We quantify the oceanic sink for anthropogenic carbon dioxide (CO2) over the period 1994 to 2007 by using observations from the global repeat hydrography program and contrasting them to observations from the 1990s. Using a linear regression–based method, we find a global increase in the anthropogenic CO2inventory of 34 ± 4 petagrams of carbon (Pg C) between 1994 and 2007. This is equivalent to an average uptake rate of 2.6 ± 0.3 Pg C year−1and represents 31 ± 4% of the global anthropogenic CO2emissions over this period. Although this global ocean sink estimate is consistent with the expectation of the ocean uptake having increased in proportion to the rise in atmospheric CO2, substantial regional differences in storage rate are found, likely owing to climate variability–driven changes in ocean circulation.

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....

The global ocean is a significant sink for anthropogenic carbon (C_ant), absorbing roughly a third of human CO₂ emitted over the industrial period. Robust estimates of the magnitude and variability of the storage and distribution of C_ant in the ocean are therefore important for understanding the human impact on climate. In this synthesis we review observational and model-based estimates of the storage and transport of C_ant in the ocean. We pay particular attention to the uncertainties and potential biases inherent in different inference schemes. On a global scale, three data-based estimates of the distribution and inventory of C_ant are now available. While the inventories are found to agree within their uncertainty, there are considerable differences in the spatial distribution. We also present a review of the progress made in the application of inverse and data assimilation techniques which combine ocean interior estimates of C_ant with numerical ocean circulation models. Such methods are especially useful for estimating the air–sea flux and interior transport of C_ant, quantities that are otherwise difficult to observe directly. However, the results are found to be highly dependent on modeled circulation, with the spread due to different ocean models at least as large as that from the different observational methods used to estimate C_ant. Our review also highlights the importance of repeat measurements of hydrographic and biogeochemical parameters to estimate the storage of C_ant on decadal timescales in the presence of the variability in circulation that is neglected by other approaches. Data-based C_ant estimates provide important constraints on forward ocean models, which exhibit both broad similarities and regional errors relative to the observational fields. A compilation of inventories of C_ant gives us a "best" estimate of the global ocean inventory of anthropogenic carbon in 2010 of 155 ± 31 PgC (±20% uncertainty). This estimate includes a broad range of values, suggesting that a combination of approaches is necessary in order to achieve a robust quantification of the ocean sink of anthropogenic CO₂

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 f CO 2 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. Highprofile 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 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:

An unprecedentedly large ensemble of climate simulations with a 60-km atmospheric general circulation model and dynamical downscaling with a 20-km regional climate model has been performed to obtain probabilistic future projections of low-frequency local-scale events. The climate of the latter half of the twentieth century, the climate 4 K warmer than the preindustrial climate, and the climate of the latter half of the twentieth century without historical trends associated with the anthropogenic effect are each simulated for more than 5,000 years. From large ensemble simulations, probabilistic future changes in extreme events are available directly without using any statistical models. The atmospheric models are highly skillful in representing localized extreme events, such as heavy precipitation and tropical cyclones. Moreover, mean climate changes in the models are consistent with those in phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensembles. Therefore, the results enable the assessment of probabilistic change in localized severe events that have large uncertainty from internal variability. The simulation outputs are open to the public as a database called “Database for Policy Decision Making for Future Climate Change” (d4PDF), which is intended to be utilized for impact assessment studies and adaptation planning for global warming.