scholarly journals Tree-ring reconstructions of temperature and sea-level pressure variability associated with the warm-season Arctic Oscillation since AD 1650

2003 ◽  
Vol 30 (11) ◽  
Author(s):  
Rosanne D. D'Arrigo
Keyword(s):  
Sea Level ◽  
Tree Ring ◽  
Arctic Oscillation ◽  
Warm Season ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
And Sea Level

scholarly journals The Response of Local Precipitation and Sea Level Pressure to Hadley Cell Expansion

2017 ◽  
Vol 44 (20) ◽  
pp. 10,573-10,582 ◽  
Author(s):  
Daniel F. Schmidt ◽  
Kevin M. Grise
Keyword(s):  
Sea Level ◽  
Cell Expansion ◽  
Hadley Cell ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
And Sea Level

scholarly journals Evaluation of a Mesoscale Short-Range Ensemble Forecast System over the Northeast United States

2007 ◽  
Vol 22 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Matthew S. Jones ◽  
Brian A. Colle ◽  
Jeffrey S. Tongue
Keyword(s):  
United States ◽  
Wind Speed ◽  
Sea Level ◽  
Short Range ◽  
Warm Season ◽  
Ensemble Forecast ◽  
Cool Season ◽  
Forecast System ◽  
Sea Level Pressure ◽  
Level Pressure

Abstract A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

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.

Influence of the Antarctic Oscillation, the Pacific–South American modes and the El Niño–Southern Oscillation on the Antarctic surface temperature and pressure variations

2011 ◽  
Vol 24 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Lejiang Yu ◽  
Zhanhai Zhang ◽  
Mingyu Zhou ◽  
Sharon Zhong ◽  
Donald Lenschow ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Level ◽  
Southern Oscillation ◽  
South American ◽  
Antarctic Oscillation ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
The Pacific ◽  
The Antarctic ◽  
And Sea Level

AbstractIn this study, the impacts of the Antarctic Oscillation (AAO), the Pacific–South American teleconnection (PSA) and the El Niño–Southern Oscillation (ENSO) on Antarctic sea level pressure and surface temperature are investigated using surface observational data, European Centre for Medium-Range Weather Forecasts (ECMWF) 40 Year Re-analysis (ERA-40) and the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) re-analysis data from 1958–2001. There is the most significant correlation between PSA and Antarctic sea level pressure and surface temperature in the northern Antarctic Peninsula during four seasons. But the correlation between Southern Oscillation Index and surface temperature and sea level pressure is significant at some stations only in spring. The three indices can explain a large portion of the trends found in sea level pressure and temperature at some stations, but not at all stations. Among the three indices the most important contribution to the trends in the two surface variables comes from AAO, followed by PSA, and finally by ENSO. The two re-analysis datasets show great similarity for the trends in surface temperature and sea level pressure in April–May and October–November, but not December–February. In summer the trends in surface temperature and sea level pressure in East Antarctica for ERA-40 re-analysis are opposite to those of NCEP re-analysis.

Improved Skill of Northern Hemisphere Winter Surface Temperature Predictions Based on Land–Atmosphere Fall Anomalies

2007 ◽  
Vol 20 (16) ◽  
pp. 4118-4132 ◽  
Author(s):  
Judah Cohen ◽  
Christopher Fletcher
Keyword(s):  
Snow Cover ◽  
Sea Level ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Northern Eurasia ◽  
Dynamical Models ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Skill Scores ◽  
And Sea Level

Abstract A statistical forecast model, referred to as the snow-cast (sCast) model, has been developed using observed October mean snow cover and sea level pressure anomalies to predict upcoming winter land surface temperatures for the extratropical Northern Hemisphere. In operational forecasts since 1999, snow cover has been used for seven winters, and sea level pressure anomalies for three winters. Presented are skill scores for these seven real-time forecasts and also for 33 winter hindcasts (1972/73–2004/05). The model demonstrates positive skill over much of the eastern United States and northern Eurasia—regions that have eluded skillful predictions among the existing major seasonal forecast centers. Comparison with three leading dynamical forecast systems shows that the statistical model produces superior skill for the same regions. Despite the increasing complexity of the dynamical models, they continue to derive their forecast skill predominantly from tropical atmosphere–ocean coupling, in particular from ENSO. Therefore, in the Northern Hemisphere extratropics, away from the influence of ENSO, the sCast model is expected to outperform the dynamical models into the foreseeable future.

Prediction of summer rainfall in Pakistan from global sea-surface temperature and sea-level pressure

Weather ◽  
2017 ◽  
Vol 72 (3) ◽  
pp. 76-84 ◽  
Author(s):  
Muhammad Adnan ◽  
Nadia Rehman ◽  
Shaukat Ali ◽  
Shahbaz Mehmood ◽  
Kaleem Anwar Mir ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Level ◽  
Summer Rainfall ◽  
Sea Surface ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Global Sea Surface Temperature ◽  
And Sea Level
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