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Substantial influence of vapour buoyancy on tropospheric air temperature and subtropical cloud

Nature Geoscience volume 15, pages 781–788 (2022)Cite this article

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Abstract

The molar mass of water vapour is less than that of dry air, making humid air lighter than dry air at the same temperature and pressure. This effect is known as vapour buoyancy and has been considered negligibly small in large-scale climate dynamics. Here, we use theory, reanalysis data and a hierarchy of climate models to show that vapour buoyancy has a similar magnitude to thermal buoyancy in the tropical free troposphere. We further show that vapour buoyancy makes cold air rise and increases subtropical stratiform low clouds by up to 70% of its climatological value. However, some widely used climate models fail to represent vapour buoyancy in the governing equations. This flaw leads to inaccurate simulations of cloud distributions—the largest uncertainty in predicting climate change.

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Fig. 1: Cold air rises in the tropical free troposphere.
Fig. 2: VB and thermal buoyancy compensate each other to maintain a horizontally uniform buoyancy field in the tropical free troposphere.
Fig. 3: Changes in atmospheric temperature and clouds due to VB in aqua-planet simulations.
Fig. 4: Low clouds and VB in comprehensive simulations.
Fig. 5: Low clouds and VB in atmosphere-only CMIP6 models.
Fig. 6: Low clouds and VB in atmosphere–ocean coupled CMIP6 models.

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Data availability

ERA-Interim can be accessed at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim. The AMIP and CMIP outputs used in this study can be obtained from the CMIP6 archives at https://esgf-node.llnl.gov/projects/esgf-llnl/. The AM2 simulation data are available at: https://ucdavis.app.box.com/file/994275826488?s=crb3jch5h94tv4ahqzrywa2lal6dako7.

Code availability

The GFDL-AM2 model should be publicly available at https://www.gfdl.noaa.gov/modeling-systems-group-public-releases/. The modified version is available at https://www.yang-climate-group.org/models.

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Acknowledgements

This study is supported by a Laboratory-Directed Research and Development (LDRD) Award at LBNL (D.Y.), a Packard Fellowship for Science and Engineering (D.Y.) and an NSF CAREER Award (D.Y.). D.Y. and W.Z. are also supported by the U.S. DOE Office of Science Biological and Environmental Research as part of the Regional and Global Modeling and Analysis program. D.Y. thanks S.-P. Xie and Z. Tan for helpful feedback on an earlier draft.

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Authors and Affiliations

  1. University of California, Davis, CA, USA

    Da Yang & Seth D. Seidel

  2. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Da Yang & Seth D. Seidel

  3. Pacific Northwest National Laboratory, Richland, WA, USA

    Wenyu Zhou

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Contributions

D.Y. designed the research, analysed results and wrote the paper. W.Z. performed numerical simulations. S.D.S. analysed results. All authors contributed to interpreting the results and editing the paper.

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Correspondence to Da Yang.

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Nature Geoscience thanks Zhaohua Wu, Martin Singh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson.

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Extended data

Extended Data Fig. 1 Annual mean, zonal mean climatology for the CNTL and MD1 simulations.

(a-d) show temperature (K), specific humidity (g/kg), cloud fraction, and vertical velocity (Pa/s) for the CNTL simulation. (e-h) are identical to (a-d), except for the MD1 simulation. (i-l) show differences between the CNTL and MD1 simulations in the corresponding fields.

Extended Data Fig. 2 Differences in clear-sky radiative heating rate between CNTL and MD1 aqua-planet simulations.

(a) Total clear-sky radiative heating rate response (K/day). (b) Partial clear-sky radiative heating rate response due to changes in temperature (K/day). (c) Partial clear-sky radiative heating rate response due to changes in specific humidity (K/day). (d) Sum of (b) and (c).

Extended Data Fig. 3 Surface buoyancy fluxes in aqua-planet simulations (a-b) and comprehensive simulations (c-d).

(a) Surface buoyancy fluxes in CNTL (blue) and MD1 (red). (b) Surface buoyancy fluxes in CNTL (blue) and MD2 (red). (c) Difference in surface buoyancy fluxes between CNTL and MD1 in the comprehensive simulations. (d) Difference in surface buoyancy fluxes between CNTL and MD2 in the comprehensive simulations.

Extended Data Fig. 4 Low clouds and VB in comprehensive simulations.

This figure is identical to Fig. 4a-d, except for CNTL and MD2.

Extended Data Fig. 5 Horizontal temperature difference versus latitude.

(a-w) CMIP simulations. (a) Results of a model from Group A (blue curve in FC. 2C). (b) Results of a model from B (the NASA GISS model; green curve in Fig. 2C). (x) ERA results. In each panel, the red solid line is directly diagnosed temperature difference \({{\Delta }}T\), and the black dashed line is vapor buoyancy-induced temperature difference \({{\Delta }}T_{{{{\mathrm{vb}}}}}\), which is calculated using Eq. (1). We divide the models into two groups: one properly incorporates VB (Group A), and the other does not (Group B). Group B include models in (b) NASA GISS-E2-1-G, (g) CAS-ESM2-0, (i) CNRM-CM6-1, (m) FGOALS-g3, (p) IITM-ESM, and (q) IPSL-CM6A-LR, in which temperature is horizontally uniform, and \({{\Delta }}T_{{{{\mathrm{vb}}}}}\) cannot explain \({{\Delta }}T\). Quantitatively, we identify Group B as those models with \({{\Delta }}T\) < 0.1 K at −10° latitude. All other models belong to Group A. This figure is created using boreal summer data.

Extended Data Fig. 6 Low cloud fraction (LCF) at 925 hPa of Group B models in northern-hemisphere summer.

(a) CAS-ESM2-0. (b) NASA GISS-E2-1-G. (c) CNRM-CM6-1. (d) IITM-ESM. (e) FGOALS-g3. (f) IPSL-CM6A-LR.

Extended Data Fig. 7 Low cloud fraction (LCF) at 925 hPa of Group B models in annual mean.

(a) CAS-ESM2-0. (b) NASA GISS-E2-1-G. (c) CNRM-CM6-1. (d) IITM-ESM. (e) FGOALS-g3. (f) IPSL-CM6A-LR.

Extended Data Fig. 8 Difference in stratiform low cloud fraction (LCF) at 925 hPa in northern-hemisphere summer.

Each panel is calculated as the difference between the average LCF of Group A models and LCF of individual Group B models. All panels correspond to the same model in Extended Data Fig. 6. Positive values mean that Group B models have lower LCF.

Extended Data Fig. 9 Difference in stratiform low cloud fraction (LCF) at 925 hPa in annual mean.

Each panel is calculated as the difference between the average LCF of Group A models and LCF of individual Group B models. All panels correspond to the same model in Extended Data Fig. 6. Positive values mean that Group B models have lower LCF.

Extended Data Fig. 10 Robust LTS-LCF relationship for prescribed-SST and coupled simulations with different box regions.

(a, b) LTS-LCF relationship for prescribed-SST simulations with different box regions. (c, d) LTS-LCF relationship for coupled simulations with different box regions. (a, c) The LTS-LCF relationship using box regions in Fig. 5. (b, d) The LTS-LCF relationship using box regions in Fig. 6. The color scheme is identical to that of Fig. 5d.

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Yang, D., Zhou, W. & Seidel, S.D. Substantial influence of vapour buoyancy on tropospheric air temperature and subtropical cloud. Nat. Geosci. 15, 781–788 (2022). https://doi.org/10.1038/s41561-022-01033-x

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