Search Results

You are looking at 11 - 20 of 108 items for :

  • Arctic Oscillation x
  • Journal of Hydrometeorology x
  • Refine by Access: All Content x
Clear All
Natalie Teale and David A. Robinson

water vapor fluxes to precipitation to quantify each flux’s contribution to the annual regional precipitation. Additionally, time series analyses of these fluxes are being conducted to determine if and, if so, how these flux patterns have changed over the past century. Also to be examined are possible changes in the seasonality and phase of precipitation produced by each of the fluxes, as well as relationships between the North Atlantic Oscillation, the Pacific North American pattern, and other

Restricted access
M. Alves, D. F. Nadeau, B. Music, F. Anctil, and A. Parajuli

the topsoil layer (within ~0.15 m deep from the skin soil surface). Blue, green, and red lines correspond to CTL, RNL, and RNL-ObsP simulations, respectively. The soil moisture simulations at ON-OMW ( Fig. 10c ) show reasonable agreement with observations for only some parts of the snow accumulation period (DJFM) and late snowmelt period (May). For the most part, however, CLASS results greatly disagree with observations, showing many oscillations (peaks) that are not shown by the measurements. Its

Free access
Yafang Zhong, Jason A. Otkin, Martha C. Anderson, and Christopher Hain

.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2 Crow , W. T. , R. Dongryeol , and J. S. Famiglietti , 2005 : Upscaling of field-scale soil moisture measurements using distributed land surface modeling . Adv. Water Resour. , 28 , 1 – 14 , . 10.1016/j.advwatres.2004.10.004 Czaja , A. , and C. Frankignoul , 2002 : Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation . J. Climate , 15 , 606 – 623 ,

Free access
Scott Lee Sellars, Xiaogang Gao, and Soroosh Sorooshian

are well-known climate states [e.g., the Arctic Oscillation (AO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO)] that often impact precipitation and temperature over the western United States ( Redmond and Koch 1991 ; Dracup and Kahya 1994 ; Cayan et al. 1999 ; Barlow et al. 2001 ; Cook et al. 2004 , 2007 ). Research has shown similarities in both the physical precipitation features (e.g., area averages) and the particular climate phenomena that influence the

Full access
David Barriopedro, Célia M. Gouveia, Ricardo M. Trigo, and Lin Wang

), although such relationship is nonstationary ( Kumar et al. 1999 ; R. Wu and B. Wang 2002 ). Other reported factors affecting seasonal precipitation in China are the Arctic Oscillation/North Atlantic Oscillation (AO/NAO; Gong and Wang 2003 ; Sung et al. 2006 ), the stationary planetary waves ( Chen et al. 2005 ), the Antarctic Oscillation (AAO; Nan and Li 2003 ), dynamic and thermal effects of the Tibetan Plateau ( Wu and Zhang 1998 ; Hsu and Liu 2003 ), and the Eurasian snow cover ( Zhang et al

Full access
Michael J. DeFlorio, Duane E. Waliser, Bin Guan, David A. Lavers, F. Martin Ralph, and Frédéric Vitart

or not additional forecast skill and utility can be gained during certain phases of climate mode variability. The relationship between AR forecast utility and climate variations is examined here using the El Niño–Southern Oscillation (ENSO), the Arctic Oscillation (AO), and the Pacific–North America (PNA) teleconnection pattern. These climate variations are a focus because AR frequency is sensitive to the phase of each of these modes over particular regions around the globe ( GW2015 ). In

Full access
Edwin Sumargo and Daniel R. Cayan

cloud variability and climate patterns are also examined using a set of low-frequency weather anomalies metrics commonly known as teleconnection indices ( Wallace and Gutzler 1981 ; Franzke et al. 2001 ). These metrics include the monthly versions of Pacific–North American (PNA), the Arctic Oscillation (AO), and the Niño-3.4 indices from the NOAA Climate Prediction Center database ( ). 3. Methods a. Determining the clear-sky albedo and the cloud albedo GOES albedo α

Full access
J. E. Cherry, L-B. Tremblay, M. Stieglitz, G. Gong, and S. J. Déry

. 16657 – 16672 . 10.1029/1999JD900158 Déry, S. J. , and Yau M. K. , 2002 : Large-scale mass balance effects of blowing snow and surface sublimation. J. Geophys. Res. , 107 . 4679, doi:10.1029/2001JD001251 . Déry, S. J. , and Wood E. F. , 2004 : Teleconnection between the Arctic Oscillation and Hudson Bay river discharge. Geophys. Res. Lett. , 31 . L18205, doi:10.1029/2004GL020729 . Déry, S. J. , Crow W. T. , Stieglitz M. , and Wood E. F. , 2004 : Modeling snowcover

Full access
A. Rinke, C. Melsheimer, K. Dethloff, and G. Heygster

thermodynamic influences. The spatial distribution of TWV is strongly coupled with temperature via the thermodynamic constraint of the Clausius–Clapeyron equation (temperature dependence of the saturation water vapor pressure). However, the spatial variability of Arctic TWV is dominated by changes in the large-scale dynamics, accompanied by changes in cyclone paths and/or intensity. Previous studies found a clear dependence of the Arctic moisture budget on the North Atlantic Oscillation–Arctic Oscillation

Full access
Hengchun Ye, Steve Ladochy, Daqing Yang, Tingjun Zhang, Xuebin Zhang, and Mark Ellison

, Peterson et al. (2002) suggested that increased Arctic river discharges are correlated with the increasing trends in the North Atlantic Oscillation (NAO) and global surface air temperatures through enhanced moisture transport into the Arctic. The observed earlier snowmelt and shift of peak flow a few days earlier is a response to increasing spring air temperature ( Yang et al. 2002 ). This directly affects flooding intensity and frequency in the region ( Burn 1997 ; Cunderlik and Burn 2002 ). River

Full access