The use of the multi-model ensemble in probabilistic climate projections

Tebaldi, Claudia; Knutti, Reto

doi:10.1098/rsta.2007.2076

Cited by 1,520 publications

(1,252 citation statements)

References 64 publications

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“…Probabilistic and stochastic approaches should not be confused with current multi-model ensemble climate projections (e.g. Tebaldi & Knutti, 2007). A stochastic framework for future climatic uncertainty has been studied recently by in a stationary setting.…”

Section: Discussionmentioning

confidence: 99%

On the credibility of climate predictions

Koutsoyiannis

Efstratiadis

Mamassis

et al. 2008

Hydrological Sciences Journal

140

100

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Geographically distributed predictions of future climate, obtained through climate models, are widely used in hydrology and many other disciplines, typically without assessing their reliability. Here we compare the output of various models to temperature and precipitation observations from eight stations with long (over 100 years) records from around the globe. The results show that models perform poorly, even at a climatic (30-year) scale. Thus local model projections cannot be credible, whereas a common argument that models can perform better at larger spatial scales is unsupported.Key words climate models; general circulation models; falsifiability; climate change; Hurst-Kolmogorov climate De la crédibilité des prévisions climatiquesRésumé Des prévisions distribuées dans l'espace du climat futur, obtenues à l'aide de modèles climatiques, sont largement utilisées en hydrologie et dans de nombreuses autres disciplines, en général sans évaluation de leur confiance. Nous comparons ici les sorties de plusieurs modèles aux observations de température et de précipitation de huit stations réparties sur la planète qui disposent de longues chroniques (plus de 100 ans). Les résultats montrent que les modèles ont de faibles performances, y compris à une échelle climatique (30 ans). Les projections locales des modélisations ne peuvent donc pas être crédibles, alors que l'argument courant selon lequel les modèles ont de meilleures performances à des échelles spatiales plus larges n'est pas vérifié.

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Section: Discussionmentioning

confidence: 99%

On the credibility of climate predictions

Koutsoyiannis

Efstratiadis

Mamassis

et al. 2008

Hydrological Sciences Journal

140

100

View full text Add to dashboard Cite

show abstract

“…Although the models differed in the projected changes most models indicated a clear drying trend. However, the use of a larger number of GCMs outputs would better represent the structural uncertainty in climate models (Tebaldi and Knutti 2007).…”

Section: Discussionmentioning

confidence: 99%

Preserving the world second largest hypersaline lake under future irrigation and climate change

Shadkam

Ludwig

Vliet

et al. 2016

Science of The Total Environment

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Abstract. Urmia Lake, the world second largest hypersaline lake, has been largely desiccated over the last two decades resulting in socio-environmental consequences similar or even larger than the Aral Sea disaster. To rescue the lake a new water management plan has been proposed, a rapid 40% decline in irrigation water use replacing a former plan which intended to develop reservoirs and irrigation. However, none of these water management plans, which have large socio-economic impacts, have been assessed under future changes in climate and water availability. By adapting a method of environmental flow requirements (EFRs) for hypersaline lakes, we estimated annually 3.7•10 9 m 3 water is needed to preserve Urmia Lake. Then, the Variable Infiltration Capacity (VIC) hydrological model was forced with bias-corrected climate model outputs for both the lowest (RCP2.6) and highest (RCP8.5) greenhouse-gas concentration scenarios to estimate future water availability and impacts of water management strategies. Results showed a 10% decline in future water availability in the basin under RCP2.6 and 27% under RCP8.5. Our results showed that if future climate change is highly limited (RCP2.6) inflow can be just enough to meet the EFRs by implementing the reduction irrigation plan. However, under more rapid climate change scenario (RCP8.5) reducing irrigation water use will not be enough to save the lake and more drastic measures are needed. Our results showed that future water management plans are not robust under climate change in this region. Therefore, an integrated approach of future land-water use planning and climate change adaptation is therefore needed to improve future water security and to reduce the desiccating of this hypersaline lake.

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“…The latter criterion is a necessity in using multi-model ensemble approach, which is the mutual independence between model realizations. Climatic models proposed by various groups could be assumed to be independent to a certain extent; nevertheless, these models may have similar elements or contain similar underlying theories for their parameterizations (Tebaldi and Knutti, 2007). To ensure the preservation of the relative independence among models, whenever multiple or revised versions of similar climate model are available, only a single version of such GCM is used.…”

Section: Datamentioning

confidence: 99%

“…The multi-model ensemble technique being implemented is taken from the work by Tebaldi et al (2007), which proposed the Bayesian statistical model. Information from several GCMs and observations (i.e., factors of change) are merged to find the Probability Density Functions (PDFs) of future changes for a particular climatic variable at the regional scale.…”

Section: Bayesian Approachmentioning

confidence: 99%

Climate Projections of Future Extreme Events in Malaysia

Syafrina¹,

Zalina²,

Norzaida³

2017

American Journal of Applied Sciences

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In Malaysia, extreme rainfall events are often linked to a number of environmental disasters such as landslides, monsoonal and flash floods. In response to the negative impacts of such disaster, studies assessing the changes and projections of extreme rainfall are vital in order to gather climate change information for better management of hydrological processes. This study investigates the changes and projections of extreme rainfall over Peninsular Malaysia for the period 2081-2100 based on the RCP 6.0 scenario. In particular, this study adopted the statistical downscaling method which enables high resolution, such as hourly data, to be used for the input. Short duration and high intensity convective rainfall is a normal feature of tropical rainfall especially in the western part of the peninsular. The proposed method, the Advanced Weather Generator model is constructed based on thirty years of hourly rainfall data from forty stations. To account for uncertainties, an ensemble multi-model of five General Circulation Model realizations is chosen to generate projections of extreme rainfall for the period 2081-2100. Results of the study indicate a possible increase in future extreme events for both the hourly and 24 h extreme rainfall with the latter showing a wider spatial distribution of increase.

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The use of the multi-model ensemble in probabilistic climate projections

Cited by 1,520 publications

References 64 publications

On the credibility of climate predictions

On the credibility of climate predictions

Preserving the world second largest hypersaline lake under future irrigation and climate change

Climate Projections of Future Extreme Events in Malaysia

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