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Disentangling and quantifying contributions of distinct forcing factors to the observed global sea level pressure field

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Abstract

Variations of the global sea level pressure (SLP) field reflect atmospheric and oceanic influences and have a profound influence on temperature, precipitation and the global carbon cycle. The impact of various forcing factors on this field was investigated mainly based on numerical simulations. Alternatively, here we identify and quantify the influences of various forcing factors on observational, reanalysis and simulated SLP fields. By applying canonical correlation analysis (CCA) on the aforementioned data sets, we separated and quantified the impact of increase CO2 concentration, El Niño–Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO) and solar forcing on the global SLP field, based on their associations with known footprints on the sea surface temperature (SST). Together, their corresponding SLP spatial structures explain ~ 60% of the observed variance. Whereas the atmospheric CO2 concentration has the most prominent impact on the global SLP field, explaining 28% of variance, ENSO and AO account for 9% each. The solar forcing and AMO explain 7%, respectively 6% of global SLP variance. Similar spatial structures corresponding to the same forcing factors are identified based on the reanalysis SLP data. CCA applied on simulated SLP fields derived from six CMIP5 model simulations captures only the spatial structures of atmospheric CO2 concentration, ENSO, AAO and AO. Such a decomposition of the global pressure field based on a linear combination of coupled SST-SLP pairs provide a reference against which one could validate the performance of general circulation models in simulating the lower atmosphere dynamics.

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Acknowledgements

This work was supported by The Deutsche Bundesstiftung Umwelt DBU (German Federal Environmental Foundation), through the MOE-Austauschstipendienprogramm. We acknowledge Dr.Norel Rimbu for his valuable comments that helped to improve the manuscript. The HadISST and HadSLP2 data sets were provided by the British Met Office, Hadley Centre (https://www.metoffice.gov.uk). The NCEP/NCAR SLP, ERSST.v5 SST data and the climate indices used were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. We acknowledge the World Climate Research Program’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table S2) for producing and making available their model output. CMIP5 model output data was downloaded from KNMI Climate Explorer (https://climexp.knmi.nl).

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  1. Faculty of Physics, University of Bucharest, Bucharest, Romania

    Petru Vaideanu & Mihai Dima

  2. Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany

    Petru Vaideanu, Mihai Dima & Monica Ionita

  3. National Institute of Research and Development for Optoelectronics, Măgurele, Romania

    Razvan Pirloaga

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Vaideanu, P., Dima, M., Pirloaga, R. et al. Disentangling and quantifying contributions of distinct forcing factors to the observed global sea level pressure field. Clim Dyn 54, 1453–1467 (2020). https://doi.org/10.1007/s00382-019-05067-7

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