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Volume 10, issue 23
Atmos. Chem. Phys., 10, 11519–11533, 2010
https://doi.org/10.5194/acp-10-11519-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 10, 11519–11533, 2010
https://doi.org/10.5194/acp-10-11519-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 Dec 2010

06 Dec 2010

Diurnal variations of humidity and ice water content in the tropical upper troposphere

P. Eriksson1, B. Rydberg1, M. Johnston1, D. P. Murtagh1, H. Struthers2, S. Ferrachat3, and U. Lohmann3 P. Eriksson et al.
  • 1Department of Earth and Space Sciences, Chalmers University of Technology, Gothenburg, Sweden
  • 2Department of Applied Environmental Science, Stockholm University, Sweden
  • 3Institute of Atmospheric and Climate Science, ETH Zurich, Switzerland

Abstract. Observational results of diurnal variations of humidity from Odin-SMR and AURA-MLS, and cloud ice mass from Odin-SMR and CloudSat are presented for the first time. Comparisons show that the retrievals of humidity and cloud ice from these two satellite combinations are in good agreement. The retrieved data are combined from four almost evenly distributed times of the day allowing mean values, amplitudes and phases of the diurnal variations around 200 hpa to be estimated. This analysis is applied to six climatologically distinct regions, five located in the tropics and one over the subtropical northern Pacific Ocean. The strongest diurnal cycles are found over tropical land regions, where the amplitude is ~7 RHi for humidity and ~50% for ice mass. The greatest ice mass for these regions is found during the afternoon, and the humidity maximum is observed to lag this peak by ~6 h. Over tropical ocean regions the variations are smaller and the maxima in both ice mass and humidity are found during the early morning. Observed results are compared with output from three climate models (ECHAM, EC-EARTH and CAM3). Direct measurement-model comparisons were not possible because the measured and modelled cloud ice masses represent different quantities. To make a meaningful comparison, the amount of snow had to be estimated from diagnostic parameters of the models. There is a high probability that the models underestimate the average ice mass (outside the 1-σ uncertainty). The models also show clear deficiencies when it comes to amplitude and phase of the regional variations, but to varying degrees.

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