FN Archimer Export Format
PT J
TI The Signal‐to‐Noise Paradox for Interannual Surface Atmospheric Temperature Predictions
BT 
AF Sévellec, Florian
   Drijfhout, S. S.
AS 1:1,2;2:2,3,4;
FF 1:;2:;
C1 Laboratoire d'Océanographie Physique et Spatiale, Univ.‐Brest CNRS IRD Ifremer Brest ,France
   Ocean and Earth ScienceUniversity of Southampton Southampton, UK
   Koninklijk Nederlands Meteorologisch Instituut De Bilt ,Netherlands
   Institute for Marine and Atmospheric ScienceUniversity of Utrecht Utrecht ,Netherlands
C2 CNRS, FRANCE
   UNIV SOUTHAMPTON, UK
   ROYAL NETHERLANDS METEOROL INST, NETHERLANDS
   UNIV UTRECHT, NETHERLANDS
UM LOPS
IN WOS Cotutelle UMR
   copubli-europe
IF 4.497
TC 9
UR https://archimer.ifremer.fr/doc/00514/62519/66825.pdf
   https://archimer.ifremer.fr/doc/00514/62519/66826.pdf
LA English
DT Article
AB The “signal‐to‐noise paradox” implies that climate models are better at predicting observations than themselves. Here, it is shown that this apparent paradox is expected when the relative level of predicted signal is weaker in models than in observations. In the presence of model error, the paradox only occurs in the range of small signal‐to‐noise ratio of the model, occurring for even smaller model signal‐to‐noise ratio with increasing model error. This paradox is always a signature of the prediction unreliability. Applying this concept to noninitialized simulations of Surface Atmospheric Temperature (SAT) of the CMIP5 database, under the assumption that prediction skill is associated with persistence, shows that global mean SAT is marginally less persistent in models than in observations. However, at a local scale, the analysis suggests that ∼70% of the globe exhibits the signal‐to‐noise paradox for local SAT interannual forecasts and that the Signal‐to‐Noise Paradox occurs especially over the oceans. 
PY 2019
PD AUG
SO Geophysical Research Letters
SN 0094-8276
PU American Geophysical Union (AGU)
VL 46
IS 15
UT 000483812500047
BP 9031
EP 9041
DI 10.1029/2019GL083855
ID 62519
ER

EF