%0 Journal Article %1 scaife2018signaltonoise %A Scaife, Adam A. %A Smith, Doug %D 2018 %J npj Climate and Atmospheric Science %K climatechange colleagues decadal review seasonal signaltonoise statistics %N 1 %P 28-- %R 10.1038/s41612-018-0038-4 %T A signal-to-noise paradox in climate science %U https://doi.org/10.1038/s41612-018-0038-4 %V 1 %X We review the growing evidence for a widespread inconsistency between the low strength of predictable signals in climate models and the relatively high level of agreement they exhibit with observed variability of the atmospheric circulation. This discrepancy is particularly evident in the climate variability of the Atlantic sector, where ensemble predictions using climate models generally show higher correlation with observed variability than with their own simulations, and higher correlations with observations than would be expected from their small signal-to-noise ratios, hence a ‘signal-to-noise paradox’. This unusual behaviour has been documented in multiple climate prediction systems and in the response to a number of different sources of climate variability. However, we also note that the total variance in the models is often close in magnitude to the observed variance, and so it is not a simple matter of models containing too much variability. Instead, the proportion of Atlantic climate variance that is predictable in climate models appears to be too weak in amplitude by a factor of two, or perhaps more. In this review, we provide a range of examples from existing studies to build the case for a problem that is common across different climate models, common to several different sources of climate variability and common across a range of timescales. We also discuss the wider implications of this intriguing paradox.

@article{scaife2018signaltonoise, abstract = {We review the growing evidence for a widespread inconsistency between the low strength of predictable signals in climate models and the relatively high level of agreement they exhibit with observed variability of the atmospheric circulation. This discrepancy is particularly evident in the climate variability of the Atlantic sector, where ensemble predictions using climate models generally show higher correlation with observed variability than with their own simulations, and higher correlations with observations than would be expected from their small signal-to-noise ratios, hence a ‘signal-to-noise paradox’. This unusual behaviour has been documented in multiple climate prediction systems and in the response to a number of different sources of climate variability. However, we also note that the total variance in the models is often close in magnitude to the observed variance, and so it is not a simple matter of models containing too much variability. Instead, the proportion of Atlantic climate variance that is predictable in climate models appears to be too weak in amplitude by a factor of two, or perhaps more. In this review, we provide a range of examples from existing studies to build the case for a problem that is common across different climate models, common to several different sources of climate variability and common across a range of timescales. We also discuss the wider implications of this intriguing paradox.}, added-at = {2018-07-19T09:23:39.000+0200}, author = {Scaife, Adam A. and Smith, Doug}, biburl = {https://www.bibsonomy.org/bibtex/22688b12063f122a5a7d5d5f9b992a0d9/pbett}, description = {A signal-to-noise paradox in climate science | npj Climate and Atmospheric Science}, doi = {10.1038/s41612-018-0038-4}, interhash = {76f55520d463114d3cc21f4397b38de7}, intrahash = {2688b12063f122a5a7d5d5f9b992a0d9}, issn = {23973722}, journal = {npj Climate and Atmospheric Science}, keywords = {climatechange colleagues decadal review seasonal signaltonoise statistics}, number = 1, pages = {28--}, refid = {Scaife2018}, timestamp = {2020-08-12T10:43:59.000+0200}, title = {A signal-to-noise paradox in climate science}, url = {https://doi.org/10.1038/s41612-018-0038-4}, volume = 1, year = 2018 }