Articles | Volume 25, issue 1
https://doi.org/10.5194/hess-25-121-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-121-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
07 Jan 2021
Research article | Highlight paper |
| 07 Jan 2021
| 07 Jan 2021
Intercomparison of freshwater fluxes over ocean and investigations into water budget closure
Marloes Gutenstein
CORRESPONDING AUTHOR
Deutscher Wetterdienst, Offenbach, Germany
Karsten Fennig
Deutscher Wetterdienst, Offenbach, Germany
Marc Schröder
Deutscher Wetterdienst, Offenbach, Germany
Tim Trent
Earth Observation Science, University of Leicester, Leicester, UK
Stephan Bakan
Max Planck Institute for Meteorology, Hamburg, Germany
J. Brent Roberts
NASA Marshall Space Flight Center, Huntsville, AL, USA
Franklin R. Robertson
NASA Marshall Space Flight Center, Huntsville, AL, USA
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Cited articles
Adler, R. F., Gu, G., and Huffman, G. J.: Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP), J. Appl. Meteorol. Clim., 51, 84–99, https://doi.org/10.1175/JAMC-D-11-052.1, 2012. a, b
Allan, R. P., Barlow, M., Byrne, M. P., Cherchi, A., Douville, H., Fowler, H. J., Gan, T. Y., Pendergrass, A. G., Rosenfeld, D., Swann, A. L. S., Wilcox, L. J., and Zolina, O.: Advances in understanding large-scale responses of the water cycle to climate change, Ann. NY Acad. Sci., 1472, 49–75, https://doi.org/10.1111/nyas.14337, 2020. a, b, c, d, e, f, g, h, i, j
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and the hydrologic cycle, Nature, 419, 228–232, https://doi.org/10.1038/nature01092, 2002.
a, b
Andersson, A., Fennig, K., Klepp, C., Bakan, S., Graßl, H., and Schulz, J.: The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3, Earth Syst. Sci. Data, 2, 215–234, https://doi.org/10.5194/essd-2-215-2010, 2010. a, b, c, d
Andersson, A., Klepp, C., Fennig, K., Bakan, S., Graßl, H., and Schulz, J.: Evaluation of HOAPS-3 Ocean Surface Freshwater Flux Components, J. Appl. Meteorol. Clim., 50, 379–398, https://doi.org/10.1175/2010JAMC2341.1, 2011. a, b
Andersson, A., Graw, K., Schröder, M., Fennig, K., Liman, J., Bakan, S., Hollmann, R., and Klepp, C.: Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS 4.0, Satellite Application Facility on Climate Monitoring, Data set, https://doi.org/10.5676/EUM_SAF_CM/HOAPS/V002, 2017. a, b
Bauer, P., Moreau, E., Chevallier, F., and O'keeffe, U.: Multiple-scattering microwave radiative transfer for data assimilation applications, Q. J. Roy. Meteorol. Soc., 132, 1259–1281, https://doi.org/10.1256/qj.05.153, 2006. a
Bentamy, A., Katsaros, K. B., Ez, A. M. M.-N., Drennan, W. M., Forde, E. B., and Roquet, H.: Satellite Estimates of Wind Speed and Latent Heat Flux over the Global Oceans, J. Climate, 16, 637–656, https://doi.org/10.1175/1520-0442(2003)016<0637:SEOWSA>2.0.CO;2 2003. a, b
Bentamy, A., Grodsky, S. A., Katsaros, K., Mestas-Nuñez, A. M., Blanke, B., and Desbiolles, F.: Improvement in air–sea flux estimates derived from satellite observations, Int. J. Remote Sens., 34, 5243–5261, https://doi.org/10.1080/01431161.2013.787502, 2013. a, b, c, d
Bentamy, A., Grodsky, S. A., Elyouncha, A., Chapron, B., and Desbiolles, F.: Homogenization of scatterometer wind retrievals, Int. J. Climatol., 37, 870–889, https://doi.org/10.1002/joc.4746, 2017a. a
Berg, W., Kroodsma, R., Kummerow, C. D., and McKague, D. S.: Fundamental Climate Data Records of Microwave Brightness Temperatures, Remote Sens.-Basel, 10, 1306, https://doi.org/10.3390/rs10081306, 2018. a, b
Berrisford, P., Kållberg, P., Kobayashi, S., Dee, D., Uppala, S., Simmons, A. J., Poli, P., and Sato, H.: Atmospheric conservation properties in ERA-Interim, Q. J. Roy. Meteorol. Soc., 137, 1381–1399, https://doi.org/10.1002/qj.864, 2011. a, b, c, d
Berry, D. I. and Kent, E. C.: Air–Sea fluxes from ICOADS: the
construction of a new gridded dataset with uncertainty estimates, Int. J. Climatol., 31, 987–1001, https://doi.org/10.1002/joc.2059, 2011. a
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015. a
Bradley, E. F., Fairall, C. W., Hare, J. E., and Grachev, A. A.: An old and improved bulk algorithm for air–sea fluxes: COARE 2.6A, in: Preprints, 14th Symp. on Boundary Layer and Turbulence, Aspen, CO, Amer. Meteor. Soc., 294–296, available at: https://ams.confex.com/ams/AugAspen/techprogram/paper_14695.htm (last access: January 2021), 2000. a
Brown, P. J. and Kummerow, C. D.: An Assessment of Atmospheric Water Budget Components over Tropical Oceans, J. Climate, 27, 2054–2071, https://doi.org/10.1175/JCLI-D-13-00385.1, 2014. a, b
Burdanowitz, J.: Point-to-area validation of passive microwave satellite precipitation with shipboard disdrometers, PhD Thesis, Universität Hamburg, Hamburg, https://doi.org/10.17617/2.2385648, 2017. a
Chou, S.-H., Nelkin, E., Ardizzone, J., Atlas, R. M., and Shie, C.-L.:
Surface Turbulent Heat and Momentum Fluxes over Global Oceans Based on the
Goddard Satellite Retrievals, Version 2 (GSSTF2), J. Climate, 16, 3256–3273,
https://doi.org/10.1175/1520-0442(2003)016<3256:STHAMF>2.0.CO;2, 2003. a
Clark, E. A., Sheffield, J., van Vliet, M. T. H., Nijssen, B., and Lettenmaier, D. P.: Continental Runoff into the Oceans (1950–2008), J. Hydrometeorol., 16, 1502–1520, https://doi.org/10.1175/JHM-D-14-0183.1, 2015. a, b
Clayson, C. A. and Brown, J.: Ocean surface bundle Climate Algorithm Theoretical Basis Document, NOAA Climate Data Record Program [CDRP-ATBD-0578] Rev. 2, available at: https://www.ncdc.noaa.gov/cdr/ (last access: January 2021), 2016. a
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.: The Twentieth Century Reanalysis Project, Q. J. Roy. Meteorol. Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011. a
Dagan, G., Stier, P., and Watson-Parris, D.: Analysis of the Atmospheric Water Budget for Elucidating the Spatial Scale of Precipitation Changes Under Climate Change, Geophys. Res. Lett., 46, 10504–10511, https://doi.org/10.1029/2019GL084173, 2019. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., Berg, L. van de, Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hòlm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., Rosnay, P. de, Tavolato, C., Thépaut, J.-N., and Vitart, F.: The
ERA-Interim reanalysis: configuration and performance of the data assimilation
system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Donlon, C. J., Minnett, P. J., Gentemann, C., Nightingale, T. J.,
Barton, I. J., Ward, B., and Murray, M. J.: Toward Improved Validation of
Satellite Sea Surface Skin Temperature Measurements for Climate Research, J. Climate, 15, 353–369,
https://doi.org/10.1175/1520-0442(2002)015<0353:TIVOSS>2.0.CO;2, 2002. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system, Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017, 2012. a, b
ECMWF: IFS Documentation CY41R2, ECMWF, available at:
https://www.ecmwf.int/en/elibrary/16648-part-iv-physical-processes (last access: January 2021), 2016. a
ECMWF: ERA5 monthly averaged data on single levels from 1979 to present, Data set, https://doi.org/10.24381/cds.f17050d7, 2019. a
Edson, J. B., Jampana, V., Weller, R. A., Bigorre, S. P., Plueddemann, A. J., Fairall, C. W., Miller, S. D., Mahrt, L., Vickers, D., and Hersbach, H.: On the Exchange of Momentum over the Open Ocean, J. Phys. Oceanogr., 43, 1589–1610, https://doi.org/10.1175/JPO-D-12-0173.1, 2013. a
Fairall, C. W., Bradley, E. F., Rogers, D. P., Edson, J. B., and Young, G. S.: Bulk parameterization of air-sea fluxes for Tropical Ocean-Global Atmosphere Coupled-Ocean Atmosphere Response Experiment, J. Geophys. Res.-Oceans, 101, 3747–3764, https://doi.org/10.1029/95JC03205, 1996. a
Fairall, C. W., Bradley, E. F.,
Hare, J. E., Grachev, A. A., and Edson, J. B.: Bulk Parameterization of
Air–Sea Fluxes: Updates and Verification for the COARE Algorithm, J. Climate, 16, 571–591, https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2, 2003. a, b
Fennig, K., Schröder, M., and Hollmann, R.: Fundamental Climate Data Record of Microwave Imager Radiances, Edition 3, Dataset, Satellite Application Facility on Climate Monitoring, Data set, https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003, 2017. a, b
Fennig, K., Schröder, M., Andersson, A., and Hollmann, R.: A Fundamental Climate Data Record of SMMR, SSM/I, and SSMIS brightness temperatures, Earth Syst. Sci. Data, 12, 647–681, https://doi.org/10.5194/essd-12-647-2020, 2020. a, b
Gehne, M., Hamill, T. M., Kiladis, G. N., and Trenberth, K. E.: Comparison of Global Precipitation Estimates across a Range of Temporal and Spatial Scales, J. Climate, 29, 7773–7795, https://doi.org/10.1175/JCLI-D-15-0618.1, 2016. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2),
J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019. a, b
GPCP – Mesoscale Atmospheric Processes Branch/Laboratory for
Atmospheres/Earth Sciences Division/Science and Exploration
Directorate/Goddard Space Flight Center/NASA, and Earth System Science
Interdisciplinary Center/University of Maryland: GPCP Version 1.3 One-Degree
Daily Precipitation Data Set, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory,
https://doi.org/10.5065/PV8B-HV76, 2018. a, b
Graw, K., Kinzel, J., Schröder, M., Fennig, K., and Andersson, A.: Algorithm Theoretical Baseline Document HOAPS version 4.0, EUMETSAT CM SAF, https://doi.org/10.5676/EUM_SAF_CM/HOAPS/V002, 2017 a
Hegerl, G. C., Black, E., Allan, R. P., Ingram, W. J., Polson, D., Trenberth, K. E., Chadwick, R. S., Arkin, P. A., Sarojini, B. B., Becker, A., Dai, A., Durack, P. J., Easterling, D., Fowler, H. J., Kendon, E. J., Huffman, G. J., Liu, C., Marsh, R., New, M., Osborn, T. J., Skliris, N., Stott, P. A., Vidale, P.-L., Wijffels, S. E., Wilcox, L. J., Willett, K. M., and Zhang, X.: Challenges in Quantifying Changes in the Global Water Cycle, B. Am. Meteorol. Soc., 96, 1097–1115, https://doi.org/10.1175/BAMS-D-13-00212.1, 2014. a
Held, I. M. and Soden, B. J.: Robust Responses of the Hydrological Cycle to Global Warming, J. Climate, 19, 5686–5699, https://doi.org/10.1175/JCLI3990.1, 2006. a, b
Henderson-Sellers, B.: A new formula for latent heat of vaporization of water as a function of temperature, Q. J. Roy. Meteorol. Soc., 110, 1186–1190, https://doi.org/10.1002/qj.49711046626, 1984. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 2020, 1–51, 2020. a, b, c, d, e, f, g, h, i
Hollinger, J. P., Peirce, J. L., and Poe, G. A.: SSM/I Instrument Evaluation, IEEE T. Geosci. Remote, 28, 781–790, 1990. a
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S., Joyce, R., McGavock, B., and Susskind, J.: Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations, J. Hydrometeorol., 2, 36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2, 2001. a, b
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S., Hnilo, J. J., Fiorino, M., and Potter, G. L.: NCEP–DOE AMIP-II Reanalysis (R-2), B. Am. Meteorol. Soc., 83, 1631–1644, https://doi.org/10.1175/BAMS-83-11-1631, 2002. a
Kennedy, J., Reyner, N., Millington, S. C., and Saunby, M: The Met
Office Hadley Centre sea ice and sea-surface temperature data set, version 2,
part 2: seasurface temperature analysis, available at:
http://www.metoffice.gov.uk/hadobs/hadisst2/ (last access: January 2021), 2016. a
Kidd, C. and Huffman, G.: Global precipitation measurement, Meteorol. Appl., 18, 334–353, https://doi.org/10.1002/met.284, 2011. a, b, c
Kinzel, J., Fennig, K., Schröder, M., Andersson, A., Bumke, K., and Hollmann, R.: Decomposition of Random Errors Inherent to HOAPS-3.2 Near-Surface Humidity Estimates Using Multiple Triple Collocation Analysis, J. Atmos. Ocean. Tech., 33, 1455–1471, https://doi.org/10.1175/JTECH-D-15-0122.1, 2016. a, b, c
Knutti, R. and Sedláček, J.: Robustness and uncertainties in the new CMIP5 climate model projections, Nat. Clim. Change, 3, 369–373, https://doi.org/10.1038/nclimate1716, 2013. a
Kunkee, D. B., Poe, G. A., Swadley, S. D., Hong, Y., Wessel, J. E., and
Uliana, E. A.: Design and Evaluation of the First Special Sensor Microwave
Imager/Sounder, IEEE T. Geosci. Remote, 46, 863–883, 2008. a
Liepert, B. G. and Previdi, M.: Inter-model variability and biases of the global water cycle in CMIP3 coupled climate models, Environ. Res. Lett., 7, 014006, https://doi.org/10.1088/1748-9326/7/1/014006, 2012. a, b, c, d
Liman, J., Schröder, M., Fennig, K., Andersson, A., and Hollmann, R.: Uncertainty characterization of HOAPS 3.3 latent heat-flux-related parameters, Atmos. Meas. Tech., 11, 1793–1815, https://doi.org/10.5194/amt-11-1793-2018, 2018. a
Masunaga, H., Schröder, M., Furuzawa, F. A., Kummerow, C.,
Rustemeier, E., and Schneider, U.: Inter-product biases in global
precipitation extremes, Environ. Res. Lett., 14, 125016, https://doi.org/10.1088/1748-9326/ab5da9, 2019. a, b
Oki, T. and Kanae, S.: Global Hydrological Cycles and World Water Resources, Science, 313, 1068–1072, https://doi.org/10.1126/science.1128845, 2006. a, b, c, d
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S.,
and Schlax, M. G.: Daily High-Resolution-Blended Analyses for Sea Surface
Temperature, J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1, 2007. a, b, c
Roberts, J. B., Clayson, C. A., Robertson, F. R., and Jackson, D. L.:
Predicting near-surface atmospheric variables from Special Sensor
Microwave/Imager using neural networks with a first-guess approach, J. Geophys. Res.-Atmos., 115, D19113, https://doi.org/10.1029/2009JD013099, 2010. a
Roberts, J. B., Clayson, C. A., and Robertson, F. R.: Improving Near-Surface Retrievals of Surface Humidity Over the Global Open Oceans From Passive Microwave Observations, Earth Space Sci., 6, 1220–1233, https://doi.org/10.1029/2018EA000436, 2019. a, b, c
Roberts, J. B., Clayson, C. A., and Robertson, F. R.: SeaFlux v3: An updated climate data record of ocean turbulent fluxes, https://doi.org/10.5067/SEAFLUX/DATA101, 2020. a, b, c
Robertson, F. R., Bosilovich, M. G., Roberts, J. B., Reichle, R. H., Adler, R., Ricciardulli, L., Berg, W., and Huffman, G. J.: Consistency of Estimated Global Water Cycle Variations over the Satellite Era, J. Climate, 27, 6135–6154, https://doi.org/10.1175/JCLI-D-13-00384.1, 2014. a, b, c
Robertson, F. R., Roberts, J. B., Bosilovich, M. G., Bentamy, A., Schröder, M., Tomita, H., Clayson, C. A., Compo, G. P., Fennig, K., Gutenstein, M., Kobayashi, C., Sardeshmukh, P., and Slivinski, L. C.: Ocean Latent Heat Flux Uncertainties at Interannual to Inter-decadal Scales in Satellite Retrievals and Reduced Observation Reanalyses, J. Climate, 33, 8415–8437, https://doi.org/10.1175/JCLI-D-19-0954.1, 2020. a
Rodell, M., Beaudoing, H. K., L'Ecuyer, T. S., Olson, W. S., Famiglietti, J. S., Houser, P. R., Adler, R., Bosilovich, M. G., Clayson, C. A., Chambers, D., Clark, E., Fetzer, E. J., Gao, X., Gu, G., Hilburn, K., Huffman, G. J., Lettenmaier, D. P., Liu, W. T., Robertson, F. R., Schlosser, C. A., Sheffield, J., and Wood, E. F.: The Observed State of the Water Cycle in the Early
Twenty-First Century, J. Climate, 28, 8289–8318,
https://doi.org/10.1175/JCLI-D-14-00555.1, 2015. a, b, c, d, e, f, g, h, i
Sapiano, M. R. P., Berg, W. K., McKague, D. S., and Kummerow, C. D.: Toward an Intercalibrated Fundamental Climate Data Record of the SSM/I Sensors, IEEE T. Geosci. Remote, 51, 1492–1503, https://doi.org/10.1109/TGRS.2012.2206601, 2013. a, b
Schlosser, C. A. and Houser, P. R.: Assessing a Satellite-Era Perspective of the Global Water Cycle, J. Climate, 20, 1316–1338, https://doi.org/10.1175/JCLI4057.1, 2007. a, b, c
Seager, R. and Henderson, N.: Diagnostic Computation of Moisture Budgets in the ERA-Interim Reanalysis with Reference to Analysis of CMIP-Archived Atmospheric Model Data, J. Climate, 26, 7876–7901, https://doi.org/10.1175/JCLI-D-13-00018.1, 2013. a
Shie, C.-L., Tao, W.-K., and Simpson, J.: A note on the relationship between temperature and water vapor over oceans, including sea surface temperature effects, Adv. Atmos. Sci., 23, 141–148, https://doi.org/10.1007/s00376-006-0014-5, 2006. a
Shie, C.-L., Chiu, L. S., Adler, R., Nelkin, E., Lin, I.-I., Xie, P., Wang, F.-C., Chokngamwong, R., Olson, W., and Chu, D. A.: A note on reviving the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF) dataset, Adv. Atmos. Sci., 26, 1071–1080, https://doi.org/10.1007/s00376-009-8138-z, 2009. a
Stephens, G. L., Li, J., Wild, M., Clayson, C. A., Loeb, N., Kato, S., L'Ecuyer, T., Stackhouse, P. W., Lebsock, M., and Andrews, T.: An update on Earth's energy balance in light of the latest global observations, Nat. Geosci., 5, 691–696, https://doi.org/10.1038/ngeo1580, 2012. a
Tapiador, F. J., Navarro, A., Levizzani, V., García-Ortega, E., Huffman, G. J., Kidd, C., Kucera, P. A., Kummerow, C. D., Masunaga, H., Petersen, W. A., Roca, R., Sànchez, J.-L., Tao, W.-K., and Turk, F. J.: Global precipitation measurements for validating climate models, Atmos. Res., 197, 1–20, https://doi.org/10.1016/j.atmosres.2017.06.021, 2017. a, b, c
Tomita, H., Hihara, T., and Kubota, M.: Improved Satellite Estimation of Near-Surface Humidity Using Vertical Water Vapor Profile Information, Geophys. Res. Lett., 45, 899–906, https://doi.org/10.1002/2017GL076384, 2018. a, b
Trenberth, K. E. and Fasullo, J. T.: Regional Energy and Water Cycles: Transports from Ocean to Land, J. Climate, 26, 7837–7851, https://doi.org/10.1175/JCLI-D-13-00008.1, 2013.
a
Trenberth, K. E. and Stepaniak, D. P.: Indices of El Niño Evolution, J. Climate, 14, 1697–1701, https://doi.org/10.1175/1520-0442(2001)014<1697:LIOENO>2.0.CO;2, 2001. a
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's Global Energy Budget, B. Am. Meteorol. Soc., 90, 311–324, https://doi.org/10.1175/2008BAMS2634.1, 2009. a
Trenberth, K. E., Fasullo, J. T., and Mackaro, J.: Atmospheric Moisture Transports from Ocean to Land and Global Energy Flows in Reanalyses, J. Climate, 24, 4907–4924, https://doi.org/10.1175/2011JCLI4171.1, 2011. a, b
Wentz, F. J., Ricciardulli, L., Hilburn, K., and Mears, C.: How Much More Rain Will Global Warming Bring?, Science, 317, 233–235, https://doi.org/10.1126/science.1140746, 2007. a, b, c
Wilkinson, K., von Zabern, M., and Scherzer, J.: Global Freshwater Fluxes into the World Oceans: Technical Report prepared for the GRDC, GRDC Report 44, UDATA Umweltschutz und Datenanalyse, Neustadt/Weinstraße, Germany, https://doi.org/10.5675/GRDC_Report_44, 2014. a
Yin, J. and Porporato, A.: Looking Up or Looking Down? Hydrologic and Atmospheric Perspectives on Precipitation and Evaporation Variability, Geophys. Res. Lett., 46, 11968–11971, https://doi.org/10.1029/2019GL085466, 2019. a
Yu, L., Jin, X., and Weller, R. A.: Multidecade Global Flux Datasets
from the Objectively Analyzed Air-sea Fluxes (OAFlux) Project: Latent and
sensible heat fluxes, ocean evaporation, and related surface meteorological
variables, OAFlux Project Technical Report OA-2008-01, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, 64 pp., 2008. a, b, c, d
Short summary
The net exchange of water between the surface and atmosphere is mainly determined by the freshwater flux: the difference between evaporation (E) and precipitation (P), or E−P. Although there is consensus among modelers that with a warming climate E−P will increase, evidence from satellite data is still not conclusive, mainly due to sensor calibration issues. We here investigate the degree of correspondence among six recent
satellite-based climate data records and ERA5 reanalysis E−P data.
The net exchange of water between the surface and atmosphere is mainly determined by the...
