Identification

Title

Climate model large ensembles as test beds for applied compound event research

Abstract

<p><span style="-webkit-text-stroke-width:0px;color:rgb(31, 31, 31);display:inline !important;float:none;font-family:ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif;font-size:16px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:start;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;">Some of the most impactful climate and weather events result from compounding drivers. To robustly assess the current and future risk from such compound events, a better understanding of the associated sources of uncertainty is needed. Internal variability confounds detection and attribution of human-induced climate change and imposes irreducible limits on the accuracy of climate projections. Response uncertainty can lead to divergent projections for many societally important quantities such as precipitation. Combined with unknown future greenhouse gas emissions, these uncertainties can result in a socio-economically paralyzing range of future storylines. Climate model large ensembles are uniquely positioned to assess these uncertainties and are rightfully gaining popularity in compound event research, but they need to be accompanied by rigorous model validation and robust observational constraints to reach their full potential in terms of usefulness for practitioners. This perspective discusses these opportunities and challenges at the example of water resources and provides an outlook on application-oriented compound event research with large ensembles.</span></p>

Resource type

document

Resource locator

Unique resource identifier

code

https://n2t.net/ark:/85065/d73x8bxn

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

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Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2024-11-01T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

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name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact