Articles | Volume 28, issue 9
https://doi.org/10.5194/hess-28-2123-2024
© 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/hess-28-2123-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 May 2024
Research article |
| 15 May 2024
| 15 May 2024
Global total precipitable water variations and trends over the period 1958–2021
Nenghan Wan
Department of Agronomy, Kansas Climate Center, Kansas State University, Manhattan, KS, USA
Xiaomao Lin
CORRESPONDING AUTHOR
Department of Agronomy, Kansas Climate Center, Kansas State University, Manhattan, KS, USA
Roger A. Pielke Sr.
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
Xubin Zeng
Climate Dynamics and Hydrometeorology Center at the University of Arizona, Tucson, AZ, USA
Amanda M. Nelson
National Center of Alluvial Aquifer Research, USDA-ARS Sustainable Water Management Research Unit, Stoneville, MS, USA
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Cited articles
AIRS Science Team and Teixeira, J.: AIRS/Aqua L3 Daily Standard Physical Retrieval (AIRS-only) 1 degree × 1 degree V006, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/Aqua/AIRS/DATA303, 2013.
Allan, D. and Allan, R. P.: Seasonal Changes in the North Atlantic Cold Anomaly: The influence of cold surface waters from coastal greenland and warming trends associated with variations in subarctic sea ice cover, J. Geophys. Res.-Oceans, 124, 9040–9052, https://doi.org/10.1029/2019JC015379, 2020.
Allan, R. P., Liu, C., Zahn, M., Lavers, D. A., Koukouvagias, E., and Bodas-Salcedo, A.: Physically consistent responses of the global atmospheric hydrological cycle in models and observations, Surv. Geophys., 35, 533–552, https://doi.org/10.1007/s10712-012-9213-z, 2014.
Allan, R. P., Willett, K. M., John, V. O., and Trent, T.: Global changes in water vapor 1979–2020, J. Geophys. Res., 127, e2022JD036728, https://doi.org/10.1029/2022JD036728, 2022.
Alshawaf, F., Balidakis, K., Dick, G., Heise, S., and Wickert, J.: Estimating trends in atmospheric water vapor and temperature time series over Germany, Atmos. Meas. Tech., 10, 3117–3132, https://doi.org/10.5194/amt-10-3117-2017, 2017.
Andrews, T., Bodas-Salcedo, A., Gregory, J. M., Dong, Y., Armour, K. C., Paynter, D., Lin, P., Modak, A., Mauritsen, T., Cole, J. N. S., Medeiros, B., Benedict, J. J., Douville, H., Roehrig, R., Koshiro, T., Kawai, H., Ogura, T., Dufresne, J.-L., Allan, R. P., and Liu, C.: On the effect of historical SST patterns on radiative feedback, J. Geophys. Res.-Atmos., 127, e2022JD036675, https://doi.org/10.1029/2022JD036675, 2022.
Bock, O.: Global GNSS integrated water vapour data, 1994–2021, AERIS [data set], https://en.aeris-data.fr/landing-page/?uuid=df7cf172-31fb-4d17-8f00-1a9293eb3b95 (last access: 13 May 2024), 2022.
Borger, C., Beirle, S., and Wagner, T.: Analysis of global trends of total column water vapour from multiple years of OMI observations, Atmos. Chem. Phys., 22, 10603–10621, https://doi.org/10.5194/acp-22-10603-2022, 2022.
Byrne, M. P. and O'Gorman, P. A.: Trends in continental temperature and humidity directly linked to ocean warming, Natl. Acad. Sci. U. S. A., 115, 4863–4868, https://doi.org/10.1073/pnas.1722312115, 2018.
Chen, B. and Liu, Z.: Global water vapor variability and trend from the latest 36 year (1979 to 2014) data of ECMWF and NCEP reanalyses, radiosonde, GPS, and microwave satellite, J. Geophys. Res., 121, 11,442-11,462, https://doi.org/10.1002/2016JD024917, 2016.
Cheng, L., Abraham, J., Trenberth, K. E., Boyer, T., Mann, M. E., Zhu, J., Wang, F., Yu, F., Locarnini, R., Fasullo, J., Zheng, F., Li, Y., Zhang, B., Wan, L., Chen, X., Wang, D., Feng, L., Song, X., Liu, Y., Reseghetti, F., Simoncelli, S., Gouretski, V., Chen, G., Mishonov, A., Reagan, J., Von Schuckmann, K., Pan, Y., Tan, Z., Zhu, Y., Wei, W., Li, G., Ren, Q., Cao, L., and Lu, Y.: New record ocean temperatures and related climate indicators in 2023, Adv. Atmos. Sci., 2024, 1–15, https://doi.org/10.1007/s00376-024-3378-5, 2024.
Clem, K. R., Fogt, R. L., Turner, J., Lintner, B. R., Marshall, G. J., Miller, J. R., and Renwick, J. A.: Record warming at the South Pole during the past three decades, Nat. Clim. Change, 10, 762–770, https://doi.org/10.1038/s41558-020-0815-z, 2020.
Dai, A., Wang, J., Thorne, P. W., Parker, D. E., Haimberger, L., and Wang, X. L.: A new approach to homogenize daily radiosonde humidity data, J. Climate, 24, 965–991, 2011.
Dee, D., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M., Balsamo, G., and Bauer, D. P.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011.
Ding, J., Chen, J., and Tang, W.: Increasing trend of precipitable water vapor in Antarctica and Greenland, in: China Satellite Navigation Conference (CSNC 2022) Proceedings, 7 May 2022, Singapore, 286–296, https://doi.org/10.1007/978-981-19-2588-7_27, 2022.
Dong, B. and Dai, A.: The influence of the Interdecadal Pacific Oscillation on temperature and precipitation over the globe, Clim. Dynam., 45, 2667–2681, https://doi.org/10.1007/s00382-015-2500-x, 2015.
Douville, H. and Willett, K. M.: A drier than expected future, supported by near-surface relative humidity observations, Sci. Adv., 9, eade6253, https://doi.org/10.1126/sciadv.ade6253, 2022.
Douville, H., Qasmi, S., Ribes, A., and Bock, O.: Global warming at near-constant tropospheric relative humidity is supported by observations, Commun. Earth Environ., 3, 1–7, https://doi.org/10.1038/s43247-022-00561-z, 2022.
Drijfhout, S., Oldenborgh, G. J. van, and Cimatoribus, A.: Is a decline of AMOC causing the warming hole above the North Atlantic in observed and modeled warming patterns?, J. Climate, 25, 8373–8379, https://doi.org/10.1175/JCLI-D-12-00490.1, 2012.
Dunning, C. M., Black, E. C. L., and Allan, R. P.: The onset and cessation of seasonal rainfall over Africa, J. Geophys. Res., 121, 11, 405–11, 424, https://doi.org/10.1002/2016JD025428, 2016.
Durre, I., Xungang, Y., Vose, R. S., Applequist, S., and Arnfield, J.: Integrated Global Radiosonde Archive (IGRA), Version 2, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V5X63K0Q, 2016.
Fasullo, J.: A mechanism for land–ocean contrasts in global monsoon trends in a warming climate, Clim. Dynam., 39, 1137–1147, https://doi.org/10.1007/s00382-011-1270-3, 2012.
Garfinkel, C. I., Gordon, A., Oman, L. D., Li, F., Davis, S., and Pawson, S.: Nonlinear response of tropical lower-stratospheric temperature and water vapor to ENSO, Atmos. Chem. Phys., 18, 4597–4615, https://doi.org/10.5194/acp-18-4597-2018, 2018.
Graversen, R. G.: Do changes in the midlatitude circulation have any impact on the Arctic surface air temperature trend?, J. Climate, 19, 5422–5438, 2006.
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, 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.
He, C., Clement, A. C., Cane, M. A., Murphy, L. N., Klavans, J. M., and Fenske, T. M.: A north atlantic warming hole without ocean circulation, Geophys. Res. Lett., 49, e2022GL100420, https://doi.org/10.1029/2022GL100420, 2022.
He, M., Qin, J., Lu, N., and Yao, L.: Assessment of ERA5 near-surface air temperatures over global oceans by combining MODIS sea surface temperature products and in situ observations, IEEE J. Sel. Top. Appl., 16, 8442–8455, https://doi.org/10.1109/JSTARS.2023.3312810, 2023.
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.
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. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N: ERA5 monthly averaged data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.6860a573, 2023.
Hirsch, R. M., Slack, J. R., and Smith, R. A.: Techniques of trend analysis for monthly water quality data, Water Resour. Res., 18, 107–121, 1982.
Japan Meteorological Agency: Updated monthly. JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6HH6H41, 2013.
Jin, M. and Dickinson, R. E.: Land surface skin temperature climatology: benefitting from the strengths of satellite observations, Environ. Res. Lett., 5, 044004, https://doi.org/10.1088/1748-9326/5/4/044004, 2010.
Jin, M., Dickinson, R. E., and Vogelmann, A. M.: A Comparison of CCM2–BATS Skin Temperature and surface-air temperature with satellite and surface observations, J. Climate, 10, 1505–1524, https://doi.org/10.1175/1520-0442(1997)010<1505:ACOCBS>2.0.CO;2, 1997.
Joshi, M. M., Gregory, J. M., Webb, M. J., Sexton, D. M. H., and Johns, T. C.: Mechanisms for the land/sea warming contrast exhibited by simulations of climate change, Clim. Dynam., 30, 455–465, https://doi.org/10.1007/s00382-007-0306-1, 2008.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 reanalysis: general specifications and basic characteristics, J. Meteorol. Soc. Jpn., 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Lacis, A. A., Schmidt, G. A., Rind, D., and Ruedy, R. A.: Atmospheric CO2: Principal control knob governing Earth's temperature, Science, 330, 356–359, 2010.
Li, L., Lozier, M. S., and Li, F.: Century-long cooling trend in subpolar North Atlantic forced by atmosphere: an alternative explanation, Clim. Dynam., 58, 2249–2267, https://doi.org/10.1007/s00382-021-06003-4, 2022.
Long, C. S., Fujiwara, M., Davis, S., Mitchell, D. M., and Wright, C. J.: Climatology and interannual variability of dynamic variables in multiple reanalyses evaluated by the SPARC Reanalysis Intercomparison Project (S-RIP), Atmos. Chem. Phys., 17, 14593–14629, https://doi.org/10.5194/acp-17-14593-2017, 2017.
Madonna, F., Tramutola, E., Sy, S., Serva, F., Proto, M., Rosoldi, M., Gagliardi, S., Amato, F., Marra, F., Fassò, A., Gardiner, T., and Thorne, P. W.: The new Radiosounding HARMonization (RHARM) data set of homogenized radiosounding temperature, humidity, and wind profiles with uncertainties, J. Geophys. Res., 127, e2021JD035220, https://doi.org/10.1029/2021JD035220, 2022.
Masson-Delmotte, V., Zhai, P., Pörtner, H. O., Roberts, D., Skea, J., and Shukla, P. R.: Global Warming of 1.5 °C: IPCC Special Report on impacts of global warming of 1.5 °C above pre-industrial levels in context of strengthening response to climate change, Sustainable Development, and Efforts to Eradicate Poverty, Cambridge University Press, ISBN 9781009157940, 2022.
Morice, C. P., Kennedy, J. J., Rayner, N. A., Winn, J. P., Hogan, E., Killick, R. E., Dunn, R. J. H., Osborn, T. J., Jones, P. D., and Simpson, I. R.: An updated assessment of near-surface temperature change From 1850: The HadCRUT5 data set, J. Geophys. Res., 126, e2019JD032361, https://doi.org/10.1029/2019JD032361, 2021.
O'Gorman, P. A. and Muller, C. J.: How closely do changes in surface and column water vapor follow Clausius–Clapeyron scaling in climate change simulations?, Environ. Res. Lett., 5, 025207, https://doi.org/10.1088/1748-9326/5/2/025207, 2010.
Parracho, A. C., Bock, O., and Bastin, S.: Global IWV trends and variability in atmospheric reanalyses and GPS observations, Atmos. Chem. Phys., 18, 16213–16237, https://doi.org/10.5194/acp-18-16213-2018, 2018.
Patel, V. K. and Kuttippurath, J.: Increase in iropospheric water vapor amplifies global warming and climate change, Ocean-Land-Atmosphere Research, 2, 0015, https://doi.org/10.34133/olar.0015, 2023.
Pielke, R., Nielsen-Gammon, J., Davey, C., Angel, J., Bliss, O., Doesken, N., Cai, M., Fall, S., Niyogi, D., Gallo, K., and Hale, R.: Documentation of uncertainties and biases associated with surface temperature measurement sites for climate change assessment, B. Am. Meteorol. Soc., 88, 913–928, https://doi.org/10.1175/BAMS-88-6-913, 2007.
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Commun. Earth Environ., 3, 1–10, https://doi.org/10.1038/s43247-022-00498-3, 2022.
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., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003.
Ruckstuhl, C., Philipona, R., Morland, J., and Ohmura, A.: Observed relationship between surface specific humidity, integrated water vapor, and longwave downward radiation at different altitudes, J. Geophys. Res.-Atmos., 112, D03302, https://doi.org/10.1029/2006JD007850, 2007.
Santer, B. D., Wigley, T. M. L., Gleckler, P. J., Bonfils, C., Wehner, M. F., AchutaRao, K., Barnett, T. P., Boyle, J. S., Bruggemann, W., Fiorino, M., and Gillett, N.: Causes of ocean surface temperature changes in Atlantic and Pacific tropical cyclogenesis regions, P. Natl. Acad. Sci. USA, 103, 13905–13910, 2006.
Santer, B. D., Taylor, K. E., Gleckler, P. J., Bonfils, C., Barnett, T. P., Pierce, D. W., Wigley, T. M. L., Mears, C., Wentz, F. J., Brüggemann, W., Gillett, N. P., Klein, S. A., Solomon, S., Stott, P. A., and Wehner, M. F.: Incorporating model quality information in climate change detection and attribution studies, P. Natl. Acad. Sci. USA, 106, 14778–14783, https://doi.org/10.1073/pnas.0901736106, 2009.
Santer, B. D., Po-Chedley, S., Mears, C., Fyfe, J. C., Gillett, N., Fu, Q., Painter, J. F., Solomon, S., Steiner, A. K., Wentz, F. J., Zelinka, M. D., and Zou, C.-Z.: Using climate model simulations to constrain observations, J. Climate, 34, 6281–6301, https://doi.org/10.1175/JCLI-D-20-0768.1, 2021.
Schröder, M., Lockhoff, M., Forsythe, J. M., Cronk, H. Q., Haar, T. H. V., and Bennartz, R.: The GEWEX water vapor assessment: results from intercomparison, trend, and homogeneity analysis of total column water vapor, J. Appl. Meteorol. Clim., 55, 1633–1649, https://doi.org/10.1175/JAMC-D-15-0304.1, 2016.
Screen, J. A. and Simmonds, I.: The central role of diminishing sea ice in recent Arctic temperature amplification, Nature, 464, 1334–1337, 2010.
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau, J. Am. Stat. Assoc., 63, 1379–1389, 1968.
Shao, X., Ho, S.-P., Jing, X., Zhou, X., Chen, Y., Liu, T.-C., Zhang, B., and Dong, J.: Characterizing the tropospheric water vapor spatial variation and trend using 2007–2018 COSMIC radio occultation and ECMWF reanalysis data, Atmos. Chem. Phys., 23, 14187–14218, https://doi.org/10.5194/acp-23-14187-2023, 2023.
Soden, B. J., Wetherald, R. T., Stenchikov, G. L., and Robock, A.: Global Cooling After the Eruption of Mount Pinatubo: A Test of Climate Feedback by Water Vapor, Science, 296, 727–730, https://doi.org/10.1126/science.296.5568.727, 2002.
Simmons, A. J., Berrisford, P., Dee, D. P., Hersbach, H., Hirahara, S., and Thépaut, J. N.: A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets, Q. J. Roy. Meteor. Soc., 143, 101–119, https://doi.org/10.1002/qj.2949, 2017.
Simpson, I. R., McKinnon, K. A., Kennedy, D., Lawrence, D. M., Lehner, F., and Seager, R.: Observed humidity trends in dry regions contradict climate models, P. Natl. Acad. Sci. USA, 121, e2302480120, https://doi.org/10.1073/pnas.2302480120, 2023.
Swaminathan, R., Parker, R. J., Jones, C. G., Allan, R. P., Quaife, T., Kelley, D. I., Mora, L. de, and Walton, J.: The physical climate at global warming thresholds as seen in the U. K. Earth System Model, J. Climate, 35, 29–48, https://doi.org/10.1175/JCLI-D-21-0234.1, 2022.
Taszarek, M., Pilguj, N., Allen, J. T., Gensini, V., Brooks, H. E., and Szuster, P.: Comparison of convective parameters derived from ERA5 and MERRA-2 with rawinsonde data over Europe and North America, J. Climate, 34, 3211–3237, https://doi.org/10.1175/JCLI-D-20-0484.1, 2021.
Theil, H.: A rank-invariant method of linear and polynomial regression analysis, in: Henri Theil's contributions to economics and econometrics, Springer, 345–381, https://doi.org/10.1007/978-94-011-2546-8, 1992.
Tian, B., Manning, E., Roman, J., Thrastarson, H., Fetzer, E., and Monarrez, R.: AIRS version 7 level 3 product user guide, Jet Propulsion Laboratory, California Institute of Technology, https://airs.jpl.nasa.gov/data/products/v7-L2-L3/(last access: 3 June 2021), 2020.
Trenberth, K. E.: Atmospheric moisture residence times and cycling: implications for rainfall rates and climate change, Climatic Change, 39, 667–694, https://doi.org/10.1023/A:1005319109110, 1998.
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The changing character of precipitation, B. Am. Meterol. Soc., 84, 1205–1218, https://doi.org/10.1175/BAMS-84-9-1205, 2003.
Trenberth, K. E., Fasullo, J., and Smith, L.: Trends and variability in column-integrated atmospheric water vapor, Clim. Dynam., 24, 741–758, https://doi.org/10.1007/s00382-005-0017-4, 2005.
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, 2011.
Trenberth, K. E., Zhang, Y., Fasullo, J. T., and Taguchi, S.: Climate variability and relationships between top-of-atmosphere radiation and temperatures on Earth, J. Geophys. Res.-Atmos., 120, 3642–3659, https://doi.org/10.1002/2014JD022887, 2015.
Trent, T., Schroeder, M., Ho, S.-P., Beirle, S., Bennartz, R., Borbas, E., Borger, C., Brogniez, H., Calbet, X., Castelli, E., Compo, G. P., Ebisuzaki, W., Falk, U., Fell, F., Forsythe, J., Hersbach, H., Kachi, M., Kobayashi, S., Kursinsk, R. E., Loyola, D., Luo, Z., Nielsen, J. K., Papandrea, E., Picon, L., Preusker, R., Reale, A., Shi, L., Slivinski, L., Teixeira, J., Vonder Haar, T., and Wagner, T.: Evaluation of Total Column Water Vapour Products from Satellite Observations and Reanalyses within the GEWEX Water Vapor Assessment, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2808, 2023.
Urraca, R. and Gobron, N.: Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends, The Cryosphere, 17, 1023–1052, https://doi.org/10.5194/tc-17-1023-2023, 2023.
Wagner, T., Beirle, S., Grzegorski, M., Sanghavi, S., and Platt, U.: El Niño induced anomalies in global data sets of total column precipitable water and cloud cover derived from GOME on ERS-2, J. Geophys. Res., 110, https://doi.org/10.1029/2005JD005972, 2005.
Wang, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019.
Wang, J., Dai, A., and Mears, C.: Global water vapor trend from 1988 to 2011 and its diurnal asymmetry based on GPS, radiosonde, and microwave satellite measurements, J. Climate, 29, 5205–5222, https://doi.org/10.1175/JCLI-D-15-0485.1, 2016.
Wang, X. L.: Penalized maximal F test for detecting undocumented mean shift without trend change, J. Atmos. Ocean. Tech., 25, 368–384, https://doi.org/10.1175/2007JTECHA982.1, 2018.
Wang, X. L. and Feng, Y.: RHtestsV4 user manual, Climate Research Division, ASTD, STB, Environment Canada [Software], http://etccdi.pacificclimate.org/software.shtml (last access: 13 May 2024), 2013.
Wang, X. L., Swail, V. R., and Zwiers, F. W.: Climatology and changes of extratropical cyclone activity: Comparison of ERA-40 with NCEP–NCAR reanalysis for 1958–2001, J. Climate, 19, 3145–3166, https://doi.org/10.1175/JCLI3781.1, 2006.
Wentz, F. J., Hilburn, K. A., and Smith, D. K.: Remote sensing systems DMSP [SSM/I or SSMIS][Daily, 3-Day, Weekly, Monthly] environmental suite on 0.25° grid, Version 7, Remote Sensing Systems, Santa Rosa, CA [data set], https://www.remss.com/missions/ssmi (last access: 13 May 2024), 2015.
Willett, K. M.: HadISDH.extremes Part I: A gridded wet bulb temperature extremes index product for climate monitoring, Adv. Atmos. Sci., 40, 1952–1967, https://doi.org/10.1007/s00376-023-2347-8, 2023.
Xiong, J., Guo, S., Abhishek, Chen, J., and Yin, J.: Global evaluation of the “dry gets drier, and wet gets wetter” paradigm from a terrestrial water storage change perspective, Hydrol. Earth Syst. Sci., 26, 6457–6476, https://doi.org/10.5194/hess-26-6457-2022, 2022.
Yuan, P., Hunegnaw, A., Alshawaf, F., Awange, J., Klos, A., Teferle, F. N., and Kutterer, H.: Feasibility of ERA5 integrated water vapor trends for climate change analysis in continental Europe: An evaluation with GPS (1994–2019) by considering statistical significance, Remote Sens. Environ., 260, 112416, https://doi.org/10.1016/j.rse.2021.112416, 2021.
Yuan, P., Van Malderen, R., Yin, X., Vogelmann, H., Jiang, W., Awange, J., Heck, B., and Kutterer, H.: Characterisations of Europe's integrated water vapour and assessments of atmospheric reanalyses using more than 2 decades of ground-based GPS, Atmos. Chem. Phys., 23, 3517–3541, https://doi.org/10.5194/acp-23-3517-2023, 2023.
Zeng, X., Reeves Eyre, J. J., Dixon, R. D., and Arevalo, J.: Quantifying the occurrence of record hot years through normalized warming trends, Geophys. Res. Lett., 48, e2020GL091626, 2021.
Zhai, P. and Eskridge, R. E.: Atmospheric water vapor over China, J Climate, 10, 2643–2652, https://doi.org/10.1175/1520-0442(1997)010<2643:AWVOC>2.0.CO;2, 1997.
Zhang, W., Lou, Y., Cao, Y., Liang, H., Shi, C., Huang, J., Liu, W., Zhang, Y., and Fan, B.: Corrections of radiosonde-based precipitable water using ground-based GPS and applications on historical radiosonde data over China, J. Geophys. Res., 124, 3208–3222, https://doi.org/10.1029/2018JD029662, 2019.
Zhang, W., Wang, L., Yu, Y., Xu, G., Hu, X., Fu, Z., and Cui, C.: Global evaluation of the precipitable-water-vapor product from MERSI-II (Medium Resolution Spectral Imager) on board the Fengyun-3D satellite, Atmos. Meas. Tech., 14, 7821–7834, https://doi.org/10.5194/amt-14-7821-2021, 2021.
Zhang, X., Sorteberg, A., Zhang, J., Gerdes, R., and Comiso, J. C.: Recent radical shifts of atmospheric circulations and rapid changes in Arctic climate system, Geophys. Res. Lett., 35, L22701, https://doi.org/10.1029/2008GL035607, 2008.
Zhou, C., Wang, J., Dai, A., and Thorne, P. W.: A new approach to homogenize global subdaily radiosonde temperature data from 1958 to 2018, J. Climate, 34, 1163–1183, https://doi.org/10.1175/JCLI-D-20-0352.1, 2021.
Zhuang, J.: xESMF: universal regridder for geospatial data, GitHub, GitHub repository, [Software], Zenodo, https://doi.org/10.5281/zenodo.1134366, 2018.
Zveryaev, I. I. and Allan, R. P.: Water vapor variability in the tropics and its links to dynamics and precipitation, J. Geophys. Res.-Atmos., 110, D21112, https://doi.org/10.1029/2005JD006033, 2005.
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Short summary
Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water...
