EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

An observational constraint on the uncertainty in stratospheric water vapour projections

Peer Nowack1,2,3, Paulo Ceppi2, Sean Davis4, Gabriel Chiodo5, Will Ball6, Mohamadou A. Diallo7, Birgit Hassler8, Yue Jia4,9, James Keeble10,11, and Manoj Joshi1
Peer Nowack et al.
  • 1University of East Anglia, School of Environmental Sciences, Climatic Research Unit, UK
  • 2Grantham Institute and Department of Physics, Imperial College London, UK
  • 3Data Science Institute, Imperial College London, UK
  • 4NOAA Chemical Sciences Laboratory, Boulder, USA
  • 5Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
  • 6Department of Geoscience and Remote Sensing, TU Delft, The Netherlands
  • 7Institute of Energy and Climate Research, Stratosphere (IEK-7), Forschungszentrum Jülich, Germany
  • 8Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 9Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, USA
  • 10Yusuf Hamied Department of Chemistry, University of Cambridge, UK
  • 11National Centre for Atmospheric Science (NCAS), University of Cambridge, UK

Future increases in stratospheric water vapour (SWV) risk amplifying climate change and slowing down the recovery of the ozone layer. However, state-of-the-art climate models strongly disagree on the magnitude of these increases under global warming1,2. Uncertainty primarily arises from the challenges inherent in modelling the many complex processes leading to dehydration of air during its tropical ascent into the stratosphere3. Here we derive an observational constraint on this longstanding uncertainty factor in Earth's climate change response. Following a statistical learning approach4,5, we infer historical co-variations between the UTLS temperature structure and tropical lower SWV concentrations. For climate models, we demonstrate that these historically constrained relationships are also highly predictive of the SWV response under strong 4xCO2 forcing. By extension, we obtain an observationally constrained range for concentration changes per degree of global warming of 0.31±0.39 ppmv K-1 (90% confidence interval). Our constraint represents a 50% decrease in the 95th percentile of the climate model uncertainty distribution, which has major implications for surface warming, ozone recovery, and the tropospheric circulation response under climate change.

Across 61 climate models from the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP), we therefore find that a large fraction of future model projections is inconsistent with observational evidence. In particular, frequently projected strong increases (>1 ppmv K-1) are highly unlikely. We further demonstrate that our constraint on tropical lower SWV can be translated into also reduced uncertainty in the radiative SWV feedback (by 0.05 W m-2 K-1). This uncertainty reduction is comparable in size to the overall feedback responses in biogenic volatile organic compounds (BVOCs) or ozone6, and is thus of great relevance for policymakers.


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[2] Keeble, J. et al. Evaluating stratospheric ozone and water vapour changes in CMIP6 models from 1850 to 2100. Atmospheric Chemistry and Physics 21, 5015–5061 (2021),

[3] Fueglistaler, S. et al. Tropical tropopause layer. Reviews of Geophysics 47, RG1004 (2009),

[4] Ceppi, P. and Nowack, P. Observational evidence that cloud feedback amplifies global warming. PNAS 118, e2026290118 (2021),

[5] Nowack, P. et al. Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations. Environmental Research Letters 13, 104016 (2018),

[6] Szopa, S. et al. Short-lived climate forcers. 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, Cambridge University Press, Cambridge, UK and New York, USA, 817–922 (2021).


How to cite: Nowack, P., Ceppi, P., Davis, S., Chiodo, G., Ball, W., Diallo, M. A., Hassler, B., Jia, Y., Keeble, J., and Joshi, M.: An observational constraint on the uncertainty in stratospheric water vapour projections, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2943,, 2023.