How representative is the spread of climate projections from the 5 CMIP5 GCMs used in ISI-MIP?

McSweeney, C.; Jones, R. G.

doi:10.1016/j.cliser.2016.02.001

Cited by 137 publications

(94 citation statements)

References 15 publications

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“…Moreover, 60 to 90% of the full range of future projections of 36 CMIP5 GCMs was captured by the five-model subset used for temperature and 40 to 80% of the full range of future projections for precipitation in the two catchments (from Fig. 1 in McSweeney and Jones 2016). This information provides a useful context for interpreting the results and may facilitate comparison of results across impact studies based on different ensemble subsets.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Impacts of 1.5 and 2 °C global warming on water availability and extreme hydrological events in Yiluo and Beijiang River catchments in China

Liu

Wang

et al. 2017

Climatic Change

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In the discussion of climate impacts, 1.5 and 2°C have become iconic values. This study examines the impacts of 1.5 and 2°C global warming on water availability, runoff seasonality, and extreme monthly and daily runoff in two catchments, using the semidistributed hydrological model Hydrologiska Byråns Vattenbalansavdelning-D, based on a combination of five global climate models (GCMs) and four representative concentration pathways (RCPs). Subsequently, quantitative assessments were made for projection uncertainties from GCMs and RCPs. The two catchments are the Yiluo River catchment (YLC) in northern China and the Beijiang River catchment (BJC) in southern China. The results indicate wetter flood seasons for YLC and warmer mean annual temperatures, drier springs, and more severe floods over long return periods (25 and 50 years) for both catchments. The change magnitude of most indicators is expected to be larger in YLC than in BJC. Mean annual temperatures in both catchments are expected to have smaller changes under the 1.5°C scenario than under the 2.0°C scenario. However, the change magnitude of the other hydrological variables is projected to be approximately equivalent for both catchments under both scenarios. Uncertainties of projected impacts from GCMs are generally larger than those from RCP scenarios, for both catchments and warming scenarios, with the exception of mean annual temperature of BJC. These findings indicate that effective measures are required to Climatic Change (2017) 145:145-158 DOI 10.1007/s10584-017-2072 Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10584-017-2072-3) contains supplementary material, which is available to authorized users.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Impacts of 1.5 and 2 °C global warming on water availability and extreme hydrological events in Yiluo and Beijiang River catchments in China

Liu

Wang

et al. 2017

Climatic Change

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show abstract

“…Moreover, the uncertainty attributed to the climate projections is likely to be underestimated due to the relatively small, though widely used, model ensembles [62]. Other factors such as the bias correction of climate projections, the method for assessing the year of exceeding SWLs, and the spatial resolution of the input data, surely do influence results though probably to a smaller degree, and do not affect the direction of the projected changes.…”

Section: Discussionmentioning

confidence: 99%

Multi-Model Projections of River Flood Risk in Europe under Global Warming

Alfieri

Dottori

Betts

et al. 2018

Climate

116

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Abstract:Knowledge on the costs of natural disasters under climate change is key information for planning adaptation and mitigation strategies of future climate policies. Impact models for large scale flood risk assessment have made leaps forward in the past few years, thanks to the increased availability of high resolution climate projections and of information on local exposure and vulnerability to river floods. Yet, state-of-the-art flood impact models rely on a number of input data and techniques that can substantially influence their results. This work compares estimates of river flood risk in Europe from three recent case studies, assuming global warming scenarios of 1.5, 2, and 3 degrees Celsius from pre-industrial levels. The assessment is based on comparing ensemble projections of expected damage and population affected at country level. Differences and common points between the three cases are shown, to point out main sources of uncertainty, strengths, and limitations. In addition, the multi-model comparison helps identify regions with the largest agreement on specific changes in flood risk. Results show that global warming is linked to substantial increase in flood risk over most countries in Central and Western Europe at all warming levels. In Eastern Europe, the average change in flood risk is smaller and the multi-model agreement is poorer.

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“…The first phase of the Inter-Sectoral Impacts Model Intercomparison Project (ISI-MIP; Warszawski et al 2014) utilized a common set of 5 GCMs for all sectoral impacts assessments owing to the need for consistency across regions and sectors. The ramifications of choosing this subset, based in part upon which GCMs available at the time, continues to be explored (McSweeney and Jones 2016). Early AgMIP regional integrated assessments also used 5 GCMs selected according to prominence in publications, length of participation in CMIP, spatial resolution, and historical monsoon patterns (Ruane et al 2015b).…”

Section: Introductionmentioning

confidence: 99%

Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment

Ruane

McDermid

2017

Earth Perspectives

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We present the Representative Temperature and Precipitation (T&P) GCM Subsetting Approach developed within the Agricultural Model Intercomparison and Improvement Project (AgMIP) to select a practical subset of global climate models (GCMs) for regional integrated assessment of climate impacts when resource limitations do not permit the full ensemble of GCMs to be evaluated given the need to also focus on impacts sector and economics models. Subsetting inherently leads to a loss of information but can free up resources to explore important uncertainties in the integrated assessment that would otherwise be prohibitive. The Representative T&P GCM Subsetting Approach identifies five individual GCMs that capture a profile of the full ensemble of temperature and precipitation change within the growing season while maintaining information about the probability that basic classes of climate changes (relatively cool/wet, cool/dry, middle, hot/wet, and hot/dry) are projected in the full GCM ensemble. We demonstrate the selection methodology for maize impacts in Ames, Iowa, and discuss limitations and situations when additional information may be required to select representative GCMs. We then classify 29 GCMs over all land areas to identify regions and seasons with characteristic diagonal skewness related to surface moisture as well as extreme skewness connected to snow-albedo feedbacks and GCM uncertainty. Finally, we employ this basic approach to recognize that GCM projections demonstrate coherence across space, time, and greenhouse gas concentration pathway. The Representative T&P GCM Subsetting Approach provides a quantitative basis for the determination of useful GCM subsets, provides a practical and coherent approach where previous assessments selected solely on availability of scenarios, and may be extended for application to a range of scales and sectoral impacts.

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How representative is the spread of climate projections from the 5 CMIP5 GCMs used in ISI-MIP?

Cited by 137 publications

References 15 publications

Impacts of 1.5 and 2 °C global warming on water availability and extreme hydrological events in Yiluo and Beijiang River catchments in China

Impacts of 1.5 and 2 °C global warming on water availability and extreme hydrological events in Yiluo and Beijiang River catchments in China

Multi-Model Projections of River Flood Risk in Europe under Global Warming

Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment

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