Articles | Volume 16, issue 8
https://doi.org/10.5194/essd-16-3517-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3517-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
02 Aug 2024
Data description article |
| 02 Aug 2024
| 02 Aug 2024
IAPv4 ocean temperature and ocean heat content gridded dataset
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Yuying Pan
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Zhetao Tan
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Huayi Zheng
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Yujing Zhu
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Wangxu Wei
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Huifeng Yuan
Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Guancheng Li
Eco-Environmental Monitoring and Research Center, Pearl River Valley and South China Sea Ecology and Environment Administration, Ministry of Ecology and Environment, PRC, Guangzhou, 510611, China
Hanlin Ye
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Viktor Gouretski
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Yuanlong Li
Institute of Oceanography, Chinese Academy of Sciences, Qingdao, 266000, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Kevin E. Trenberth
National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307, USA
Department of Physics, University of Auckland, Tāmaki Makaurau / Auckland, Aotearoa / New Zealand
John Abraham
University of St. Thomas, School of Engineering, 2115 Summit Ave, St Paul, MN 55105, USA
Yuchun Jin
Institute of Oceanography, Chinese Academy of Sciences, Qingdao, 266000, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Franco Reseghetti
Istituto Nazionale di Geofisica e Vulcanologia, 40127, Bologna, Italy
Xiaopei Lin
Frontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory, Ocean University of China, Qingdao, 266100, China
Bin Zhang
Institute of Oceanography, Chinese Academy of Sciences, Qingdao, 266000, China
Gengxin Chen
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
University of Chinese Academy of Sciences, Beijing, 101408, China
Michael E. Mann
Dept. of Earth and Environmental Science, University of Pennsylvania, Philadelphia, PA, USA
Jiang Zhu
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 101408, China
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Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Zhenming Wang, Shaoqing Zhang, Yishuai Jin, Yinglai Jia, Yangyang Yu, Yang Gao, Xiaolin Yu, Mingkui Li, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev., 16, 705–717, https://doi.org/10.5194/gmd-16-705-2023, https://doi.org/10.5194/gmd-16-705-2023, 2023
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To improve the numerical model predictability of monthly extended-range scales, we use the simplified slab ocean model (SOM) to restrict the complicated sea surface temperature (SST) bias from a 3-D dynamical ocean model. As for SST prediction, whether in space or time, the WRF-SOM is verified to have better performance than the WRF-ROMS, which has a significant impact on the atmosphere. For extreme weather events such as typhoons, the predictions of WRF-SOM are in good agreement with WRF-ROMS.
Tian Tian, Lijing Cheng, Gongjie Wang, John Abraham, Wangxu Wei, Shihe Ren, Jiang Zhu, Junqiang Song, and Hongze Leng
Earth Syst. Sci. Data, 14, 5037–5060, https://doi.org/10.5194/essd-14-5037-2022, https://doi.org/10.5194/essd-14-5037-2022, 2022
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A high-resolution gridded dataset is crucial for understanding ocean processes at various spatiotemporal scales. Here we used a machine learning approach and successfully reconstructed a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 (monthly) by merging in situ salinity profile observations with high-resolution satellite remote-sensing data. This new product could be useful in various applications in ocean and climate fields.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708, https://doi.org/10.5194/gmd-15-6695-2022, https://doi.org/10.5194/gmd-15-6695-2022, 2022
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To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Jingzhe Sun, Yingjing Jiang, Shaoqing Zhang, Weimin Zhang, Lv Lu, Guangliang Liu, Yuhu Chen, Xiang Xing, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev., 15, 4805–4830, https://doi.org/10.5194/gmd-15-4805-2022, https://doi.org/10.5194/gmd-15-4805-2022, 2022
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An online ensemble coupled data assimilation system with the Community Earth System Model is designed and evaluated. This system uses the memory-based information transfer approach which avoids frequent I/O operations. The observations of surface pressure, sea surface temperature, and in situ temperature and salinity profiles can be effectively assimilated into the coupled model. That will facilitate a long-term high-resolution climate reanalysis once the algorithm efficiency is much improved.
Guorong Zhong, Xuegang Li, Jinming Song, Baoxiao Qu, Fan Wang, Yanjun Wang, Bin Zhang, Xiaoxia Sun, Wuchang Zhang, Zhenyan Wang, Jun Ma, Huamao Yuan, and Liqin Duan
Biogeosciences, 19, 845–859, https://doi.org/10.5194/bg-19-845-2022, https://doi.org/10.5194/bg-19-845-2022, 2022
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A predictor selection algorithm was constructed to decrease the predicting error in the surface ocean partial pressure of CO2 (pCO2) mapping by finding better combinations of pCO2 predictors in different regions. Compared with previous research using the same combination of predictors in all regions, using different predictors selected by the algorithm in different regions can effectively decrease pCO2 predicting errors.
Zhaohui Chen, Parvadha Suntharalingam, Andrew J. Watson, Ute Schuster, Jiang Zhu, and Ning Zeng
Biogeosciences, 18, 4549–4570, https://doi.org/10.5194/bg-18-4549-2021, https://doi.org/10.5194/bg-18-4549-2021, 2021
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As the global temperature continues to increase, carbon dioxide (CO2) is a major driver of this global warming. The increased CO2 is mainly caused by emissions from fossil fuel use and land use. At the same time, the ocean is a significant sink in the carbon cycle. The North Atlantic is a critical ocean region in reducing CO2 concentration. We estimate the CO2 uptake in this region based on a carbon inverse system and atmospheric CO2 observations.
Weiqi Xu, Chun Chen, Yanmei Qiu, Ying Li, Zhiqiang Zhang, Eleni Karnezi, Spyros N. Pandis, Conghui Xie, Zhijie Li, Jiaxing Sun, Nan Ma, Wanyun Xu, Pingqing Fu, Zifa Wang, Jiang Zhu, Douglas R. Worsnop, Nga Lee Ng, and Yele Sun
Atmos. Chem. Phys., 21, 5463–5476, https://doi.org/10.5194/acp-21-5463-2021, https://doi.org/10.5194/acp-21-5463-2021, 2021
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Here aerosol volatility and viscosity at a rural site (Gucheng) and an urban site (Beijing) in the North China Plain (NCP) were investigated in summer and winter. Our results showed that organic aerosol (OA) in winter in the NCP is more volatile than that in summer due to enhanced primary emissions from coal combustion and biomass burning. We also found that OA existed mainly as a solid in winter in Beijing but as semisolids in Beijing in summer and Gucheng in winter.
Lei Kong, Xiao Tang, Jiang Zhu, Zifa Wang, Jianjun Li, Huangjian Wu, Qizhong Wu, Huansheng Chen, Lili Zhu, Wei Wang, Bing Liu, Qian Wang, Duohong Chen, Yuepeng Pan, Tao Song, Fei Li, Haitao Zheng, Guanglin Jia, Miaomiao Lu, Lin Wu, and Gregory R. Carmichael
Earth Syst. Sci. Data, 13, 529–570, https://doi.org/10.5194/essd-13-529-2021, https://doi.org/10.5194/essd-13-529-2021, 2021
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China's air pollution has changed substantially since 2013. Here we have developed a 6-year-long high-resolution air quality reanalysis dataset over China from 2013 to 2018 to illustrate such changes and to provide a basic dataset for relevant studies. Surface fields of PM2.5, PM10, SO2, NO2, CO, and O3 concentrations are provided, and the evaluation results indicate that the reanalysis dataset has excellent performance in reproducing the magnitude and variation of air pollution in China.
Cited articles
Abraham, J. P. and Cheng, L.: Intersection of Climate Change, Energy, and Adaptation, Energies, 15, 5886, https://doi.org/10.3390/en15165886, 2022.
Abraham, J. P., Baringer, M., Bindoff, N. L., Boyer, T., Cheng, L. J., Church, J. A., Conroy, J. L., Domingues, C. M., Fasullo, J. T., Gilson, J., Goni, G., Good, S. A., Gorman, J. M., Gouretski, V., Ishii, M., Johnson, G. C., Kizu, S., Lyman, J. M., Macdonald, A. M., Minkowycz, W. J., Moffitt, S. E., Palmer, M. D., Piola, A. R., Reseghetti, F., Schuckmann, K., Trenberth, K. E., Velicogna, I., and Willis, J. K.: A review of global ocean temperature observations: Implications for ocean heat content estimates and climate change, Rev. Geophys., 51, 450–483, https://doi.org/10.1002/rog.20022, 2013.
Abraham, J. P., Cheng, L., Mann, M. E., Trenberth, K., and von Schuckmann, K.: The ocean response to climate change guides both adaptation and mitigation efforts, Atmos. Ocean. Sci. Lett., 15, 100221, https://doi.org/10.1016/j.aosl.2022.100221, 2022.
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC), SEANOE [data set], https://doi.org/10.17882/42182, 2000.
Bagnell, A. and DeVries, T.: 20(th) century cooling of the deep ocean contributed to delayed acceleration of Eart”s energy imbalance, Nat. Commun., 12, 4604, https://doi.org/10.1038/s41467-021-24472-3, 2021.
Barker, P. M. and McDougall, T. J.: Two Interpolation Methods Using Multiply-Rotated Piecewise Cubic Hermite Interpolating Polynomials, J. Atmos. Ocean Technol., 37, 605–619, https://doi.org/10.1175/JTECH-D-19-0211.1, 2020.
Barnoud, A., Pfeffer, J., Guérou, A., Frery, M.-L., Siméon, M., Cazenave, A., Chen, J., Llovel, W., Thierry, V., Legeais, J.-F., and Ablain, M.: Contributions of Altimetry and Argo to Non-Closure of the Global Mean Sea Level Budget Since 2016, Geophys. Res. Lett., 48, e2021GL092824, https://doi.org/10.1029/2021GL092824, 2021.
Barnoud, A., Pfeffer, J., Cazenave, A., Fraudeau, R., Rousseau, V., and Ablain, M.: Revisiting the global mean ocean mass budget over 2005–2020, Ocean Sci., 19, 321–334, https://doi.org/10.5194/os-19-321-2023, 2023.
Bindoff, N. L., Cheung, W. W. L., Kairo, J. G., Arístegui, J., Guinder, V. A., Hallberg, R., Hilmi, N., Jiao, N., and Karim, M. S.: Changing Ocean, Marine Ecosystems, and Dependent Communities, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 447–587, https://doi.org/10.1017/9781009157964.007, 2019.
Boyer, T., Domingues, C. M., Good, S. A., Johnson, G. C., Lyman, J. M., Ishii, M., Gouretski, V., Willis, J. K., Antonov, J., Wijffels, S., Church, J. A., Cowley, R., and Bindoff, N. L.: Sensitivity of Global Upper Ocean Heat Content Estimates to Mapping Methods, XBT Bias Corrections, and Baseline Climatologies, J. Clim., 29, 4817–4842, https://doi.org/10.1175/JCLI-D-15-0801.1, 2016.
Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G., and Saba, V.: Observed fingerprint of a weakening Atlantic Ocean overturning circulation, Nature, 556, 191–196, https://doi.org/10.1038/s41586-018-0006-5, 2018.
Cane, M. A. and Zebiak, S. E.: A theory for El Niño and the southern oscillation, Science, 228, 1085–1087, https://doi.org/10.1126/science.228.4703.1085, 1985.
Cheng, L.: Sensitivity of Ocean Heat Content to Various Instrumental Platforms in Global Ocean Observing System, Ocean-Land-Atmos. Res., 3, 0037, https://doi.org/10.34133/olar.0037, 2024a.
Cheng, L. and Zhu, J.: Uncertainties of the Ocean Heat Content Estimation Induced by Insufficient Vertical Resolution of Historical Ocean Subsurface Observations, J. Atmos. Ocean Technol., 31, 1383–1396, https://doi.org/10.1175/JTECH-D-13-00220.1, 2014.
Cheng, L. and Zhu, J.: Influences of the Choice of Climatology on Ocean Heat Content Estimation, J. Atmos. Ocean Technol., 32, 388–394, https://doi.org/10.1175/JTECH-D-14-00169.1, 2015.
Cheng, L. and Zhu, J.: Benefits of CMIP5 Multimodel Ensemble in Reconstructing Historical Ocean Subsurface Temperature Variations, J. Clim., 29, 5393–5416, https://doi.org/10.1175/JCLI-D-15-0730.1, 2016.
Cheng, L., Zhu, J., Cowley, R., Boyer, T., and Wijffels, S.: Time, Probe Type, and Temperature Variable Bias Corrections to Historical Expendable Bathythermograph Observations, J. Atmos. Ocean. Technol., 31, 1793–1825, https://doi.org/10.1175/jtech-d-13-00197.1, 2014.
Cheng, L., Abraham, J., Goni, G., Boyer, T., Wijffels, S., Cowley, R., Gouretski, V., Reseghetti, F., Kizu, S., Dong, S., Bringas, F., Goes, M., Houpert, L., Sprintall, J., and Zhu, J.: XBT Science: Assessment of Instrumental Biases and Errors, B. Am. Meteorol. Soc., 97, 924–933, https://doi.org/10.1175/BAMS-D-15-00031.1, 2016.
Cheng, L., Trenberth, K. E., Fasullo, J., Boyer, T., Abraham, J., and Zhu, J.: Improved estimates of ocean heat content from 1960 to 2015, Sci. Adv., 3, e1601545, https://doi.org/10.1126/sciadv.1601545, 2017.
Cheng, L., Luo, H., Boyer, T., Cowley, R.. Abraham, J., Gouretski, V., Reseghetti, F., and Zhu, J.: How Well Can We Correct Systematic Errors in Historical XBT Data?, J. Atmos. Ocean. Technol., 35, 1103–1125, https://doi.org/10.1175/JTECH-D-17-0122.1, 2018.
Cheng, L., Trenberth, K. E., Fasullo, J. T., Mayer, M., Balmaseda, M., and Zhu, J.: Evolution of Ocean Heat Content Related to ENSO, J. Clim., 32, 3529–3556, https://doi.org/10.1175/jcli-d-18-0607.1, 2019.
Cheng, L., Trenberth, K. E., Gruber, N., Abraham, J. P., Fasullo, J. T., Li, G., Mann, M. E., Zhao, X., and Zhu, J.: Improved Estimates of Changes in Upper Ocean Salinity and the Hydrological Cycle, J. Clim., 33, 10357–10381, https://doi.org/10.1175/JCLI-D-20-0366.1, 2020.
Cheng, L., von Schuckmann, K., Abraham, J. P., Trenberth, K. E., Mann, M. E., Zanna, L., England, M. H., Zika, J. D., Fasullo, J. T., Yu, Y., Pan, Y., Zhu, J., Newsom, E. R., Bronselaer, B., and Lin, X.: Past and future ocean warming, Nat. Rev. Earth Environ., 3, 776–794, https://doi.org/10.1038/s43017-022-00345-1, 2022a.
Cheng, L., Foster, G., Hausfather, Z., Trenberth, K. E., and Abraham, J.: Improved quantification of the rate of ocean warming, J. Clim., 35, 4827–4840, https://doi.org/10.1175/jcli-d-20-0366.1, 2022b.
Cheng, L., Tan, Z., Pan, Y., Zheng, H., Zhu, Y., Wei, W., Du, J., Li, G., Ye, H., Gourteski, V.: IAP temperature 1° gridded analysis product (IAPv4), CODC [data set], https://doi.org/10.12157/IOCAS.20240117.002, 2024a.
Cheng, L., Tan, Z., Pan, Y., Zheng, H., Zhu, Y., Wei, W., Du, J., Li, G., Ye, H., and Gourteski, V.: IAP global ocean heat content 1° gridded analysis product (IAPv4), CODC [data set], https://doi.org/10.12157/IOCAS.20240117.001, 2024b.
Chu, P. C. and Fan, C.: Global climatological data of ocean thermohaline parameters derived from WOA18, Sci. Data, 10, 408, https://doi.org/10.1038/s41597-023-02308-7, 2023.
Comiso, J. C., Meier, W. N., and Gersten, R.: Variability and trends in the Arctic Sea ice cover: Results from different techniques, J. Geophys. Res.-Ocean., 122, 6883–6900, https://doi.org/10.1002/2017JC012768, 2017.
Cowley, R., Killick, R. E., Boyer, T., Gouretski, V., Reseghetti, F., Kizu, S., Palmer, M. D., Cheng, L., Storto, A., Le Menn, M., Simoncelli, S., Macdonald, A. M., and Domingues, C. M.: International Quality-Controlled Ocean Database (iQuOD) v0.1: The Temperature Uncertainty Specification, Front. Mar. Sci., 8, 689695, https://doi.org/10.3389/fmars.2021.689695, 2021.
Dangendorf, S., Frederikse, T., Chafik, L., Klinck, J. M., Ezer, T., and Hamlington, B. D.: Data-driven reconstruction reveals large-scale ocean circulation control on coastal sea level, Nat. Clim. Change, 11, 514–520, https://doi.org/10.1038/s41558-021-01046-1, 2021.
de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A., and Iudicone, D.: Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology, J. Geophys. Res.-Ocean., 109, C12003, https://doi.org/10.1029/2004JC002378, 2004.
Desbruyères, D., McDonagh, E. L., King, B. A., and Thierry, V.: Global and full-depth ocean temperature trends during the early twenty-first century from Argo and repeat hydrography, J. Clim., 30, 1985–1997, 2017.
England, M. H., McGregor, S., Spence, P., Meehl, G. A., Timmermann, A., Cai, W., Gupta, A. S., McPhaden, M. J., Purich, A., and Santoso, A.: Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus, Nat. Clim. Change, 4, 222–227, https://doi.org/10.1038/nclimate2106, 2014.
Fasullo, J. T. and Nerem, R. S.: Altimeter-era emergence of the patterns of forced sea-level rise in climate models and implications for the future, P. Natl. Acad. Sci. USA, 115, 12944–12949, https://doi.org/10.1073/pnas.1813233115, 2018.
Frederikse, T., Jevrejeva, S., Riva, R. E. M., and Dangendorf, S.: A Consistent Sea-Level Reconstruction and Its Budget on Basin and Global Scales over 1958–2014, J. Clim., 31, 1267–1280, https://doi.org/10.1175/JCLI-D-17-0502.1, 2018.
Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., and Wu, Y.-H.: The causes of sea level rise since 1900, Nature, 584, 393–397, https://doi.org/10.1038/s41586-020-2591-3, 2020 (data set available at https://zenodo.org/records/3862995).
Garcia, H. E., Boyer, T. P., Locarnini, R. A., Baranova, O. K., and Zweng, M. M.: World Ocean Database 2018: User's Manual, T. E. A. V. Mishonov, MD NOAA Atlas NESDIS 87, NOAA, Silver Spring, 2018
Goni, G. J., Sprintall, J., Bringas, F., Cheng, L., Cirano, M., Dong, S., Domingues, R., Goes, M., Lopez, H., Morrow, R., Rivero, U., Rossby, T., Todd, R. E., Trinanes, J., Zilberman, N., Baringer, M., Boyer, T., Cowley, R., Domingues, Hutchinson, K., Kramp, M., Mata, M. M., Reseghetti, F., Sun, C., Bhaskar, T. U., and Volkov, D.: More Than 50 Years of Successful Continuous Temperature Section Measurements by the Global Expendable Bathythermograph Network, Its Integrability, Societal Benefits, and Future, Fron. Mar. Sci., 6, 452, https://doi.org/10.3389/fmars.2019.00452, 2019.
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, J. Geophys. Res.-Ocean., 118, 6704–6716, https://doi.org/10.1002/2013jc009067, 2013 (data set available at https://www.metoffice.gov.uk/hadobs/en4/index.html).
Gouretski, V. and Cheng, L.: Correction for Systematic Errors in the Global Dataset of Temperature Profiles from Mechanical Bathythermographs, J. Atmos. Ocean. Technol., 37, 841–855, https://doi.org/10.1175/jtech-d-19-0205.1, 2020.
Gouretski, V. and Koltermann, K. P.: WOCE global hydrographic climatolog, Berichte BSH, 35, 1–52, 2004.
Gouretski, V. and Koltermann, K. P.: How much is the ocean really warming?, Geophys. Res. Lett., 34, L01610, https://doi.org/10.1029/2006GL027834, 2007.
Gouretski, V. and Reseghetti, F.: On depth and temperature biases in bathythermograph data: Development of a new correction scheme based on analysis of a global ocean database, Deep-Sea Res., 57, 812–833, https://doi.org/10.1016/j.dsr.2010.03.011, 2010.
Gouretski, V., Kennedy, J., Boyer, T., and Köhl, A.: Consistent near-surface ocean warming since 1900 in two largely independent observing networks, Geophys. Res. Lett., 39, L19606, https://doi.org/10.1029/2012GL052975, 2012.
Gouretski, V., Cheng, L., and Boyer, T.: On the Consistency of the Bottle and CTD Profile Data, J. Atmos. Ocean Technol., 39, 1869–1887, https://doi.org/10.1175/JTECH-D-22-0004.1, 2022.
Gouretski, V., Roquet, F., and Cheng, L.: Measurement biases in ocean temperature profiles from marine mammal data loggers, J. Atmos. Ocean Technol., 41, 629–645, https://doi.org/10.1175/JTECH-D-23-0081.1, 2024.
Gulev, S. K., Thorne, P. W., Ahn, J., Dentener, F. J., Domingues, C. M., Gerland, S., Gong, D., Kaufman, D. S., Nnamchi, H. C., Quaas, J., Rivera, J. A., Sathyendranath, S., Smith, S. L., Trewin, B., von Schuckmann, K., and Vose, R. S.: Changing State of the Climate System Supplementary Material, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 287– 422, https://doi.org/10.1017/9781009157896.004, 2021.
Hakuba, M. Z., Frederikse, T., and Landerer, F. W.: Earth's Energy Imbalance From the Ocean Perspective (2005–2019), Geophys. Res. Lett., 48, e2021GL093624, https://doi.org/10.1029/2021GL093624, 2021.
Hansen, J., Sato, M., Kharecha, P., and von Schuckmann, K.: Eart”s energy imbalance and implications, Atmos. Chem. Phys., 11, 13421–13449, https://doi.org/10.5194/acp-11-13421-2011, 2011.
Hirahara, S., Ishii, M., and Fukuda, Y.: Centennial-Scale Sea Surface Temperature Analysis and Its Uncertainty, J. Clim., 27, 57-75, https://doi.org/10.1175/JCLI-D-12-00837.1, 2014 (data set available at https://psl.noaa.gov/data/gridded/data.cobe2.html).
Holte, J., Talley, L. D., Gilson, J., and Roemmich, D.: An Argo mixed layer climatology and database, Geophys. Res. Lett., 44, 5618–5626, https://doi.org/10.1002/2017GL073426, 2017.
Hosoda, S., Ohira, T., and Nakamura, T.: Monthly mean dataset of global oceanic temperature and salinity derived from Argo float observations, JAMSTEC Rep. Res. Dev., 8, 47–59, https://doi.org/10.5918/jamstecr.8.47, 2008.
Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., and Zhang, H.-M.: Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons, J. Clim., 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1, 2017 (data set available at https://www1.ncdc.noaa.gov/pub/data/cmb/ersst/v5/netcdf/).
Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021.
IPCC,: Annex I: Observational Products, edited by: Trewin, B., in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B.: Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2061–2086, https://doi.org/10.1017/9781009157896.015, 2021.
Ishii, M. and Kimoto, M.: Reevaluation of historical ocean heat content variations with time-varying XBT and MBT depth bias corrections, J. Oceanogr., 65, 287–299, https://doi.org/10.1007/s10872-009-0027-7, 2009.
Ishii, M., Shouji, A., Sugimoto, S., and Matsumoto, T.: Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection, Int. J. Climatol., 25, 865–879, https://doi.org/10.1002/joc.1169, 2005.
Ishii, M., Fukuda, Y., Hirahara, S., Yasui, S., Suzuki, T., and, Sato K.: Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets, Sola, 13, 163–167, https://doi.org/10.2151/sola.2017-030, 2017 (data set available at https://www.data.jma.go.jp/gmd/kaiyou/english/ohc/ohc_global_en.html).
Jin, F.-F.: An Equatorial Ocean Recharge Paradigm for ENSO, Part I: Conceptual Model, J. Atmos. Sci., 54, 811–829, https://doi.org/10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2, 1997.
Jin, Y., Li, Y., Cheng, L., Duan, J., Li, R., and Wang, F.: Ocean heat content increase of the Maritime Continent since the 1990s, Geophys. Res. Lett., 51, e2023GL107526, https://doi.org/10. 1029/2023GL107526, 2024.
Johns, W. E., Elipot, S., Smeed, D. A., Moat, B., King, B, Volkov, D. L., and Smith, R. H.: Towards two decades of Atlantic Ocean mass and heat transports at 26.5° N, Philos. T. R. Soc. A, 381, 20220188, 2023.
Johnson, G. C., Purkey, S. G., Zilberman, N. V., and Roemmich, D.: Deep Argo Quantifies Bottom Water Warming Rates in the Southwest Pacific Basin, Geophys. Res. Lett., 46, 2662–2669, https://doi.org/10.1098/rsta.2022.0188, 2019.
Katsumata, K., Purkey, S. G., Cowley, R., Sloyan, B. M., Diggs, S. C., Moore, T. S., Talley, L. D., and Swift, J. H.: GO-SHIP Easy Ocean: Gridded ship-based hydrographic section of temperature, salinity, and dissolved oxygen, Sci. Data, 9, 103, https://doi.org/10.1038/s41597-022-01212-w, 2022.
Kennedy, J.: A review of uncertainty in in situ measurements and data sets of sea surface temperature, Rev. Geophys., 52, 1–32, https://doi.org/10.1002/2013RG000434, 2014.
Levitus, S., Antonov, J. I., Boyer, T. P., Locarnini, R. A., Garcia, H. E., and Mishonov, A. V.: Global ocean heat content 1955–2008 in light of recently revealed instrumentation problems. Geophys. Res. Lett., 36, L07608, https://doi.org/10.1029/2008GL037155, 2009.
Levitus, S., Antonov, J. I., Boyer, T. P., Baranova, O. K., Garcia, H. E., Locarnini, R. A., Mishonov, A. V, Reagan, J. R., Seidov, D., Yarosh, E. S., and Zweng, M. M.: World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010, Geophys. Res. Lett., 39, L10603, https://doi.org/10.1029/2012GL051106, 2012 (data set available at https://www.ncei.noaa.gov/products/).
Li, G., Cheng, L., Zhu, J., Trenberth, K. E., Mann, M. E., and Abraham, J. P.: Increasing ocean stratification over the past half-century, Nat. Clim. Change, 10, 1116–1123, https://doi.org/10.1038/s41558-020-00918-2, 2020.
Li, H., Xu, F., Zhou, W., Wang, D., Wright, J. S., Liu, Z., and Lin, Y.: Development of a global gridded Argo data set with Barnes successive corrections, J. Geophys. Res.-Ocean., 122, 866–889, https://doi.org/10.1002/2016JC012285, 2017 (data set available at https://argo.ucsd.edu/data/argo-data-products/).
Li, Y., Church, J. A., McDougall, T. J., and Barker, P. M.: Sensitivity of Observationally Based Estimates of Ocean Heat Content and Thermal Expansion to Vertical Interpolation Schemes, Geophys. Res. Lett., 49, e2022GL101079, https://doi.org/10.1029/2022GL101079, 2022.
Lian, T., Wang, J., Chen, D., Liu, T., and Wang, D.: A Strong 2023/24 El Niño is Staged by Tropical Pacific Ocean Heat Content Buildup, Ocean-Land-Atmos. Res., 2, 0011, https://doi.org/10.34133/olar.0011, 2023.
Liu, C. and Allan, R.: Reconstructions of the radiation fluxes at the top of atmosphere and net surface energy flux: DEEP-C version 5.0, University of Reading Dataset [data set], https://doi.org/10.17864/1947.000347, 2022.
Liu, C., Allan, R. P., Mayer, M., Hyder, P., Loeb, N. G., Roberts, C. D., Valdivieso, M., Edwards, J. M., and Vidale, P.-L.: Evaluation of satellite and reanalysis-based global net surface energy flux and uncertainty estimates, J. Geophys. Res.- Atmos., 122, 6250–6272, https://doi.org/10.1002/2017JD026616, 2017.
Liu, C., Allan, R. P., Mayer, M., Hyder, P., Desbruyères, D., Cheng, L., Xu, J., Xu, F., and Zhang, Y.: Variability in the global energy budget and transports 1985–2017, Clim. Dynam., 55, 3381–3396, https://doi.org/10.1007/s00382-020-05451-8, 2020.
Loeb, N. G., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F., Kato, S., Manalo-Smith, N., and Wong, T.: Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation Budget, J. Clim., 22, 748–766, https://doi.org/10.1175/2008JCLI2637.1, 2009.
Loeb, N. G., Thorsen, T. J., Norris, J. R., Wang, H., and Su, W.: Changes in Earth's energy budget during and after the “Pause” in global warming: An observational perspective, Climate, 6, 62, https://doi.org/10.3390/cli6030062, 2018.
Loeb, N. G., Johnson, G. C., Thorsen, T. J., Lyman, J. M., Rose, F. G., and Kato, S.: Satellite and Ocean Data Reveal Marked Increase in Earth's Heating Rate, Geophys. Res. Lett., 48, e2021GL093047, https://doi.org/10.1029/2021gl093047, 2021 (data set available at https://asdc.larc.nasa.gov/project/CERES).
Lyman, J. M. and Johnson, G. C.: Estimating Global Ocean Heat Content Changes in the Upper 1800 m since 1950 and the Influence of Climatology Choice, J. Clim., 27, 1945–1957, https://doi.org/10.1175/JCLI-D-12-00752.1, 2014.
Lyman, J. M. and Johnson, G. C.: Global High-Resolution Random Forest Regression Maps of Ocean Heat Content Anomalies Using In Situ and Satellite Data, J. Atmos. Ocean. Technol., 40, 575–586, https://doi.org/10.1175/JTECH-D-22-0058.1, 2023.
Lyman, J. M., Good, S. A., Gouretski, V. V., Ishii, M., Johnson, G. C., Palmer, M. D., Smith, D. M., and Willis, J. K.: Robust warming of the global upper ocean, Nature, 465, 334–337, https://doi.org/10.1038/nature09043, 2010.
Mann, M. E.: Beyond the Hockey Stick: Climate Lessons from The Common Era, P. Natl. Acad. Sci. USA, 118, e2112797118, https://doi.org/10.1073/pnas.2112797118, 2021.
Mayer, J., Mayer, M., and Haimberger, L.: Consistency and Homogeneity of Atmospheric Energy, Moisture, and Mass Budgets in ERA5, J. Clim., 34, 3955–3974, https://doi.org/10.1175/JCLI-D-20-0676.1, 2021.
Mayer, M., Alonso Balmaseda, M., and Haimberger, L.: Unprecedented 2015/2016 Indo-Pacific Heat Transfer Speeds Up Tropical Pacific Heat Recharge, Geophys. Res. Lett., 45, 3274–3284, https://doi.org/10.1002/2018GL077106, 2018.
Mayer, M., Tietsche, S., Haimberger, L., Tsubouchi, T., Mayer, J., and Zuo, H.: An Improved Estimate of the Coupled Arctic Energy Budget, J. Clim., 32, 7915–7934, https://doi.org/10.1175/JCLI-D-19-0233.1, 2019.
McDougall, T. J. and Barker, P. M.: Getting started with TEOS-10 and the Gibbs Seawater (GSW) Oceanographic Toolbox, 28 pp., SCOR/IAPSO WG127, Intergovernmental Oceanographic Commission, ISBN 978-0-646-55621-5, 2011.
McPhaden, M. J.: A 21st century shift in the relationship between enso sst and warm water volume anomalies, Geophys. Res. Lett., 39, 9706, https://doi.org/10.1029/2012GL051826, 2012.
McMahon, C. R., Roquet, F., Baudel, S., Belbeoch, M., Bestley, S., Blight, C., Boehme, L., Carse, F., Costa, D. P., Fedak, M. A., Guinet, C., Harcourt, R., Heslop, E., Hindell, M. A., Hoenner, X., Holland, K., Holland, M., Jaine, F. R. A., Jeanniard du Dot, T., Jonsen, I., Keates, T. R., Kovacs, K. M., Labrousse, S., Lovell, P., Lydersen, C., March, D., Mazloff, M., McKinzie, M. K., Muelbert, M. M. C., O’Brien, K., Phillips, L., Portela, E., Pye, J., Rintoul, S., Sato, K., Sequeira, A. M. M., Simmons, S. E., Tsontos, V. M., Turpin, V., van Wijk, E., Vo, D., Wege, M., Whoriskey, F. G., Wilson K., and Woodward, B.: Animal Borne Ocean Sensors – AniBOS – An Essential Component of the Global Ocean Observing System, Front. Mar. Sci., 8, 751840, https://doi.org/10.3389/fmars.2021.751840, 2021.
Meyssignac, B., Boyer, T., Zhao, Z., Hakuba, M. Z., Landerer, F. W., Stammer, D., Köhl, A., Kato, S., L'Ecuyer, T., Ablain, M., Abraham, J. P., Blazquez, A., Cazenave, A., Church, J. A., Cowley, R., Cheng, L., Domingues, C. M., Giglio, D., Gouretski, V., Ishii, M., Johnson, G. C., Killick, R. E., Legler, D., Llovel, W., Lyman, J., Palmer, M. D., Piotrowicz, S., Purkey, S. G., Roemmich, D., Roca, R., Savita, A., Schuckmann, K. von, Speich, S., Stephens, G., Wang, G., Wijffels, S. E., and Zilberman, N.: Measuring Global Ocean Heat Content to Es- timate the Earth Energy Imbalance, Front. Mar. Sci., 6, 432, https://doi.org/10.3389/fmars.2019.00432, 2019.
Minière, A., von Schuckmann, K., Sallée, J.-B., and Vogt, L.: Robust acceleration of Earth system heating observed over the past six decades, Sci. Rep., 13, 22975, https://doi.org/10.1038/s41598-023-49353-1, 2024
Nerem, R. S., Beckley, B. D., Fasullo, J. T., Hamlington, B. D., Masters, D., and Mitchum, G. T.: Climate-change–driven accelerated sea-level rise detected in the altimeter era, P. Natl. Acad. Sci. USA, 115, 2022–2025, https://doi.org/10.1073/pnas.1717312115, 2018.
O'Carroll, A. G., Armstrong, E. M., Beggs, H. M., Bouali, M., Casey, K. S., Corlett, G. K., Dash, P., Donlon, C. J., Gentemann, C. L., Høyer, J. L., Ignatov, A., Kabobah, K., Kachi, M., Kurihara, Y., Karagali, I., Maturi, E., Merchant, C. J., Marullo, S., Minnett, P. J., Pennybacker, M., Ramakrishnan, B., Ramsankaran, R. Santoleri, R., Sunder, S., Saux Picart, S. Vázquez-Cuervo, J., and Wimmer, W.: Observational Needs of Sea Surface Temperature, Front. Mar. Sci., 6, 420, https://doi.org/10.3389/fmars.2019.00420, 2019.
Palmer, M. D. and Haines, K.: Estimating Oceanic Heat Content Change Using Isotherms, J. Clim., 22, 4953–4969, https://doi.org/10.1175/2009JCLI2823.1, 2009.
Purkey, S. G. and Johnson, G. C.: Warming of Global Abyssal and Deep Southern Ocean Waters between the 1990s and 2000s: Contributions to Global Heat and Sea Level Rise Budgets, J. Clim., 23, 6336–6351, https://doi.org/10.1175/2010jcli3682.1, 2010.
Rahmstorf, S., Box, J., Feulner, G., Mann, M. E., Robinson, A., Rutherford, S., and Schaffernicht, E.: Exceptional 20th-Century slowdown in Atlantic Ocean overturning, Nat. Clim. Change, 5, 475–480, 2015.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res.-Atmos., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003 (data set available at https://www.metoffice.gov.uk/hadobs/hadisst/).
Reiniger, R. F. and Ross, C. K.: A method of interpolation with application to oceanographic data, Deep-Sea Res., 15, 185–193, https://doi.org/10.1016/0011-7471(68)90040-5, 1968.
Rhein, M., Rintoul, S. R., Aoki, S., Campos, E., Chambers, D., Feely, R. A., Gulev, S., Johnson, G. C., Josey, S. A., Kostianoy, A., Mauritzen, C., Roemmich, D., Talley, L. D., and Wang, F.: Observations: Ocean, in: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/CBO9781107415324.010, 2013.
Roemmich, D. and Gilson, J.: The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program, Prog. Oceanogr., 82, 81–100, https://doi.org/10.1016/j.pocean.2009.03.004, 2009 (data set available at http://sio-argo.ucsd.edu/RG_Climatology.html).
Roemmich, D. and Gilson, J.: The global ocean imprint of ENSO, Geophys. Res. Lett., 38, L13606, https://doi.org/10.1029/2011GL047992, 2011.
Roemmich, D., Alford, M. H., Claustre, H., Johnson, K., King, B., Moum, J., Oke, P., Owens, W. B., Pouliquen, S., Purkey, S., Scanderbeg, M., Suga, T., Wijffels, S., Zilberman, N., Bakker, D., Baringer, M., Belbeoch, M., Bittig, H. C., Boss, E., Calil, P., Carse, F., Carval, T., Chai, F., Conchubhair, D. Ó., d’Ortenzio, F., Dall’Olmo, G., Desbruyeres, D., Fennel, K., Fer, I., Ferrari, R., Forget, G., Freeland, H., Fujiki, T., Gehlen, M., Greenan, B., Hallberg, R., Hibiya, T., Hosoda, S., Jayne, S., Jochum, M., Johnson, G. C., Kang, K., Kolodziejczyk, N., Körtzinger, A., L. Traon, P.-Y., Lenn, Y.-D., Maze, G., Mork, K. A., Morris, T., Nagai, T., Nash, J., Garabato, A. N., Olsen, A., Pattabhi, R. R., Prakash, S., Riser, S., Schmechtig, C., Schmid, C., Shroyer, E., Sterl, A., Sutton, P., Talley, L., Tanhua, T., Thierry, V., Thomalla, S., Toole, J., Troisi, A., Trull, T. W., Turton, J., Velez-Belchi, P. J., Walczowski, W., Wang, H., Wanninkhof, R., Waterhouse, A. F., Waterman, S., Watson, A., Wilson, C., Wong, A. P. S., Xu, J., and Yasuda, I.: On the Future of Argo: A Global, Full-Depth, Multi-Disciplinary Array, Front. Mar. Sci., 6, 439, https://doi.org/10.3389/fmars.2019.00439, 2019.
Savita, A., Domingues, C. M., Boyer, T., Gouretski, V., Ishii, M., Johnson, G. C., Lyman, J. M., Willis, J. K., Marsland, S. J., Hobbs, W., Church, J. A., Monselesan, D. P., Dobrohotoff, P., Cowley, R., and Wijffels, S. E.: Quantifying Spread in Spatiotemporal Changes of Upper-Ocean Heat Content Estimates: An Internationally Coordinated Comparison, J. Clim., 35, 851–875, https://doi.org/10.1175/JCLI-D-20-0603.1, 2022.
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.: Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011.
Sloyan, B. M., Wanninkhof, R., Kramp, M., Johnson, G. C., Talley, L. D., Tanhua, T., McDonagh, E., Cusack, C., O'Rourke, E., McGovern, E., Katsumata, K., Diggs, S., Hummon, J., Ishii, M., Azetsu-Scott, K., Boss, E., Ansorge, I., Perez, F. F., Mercier, H., Williams, M. J. M., Anderson, L., Lee, J. H., Murata, A., Kouketsu, S., Jeansson, E., Hoppema, M., and Campos, E.: The Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP): A Platform for Integrated Multidisciplinary Ocean Science, Front. Mar. Sci., 6, p. 445, https://doi.org/10.3389/fmars.2019.00445, 2019.
Su, H., Zhang, H., Geng, X., Qin, T., Lu, W., and Yan, X.: OPEN: a new estimation of global ocean heat content for upper 2000 meters from remote sensing data, Remote Sens., 12, 2294, https://doi.org/10.3390/rs12142294, 2020.
Sun, D., Li, F., Jing, Z., Hu, S., and Zhang, B.: Frequent marine heatwaves hidden below the surface of the global ocean, Nat. Geosci., 16, 1099–1104, https://doi.org/10.1038/s41561-023-01325-w, 2023.
Tan, Z., Zhang, B., Wu, X., Dong, M., and Cheng, L.: Quality control for ocean observations: From present to future, Sci. China Earth Sci., 65, 215–233, https://doi.org/10.1007/s11430-021-9846-7, 2022.
Tan, Z., Cheng, L., Gouretski, V., Zhang, B., Wang, Y., Li, F., Liu, Z., and Zhu, J.: A new automatic quality control system for ocean profile observations and impact on ocean warming estimate, Deep-Sea Res. Pt. I, 194, 103961, https://doi.org/10.1016/j.dsr.2022.103961, 2023.
Trenberth, K. E.: The Changing Flow of Energy Through the Climate System, Cambridge University Press, https://doi.org/10.1017/9781108979030, 2022.
Trenberth, K. E. and Fasullo, J. T.: Atlantic meridional heat transports computed from balancing Earth's energy locally, Geophys. Res. Lett., 44, 1919–1927, https://doi.org/10.1002/2016gl072475, 2017.
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's Global Energy Budget, Bull. Am. Meteorol. Soc., 90, 311–324, https://doi.org/10.1175/2008bams2634.1, 2009.
Trenberth, K. E., Fasullo, J. T., Von Schuckmann, K., and Cheng, L.: Insights into Earth's Energy Imbalance from Multiple Sources, J. Clim., 29, 7495–7505, https://doi.org/10.1175/jcli-d-16-0339.1, 2016.
Trenberth, K. E., Zhang, Y., Fasullo, J. T., and Cheng, L.: Observation-Based Estimates of Global and Basin Ocean Meridional Heat Transport Time Series, J. Clim., 32, 4567–4583, https://doi.org/10.1175/jcli-d-18-0872.1, 2019.
Thresher, A., Cowley, R., and Wijffels, S.: QuOTA dataset (Quality-controlled Ocean Temperature Archive), Commonwealth Scientific and Industrial Research Organisation (CSIRO) [data set], https://doi.org/10.25919/5ec357563bd3e, 2008.
von Schuckmann, K. and Le Traon, P. Y.: How well can we derive Global Ocean Indicators from Argo data?, Ocean Sci., 7, 783–791, https://doi.org/10.5194/os-7-783-2011, 2011.
von Schuckmann, K., Cheng, L., Palmer, M. D., Hansen, J., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruyères, D., Domingues, C., García-García, A., Gentine, P., Gilson, J., Gorfer, M., Haim- berger, L., Ishii, M., Johnson, G. C., Killick, R., King, B. A., Kirchengast, G., Kolodziejczyk, N., Lyman, J., Marzeion, B., Mayer, M., Monier, M., Monselesan, D. P., Purkey, S., Roemmich, D., Schweiger, A., Seneviratne, S. I., Shepherd, A., Slater, D. A., Steiner, A. K., Straneo, F., Timmermans, M.-L., and Wijffels, S. E.: Heat stored in the Earth system: where does the energy go?, Earth Syst. Sci. Data, 12, 2013–2041, https://doi.org/10.5194/essd-12-2013-2020, 2020.
von Schuckmann, K., Palmer, M. D., Trenberth, K. E., Cazenave, A., Chambers, D., Champollion, N., Hansen, J., Josey, S. A., Loeb, N., Mathieu, P.-P., Meyssignac, B., and Wild, M.: An imperative to monitor Earth's energy imbalance, Nat. Clim. Change, 6, 138–144, https://doi.org/10.1038/nclimate2876, 2016.
von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, F. J., Kirchengast, G., Adusumilli, S., Straneo, F., Allan, R., Barker, P. M., Beltrami, H., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C., García-García, A., Giglio, D., Gilson, J., Gorfer, M., Haimberger, L., Hendricks, S., Hosoda, S., Johnson, G. C., Killick, R., King, B. A., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Langer, M., Lavergne, T., Li, Y., Lyman, J., Marzeion, B., Mayer, M., MacDougall, A., Lawrence, I., McDougall, T., Monselesan, D. P., Nitzbon, J., Otosaka, I., Peng, J., Purkey, S., Roemmich, D., Sato, K., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S. I., Simons, L., Slater, D. A., Slater, T., Smith, N., Steiner, A. K., Suga, T., Szekely, T., Thiery, W., Timmermanns, M.-L., Vanderkelen, I., Wijffels, S. E., Wu, T., and Zemp, M.: Heat stored in the Earth system 1960–2020: Where does the energy go?, World Data Center for Climate (WDCC) at DKRZ [data set], https://hdl.handle.net/21.14106/279f535efb48324f4f604bb390f74deadf268812, 2022.
von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, F. J., Kirchengast, G., Adusumilli, S., Straneo, F., Ablain, M., Allan, R. P., Barker, P. M., Beltrami, H., Blazquez, A., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C. M., García-García, A., Giglio, D., Gilson, J. E., Gorfer, M., Haimberger, L., Hakuba, M. Z., Hendricks, S., Hosoda, S., Johnson, G. C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Landerer, F. W., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marti, F., Marzeion, B., Mayer, M., MacDougall, A. H., McDougall, T., Monselesan, D. P., Nitzbon, J., Otosaka, I., Peng, J., Purkey, S., Roemmich, D., Sato, K., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S. I., Simons, L., Slater, D. A., Slater, T., Steiner, A. K., Suga, T., Szekely, T., Thiery, W., Timmermans, M. L., Vanderkelen, I., Wjiffels, S. E., Wu, T., and Zemp, M.: Heat stored in the Earth system 1960–2020: where does the energy go?, Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, 2023.
Wang, F., Shen, Y., Chen, Q., and Sun, Y.: Reduced misclosure of global sea-level budget with updated Tongji-Grace2018 solution, Sci. Rep.-UK, 11, 17667, https://doi.org/10.1038/s41598-021-96880-w, 2021.
Watkins, M. M., Wiese, D. N., Yuan, D. N., Boening, C., and Landerer, F. W.: Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res.-Sol. Ea., 120, 2648–2671, https://doi.org/10.1002/2014JB011547, 2015 (data set available at https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/).
Wijffels, S. E., Willis, J., Domingues, C. M., Barker, P., White, N. J., Gronell, A., Ridgway, K., and Church, J. A.: Changing Expendable Bathythermograph Fall Rates and Their Impact on Estimates of Thermosteric Sea Level Rise, J. Clim., 21, 5657–5672, https://doi.org/10.1175/2008jcli2290.1, 2008.
WMO: State of the Global Climate 2021, WMO-No. 1290, ISBN: 978-92-63-11290-3, 2022.
Wong, A. P. S., Wijffels, S. E., Riser, S. C., Pouliquen, S., Hosoda, S., Roemmich, D., Gilson, J., Johnson, G. C., Martini, K., Murphy, D. J., Scanderbeg, M., Bhaskar, T. V. S. U., Buck, J. J. H., Merceur, F., Carval, T., Maze, G., Cabanes, C., André, X., Poffa, N., Yashayaev, I., Barker, P. M., Guinehut, S., Belbéoch, M., Ignaszewski, M., Baringer, M. O. N., Schmid, C., Lyman, J. M., McTaggart, K. E., Purkey, S. G., Zilberman, N., Alkire, M. B., Swift, D., Owens, W. B., Jayne, S. R., Hersh, C., Robbins, P., West-Mack, D., Bahr, F., Yoshida, S., Sutton, P. J. H., Cancouët, R., Coatanoan, C., Dobbler, D., Juan, A. G., Gourrion, J., Kolodziejczyk, N., Bernard, V., Bourlès, B., Claustre, H., D'Ortenzio, F., Le Reste, S., Le Traon, P.-Y., Rannou, J.-P., Saout-Grit, C., Speich, S., Thierry, V., Verbrugge, N., Angel-Benavides, I. M., Klein, B., Notarstefano, G., Poulain, P.-M., Vélez-Belchí, P., Suga, T., Ando, K., Iwasaska, N., Kobayashi, T., Masuda, S., Oka, E., Sato, K., Nakamura, T., Sato, K., Takatsuki, Y., Yoshida, T., Cowley, R., Lovell, J. L., Oke, P. R., van Wijk, E. M., Carse, F., Donnelly, M., Gould, W. J., Gowers, K., King, B. A., Loch, S. G., Mowat, M., Turton, J., Rama Rao, E. P., Ravichandran, M., Freeland, H. J., Gaboury, I., Gilbert, D., Greenan, B. J. W., Ouellet, M., Ross, T., Tran, A., Dong, M., Liu, Z., Xu, J., Kang, K., Jo, H., Kim, S.-D., and Park, H.-M.: Argo Data 1999–2019: Two Million Temperature-Salinity Profiles and Subsurface Velocity Observations From a Global Array of Profiling Floats, Front. Mar. Sci., 7, 700, https://doi.org/10.3389/fmars.2020.00700, 2020.
Yashayaev, I.: Hydrographic changes in the Labrador Sea 1960–2005, Prog. Oceanogr., 73, 242–276, https://doi.org/10.1016/j.pocean.2007.04.015, 2007.
Yashayaev, I. and Loder, J. W.: Further intensification of deep convection in the Labrador Sea in 2016, Geophys. Res. Lett., 44, 1429–1438, https://doi.org/10.1002/2016GL071668, 2017.
Zanna, L., Khatiwala, S., Gregory, J. M., Ison, J., and Heimbach, P.: Global reconstruction of historical ocean heat storage and transport, P. Natl. Acad. Sci. USA, 116, 1126–1131, https://doi.org/10.1073/pnas.1808838115, 2019.
Zhang, B., Cheng, L., Tan, Z., Gouretski, V., Li, F., Pan, Y., Yuan, H., Ren, H., Reseghetti, F., Zhu, J., and Wang, F.: CAS-Ocean Data Center, Global Ocean Science Database (CODCv1): temperature, Marine Science Data Center of the Chinese Academy of Science, https://doi.org/10.12157/IOCAS.20230525.001, 2024a.
Zhang, B., Cheng, L., Tan, Z. Gouretski, V., Li, F., Pan, Y., Yuan, H., Ren, H., Reseghetti, F., Zhu, J., and Wang, F.: CODC-v1: a quality-controlled and bias-corrected ocean temperature profile database from 1940–2023, Sci. Data, 11, 666, https://doi.org/10.1038/s41597-024-03494-8, 2024b.
Zhang, J. and Rothrock, D. A.: Modeling Global Sea Ice with a Thickness and Enthalpy Distribution Model in Generalized Curvilinear Coordinates, Mon. Weather Rev., 131, 845–861, https://doi.org/10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2, 2003.
Zhang, X., Church, J. A., Platten, S. M., and Monselesan, D.: Projection of subtropical gyre circulation and associated sea level changes in the Pacific based on CMIP3 climate models, Clim. Dynam., 43, 131–144, https://doi.org/10.1007/s00382-013-1902-x, 2014.
Short summary
Observational gridded products are essential for understanding the ocean, the atmosphere, and climate change; they support policy decisions and socioeconomic developments. This study provides an update of an ocean subsurface temperature and ocean heat content gridded product, named the IAPv4 data product, which is available for the upper 6000 m (119 levels) since 1940 (more reliable after ~1955) for monthly and 1° × 1° temporal and spatial resolutions.
Observational gridded products are essential for understanding the ocean, the atmosphere, and...
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