Natural variability in the surface ocean carbonate ion concentration

[摘要] We investigate variability in the surface ocean carbonate ionconcentration ([CO₃²⁻]) on the basis of a~long controlsimulation with an Earth System Model.The simulationis run with a prescribed, pre-industrial atmospheric CO₂concentration for 1000 years, permitting investigation ofnatural [CO₃²⁻] variability on interannual tomulti-decadal timescales.We find high interannual variability insurface [CO₃²⁻] in the tropical Pacific and at theboundaries between the subtropical and subpolar gyres in theNorthern Hemisphere, and relatively low interannual variability inthe centers of the subtropical gyres and in the Southern Ocean.Statistical analysis of modeled [CO₃²⁻] variance andautocorrelation suggests that significant anthropogenic trends inthe saturation state of aragonite (Ω_aragonite) arealready or nearly detectable at the sustained, open-ocean time seriessites, whereas several decades of observations are required todetect anthropogenic trends in Ω_aragonite in thetropical Pacific, North Pacific, and North Atlantic.The detectiontimescale for anthropogenic trends in pH is shorter than that forΩ_aragonite, due to smaller noise-to-signal ratiosand lower autocorrelation in pH.In the tropical Pacific, theleading mode of surface [CO₃²⁻] variability is primarilydriven by variations in the vertical advection of dissolvedinorganic carbon (DIC) in association with El Niño–SouthernOscillation.In the North Pacific, surface [CO₃²⁻]variability is caused by circulation-driven variations in surfaceDIC and strongly correlated with the Pacific Decadal Oscillation,with peak spectral power at 20–30-year periods. NorthAtlantic [CO₃²⁻] variability is also driven by variationsin surface DIC, and exhibits weak correlations with both the NorthAtlantic Oscillation and the Atlantic Multidecadal Oscillation.Asthe scientific community seeks to detect the anthropogenic influenceon ocean carbonate chemistry, these results will aid theinterpretation of trends calculated from spatially andtemporally sparse observations.

[学科分类] 地球化学与岩石