Tuesday, 12 January 2016: 3:45 PM
La Nouvelle C ( New Orleans Ernest N. Morial Convention Center)
To gain a clearer understanding of long-term natural versus forced changes of the surface atmospheric circulation and storminess, we have investigated large ensembles of three distinct types of observational datasets and model simulations of the 1874 to 2010 period. The first type is a newly available 56-member ensemble of 6-hourly observational global atmospheric reanalyses, the 20CRv2c dataset. The second type, referred to as the AMIP20v2c dataset, is a 56-member ensemble of atmospheric GCM simulations of the same period using the same NCEP AGCM used to produce the 20CRv2c dataset, and with identical specifications of time-varying SST, sea ice, and radiative forcings. The third type is a multi-model ensemble of 62 CMIP5 climate model simulations of the same period with observed radiative forcings. These three types of datasets enable cleaner separations of the radiatively forced versus internal natural climate variations than has previously been possible.
The most important result from this study is that the large observed trends in many circulation variables evident over the second half (1943-2010) of the period are much weaker or non-existent when considered over the full period (1874-2010), and are associated with even weaker long-term storminess trends (defining the storminess at each gridpoint as the r.m.s. of 24-hr SLP differences). This has important implications for the atmospheric circulation response to global warming, and casts doubt on inferences about this response drawn in many studies from considering only the second half (or even shorter subsets) of the record. Consistent with the weak observed long-term circulation trends, the ensemble-mean long-term trends in the AMIP20v2c and CMIP5 simulations are also weak in most regions of the globe, except in the extratropical southern hemisphere. We also provide evidence that the trends in the CMIP5 simulations are generally compromised by their misrepresentation of tropical SST changes.
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