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Table 1. Ten driest and wettest summers (mean Z-index) between 1929 and 1999 (ranked by severity). Driest Years Wettest Years Year Z-Index Year Z-Index 1961 –2.52 1993 4.00 1936 –2.51 1944 1.95 1988 –2.48 1951 1.63 1934 –2.13 1965 1.62 1931 –1.73 1995 1.54 1933 –1.40 1947 1.52 1929 –1.24 1999 1.36 1940 –1.23 1942 1.33 1959 –1.09 1975 1.30 1937 –0.99 1968 1.28 Figure 4. Composite snowfall anomalies (mm) in winter, and spring associated with the five wettest summers (1993, 1944, 1951, 1965, 1995) and the five driest summers (1961, 1936, 1988, 1934, 1931). DISCUSSION The observational data demonstrate that below (above) normal snowfall in winter/spring is generally associated with anomalously dry (wet) summers in the northern Great Plains. It is hypothesized that below normal snowfall is linked to summer drought via negative soil moisture anomalies in spring and early summer that reduce local moisture recycling. Our findings appear to support those of Namias (1991), who suggested that reduced soil moisture during the late 70
winter/early spring could contribute to a warm, dry summer in the region by reducing the amount of local moisture recycling and by modifying the large-scale atmospheric circulation. The strength of the relationship between winter/spring snowfall and summer moisture anomalies has varied significantly over space and time. Linear correlations between April–May snowfall anomalies and summer moisture conditions ranged from approximately zero (1955–1969) to 0.82 (1971–1985). Other empirical studies have also found that the strength of the relationship between land surface conditions (e.g., snow cover and soil moisture) and precipitation has varied significantly during the 20 th century (Gutzler, 2000; Hu and Feng, 2002; Zhu et al., 2005). Hu and Feng (2004) suggest that the relationship between land surface conditions and precipitation patterns over the North American Monsoon region is modulated by sea surface temperature (SST) anomalies in the Pacific Ocean. They found that when SST anomalies were strong (weak), land surface conditions tend to have less (more) influence. Therefore it is hypothesized that atmospheric and/or oceanic forcings are modulating the relationship between snowfall and summer moisture conditions in the northern Great Plains. However, the reasons for the differential influence of winter versus spring (April–May) snowfall (Figure 2) are unknown and merit future study. Relationships between April–May snowfall and summer moisture anomalies also varied spatially. The strongest relationships were found in southern Manitoba and South Dakota and statistically significant correlations were present across approximately 56% of the study region. Previous research has also demonstrated that the coupling between land surface conditions (e.g., soil moisture and snow) and precipitation can be highly spatially variable (Lo and Clark, 2002; Koster et al., 2004; Dominguez et al., 2006). Our results demonstrate that even within a relatively small area there can be substantial differences in the strength of the relationship between spring snowfall and summer moisture anomalies. The relationship between spring snowfall and summer moisture may be non-linear since it appears that snowfall anomalies must exceed some minimum threshold before they have a significant (and consistent) influence on summer moisture conditions. The mean correlation between April–May snowfall and summer moisture anomalies increased from 0.22 (all years) to 0.49 when only the years with snowfall anomalies more than one standard deviation above/below the mean were considered. The lack of spatial and temporal stability in the relationship between snowfall and summer moisture anomalies has significant implications for understanding and forecasting the occurrence of severe hydrologic events (e.g., floods and droughts). Additional study is needed to identify the factors that are responsible for modulating the strength of the snowfall-summer moisture relationship over space and time. Although spring snowfall conditions can, in some cases, explain more than half of the variance in summer moisture, the lack of spatial and temporal stability in this relationship limits its utility for producing accurate forecasts of summer droughts in the northern Great Plains. ACKNOWLEDGEMENTS A version of this paper has been submitted to Geophysical Research Letters. The authors would like to thank Tom Mote for providing the snowfall data and Dan Leathers for reviewing an earlier version of this manuscript. REFERENCES Alley WM. 1984. The Palmer Drought Severity Index: Limitation and assumptions. J. Clim. Appl. Meteorol. 23: 1100–1109. Déry SJ, Sheffield J, Wood EF. 2005. Connectivity between Eurasian snow cover extent and Canadian snow water equivalent and river discharge. J. Geophys. Res. 110: D23106, doi:10.1029/2005JD006173. 71
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Proceedings of the 63rd ANNUAL EAST
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FOREWORD T his proceedings volume c
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CONTENTS Foreword..................
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STATEMENT OF PURPOSE The Eastern Sn
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EXECUTIVES FOR THE 63rd EASTERN SNO
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THE PRESIDENT’S PAGE The 63rd ann
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Weisnet Medal for Best Student Pape
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3 63 rd EASTERN SNOW CONFERENCE New
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Figure 1. Layer 1 represents the so
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Figure 4. The general layout of the
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lue color represented no convection
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Total Density of Water Profiles The
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Figure 11(a). Simulated snow grain
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Jordan R, 1991. A one-dimensional t
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Campbell Scientific Award for Best
Page 31 and 32: 19 63 rd EASTERN SNOW CONFERENCE Ne
Page 41 and 42: R ∑i ∑ n 2 = 1 NS = − n i = 1
Page 49 and 50: Snow and Climate 37
Page 53 and 54: Spatial distributions of changes in
Page 55 and 56: Snow and Climate Posters 43
Page 59 and 60: Figure 2 presents correlation maps
Page 61 and 62: CONCLUSIONS Three regional-continen
Page 65 and 66: Discharge (mm) 20 18 16 14 12 10 8
Page 67 and 68: ABSTRACT 55 63 rd EASTERN SNOW CONF
Page 69 and 70: This paper analyzes the synoptic pa
Page 71 and 72: Cyclones that track west of the App
Page 73 and 74: The synoptic-scale atmospheric circ
Page 75 and 76: CONCLUSIONS The record snowfall in
Page 79 and 80: correlations between April-May snow
Page 81: Figure 3. Linear correlations betwe
Page 85 and 86: Snow Remote Sensing 73
Page 89 and 90: height averaged 11.4 m with an aver
Page 91 and 92: strings were buried 20 - 30 cm belo
Page 93 and 94: Although the λE/Rn fraction was on
Page 95 and 96: estimated snow depth based on the 2
Page 97 and 98: important to note that the average
Page 99 and 100: Schmidt, R. A., C. A. Troendle, and
Page 103 and 104: METHODS The IMS Product The IMS was
Page 105 and 106: Figure 1. Microwave spectral charac
Page 107 and 108: snow depth and SWE on a weekly basi
Page 109 and 110: Figure 4. Example of blended SWE pr
Page 111 and 112: Evaluation of the global distributi
Page 113 and 114: Figure 9. Inter-comparison plots of
Page 115 and 116: Helfrich, S.R., D. McNamara, B.H.Ra
Page 117 and 118: 105 63 rd EASTERN SNOW CONFERENCE N
Page 119 and 120: and validated regionally so they ca
Page 121 and 122: 109 Test Site Figure 2. Variation o
Page 123 and 124: Correlation Coe. Correlation Coe. C
Page 125 and 126: Figure 8. SSM/I scattering signatur
Page 127 and 128: Considering the facts mentioned abo
Page 129 and 130: Besides the temporal validation, th
Page 131 and 132: RMSE Non linear Azar Chang Goodison
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63 nd EASTERN SNOW CONFERENCE Newar
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http://www.ccin.ca); from simulatio
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over the period 1988-2000. With the
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Figure 3: Explained variance (R 2 )
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correspondence between simulated an
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values. Continued development of ne
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National Climatic Data Center (2005
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Snow Remote Sensing Posters 135
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ABSTRACT 63 rd EASTERN SNOW CONFERE
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day and has a mean pixel resolution
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Table 1. Wheaton River basin EASE-G
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The SSM/I snowmelt onset algorithm
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coarse-resolution pixel with the hi
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Figure 5. (a) Scatterplot of snowpa
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For both 2004 and 2005, the AMSR-E
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threshold of ±18 K that reflects t
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ABSTRACT 153 63 rd EASTERN SNOW CON
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DATA USED SSM/I & QuikSCAT Coverage
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157 63 rd EASTERN SNOW CONFERENCE N
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180 160 140 120 100 80 60 40 20 0 1
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independent of regions. This greatl
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slightly vary the IMS with this pro
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imagery, the higher latitudes rely
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MODIS Land Science Team, NASA Godda
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FUTURE OF IMS Enhancements to the I
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information never present before on
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Brubaker, K.L., R. Pinker and E. De
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The Sierra Nevada del Cocuy region
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Colombia Venezuela Glacier Series R
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Table 4. Glacier Areas of the Ruiz-
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Pico Bonpland Massif-Sinigüis Glac
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Linder W, Jordan E, 1991. Ice-mass
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Snowpack Processes 193
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63 rd EASTERN SNOW CONFERENCE Newar
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In the following paragraph the main
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Figure 1: Variation of the season a
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Figure 3: 8-day composite maps (24
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The third pair (Figure 5) represent
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Figure 9: Time evolution of SWE ave
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melting occurs, whereas at the high
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Colbeck SC, Anderson EA. 1982. The
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A B Figure 1. Low-magnification sec
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GB ridge GB groove Figure 3. Higher
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CONCLUSION Figure 5. Indexed electr
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219 63rd EASTERN SNOW CONFERENCE De
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Meteorological data for the winter
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monthly probability adjusted data 4
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225 Pullman WA Rawlins WY Leadville
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The daily wind speed can exceed 6.5
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NCDC. 2006. National Climate Data C
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and mass balances for 540 environme
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Temperature, precipitation, windspe
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the left in response to snow compac
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Pinched-cone dimensions for each sl
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Terrain slope (degrees) Terrain slo
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ACKNOWLEDGEMENTS This work was fund
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Glacial and Periglacial Processes 2
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From linear regression the constant
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DATA COLLECTION Figure 1. Location
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Table 1. Comparison of GPS measured
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Transverse Velocity Profiles Surfac
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is shown in Figure 4. This variatio
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Table 4. The calculated volume flux
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Figure 1. Cordillera Blanca regiona
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MATERIALS AND METHODS Field Measure
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Data analysis techniques We synthes
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season, but unacceptable for the dr
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significant, although more informat
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(a) Liquid Water (mm) Dry Period: M
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Future Work We are installing addit
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Shuttleworth, W.J., and J.S. Wallac
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Snow and Periglacial Processes Post
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No one to date has shown why snowfl
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APPENDIX A Northern Hemisphere Year
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62nd ESC Papers 291
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62 nd EASTERN SNOW CONFERENCE Water
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each other; and, they both decrease
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297 62 nd EASTERN SNOW CONFERENCE W
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comes in the form of precipitation.
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averaged to reduce bias in this var
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RESULTS Figure 2. Flow chart of reg
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Table 6: Actual-Conditions SWE, dep
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307 Table 9: Regression results bet
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Goita, K., A. Walker, B. Goodison,
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TO ERR IS HUMAN, TO FORGET IT WOULD
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