Use of Representative Climate Futures in impact and adaptation assessment

Whetton, Penny; Hennessy, Kevin; Clarke, John Mason; McInnes, Kathleen L.; Kent, David M.

doi:10.1007/s10584-012-0471-z

Cited by 115 publications

(92 citation statements)

References 15 publications

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“…Approaches for selecting climate models for downscaling are discussed in various papers (e.g. Wilby et al, 2009;Whetton et al, 2012). Criteria of particular importance in selecting climate models for impact studies include (a) that the climate models under historical conditions accurately represent the processes or features that are of particular relevance to the impact study and (b) that the climate models sample the range of projected change in the features of interest (Whetton et al, 2012).…”

Section: Design Of Model Studymentioning

confidence: 99%

Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore

et al. 2016

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Abstract.Singapore is an island state with considerable population, industries, commerce and transport located in coastal areas at elevations less than 2 m making it vulnerable to sea level rise. Mitigation against future inundation events requires a quantitative assessment of risk. To address this need, regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events have been combined to explore potential changes to coastal flood risk over the 21st century. Local changes in time-mean sea level were evaluated using the process-based climate model data and methods presented in the United Nations Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Regional surge and wave solutions extending from 1980 to 2100 were generated using ∼ 12 km resolution surge (Nucleus for European Modelling of the Ocean -NEMO) and wave (WaveWatchIII) models. Ocean simulations were forced by output from a selection of four downscaled (∼ 12 km resolution) atmospheric models, forced at the lateral boundaries by global climate model simulations generated for the IPCC AR5. Long-term trends in skew surge and significant wave height were then assessed using a generalised extreme value model, fit to the largest modelled events each year. An additional atmospheric solution downscaled from the ERA-Interim global reanalysis was used to force historical ocean model simulations extending from 1980 to 2010, enabling a quantitative assessment of model skill. Simulated historical sea-surface height and significant wave height time series were compared to tide gauge data and satellite altimetry data, respectively. Central estimates of the long-term mean sea level rise at Singapore by 2100 were projected to be 0.52 m (0.74 m) under the Representative Concentration Pathway (RCP)4.5 (8.5) scenarios. Trends in surge and significant wave height 2-year return levels were found to be statistically insignificant and/or physically very small under the more severe RCP8.5 scenario. We conclude that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century. We note that the largest recorded surge residual in the Singapore Strait of ∼ 84 cm lies between the central and upper estimates of sea level rise by 2100, highlighting the vulnerability of the region.

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Section: Design Of Model Studymentioning

confidence: 99%

Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore

et al. 2016

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“…Ranges of change are presented by the 10 th , 50 th (median) and 90 th percentile of the range in each model group. The 10 th and 90 th percentiles are considered as a plausible 'dry case' and 'wet case' when considering the range of projections in a framework similar to Representative Climate Futures (Whetton et al 2012). We also look at the spatial pattern of change in each study, primarily to examine any artefacts behind the area-averages but also to examine any features in the change signal at scales smaller than the clusters.…”

Section: Comparing and Combining Projection Distributionsmentioning

confidence: 99%

“…We use an approach similar to the Representative Climate Futures framework of Whetton et al (2012), where projections can be categorised and compared in multiple categories and on multiple dimensions (such as 'wetter' or 'drier' and 'hotter' and 'much hotter' for temperature and rainfall dimensions).…”

Section: Introductionmentioning

confidence: 99%

Comparison of various climate change projections of eastern Australian rainfall

Grose¹,

Bhend²,

Argüeso³

et al. 2015

AMOJ

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The Australian eastern seaboard is a distinct climate entity from the interior of the continent, with different climatic influences on each side of the Great Dividing Range. Therefore, it is plausible that downscaling of global climate models could reveal meaningful regional detail, or 'added value', in the climate change signal of mean rainfall change in eastern Australia under future scenarios. However, because downscaling is typically done using a limited set of global climate models and downscaling methods, the results from a downscaling study may not represent the range of uncertainty in plausible projected change for a region suggested by the ensemble of host global climate models. A complete and unbiased representation of the plausible changes in the climate is essential in producing climate projections useful for future planning. As part of this aim it is important to quantify any differences in the change signal between global climate models and downscaling, and understand the cause of these differences in terms of plausible added regional detail in the climate change signal, the impact of sub-sampling global climate models and the effect of the downscaling models themselves. Here we examine rainfall projections in eastern Australia under a high emissions scenario by late in the century from ensembles of global climate models, two dynamical downscaling models and one statistical downscaling model. We find no cases where all three downscaling methods show the same clear regional spatial detail in the change signal that is distinct from the host models. However, some downscaled projections suggest that the eastern seaboard could see little change in spring rainfall, in contrast to the substantial rainfall decrease inland. The change signal in the downscaled outputs is broadly similar at the large scale in the various model outputs, with a few notable exceptions. For example, the model median from dynamical downscaling projects a rainfall increase over the entirety of eastern Australia in autumn that is greater than the global models. Also, there are some instances where a downscaling method produces changes outside the range of host models over eastern Australia as a whole, thus expanding the projected range of uncertainty. Results are particularly uncertain for summer, where no two downscaling studies clearly agree. There are also some confounding factors from the model configuration used in downscaling, where the particular zones used for statistical models and the model components used in dynamical models have an influence on results and produce additional uncertainty.

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“…For example, the Climate Futures Tool (Whetton et al 2012) has been applied in Australia, the Pacific Islands and Vietnam, as a decision-support tool to aid the use of regional climate projections for impact assessment and adaptation planning. The tool helps users navigate a wide range of information to better inform their impact and adaptation decisions.…”

Section: Discussionmentioning

confidence: 99%

Providing future climate projections using multiple models and methods: insights from the Philippines

et al. 2018

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To meet the growing demand for climate change information to guide national and local adaptation decision-making in the Philippines, the climate science and services community is producing an increasing volume of future climate data using a range of modelling approaches. However, there is a significant methodological challenge in how to best compare and combine information produced using different models and methods. In this paper, we present the landscape of climate model data available in the Philippines and show how multimodel, multi-method climate projections are being used and communicated to inform climate Climatic Change (2018) change policy and planning, focusing on the agriculture sector. We highlight the importance of examining and communicating methodological strengths and weaknesses as well as understanding the needs and capabilities of different user communities. We discuss the assessment of projections from different methods, including global and regional downscaled simulations, and discuss ways to summarise and communicate this information to stakeholders using coproduction approaches. The paper concludes with perspectives on how to best use an Bensemble of opportunity^to construct defensible, plausible and usable climate projections.

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Use of Representative Climate Futures in impact and adaptation assessment

Cited by 115 publications

References 15 publications

Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore

Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore

Comparison of various climate change projections of eastern Australian rainfall

Providing future climate projections using multiple models and methods: insights from the Philippines

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