A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach

Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than 2 centuries. As the ocean stores most of the carbon on our planet, there is an important task in unraveling the natural and anthropogenic processes that drive the carbon cycle at different spatial and temporal scales. We contribute to this by designing a global monthly climatology of total dissolved inorganic carbon (TCO2), which offers a robust basis in carbon cycle modeling but also for other studies related to this cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured to extract from the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) and the Lamont–Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set of variables related to the former's variability. The global root mean square error (RMSE) of mapping TCO2 is relatively low for the two datasets (GLODAPv2.2019: 7.2 µmol kg−1; LDEO: 11.4 µmol kg−1) and also for independent data, suggesting that the network does not overfit possible errors in data. The ability of NNGv2LDEO to capture the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in 10 time series stations spread over different regions of the ocean (RMSE: 3.6 to 13.2 µmol kg−1). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity, and oxygen from the World Ocean Atlas 2013 and phosphate, nitrate, and silicate computed from a neural network fed with the previous fields. The resolution is 1∘×1∘ in the horizontal, 102 depth levels (0–5500 m), and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution, and it is centered around the year 1995. The uncertainty of the climatology is low when compared with climatological values derived from measured TCO2 in the largest time series stations. Furthermore, a computed climatology of partial pressure of CO2 (pCO2) from a previous climatology of total alkalinity and the present one of TCO2 supports the robustness of this product through the good correlation with a widely used pCO2 climatology (Landschützer et al., 2017). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/10551, Broullón et al., 2020)

Data Management Strategy to Improve Global Use of Ocean Acidification Data and Information

International audienceOcean acidification (OA) refers to the general decrease in pH of the global ocean as a result of absorbing anthropogenic CO2 emitted in the atmosphere since preindustrial times (Sabine et al., 2004). There is, however, considerable variability in ocean acidification, and many careful measurements need to be made and compared in order to obtain scientifically valid information for the assessment of patterns, trends, and impacts over a range of spatial and temporal scales, and to understand the processes involved. A single country or institution cannot undertake measurements of worldwide coastal and open ocean OA changes; therefore, international cooperation is needed to achieve that goal. The OA data that have been, and are being, collected represent a significant public investment. To this end, it is critically important that researchers (and others) around the world are easily able to find and use reliable OA information that range from observing data (from time-series moorings, process studies, and research cruises), to biological response experiments (e.g., mesocosm), data products, and model output. [...

A new global interior ocean mapped climatology: the 1° × 1° GLODAP version 2

We present a mapped climatology (GLODAPv2.2016b) of ocean biogeochemical variables based on the new GLODAP version 2 data product (Olsen et al., 2016; Key et al., 2015), which covers all ocean basins over the years 1972 to 2013. The quality-controlled and internally consistent GLODAPv2 was used to create global 1°  ×  1° mapped climatologies of salinity, temperature, oxygen, nitrate, phosphate, silicate, total dissolved inorganic carbon (TCO2), total alkalinity (TAlk), pH, and CaCO3 saturation states using the Data-Interpolating Variational Analysis (DIVA) mapping method. Improving on maps based on an earlier but similar dataset, GLODAPv1.1, this climatology also covers the Arctic Ocean. Climatologies were created for 33 standard depth surfaces. The conceivably confounding temporal trends in TCO2 and pH due to anthropogenic influence were removed prior to mapping by normalizing these data to the year 2002 using first-order calculations of anthropogenic carbon accumulation rates. We additionally provide maps of accumulated anthropogenic carbon in the year 2002 and of preindustrial TCO2. For all parameters, all data from the full 1972–2013 period were used, including data that did not receive full secondary quality control. The GLODAPv2.2016b global 1°  ×  1° mapped climatologies, including error fields and ancillary information, are available at the GLODAPv2 web page at the Carbon Dioxide Information Analysis Center (CDIAC; doi:10.3334/CDIAC/OTG.NDP093_GLODAPv2)

A multi-decade record of high quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) “living data” publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID

Autonomous Seawater \u3ci\u3ep\u3c/i\u3eCO\u3csub\u3e2\u3c/sub\u3e and pH Time Series From 40 Surface Buoys and the Emergence of Anthropogenic Trends

Ship-based time series, some now approaching over 3 decades long, are critical climate records that have dramatically improved our ability to characterize natural and anthropogenic drivers of ocean carbon dioxide (CO2) uptake and biogeochemical processes. Advancements in autonomous marine carbon sensors and technologies over the last 2 decades have led to the expansion of observations at fixed time series sites, thereby improving the capability of characterizing sub-seasonal variability in the ocean. Here , we present a data product of 40 individual autonomous moored surface ocean pCO2 (partial pressure of CO2) time series established between 2004 and 2013, 17 also include autonomous pH measurements. These time series characterie a wide range of surface ocean carbonate conditions in diffferent oceanic (17 sites), coastal (13 sites), and coral reef (10 sites) regimes. A time of trend emergence (ToE) methodology applied ot the time series that exhibit well-constrained daily to interannual variability and an estimate of decadal variability indicates that the length of sustained observations necessary to detect statistically significant anthropogenic trends varies by marine environment. The ToE estisites, and 9 to 22 years at the coral reef sites. Only two open ocean pCO2 and pH range from 8 to 15 years at the open ocean sites, 16 to 41 years at the coastal sites, and 9 to 22 years at the coral reef sites. Only two open ocean pCO2 time series, Woods Hole Oceanographic Institution Hawaii Ocean Time-series Station (WHOTS) in the subtropical North Pacific and Stratus n the South Pacific gyre, have been deployed longer than the estimated trend detection time and, for these, deseasoned monthly means show estimated anthropogenic trends of 1.9 ± 0.3 and 1.6 ± 0.3 μatm yr-1, respectively. In the future, it is possible that updates to this product will allow for the estimation of anthropogenic trends at more sites; however, the product currently provides a valuable tool in an accessible format for evaluating climatology and natural variability of surface ocean carbonate chemistry in a variety of regions. Data are available at https://doi.org/10.7289/V5DB8043 and https://www.nodc.noaa.gov/ocads/oceans/Moorings/ndp097.html (Sutton et al., 2018)

Best practice data standards for discrete chemical oceanographic observations

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jiang, L.-Q., Pierrot, D., Wanninkhof, R., Feely, R. A., Tilbrook, B., Alin, S., Barbero, L., Byrne, R. H., Carter, B. R., Dickson, A. G., Gattuso, J.-P., Greeley, D., Hoppema, M., Humphreys, M. P., Karstensen, J., Lange, N., Lauvset, S. K., Lewis, E. R., Olsen, A., Pérez, F. F., Sabine, C., Sharp, J. D., Tanhua, T., Trull, T. W., Velo, A., Allegra, A. J., Barker, P., Burger, E., Cai, W-J., Chen, C-T. A., Cross, J., Garcia, H., Hernandez-Ayon J. M., Hu, X., Kozyr, A., Langdon, C., Lee., K, Salisbury, J., Wang, Z. A., & Xue, L. Best practice data standards for discrete chemical oceanographic observations. Frontiers in Marine Science, 8, (2022): 705638, https://doi.org/10.3389/fmars.2021.705638.Effective data management plays a key role in oceanographic research as cruise-based data, collected from different laboratories and expeditions, are commonly compiled to investigate regional to global oceanographic processes. Here we describe new and updated best practice data standards for discrete chemical oceanographic observations, specifically those dealing with column header abbreviations, quality control flags, missing value indicators, and standardized calculation of certain properties. These data standards have been developed with the goals of improving the current practices of the scientific community and promoting their international usage. These guidelines are intended to standardize data files for data sharing and submission into permanent archives. They will facilitate future quality control and synthesis efforts and lead to better data interpretation. In turn, this will promote research in ocean biogeochemistry, such as studies of carbon cycling and ocean acidification, on regional to global scales. These best practice standards are not mandatory. Agencies, institutes, universities, or research vessels can continue using different data standards if it is important for them to maintain historical consistency. However, it is hoped that they will be adopted as widely as possible to facilitate consistency and to achieve the goals stated above.Funding for L-QJ and AK was from NOAA Ocean Acidification Program (OAP, Project ID: 21047) and NOAA National Centers for Environmental Information (NCEI) through NOAA grant NA19NES4320002 [Cooperative Institute for Satellite Earth System Studies (CISESS)] at the University of Maryland/ESSIC. BT was in part supported by the Australia’s Integrated Marine Observing System (IMOS), enabled through the National Collaborative Research Infrastructure Strategy (NCRIS). AD was supported in part by the United States National Science Foundation. AV and FP were supported by BOCATS2 Project (PID2019-104279GB-C21/AEI/10.13039/501100011033) funded by the Spanish Research Agency and contributing to WATER:iOS CSIC interdisciplinary thematic platform. MH was partly funded by the European Union’s Horizon 2020 Research and Innovation Program under grant agreement N°821001 (SO-CHIC)

The annual update GLODAPv2.2023: the global interior ocean biogeochemical data product

The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface to bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2023 is an update of the previous version, GLODAPv2.2022 (Lauvset et al., 2022). The major changes are as follows: data from 23 new cruises were added. In addition, a number of changes were made to the data included in GLODAPv2.2022. GLODAPv2.2023 includes measurements from more than 1.4 million water samples from the global oceans collected on 1108 cruises. The data for the now 13 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, chlorofluorocarbon-11 (CFC-11), CFC-12, CFC-113, CCl4, and SF6) have undergone extensive quality control with a focus on the systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but converted to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For the present annual update, adjustments for the 23 new cruises were derived by comparing those data with the data from the 1085 quality-controlled cruises in the GLODAPv2.2022 data product using crossover analysis. SF6 data from all cruises were evaluated by comparison with CFC-12 data measured on the same cruises. For nutrients and ocean carbon dioxide (CO2), chemistry comparisons to estimates based on empirical algorithms provided additional context for adjustment decisions. The adjustments that we applied are intended to remove potential biases from errors related to measurement, calibration, and data-handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), were not subjected to bias comparison or adjustments. The original data, their documentation, and DOI codes are available at the Ocean Carbon and Acidification Data System of NOAA National Centers for Environmental Information (NCEI), which also provides access to the merged data product. This is provided as a single global file and as four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under https://doi.org/10.25921/zyrq-ht66 (Lauvset et al., 2023). These bias-adjusted product files also include significant ancillary and approximated data, which were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2023 methods and provides a broad overview of the secondary quality control procedures and results

The development of the data system and growth in data sharing

A great wealth of ocean data exists, for a wide range of disciplines, derived from in-situ and remote sensing observing platforms, in real-time, near-real-time and delayed mode. These data are acquired as part of routine monitoring activities and as part of scientific surveys by a few thousand institutes and agencies all around the world. Both the means to acquire these data and the way in which they are used have changed greatly in the past ten years. Over the last decade, information technology has progressed a great deal. It presently allows the exchange of gigabytes of data and more via the Internet in developed countries. In the late nineties, it was considered high technology to provide data on CDROM rather than on magnetic tapes and only small datasets were distributed via the Internet. Nowadays CDROMs are considered as a backup delivery system especially for countries with poor Internet connections. The explosion in use of the Internet has provided new communications capabilities, new tools, and a new way of using computers. The nature of requirements from government agencies have changed: they want to know or estimate what the future of the earth will look like and what will be the impact on their territories due to climate change issues, ocean health monitoring and fisheries assessment, but they can't pay the full bill for the data acquisition. Therefore, they are pushing, and nowadays more often imposing, a change in data policy and a move towards increased data sharing, in which data acquired with public funds should be freely available to the community. Moreover, the nature of science itself has changed. Investigators and research funding agencies are looking for context, impacts, and synthesis, rather than just focusing on individual, well-defined processes. Most scientists need the data collected by others as well as their own. They cannot do their work using only data they have collected themselves. Finally, the growth of operational oceanographic services, based on downscaling of global model results, is really important. These are in demand by users especially for real/near real-time data just as operational meteorology has been doing for a long time

A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]

The item is made of 6 files: 1) README.txt; 2) TCO2_NNGv2LDEO_climatology.nc contains the climatology of TCO2 centered in 1995 and computed with NNGv2LDEO in netcdf4 format; 3) pCO2_NNGv2LDEO_climatology.nc contains the climatology of pCO2 centered in 1995 and computed with NNGv2LDEO and NNGv2 of Broullón et al. (2019) in netcdf4 format ; 4) NNGv2LDEO.mat is the neural network object used to create the climatology of TCO2; 5) TCO2NNWOA13.mp4 is a video of the surface climatology, 3 vertical sections in the Pacific Ocean, Atlantic Ocean and Indian Ocean and, the variation in depth of one month (April); 6) Example.rar contains an example matrix of inputs and targets to the neural network, the NNGv2LDEO.mat and a MATLAB script to compute TCO2 with NNGv2LDEOThis research was supported by Ministerio de Educación, Cultura y Deporte (FPU grant FPU15/06026), Ministerio de Economía y Competitividad through the ARIOS (CTM2016-76146-C3-1-R) project co-funded by the Fondo Europeo de Desarrollo Regional 2014-2020 (FEDER) and EU Horizon 2020 through the AtlantOS project (grant agreement 633211)Peer reviewe