Uncertainty in regional climate modelling: A review

Foley, Aideen

doi:10.1177/0309133310375654

Cited by 222 publications

(148 citation statements)

References 121 publications

Supporting

Mentioning

142

Contrasting

Unclassified

Order By: Relevance

“…In literature, this is often termed the climate modelling 'chain', which, as new uncertainties are introduced at each modelling step, leads to the 'uncertainty cascade' (Mitchell and Hulme 1999;Foley 2010). It is worth pointing out that the uncertainty cascade does not necessarily lead to an 15 increase in uncertainty at each step, as the modelling steps depend on each other in a non-linear fashion.…”

Section: Uncertainties In the Climate Modelling Chainmentioning

confidence: 99%

Climate uncertainty in flood protection planning

Dittes

Špačková

Schoppa

et al. 2017

Preprint

View full text Add to dashboard Cite

Technical flood protection is a necessary part of integrated strategies to protect riverine settlements from extreme floods. Many technical flood protection measures, such as dikes and protection walls, are costly to adapt after their initial construction. This poses a challenge to decision makers as there is large uncertainty in how the required protection level will change during the measure life time, which is typically many decades long. Flood protection requirements should account for multiple future 10 uncertain factors: socio-economic, e.g. whether the population and with it the damage potential grows or falls; technological, e.g. possible advancements in flood protection; and climatic, e.g. whether extreme discharge will become more frequent or not. We focus here on the planning implications of the uncertainty in extreme discharge. We account for the sequential nature of the decision process, in which the adequacy of the protection is regularly revised in the future based on the discharges that have been observed by that point and that reduce uncertainty. For planning purposes, we categorize uncertainties as either 15 'visible', if they can be quantified from available catchment data, or 'hidden', if they cannot be quantified from catchment data and must be estimated, e.g. from literature. It is vital to consider the hidden uncertainty, since in practical applications only a limited amount of information (e.g. through projections, historic record) is available. We use a Bayesian approach to quantify the visible uncertainties and combine them with an estimate of the hidden uncertainties to learn a joint probability distribution of the parameters of extreme discharge. The methodology is integrated into an optimization framework and applied to a pre-20 alpine case study to give a quantitative, cost-optimal recommendation on the required amount of flood protection.

show abstract

Section: Uncertainties In the Climate Modelling Chainmentioning

confidence: 99%

Climate uncertainty in flood protection planning

Dittes

Špačková

Schoppa

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…The IPCC Special Report on Emissions Scenarios [6] discusses several factors that impact on the atmospheric greenhouse gas concentrations projected over the present century: population growth, economic and social development, the development and utilization of carbon-free energy sources and technology and changes to agricultural practices and land use. The four storylines on which the SRES scenarios are based capture just some of the ways in which these driving forces might change [7]. Uncertainty in climate science is a case of 'imperfect knowledge and what Gershon [8] identifies as 'causes of imperfect knowledge are all present.…”

Section: Uncertainty Of Climate Change Scenariousmentioning

confidence: 99%

Assessment of Runoff Changes under Climate Change Scenarios in the Dam Basin of Ekbatan, Hamedan Iran

Nazari¹,

Kardavany²,

Farajirad³

et al. 2016

J Climatol Weather Forecasting

View full text Add to dashboard Cite

In this study, the uncertainty of the effects of climate change on temperature, rainfall and runoff in the watershed done using the output of models MPEH5, HADCM3 and IPCM4 under two scenarios for 2045-2065 period. After performance of LARS-WG model for downscaling of rainfall and temperature variations, the monthly change rainfall and temperature evaluated for the 2065-2045 period relative to 2010-1983 base period. Results showed that all three models based on two scenarios reduce the amount of rainfall and increases in average temperature in the region. The average annual temperature rise according to the scenario A2, 2/12°C and scenario B1, 1/12°C. The amount of rainfall decreases for the period 2045-2065 under the scenario A2, -6/1 and the scenario B1, -1/4 per cent. To study the effects of climate change on monthly runoff regime, production variables was used to the model of rainfall-runoff IHACRES after that runoff was predicted for the period 2065-2045. The results showed that annual river flow reduced under the A2 scenario -17/2 percent and the B1 scenario -19/4 per cent. The overall results showed that despite the uncertainty in climate models MPEH5, HADCM3 and IPCM4 under the A2 and B1 in the amount of temperature increase and rainfall decreases and negative effects of these changes on the runoff area.

show abstract

“…Aussagen über die zukünftige Entwicklung des Klimas sind immer mit Unsicherheiten behaftet (Foley 2010 Sowohl in der globalen Klimamodellierung (Projekte CMIP5, MIKLIP) als auch in regionalen Ensembles (Projekte PRU-DENCE, ENSEMBLES, EURO-CORDEX) werden ein oder mehrere dieser Ensemblemethoden verwendet (Taylor et al 2012;van der Linden und Mitchell 2009;Jacob et al 2014).…”

Section: Erzeugung Und Interpretation Von Ensemblesunclassified