Lower Stratospheric Water Vapor Variations Diagnosed from Satellite Observations, Reanalysis Data, and a Chemistry–Climate Model

基于卫星观测、再分析资料和化学气候模式分析平流层低层水汽变化

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  • Corresponding author: Yan XIA, xiayan@bnu.edu.cn
  • Funds:

    Supported by the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0604), Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO-2020-09), and Fundamental Research Funds for the Central Universities. Y. Y. Hu is supported by the National Natural Science Foundation of China (41530423, 41761144072, and 41888101). Y. Huang acknowledges grants from the Discovery Program of the Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-04511) and from the Canadian Space Agency (16SUASURDC)

  • doi: 10.1007/s13351-021-0193-0

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  • Stratospheric water vapor variations, which may play an important role in surface climate, have drawn extensive studies. Here, the variation in stratospheric water vapor is investigated by using data from observations of the Microwave Limb Sounder (MLS) on the Aura satellite, from the ECMWF Interim Reanalysis (ERAI), and simulations by the Whole Atmosphere Community Climate Model (WACCM). We find that the differences of annual mean stratospheric water vapor among these datasets may be partly caused by the differences in vertical transports. Using budget analysis, we find that the upward transport of water vapor at 100 hPa is mainly located over the Pacific warm pool region and South America in the equatorial tropics in boreal winter and over the southeast of the South Asian high and south of North America in boreal summer. It is found that temperature averaged over regions with upward transport is a better indicator of interannual variability of tropical mean stratospheric water vapor than the tropical mean temperature. It seems that the distributions of the seasonal cycle amplitude of lower stratospheric water vapor in the tropics can also be impacted by the vertical transport. The radiative effects of the interannual changes in water vapor in the lowermost stratosphere are underestimated by approximately 29% in both ERAI and WACCM compared to MLS, although the interannual variations of water vapor in the lowermost stratosphere are dramatically overestimated in ERAI and WACCM. The results here indicate that the radiative effect of long-term changes in water vapor in the lowermost stratosphere may be underestimated in both ERAI and WACCM simulations.
    在气候变化中起着重要作用的平流层水汽变化最近获得了广泛关注。本文使用Aura MLS观测资料、ECMWF再分析(ERA-Interim)和全大气气候模式(WACCM)模拟分析了平流层水汽的变化特征(包括年平均气候态、季节变化和年际变化)。研究发现,不同数据中年平均平流层水汽的部分差异有可能来自垂直输送。通过收支分析,我们发现夏季水汽在100 hPa的向上输送主要位于南亚高压东南部和北美洲的南部区域。相比于整个热带区域平均,在这些上升区域的温度平均能够更好地表征热带平流层水汽的年际变率。垂直输送还能影响热带平流层低层水汽的季节循环振幅的分布。通过辐射分析发现,相比于Aura MLS观测资料,热带外平流层最底层区域水汽年际变化的辐射效应被ERA-Interim和WACCM低估了约29%。这个结果表明平流层水汽长期变化的辐射效应也可能被ERA-Interim和WACCM低估。
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  • Fig. 1.  Annual and zonal mean water vapor (10−6 mol mol−1) averaged from 2005 to 2012 in (a) MLS, (b) ERAI, (c) SD-WACCM, and (d) SC-WACCM. The solid black line indicates the tropopause in ERAI and SD-WACCM. The dotted black line denotes the tropopause in SC-WACCM.

    Fig. 2.  Fractional differences of water vapor (%) in (a) ERAI, (b) SD-WACCM, and (c) SC-WACCM with respect to MLS. Stippled regions are places with statistically significant levels higher than the 95% confidence level (Student’s t test).

    Fig. 3.  Time–pressure distributions of water vapor (10−6 mol mol−1) averaged over 10°S–10°N in (a) MLS, (b) ERAI, (d) SD-WACCM, and (f) SC-WACCM; the differences between (c) ERAI and MLS, (e) SD-WACCM and MLS, and (g) SC-WACCM and MLS.

    Fig. 4.  Geographic distributions of the monthly mean water vapor (10−6 mol mol−1) at 100 hPa in (a, c, e, g) February and (b, d, f, h) August in (a, b) MLS, (c, d) ERAI, (e, f) SD-WACCM, and (g, h) SC-WACCM. The vectors indicate wind fields (m s−1), and the blue contours denote temperature (K). In (a) and (b), the wind and temperature results from ERAI are used.

    Fig. 5.  As in Fig. 4, but for water vapor changes (10−6 mol mol−1 month−1) due to vertical advection in (a, c, e) February and (b, d, f) August in (a, b) ERAI, (c, d) SD-WACCM, and (e, f) SC-WACCM. The vectors and contour lines are the same as in Fig. 4.

    Fig. 6.  As in Fig. 5, but for water vapor changes due to horizontal advection.

    Fig. 7.  (a) The relationship between monthly mean water vapor and temperature averaged from 30°S to 30°N at 100 hPa in MLS (black), ERAI (red), SD-WACCM (blue), and SC-WACCM (green). (b) The relationship between monthly mean water vapor averaged from 30°S to 30°N and temperature averaged over the upward transport in the tropics in Fig. 5 at 100 hPa. The correlation coefficients between the water vapor and temperature are labeled in the bottom right corner in each panel.

    Fig. 8.  As in Fig. 1, but for the annual cycle amplitude of water vapor (10−6 mol mol−1) averaged from 2005 to 2012.

    Fig. 9.  As in Fig. 1, but for the amplitude of the interannual variation in water vapor (10−6 mol mol−1).

    Fig. 10.  As in Fig. 1, but for the ratio (%) between the amplitude of the interannual variation and annual mean water vapor.

  • [1]

    Banerjee, A., G. Chiodo, M. Previdi, et al., 2019: Stratospheric water vapor: an important climate feedback. Climate Dyn., 53, 1697–1710. doi: 10.1007/s00382-019-04721-4.
    [2]

    Chandran, A., R. R. Garcia, R. L. Collins, et al., 2013: Secondary planetary waves in the middle and upper atmosphere following the stratospheric sudden warming event of January 2012. Geophys. Res. Lett., 40, 1861–1867. doi: 10.1002/grl.50373.
    [3]

    de F. Forster, P. M., and K. P. Shine, 2002: Assessing the climate impact of trends in stratospheric water vapor. Geophys. Res. Lett., 29, 1086. doi: 10.1029/2001GL013909.
    [4]

    Dee, D. P., S. M. Uppala, A. J. Simmons, et al., 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597. doi: 10.1002/qj.828.
    [5]

    Dessler, A. E., M. R. Schoeberl, T. Wang, et al., 2013: Stratospheric water vapor feedback. Proc. Natl. Acad. Sci. USA, 110, 18,087–18,091. doi: 10.1073/pnas.1310344110.
    [6]

    Dessler, A. E., H. Ye, T. Wang, et al., 2016: Transport of ice into the stratosphere and the humidification of the stratosphere over the 21st century. Geophys. Res. Lett., 43, 2323–2329. doi: 10.1002/2016GL067991.
    [7]

    Flato, G., J. Marotzke, B. Abiodun, et al., 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker, D. Qin, G.-K. Plattner, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 741–866, doi: 10.1017/CBO9781107415324.020.
    [8]

    Fueglistaler, S., and P. H. Haynes, 2005: Control of interannual and longer-term variability of stratospheric water vapor. J. Geophys. Res. Atmos., 110, D24108. doi: 10.1029/2005JD006019.
    [9]

    Fueglistaler, S., M. Bonazzola, P. H. Haynes, et al., 2005: Stratospheric water vapor predicted from the Lagrangian temperature history of air entering the stratosphere in the tropics. J. Geophys. Res. Atmos., 110, D08107. doi: 10.1029/2004JD005516.
    [10]

    Garcia, R. R., D. R. Marsh, D. E. Kinnison, et al., 2007: Simulation of secular trends in the middle atmosphere, 1950–2003. J. Geophys. Res. Atmos., 112, D09301. doi: 10.1029/2006JD007485.
    [11]

    Garcia, R. R., M. López-Puertas, B. Funke, et al., 2014: On the distribution of CO2 and CO in the mesosphere and lower thermosphere. J. Geophys. Res. Atmos., 119, 5700–5718. doi: 10.1002/2013JD021208.
    [12]

    Gettelman, A., M. I. Hegglin, S.-W. Son, et al., 2010: Multimodel assessment of the upper troposphere and lower stratosphere: Tropics and global trends. J. Geophys. Res. Atmos., 115, D00M08. doi: 10.1029/2009JD013638.
    [13]

    Hegglin, M. I., S. Tegtmeier, J. Anderson, et al., 2013: SPARC Data Initiative: Comparison of water vapor climatologies from international satellite limb sounders. J. Geophys. Res. Atmos., 118, 11,824–11,846. doi: 10.1002/jgrd.50752.
    [14]

    Hoffmann, L., G. Günther, D. Li, et al., 2019: From ERA-Interim to ERA5: the considerable impact of ECMWF’s next-generation reanalysis on Lagrangian transport simulations. Atmos. Chem. Phys., 19, 3097–3124. doi: 10.5194/acp-19-3097-2019.
    [15]

    Huang, Y., and M. B. Shahabadi, 2014: Why logarithmic? A note on the dependence of radiative forcing on gas concentration. J. Geophys. Res. Atmos., 119, 13,683–13,689. doi: 10.1002/2014JD022466.
    [16]

    Huang, Y., M. H. Zhang, Y. Xia, et al., 2016: Is there a stratospheric radiative feedback in global warming simulations? Climate Dyn., 46, 177–186. doi: 10.1007/s00382-015-2577-2.
    [17]

    Huang, Y., Y. Xia, and X. X. Tan, 2017: On the pattern of CO2 radiative forcing and poleward energy transport. J. Geophys. Res. Atmos., 122, 10,578–10,593. doi: 10.1002/2017JD027221.
    [18]

    James, R., M. Bonazzola, B. Legras, et al., 2008: Water vapor transport and dehydration above convective outflow during Asian monsoon. Geophys. Res. Lett., 35, L20810. doi: 10.1029/2008GL035441.
    [19]

    Jiang, J. H., H. Su, S. Pawson, et al., 2010: Five year (2004–2009) observations of upper tropospheric water vapor and cloud ice from MLS and comparisons with GEOS-5 analyses. J. Geophys. Res. Atmos., 115, D15103. doi: 10.1029/2009JD013256.
    [20]

    Jiang, J. H., H. Su, C. X. Zhai, et al., 2012: Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations. J. Geophys. Res. Atmos., 117, D14105. doi: 10.1029/2011JD017237.
    [21]

    Jiang, J. H., H. Su, C. X. Zhai, et al., 2015: An assessment of upper troposphere and lower stratosphere water vapor in MERRA, MERRA2, and ECMWF reanalyses using Aura MLS observations. J. Geophys. Res. Atmos., 120, 11,468–11,485. doi: 10.1002/2015JD023752.
    [22]

    Kunz, A., L. L. Pan, P. Konopka, et al., 2011: Chemical and dynamical discontinuity at the extratropical tropopause based on START08 and WACCM analyses. J. Geophys. Res. Atmos., 116, D24302. doi: 10.1029/2011JD016686.
    [23]

    Li, J.-L., D. E. Waliser, J. H. Jiang, et al., 2005: Comparisons of EOS MLS cloud ice measurements with ECMWF analyses and GCM simulations: Initial results. Geophys. Res. Lett., 32, L18710. doi: 10.1029/2005GL023788.
    [24]

    Li, J.-L. F., D. Waliser, C. Woods, et al., 2008: Comparisons of satellites liquid water estimates to ECMWF and GMAO analyses, 20th century IPCC AR4 climate simulations, and GCM simulations. Geophys. Res. Lett., 35, L19710. doi: 10.1029/2008GL035427.
    [25]

    Livesey, N. J., W. G. Read, L. Froidevaux, et al., 2011: EOS MLS Version V3.3 Level 2 Data Quality and Description Document. Technical Report JPL D-33509, Jet Propulsion Laboratory, Pasadena, CA, 162 pp.
    [26]

    Madonna, E., H. Wernli, H. Joos, et al., 2014: Warm conveyor belts in the ERA-Interim dataset (1979–2010). Part I: Climatology and potential vorticity evolution. J. Climate, 27, 3–26. doi: 10.1175/JCLI-D-12-00720.1.
    [27]

    Marsh, D. R., 2011: Chemical–dynamical coupling in the mesosphere and lower thermosphere. Aeronomy of the Earth’s Atmosphere and Ionosphere, M. A. Abdu, and D. Pancheva, Eds., Springer, Dordrecht, 3–17, doi: 10.1007/978-94-007-0326-1_1.
    [28]

    Maycock, A. C., M. M. Joshi, K. P. Shine, et al., 2013: The circulation response to idealized changes in stratospheric water vapor. J. Climate, 26, 545–561. doi: 10.1175/JCLI-D-12-00155.1.
    [29]

    Mote, P. W., K. H. Rosenlof, M. E. McIntyre, et al., 1996: An atmospheric tape recorder: The imprint of tropical tropopause temperatures on stratospheric water vapor. J. Geophys. Res. Atmos., 101, 3989–4006. doi: 10.1029/95JD03422.
    [30]

    Oltmans, S. J., H. Vömel, D. J. Hofmann, et al., 2000: The increase in stratospheric water vapor from balloonborne, frostpoint hygrometer measurements at Washington, D.C., and Boulder, Colorado. Geophys. Res. Lett., 27, 3453–3456. doi: 10.1029/2000GL012133.
    [31]

    Pierce, D. W., T. P. Barnett, E. J. Fetzer, et al., 2006: Three-dimensional tropospheric water vapor in coupled climate models compared with observations from the AIRS satellite system. Geophys. Res. Lett., 33, L21701. doi: 10.1029/2006GL027060.
    [32]

    Randel, W. J., and E. J. Jensen, 2013: Physical processes in the tropical tropopause layer and their roles in a changing climate. Nat. Geosci., 6, 169–176. doi: 10.1038/ngeo1733.
    [33]

    Read, W. G., A. Lambert, J. Bacmeister, et al., 2007: Aura Microwave Limb Sounder upper tropospheric and lower stratospheric H2O and relative humidity with respect to ice validation. J. Geophys. Res. Atmos., 112, D24S35. doi: 10.1029/2007JD008752.
    [34]

    Reutter, P., P. Neis, S. Rohs, et al., 2020: Ice supersaturated regions: properties and validation of ERA-Interim reanalysis with IAGOS in situ water vapour measurements. Atmos. Chem. Phys., 20, 787–804. doi: 10.5194/acp-20-787-2020.
    [35]

    Rienecker, M. M., M. J. Suarez, R. Todling, et al., 2008: The GEOS-5 Data Assimilation System—Documentation of Versions 5.0.1, 5.1.0, and 5.2.0. NASA/TM–2008–104606, Vol. 27, Goddard Space Flight Center, Greenbelt, Maryland, 118 pp.
    [36]

    Riese, M., F. Ploeger, A. Rap, et al., 2012: Impact of uncertainties in atmospheric mixing on simulated UTLS composition and related radiative effects. J. Geophys. Res. Atmos., 117, D16305. doi: 10.1029/2012JD017751.
    [37]

    Sakazaki, T., M. Fujiwara, C. Mitsuda, et al., 2013: Diurnal ozone variations in the stratosphere revealed in observations from the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) on board the International Space Station (ISS). J. Geophys. Res. Atmos., 118, 2991–3006. doi: 10.1002/jgrd.50220.
    [38]

    Shahabadi, M. B., and Y. Huang, 2014: Logarithmic radiative effect of water vapor and spectral kernels. J. Geophys. Res. Atmos., 119, 6000–6008. doi: 10.1002/2014JD021623.
    [39]

    Shindell, D. T., 2001: Climate and ozone response to increased stratospheric water vapor. Geophys. Res. Lett., 28, 1551–1554. doi: 10.1029/1999GL011197.
    [40]

    Smith, K. L., R. R. Neely, D. R. Marsh, et al., 2014: The Specified Chemistry Whole Atmosphere Community Climate Model (SC-WACCM). J. Adv. Model. Earth Syst., 6, 883–901. doi: 10.1002/2014MS000346.
    [41]

    Solomon, S., K. H. Rosenlof, R. W. Portmann, et al., 2010: Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science, 327, 1219–1223. doi: 10.1126/science.1182488.
    [42]

    Straub, C., B. Tschanz, K. Hocke, et al., 2012: Transport of mesospheric H2O during and after the stratospheric sudden warming of January 2010: observation and simulation. Atmos. Chem. Phys., 12, 5413–5427. doi: 10.5194/acp-12-5413-2012.
    [43]

    Su, H., D. E. Waliser, J. H. Jiang, et al., 2006: Relationships of upper tropospheric water vapor, clouds and SST: MLS observations, ECMWF analyses and GCM simulations. Geophys. Res. Lett., 33, L22802. doi: 10.1029/2006GL027582.
    [44]

    Tandon, N. F., L. M. Polvani, and S. M. Davis, 2011: The response of the tropospheric circulation to water vapor–like forcings in the stratosphere. J. Climate, 24, 5713–5720. doi: 10.1175/JCLI-D-11-00069.1.
    [45]

    Tian, W. S., M. P. Chipperfield, and D. R. Lü, 2009: Impact of increasing stratospheric water vapor on ozone depletion and temperature change. Adv. Atmos. Sci., 26, 423–437. doi: 10.1007/s00376-009-0423-3.
    [46]

    Tweedy, O. V., V. Limpasuvan, Y. J. Orsolini, et al., 2013: Nighttime secondary ozone layer during major stratospheric sudden warmings in specified-dynamics WACCM. J. Geophys. Res. Atmos., 118, 8346–8358. doi: 10.1002/jgrd.50651.
    [47]

    Uma, K. N., S. K. Das, and S. S. Das, 2014: A climatological perspective of water vapor at the UTLS region over different global monsoon regions: observations inferred from the Aura-MLS and reanalysis data. Climate Dyn., 43, 407–420. doi: 10.1007/s00382-014-2085-9.
    [48]

    Wang, X. Y., Y. T. Wu, W.-W. Tung, et al., 2018: The simulation of stratospheric water vapor over the Asian summer monsoon in CESM1(WACCM) models. J. Geophys. Res. Atmos., 123, 11,377–11,391. doi: 10.1029/2018JD028971.
    [49]

    Wang, Y., H. Su, J. H. Jiang, et al., 2017: The linkage between stratospheric water vapor and surface temperature in an observation-constrained coupled general circulation model. Climate Dyn., 48, 2671–2683. doi: 10.1007/s00382-016-3231-3.
    [50]

    Waters, J. W., L. Froidevaux, R. S. Harwood, et al., 2006: The Earth Observing System Microwave Limb Sounder (EOS MLS) on the Aura satellite. IEEE Trans. Geosci. Remote Sens., 44, 1075–1092. doi: 10.1109/TGRS.2006.873771.
    [51]

    Wegner, T., D. E. Kinnison, R. R. Garcia, et al., 2013: Simulation of polar stratospheric clouds in the specified dynamics version of the whole atmosphere community climate model. J. Geophys. Res. Atmos., 118, 4991–5002. doi: 10.1002/jgrd.50415.
    [52]

    Xia, Y., Y. Huang, Y. Y. Hu, et al., 2019: Impacts of tropical tropopause warming on the stratospheric water vapor. Climate Dyn., 53, 3409–3418. doi: 10.1007/s00382-019-04714-3.
    [53]

    Xia, Y., Y. Y. Hu, and J. P. Liu, 2020a: Comparison of trends in the Hadley circulation between CMIP6 and CMIP5. Sci. Bull., 65, 1667–1674. doi: 10.1016/j.scib.2020.06.011.
    [54]

    Xia, Y., Y. Huang, and Y. Y. Hu, 2020b: Robust acceleration of stratospheric moistening and its radiative feedback under greenhouse warming. J. Geophys. Res. Atmos., 125, e2020JD033090. doi: 10.1029/2020JD033090.
    [55]

    Yan, X. L., J. S. Wright, X. D. Zheng, et al., 2016: Validation of Aura MLS retrievals of temperature, water vapour and ozone in the upper troposphere and lower–middle stratosphere over the Tibetan Plateau during boreal summer. Atmos. Meas. Tech., 9, 3547–3566. doi: 10.5194/amt-9-3547-2016.
    [56]

    Yuan, T., B. Thurairajah, C.-Y. She, et al., 2012: Wind and temperature response of midlatitude mesopause region to the 2009 Sudden Stratospheric Warming. J. Geophys. Res. Atmos., 117, D09114. doi: 10.1029/2011JD017142.
    [57]

    Zhan, R. F., and Y. Q. Wang, 2012: Contribution of tropical cyclones to stratosphere-troposphere exchange over the northwest Pacific: Estimation based on AIRS satellite retrievals and ERA-Interim data. J. Geophys. Res. Atmos., 117, D12112. doi: 10.1029/2012JD017494.
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Lower Stratospheric Water Vapor Variations Diagnosed from Satellite Observations, Reanalysis Data, and a Chemistry–Climate Model

    Corresponding author: Yan XIA, xiayan@bnu.edu.cn
  • 1. College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
  • 2. Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 3. Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec H3A 0B9, Canada
  • 4. Laboratory for Climate and Ocean–Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Funds: Supported by the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0604), Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO-2020-09), and Fundamental Research Funds for the Central Universities. Y. Y. Hu is supported by the National Natural Science Foundation of China (41530423, 41761144072, and 41888101). Y. Huang acknowledges grants from the Discovery Program of the Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-04511) and from the Canadian Space Agency (16SUASURDC)

Abstract: Stratospheric water vapor variations, which may play an important role in surface climate, have drawn extensive studies. Here, the variation in stratospheric water vapor is investigated by using data from observations of the Microwave Limb Sounder (MLS) on the Aura satellite, from the ECMWF Interim Reanalysis (ERAI), and simulations by the Whole Atmosphere Community Climate Model (WACCM). We find that the differences of annual mean stratospheric water vapor among these datasets may be partly caused by the differences in vertical transports. Using budget analysis, we find that the upward transport of water vapor at 100 hPa is mainly located over the Pacific warm pool region and South America in the equatorial tropics in boreal winter and over the southeast of the South Asian high and south of North America in boreal summer. It is found that temperature averaged over regions with upward transport is a better indicator of interannual variability of tropical mean stratospheric water vapor than the tropical mean temperature. It seems that the distributions of the seasonal cycle amplitude of lower stratospheric water vapor in the tropics can also be impacted by the vertical transport. The radiative effects of the interannual changes in water vapor in the lowermost stratosphere are underestimated by approximately 29% in both ERAI and WACCM compared to MLS, although the interannual variations of water vapor in the lowermost stratosphere are dramatically overestimated in ERAI and WACCM. The results here indicate that the radiative effect of long-term changes in water vapor in the lowermost stratosphere may be underestimated in both ERAI and WACCM simulations.

基于卫星观测、再分析资料和化学气候模式分析平流层低层水汽变化

在气候变化中起着重要作用的平流层水汽变化最近获得了广泛关注。本文使用Aura MLS观测资料、ECMWF再分析(ERA-Interim)和全大气气候模式(WACCM)模拟分析了平流层水汽的变化特征(包括年平均气候态、季节变化和年际变化)。研究发现,不同数据中年平均平流层水汽的部分差异有可能来自垂直输送。通过收支分析,我们发现夏季水汽在100 hPa的向上输送主要位于南亚高压东南部和北美洲的南部区域。相比于整个热带区域平均,在这些上升区域的温度平均能够更好地表征热带平流层水汽的年际变率。垂直输送还能影响热带平流层低层水汽的季节循环振幅的分布。通过辐射分析发现,相比于Aura MLS观测资料,热带外平流层最底层区域水汽年际变化的辐射效应被ERA-Interim和WACCM低估了约29%。这个结果表明平流层水汽长期变化的辐射效应也可能被ERA-Interim和WACCM低估。
1.   Introduction
  • Stratospheric water vapor abundance affects stratospheric temperature and chemistry and surface climate (Shindell, 2001; de F. Forster and Shine, 2002). Change in stratospheric water vapor may have significant impacts on the rate of surface warming (Solomon et al., 2010) and tropospheric circulation (Tandon et al., 2011; Maycock et al., 2013; Xia et al., 2020a). Previous studies have estimated the stratospheric water vapor feedback under greenhouse warming and indicated that this feedback significantly contributes to the overall climate sensitivity (Dessler et al., 2013; Banerjee et al., 2019; Xia et al., 2020b). Although it is increasingly recognized that variations in stratospheric water vapor have a significant influence on the earth system, observations are still limited. Balloon observations from a single site in Boulder, Colorado, which began in 1980, represent the longest series of observations of stratospheric water vapor (Oltmans et al., 2000). There were no high-quality, global-satellite observations from multiple platforms until the 1990s. Therefore, reanalysis and general circulation models (GCMs) are necessary tools to investigate the climate impact of variations in stratospheric water vapor.

    Stratospheric water vapor still has large biases in reanalysis and simulations. Reanalysis data generally overestimate water vapor in the upper troposphere by up to 150% compared to Microwave Limb Sounder (MLS) observations (Jiang et al., 2015). Vertical and horizontal transport of water vapor in the stratosphere also have large biases and spreads in the reanalysis. Simulations of stratospheric water vapor by different climate models have been evaluated, and it has been found that most of the models have large water vapor biases in the lower stratosphere (Gettelman et al., 2010). Wang et al. (2018) compared stratospheric water vapor from Whole Atmosphere Community Climate Model (WACCM) simulations to those from satellite observations. They found that WACCM generally tends to simulate a stratospheric water vapor maximum over the central Pacific Ocean, while the maximum is located over the Asian summer monsoon region. The reduction in stratospheric water vapor after 2000, which caused surface cooling (Solomon et al., 2010; Wang et al., 2017), cannot be reproduced by the models (Flato et al., 2013). The simulated trends of stratospheric water vapor under greenhouse warming have large uncertainties among different models in phase 5 of the Coupled Model Intercomparison Project (CMIP5; Huang et al., 2016). Previous studies have attempted to quantify the climate effects of stratospheric water vapor using reanalysis data and GCM simulations (Tian et al., 2009; Riese et al., 2012; Dessler et al., 2013; Huang et al., 2016). Therefore, it is necessary to evaluate and understand the variations in stratospheric water vapor in reanalysis and simulations. It is important to clarify whether reanalysis and simulations can reproduce the amplitudes of seasonal and interannual variabilities in stratospheric water vapor found in observations.

    It is generally accepted that variations in stratospheric water vapor are mostly controlled by the tropical tropopause temperature (Fueglistaler and Haynes, 2005; Fueglistaler et al., 2005; Randel and Jensen, 2013; Xia et al., 2019). However, it is possible to alter stratospheric water vapor by changing transport pathways from the troposphere to the stratosphere but not by changing the mean temperature (Gettelman et al., 2010), which means that the amplitude of the variations in stratospheric water vapor may be different even with the same temperature change in reanalysis and simulations. To better understand the biases in reanalysis and modeling data, we decompose the water vapor budget into terms that include horizontal transport, vertical transport, and a physics-tendency term, including all moist (phase change) and subgrid processes, using the high-frequency output of the reanalysis and model simulations.

    In this paper, we examine the seasonal and interannual variabilities in stratospheric water vapor and their radiative effects in the reanalysis, specified-dynamics (SD) simulation, and specified-chemistry (SC) simulation using a chemistry climate model, WACCM. Specified-chemistry WACCM (SC-WACCM) is generally used to make long-term simulations of stratospheric water vapor, as well as predictions of future changes, driven by time-dependent boundary conditions for greenhouse gases, sea surface temperatures, and sea ice concentrations (Dessler et al., 2016; Wang et al., 2018). In more recent years, modeling groups have implemented “specified dynamics” versions that are constrained to meteorological fields (e.g., surface pressure, temperature, and winds). The specified-dynamics WACCM (SD-WACCM) version has also been used to study dynamical processes that affect stratospheric water vapor (Xia et al., 2019). Our purpose is to investigate the variations of lower stratospheric water vapor and reveal how the water vapor transport pathways impact the variability using typical reanalysis and these two types of model runs from WACCM and determine which transport pathway is the closest to satellite observations. We describe the datasets and models that we use and the configuration of the experiments in Section 2. The seasonal and interannual variations in stratospheric water vapor are analyzed in Section 3.

2.   Data and models
  • Stratospheric water vapor measurements by the MLS provide important information that can be used to evaluate the water vapor fields from the reanalysis and simulations. Here, we use satellite observations, version-3.3-Level-2-Aura-MLS (Waters et al., 2006), of water vapor and the volume mixing ratio product as described by Read et al. (2007) and Livesey et al. (2011). The vertical resolution of the MLS is approximately 3 km, and the horizontal resolution is approximately 7 km across the track and approximately 200–300 km along the track. Aura-MLS water vapor data, which are available at above 316 hPa, are widely used to evaluate model simulations and reanalysis data (Li et al., 2005, 2008; Pierce et al., 2006; Su et al., 2006; Jiang et al., 2010, 2012, 2015). Zonal and daily mean MLS water vapor are calculated by averaging samples at the same latitude and on the same day. MLS version 3.3 water vapor product generally shows very good to excellent agreement with collocated measurements, excepting a dry bias in the upper troposphere (121–261 hPa; Hegglin et al., 2013; Yan et al., 2016). In this study, we use the 8-yr (2005–2012) MLS observational record.

  • We use ERAI (Dee et al., 2011) from the same time period (2005–2012). The 6-h instantaneous specific humidity, temperature, and wind data from ERAI are used for evaluation. Reanalysis data are produced with a T255 horizontal resolution (~0.71° × 0.71°) and 60 vertical levels. A recent validation study has demonstrated that stratospheric water vapor in ERAI generally compares well with the new version of reanalysis data from the ECMWF, ERA5, excepting a moist bias in the lowermost stratosphere at mid and high latitudes over 200–300 hPa (Hoffmann et al., 2019). Many studies in this decade regarding the lower stratospheric region are based on the ERAI reanalysis (Riese et al., 2012; Zhan and Wang, 2012; Madonna et al., 2014; Uma et al., 2014; Reutter et al., 2020). Also, ERAI is still used in many ongoing investigations. Therefore, an investigation of stratospheric water vapor using ERAI is still valuable.

  • The WACCM, derived from NCAR, is a fully coupled chemistry climate model [see Garcia et al. (2007) and the references therein]. The model domain of WACCM extends from the earth’s surface to the lower thermosphere (~145 km or 4.5 × 10−6 hPa). Here, we use two kinds of configurations of WACCM, SD-WACCM and SC-WACCM, compiled from the Community Earth System Model version 1.2 (CESM 1.2).

    The SD-WACCM used here is performed by using Goddard Earth Observing System, version 5 (GEOS-5) Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis (Rienecker et al., 2008) to constrain the dynamics and temperature in the troposphere and stratosphere (below ~50 km). In SD-WACCM, meridional and zonal winds, temperature, and surface pressure are relaxed to the reanalysis data at every model time step (1800 s) by using the approach described in Kunz et al. (2011). The strength of the relaxation is linearly reduced between 0.79 hPa (~50 km) and 0.19 hPa (~60 km) such that the meteorological fields in the model become fully interactive above 60 km. SD-WACCM has more vertical levels (88 levels) than the standard version of WACCM (66 levels). The vertical resolution in the troposphere is higher in SD-WACCM than in the standard version of WACCM. SD-WACCM is widely used to study particular meteorological events (Marsh, 2011; Yuan et al., 2012; Chandran et al., 2013), their impacts on the chemistry and microphysics (Sakazaki et al., 2013; Tweedy et al., 2013; Wegner et al., 2013), and the variation and distribution of trace gases in the stratosphere and above (Straub et al., 2012; Garcia et al., 2014). Here, we set the strength of the relaxation to 10% toward the reanalysis and use SD-WACCM to determine whether stratospheric water vapor can be better simulated with the improvement of the dynamics and temperature compared to SC-WACCM.

    SC-WACCM is configured with prescribed, rather than interactive chemistry. Methane oxidation and the photolysis of water vapor in the middle atmosphere are retained by parameterization (Smith et al., 2014). Because there is no ozone chemistry in SC-WACCM, the ozone concentrations are prescribed by using zonal and monthly mean fields computed by a companion integration with WACCM. Smith et al. (2014) found that the tropical water vapor tape recorder simulated by SC-WACCM, without the expense of running interactive chemistry, has remarkable similarities with the standard version. To examine the stratospheric water vapor in SC-WACCM, we used the simulation with the prescribed time-varying atmospheric conditions, sea surface temperature, and ice coverage from the observations.

    Both SD-WACCM and SC-WACCM have the same horizontal resolution of 1.9° × 2.5° (latitude × longitude), with 3-h instantaneous outputs of specific humidity, temperature, and winds. For comparison with MLS, both simulations are integrated from 2005 to 2012.

  • Our purpose here is to investigate the transport pathways of water vapor into the lower stratosphere. For this purpose, spatial distributions of the transport are required. Therefore, an advection equation is applied at 100 hPa in the tropical tropopause layer to reveal the vertical and horizontal transport from the large-scale atmospheric circulation (e.g., Asian monsoon).

    $$ \frac{{\rm{d}}q}{{\rm{d}}t}=\frac{\partial q}{\partial t}+\left(u\frac{\partial q}{\partial x}+v\frac{\partial q}{\partial y}\right)+\omega \frac{\partial q}{\partial p} .$$ (1)

    The total water vapor tendency $\dfrac{{\rm{d}}q}{{\rm{d}}t}$ is decomposed into the water vapor change because all local, physics processes $\dfrac{\partial q}{\partial t}$ consist of moist and subgrid-scale processes, horizontal transport (the summation of zonal transport and meridional transport, $u\dfrac{\partial q}{\partial x}+v\dfrac{\partial q}{\partial y}$), and vertical transport $\mathrm{\omega }\dfrac{\partial q}{\partial p}$. The transport terms are calculated by using the high-frequency output and 6- and 3-h data in ERAI and WACCM, respectively. There is a direct output of the total physics tendency in WACCM. The physics tendency is calculated by subtracting the transport terms from the total water vapor tendency in ERAI. To explain the monthly mean bias of the stratospheric water vapor, the high-frequency water vapor tendency for each component is integrated to obtain its contribution to the monthly water vapor change. It is interesting to note that the total water vapor tendency $\dfrac{{\rm{d}}q}{{\rm{d}}t}$ in the tropical tropopause layer, which results from the counteraction between the transport and physics processes, is an order of magnitude smaller than both the transport and physics tendencies. To examine the closure of the budget, we compare the calculated transport terms $\left(u\dfrac{\partial q}{\partial x}+v\dfrac{\partial q}{\partial y}\right)+\omega \dfrac{\partial q}{\partial p}$ to the direct output in WACCM. We find that the diagnosed water vapor changes due to transport capture the geographic patterns and magnitudes of the simulated transport terms extremely well.

3.   Results
  • Figure 1 shows the annual and zonal mean water vapor concentrations averaged from 2005 to 2012 in MLS, ERAI, SD-WACCM, and SC-WACCM. Water vapor dramatically decreases with increasing altitude and reaches its minimum in the lower stratosphere at approximately 80 hPa and then increases in the stratosphere with increasing altitude. The minimum in the tropical lower stratosphere, which is caused by strong dehydration due to the cold tropical tropopause, reaches approximately 3.6 × 10−6 mol mol−1. This dry signal is then transported upward into the middle stratosphere by the ascending branch of the Brewer–Dobson circulation (BDC) and poleward to high latitudes in both hemispheres. Both ERAI and WACCM capture the distribution of stratospheric water vapor well. The propagation of dry air in ERAI and SC-WACCM reaches higher altitudes in the tropics than that in MLS (Figs. 1b, d), while SD-WACCM underestimates this vertical transport (Fig. 1c). Because it is cold in the Antarctic lower stratosphere, particularly in austral winter, strong dehydration results in another minimum center of water vapor. This dehydration is overestimated in SC-WACCM, where water vapor is much lower in lower and middle stratosphere in the Antarctic in the simulation (Fig. 1d).

    Figure 1.  Annual and zonal mean water vapor (10−6 mol mol−1) averaged from 2005 to 2012 in (a) MLS, (b) ERAI, (c) SD-WACCM, and (d) SC-WACCM. The solid black line indicates the tropopause in ERAI and SD-WACCM. The dotted black line denotes the tropopause in SC-WACCM.

    Compared to MLS, both ERAI and WACCM significantly overestimate the water vapor in the upper troposphere by approximately 50%–60% and in the lowermost stratosphere in both hemispheres by more than 120% (Fig. 2). ERAI and SC-WACCM overestimate the water vapor in the tropical tropopause layer by approximately 6%–8%, while they underestimate the stratospheric water vapor. In contrast, SD-WACCM overestimates the stratospheric water vapor by approximately 10%, particularly at approximately 50 hPa in the tropics. SD-WACCM slightly underestimates the water vapor in the tropical tropopause layer by approximately 2%.

    Figure 2.  Fractional differences of water vapor (%) in (a) ERAI, (b) SD-WACCM, and (c) SC-WACCM with respect to MLS. Stippled regions are places with statistically significant levels higher than the 95% confidence level (Student’s t test).

    Figure 3 shows the time–pressure distributions of the water vapor averaged over 10°S–10°N from 2007 to 2011 in MLS, ERAI, SD-WACCM, and SC-WACCM. We note that the water vapor in the tropical tropopause layer has stronger seasonal variations than that in the middle and upper stratosphere, which are imposed by the strong annual cycle of the tropical tropopause temperatures (Randel and Jensen, 2013). Both dry and moist air are transported from the tropical tropopause layer through the ascending branch of the BDC during boreal winter and summer, respectively. This so-called tape recorder effect (Mote et al., 1996) indicates that the concentration of stratospheric water vapor is largely impacted by the water vapor in the tropical tropopause layer, which itself is controlled by the tropical tropopause temperature (Fueglistaler and Haynes, 2005; Fueglistaler et al., 2005; Randel and Jensen, 2013).

    Figure 3.  Time–pressure distributions of water vapor (10−6 mol mol−1) averaged over 10°S–10°N in (a) MLS, (b) ERAI, (d) SD-WACCM, and (f) SC-WACCM; the differences between (c) ERAI and MLS, (e) SD-WACCM and MLS, and (g) SC-WACCM and MLS.

    It is interesting to note that the biases in the middle stratosphere and lower stratosphere have opposite signs in Fig. 2, although water vapor enters the stratosphere mainly through the tropical tropopause. This can be partly explained by the differences in vertical transport (Xia et al., 2019). Relatively dry air is transported into the stratosphere from the tropical tropopause layer in boreal winter, while relatively moist air is transported into the stratosphere in boreal summer. Therefore, water vapor in the middle stratosphere could be largely impacted by the strength of the vertical transport of both dry and moist air. Compared to MLS, the upward speeds of vertical propagation are stronger in ERAI (Figs. 3a, b), which is consistent with the results in Jiang et al. (2015). We find that the propagation of both moist and dry air is stronger in ERAI than in MLS (Fig. 3c). Interestingly, the enhancement of the propagation of dry air is stronger than that of moist air in ERAI, particularly above ~50 hPa (Fig. 3c), which results in a dry bias in the middle stratosphere despite the moist bias in the lower stratosphere below 50 hPa. In contrast, SD-WACCM underestimates the vertical propagation of dry air but overestimates the vertical propagation of moist air (Figs. 3d, e), which leads to a moist bias in the middle stratosphere despite the dry bias in the tropical tropopause. Compared to MLS, SC-WACCM underestimates the vertical propagation of both dry and moist air (Figs. 3f, g). The change in the propagation of moist air is stronger than that of dry air, which results in a dry bias in the middle stratosphere despite the moist bias in the lower stratosphere below 50 hPa in SC-WACCM. These three cases indicate that water vapor in the middle stratosphere is largely impacted by the strength of the vertical propagation of both dry and moist air.

    Figure 4 shows the geographic distributions of monthly mean water vapor at 100 hPa in February and August in MLS, ERAI, SD-WACCM, and SC-WACCM. The changes in February and August are presented here to represent the situations in boreal winter and summer, respectively. It is found that the tropical water vapor concentration is low in February, particularly in the Pacific warm pool region, due to the relatively cold tropical tropopause, and it is high in August, particularly in southeast of the South Asian high and south of North America (Fig. 4). The high concentration of water vapor centers over the Asian and American summer monsoon regions in August, which is consistent with the results in James et al. (2008).

    Figure 4.  Geographic distributions of the monthly mean water vapor (10−6 mol mol−1) at 100 hPa in (a, c, e, g) February and (b, d, f, h) August in (a, b) MLS, (c, d) ERAI, (e, f) SD-WACCM, and (g, h) SC-WACCM. The vectors indicate wind fields (m s−1), and the blue contours denote temperature (K). In (a) and (b), the wind and temperature results from ERAI are used.

    To better understand the transport pathways in ERAI and WACCM, we calculate the water vapor changes due to horizontal and vertical transport following the method presented in Section 2.4. The water vapor changes due to vertical and horizontal transport are shown in Figs. 5, 6, respectively. We find that the water vapor increases due to vertical transport at 100 hPa are mainly located over the Pacific warm pool region and South America in the equatorial tropics in boreal winter and over the southeast of the South Asian high and south of North America in boreal summer (Fig. 5). It is interesting to note that vertical transport can well explain the moist centers in boreal summer in Fig. 4. The upward transport in boreal winter is mainly located in the coldest regions lower than 190 K, which limits the water vapor entering into the stratosphere due to dehydration processes. The water vapor increases due to horizontal transport at 100 hPa are located over central Pacific and Atlantic (Fig. 6), which helps the water vapor mixing in the tropics. The clockwise circulation of the South Asian high in boreal summer transports air with high water vapor concentration into the cold regions lower than 195 K, which also limits the water vapor in the tropical tropopause layer.

    Figure 5.  As in Fig. 4, but for water vapor changes (10−6 mol mol−1 month−1) due to vertical advection in (a, c, e) February and (b, d, f) August in (a, b) ERAI, (c, d) SD-WACCM, and (e, f) SC-WACCM. The vectors and contour lines are the same as in Fig. 4.

    Figure 6.  As in Fig. 5, but for water vapor changes due to horizontal advection.

    Previous studies have demonstrated that stratospheric water vapor is predominantly controlled by the variations in the tropical tropopause temperature (Fueglistaler and Haynes, 2005; Fueglistaler et al., 2005; Randel and Jensen, 2013). We find that the monthly and tropical mean water vapor is proportional to the tropical mean temperature at 100 hPa in MLS, ERAI, SD-WACCM, and SC-WACCM (Fig. 7a), which is consistent with previous studies. The correlation coefficients between tropical mean water vapor and temperature are 0.86, 0.93, 0.90, and 0.77 in MLS, ERAI, SD-WACCM, and SC-WACCM, respectively. Since upward transport is the main source of stratospheric water vapor, we also investigate the relationship between the tropical mean water vapor and temperature averaged over the upward transport in the tropics in Fig. 5 at 100 hPa. It is found that the correlation coefficients increase to 0.87, 0.94, 0.92, and 0.82 in MLS, ERAI, SD-WACCM, and SC-WACCM, respectively.

    Figure 7.  (a) The relationship between monthly mean water vapor and temperature averaged from 30°S to 30°N at 100 hPa in MLS (black), ERAI (red), SD-WACCM (blue), and SC-WACCM (green). (b) The relationship between monthly mean water vapor averaged from 30°S to 30°N and temperature averaged over the upward transport in the tropics in Fig. 5 at 100 hPa. The correlation coefficients between the water vapor and temperature are labeled in the bottom right corner in each panel.

    The amplitude of the annual cycle of water vapor, measured by the difference between the peak and trough of the cycle by using the daily mean data, decreases with increasing altitude (Fig. 8). The amplitude peaks in the tropics and decreases toward the two polar regions. The amplitude peaks in the Northern Hemisphere (NH) subtropics, reaching approximately 3.5 × 10−6 mol mol−1 in the tropical tropopause layer, due to vertical transport by the summer monsoon, and propagates upward into the stratosphere by the upwelling branch of the BDC. This peak then shifts from the NH subtropics to the equator in the lower stratosphere at approximately 80 hPa. We find that there is a double-peak pattern [over the Southern Hemisphere (SH) and NH subtropics] in the tropical tropopause layer in SD-WACCM (Fig. 8c), which results from the vertical transport in both the SH and NH subtropics in boreal summer (Fig. 5d). The vertical transport in SC-WACCM (Fig. 5f) results in a triple-peak distribution (over the SH and NH subtropics and the equator) of the amplitude in the tropical tropopause layer (Fig. 8d).

    Figure 8.  As in Fig. 1, but for the annual cycle amplitude of water vapor (10−6 mol mol−1) averaged from 2005 to 2012.

    The amplitude of the interannual variation in water vapor, as measured by the standard deviation of the annual mean water vapor from 2005 to 2012, decreases with increasing altitude (Fig. 9), which is similar to the distribution of the amplitude of the seasonal cycle (Fig. 8). In MLS, the strongest interannual variation in the tropical stratosphere, which reaches 0.3 × 10−6 mol mol−1, is located at approximately 83 hPa at the equator instead of the tropical tropopause (Fig. 9a). Similar patterns can be seen in ERAI and SD-WACCM (Figs. 9b, c). We find that both ERAI and WACCM largely overestimate the interannual variation in the lowermost stratosphere.

    Figure 9.  As in Fig. 1, but for the amplitude of the interannual variation in water vapor (10−6 mol mol−1).

    It was found that the radiative effect of water vapor on the outgoing longwave radiation at the top of the atmosphere (TOA) is proportional to the logarithm of its concentration (Huang and Shahabadi, 2014; Shahabadi and Huang, 2014). The radiative effect of the water vapor change is proportional to the change in the logarithm of the water vapor concentration, which approximately equals the fractional change in water vapor. The radiative effect of the interannual change in water vapor, therefore, can be measured by the ratio between the amplitude of the interannual variation and the annual mean water vapor (Fig. 10). The largest variation occurs in the upper troposphere and lower stratosphere (UTLS) and decreases with altitude. Interestingly, the radiative effect is most sensitive to the water vapor change in the UTLS region (Riese et al., 2012).

    Figure 10.  As in Fig. 1, but for the ratio (%) between the amplitude of the interannual variation and annual mean water vapor.

    To quantify this radiative effect, we calculate the radiative effect of the interannual variation in the water vapor at TOA using the kernel method, following Huang et al. (2017). The global and annual mean radiative effects of the interannual variation in water vapor above 261 hPa are approximately 0.2, 0.14, 0.17, and 0.15 W m−2 at TOA in MLS, ERAI, SD-WACCM, and SC-WACCM, respectively. This radiative effect is underestimated in both ERAI and WACCM.

    Huang et al. (2016) analyzed the stratospheric water vapor response to 4 × CO2 using 12 models of CMIP5 with the similar method. They found that the most noticeable stratospheric water vapor increase occurs in the lowermost stratosphere adjacent to the tropical upper-troposphere region where atmospheric moistening is maximized. Although the global mean stratospheric water vapor feedback is insignificant, stratospheric water vapor plays a non-negligible role in local radiative feedback, particularly in the extratropics, due to the increase in water vapor in the lowermost stratosphere. These results illustrate that the variation in water vapor in the lowermost stratosphere is important for the evaluation of the radiative effect of stratospheric water vapor. Here, we find that the interannual variations in water vapor in the lowermost stratosphere are largely overestimated in both ERAI and WACCM. However, the radiative effects of the interannual variation in water vapor in the lowermost stratosphere (averaged over 261–100 hPa and poleward of 45°), which is approximately 0.07 W m−2 in MLS, are underestimated by approximately 33%, 27%, and 20% in ERAI, SD-WACCM, and SC-WACCM, respectively. To better predict the climate impact of stratospheric water vapor, we need to further understand and improve the transport pathways and seasonal and interannual variations in stratospheric water vapor in climate models.

4.   Conclusions
  • We investigate the annual mean and seasonal and interannual variations in stratospheric water vapor diagnosed from MLS satellite observations, ERAI, and simulations by SD-WACCM and SC-WACCM. Both ERAI and SC-WACCM overestimate the annual mean water vapor in the tropical tropopause layer but underestimate it in the stratosphere above ~50 hPa compared to MLS observations. SD-WACCM significantly overestimates the water vapor concentration in the stratosphere above ~80 hPa. We find that these bias patterns can be partly explained by the differences in the vertical transport of moist and dry air from the tropical tropopause layer, which is consistent with the results in Xia et al. (2019).

    Using budget analysis, we find that the upward transport of water vapor at 100 hPa is mainly located over the Pacific warm pool region and South America in the equatorial tropics in boreal winter and over the southeast of the South Asian high and south of North America in boreal summer. The water vapor increases due to horizontal transport at 100 hPa, which helps the water vapor mixing in the tropics, are located over central Pacific and Atlantic. It is found that temperature averaged over regions with upward transport is a better indicator of interannual variability of tropical mean stratospheric water vapor than the tropical mean temperature. It seems that the distributions of the seasonal cycle amplitude of lower stratospheric water vapor in the tropics can also be impacted by the vertical transport.

    The overall radiative effects of the interannual changes in water vapor above 261 hPa are underestimated by approximately 30%, 15%, and 25% compared to MLS in ERAI, SD-WACCM, and SC-WACCM, respectively. The water vapor concentration and its seasonal variations in the lowermost stratosphere are dramatically overestimated in both ERAI and WACCM. However, it is important to note that the radiative effects of the interannual variation in water vapor in the lowermost stratosphere are underestimated by approximately 29% in both ERAI and WACCM. The results here indicate that the radiative effect of long-term changes in water vapor in the lowermost stratosphere may be underestimated in both ERAI reanalysis and WACCM simulations.

    Acknowledgments. We thank Dr. Jian Yue and two anonymous reviewers for helpful comments. We thank ECMWF for providing the ERAI reanalysis data, which are freely available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector?tab=form. Aura MLS satellite observations of Microwave Limb Sounder are available at https://earthdata.nasa.gov/earth-observation-data/near-real-time/download-nrt-data/mls-nrt.

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