The Canadian Centre for Climate Modelling and Analysis global coupled model and its climate

Flato, Gregory M.; Boer, G. J.; Lee, Warren G.; McFarlane, N. A.; Ramsden, D.; Reader, M. C.; Weaver, Andrew J.

doi:10.1007/s003820050339

Cited by 541 publications

(284 citation statements)

References 37 publications

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“…As a large number of climate change experiments have been completed with varying results, the modeling center of choice was selected based on a set of reliability criteria determined by the Intergovernmental Panel on Climate Change, Task Group on Scenarios for Climate Impact Assessment (IPCC-TGCIA) (IPCC-TGCIA 1999). Based on the criteria of vintage, resolution, and validity, we selected the Canadian Global Coupled Model (CGCM1) produced by the Canadian Centre for Climate Modelling and Analysis (Flato, Boer et al 2000).…”

Section: Climate Model Forecastmentioning

confidence: 99%

“…The reliability of these climate projections is reflected in the model's reproduction of present-day climate and historical variation. The model accurately predicts the observed 0.6°C increase in global mean temperature over the past century (Flato, Boer et al 2000). Climate change data for each variable and integration were obtained for three time points, the 2020's (2010-2039), the 2050's (2040-2069) and the 2080's (2070-2099), from the IPCC Data Distribution Centre.…”

Section: Climate Model Forecastmentioning

confidence: 99%

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Effect of Climate Change on Lyme Disease Risk in North America

2005

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An understanding of the influence of climate change on Ixodes scapularis, the main vector of Lyme disease in North America, is a fundamental component in assessing changes in the spatial distribution of human risk for the disease. We used a climate suitability model of I. scapularis to examine the potential effects of global climate change on future Lyme disease risk in North America. A climatebased logistic model was first used to explain the current distribution of I. scapularis in North America. Climate change scenarios were then applied to extrapolate the model in time and produce forecasts of vector establishment. The spatially modeled relationship between I. scapularis presence and large-scale environmental data generated the current pattern of I. scapularis across North America with an accuracy of 89% (p<0.0001). Extrapolation of the model revealed a significant expansion of I. scapularis north into Canada with an increase in suitable habitat of 213% by the 2080's. Climate change will also result in a retraction of the vector from southern United States, and movement into the central United States. This report predicts the effect of climate change on Lyme disease risk and specifically forecasts the emergence of a tick-borne infectious disease in Canada. Our modeling approach could thus be used to outline where future control strategies and prevention efforts need to be applied.

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Section: Climate Model Forecastmentioning

confidence: 99%

Section: Climate Model Forecastmentioning

confidence: 99%

Effect of Climate Change on Lyme Disease Risk in North America

2005

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“…Our estimates of the GS signal, and of natural climate variability, are constructed from two versions of a coupled global climate model. The models used are the Canadian Center for Climate Modelling and Analysis (CCCma) CGCM1 Boer et al 2000) and CGCM2 (Flato and Boer 2001). Details of the models and an extensive archive of model output are available from the CCCma Web site (http:// www.cccma.ec.gc.ca).…”

Section: A Datamentioning

confidence: 99%

“…Vector ␤ contains one element for each signal taken into account. The signal pattern estimates contained in the columns of X are typically obtained by using a coupled climate system model (see, e.g., Flato et al 2000) to simulate the response to known forcing changes in the twentieth century. Vector is assumed to be a realization of a Gaussian random vector consisting of correlated elements.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Climate Change Detection and Attribution Assessment

et al. 2005

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A Bayesian analysis of the evidence for human-induced climate change in global surface temperature observations is described. The analysis uses the standard optimal detection approach and explicitly incorporates prior knowledge about uncertainty and the influence of humans on the climate. This knowledge is expressed through prior distributions that are noncommittal on the climate change question. Evidence for detection and attribution is assessed probabilistically using clearly defined criteria. Detection requires that there is high likelihood that a given climate-model-simulated response to historical changes in greenhouse gas concentration and sulphate aerosol loading has been identified in observations. Attribution entails a more complex process that involves both the elimination of other plausible explanations of change and an assessment of the likelihood that the climate-model-simulated response to historical forcing changes is correct. The Bayesian formalism used in this study deals with this latter aspect of attribution in a more satisfactory way than the standard attribution consistency test. Very strong evidence is found to support the detection of an anthropogenic influence on the climate of the twentieth century. However, the evidence from the Bayesian attribution assessment is not as strong, possibly due to the limited length of the available observational record or sources of external forcing on the climate system that have not been accounted for in this study. It is estimated that strong evidence from a Bayesian attribution assessment using a relatively stringent attribution criterion may be available by 2020.

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“…The bioclimatic predictors for future scenarios (year 2080) were obtained from the CCCMA-CGCM2 climate model (Flato et al 2000) through the CIAT database (http://www.ccafs-climate.org, Ramirez and Jarvis 2008). The A2 and B2 scenarios from the 4th assessment report of the Intergovernmental Panel on Climate Change (IPCC 2007) were used.…”

Section: Future Potential Distributionmentioning

confidence: 99%