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M. D. Frías, S. Herrera, A. S. Cofiño, and J. M. Gutiérrez

1. Introduction Seasonal forecasting is a promising research field with enormous potential influence in different socioeconomic sectors, such as agriculture ( Challinor et al. 2005 ), health ( Thompson et al. 2006 ), and energy ( García-Morales and Dubus 2007 ). The goal is predicting climate seasonal anomalies a few months in advance, and its prospects have been discussed many times ( Palmer and Anderson 1994 ; Trenberth 1997 ; Goddard et al. 2001 ; Troccoli et al. 2008 ). In the previous

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D. W. Shin, G. A. Baigorria, Y-K. Lim, S. Cocke, T. E. LaRow, James J. O’Brien, and James W. Jones

1. Introduction If we have a reliable seasonal climate forecast at the beginning of the crop growing season, we can estimate the upcoming season crop yield amount reasonably well using a dynamic crop model. This would be very beneficial in helping farmers and/or crop decision makers to prepare for the crop growing season ( Jones et al. 2000 ; Hansen 2002 ; Cabrera et al. 2009 ). Farmers may take certain mitigation measures (e.g., changing planting date, adopting different crop variety) or

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Lifeng Luo and Eric F. Wood

these stream gauges are indicated in Fig. 3 . As discussed earlier, the simulated streamflow agrees well with observations, but there are periods with significant differences that may arise from water management, especially reservoir operations. One way to avoid these complicating effects is to verify the seasonal hydrologic predictions against offline streamflow simulated with our best-known, in situ forcing data. An analysis of Fig. 8 leads to similar results, as does the precipitation and soil

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Yann Friocourt, Bruno Blanke, Sybren Drijfhout, and Sabrina Speich

Greenland low-pressure atmospheric cells between summer and winter ( Bakun and Nelson 1991 ). Similarly, Pingree et al. (1999) were able to partially relate observed flow reversals in the vicinity of Goban Spur to seasonal changes in the mean wind stress as well as in the large-scale oceanic density structure. This study aims at evaluating the relative effects of local wind stress and seasonal changes in the large-scale density structure of the northeastern Atlantic Ocean on the variability of the

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J. D. Tamerius, M. S. Perzanowski, L. M. Acosta, J. S. Jacobson, I. F. Goldstein, J. W. Quinn, A. G. Rundle, and J. Shaman

models for indoor relative humidity (%), while outdoor specific humidity is greater than 10 g kg −1 . The coefficients for univariate models for each of the variables present in the multivariate models are included in the first column. 4. Discussion The preceding analysis shows a number of interesting seasonal effects, especially with regard to indoor temperature. During the warm season (outdoor temperature greater than 15°C) outdoor temperature is the strongest predictor of indoor temperature. The

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Ipshita Majhi and Daqing Yang

constructions, interbasin water diversions, and water withdrawal for industrial and agricultural uses also affect river discharge regimes and changes over space and time ( Miah 2002 ; Vörösmarty et al. 1997 ; Revenga et al. 1998 ; Dynesius and Nilsson 1994 ). Slow economic growth and low population in the high-latitude regions have resulted in low impact by humans ( Shiklomanov et al. 2000 ; Lammers et al. 2001 ). Among the human impacts, reservoir regulation has the most direct effects on hydrologic

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Xiaogang Shi, Andrew W. Wood, and Dennis P. Lettenmaier

upstream diversion and storage effects had been removed. All forecast points have lengthy streamflow records (spanning at least the period 1971–2001) of high quality. The basins represent different hydroclimatic conditions and are distributed across the western United States. The seasonal hydrologic cycle of all basins is dominated by spring snowmelt runoff. Six of the gages, the Animas River at Durango (ANIMA), Bruneau River near Hot Spring (BRUNE), Yellowstone River (YELLO), Salmon River at Whitebird

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H. Biemans, R. W. A. Hutjes, P. Kabat, B. J. Strengers, D. Gerten, and S. Rost

expect to be the largest source of uncertainty from input data. For water resources assessments, the intra-annual dynamics of discharge are important, because both water demand and supply vary throughout the year. Therefore, the impact of uncertainty should also be investigated on a seasonal time scale. The objective of this paper is to quantify the global distribution of the uncertainty in annual as well as seasonal estimates of precipitation on a basin scale and the resulting uncertainty in

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Katrina S. Virts, John M. Wallace, Michael L. Hutchins, and Robert H. Holzworth

evening and a maximum during the morning. In contrast, wintertime lightning peaks around 1900 LT, at the time of minimum precipitation, and declines steadily through the night to a minimum in the hours after sunrise. Interestingly, the results in Fig. 2 indicate that lightning frequencies during early evening (from 1700 to 2000 LT) are of comparable magnitude during summer and winter. Seasonal-mean precipitation and lightning over the eastern United States and western Atlantic are shown in Figs. 3

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Nikolaos Christidis, Peter A. Stott, Simon Brown, David J. Karoly, and John Caesar

crops like rice ( Peng et al. 2004 ) and also increases in insect and pest diseases ( Patterson et al. 1999 ). In addition to crops, there are numerous studies that report regional phenological changes in various other plant species and also forestry ( Cayan et al. 2001 ; Matsumoto et al. 2003 ; Zhou et al. 2001 ). Seasonal activities of animals are also affected by changes in the growing season. Examples include changes in the return dates of migrant birds ( Cotton 2003 ), in egg laying and

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