Global Surface Ocean Acidification Indicators From 1750 to 2100

Type Article
Date 2023-03
Language English
Author(s) Jiang Li-Qing1, 2, Dunne John3, Carter Brendan R.4, 5, Tjiputra Jerry F.6, Terhaar Jens7, 8, 9, Sharp Jonathan D.4, 5, Olsen Are10, 11, Alin Simone, Bakker Dorothee C. E.12, Feely Richard A.5, Gattuso Jean-Pierre13, 14, Hogan Patrick15, Ilyina Tatiana16, Lange Nico17, Lauvset Siv K.6, Lewis Ernie R.18, Lovato Tomas19, Palmieri Julien20, Santana-Falcon Yeray21, Schwinger Joerg6, Seferian Roland21, Strand Gary22, Swart Neil23, Tanhua Toste17, Tsujino Hiroyuki24, Wanninkhof Rik25, Watanabe Michio26, 27, Yamamoto Akitomo26, 27, Ziehn TiloORCID28
Affiliation(s) 1 : Univ Maryland, Cooperat Inst Satellite Earth Syst Studies, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 21201 USA.
2 : NOAA, NESDIS Natl Ctr Environm Informat, Silver Spring, MD 20910 USA.
3 : NOAA, OAR Geophys Fluid Dynam Lab, Princeton, NJ USA.
4 : Univ Washington, Cooperat Inst Climate Ocean & Ecosyst Studies, Seattle, WA USA.
5 : NOAA, OAR Pacific Marine Environm Lab, Seattle, WA USA.
6 : Bjerknes Ctr Climate Res, NORCE Norwegian Res Ctr, Bergen, Norway.
7 : Woods Hole Oceanog Inst, Dept Marine Chem & Geochem, Woods Hole, MA USA.
8 : Univ Bern, Phys Inst, Climate & Environm Phys, Bern, Switzerland.
9 : Univ Bern, Oeschger Ctr Climate Change Res, Bern, Switzerland.
10 : Univ Bergen, Geophys Inst, Bergen, Norway.
11 : Bjerknes Ctr Climate Res, Bergen, Norway.
12 : Univ East Anglia, Ctr Ocean & Atmospher Sci, Sch Environm Sci, Norwich, England.
13 : Sorbonne Univ, CNRS, Lab Oceanog Villefranche, Villefranche Sur Mer, France.
14 : Inst Sustainable Dev & Int Relat, Paris, France.
15 : NOAA, NESDIS Natl Ctr Environm Informat, Stennis Space Ctr, MS USA.
16 : Max Planck Inst Meteorol, Hamburg, Germany.
17 : GEOMAR Helmholtz Ctr Ocean Res Kiel, Kiel, Germany.
18 : Brookhaven Natl Lab, Upton, NY USA.
19 : Fdn Ctr Euro Mediterraneo Cambiamenti Climat, Ocean Modeling & Data Assimilat Div, CMCC, Bologna, Italy.
20 : Natl Oceanog Ctr, European Way, Southampton, England.
21 : Univ Toulouse, CNRM, Meteo France, CNRS, Toulouse 1, France.
22 : US Natl Ctr Atmospher Res, Boulder, CO USA.
23 : Univ Victoria, Canadian Ctr Climate Modelling & Anal, Victoria, BC, Canada.
24 : JMA Meteorol Res Inst, Tsukuba, Japan.
25 : NOAA, OAR Atlantic Oceanog & Meteorol Lab, Miami, FL USA.
26 : Japan Agcy Marine Earth Sci & Technol JAMSTEC, Res Inst Global Change, Yokosuka, Kanagawa, Japan.
27 : Univ Tokyo, Atmosphere & Ocean Res Inst, Chiba, Japan.
28 : CSIRO Oceans & Atmosphere, Aspendale, Vic, Australia.
Source Journal Of Advances In Modeling Earth Systems (Amer Geophysical Union), 2023-03 , Vol. 15 , N. 3 , P. e2022MS003563 (23p.)
DOI 10.1029/2022MS003563
WOS© Times Cited 13
Keyword(s) ocean acidification indicators, pH, aragonite saturation state, global surface ocean, Earth System Models, Shared Socioeconomic Pathways
Abstract

Accurately predicting future ocean acidification (OA) conditions is crucial for advancing OA research at regional and global scales, and guiding society's mitigation and adaptation efforts. This study presents a new model-data fusion product covering 10 global surface OA indicators based on 14 Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with three recent observational ocean carbon data products. The indicators include fugacity of carbon dioxide, pH on total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle Factor, total dissolved inorganic carbon content, and total alkalinity content. The evolution of these OA indicators is presented on a global surface ocean 1 degrees x 1 degrees grid as decadal averages every 10 years from preindustrial conditions (1750), through historical conditions (1850-2010), and to five future Shared Socioeconomic Pathways (2020-2100): SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. These OA trajectories represent an improvement over previous OA data products with respect to data quantity, spatial and temporal coverage, diversity of the underlying data and model simulations, and the provided SSPs. The generated data product offers a state-of-the-art research and management tool for the 21st century under the combined stressors of global climate change and ocean acidification. The gridded data product is available in NetCDF at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html, and global maps of these indicators are available in jpeg at: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-datasystem/synthesis/surface-oa-indicators.html. Plain Language Summary A new data product, based on the latest computer simulations and observational data, offers improved projections of ocean acidification (OA) conditions from the start of the Industrial Revolution in 1750 to the end of the 21st century. These projections will support OA research at regional and global scales, and provide essential information to guide OA mitigation and adaptation efforts for various sectors, including fisheries, aquaculture, tourism, marine resource decision-makers, and the general public.

Full Text
File Pages Size Access
Publisher's official version 23 8 MB Open access
Supporting Information S1 57 11 MB Open access
Top of the page

How to cite 

Jiang Li-Qing, Dunne John, Carter Brendan R., Tjiputra Jerry F., Terhaar Jens, Sharp Jonathan D., Olsen Are, Alin Simone, Bakker Dorothee C. E., Feely Richard A., Gattuso Jean-Pierre, Hogan Patrick, Ilyina Tatiana, Lange Nico, Lauvset Siv K., Lewis Ernie R., Lovato Tomas, Palmieri Julien, Santana-Falcon Yeray, Schwinger Joerg, Seferian Roland, Strand Gary, Swart Neil, Tanhua Toste, Tsujino Hiroyuki, Wanninkhof Rik, Watanabe Michio, Yamamoto Akitomo, Ziehn Tilo (2023). Global Surface Ocean Acidification Indicators From 1750 to 2100. Journal Of Advances In Modeling Earth Systems, 15(3), e2022MS003563 (23p.). Publisher's official version : https://doi.org/10.1029/2022MS003563 , Open Access version : https://archimer.ifremer.fr/doc/00842/95432/