curt; AchutaRao, Krishna; Cubasch, Ulrich; Jones, Phil; Lambert, Steven J.; Mann, Michael; Phillips, T. J.; Taylor, Karl E.

doi:10.1016/s0921-8181(02)00193-5

Cited by 334 publications

(256 citation statements)

References 52 publications

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“…We analyze simulations of global surface temperature generated by seventeen of the models that participated in CMIP2 (Covey et al, 2003). As part of this comparison, the models simulate the same experiment -the so-called one percent experiment.…”

Section: Methodsmentioning

confidence: 99%

The Relationship Between Radiative Forcing and Temperature: What Do Statistical Analyses of the Instrumental Temperature Record Measure?

2006

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Abstract.Comparing statistical estimates for the long-run temperature effect of doubled CO 2 with those generated by climate models begs the question, is the long-run temperature effect of doubled CO 2 that is estimated from the instrumental temperature record using statistical techniques consistent with the transient climate response, the equilibrium climate sensitivity, or the effective climate sensitivity. Here, we attempt to answer the question, what do statistical analyses of the observational record measure, by using these same statistical techniques to estimate the temperature effect of a doubling in the atmospheric concentration of carbon dioxide from seventeen simulations run for the Coupled Model Intercomparison Project 2 (CMIP2). The results indicate that the temperature effect estimated by the statistical methodology is consistent with the transient climate response and that this consistency is relatively unaffected by sample size or the increase in radiative forcing in the sample.

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Section: Methodsmentioning

confidence: 99%

The Relationship Between Radiative Forcing and Temperature: What Do Statistical Analyses of the Instrumental Temperature Record Measure?

2006

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“…As always, it is difficult to determine whether or not the models are "good enough" to be trusted when used to make predictions for the future (Covey et al 2003). In addition, high uncertainty exists when climate is simulated over the TP by GCMs (Cui et al 2006(Cui et al , 2007a or Regional Climate models (RCM; Cui et al 2007b;Christensen et al 2007).…”

Section: Future Scenarios Of Land Cover On the Tibetan Plateaumentioning

confidence: 99%

Recent land cover changes on the Tibetan Plateau: a review

2009

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This paper reviews the land cover changes on the Tibetan Plateau during the last 50 years partly caused by natural climate change and, more importantly, influenced by human activities. Recent warming trends on the plateau directly influence the permafrost and snow melting and will impact on the local ecosystem greatly. Human activities increased rapidly on the plateau during the last half century and have significant impacts on land use. Significant land cover changes on the Tibetan Plateau include permafrost and grassland degradation, urbanization, deforestation and desertification. These changes not only impact on local climate and environment, but also have important hydrological implications for the rivers which originate from the plateau. The most noticeable disasters include the flooding at the upper reaches of Yangtze River and droughts along the middle and lower reaches of Yellow River. Future possible land cover changes under future global climate warming are important but hard to assess due to the deficits of global climate model in this topographically complex area. Integrated investigation of climate and ecosystems, including human-beings, are highly recommended for future studies.

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“…Over the past ten years, considerable effort has gone into improving global climate simulation and prediction from coupled models, driven largely by the Coupled Model Intercomparison Project (CMIP) [Meehl et al, 2005]. While the overall level of CMIP model internal climate variability appears to be within the observed range [Covey et al, 2003;Coquard et al, 2004;Harvey, 2004], no studies have focused specifically on snow cover.…”

Section: Introductionmentioning

confidence: 99%

Decadal to century scale trends in North American snow extent in coupled atmosphere‐ocean general circulation models

Frei

Gong

2005

Geophysical Research Letters

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20th and 21st century decadal scale trends and variability in winter North American snow cover extent (NA‐SCE) are investigated using coupled atmosphere‐ocean general circulation model experiments participating in the upcoming Intergovernmental Panel on Climate Change Fourth Assessment Report. Significant between‐model variability is found, with most models underestimating mean NA‐SCE. 20th century simulations are poorly correlated with observations, and, while individual ensemble members capture the magnitude of decadal scale variability, the variability of the signal is dampened in the ensemble mean, indicating that decadal‐scale NA‐SCE variability is associated predominantly with internal model variability rather than external forcing. Two 21st century emission scenarios with realistic (moderate or significant) greenhouse gas emission rates produce decreasing NA‐SCE trends, while one unrealistic scenario with fixed concentrations produces little or no NA‐SCE trend. These results suggest that snow cover may be a sensitive indicator of climate change, and that North American snow extent will probably decrease in response to greenhouse gas emissions, although the magnitude of the response may be nonlinear.

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An overview of results from the Coupled Model Intercomparison Project

Cited by 334 publications

References 52 publications

The Relationship Between Radiative Forcing and Temperature: What Do Statistical Analyses of the Instrumental Temperature Record Measure?

The Relationship Between Radiative Forcing and Temperature: What Do Statistical Analyses of the Instrumental Temperature Record Measure?

Recent land cover changes on the Tibetan Plateau: a review

Decadal to century scale trends in North American snow extent in coupled atmosphere‐ocean general circulation models

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