3.4.2.3.1 Surface air temperature and sea level pressure

2005 ◽  
pp. 297-305
Author(s):  
P. Speth ◽  
R. A. Madden
Keyword(s):  
Sea Level ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
And Sea Level

scholarly journals Recent atmospheric changes and future projections along the Saudi Arabian Red Sea Coast

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Abdulhakim Bawadekji ◽  
Kareem Tonbol ◽  
Nejib Ghazouani ◽  
Nidhal Becheikh ◽  
Mohamed Shaltout
Keyword(s):  
Relative Humidity ◽  
Sea Level ◽  
Air Temperature ◽  
Surface Wind ◽  
Surface Air Temperature ◽  
Saudi Arabian ◽  
Sea Level Pressure ◽  
Ensemble Mean ◽  
Red Sea Coast ◽  
Sea Coast

AbstractRecent and future climate diagrams (surface air temperature, surface relative humidity, surface wind, and mean sea level pressure) for the Saudi Arabian Red Sea Coast are analysed based on hourly observations (2016–2020) and hourly ERA5 data (1979–2020) with daily GFDL mini-ensemble means (2006–2100). Moreover, GFDL mini-ensemble means are calculated based on the results of three GFDL simulations (GFDL-CM3, GFDL-ESM2M, and GFDL-ESM2G). Observation data are employed to describe the short-term current weather variability. However, ERA5 data are considered to study the long-term current weather variability after bias removal via a comparison to observations. Finally, a bias correction statistical model was developed by matching the cumulative distribution functions (CDFs) of corrected ERA5 and mini-ensemble mean data over 15 years (2006–2020). The obtained local statistic were used to statically downscale GFDL mini-ensemble means to study the future uncertainty in the atmospheric parameters studied. There occurred significant spatial variability across the study area, especially regarding the surface air temperature and relative humidity, based on monthly analysis of both observation and ERA5 data. Moreover, the results indicated that the ERA5 data suitably describe Tabuk, Jeddah and Jizan weather conditions with a marked spatial variability. The best performance of ERA5 surface air temperature and relative humidity (surface wind speed and sea level pressure) data was detected in Tabuk (Jeddah). These data for the Saudi Arabian Red Sea coast, 1979–2020, exhibit significant positive trends of the surface air temperature and surface wind speed and significant negative trends of the relative humidity and sea level pressure. The GFDL mini-ensemble mean projection result, up to 2100, contains a significant bias in the studied weather parameters. This is partly attributed to the coarse GFDL resolution (2° × 2°). After bias removal, the statistically downscaled simulations based on the GFDL mini-ensemble mean indicate that the climate in the study area will experience significant changes with a large range of uncertainty according to the considered scenario and regional variations.

scholarly journals Daily to intraseasonal oscillations at Antarctic research station Neumayer

2013 ◽  
Vol 26 (2) ◽  
pp. 193-204 ◽  
Author(s):  
N. Rimbu ◽  
G. Lohmann ◽  
G. König-Langlo ◽  
C. Necula ◽  
M. Ionita
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Sea Level ◽  
Intraseasonal Variability ◽  
Research Station ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Intraseasonal Oscillations ◽  
Antarctic Research ◽  
And Sea Level

AbstractHigh temporal resolution (three hours) records of temperature, wind speed and sea level pressure recorded at Antarctic research station Neumayer (70°S, 8°W) during 1982–2011 are analysed to identify oscillations from daily to intraseasonal timescales. The diurnal cycle dominates the three-hourly time series of temperature during the Antarctic summer and is almost absent during winter. In contrast, the three-hourly time series of wind speed and sea level pressure show a weak diurnal cycle. The dominant pattern of the intraseasonal variability of these quantities, which captures the out-of-phase variation of temperature and wind speed with sea level pressure, shows enhanced variability at timescales of ∼ 40 days and ∼ 80 days, respectively. Correlation and composite analysis reveal that these oscillations may be related to tropical intraseasonal oscillations via large-scale eastward propagating atmospheric circulation wave-trains. The second pattern of intraseasonal variability, which captures in-phase variations of temperature, wind and sea level pressure, shows enhanced variability at timescales of ∼ 35, ∼ 60 and ∼ 120 days. These oscillations are attributed to the Southern Annular Mode/Antarctic Oscillation (SAM/AAO) which shows enhanced variability at these timescales. We argue that intraseasonal oscillations of tropical climate and SAM/AAO are related to distinct patterns of climate variables measured at Neumayer.

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