You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
Natural variability in the surface ocean carbonate ion concentration
Copyright policy )Natural variability in the surface ocean carbonate ion concentration
We investigate variability in the surface ocean carbonate ion concentration ([CO32−]) on the basis of a~long control simulation with an Earth System Model. The simulation is run with a prescribed, pre-industrial atmospheric CO2 concentration for 1000 years, permitting investigation of natural [CO32−] variability on interannual to multi-decadal timescales. We find high interannual variability in surface [CO32−] in the tropical Pacific and at the boundaries between the subtropical and subpolar gyres in the Northern Hemisphere, and relatively low interannual variability in the centers of the subtropical gyres and in the Southern Ocean. Statistical analysis of modeled [CO32−] variance and autocorrelation suggests that significant anthropogenic trends in the saturation state of aragonite (Ωaragonite) are already or nearly detectable at the sustained, open-ocean time series sites, whereas several decades of observations are required to detect anthropogenic trends in Ωaragonite in the tropical Pacific, North Pacific, and North Atlantic. The detection timescale for anthropogenic trends in pH is shorter than that for Ωaragonite, due to smaller noise-to-signal ratios and lower autocorrelation in pH. In the tropical Pacific, the leading mode of surface [CO32−] variability is primarily driven by variations in the vertical advection of dissolved inorganic carbon (DIC) in association with El Niño–Southern Oscillation. In the North Pacific, surface [CO32−] variability is caused by circulation-driven variations in surface DIC and strongly correlated with the Pacific Decadal Oscillation, with peak spectral power at 20–30-year periods. North Atlantic [CO32−] variability is also driven by variations in surface DIC, and exhibits weak correlations with both the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation. As the scientific community seeks to detect the anthropogenic influence on ocean carbonate chemistry, these results will aid the interpretation of trends calculated from spatially and temporally sparse observations.
- University of Colorado Boulder United States
- University of Colorado System United States
- Climate Analysis Section Climate and Global Dynamics Division National Center for Atmospheric Research United States
- University Corporation for Atmospheric Research United States
- Climate and Global Dynamics Laboratory United States
Library of Congress Subject Headings: lcsh:Life lcsh:QH540-549.5 lcsh:QE1-996.5 lcsh:Geology lcsh:QH501-531 lcsh:Ecology
Microsoft Academic Graph classification: engineering.material chemistry.chemical_compound Ocean gyre Atlantic multidecadal oscillation geography geography.geographical_feature_category Advection Aragonite Northern Hemisphere Oceanography chemistry North Atlantic oscillation engineering Carbonate Pacific decadal oscillation
Ecology, Evolution, Behavior and Systematics, Earth-Surface Processes
Ecology, Evolution, Behavior and Systematics, Earth-Surface Processes
Library of Congress Subject Headings: lcsh:Life lcsh:QH540-549.5 lcsh:QE1-996.5 lcsh:Geology lcsh:QH501-531 lcsh:Ecology
Microsoft Academic Graph classification: engineering.material chemistry.chemical_compound Ocean gyre Atlantic multidecadal oscillation geography geography.geographical_feature_category Advection Aragonite Northern Hemisphere Oceanography chemistry North Atlantic oscillation engineering Carbonate Pacific decadal oscillation
5416
Antonov, J. I., Seidov, D., Boyer, T. P., Locarnini, R. A., Mishonov, A. V., Garcia, H. E., Baranova, O. K., Zweng, M. M., and Johnson, D. R.: World Ocean Atlas 2009, Volume 2: Salinity, in: NOAA Atlas NESDIS 69, edited by: Levitus, S., US Government Printing Office, Washington, DC, 184 pp., 2010.
Bates, N. R., Astor, Y. M., Church, M. J., Currie, K., Dore, J. E., Gonzalez-Davila, M., Lorenzoni, L., MullerKarger, F., Olaffson, J., and Santana-Casiano, J. M.: A timeseries view of changing ocean chemistry due to ocean uptake of anthropogenic CO2 and ocean acidification, Oceanography, 27, 126-141, 2014.
Beaulieu, C., Henson, S. A., Sarmiento, Jorge L., Dunne, J. P., Doney, S. C., Rykaczewski, R. R., and Bopp, L.: Factors challenging our ability to detect long-term trends in ocean chlorophyll, Biogeosciences, 10, 2711-2724, doi:10.5194/bg-10-2711- 2013, 2013.
Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., and Bladé, I.: The effective number of spatial degrees of freedom of a time-varying field, J. Climate, 12, 1990-2009, 1999.
Brix, H., Gruber, N., and Keeling, C. D.: Interannual variability of the upper ocean carbon cycle at station ALOHA near Hawaii, Global Biogeochem. Cy., 18, GB4016, doi:10.1029/2004GB002245, 2004.
Ciais, P. and Sabine, C.: Carbon and other biogeochemical cycles, in: Climate Change 2013: the Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M. M. B., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK, New York, NY, USA, 1535 pp., 2013.
Conrad, C. J. and Lovenduski, N. S.: Climate-driven variability in the Southern Ocean carbonate system, J. Climate, 28, 5335- 5350, 2015.
Danabasoglu, G., Bates, S. C., Briegleb, B. P., Jayne, S. R., Jochum, M., Large, W. G., Peacock, S., and Yeager, S. G.: The CCSM4 ocean component, J. Climate, 25, 1361-1389, 2012.
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate change projections: the role of internal variability, Clim. Dynam., 38, 527-546, 2012a. [OpenAIRE]
Deser, C., Phillips, A. S., Tomas, R. A., Okumura, Y. M., Alexander, M. A., Capotondi, A., Scott, J. D., Kwon, Y.-O., and Ohba, M.: ENSO and Pacific decadal variability in the Community Climate System Model Version 4, J. Climate, 25, 2622- 2651, 2012b.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).24 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).24 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!
- National Science Foundation (NSF)
- 1155240
- Directorate for Geosciences | Division of Ocean Sciences
- University of Colorado Boulder United States
- University of Colorado System United States
- Climate Analysis Section Climate and Global Dynamics Division National Center for Atmospheric Research United States
- University Corporation for Atmospheric Research United States
- Climate and Global Dynamics Laboratory United States
- Institute of Arctic and Alpine Research United States
- National Center for Atmospheric Research United States
We investigate variability in the surface ocean carbonate ion concentration ([CO32−]) on the basis of a~long control simulation with an Earth System Model. The simulation is run with a prescribed, pre-industrial atmospheric CO2 concentration for 1000 years, permitting investigation of natural [CO32−] variability on interannual to multi-decadal timescales. We find high interannual variability in surface [CO32−] in the tropical Pacific and at the boundaries between the subtropical and subpolar gyres in the Northern Hemisphere, and relatively low interannual variability in the centers of the subtropical gyres and in the Southern Ocean. Statistical analysis of modeled [CO32−] variance and autocorrelation suggests that significant anthropogenic trends in the saturation state of aragonite (Ωaragonite) are already or nearly detectable at the sustained, open-ocean time series sites, whereas several decades of observations are required to detect anthropogenic trends in Ωaragonite in the tropical Pacific, North Pacific, and North Atlantic. The detection timescale for anthropogenic trends in pH is shorter than that for Ωaragonite, due to smaller noise-to-signal ratios and lower autocorrelation in pH. In the tropical Pacific, the leading mode of surface [CO32−] variability is primarily driven by variations in the vertical advection of dissolved inorganic carbon (DIC) in association with El Niño–Southern Oscillation. In the North Pacific, surface [CO32−] variability is caused by circulation-driven variations in surface DIC and strongly correlated with the Pacific Decadal Oscillation, with peak spectral power at 20–30-year periods. North Atlantic [CO32−] variability is also driven by variations in surface DIC, and exhibits weak correlations with both the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation. As the scientific community seeks to detect the anthropogenic influence on ocean carbonate chemistry, these results will aid the interpretation of trends calculated from spatially and temporally sparse observations.