the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Jun 2026
A 15 Year Climatology and Trend Study of Tropospheric Relative Humidity and Temperature over Switzerland based on Raman Lidar Measurements
Abstract. Water vapor is the most important greenhouse gas, yet large uncertainties remain regarding its evolution in a warming climate, particularly for relative humidity (RH). In this study we use 15 years (2010–2024) of nighttime Raman lidar measurements from the Raman Lidar for Meteorological Observations (RALMO) in Payerne, Switzerland, to investigate free-tropospheric RH changes under clear-sky conditions. We reprocessed and homogenized the data set and derived a climatology and seasonal trends between 3 and 10 km. The temperature climatology captures the expected annual cycle with an amplitude decreasing from about 10 K near the surface to 2 K near the tropopause. The RH climatology exhibits an annual cycle in the lower and upper troposphere and a semi-annual cycle in the mid-troposphere, reflecting the influence of large-scale circulation and moisture transport. Consistent tropospheric warming of approximately 1–3 K per decade is observed, with the strongest and statistically significant trends occurring in winter and summer. In contrast, RH trends are generally small and not statistically significant, suggesting that RH has remained approximately constant over Switzerland despite atmospheric warming. Our results imply that moisture supply is sufficient in the Alpine region to compensate for the atmosphere’s increasing capacity to hold water. This work is one of few long-term studies of RH in the free troposphere where such analyses remain limited by the scarcity of suitable datasets.
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Status: open (until 25 Sep 2026)
- RC1: 'Comment on egusphere-2026-2823', Anonymous Referee #1, 26 Aug 2026 reply
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RC2: 'Comment on egusphere-2026-2823', Anonymous Referee #2, 07 Sep 2026
reply
This paper analyzes a 15 year temperature and water vapor dataset observed at Payerne Switzerland. The observations were primarily made by a Raman lidar, but the same analysis was performed on a full radiosonde datasets and a subset of the radiosondes that were selected to match the Raman lidar observations. They focused primarily on the trends observed between 3 and 10 km (i.e., 700 mb to 250 mb), separating the results by season. The authors wrote a well-organized and clear manuscript, and it was a pleasure to read. I only have a few questions / suggestions I would like to make:
- A key point to this paper is the calibration method applied to the Raman lidar temperature and RH fields. They point to a “ground-based internal calibration method developed by Jayaweera et al. (2025)”. Given the importance of this calibration to the paper, I would like to have it summarized in a new paragraph within this paper
- The key baseline variables measured by a Raman lidar are ambient temperature and water vapor mixing ratio. Thus, the RH field includes the uncertainties of both baseline variables. I think this paper would be much stronger if a similar trend analysis (e.g., Fig 12) could be performed with the water vapor mixing ratio. That should help make the point that even though temperature is increasing, the reason RH is not increasing is due to the decrease in the water vapor mixing ratio.
- Line 189 on filling the COVID gap: was the linear relationship applied to temperature and RH simultaneously? Or was the temperature gap filled first, and then the RH gap filled? Or was the temperature and water vapor mixing ratio gaps filled, and then the RH was computed? (This connects with the previous point about mixing ratio being a more baseline observation than RH)
- Paragraph starting line 235: November is also one of the more poorly sampled months; is it possible that the increase in the RH seen in November is due to sampling?
- Line 249: The authors point out that there is more temperature variability from the RALMO obs due to instrument noise. Instrument noise would also affect the water vapor mixing ratio, and thus RH would have two instrumental sources of noise. The authors have nicely indicated the amount of temperature variability that comes from the lidar here, but how much RH variability comes from the lidar noise?
- Fig 8: Do you have a hypothesis why the RALMO observations show a distinct lower amount of variability throughout the entire troposphere in November? Does the matched radiosonde dataset show this reduction also?
- Line 413: I do not agree with the statement that the clear sky sampling bias is negligible (my interpretation of “do not depend critically”). In Fig 11 (especially) and also Fig 12, the all 95% confidence interval is markedly wider for the “all sonde” than for the lidar. Even the “sonde selected” results generally have wider 95% confidence intervals than the lidar, and often much larger. The authors need to be more careful with this particular conclusion, as what they have written is not supported by the analysis.
Minor points:
- Line 52 “The Raman Lidar for …” should be the start of a new paragraph
- Fig 4: please explain those few lidar points that have RH significantly higher than 100%. I have presumed throughout this document that RH is reported as RH with respect to liquid at all heights; is this correct? (It should be stated in the paper regardless).
- Line 290: Fall does not need to be capitalized
Citation: https://doi.org/10.5194/egusphere-2026-2823-RC2
Data sets
Tropospheric Temperature and Relative Humidity Profile Record from Raman Lidar over Payerne (Switzerland) Y. V. Jayaweera et al. https://doi.org/10.5281/zenodo.20034242
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This manuscript presents a carefully conducted study that explores mid and upper tropospheric trends in temperature and relative humidity. I found the methodology utilizing Raman lidar and radiosondes to be appropriate, the analysis to be thorough, and the conclusions to not only be well supported by the results but support many papers exploring similar trends. The manuscript is also clearly written and logically structured. I did not identify any major or minor scientific concerns that would require revision and I find the manuscript to be a strong contribution to the literature and have no substantive comments for the authors. One potential direction for future research that arises from this study is determining how reanalysis or model outputs compare to trends identified here. I do not consider this necessary for the present manuscript, but I think exploring that in a future study could provide more dates to help fill in observational gaps in the analysis.