An update to the Surface Ocean CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; Atlas (SOCAT version 2)

Bakker, D. C. E.; Pfeil, Benjamin; Smith, Karl M.; Hankin, S.; Olsen, Are; Alin, Simone R.; Cosca, Catherine E; Harasawa, S.; Kozyr, Alex; Nojiri, Yukihiro; O’Brien, Kevin; Schuster, Ute; Telszewski, Maciej; Tilbrook, Bronte; Wada, Chisato; Akl, John; Barbero, Leticia; Bates, Nicholas R.; Boutin, Jacqueline; Bozec, Yann; Cai, Wei‐Jun; Castle, R. D.; Chávez, Francisco P.; Chen, L.; Chierici, Melissa; Currie, Kim; Baar, H. J. W. de; Evans, Wiley; Feely, Richard A.; Fransson, Agneta; Gao, Zhiqian; Hales, Burke; Hardman-Mountford, Nick J.; Hoppema, Mario; Huang, Wei‐Jen; Hunt, Christopher W; Huss, B.; Ichikawa, Tadafumi; Johannessen, Truls; Jones, Elizabeth M.; Jones, Steve; Jütterström, Sara; Kitidis, Vassilis; Körtzinger, Arne; Landschützer, Peter; Lauvset, Siv K.; Lefèvre, Nathalie; Manke, A.; Mathis, Jeremy T.; Merlivat, Liliane; Metzl, Nicolas; Murata, Akihiko; Newberger, Timothy; Omar, Abdirahman M; Ono, Tsueno; Park, G.-H.; Paterson, K.; Pierrot, Denis; Rı́os, Aida F.; Sabine, Christopher L.; Saito, Shu; Salisbury, Joe; Sarma, V. V. S. S.; Schlitzer, Reiner; Sieger, Rainer; Skjelvan, Ingunn; Steinhoff, Tobias; Sullivan, Kevin F.; Sun, H.; Sutton, Adrienne J.; Suzuki, T.; Sweeney, C.; Takahashi, Taro; Tjiputra, Jerry; Tsurushima, Nobuo; Heuven, Steven M. A. C. van; Vandemark, D.; Vlahos, Penny; Wallace, Douglas W.R.; Wanninkhof, Rik; Watson, Andrew J.

doi:10.5194/essd-6-69-2014

Cited by 2,839 publications

(160 citation statements)

References 65 publications

Supporting

Mentioning

155

Contrasting

Order By: Relevance

“…The few existing studies of Norwegian fjords confirm the above picture; i.e. they act as an annual net sink for atmospheric CO 2 (Fransson et al, 2014;Omar et al, 2005).…”

Section: A M Omar Et Al: Aragonite Saturation States and Ph In Wesmentioning

confidence: 57%

“…However, only a few studies on the carbon cycle of Norwegian fjords exist and these are only from the high Arctic at Svalbard (Fransson et al, 2014;Omar et al, 2005). Generally, in the Northern Hemisphere, high latitude coastal regions are thought to be sinks for atmospheric CO 2 , while low-latitude regions are thought to be CO 2 sources Cai et al, 2006;Chavez et al, 2007;Chen and Borges, 2009).…”

Section: A M Omar Et Al: Aragonite Saturation States and Ph In Wesmentioning

confidence: 99%

See 1 more Smart Citation

Aragonite saturation states and pH in western Norwegian fjords: seasonal cycles and controlling factors, 2005–2009

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract. The uptake of anthropogenic carbon dioxide (CO2) by the ocean leads to a process known as ocean acidification (OA), which lowers the aragonite saturation state (ΩAr) and pH, and this is poorly documented in coastal environments including fjords due to lack of appropriate observations.Here we use weekly underway data from the Voluntary Observing Ships (VOS) program covering the period 2005–2009 combined with data from research cruises to estimate ΩAr and pH values in several adjacent western Norwegian fjords, and to evaluate how seawater CO2 chemistry drives their variations in response to physical and biological factors.The OA parameters in the surface waters of the fjords are subject to strong seasonal and spatially coherent variations. These changes are governed by the seasonal changes in temperature, salinity, formation and decay of organic matter, and vertical mixing with deeper, carbon-rich coastal water. Annual mean pH and ΩAr values were 8.13 and 2.21, respectively. The former varies from minimum values ( ≈  8.05) in late December – early January to maximum values of around 8.2 during early spring (March–April) as a consequence of the phytoplankton spring bloom, which reduces dissolved inorganic carbon (DIC). In the following months, pH decreases in response to warming. This thermodynamic decrease in pH is reinforced by the deepening of the mixed layer, which enables carbon-rich coastal water to reach the surface, and this trend continues until the low winter values of pH are reached again. ΩAr, on the other hand, reaches its seasonal maximum (> 2.5) in mid- to late summer (July–September), when the spring bloom is over and pH is decreasing. The lowest ΩAr values ( ≈  1.3–1.6) occur during winter (January–March), when both pH and sea surface temperature (SST) are low and DIC is its highest. Consequently, seasonal ΩAr variations align with those of SST and salinity normalized DIC (nDIC).We demonstrate that underway measurements of fugacity of CO2 in seawater (fCO2) and SST from VOS lines combined with high frequency observations of the complete carbonate system at strategically placed fixed stations provide an approach to interpolate OA parameters over large areas in the fjords of western Norway.

show abstract

“…The few existing studies of Norwegian fjords confirm the above picture; i.e. they act as an annual net sink for atmospheric CO 2 (Fransson et al, 2014;Omar et al, 2005).…”

Section: A M Omar Et Al: Aragonite Saturation States and Ph In Wesmentioning

confidence: 57%

Section: A M Omar Et Al: Aragonite Saturation States and Ph In Wesmentioning

confidence: 99%

Aragonite saturation states and pH in western Norwegian fjords: seasonal cycles and controlling factors, 2005–2009

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…The measurements were from the Surface Ocean CO 2 Atlas (SOCAT v3; Bakker et al, 2014Bakker et al, , 2015, which contains 14.5 million data to the end of 2014. This was extended with 1.4 million additional measurements over years 2013-2014 (see data attribution Table A1 in Appendix A), submitted to SOCAT but not yet fully quality controlled following standard SOCAT procedures.…”

Section: Observation-based Estimatesmentioning

confidence: 99%

Global Carbon Budget 2015

Quéré

Moriarty

Andrew

et al. 2015

Earth Syst. Sci. Data

Self Cite

668

485

View full text Add to dashboard Cite

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry (E FF ) are based on energy statistics and cement production data, while emissions from land-use change (E LUC ), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth (G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink (S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink (S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO 2 , and land-cover change (some including nitrogen-carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ , reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), E FF was 9.0 ± 0.5 GtC yr −1 , E LUC was 0.9 ± 0.5 GtC yr −1 , G ATM was 4.4 ± 0.1 GtC yr −1 , S OCEAN was 2.6 ± 0.5 GtC yr −1 , and S LAND was 3.0 ± 0.8 GtC yr −1 . For the year 2014 alone, E FF grew to 9.8 ± 0.5 GtC yr −1 , 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yr −1 that took place during 2005-2014. Also, for 2014, E LUC was 1.1 ± 0.5 GtC yr −1 , G ATM was 3.9 ± 0.2 GtC yr −1 , S OCEAN was 2.9 ± 0.5 GtC yr −1 , and S LAND was 4.1 ± 0.9 GtC yr −1 . G ATM was lower in 2014 compared to the past decade (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), reflecting a larger S LAND for that year. The global atmospheric CO 2 concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in E FF will be near or slightly below zero, with a projection of −0.6 [range of −1.6 to +0....

show abstract

“…The increasing availability of quality-controlled global biogeochemical data sets for the historical period (e.g. Surface Ocean CO 2 Atlas Version 2 (SOCAT v2, Bakker et al, 2014)) provides further opportunity to evaluate model performance on multi-decadal timescales. Recent analyses of CMIP5 ESMs indicate that persistent biases exist in simulated biogeochemical variables, for instance as identified in ocean oxygen (Andrews et al, 2013) and carbon cycle fields derived from CMIP5 historical experiments.…”

Section: Marine Biogeochemistrymentioning

confidence: 99%

ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP

et al. 2016

View full text Add to dashboard Cite

Abstract.A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected essential climate variables (ECVs), a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases, and soil hydrology-climate interactions, as well as atmospheric CO 2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard namelists for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The Earth System Model Evaluation Tool (ESMValTool) is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the Coupled Model Intercomparison Project (CMIP) ensemble. This will facilitate and improve ESM evaluation beyond the stateof-the-art and aims at supporting such activities within CMIP and at individual modelling centres. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user.

show abstract

An update to the Surface Ocean CO<sub>2</sub> Atlas (SOCAT version 2)

Cited by 2,839 publications

References 65 publications

Aragonite saturation states and pH in western Norwegian fjords: seasonal cycles and controlling factors, 2005–2009

Aragonite saturation states and pH in western Norwegian fjords: seasonal cycles and controlling factors, 2005–2009

Global Carbon Budget 2015

ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP

Contact Info

Product

Resources

About