Accessibility navigation


An assessment of CMIP6 climate signals and biases in temperature, precipitation and soil moisture over Europe

Osso, A. ORCID: https://orcid.org/0000-0001-5653-4886, Craig, P. ORCID: https://orcid.org/0000-0001-9213-4599 and Allan, R. P. ORCID: https://orcid.org/0000-0003-0264-9447 (2023) An assessment of CMIP6 climate signals and biases in temperature, precipitation and soil moisture over Europe. International Journal of Climatology, 43 (12). pp. 5698-5719. ISSN 0899-8418

[img]
Preview
Text (Open Access) - Published Version
· Available under License Creative Commons Attribution Non-commercial No Derivatives.
· Please see our End User Agreement before downloading.

14MB
[img] Text - Accepted Version
· Restricted to Repository staff only

12MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1002/joc.8169

Abstract/Summary

The CMIP6 projections constitute the basis of our latest understanding of the climate response to anthropogenic forcing. However, there is still considerable uncertainty in the projections, especially at the regional scale. One way to constrain the uncertainty is by comparing the models historical climate change signals against observations and investigate the physical reasons for divergences. Here, we assess the signal-to-noise ratio (S/N) of surface air temperature (SAT), precipitation (PREC) and soil moisture (SM) over Europe for a set of CMIP6 historical simulations and compare them against the E-OBS observational product and the ERA5 reanalysis. We found considerable divergences between the CMIP6 ensemble mean S/N and that of E-OBS and ERA5, as well as between ERA5 and E-OBS. The latter indicates that the S/N is affected by data coverage. We show that the differences among model signals are associated with different atmospheric circulation responses. We also investigate the potential relationships between the models' signals and climatological biases, and we found evidence that the models with a warm climatological bias in southern Europe tend to have smaller SAT signals (warm less). Finally, we found no apparent relationship between SM biases and the warming signal, suggesting that the mechanism by which SM–atmosphere interactions affect climate variability does not explain the mean changes. However, there is a tendency for models with higher SM to dry faster than models with lower SM.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary Research Centres (IDRCs) > Walker Institute
Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:112504
Publisher:Wiley

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation