69 citations as recorded by crossref.

1. Magnitude, Trends, and Variability of the Global Ocean Carbon Sink From 1985 to 2018 T. DeVries et al. 10.1029/2023GB007780
2. CMEMS-LSCE: a global, 0.25°, monthly reconstruction of the surface ocean carbonate system T. Chau et al. 10.5194/essd-16-121-2024
3. Spatial and temporal variations in sea surface pCO2 and air-sea flux of CO2 in the Bering Sea revealed by satellite-based data during 2003–2019 S. Zhang et al. 10.3389/fmars.2023.1099916
4. ML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrients B. Wang et al. 10.5194/gmd-13-4253-2020
5. Reconstructing ocean subsurface salinity at high resolution using a machine learning approach T. Tian et al. 10.5194/essd-14-5037-2022
6. The Distribution of pCO2W and Air-Sea CO2 Fluxes Using FFNN at the Continental Shelf Areas of the Arctic Ocean I. Wrobel-Niedzwiecka et al. 10.3390/rs14020312
7. Variability in the Global Ocean Carbon Sink From 1959 to 2020 by Correcting Models With Observations V. Bennington et al. 10.1029/2022GL098632
8. Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis Z. Wang et al. 10.5194/essd-15-1711-2023
9. Research ReportDiurnal global ocean surface pCO2 and air–sea CO2 flux reconstructed from spaceborne LiDAR data S. Zhang et al. 10.1093/pnasnexus/pgad432
10. Observed Regional Fluxes to Constrain Modeled Estimates of the Ocean Carbon Sink A. Fay & G. McKinley 10.1029/2021GL095325
11. An Assessment of CO2 Uptake in the Arctic Ocean From 1985 to 2018 S. Yasunaka et al. 10.1029/2023GB007806
12. Variability of North Atlantic CO<sub>2</sub> fluxes for the 2000–2017 period estimated from atmospheric inverse analyses Z. Chen et al. 10.5194/bg-18-4549-2021
13. Sparse observations induce large biases in estimates of the global ocean CO 2 sink: an ocean model subsampling experiment J. Hauck et al. 10.1098/rsta.2022.0063
14. Laboratory experiments on CO2 gas exchange with wave breaking S. Li et al. 10.1175/JPO-D-20-0272.1
15. The increasing big gap of carbon sink between the western and eastern Pacific in the last three decades G. Zhong et al. 10.3389/fmars.2022.1088181
16. Quantification of Chaotic Intrinsic Variability of Sea‐Air CO2 Fluxes at Interannual Timescales M. Gehlen et al. 10.1029/2020GL088304
17. Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7 H. Peiro et al. 10.5194/acp-22-1097-2022
18. Distribution and long-term change of the sea surface carbonate system in the Mozambique Channel (1963–2019) C. Lo Monaco et al. 10.1016/j.dsr2.2021.104936
19. Modern air-sea flux distributions reduce uncertainty in the future ocean carbon sink G. McKinley et al. 10.1088/1748-9326/acc195
20. Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning J. Krasting et al. 10.1038/s43247-022-00419-4
21. Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning A. Denvil-Sommer et al. 10.5194/gmd-16-2995-2023
22. Data-based estimates of interannual sea–air CO<sub>2</sub> flux variations 1957–2020 and their relation to environmental drivers C. Rödenbeck et al. 10.5194/bg-19-2627-2022
23. The impact of seasonality on the annual air-sea carbon flux and its interannual variability P. Rustogi et al. 10.1038/s41612-023-00378-3
24. Comparison of Machine Learning Approaches for Reconstructing Sea Subsurface Salinity Using Synthetic Data T. Tian et al. 10.3390/rs14225650
25. A comparative assessment of the uncertainties of global surface ocean CO<sub>2</sub> estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? L. Gregor et al. 10.5194/gmd-12-5113-2019
26. The quiet crossing of ocean tipping points C. Heinze et al. 10.1073/pnas.2008478118
27. Plant gross primary production, plant respiration and carbonyl sulfide emissions over the globe inferred by atmospheric inverse modelling M. Remaud et al. 10.5194/acp-22-2525-2022
28. Global Carbon Budget 2020 P. Friedlingstein et al. 10.5194/essd-12-3269-2020
29. How Well Do We Understand the Land‐Ocean‐Atmosphere Carbon Cycle? D. Crisp et al. 10.1029/2021RG000736
30. Explicit Physical Knowledge in Machine Learning for Ocean Carbon Flux Reconstruction: The pCO2‐Residual Method V. Bennington et al. 10.1029/2021MS002960
31. SeaFlux: harmonization of air–sea CO<sub>2</sub> fluxes from surface <i>p</i>CO<sub>2</sub> data products using a standardized approach A. Fay et al. 10.5194/essd-13-4693-2021
32. Surface ocean CO2 concentration and air-sea flux estimate by machine learning with modelled variable trends J. Zeng et al. 10.3389/fmars.2022.989233
33. A seamless ensemble-based reconstruction of surface ocean <i>p</i>CO<sub>2</sub> and air–sea CO<sub>2</sub> fluxes over the global coastal and open oceans T. Chau et al. 10.5194/bg-19-1087-2022
34. The Ocean Carbon Cycle T. DeVries 10.1146/annurev-environ-120920-111307
35. Decrease in air-sea CO2 fluxes caused by persistent marine heatwaves A. Mignot et al. 10.1038/s41467-022-31983-0
36. Global Carbon Budget 2021 P. Friedlingstein et al. 10.5194/essd-14-1917-2022
37. Towards monitoring the CO2 source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO2 mole fraction V. Thilakan et al. 10.5194/acp-22-15287-2022
38. Amplification of the Ocean Carbon Sink During El Niños: Role of Poleward Ekman Transport and Influence on Atmospheric CO2 E. Liao et al. 10.1029/2020GB006574
39. Applications of Machine Learning in Chemical and Biological Oceanography B. Sadaiappan et al. 10.1021/acsomega.2c06441
40. External Forcing Explains Recent Decadal Variability of the Ocean Carbon Sink G. McKinley et al. 10.1029/2019AV000149
41. Estimating marine carbon uptake in the northeast Pacific using a neural network approach P. Duke et al. 10.5194/bg-20-3919-2023
42. Carbon Sinks and Variations of pCO2 in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach Y. Wang et al. 10.1109/JSTARS.2021.3066552
43. Carbon Cycle Response to Temperature Overshoot Beyond 2°C: An Analysis of CMIP6 Models I. Melnikova et al. 10.1029/2020EF001967
44. Global Carbon Budget 2019 P. Friedlingstein et al. 10.5194/essd-11-1783-2019
45. Revised estimates of ocean-atmosphere CO2 flux are consistent with ocean carbon inventory A. Watson et al. 10.1038/s41467-020-18203-3
46. Ocean carbonate system variability in the North Atlantic Subpolar surface water (1993–2017) C. Leseurre et al. 10.5194/bg-17-2553-2020
47. Sea surface pCO2 variability and air-sea CO2 exchange in the coastal Sudanese Red Sea E. Ali et al. 10.1016/j.rsma.2021.101796
48. OceanSODA-MDB: a standardised surface ocean carbonate system dataset for model–data intercomparisons P. Land et al. 10.5194/essd-15-921-2023
49. Global trends of ocean CO2 sink and ocean acidification: an observation-based reconstruction of surface ocean inorganic carbon variables Y. Iida et al. 10.1007/s10872-020-00571-5
50. Copernicus Marine Service Ocean State Report, Issue 4 K. von Schuckmann et al. 10.1080/1755876X.2020.1785097
51. Recent trends in the wind-driven California current upwelling system Y. Quilfen et al. 10.1016/j.rse.2021.112486
52. OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification L. Gregor & N. Gruber 10.5194/essd-13-777-2021
53. The sensitivity ofpCO2reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach L. Djeutchouang et al. 10.5194/bg-19-4171-2022
54. Improved Quantification of Ocean Carbon Uptake by Using Machine Learning to Merge Global Models and pCO2 Data L. Gloege et al. 10.1029/2021MS002620
55. Ocean-driven interannual variability in atmospheric CO2 quantified using OCO-2 observations and atmospheric transport simulations Y. Guan et al. 10.3389/fmars.2024.1272415
56. Carbon Air–Sea Flux in the Arctic Ocean from CALIPSO from 2007 to 2020 S. Zhang et al. 10.3390/rs14246196
57. Quantifying the Atmospheric CO2 Forcing Effect on Surface Ocean pCO2 in the North Pacific Subtropical Gyre in the Past Two Decades S. Chen et al. 10.3389/fmars.2021.636881
58. Feasibility of reconstructing the summer basin-scale sea surface partial pressure of carbon dioxide from sparse in situ observations over the South China Sea G. Wang et al. 10.5194/essd-13-1403-2021
59. Enhance seasonal amplitude of atmospheric CO 2 by the changing Southern Ocean carbon sink J. Yun et al. 10.1126/sciadv.abq0220
60. Alternate Histories: Synthetic Large Ensembles of Sea‐Air CO2 Flux H. Olivarez et al. 10.1029/2021GB007174
61. Reconstruction of global surface ocean <i>p</i>CO<sub>2</sub> using region-specific predictors based on a stepwise FFNN regression algorithm G. Zhong et al. 10.5194/bg-19-845-2022
62. National CO2budgets (2015–2020) inferred from atmospheric CO2observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
63. The impact of the South-East Madagascar Bloom on the oceanic CO<sub>2</sub> sink N. Metzl et al. 10.5194/bg-19-1451-2022
64. Characteristics of surface physical and biogeochemical parameters within mesoscale eddies in the Southern Ocean Q. Liu et al. 10.5194/bg-20-4857-2023
65. Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald et al. 10.1088/1748-9326/ac0eb0
66. Observation system simulation experiments in the Atlantic Ocean for enhanced surface ocean <i>p</i>CO<sub>2</sub> reconstructions A. Denvil-Sommer et al. 10.5194/os-17-1011-2021
67. A monthly surface <i>p</i>CO<sub>2</sub> product for the California Current Large Marine Ecosystem J. Sharp et al. 10.5194/essd-14-2081-2022
68. Consistency and Challenges in the Ocean Carbon Sink Estimate for the Global Carbon Budget J. Hauck et al. 10.3389/fmars.2020.571720
69. From Observation to Information and Users: The Copernicus Marine Service Perspective P. Le Traon et al. 10.3389/fmars.2019.00234