THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research

By Gerald A. Meehl, C. Covey, T. Delworth, M. Latif, B. Mcavaney, J. Mitchell, R. Stouffer, K. Taylor. 2007

2,820 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/947eed49886780415ff71c823430bbe679391b0f

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Citations in the corpus

Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5 (2013, methodology): “The zonal mean average of this temperature amplification has been shown to be only weakly dependent on the scenario for the CMIP3 simulations (Meehl et al. 2007b).”
Analysis on the Dynamics of a Wave-like Teleconnection Pattern along the Summertime Asian Jet Based on a Reanalysis Dataset and Climate Model Simulations (2009, methodology): “Models (20C3M) experiments from 24 CMIP3 climate models (Meehl et al. 2007), as listed in Table 1, are analyzed and compared.”
The Role of Atmosphere Feedbacks during ENSO in the CMIP3 Models. Part III: The Shortwave Flux Feedback (2012, background): “Indeed, the ENSO properties exhibited by these models are too diverse to allow any clear projection of ENSO evolution in a warmer climate (Meehl et al. 2007; Collins et al. 2010).”
Ozone hole and Southern Hemisphere climate change (2009, positive): “[ 3 ] In this study, we demonstrate the pervasive impacts of stratospheric ozone on SH climate change by examining output from the IPCC/AR4 models [ Meehl et al. , 2007].”
A methodology for the comparison of blocking climatologies across indices, models and climate scenarios (2012, background): “…we present model results of blocking frequency from 14 Coupled Model Intercomparison Project (CMIP3) general circulation models (GCMs) for both the 20C3M (1961–2000; 40 years) and SRES A2 (2081–2100; 20 years) scenarios, where the A2 scenario represents ‘‘business as usual’’ (Meehl et al. 2007a).”
Conservation and climate change: Assessing the vulnerability of snow leopard habitat to treeline shift in the Himalaya (2012, positive): “We analyzed the potential change in the alpine and forest zones under three IPCC-4 climate scenarios: low emissions (B1), medium–low emissions (A1B), and high emissions (A2) (Meehl et al., 2007a).”
Separating signal and noise in atmospheric temperature changes: The importance of timescale (2011, methodology): “[10] To obtain model estimates of forced and unforced TLT changes, we use output from phase 3 of the Coupled Model Intercomparison Project (CMIP‐3) [Meehl et al., 2007; Intergovernmental Panel on Climate Change, 2007], which was an important scientific resource for the Fourth Assessment Report of…”
Projecting twenty-first century regional sea-level changes (2014, methodology): “Because there are no such relations available for CMIP5, we derived a relation between SMB and temperature from IPCC AR4, and applied this to CMIP5 temperature data to obtain SMB estimates.”
Climate model dependence and the replicate Earth paradigm (2013, methodology): “…mean to represent “best guess” 74 scenarios in the Intergovernmental Panel on Climate Change’s Fourth Assessment Report 75 (henceforth IPCC AR4; Meehl et al, 2007b), and was reinforced in the recent Expert Meeting on 76 Assessing and Combining Multi-model Climate Projections in 2010…”
ENSO stability in coupled climate models and its association with mean state (2013, positive): “In particular, the Coupled Model Intercomparison Project phase 3 (CMIP3; Meehl et al. 2007) models generally underestimate thermodynamic damping and positive feedbacks including zonal advective and thermocline feedbacks (Kim and Jin 2011b; Lloyd et al. 2009), which are responsible for ENSO…”
Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator (2012, background): “Although less formal, there was also a close connection between CFMIP1 and the corresponding third Coupled Model Intercomparison Project (CMIP3) [Meehl et al., 2007].”
Improved Atlantic winter blocking in a climate model (2011, methodology): “Models used in the World Climate Research Programme’s (WCRP’s) Coupled Model Intercomparison Project phase 3 dataset [Meehl et al., 2007] also show this error [Scaife et al., 2010].”
Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. (2009, methodology): “jsp) (Meehl et al. 2007) utilized extensively by the IPCC AR4 assessments.”
History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble (2013, methodology): “We discuss this issue and derive an estimate for it under an interpretation of model discrepancy variance as a tolerance to climate model error, using a multi-model ensemble for the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3 (CMIP3) (Meehl et al. 2007).”
Sources of multi-decadal variability in Arctic sea ice extent (2012, positive): “1 and GISS-ER which are the only models for which suitably long control integrations can be obtained from the CMIP3 database (Meehl et al 2007).”
The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3 (2011, background): “AM3 compares favorably to models in the Atmospheric Model Intercomparison Program (AMIP) at the Project for Climate Model Diagnosis and Intercomparison (PCMDI) for phase 3 of the Climate Model Intercomparison Project (CMIP3) (Meehl et al. 2007) whose coupled simulations have performed well (Reichler”
Climate model genealogy (2011, methodology): “The most recent coordinated ensemble used here is from the World Climate Research Project (WCRP) Coupled Model Intercomparison Project Phase 3 (CMIP3) [ Meehl et al., 2007 ].”