The Amazon Water Cycle: Perspectives from Water Budget Closure and Ocean Salinity

J. E. Jack Reeves Eyre Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

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Xubin Zeng Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

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Abstract

Global and regional water cycle includes precipitation, water vapor divergence, and change of column water vapor in the atmosphere, and land surface evapotranspiration, terrestrial water storage change, and river discharge, which is linked to ocean salinity near the river mouth. The water cycle is a crucial component of the Earth system, and numerous studies have addressed its individual components (e.g., precipitation). Here we assess, for the first time, if remote sensing and reanalysis datasets can accurately and self-consistently portray the Amazon water cycle. This is further assisted with satellite ocean salinity measurements near the mouth of the Amazon River. The widely used practice of taking the mean of an ensemble of datasets to represent water cycle components (e.g., precipitation) can produce large biases in water cycle closure. Closure is achieved with only a small subset of data combinations (e.g., ERA5 precipitation and evapotranspiration plus GRACE satellite terrestrial water storage), which rules out the lower precipitation and higher evapotranspiration estimates, providing valuable constraints on assessments of precipitation, evapotranspiration, and their ratio. The common approach of using the Óbidos stream gauge (located hundreds of kilometers from the river mouth) multiplied by a constant (1.25) to represent the entire Amazon discharge is found to misrepresent the seasonal cycle, and this can affect the apparent influence of Amazon discharge on tropical Atlantic salinity.

Current affiliation: Cooperative Institute for Climate, Ocean and Ecosystem Studies, University of Washington, Seattle, Washington.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0309.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jack Reeves Eyre, jack.reeveseyre@gmail.com

Abstract

Global and regional water cycle includes precipitation, water vapor divergence, and change of column water vapor in the atmosphere, and land surface evapotranspiration, terrestrial water storage change, and river discharge, which is linked to ocean salinity near the river mouth. The water cycle is a crucial component of the Earth system, and numerous studies have addressed its individual components (e.g., precipitation). Here we assess, for the first time, if remote sensing and reanalysis datasets can accurately and self-consistently portray the Amazon water cycle. This is further assisted with satellite ocean salinity measurements near the mouth of the Amazon River. The widely used practice of taking the mean of an ensemble of datasets to represent water cycle components (e.g., precipitation) can produce large biases in water cycle closure. Closure is achieved with only a small subset of data combinations (e.g., ERA5 precipitation and evapotranspiration plus GRACE satellite terrestrial water storage), which rules out the lower precipitation and higher evapotranspiration estimates, providing valuable constraints on assessments of precipitation, evapotranspiration, and their ratio. The common approach of using the Óbidos stream gauge (located hundreds of kilometers from the river mouth) multiplied by a constant (1.25) to represent the entire Amazon discharge is found to misrepresent the seasonal cycle, and this can affect the apparent influence of Amazon discharge on tropical Atlantic salinity.

Current affiliation: Cooperative Institute for Climate, Ocean and Ecosystem Studies, University of Washington, Seattle, Washington.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0309.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jack Reeves Eyre, jack.reeveseyre@gmail.com

Supplementary Materials

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  • Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and Coauthors, 2018: GPCP version 2.3 combined precipitation data set (updated monthly). NOAA/OAR/ESRL PSL, accessed 7 February 2020, https://psl.noaa.gov/data/gridded/data.gpcp.html.

  • Bao, S., H. Wang, R. Zhang, H. Yan, and J. Chen, 2019: Comparison of satellite-derived sea surface salinity products from SMOS, Aquarius, and SMAP. J. Geophys. Res. Oceans, 124, 19321944, https://doi.org/10.1029/2019JC014937.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beaudoing, H. K., and M. Rodell, 2020: GLDAS Noah land surface model L4 monthly 1.0 × 1.0 degree version 2.1. NASA Goddard Space Flight Center Hydrological Sciences Laboratory, and Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed 25 February 2020, https://doi.org/10.5067/LWTYSMP3VM5Z.

    • Crossref
    • Export Citation
  • Boutin, J., J.-L. Vergely, and D. Khvorostyanov, 2018a: SMOS SSS L3 maps generated by CATDS CEC LOCEAN, debias version 3.0. SEA scieNtific Open data Edition (SEANOE), accessed 27 February 2019, https://www.catds.fr/Products/Available-products-from-CEC-OS/CEC-Locean-L3-Debiased-v3.

  • Boutin, J., and Coauthors, 2018b: New SMOS Sea Surface Salinity with reduced systematic errors and improved variability. Remote Sens. Environ., 214, 115134, https://doi.org/10.1016/j.rse.2018.05.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coles, V. J., M. T. Brooks, J. Hopkins, M. R. Stukel, P. L. Yager, and R. R. Hood, 2013: The pathways and properties of the Amazon River plume in the tropical North Atlantic Ocean. J. Geophys. Res. Oceans, 118, 68946913, https://doi.org/10.1002/2013JC008981.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Copernicus Climate Change Service, 2017: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, monthly averaged data on single levels from 1979 to present. Copernicus Climate Change Service Climate Data Store (CDS), accessed 21 August 2019, doi:10.24381/cds.f17050d7.

    • Crossref
    • Export Citation
  • Dahle, C., and M. Murböck, 2019: Post-processed GRACE/GRACE-FO Geopotential GSM coefficients GFZ RL06 (Level-2B Product), version 0001. GFZ Data Services, accessed 24 September 2019, https://doi.org/10.5880/GFZ.GRAVIS_06_L2B.

    • Crossref
    • Export Citation
  • Dahle, C., and Coauthors, 2019: The GFZ GRACE RL06 monthly gravity field time series: Processing details and quality assessment. Remote Sens., 11, 2116, https://doi.org/10.3390/rs11182116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeor., 3, 660687, https://doi.org/10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., T. Qian, K. E. Trenberth, and J. D. Milliman, 2009: Changes in continental freshwater discharge from 1948 to 2004. J. Climate, 22, 27732792, https://doi.org/10.1175/2008JCLI2592.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eicker, A., E. Forootan, A. Springer, L. Longuevergne, and J. Kusche, 2016: Does GRACE see the terrestrial water cycle “intensifying”? J. Geophys. Res. Atmos., 121, 733745, https://doi.org/10.1002/2015JD023808.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eltahir, E. B., and R. L. Bras, 1994: Precipitation recycling in the Amazon basin. Quart. J. Roy. Meteor. Soc., 120, 861880, https://doi.org/10.1002/qj.49712051806.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fore, A. G., S. H. Yueh, W. Tang, B. W. Stiles, and A. K. Hayashi, 2016: Combined active/passive retrievals of ocean vector wind and sea surface salinity with SMAP. IEEE Trans. Geosci. Remote Sens., 54, 73967404, https://doi.org/10.1109/TGRS.2016.2601486.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fournier, S., D. Vandemark, L. Gaultier, T. Lee, B. Jonsson, and M. M. Gierach, 2017: Interannual variation in offshore advection of Amazon-Orinoco plume waters: Observations, forcing mechanisms, and impacts. J. Geophys. Res. Oceans, 122, 89668982, https://doi.org/10.1002/2017JC013103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Funk, C., and Coauthors, 2014a: CHIRPS version 2.0, monthly averages (updated monthly). Climate Hazards Center, University of California Santa Barbara, accessed 5 February 2020, https://doi.org/10.15780/G2RP4Q.

    • Crossref
    • Export Citation
  • Funk, C., and Coauthors, 2014b: A quasi-global precipitation time series for drought monitoring. U.S. Geological Survey Data Series 832, 4 pp., https://doi.org/10.3133/DS832.

    • Crossref
    • Export Citation
  • Gebremichael, M., and W. F. Krajewski, 2005: Modeling distribution of temporal sampling errors in area-time-averaged rainfall estimates. Atmos. Res., 73, 243259, https://doi.org/10.1016/j.atmosres.2004.11.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Getirana, A. C. V., and Coauthors, 2014: Water balance in the Amazon Basin from a land surface model ensemble. J. Hydrometeor., 15, 25862614, https://doi.org/10.1175/JHM-D-14-0068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gouveia, N. A., D. F. M. Gherardi, and L. E. O. C. Aragão, 2019: The role of the Amazon River plume on the intensification of the hydrological cycle. Geophys. Res. Lett., 46, 12 22112 229, https://doi.org/10.1029/2019GL084302.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guimberteau, M., and Coauthors, 2012: Discharge simulation in the sub-basins of the Amazon using ORCHIDEE forced by new datasets. Hydrol. Earth Syst. Sci., 16, 911935, https://doi.org/10.5194/hess-16-911-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hellweger, F. L., and A. L. Gordon, 2002: Tracing Amazon River water into the Caribbean Sea. J. Mar. Res., 60, 537549, https://doi.org/10.1357/002224002762324202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • HYBAM, 2019: Amazon discharge at Óbidos and Amazon basin cartography. SO-HYBAM, accessed 12 August 2019, https://www.ore-hybam.org.

  • Kerr, Y., N. Reul, M. Martín-Neira, M. Drusch, A. Alvera-Azcarate, J.-P. Wigneron, and S. Mecklenburg, 2016: ESA’s soil moisture and ocean salinity mission—Achievements and applications after more than 6 years in orbit. Remote Sens. Environ., 180, 12, https://doi.org/10.1016/j.rse.2016.03.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, V., 2019: The GDAP integrated product. GEWEX News, Vol. 29, No. 3, International GEWEX Project Office, Silver Spring, MD, 1–16, https://www.gewex.org/gewex-content/files_mf/1568309644Aug2019.pdf.

  • Kuper, R., and S. Kröpelin, 2006: Climate-controlled Holocene occupation in the Sahara: Motor of Africa’s evolution. Science, 313, 803807, https://doi.org/10.1126/science.1130989.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Large, W. G., and S. G. Yeager, 2004: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies. NCAR Tech. Note NCAR/TN-460+STR, 105 pp., https://doi.org/10.5065/D6KK98Q6.

    • Crossref
    • Export Citation
  • Large, W. G., and S. G. Yeager, 2009: The global climatology of an interannually varying air–sea flux data set. Climate Dyn., 33, 341364, https://doi.org/10.1007/S00382-008-0441-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, T., G. Lagerloef, M. M. Gierach, H.-Y. Kao, S. Yueh, and K. Dohan, 2012: Aquarius reveals salinity structure of tropical instability waves. Geophys. Res. Lett., 39, L12610, https://doi.org/10.1029/2012GL052232.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mansanarez, V., J. L. Coz, B. Renard, M. Lang, G. Pierrefeu, and P. Vauchel, 2016: Bayesian analysis of stage-fall-discharge rating curves and their uncertainties. Water Resour. Res., 52, 74247443, https://doi.org/10.1002/2016WR018916.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., 2005: Characteristics and spatio-temporal variability of the Amazon River basin water budget. Climate Dyn., 24, 1122, https://doi.org/10.1007/s00382-004-0461-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martens, B., and Coauthors, 2017a: GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev., 10, 19031925, https://doi.org/10.5194/gmd-10-1903-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martens, B., and Coauthors, 2017b: The Global Land Evaporation Amsterdam model, version 3.3b. Ghent University, accessed 24 September 2019, https://www.gleam.eu/.

  • Masson, S., and P. Delecluse, 2001: Influence of the Amazon River runoff on the tropical Atlantic. Phys. Chem. Earth, 26B, 137142, https://doi.org/10.1016/S1464-1909(00)00230-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meissner, T., F. J. Wentz, and D. M. Le Vine, 2018: The salinity retrieval algorithms for the NASA Aquarius version 5 and SMAP version 3 releases. Remote Sens., 10, 1121, https://doi.org/10.3390/rs10071121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Micklin, P., 2016: The future Aral Sea: Hope and despair. Environ. Earth Sci., 75, 844, https://doi.org/10.1007/s12665-016-5614-5.

  • Miguez-Macho, G., and Y. Fan, 2012: The role of groundwater in the Amazon water cycle: 2. Influence on seasonal soil moisture and evapotranspiration. J. Geophys. Res., 117, D15114, https://doi.org/10.1029/2012JD017540.

    • Search Google Scholar
    • Export Citation
  • Miralles, D. G., T. R. H. Holmes, R. A. M. D. Jeu, J. H. Gash, A. G. C. A. Meesters, and A. J. Dolman, 2011: Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci., 15, 453469, https://doi.org/10.5194/hess-15-453-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, B., and Coauthors, 2011: Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophys. Res. Lett., 38, L06402, https://doi.org/10.1029/2010GL046230.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, B., and Coauthors, 2013: Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis. Hydrol. Earth Syst. Sci., 17, 37073720, https://doi.org/10.5194/hess-17-3707-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NASA GMAO, 2015a: MERRA-2 instM_2d_int_Nx: 2d,Monthly mean, Instantaneous, Single-Level, Assimilation, Vertically Integrated Diagnostics V5.12.4. Global Modeling and Assimilation Office, Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed 14 April 2020, https://doi.org/10.5067/KVTU1A8BWFSJ.

    • Crossref
    • Export Citation
  • NASA GMAO, 2015b: MERRA-2 tavgM_2d_flx_Nx: 2d,Monthly mean, Time-Averaged, Single-Level, Assimilation, Surface Flux Diagnostics V5.12.4. Global Modeling and Assimilation Office, Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed 14 April 2020, https://doi.org/10.5067/0JRLVL8YV2Y4.

    • Crossref
    • Export Citation
  • NASA GMAO, 2015c: MERRA-2 tavgM_2d_int_Nx: 2d,Monthly mean, Time-Averaged, Single-Level, Assimilation, Vertically Integrated Diagnostics V5.12.4. Global Modeling and Assimilation Office, Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed 14 April 2020, https://doi.org/10.5067/FQPTQ4OJ22TL.

    • Crossref
    • Export Citation
  • NOAA CPC, 1997: Climate Prediction Center Merged Analysis of Precipitation (excludes NCEP Reanalysis), version v1908, updated irregularly. NOAA/OAR/ESRL PSL, accessed 17 February 2020, https://psl.noaa.gov/data/gridded/data.cmap.html.

  • Nobre, C. A., G. Sampaio, L. S. Borma, J. C. Castilla-Rubio, J. S. Silva, and M. Cardoso, 2016: Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proc. Natl. Acad. Sci. USA, 113, 10 75910 768, https://doi.org/10.1073/pnas.1605516113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paca, V. H. M., G. E. Espinoza-Dávalos, T. M. Hessels, D. M. Moreira, G. F. Comair, and W. G. M. Bastiaanssen, 2019: The spatial variability of actual evapotranspiration across the Amazon River Basin based on remote sensing products validated with flux towers. Ecol. Process., 8, 6, https://doi.org/10.1186/s13717-019-0158-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, M., A. K. Sahoo, T. J. Troy, R. K. Vinukollu, J. Sheffield, and E. F. Wood, 2012: Multisource estimation of long-term terrestrial water budget for major global river basins. J. Climate, 25, 31913206, https://doi.org/10.1175/JCLI-D-11-00300.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pellet, V., F. Aires, S. Munier, D. Fernández Prieto, G. Jordá, W. A. Dorigo, J. Polcher, and L. Brocca, 2019: Integrating multiple satellite observations into a coherent dataset to monitor the full water cycle—Application to the Mediterranean region. Hydrol. Earth Syst. Sci., 23, 465491, https://doi.org/10.5194/hess-23-465-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reeves Eyre, J. E. J., L. V. Roekel, X. Zeng, M. A. Brunke, and J.-C. Golaz, 2019: Ocean barrier layers in the Energy Exascale Earth System Model. Geophys. Res. Lett., 46, 82348243, https://doi.org/10.1029/2019GL083591.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., Q. Liu, R. D. Koster, C. S. Draper, S. P. P. Mahanama, and G. S. Partyka, 2017: Land surface precipitation in MERRA-2. J. Climate, 30, 16431664, https://doi.org/10.1175/jcli-d-16-0570.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rocha, V. M., P. R. T. da Silva, W. B. Gomes, L. A. Vergasta, and A. Jardine, 2018: Precipitation recycling in the Amazon Basin: A study using the ECMWF ERA-Interim reanalysis dataset. Rev. Dep. Geogr., 35, 7182, https://doi.org/10.11606/rdg.v35i0.139494.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodell, M., and H. K. Beaudoing, 2007: GLDAS CLM land surface model L4 monthly 1.0 × 1.0 degree V001. NASA Goddard Space Flight Center Hydrological Sciences Laboratory, Greenbelt and Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed 25 February 2020, https://doi.org/10.5067/0JNJQ8ZDZRBA.

    • Crossref
    • Export Citation
  • Rodell, M., J. S. Famiglietti, J. Chen, S. I. Seneviratne, P. Viterbo, S. Holl, and C. R. Wilson, 2004a: Basin scale estimates of evapotranspiration using GRACE and other observations. Geophys. Res. Lett., 31, L20504, https://doi.org/10.1029/2004GL020873.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodell, M., and Coauthors, 2004b: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381394, https://doi.org/10.1175/BAMS-85-3-381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodell, M., E. B. McWilliams, J. S. Famiglietti, H. K. Beaudoing, and J. Nigro, 2011: Estimating evapotranspiration using an observation based terrestrial water budget. Hydrol. Processes, 25, 40824092, https://doi.org/10.1002/hyp.8369.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodell, M., and Coauthors, 2015: The observed state of the water cycle in the early twenty-first century. J. Climate, 28, 82898318, https://doi.org/10.1175/JCLI-D-14-00555.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudzin, J. E., L. K. Shay, and B. Jaimes de la Cruz, 2019: The impact of the Amazon-Orinoco River plume on enthalpy flux and air–sea interaction within Caribbean Sea tropical cyclones. Mon. Wea. Rev., 147, 931950, https://doi.org/10.1175/MWR-D-18-0295.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sahoo, A. K., M. Pan, T. J. Troy, R. K. Vinukollu, J. Sheffield, and E. F. Wood, 2011: Reconciling the global terrestrial water budget using satellite remote sensing. Remote Sens. Environ., 115, 18501865, https://doi.org/10.1016/j.rse.2011.03.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salisbury, J., D. Vandemark, J. Campbell, C. Hunt, D. Wisser, N. Reul, and B. Chapron, 2011: Spatial and temporal coherence between Amazon River discharge, salinity, and light absorption by colored organic carbon in western tropical Atlantic surface waters. J. Geophys. Res., 116, C00H02, https://doi.org/10.1029/2011JC006989.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Save, H., 2019: CSR GRACE RL06 Mascon Solutions. Texas Data Repository Dataverse, accessed 24 September 2019, https://doi.org/10.18738/T8/UN91VR.

    • Crossref
    • Export Citation
  • Save, H., S. Bettadpur, and B. D. Tapley, 2016: High-resolution CSR GRACE RL05 mascons. J. Geophys. Res. Solid Earth, 121, 75477569, https://doi.org/10.1002/2016JB013007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schlosser, C. A., and P. R. Houser, 2007: Assessing a satellite-era perspective of the global water cycle. J. Climate, 20, 13161338, https://doi.org/10.1175/JCLI4057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seager, R., and N. Henderson, 2013: Diagnostic computation of moisture budgets in the ERA-Interim reanalysis with reference to analysis of CMIP-archived atmospheric model data. J. Climate, 26, 78767901, https://doi.org/10.1175/JCLI-D-13-00018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Syed, T. H., J. S. Famiglietti, J. Chen, M. Rodell, S. I. Seneviratne, P. Viterbo, and C. R. Wilson, 2005: Total basin discharge for the Amazon and Mississippi River basins from GRACE and a land–atmosphere water balance. Geophys. Res. Lett., 32, L24404, https://doi.org/10.1029/2005GL024851.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tapley, B. D., S. Bettadpur, M. Watkins, and C. Reigber, 2004: The Gravity Recovery and Climate Experiment: Mission overview and early results. Geophys. Res. Lett., 31, L0967, https://doi.org/10.1029/2004GL019920.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tapley, B. D., and Coauthors, 2019: Contributions of GRACE to understanding climate change. Nat. Climate Change, 9, 358369, https://doi.org/10.1038/s41558-019-0456-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tierney, J. E., F. S. R. Pausata, and P. B. deMenocal, 2017: Rainfall regimes of the Green Sahara. Sci. Adv., 3, e1601503, https://doi.org/10.1126/sciadv.1601503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and J. T. Fasullo, 2013: Regional energy and water cycles: Transports from ocean to land. J. Climate, 26, 78377851, https://doi.org/10.1175/JCLI-D-13-00008.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., J. T. Fasullo, and J. Mackaro, 2011: Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J. Climate, 24, 49074924, https://doi.org/10.1175/2011JCLI4171.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer, 2015: Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons. J. Geophys. Res. Solid Earth, 120, 26482671, https://doi.org/10.1002/2014JB011547.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wiese, D. N., D.-N. Yuan, C. Boening, F. W. Landerer, and M. M. Watkins, 2018: JPL GRACE Mascon Ocean, Ice, and Hydrology equivalent water height release 06 version 1.0, Coastal Resolution Improvement (CRI) Filtered Version 1.0. Physical Oceanography Distributed Active Archive Center, accessed 22 February 2019, https://doi.org/10.5067/TEMSC-3MJC6.

    • Crossref
    • Export Citation
  • Wigley, T. M. L., K. R. Briffa, and P. D. Jones, 1984: On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J. Climate Appl. Meteor., 23, 201213, https://doi.org/10.1175/1520-0450(1984)023<0201:OTAVOC>2.0.CO;2.

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    • Export Citation
  • Wu, J., and Coauthors, 2016: Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science, 351, 972976, https://doi.org/10.1126/science.aad5068.

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  • Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 25392558, https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

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    • Search Google Scholar
    • Export Citation
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