The next generation of scenarios for climate change research and assessment

Moss, Richard H.; Edmonds, Jae; Hibbard, Kathy; Manning, Martin; Rose, Steven K.; Vuuren, Detlef van; Carter, Timothy R.; Emori, Seita; Kainuma, Mikiko; Kram, Tom; Meehl, Gerald A.; Mitchell, J. F. B.; Nakićenović, Nebojša; Riahi, Keywan; Smith, Steven J.; Stouffer, Ronald J.; Thomson, Allison M.; Weyant, John P.; Wilbanks, Thomas J.

doi:10.1038/nature08823

Cited by 5,693 publications

(4,046 citation statements)

References 36 publications

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“…For each model we use the ‘historical’ simulations, which are forced with observed aerosol and greenhouse gas concentrations from 1850–2005, and 21st century simulations generated using high emissions Representative Concentration Pathway (RCP8.5; Moss et al , 2010). Monthly precipitation, pressure level zonal wind (U‐wind) and SST output fields from both of these experiments are used and only the first member of the ensemble simulations is utilized in order to provide consistent statistics across models.…”

Section: Data and Methodsologymentioning

confidence: 99%

Perspectives on CMIP5 model performance in the Nile River headwaters regions

Bhattacharjee

Zaitchik

2015

Intl Journal of Climatology

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Ranking the performance of global climate models (GCMs) is a notoriously difficult exercise. Multi‐model comparison studies nearly always show that each model has strengths and weaknesses relative to others, and for many purposes the multi‐model ensemble mean delivers better estimates than any individual model. Nevertheless, in regions like East Africa, where there is little consensus between models on the magnitude or sign of 21st century precipitation change, the multi‐model ensemble mean approach to climate projection provides little value for adaptation planning. Here, we consider several possible frameworks for model evaluation and ranking, and assess the differences in performance of a subset of models participating in the 5th Coupled Model Intercomparison Project (CMIP5) according to each framework. Our test case is precipitation in the Nile River headwaters regions. We find that there is little consistency in the relative performance of models across frameworks based on amount and seasonality of precipitation, interannual precipitation variability, precipitation teleconnections, and continental scale climate patterns. These analyses offer some guidance on which GCMs are most likely to provide meaningful results for specific applications, but they caution that any effort to select ‘best performing’ GCMs for the Nile River basin must carefully consider the purposes for which GCMs are being selected.

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Section: Data and Methodsologymentioning

confidence: 99%

Perspectives on CMIP5 model performance in the Nile River headwaters regions

Bhattacharjee

Zaitchik

2015

Intl Journal of Climatology

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“…Recently, Bilbao et al (2015) identified a scenario-independent sea level * Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0376.s1. pattern in response to increasing anthropogenic forcings in sea level projections for the twenty-first century, valid for all representative concentration pathway (RCP) scenarios (Moss et al 2010). These forced patterns are partly masked by the internal climate variability, which has a larger influence at regional scales than on the global scale (Hu and Deser 2013;Monselesan et al 2015).…”

Section: Introductionmentioning

confidence: 99%

The Sea Level Response to External Forcings in Historical Simulations of CMIP5 Climate Models*

et al. 2015

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Changes in Earth's climate are influenced by internal climate variability and external forcings, such as changes in solar radiation, volcanic eruptions, anthropogenic greenhouse gases (GHG), and aerosols. Although the response of surface temperature to external forcings has been studied extensively, this has not been done for sea level. Here, a range of climate model experiments for the twentieth century is used to study the response of global and regional sea level change to external climate forcings. Both the global mean thermosteric sea level and the regional dynamic sea level patterns show clear responses to anthropogenic forcings that are significantly different from internal climate variability and larger than the difference between models driven by the same external forcing. The regional sea level patterns are directly related to changes in surface winds in response to the external forcings. The spread between different realizations of the same model experiment is consistent with internal climate variability derived from preindustrial control simulations. The spread between the different models is larger than the internal variability, mainly in regions with large sea level responses. Although the sea level responses to GHG and anthropogenic aerosol forcing oppose each other in the global mean, there are differences on a regional scale, offering opportunities for distinguishing between these two forcings in observed sea level change.

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“…In the Fifth Assessment Report even more comprehensive scenarios are developed that do not only include changes in radiative forcings, but also the implications of various responses of mankind in terms of technology, mitigation, and adaptation [13].The IPCC uses several climate models. They are yielding widely different results, and come in a rough spatial resolution.…”

Section: Biotic and Abiotic Drivers Call For An Adaptation Of Forestmentioning

confidence: 99%