The 'pause' in global warming in historical context: (II). Comparing models to 
observations

Lewandowsky, S.; Cowtan, K.; Risbey, J. S.; Mann, M. E.; Steinmann, B. A.; Oreskes, N.; Rahmstorf, Stefan

doi:10.1088/1748-9326/aaf372

Datensatz

DATENSATZ AKTIONENEXPORT

Zur Ablage hinzufügen

Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben

Zeitschriftenartikel

The 'pause' in global warming in historical context: (II). Comparing models to observations

Urheber*innen

Lewandowsky, S.
External Organizations;

Cowtan, K.
External Organizations;

Risbey, J. S.
External Organizations;

Mann, M. E.
External Organizations;

Steinmann, B. A.
External Organizations;

Oreskes, N.
External Organizations;

/persons/resource/Stefan.Rahmstorf

Rahmstorf, Stefan
Potsdam Institute for Climate Impact Research;

Externe Ressourcen

https://doi.org/10.1088/1748-9326/aafbb7
(Ergänzendes Material)

Volltexte (frei zugänglich)

8332oa.pdf
(Verlagsversion), 4MB

Ergänzendes Material (frei zugänglich)

Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar

Zitation

Lewandowsky, S., Cowtan, K., Risbey, J. S., Mann, M. E., Steinmann, B. A., Oreskes, N., Rahmstorf, S. (2018): The 'pause' in global warming in historical context: (II). Comparing models to observations. - Environmental Research Letters, 13, 12, 123007.
https://doi.org/10.1088/1748-9326/aaf372

Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_22867

Zusammenfassung

We review the evidence for a putative early 21st-century divergence between global mean surface temperature (GMST) and Coupled Model Intercomparison Project Phase 5 (CMIP5) projections. We provide a systematic comparison between temperatures and projections using historical versions of GMST products and historical versions of model projections that existed at the times when claims about a divergence were made. The comparisons are conducted with a variety of statistical techniques that correct for problems in previous work, including using continuous trends and a Monte Carlo approach to simulate internal variability. The results show that there is no robust statistical evidence for a divergence between models and observations. The impression of a divergence early in the 21st century was caused by various biases in model interpretation and in the observations, and was unsupported by robust statistics.