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John Risley, Hamid Moradkhani, Lauren Hay, and Steve Markstrom

distributed watershed models that simulate streamflow in addition to various water and energy fluxes within a basin. Dibike and Coulibaly ( Dibike and Coulibaly 2005 ) compared two statistical downscaling and hydrologic modeling techniques to simulate runoff in a watershed in northern Quebec, Canada. Both downscaling methods resulted in increased winter low flow and earlier spring high flows, which was consistent with reduced freezing and increasing trends in temperature and snowmelt. Downscaled data from

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Ute C. Herzfeld, Sheldon Drobot, Wanli Wu, Charles Fowler, and James Maslanik

studies presented in this paper, we focus on MM5 temperature and precipitation grids for the model years 1992–2000 ( Wu et al. 2007 ) and compare them to several compiled or reanalyzed datasets that are frequently used in the climate research community. These include temperature and precipitation datasets from 1) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; see Gibson et al. 1997 ); 2) University of Delaware climate datasets [UDEL (MW); see Willmott and

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Mohammad Karamouz, Erfan Goharian, and Sara Nazif

(GCMs), which are the most advanced tools currently available in this field. GCMs are widely applied for understanding the climate, weather forecasting, and projecting climate change. In the past few years, various studies have investigated the hydrological impact of climate change (e.g., Boorman and Sefton 1997 ; Bergström et al. 2001 ; Gao et al. 2002 ; Christensen et al. 2004 ; Chen et al. 2007 ). Charlton et al. ( Charlton et al. 2006 ) examined the impact of climate change on flood hazard

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Kwang-Yul Kim, James J. O'Brien, and Albert I. Barcilon

an accurate ENSO forecasting technique ( Clarke and van Gorder, 2001 ). Some of the inter-ENSO variability also can be interpreted in the context of the two-mode view. A loose phase-locking tendency, variable duration of SST warming or cooling, and different onset and termination times of El Niño and La Niña seem to be reasonably explained in terms of an irregular interplay of the two modes. Finally, it should be mentioned that the two principal computational modes may not necessarily be true

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A. S. Alhumaima and S. M. Abdullaev

the Diyala River basin, a tributary of the Tigris River, Alhumaima and Abdullaev (2019) found that the normalized difference vegetation index (NDVI), measured at the peak of landscape biological productivity, has higher correlation with the precipitation amounts of the entire rainy winter season and with the temperatures of the first quarter of the year, than with corresponding monthly data. It has also been shown that the spatiotemporal NDVI forecasting of the Diyala River basin, which is

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Lauren E. Hay, Steven L. Markstrom, and Christian Ward-Garrison

vs streamflow using the five GCMs and three emission scenarios calculated for each of the 88 windows for the 14 selected basins. 5. Discussion There are numerous sources of uncertainty at each step of simulation associated with this study: uncertainty in the GCMs, in the downscaling technique, and in the hydrologic model. Starting with the GCMs, large uncertainties are associated with the representation of the physical processes, model structure, and feedbacks within the climate system ( Alley et

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Sheldon Drobot, James Maslanik, Ute Christina Herzfeld, Charles Fowler, and Wanli Wu

. 1996 ), 15-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analyses (ERA-15; Gibson et al. 1997 ), and the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU; Jones et al. 2001 ) datasets were analyzed. The results indicated that temperature differences between the NCEP1 and CRU datasets were largest in winter and smallest in summer, with NCEP1 being warmer over North America; comparisons for NCEP1 and ERA-15 were similar, whereas ERA-15 was noticeably warmer than CRU

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Walter N. Meier, James A. Maslanik, Charles W. Fowler, and Jeffrey R. Key

of the cloud-detection steps, and algorithm assumptions, as well as differences in the types of in situ data used. In another comparison, the AVHRR-derived skin temperatures correspond reasonably closely to surface air temperatures provided by meteorological forecast models [National Centers for Environmental Prediction (NCEP) reanalyses ( Kalnay et al., 1996 ) ( Table 1 and Figure 7a ); European Centre for Medium-Range Weather Forecasts (ECMWF) ( Figure 7b )], and interpolated air temperatures

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Allison L. Steiner, Dori Mermelstein, Susan J. Cheng, Tracy E. Twine, and Andrew Oliphant

information (land-cover type from Ameriflux website: http://ameriflux.lbl.gov/AmeriFluxSites/SitePages/Home.aspx ). It is likely that the flux data used in our analysis ( H , λE , and CO 2 fluxes) underestimate actual flux values because of the so-called closure problem in the eddy covariance technique ( Foken 2008 ; Franssen et al. 2010 ; Leuning et al. 2012 ; Oliphant et al. 2004 ; Oncley et al. 2007 ; Twine et al. 2000 ; Wilson et al. 2002 ). To close the energy budget, R n must be balanced

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Y. Govender, E. Cuevas, L. D. S. Sternberg, and M. R. Jury

isotopes are progressively stripped from the water vapor toward the center of the storm. The water vapor eventually rises and condenses into rainfall with lower δ 18 O and δD than before. Stable isotopic techniques using δ 18 O and δD values of water are a reliable tool to trace the origins of rainfall and groundwater and can be used to detect weather events ( Gedzelman et al. 2004 ), especially in the Caribbean, where infrequent heavy rainfall occurs in a generally dry environment ( Malmgren et al

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