%0 Journal Article %1 Scaife2010Atmospheric %A Scaife, Adam A. %A Woollings, Tim %A Knight, Jeff %A Martin, Gill %A Hinton, Tim %B Journal of Climate %D 2010 %I American Meteorological Society %J J. Climate %K nao blocking colleagues %N 23 %P 6143--6152 %R 10.1175/2010jcli3728.1 %T Atmospheric Blocking and Mean Biases in Climate Models %U http://dx.doi.org/10.1175/2010jcli3728.1 %V 23 %X Abstract Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.

@article{Scaife2010Atmospheric, abstract = {Abstract Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Scaife, Adam A. and Woollings, Tim and Knight, Jeff and Martin, Gill and Hinton, Tim}, biburl = {https://www.bibsonomy.org/bibtex/2927e6e67e20980f2d611659db4471c3f/pbett}, booktitle = {Journal of Climate}, citeulike-article-id = {9678946}, citeulike-linkout-0 = {http://journals.ametsoc.org/doi/abs/10.1175/2010JCLI3728.1}, citeulike-linkout-1 = {http://dx.doi.org/10.1175/2010jcli3728.1}, day = 23, doi = {10.1175/2010jcli3728.1}, interhash = {b77b17dac6b4e7b95a056e29b36fa542}, intrahash = {927e6e67e20980f2d611659db4471c3f}, journal = {J. Climate}, keywords = {nao blocking colleagues}, month = aug, number = 23, pages = {6143--6152}, posted-at = {2011-11-17 11:31:20}, priority = {2}, publisher = {American Meteorological Society}, timestamp = {2018-06-22T18:30:45.000+0200}, title = {Atmospheric Blocking and Mean Biases in Climate Models}, url = {http://dx.doi.org/10.1175/2010jcli3728.1}, volume = 23, year = 2010 }