--- - attrs: .reference_type: 0 Author: 'Prein, Andreas F.; Langhans, Wolfgang; Fosser, Giorgia; Ferrone, Andrew; Ban, Nikolina; Goergen, Klaus; Keller, Michael; Tölle, Merja; Gutjahr, Oliver; Feser, Frauke; Brisson, Erwan; Kollet, Stefan; Schmidli, Juerg; van Lipzig, Nicole P. M.; Leung, Ruby' DOI: 10.1002/2014RG000475 Issue: 2 Journal: Reviews of Geophysics Keywords: convection-permitting modeling; added value; climate; cloud resolving; nonhydrostatic modeling; high resolution; 3355 Regional modeling; 3314 Convective processes; 3354 Precipitation; 4321 Climate impact; 4313 Extreme events Pages: 323-361 Title: 'A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges' Volume: 53 Year: 2015 _record_number: 19700 _uuid: 561b116f-6b89-4efa-a570-fad433524afa reftype: Journal Article child_publication: /article/10.1002/2014RG000475 href: https://data.globalchange.gov/reference/561b116f-6b89-4efa-a570-fad433524afa.yaml identifier: 561b116f-6b89-4efa-a570-fad433524afa uri: /reference/561b116f-6b89-4efa-a570-fad433524afa - attrs: .reference_type: 10 Author: 'Kotamarthi, R.; L. Mearns; K. Hayhoe; C. Castro; D. Wuebbles' DOI: '10.13140/RG.2.1.1986.0085 ' Pages: 55 Publisher: 'U.S. Department of Defense, Strategic Environment Research and Development Program Report' Title: Use of Climate Information for Decision-Making and Impact Research URL: https://www.serdp-estcp.org/content/download/38568/364489/version/1/file/Use+of+Climate+Information+for+Decision-Making+Technical+Report.pdf Year: 2016 _record_number: 20591 _uuid: 76fb2a82-f93e-4db9-bed3-cb864bd9c751 reftype: Report child_publication: /report/use-climate-information-decision-making-impact-research href: https://data.globalchange.gov/reference/76fb2a82-f93e-4db9-bed3-cb864bd9c751.yaml identifier: 76fb2a82-f93e-4db9-bed3-cb864bd9c751 uri: /reference/76fb2a82-f93e-4db9-bed3-cb864bd9c751 - attrs: .reference_type: 0 Author: 'Knutti, Reto; Sedláček, Jan; Sanderson, Benjamin M.; Lorenz, Ruth; Fischer, Erich M.; Eyring, Veronika' DOI: 10.1002/2016GL072012 Issue: 4 Journal: Geophysical Research Letters Keywords: model weighting; sea ice; emergent constraint; model interdependence; 1622 Earth system modeling; 1626 Global climate models; 1990 Uncertainty Pages: 1909-1918 Title: A climate model projection weighting scheme accounting for performance and interdependence Volume: 44 Year: 2017 _record_number: 21024 _uuid: 7e20a75c-7cda-4c6d-8865-544ee639db47 reftype: Journal Article child_publication: /article/10.1002/2016GL072012 href: https://data.globalchange.gov/reference/7e20a75c-7cda-4c6d-8865-544ee639db47.yaml identifier: 7e20a75c-7cda-4c6d-8865-544ee639db47 uri: /reference/7e20a75c-7cda-4c6d-8865-544ee639db47 - attrs: .reference_type: 0 Abstract: 'An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.' Author: 'Feser, Frauke; Burkhardt Rockel; Hans von Storch; Jörg Winterfeldt; Matthias Zahn' DOI: 10.1175/2011BAMS3061.1 Issue: 9 Journal: Bulletin of the American Meteorological Society Pages: 1181-1192 Title: 'Regional climate models add value to global model data: A review and selected examples' Volume: 92 Year: 2011 _record_number: 20229 _uuid: 9f2b8aaf-ac22-48bc-bbdd-dd94368767ce reftype: Journal Article child_publication: /article/10.1175/2011BAMS3061.1 href: https://data.globalchange.gov/reference/9f2b8aaf-ac22-48bc-bbdd-dd94368767ce.yaml identifier: 9f2b8aaf-ac22-48bc-bbdd-dd94368767ce uri: /reference/9f2b8aaf-ac22-48bc-bbdd-dd94368767ce - attrs: .reference_type: 0 Abstract: 'The collection of Earth system models available in the archive of phase 5 of CMIP (CMIP5) represents, at least to some degree, a sample of uncertainty of future climate evolution. The presence of duplicated code as well as shared forcing and validation data in the multiple models in the archive raises at least three potential problems: biases in the mean and variance, the overestimation of sample size, and the potential for spurious correlations to emerge in the archive because of model replication. Analytical evidence is presented to demonstrate that the distribution of models in the CMIP5 archive is not consistent with a random sample, and a weighting scheme is proposed to reduce some aspects of model codependency in the ensemble. A method is proposed for selecting diverse and skillful subsets of models in the archive, which could be used for impact studies in cases where physically consistent joint projections of multiple variables (and their temporal and spatial characteristics) are required.' Author: Benjamin M. Sanderson; Reto Knutti; Peter Caldwell DOI: 10.1175/JCLI-D-14-00362.1 Issue: 13 Journal: Journal of Climate Keywords: 'Empirical orthogonal functions,Interpolation schemes,Numerical analysis/modeling,Statistical techniques,Climate models,Model errors' Pages: 5171-5194 Title: A representative democracy to reduce interdependency in a multimodel ensemble Volume: 28 Year: 2015 _record_number: 20241 _uuid: e0e1fdcc-b1e3-4eff-9eea-ff89dcf75c16 reftype: Journal Article child_publication: /article/10.1175/JCLI-D-14-00362.1 href: https://data.globalchange.gov/reference/e0e1fdcc-b1e3-4eff-9eea-ff89dcf75c16.yaml identifier: e0e1fdcc-b1e3-4eff-9eea-ff89dcf75c16 uri: /reference/e0e1fdcc-b1e3-4eff-9eea-ff89dcf75c16