STOIC: a study of coupled model climatology and variability in tropical ocean regions

Davey, Michael; Huddleston, M. R.; Sperber, Kenneth R.; Braconnot, Pascale; Bryan, Frank O.; Chen, D.; Colman, Robert; Cooper, C.; Cubasch, Ulrich; Delecluse, P.; DeWitt, David G.; Fairhead, Laurent; Flato, Gregory M.; Gordon, Charles T.; Hogan, Timothy F.; Ji, Ming; Kimoto, Masahide; Kitoh, A.; Knutson, Thomas R.; Latif, Mojib; Treut, Hervé Le; Li, T.; Manabe, Syukuro; Mechoso, Carlos R.; Meehl, Gerald A.; Power, Scott B.; Roeckner, Erich; Terray, Laurent; Vintzileos, Augustin; Voss, Reinhard; Wang, B.; Washington, Warren M.; Yoshikawa, Ichiro; Yu, Jin‐Yi; Yukimoto, Seiji; Zebiak, Stenhen E.

doi:10.1007/s00382-001-0188-6

Cited by 299 publications

(53 citation statements)

References 10 publications

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“…Toward the end of the twentieth century, a new era of seasonal forecast with coupled GCMs (also known as the one-tier approach) began, due to rapid progress made in coupled climate models (Latif et al 2001;Davey et al 2002;Schneider et al 2003) and due to a concerted international effort (through the Tropical Ocean-Global Atmosphere program) to monitor tropical ocean variations. Although the CGCMs still have significant systematic errors, many have demonstrated their capacity to reproduce realistic characteristics of ENSO (e.g., Latif et al 1994;Ji et al 1994;Rosati et al 1997;Kirtman and Zebiak 1997;Vintzileos et al 1999a, b;Guilyardi et al 2004) and the major modes of interannual variability for the AsianAustralian monsoon system .…”

Section: Introductionmentioning

confidence: 99%

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

et al. 2008

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We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980-2004) retrospective forecasts performed by 14 climate model systems (7 onetier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models' MME for the period of 1981-2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the

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Section: Introductionmentioning

confidence: 99%

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

et al. 2008

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“…The drift in the climate state is a common problem in coupled GCMs that are not subject to a correction of their fluxes at the oceanatmosphere interface, as, for instance, was pointed out by the Coupled Model Intercomparison Project CMIP1 (Lambert and Boer 2001). Especially in the tropical Pacific, at the heart of ENSO, a cold bias in SST with the same order of magnitude as the one in our uncorrected run is a typical characteristic of most coupled models without flux correction (Latif et al 2001;Davey et al 2002). Such an unrealistic basic state is believed to be related to a split Intertropical Convergence Zone (ITCZ) over the western Pacific, as was identified by AchutaRao and Sperber (2006) in the recent generation of models that contributed to the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4, see IPCC 2007).…”

Section: Methodsmentioning

confidence: 97%

“…Apparently, the atmospheric component of our coupled system shows a realistic response to ENSO variability only when being forced by a vigorous SST signal. All the models in the intercomparison project of Davey et al (2002) that exhibit a SD in SST in the Nino3 region (150°-90°W and 5°S-5°N) that is close to or higher than the observations exhibit low atmospheric sensitivity. The models reveal ratios of interannual variability (SD) between wind stress in the central equatorial Pacific and Nino3 SST that are substantially lower than those observed.…”

Section: Nino34 Regressionsmentioning

confidence: 99%

Sensitivity of ENSO characteristics to a new interactive flux correction scheme in a coupled GCM

Kröger

Kucharski

2010

Clim Dyn

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A fast coupled global climate model (CGCM) is used to study the sensitivity of El Niño Southern Oscillation (ENSO) characteristics to a new interactive flux correction scheme. With no flux correction applied our CGCM reveals typical bias in the background state: for instance, the cold tongue in the tropical east Pacific becomes too cold, thus degrading atmospheric sensitivity to variations of sea surface temperature (SST). Sufficient atmospheric sensitivity is essential to ENSO. Our adjustment scheme aims to sustain atmospheric sensitivity by counteracting the SST drift in the model. With reduced bias in the forcing of the atmosphere, the CGCM displays ENSO-type variability that otherwise is absent. The adjustment approach employs a one-way anomaly coupling from the ocean to the atmosphere: heat fluxes seen by the ocean are based on full SST, while heat fluxes seen by the atmosphere are based on anomalies of SST. The latter requires knowledge of the model's climatological SST field, which is accumulated interactively in the spinup phase (''training''). Applying the flux correction already during the training period (by utilizing the evolving SST climatology) is necessary for efficiently reducing the bias. The combination of corrected fluxes seen by the atmosphere and uncorrected fluxes seen by the ocean implies a restoring mechanism that counteracts the bias and allows for long stable integrations in our CGCM. A suite of sensitivity runs with varying training periods is utilized to study the effect of different levels of bias in the background state on important ENSO properties. Increased duration of training amplifies the coupled sensitivity in our model and leads to stronger amplitudes and longer periods of the Nino3.4 index, increased emphasis of warm events that is reflected in enhanced skewness, and more pronounced teleconnections in the Pacific. Furthermore, with longer training durations we observe a mode switch of ENSO in our model that closely resembles the observed mode switch related to the mid-1970s ''climate shift''.

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“…In the equatorial Atlantic, state-of-the-art coupled general circulation models (CGCMs) are not sufficient in this regard: the zonal sea surface temperature (SST) gradient is incorrectly simulated in most CGCMs with model SST cooler in the west than in the east [Davey et al, 2002]. Since the cold tongue is the integral part of the zonal mode or Atlantic Niño, this dominant climate mode in the equatorial Atlantic cannot be simulated or predicted by these CGCMs [Stockdale et al, 2006].…”

Section: Introductionmentioning

confidence: 99%

Key factors in simulating the equatorial Atlantic zonal sea surface temperature gradient in a coupled general circulation model

Tozuka¹,

Doi²,

Miyasaka³

et al. 2011

J. Geophys. Res.

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[1] Causes of the coupled model bias in simulating the zonal sea surface temperature (SST) gradient in the equatorial Atlantic are examined in three versions of the same coupled general circulation model (CGCM) differing only in the cumulus convection scheme. One version of the CGCM successfully simulates the mean zonal SST gradient of the equatorial Atlantic, in contrast to the failure of the Coupled Model Intercomparison Project phase 3 models. The present analysis shows that key factors to be successful are high skills in simulating the meridional location of the Intertropical Convergence Zone, the precipitation over northern South America, and the southerly winds along the west coast of Africa associated with the West African monsoon in boreal spring. Model biases in the Pacific contribute to the weaker precipitation over northern South America. Uncoupled experiments with the atmospheric component further confirm the importance of remote influences on the development of the equatorial Atlantic bias.

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STOIC: a study of coupled model climatology and variability in tropical ocean regions

Cited by 299 publications

References 10 publications

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

Sensitivity of ENSO characteristics to a new interactive flux correction scheme in a coupled GCM

Key factors in simulating the equatorial Atlantic zonal sea surface temperature gradient in a coupled general circulation model

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