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  • Bodeker, G., and S. Kremser, 2015: Techniques for analyses of trends in GRUAN data. Atmos. Meas. Tech., 8, 1673–1684, doi:10.5194/amt-8-1673-2015.

    • Search Google Scholar
    • Export Citation

  • Bodeker, G., and Coauthors, 2016: Reference upper-air observations for climate: From concept to reality. Bull. Amer. Meteor. Soc., 97, 123–135, doi:10.1175/BAMS-D-14-00072.1.

    • Search Google Scholar
    • Export Citation

  • Dai, A., 2006: Recent climatology, variability, and trends in global surface humidity. J. Climate, 19, 3589–3606, doi:10.1175/JCLI3816.1.

    • Search Google Scholar
    • Export Citation

  • Dai, A., 2013: The influence of the inter-decadal Pacific oscillation on US precipitation during 1923–2010. Climate Dyn., 41, 633–646, doi:10.1007/s00382-012-1446-5.

    • Search Google Scholar
    • Export Citation

  • Dai, A., K. E. Trenberth, and T. R. Karl, 1999: Effects of clouds, soil moisture, precipitation, and water vapor on diurnal temperature range. J. Climate, 12, 2451–2473, doi:10.1175/1520-0442(1999)012<2451:EOCSMP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dai, A., J. Wang, R. H. Ware, and T. Van Hove, 2002: Diurnal variation in water vapor over North America and its implications for sampling errors in radiosonde humidity. J. Geophys. Res., 107, 4090, doi:10.1029/2001JD000642.

    • Search Google Scholar
    • Export Citation

  • Dai, A., J. Wang, P. W. Thorne, D. E. Parker, L. Haimberger, and X. L. Wang, 2011: A new approach to homogenize daily radiosonde humidity data. J. Climate, 24, 965–991, doi:10.1175/2010JCLI3816.1.

    • Search Google Scholar
    • Export Citation

  • Dai, A., H. Li, Y. Sun, L.-C. Hong, LinHo, C. Chou, and T. Zhou, 2013: The relative roles of upper and lower tropospheric thermal contrasts and tropical influences in driving Asian summer monsoons. J. Geophys. Res. Atmos., 118, 7024–7045, doi:10.1002/jgrd.50565.

    • Search Google Scholar
    • Export Citation

  • Dai, A., J. C. Fyfe, S.-P. Xie, and X. Dai, 2015: Decadal modulation of global surface temperature by internal climate variability. Nat. Climate Change, 5, 555–559, doi:10.1038/nclimate2605.

    • Search Google Scholar
    • Export Citation

  • Dirksen, R. J., M. Sommer, F. J. Immler, D. F. Hurst, R. Kivi, and H. Vömel, 2014: Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosonde. Atmos. Meas. Tech., 7, 4463–4490, doi:10.5194/amt-7-4463-2014.

    • Search Google Scholar
    • Export Citation

  • Dong, B., and A. Dai, 2015: The influence of the interdecadal Pacific oscillation on temperature and precipitation over the globe. Climate Dyn., 45, 2667–2681, doi:10.1007/s00382-015-2500-x.

    • Search Google Scholar
    • Export Citation

  • Dunn, R. J. H., K. M. Willett, P. W. Thorne, E. V. Woolley, I. Durre, A. Dai, D. E. Parker, and R. S. Vose, 2012: HadISD: A quality-controlled global synoptic report database for selected variables at long-term stations from 1973–2011. Climate Past, 8, 1649–1679, doi:10.5194/cp-8-1649-2012.

    • Search Google Scholar
    • Export Citation

  • Durre, I., and J. M. Wallace, 2001: The warm season dip in diurnal temperature range over the eastern United States. J. Climate, 14, 354–360, doi:10.1175/1520-0442(2001)014<0354:TWSDID>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Easterling, D. R., and Coauthors, 1997: Maximum and minimum temperature trends for the globe. Science, 277, 364–367, doi:10.1126/science.277.5324.364.

    • Search Google Scholar
    • Export Citation

  • Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866. [Available online at http://www.climatechange2013.org/images/report/WG1AR5_Chapter09_FINAL.pdf.]

  • Gaffen, D. J., and R. J. Ross, 1999: Climatology and trends of U.S. surface humidity and temperature. J. Climate, 12, 811–828, doi:10.1175/1520-0442(1999)012<0811:CATOUS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hartmann, D. L., and Coauthors, 2013: Observations: Atmosphere and surface. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 159–254. [Available online at http://www.climatechange2013.org/images/report/WG1AR5_Chapter02_FINAL.pdf.]

  • Held, I. M., and B. J. Soden, 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25, 441–475, doi:10.1146/annurev.energy.25.1.441.

    • Search Google Scholar
    • Export Citation

  • Immler, F. J., J. Dykema, T. Gardiner, D. N. Whiteman, P. W. Thorne, and H. Vömel, 2010: Reference quality upper-air measurements: Guidance for developing GRUAN data products. Atmos. Meas. Tech., 3, 1217–1231, doi:10.5194/amt-3-1217-2010.

    • Search Google Scholar
    • Export Citation

  • Isaac, V., and W. A. Van Wijngaarden, 2012: Surface water vapor pressure and temperature trends in North America during 1948–2010. J. Climate, 25, 3599–3609, doi:10.1175/JCLI-D-11-00003.1.

    • Search Google Scholar
    • Export Citation

  • Karl, T. R., R. W. Knight, K. P. Gallo, and T. C. Peterson, 1993: A new perspective on recent global warming: Asymmetric trends of daily maximum and minimum temperature. Bull. Amer. Meteor. Soc., 74, 1007–1023, doi:10.1175/1520-0477(1993)074<1007:ANPORG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Lawrimore, J. H., M. J. Menne, B. E. Gleason, C. N. Williams, D. B. Wuertz, R. S. Vose, and J. Rennie, 2011: An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3. J. Geophys. Res., 116, D19121, doi:10.1029/2011JD016187.

    • Search Google Scholar
    • Export Citation

  • Mears, C. A., B. D. Santer, F. J. Wentz, K. E. Taylor, and M. F. Wehner, 2007: Relationship between temperature and precipitable water changes over tropical oceans. Geophys. Res. Lett., 34, L24709, doi:10.1029/2007GL031936.

    • Search Google Scholar
    • Export Citation

  • Mears, C. A., J. Wang, D. Smith, and F. J. Wentz, 2015: Intercomparison of total precipitable water measurements made by satellite-borne microwave radiometers and ground-based GPS instruments. J. Geophys. Res. Atmos., 120, 2492–2504, doi:10.1002/2014JD022694.

    • Search Google Scholar
    • Export Citation

  • Menne, M. J., and C. N. Williams Jr., 2009: Homogenization of temperature series via pairwise comparisons. J. Climate, 22, 1700–1717, doi:10.1175/2008JCLI2263.1.

    • Search Google Scholar
    • Export Citation

  • Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol., 25, 693–712, doi:10.1002/joc.1181.

    • Search Google Scholar
    • Export Citation

  • Myhre, G., and Coauthors, 2013: Anthropogenic and natural radiative forcing. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 659–740. [Available online at https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_Chapter08_FINAL.pdf.]

  • Ning, T., and Coauthors, 2016a: The uncertainty of the atmospheric integrated water vapour estimated from GNSS observations. Atmos. Meas. Tech., 9, 79–92, doi:10.5194/amt-9-79-2016.

    • Search Google Scholar
    • Export Citation

  • Ning, T., J. Wickert, Z. Deng, S. Heise, G. Dick, S. Vey, and T. Schöne, 2016b: Homogenized time series of the atmospheric water vapor content obtained from the GNSS reprocessed data. J. Climate, 29, 2443–2456, doi:10.1175/JCLI-D-15-0158.1.

    • Search Google Scholar
    • Export Citation

  • O’Gorman, P. A., and C. J. Muller, 2010: How closely do changes in surface and column water vapor follow Clausius–Clapeyron scaling in climate change simulations? Environ. Res. Lett., 5, 025207, doi:10.1088/1748-9326/5/2/025207.

    • Search Google Scholar
    • Export Citation

  • Remote Sensing Systems, 2013: Monthly mean total precipitable water data set on a 1 degree grid made from Remote Sensing Systems Version-7 Microwave Radiometer Data. Remote Sensing Systems. [Available online at ftp://ftp.discover-earth.org/vapor/monthly_1deg/.]

  • Rohde, R., and Coauthors, 2013: A new estimate of the average Earth surface land temperature spanning 1753 to 2011. Geoinf. Geostat., 1, doi:10.4172/2327-4581.1000101.

    • Search Google Scholar
    • Export Citation

  • Ross, R. J., W. P. Elliott, and D. J. Seidel, 2002: Lower-tropospheric humidity–temperature relationships in radiosonde observations and atmospheric general circulation models. J. Hydrometeor., 3, 26–38, doi:10.1175/1525-7541(2002)003<0026:LTHTRI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Rousseeuw, P. J., and A. M. Leroy, 2003: Robust Regression and Outlier Detection. Wiley Series in Probability and Mathematical Statistics, Vol. 516, Wiley, 360 pp.

    • Search Google Scholar
    • Export Citation

  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 1379–1389, doi:10.1080/01621459.1968.10480934.

    • Search Google Scholar
    • Export Citation

  • Shiu, C. J., S. C. Liu, and J. P. Chen, 2009: Diurnally asymmetric trends of temperature, humidity, and precipitation in Taiwan. J. Climate, 22, 5635–5649, doi:10.1175/2009JCLI2514.1.

    • Search Google Scholar
    • Export Citation

  • Slutz, R. J., S. J. Lubker, J. D. Hiscox, S. D. Woodruff, R. L. Jenne, D. H. Joseph, P. M. Steurer, and J. D. Elms, 1985: Comprehensive ocean-atmosphere data set, release 1. NOAA Environmental Research Laboratories Rep., 268 pp.

  • Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J. Climate, 21, 2283–2296, doi:10.1175/2007JCLI2100.1.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., and A. Dai, 2007: Effects of Mount Pinatubo volcanic eruption on the hydrological cycle as an analog of geoengineering. Geophys. Res. Lett., 34, L15702, doi:10.1029/2007GL030524.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., and J. Fasullo, 2013: An apparent hiatus in global warming? Earth’s Future, 1, 19–32, doi:10.1002/2013EF000165.

  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 1205–1217, doi:10.1175/BAMS-84-9-1205.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., J. Fasullo, and L. Smith, 2005: Trends and variability in column-integrated atmospheric water vapor. Climate Dyn., 24, 741–758, doi:10.1007/s00382-005-0017-4.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., and Coauthors, 2007: Observations: Surface and atmospheric climate change. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 235–336. [Available online at https://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter3.pdf.]

  • Trenberth, K. E., J. T. Fasullo, and J. Kiehl, 2009: Earth’s global energy budget. Bull. Amer. Meteor. Soc., 90, 311–324, doi:10.1175/2008BAMS2634.1.

    • Search Google Scholar
    • Export Citation

  • Vose, R. S., D. R. Easterling, and B. Gleason, 2005: Maximum and minimum temperature trends for the globe: An update through 2004. Geophys. Res. Lett., 32, L23822, doi:10.1029/2005GL024379.

    • Search Google Scholar
    • Export Citation

  • Wagner, T., S. Beirle, M. Grzegorski, and U. Platt, 2006: Global trends (1996–2003) of total column precipitable water observed by Global Ozone Monitoring Experiment (GOME) on ERS‐2 and their relation to near‐surface temperature. J. Geophys. Res., 111, D12102, doi:10.1029/2005JD006523.

    • Search Google Scholar
    • Export Citation

  • Wang, J., L. Zhang, A. Dai, T. Van Hove, and J. Van Baelen, 2007: A near-global, 2-hourly data set of atmospheric precipitable water from ground-based GPS measurements. J. Geophys. Res., 112, D11107, doi:10.1029/2006JD007529.

    • Search Google Scholar
    • Export Citation

  • Wang, J., L. Zhang, A. Dai, F. Immler, M. Sommer, and H. Vömel, 2013: Radiation dry bias correction of Vaisala RS92 humidity data and its impacts on historical radiosonde data. J. Atmos. Oceanic Technol., 30, 197–214, doi:10.1175/JTECH-D-12-00113.1.

    • Search Google Scholar
    • Export Citation

  • Wang, J. X., and D. J. Gaffen, 2001: Late-twentieth-century climatology and trends of surface humidity and temperature in China. J. Climate, 14, 2833–2845, doi:10.1175/1520-0442(2001)014<2833:LTCCAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wentz, F. J., 1997: A well calibrated ocean algorithm for Special Sensor Microwave Imager. J. Geophys. Res., 102, 8703–8718, doi:10.1029/96JC01751.

    • Search Google Scholar
    • Export Citation

  • Wentz, F. J., 2015: A 17-year climate record of environmental parameters derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. J. Climate, 28, 6882–6902, doi:10.1175/JCLI-D-15-0155.1.

    • Search Google Scholar
    • Export Citation

  • Wentz, F. J., and M. Schabel, 2000: Precise climate monitoring using complementary satellite data sets. Nature, 403, 414–416, doi:10.1038/35000184.

    • Search Google Scholar
    • Export Citation

  • Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will global warming bring? Science, 317, 233–235, doi:10.1126/science.1140746.

    • Search Google Scholar
    • Export Citation

  • Willett, K. M., R. J. H. Dunn, P. W. Thorne, S. Bell, M. de Podesta, D. E. Parker, P. D. Jones, and C. N. Williams Jr., 2014: HadISDH land surface multi-variable humidity and temperature record for climate monitoring. Climate Past, 10, 1983–2006, doi:10.5194/cp-10-1983-2014.

    • Search Google Scholar
    • Export Citation

  • Woodruff, S. D., and Coauthors, 2011: ICOADS release 2.5: Extensions and enhancements to the surface marine meteorological archive. Int. J. Climatol., 31, 951–967, doi:10.1002/joc.2103.

    • Search Google Scholar
    • Export Citation

  • Zhang, Y.-C., W. B. Rossow, and A. A. Lacis, 1995: Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets. Part 1: Methods and sensitivity to input data uncertainties. J. Geophys. Res., 100, 1149–1165, doi:10.1029/94JD02747.

    • Search Google Scholar
    • Export Citation

  • Zhao, T., A. Dai, and J. Wang, 2012: Trends in tropospheric humidity from 1970 to 2008 over China from a homogenized radiosonde dataset. J. Climate, 25, 4549–4567, doi:10.1175/JCLI-D-11-00557.1.

    • Search Google Scholar
    • Export Citation
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Article Type:
Research Article

Global Water Vapor Trend from 1988 to 2011 and Its Diurnal Asymmetry Based on GPS, Radiosonde, and Microwave Satellite Measurements

Junhong Wang1, Aiguo Dai2, and Carl Mears3
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  • 1 Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York
  • | 2 Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York, and National Center for Atmospheric Research, Boulder, Colorado
  • | 3 Remote Sensing Systems, Santa Rosa, California
Print Publication:
15 Jul 2016
DOI:
https://doi.org/10.1175/JCLI-D-15-0485.1
Page(s):
5205–5222
Restricted access

Abstract

This study analyzes trends in precipitable water (PW) over land and ocean from 1988 to 2011, the PW–surface temperature Ts relationship, and their diurnal asymmetry using homogenized radiosonde data, microwave satellite observations, and ground-based global positioning system (GPS) measurements. It is found that positive PW trends predominate over the globe, with larger magnitudes over ocean than over land. The PW trend is correlated with surface warming spatially over ocean with a pattern correlation coefficient of 0.51. The PW percentage increase rate normalized by Ts expressed as is larger and closer to the rate implied by the Clausius–Clapeyron (C–C) equation over ocean than over land. The 2-hourly GPS PW data show that the PW trend from 1995 to 2011 is larger at night than during daytime. Nighttime PW monthly anomalies correlate positively and significantly with nighttime minimum temperature Tmin at all stations, but this is not true for daytime PW and maximum temperature Tmax. The ratio of relative PW changes with Tmin () is larger and closer to the C–C equation’s implied value of ~7% K−1 than . This suggests that the relationship between PW and Ts at night is a better indicator of the water vapor feedback than that during daytime, when clouds and other factors also influence Ts.

Publisher’s Note: This article was revised on 13 July 2016 to correct an editing error related to the citation of Fig. 13 in section 5.

Corresponding author address: Junhong (June) Wang, Department of Atmospheric and Environmental Sciences, University at Albany, SUNY, 1400 Washington Ave., Albany, NY 12222. E-mail: jwang20@albany.edu

Abstract

This study analyzes trends in precipitable water (PW) over land and ocean from 1988 to 2011, the PW–surface temperature Ts relationship, and their diurnal asymmetry using homogenized radiosonde data, microwave satellite observations, and ground-based global positioning system (GPS) measurements. It is found that positive PW trends predominate over the globe, with larger magnitudes over ocean than over land. The PW trend is correlated with surface warming spatially over ocean with a pattern correlation coefficient of 0.51. The PW percentage increase rate normalized by Ts expressed as is larger and closer to the rate implied by the Clausius–Clapeyron (C–C) equation over ocean than over land. The 2-hourly GPS PW data show that the PW trend from 1995 to 2011 is larger at night than during daytime. Nighttime PW monthly anomalies correlate positively and significantly with nighttime minimum temperature Tmin at all stations, but this is not true for daytime PW and maximum temperature Tmax. The ratio of relative PW changes with Tmin () is larger and closer to the C–C equation’s implied value of ~7% K−1 than . This suggests that the relationship between PW and Ts at night is a better indicator of the water vapor feedback than that during daytime, when clouds and other factors also influence Ts.

Publisher’s Note: This article was revised on 13 July 2016 to correct an editing error related to the citation of Fig. 13 in section 5.

Corresponding author address: Junhong (June) Wang, Department of Atmospheric and Environmental Sciences, University at Albany, SUNY, 1400 Washington Ave., Albany, NY 12222. E-mail: jwang20@albany.edu
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