POSSIBLE LONG-TERM VARIATIONS IN STRATOSPHERIC WATER-VAPOUR CONTENT

Weather ◽  
1974 ◽  
Vol 29 (3) ◽  
pp. 107-112 ◽  
Author(s):  
J. L. Stanford
2009 ◽  
Vol 5 (H15) ◽  
pp. 537-537
Author(s):  
R. Querel ◽  
F. Kerber ◽  
R. Hanuschik ◽  
G. Lo Curto ◽  
D. Naylor ◽  
...  

Water vapour is the principle source of opacity at infrared wavelengths in the earth's atmosphere. Measurements of atmospheric water vapour serve two primary purposes when considering operation of an observatory: long-term monitoring of precipital water vapour (PWV) is useful for characterizing potential observatory sites, and real-time monitoring of PWV is useful for optimizing use, in particular for mid-IR observations.


2020 ◽  
Author(s):  
Michaela I. Hegglin

<p>Water vapour is the most important natural greenhouse gas in the atmosphere and provides a positive feedback to the climate forcing from carbon dioxide. Water vapour is also the source of hydroxyl (OH) which controls the lifetime of shorter-lived pollutants and long-lived greenhouse gases. Despite the importance of water vapour to chemistry and the radiative balance of the atmosphere, its observed long-term changes in the stratosphere are not well understood, and may even conflict with the theoretical understanding of its drivers. </p><p>I here present a new climate data record of stratospheric water vapour developed within the ESA Water Vapour Climate Change Initiative and discuss recent changes in stratospheric water vapour concentrations in the light of earlier observational studies, modelling results from the SPARC Chemistry-Climate Model Initiative, and our theoretical understanding of its drivers. In addition, the radiative forcing of surface climate and inferred changes in the Brewer-Dobson Circulation will be highlighted. </p>


2021 ◽  
Vol 13 (23) ◽  
pp. 4871
Author(s):  
Monia Negusini ◽  
Boyan H. Petkov ◽  
Vincenza Tornatore ◽  
Stefano Barindelli ◽  
Leonardo Martelli ◽  
...  

The atmospheric humidity in the Polar Regions is an important factor for the global budget of water vapour, which is a significant indicator of Earth’s climate state and evolution. The Global Navigation Satellite System (GNSS) can make a valuable contribution in the calculation of the amount of Precipitable Water Vapour (PW). The PW values retrieved from Global Positioning System (GPS), hereafter PWGPS, refer to 20-year observations acquired by more than 40 GNSS geodetic stations located in the polar regions. For GNSS stations co-located with radio-sounding stations (RS), which operate Vaisala radiosondes, we estimated the PW from RS observations (PWRS). The PW values from the ERA-Interim global atmospheric reanalysis were used for validation and comparison of the results for all the selected GPS and RS stations. The correlation coefficients between times series are very high: 0.96 for RS and GPS, 0.98 for RS and ERA in the Arctic; 0.89 for RS and GPS, 0.97 for RS and ERA in Antarctica. The Root-Mean-Square of the Error (RMSE) is 0.9 mm on average for both RS vs. GPS and RS vs. ERA in the Arctic, and 0.6 mm for RS vs. GPS and 0.4 mm for RS vs. ERA in Antarctica. After validation, long-term trends, both for Arctic and Antarctic regions, were estimated using Hector scientific software. Positive PWGPS trends dominate at Arctic sites near the borders of the Atlantic Ocean. Sites located at higher latitudes show no significant values (at 1σ level). Negative PWGPS trends were observed in the Arctic region of Greenland and North America. A similar behaviour was found in the Arctic for PWRS trends. The stations in the West Antarctic sector show a general positive PWGPS trend, while the sites on the coastal area of East Antarctica exhibit some significant negative PWGPS trends, but in most cases, no significant PWRS trends were found. The present work confirms that GPS is able to provide reliable estimates of water vapour content in Arctic and Antarctic regions too, where data are sparse and not easy to collect. These preliminary results can give a valid contribution to climate change studies.


2020 ◽  
Vol 20 (4) ◽  
pp. 2143-2159 ◽  
Author(s):  
Zhipeng Qu ◽  
Yi Huang ◽  
Paul A. Vaillancourt ◽  
Jason N. S. Cole ◽  
Jason A. Milbrandt ◽  
...  

Abstract. Stratospheric water vapour (SWV) is a climatically important atmospheric constituent due to its impacts on the radiation budget and atmospheric chemical composition. Despite the important role of SWV in the climate system, the processes controlling the distribution and variation in water vapour in the upper troposphere and lower stratosphere (UTLS) are not well understood. In order to better understand the mechanism of transport of water vapour through the tropopause, this study uses the high-resolution Global Environmental Multiscale model of the Environment and Climate Change Canada to simulate a lower stratosphere moistening event over North America. Satellite remote sensing and aircraft in situ observations are used to evaluate the quality of model simulation. The main focus of this study is to evaluate the processes that influence the lower stratosphere water vapour budget, particularly the direct water vapour transport and the moistening due to the ice sublimation. In the high-resolution simulations with horizontal grid spacing of less than 2.5 km, it is found that the main contribution to lower stratospheric moistening is the upward transport caused by the breaking of gravity waves. In contrast, for the lower-resolution simulation with horizontal grid spacing of 10 km, the lower stratospheric moistening is dominated by the sublimation of ice. In comparison with the aircraft in situ observations, the high-resolution simulations predict the water vapour content in the UTLS well, while the lower-resolution simulation overestimates the water vapour content. This overestimation is associated with the overly abundant ice in the UTLS along with a sublimation rate that is too high in the lower stratosphere. The results of this study affirm the strong influence of overshooting convection on the lower stratospheric water vapour and highlight the importance of both dynamics and microphysics in simulating the water vapour distribution in the UTLS region.


Boreas ◽  
2021 ◽  
Author(s):  
Zoltán Püspöki ◽  
Philip Leonard Gibbard ◽  
Annamária Nádor ◽  
Edit Thamó‐Bozsó ◽  
Pál Sümegi ◽  
...  

1969 ◽  
Vol 2 (6) ◽  
pp. 236-238
Author(s):  
G. W. Lord

The desired properties of magnetic alloys, semiconductor materials and similar products are usually developed by heat treatment in a stream off pure dry gas … the presence of even a few parts per million of water vapour in this gas can cause unwanted changes in such properties … to monitor the water-vapour content, a direct-reading dewpoint meter has been developed which is claimed to be more rapid and sensitive than similar meters


2021 ◽  
Vol 270 ◽  
pp. 116285
Author(s):  
Lewei Zeng ◽  
Hai Guo ◽  
Xiaopu Lyu ◽  
Beining Zhou ◽  
Zhenhao Ling ◽  
...  

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.


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