Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections

Ehud Strobach () and Golan Bel ()
Additional contact information
Ehud Strobach: University of Maryland
Golan Bel: Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev

Nature Communications, 2020, vol. 11, issue 1, 1-7

Abstract: Abstract Climate predictions are only meaningful if the associated uncertainty is reliably estimated. A standard practice is to use an ensemble of climate model projections. The main drawbacks of this approach are the fact that there is no guarantee that the ensemble projections adequately sample the possible future climate conditions. Here, we suggest using simulations and measurements of past conditions in order to study both the performance of the ensemble members and the relation between the ensemble spread and the uncertainties associated with their predictions. Using an ensemble of CMIP5 long-term climate projections that was weighted according to a sequential learning algorithm and whose spread was linked to the range of past measurements, we find considerably reduced uncertainty ranges for the projected global mean surface temperature. The results suggest that by employing advanced ensemble methods and using past information, it is possible to provide more reliable and accurate climate projections.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-020-14342-9 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14342-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-14342-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().