Articles | Volume 12, issue 5
Biogeosciences, 12, 1285–1298, 2015
Biogeosciences, 12, 1285–1298, 2015

Research article 02 Mar 2015

Research article | 02 Mar 2015

Trends and drivers in global surface ocean pH over the past 3 decades

S. K. Lauvset1,2, N. Gruber3, P. Landschützer3, A. Olsen1,2,4, and J. Tjiputra2,4 S. K. Lauvset et al.
  • 1Geophysical Institute, University of Bergen, Norway
  • 2Bjerknes Center for Climate Research, Bergen, Norway
  • 3Environmental Physics, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zurich, Switzerland
  • 4Uni Climate – Uni Research, Bergen, Norway

Abstract. We report global long-term trends in surface ocean pH using a new pH data set computed by combining fCO2 observations from the Surface Ocean CO2 Atlas (SOCAT) version 2 with surface alkalinity estimates based on temperature and salinity. Trends were determined over the periods 1981–2011 and 1991–2011 for a set of 17 biomes using a weighted linear least squares method. We observe significant decreases in surface ocean pH in ~70% of all biomes and a mean rate of decrease of 0.0018 ± 0.0004 yr−1 for 1991–2011. We are not able to calculate a global trend for 1981–2011 because too few biomes have enough data for this. In half the biomes, the rate of change is commensurate with the trends expected based on the assumption that the surface ocean pH change is only driven by the surface ocean CO2 chemistry remaining in a transient equilibrium with the increase in atmospheric CO2. In the remaining biomes, deviations from such equilibrium may reflect that the trend of surface ocean fCO2 is not equal to that of the atmosphere, most notably in the equatorial Pacific Ocean, or may reflect changes in the oceanic buffer (Revelle) factor. We conclude that well-planned and long-term sustained observational networks are key to reliably document the ongoing and future changes in ocean carbon chemistry due to anthropogenic forcing.

Short summary
This paper utilizes the SOCATv2 data product to calculate surface ocean pH. The pH data are divided into 17 biomes, and a linear regression is used to derive the long-term trend of pH in each biome. The results are consistent with the trends observed at time series stations. The uncertainties are too large for a mechanistic understanding of the driving forces behind the trend, but there are indications that concurrent changes in chemistry create spatial variability.
Final-revised paper