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Pavel Ya Groisman, Richard W. Knight, Thomas R. Karl, David R. Easterling, Bomin Sun, and Jay H. Lawrimore

major macrocirculation variables such as El Niño–Southern Oscillation, North Atlantic Oscillation, Arctic Oscillation, Pacific decadal oscillation, and with the development of the North American monsoon system ( Cayan et al. 1999 ; Gershunov and Barnett 1998 ; Mauget 2003 ; Wallace and Thompson 2002 ; Barlow et al. 1998 ; Higgins et al. 1997 ). While acknowledging these efforts, we want to point out that this study is mostly an attempt to log and summarize what we know about the long

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Sapna Rana, James McGregor, and James Renwick

corresponding to EOF1 and EOF2 of DJF zonal winds at 200 hPa represented a pattern similar to El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO)–Arctic Oscillation (AO), respectively, with a statistically significant correlation between the PC time series and winter precipitation. Syed et al. (2009) also found that both ENSO and NAO have a significant influence on winter precipitation over southwest-central Asia (including northern Pakistan, Afghanistan, and Tajikistan), where

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Helene B. Erlandsen, Ingjerd Haddeland, Lena M. Tallaksen, and Jørn Kristiansen

western part and a drier eastern part. f. Study period Interannual weather variability in Norway is influenced by the North Atlantic Oscillation (NAO), especially in winter. A negative phase of the NAO is usually concurrent with cold and dry conditions in Norway, while a positive NAO phase usually indicates warm and wet conditions. To evaluate to what degree the sensitivities found vary with weather variability, the study is conducted over a time period when the phase of the NAO changed from positive

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Kabir Rasouli, John W. Pomeroy, and Paul H. Whitfield

, 2012 : Uncertainty in climate change projections: the role of internal variability . Climate Dyn. , 38 , 527 – 546 , https://doi.org/10.1007/s00382-010-0977-x . 10.1007/s00382-010-0977-x Dornes , P. F. , J. W. Pomeroy , A. Pietroniro , S. K. Carey , and W. L. Quinton , 2008 : Influence of landscape aggregation in modelling snow-cover ablation and snowmelt runoff in a sub-arctic mountainous environment . Hydrol. Sci. J. , 53 , 725 – 740 , https://doi.org/10.1623/hysj.53.4.725 . 10

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T. G. Huntington and M. Billmire

precipitation for each basin were summarized on an annual basis. Monthly North Atlantic Oscillation data were obtained from the National Oceanic and Atmospheric Administration (NOAA) web site ( http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml ). To estimate ET for this study we used the water balance approach where the difference between precipitation ( P ) and runoff (Ru) can be described by the following equation ( Scanlon et al. 2002 ; Healy et al. 2007 ): where P − Ru is partitioned

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Tian Zhou, Bart Nijssen, Huilin Gao, and Dennis P. Lettenmaier

United States decreased by as much as 30% in June because of irrigation and reservoir regulations, while monthly streamflow increased by as much as 30% in Arctic river basins in Asia during the winter low-flow period. Oki and Kanae (2006) argue that these variations in streamflow can lead to water-related hazards such as droughts and floods if societies fail to anticipate or monitor these changes in the hydrological cycle. Furthermore, variations in reservoir storage have important implications for

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Paul A. Dirmeyer, Jiangfeng Wei, Michael G. Bosilovich, and David M. Mocko

and Sudan has a large oscillation between oceanic sources in the winter and spring and terrestrial sources during summer into fall. Much of southern Africa has a similar variation, but 6 months out of phase. The general east–west gradient over North America is maintained throughout the year but fluctuates from a predominance of marine sources in winter to a much larger portion of continental sources in summer. Most of Eurasia also shows the same annual cycle as North America. Very strong gradients

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Susan Frankenstein, Anne Sawyer, and Julie Koeberle

incoming and reflected solar radiations are used to drive the models, FASST did better at Buffalo Pass, Walton Creek, and Fool Creek during the accumulation period. Both models do very well at predicting the slope and diurnal oscillations in the ablation phase at the two Rabbit Ears ISAs, whereas SNTHERM becomes unstable at Fool Creek, melting out much quicker than observed. In all cases, FASSTi melts quicker than FASST, with the former predicting melt-out before the actual date, whereas FASST and

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Kirsten L. Findell and Elfatih A. B. Eltahir

1. Introduction a. Motivation Feedbacks from the earth's surface to the atmosphere are an instrumental part of global climatic processes. Extensive research on the El Niño–Southern Oscillation phenomenon connects anomalous sea surface temperatures (SSTs) in the eastern Pacific Ocean with dramatic shifts in weather patterns over much of the globe. Like SSTs, vegetation cover and soil moisture content control the partitioning of energy fluxes at the earth's surface, and, like SSTs, land surface

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Benjamin R. Lintner and J. David Neelin

the tropical troposphere. An advantage of QTCM1 over GCMs is the simplicity of the model framework: the QTCM1’s transparency facilitates diagnosis in ways that are not always feasible or straightforward with GCMs. The simplicity of QTCM1 has proved useful for elucidating many tropical climate phenomena, including tropical ocean–atmosphere coupling ( Su et al. 2003 ), El Niño–Southern Oscillation (ENSO) tropical teleconnections ( Neelin and Su 2005 ), climate sensitivity to global warming ( Chou

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