The Annual Cycle of Snow Cover Extent over the Northern Hemisphere as Revealed by NOAA/NESDIS Satellite Data

Geographical Reports of Tokyo Metropolitan University, 28, 113 - 132 (1993).

Updated HTML version, 9 February 2001. (Affiliation updated 25 June 2003.)

Kooiti MASUDA: Frontier Research System for Global Change, Yokohama 236-0001, Japan.
Yuki MORINAGA: School of Commerce, Meiji University, Tokyo 168-8555, Japan.
Atusi NUMAGUTI: Deceased in June 2001, formerly at Graduate School of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, Japan (also affiliated with Frontier Observational Research System for Global Change, Yokohama 236-0001, Japan)
Ayako ABE-OUCHI: Center for Climate System Research, University of Tokyo, Tokyo 153-8904, Japan (also affiliated with Frontier Research System for Global Change, Yokohama 236-0001, Japan.)

(Affiliations are as of June 2003).

Abstract: The hemispheric distribution of the timing of appearance and disappearance of snow cover is obtained. Weekly digital data of snow cover for 25 years based on NOAA satellite observations are used. In plain areas, we find zonal pattern in median despite of patchy pattern in individual years. The phase lines are in general parallel to latitudinal circles, but there is considerable east-west gradient both in Eurasia and North America. Mountainous areas are characterized by late snowmelt, large variability or both.

Key words: Snow cover, snowmelt, northern hemisphere, NOAA satellite data

1. Introduction

Snow cover is one of the most prominent features of the earth that vary seasonally. It affects the heat balance of the earth in several ways, as:

Because of its high albedo (reflectivity), it absorbs less solar radiation than bare soil or vegetated surface.
Melting snow acts as heat sink, and it keeps the ground temperature near 0 degree Celcius despite of diurnal variation of radiative fluxes.
In middle to high latitudes, snow cover is stock of water substance almost immobile during wintertime, and source of soil moisture during springtime. The wetness of soil makes the surface heat balance of summertime considerably different.

Continental snow cover is also one of the major players in interannual variation of the climate system, together with sea water temperature and monsoon rainfall (Yasunari, 1991). Hahn and Shukla (1976) showed correlation between snow cover area over Eurasia and monsoon rainfall over India in the following summer. Since then, many studies have been conducted. They are reviewed in Morinaga and Yasunari (1993).

The seasonal variation of snow cover is much larger than the interannual one. We should have better understanding of seasonal variation and then we can perhaps better understand interannual anomalies too. We begin with a primitive idea that the "front" of snow cover advances from higher latitude to lower latitude, and that it retreats oppositely. We hope that we can draw isopleths of the timing of advance and retreat. Such studies for one continent or another have been published (Anonymous, 1960; Potter, 1965). They are based on ground observations. In this paper, we try to describe a standard seasonal cycle of snow cover in the hemispheric scale using weekly data of satellite observations.

A preliminary report of this study was published as a researh grant report (Masuda et al., 1989). We used 14 years' (1973 - 1986) data there. In this paper (as published in 1993), the time period of analysis is extended to 17 years (1973 - 1989), and the analysis procedure is improved as discussed in Section 3. In this HTML version, the time period is 25 years (1973 - 1997).

2. Data

Data set

We used the weekly values of "Weekly digital northern hemisphere snow and ice product" compiled by NOAA (National Oceanic and Atmospheric Administration) NESDIS (National Environmental Satellite, Data and Information Service) of the United States. This is the data set of hemispheric-scale snow cover with the longest history that starts in November 1966. Recently, the data set is updated by Climate Prediction Center (CPC) which is part of National Centers for Environmental Prediction of NOAA, rather than NESDIS.

This data set contains information of existence (1) or non-existence (0) of snow cover at each of the 89 x 89 square grid boxes on a polar stereographic map that covers most part of the northern hemisphere. It is a digitized version of hard-copy charts. The hard- copy charts are created by manual analysis of satellite imageries - mainly those of visible channels. The satellites and sensors used for the production are listed in a table of Matson et al. (1986) and reproduced in Wiesnet et al. (1987). The digitization of data for 1966 - 1980 was done by Dewey and Heim (1982) and that for the following years is done annually within NESDIS. (This digital data set contains information of sea ice until 1980 but it does not since 1981). The size of a grid box is roughly 200 km x 200 km.

Data from the autumn of 1966 were avaliable to this study. Until 1971, data of some weeks were missing. Also, the sensor used until 1972 had lower spatial resolution (3 km) than newer VHRR and AVHRR (1 km) sensors (Matson et al., 1986). Considering these facts, we excluded these earlier years before 1973.

Problems

The method of production of this data set and its problems are discussed in the paper of Masuda and Morinaga (1990), based on Wiesnet et al. (1987) and other sources. Here, a few points that are relevant to this study are listed:

It just contains information about existence of snow cover, not about its depth.
No information can be obtained if a grid box is cloudy every day during a week. In such a case, the same condition as the previous week is assumed.
If multiple grid boxes are partially covered by snow, and if the sum of the areas is equivalent to one box, the analysts subjectively determine which box they should assign `1'.

3. Method of Analysis

Basic idea

In this paper, we calculate statistics at each grid box, and view the distribution of the statistic values geographically. It is contrary to the more popular approach that first calculates regional snow cover areas and that second analyzes its time-series.

The problems of the NOAA/NESDIS data set discussed above, in particular items (2) and (3), result in considerable statistical noise in time-series analysis at single grid boxes. We try to overcome them by using robust statistics as discussed below, and by paying attention to spatial pattern of the statistic values.

Definition of `snowmelt week' and `snowfall week'

At a certain place in a certain year, there may be multiple timings of appearance and disappearance of snow cover. We focus on the timing of the last disappearance, because we consider it more important in controling summer dryness of soil. For the appearance of snow cover, we pick up the first one. This decision does not have an independent rationale. We just wanted to treat the appearance and disappearance symmetrically. Our convention is different from that of the figures on Page 42 of USSR Agriculture Atlas (anonymous, 1960), where the first and last dates of `stable' (i.e. temporally continuous) snow cover are shown.

We define the `snowmelt week' of a grid box of a year as the week when snow cover is lastly observed between Week 8 and Week 30. (This definition is slightly different from that adopted by the previous report of Masuda et al., 1989).

We define the `snowfall week' as the week when snow cover is observed at the first time between Week 36 and Week 52. It actually corresponds to appearance of snow cover rather than just snowfall, but we use the short name for brevity.

Use of order statistics

When a batch of numerical values is sorted, the value that comes at the center is called the median. The value whose order is one fourth from the smallest [largest] is called the lower [upper] quartile, and the difference between the two quartiles is called the quartile range. It is known (e.g. Mosteller and Tukey, 1977) that these order statistics are more robust to outliers (extraordinary values) than the average and the standard deviation.

In this paper, medians and quartile ranges are calculated using 25 years' data. Here, the median is the 13th largest case, and the quartile range is the difference between the 7th largest and the 19th largest cases.

In our previous report (Masuda et al., 1989), we showed means and standard deviations of snowmelt and snowfall weeks. Then we had to exclude such cases from the numerator and denominator where the grid box is always snow-covered or always snow-free during the period under examination. In this study, if it is always snow-covered [snow-free], an arbitrary large [small] number is assigned as flag values to preserve the order. Similarly, for `snowfall week', an arbitrary small [large] number is assigned in such cases. With this convention, such terminal cases are taken into account properly.

Amendment of data

After writing the previous report (Masuda et al., 1989), we made following additional revisions to the data.

The data tape from NESDIS contained a land-sea template file used by Dewey and Heim. Time-series plot of 0 / 1 values at each box revealed that the template used since 1981 must be a little different from it. In this study, only such grid boxes that are treated as land in both templates are taken into account.
The data of Week 17 of year 1987 were damaged, where 0 and 1 are partly (but not completely) reversed. We obtained a hard-copy chart of that week and digitized it.
In some weeks between 1983 and 1997, there are physically inplausible line-like patterns, such as a belt of 0's traversing Greenland. We corrected them subjectively. It is likely to be noise of scanning hardware, and there may be still other similar errors that cannot be subjectively corrected.

4. Preliminary Facts

Orography

As background information, the orographic height of each grid box is shown in _Fig. 1. It is based on the GLOBE (Global One-kilometer-Base Elevation) data set (Hastings and Dunbar, 1999). In our 1993 paper, we used 1^o x 1^o grid-box data set of Gates and Nelson (1975) with some smoothing. Names of major mountain ranges and other places discussed later are shown in_Fig. 2 (with the smoothed orography based on Gates and Nelson 1975).

Seasonal cycle of total snow cover area

The time series of weekly values of total snow cover area is shown in _Fig. 3. The annual cycle is one decimal order-of-magnitude larger than interannual variability.

_Fig. 4 shows the same data differently. For each week of year, there are 25 values corresponding to 25 years (1973 - 1997). Maximum (1st), upper quartile (7th), median (13th), lower quartile (19th) and minimum (25th) values from the largest are selected for each week and connected. The curves do not correspond to the seasonal march of any single year. The range between the uppermost and the lowermost curves shows the whole range of sample values. The half of the sample values are contained within the range covered by the second and the fourth curves (the quartile range).

In a year, snow cover area reaches maximum at Week 1 - 8 (January to February), and minimum at Week 30 - 36 (about August). The growth in autumn season is about 1.5 times faster than the decay in spring. This feature of the seasonal cycle is qualitatively the same as documented earlier by Dewey and Heim (1982).

Frequency of snow cover

The `frequency', or empirical probability, of snow cover is the ratio of occurrence of 1's to the total count of samples in each grid box. Maps of geographic distribution of snow-cover frequency are presented by Matson et al. (1986), using data for 1967 - 1981. Our results are not much different from theirs.

_Fig. 5 shows the frequency of the whole year. Cross-hatches denote the areas where there is always snow cover. Such areas are limited to the interior of Greenland. Values larger than 90 % are found in Greenland, Canadian Arctic Archipelago, and in limited parts of Himalayas and northern Rockies. The areas where no snow cover is observed at all are left white. Most of the area to the north of 30 ^oN experience snow cover.

_Fig. 6 shows the snow cover frequency of Weeks 1 - 8, that is the season where the area attains the maximum. Most of the area in the higher latitudes has frequency larger than 90 %. The boundary of this zone is approximately the 45^oN parallel, but it is shifted northward of this latitude in the eastern part of each continent: i.e. in western and central Europe and in the `Great Plains'. To the north of this boundary, it can be assumed that stable snow cover exists continuously in winter.

5. Geographical Distribution of `Snowmelt Week' and `Snowfall Week'

Hemispheric distributions

_Figs. 7 (a) and (b) are examples of the distribution of `snowmelt week' in individual years._Fig. 8 shows the median, and_Fig. 9 shows the quartile range._Figs. 10 (a) and (b) are examples of the distribution of `snowfall week' in individual years. _Fig. 11 shows the median, and _Fig. 12 shows the quartile range.

Characteristics of maps for individual years

Both snowmelt and snowfall in a week occur in irregular patches. The size of patches that correspond to a single week is much larger in snowfall than in snowmelt. The typical spatial scale of snowmelt patch in one week is about 1000 km, while that of snowfall patch is about 3000 km or more. We consider it rational because snowfall is often caused by extra-tropical cyclones that moves about 7000 km in a week, while snowmelt is caused essentially by local heat balance. However, it may be partly artifact of the data set. When snow falls it is inevitably cloudy, and it is difficult to detect the accurate timing of snow cover set-up by observation of visible radiation.

The patterns are considerably different from year to year. We comment on just a few examples. The advance of snow cover in Eurasia from the Arctic coast to 45^oN took only 4 weeks in 1976 (Fig. 10(a)) while it took 10 weeks in 1977 (Fig. 10(b)). Snow cover existed in a large area in the western Siberia and eastern Europe as late as Week 17 in 1987 (Fig. 7(b)) while it melted much earlier in 1975 (Fig. 7(a)) there.

Different nature of plain and mountainous areas

As a result of stacking 25 samples, the patterns of median snowmelt and snowfall weeks (Figs. 8 and 11) look much more zonal in areas with elevation generally below 500 m. We will call these areas "plain areas". The patterns are still irregular in mountainous areas (above 1000 m). The areas with elevation between 500 m and 1000 m can be considered as having transitional nature between plains and mountains.

In plain areas, the isopleths are parallel to latitudinal circles as the first approximation, though the east-west gradient is evident over Europe and over the Great Plains of North America. The quartile range (Figs. 9 and 12) is generally less than 5 weeks in plain areas.

In mountainous areas, large values of the quartile range are often found. Some mountain areas, however, do not show large quartile range but are rather characterized by late snowmelt and early snowfall (in median as shown in Figs. 8 and 11).

The characteristics in plain areas and mountainous areas will be discussed more in Sections 6 and 7, respectively.

6. Characteristics of Median `Snowmelt Week' and `Snowfall Week' in Plain Areas

Speeds of north-south propagation of snowmelt and snowfall

When we compare Figs. 8 and 11, we find some assymmetry between snowmelt and snowfall. As we have already seen with Fig. 4, snowfall proceeds more rapidly than snowmelt. We also see at Fig. 11 that in median large advance of snow cover occurs in a single week: Week 45 in North America and Weeks 40 and 44 in Eurasia.

In the following analysis, we take such grid boxes where both the median `snowmelt week' and the median `snowfall week' are defined (i.e. not the terminal flag values) and plot the week numbers against latitude. In_Fig. 13, we select the area of Eurasia, to the north of 40^oN, and the longitude being between 30^oE and 120^oE. In this area, the isopleths can be regarded roughly parallel to the latitudinal circles. We will first look at the grid boxes with elevation less than 500 m (+'s and o's in Fig. 13 together). `Snowmelt' proceeds from 45^oN to 70^oN in 15 weeks, thus the speed is about 1.7^o latitude / week. `Snowfall' proceeds oppositely in the same interval in 10 weeks, the speed being 2.5^o latitude / week. Accelerated `snowfall' around Week 44 is also evident.

Dependence on elevation

The samples are sorted by the elevation of land. Difference between the 250 m - 500 m group (o's in Fig. 13) and the 0 m - 250 m group (+'s) is larger in snowmelt (about 4 weeks) than in snowfall (about 2 weeks). The samples with elevation higher than 500 m are also shown as small dots in Fig. 13. The distribution is much more irregular than o's and +'s and it is likely to be affected by regional orographic effects.

General east-west gradient

It is known that in middle and high latitudes in winter, western side of a continent tends to be warmer than eastern side. An easy explanation of this general trend is as follows:

In winter, the interior of a continent is cooler than oceans;
Accordingly, the surface air pressure is higher;
By near-geostropic balance with pressure, the winds tend to be northerly in the eastern side and southerly in the western side.

Concerning snow cover here, too, the western side experiences earlier snowmelt and later snowfall than the eastern side in both Eurasia and North America. The slant of isopleths is evident in Figs. 8 and 11 in Europe and Western Siberia as well as in plain areas of North America. The phase propagation is very roughly 1 to 2 weeks / 15^o of longitude, though it is not constant as discussed below.

Regional east-west gradient in Eurasia

The constant east-west difference in Europe and Western Siberia breaks at the Ural mountain range at 60^oE. Comparing distributions of symbols in _Fig. 14, we find that the belt of 45 - 60^oE experience anomalously late snowmelt. The western side is a hilly region and the eastern side is plain area, but the difference of average elevation is less than 250 m. It is likely that the differnce of snow depth is the controling factor. The annual maximum snow depth is generally larger than 60 cm to the west of the Urals, with a maximum of 80 cm over the mountain range. It is smaller to the east, in particular, less than 40 cm to the south of 60^oN. (Anonymous, 1960; Kotlyakov, 1968 quoted by Lydolph, 1971; Igarashi, 1992). It is likely that prevailing westerly winds are forced to uplift at the mountain range and result in large precipitation in windward and become drier in leeward.

The slanted zonal pattern appears to stop at 90^oE. At 90^oE, snowmelt is later than other places with similar latitude and elevation. There is a maximum of snow depth here to the west (windward) of the Central Siberian Highlands. The snowmelt / snowfall pattern to the east of this longitude is complicated, presumably due to complex orography.

The east-west gradient is again evident near the Pacific coast as shown in_Fig. 15. Snowmelt is as much as 8 weeks later in the grid boxes of northern Japan and Sahalin (42 - 52^oN, 135 - 150^oE) and Kamchatka (53 - 60^oN, 150 - 165^oE) than the majority of the 30^oE - 120^oE zone. Grid boxes to the east of 120^oE and to the south of 60^oN also have later snowmelt though the difference is smaller. In these areas, which are near oceans, snow depth are larger than the continental interior of eastern Siberia. It is likely that sea ice in Ohotsk and Bering Seas has also some influence, but our knowledge is insufficient to discuss it here. We just mention the fact that the seasonal cycle of sea ice has a different phase from that of snow cover. The total sea ice area of the northern hemisphere reaches maximum in March and minimum in September in the northern hemisphere (Parkinson and Cavalieri, 1989).

Regional east-west gradient in North America

Snowmelt is rapid in the `Great Plains' region at the eastern slope of the Rocky Mountains, while it is much slower in the longitudes of the Great Lakes and the Hudson Bay._Fig. 16 shows the difference in snowmelt is as large as 4 weeks / 15^o longitude, while the difference in snowfall is not so large. In this case, too, difference of snow depth is a likely cause of part of the large difference. According to Canada Department of Transport (1967) quoted by Bryson and Hare (1974), annual sum of snowfall is 80 to 120 cm in the `Great Plains' region of Canada and 200 to 300 cm in the area between the Great Lakes and the Hudson Bay. Influence of sea ice and lake ice in the eastern region may also be suggested, though we have not examined it yet.

7. Characteristics in Mountainous Regions

An attempt of categorization

Comparing Figs. 8, 9, 11 and 12 with the orography (Fig. 1), mountainous regions are characterized by

late snowmelt and early snowfall, or
large variability of snowmelt and snowfall seasons.

By `large variability', we mean both large values of the quartile range and large spatial inhomogeneity in the median. We often find these two features together. We consider that the coincidence is probably real. But it is possible to be an `aliasing' effect of this data set (problem (3) mentioned in Section 2). In case when there are snow patches of the scale smaller than a grid box, variability in judgements of the analysts who produced the digital product can result in apparent temporal variability.

In this report (HTML version), we present a tentative categorization of all areas with snow cover (not only mountains) in _Fig. 17. This figure is made by the following process. Using Fig. 8, the grid boxes where median `snowmelt week' is Week 22 or later are marked as `late snowmelt'. Also, using Figs. 9 and 12, the grid boxes where the quartile range of either snowmelt or snowfall week is 5 weeks or larger are marked as `large variability'. Grid boxes are classified according to the two criteria as follows.

1: Early snowmelt, small variability
2: Early snowmelt, large variability
3: Late snowmelt, large variability
4: Late snowmelt, small variability

Thus, this figure is made by an objective process, though the values of thresholds are determined subjectively. The areas which is always snow-covered in median year, except permanent ice, are added to the category `4'.

Category `1' (painted orange in in Fig. 17) corresponds to `plain areas' except Arctic region.

Category `4' (painted light blue in in Fig. 17) includes most of the regions around the Arctic Sea irrespective of land elevation. This category also includes part of Alaska Range, Canadian and U.S. Rockies in North America, Pamir Highlands and part of Scandinavia, Caucasus (Kavkaz), Himalayas, Altay and Sayan Mountains and Stanovoi Highlands in Eurasia. Most of these are very high mountains or mountains in higher latitudes.

Category `3' (painted green in Fig. 17) includes Canadian Rockies and Sierra Nevada in North America, part of Himalayas, Tian Shan, Caucasus and Alps in Eurasia. It is certain that pixels of this category includes parts with higher elevation that should be included in Category `4' if the data have higher spatial resolution. It is possible that the remaining parts of the pixels have the same characteristics as Category `2' and therefore Category `3' may be just an illusion due to coarse spatial resolution. Whether this suggestion holds cannot be decided based on available data.

Category `2' (painted yellow in Fig. 17) includes most of U.S. Rockies in North America, and Tibetan Plateau, part of Mongolian Plateau, and Zagros Mountains (in Iran) in Eurasia. The common factors of these areas are mountains in relatively low latitudes and low supply of water vapor which causes precipitation. In eastern Europe, this category extends to plain areas near the Baltic Sea. Explanation of this feature is not available yet.

Remark about Stanovoi Highlands

In the Stanovoi Highlands (about 55^oN, 115^oE, to the east of Lake Baikal), snowmelt occurs as late as Week 23 to 25 (June) in median. The smoothed height does not reach 1000 m, though the highest peak is nearly 3000 m high. The late snowmelt is confirmed by ground-based studies: anonymous (1960) also shows that in this region `stable' snowcover exists until the third 10-day period of May, and that the total duration of snowcover is 240 - 280 days per year. Though we cannot discuss causal relationships, this area is also known as the southernmost part of the continous permafrost zone of the world (e.g. Pewe, 1983).

8. Concluding Remarks

Summary

In plain areas, average elevation being less than approximately 500 m, we find zonal pattern of the progress and retreat of seasonal snow cover by stacking 25 years' data. The phase lines are in general parallel to latitudinal circles, but there is east-west gradient both in Eurasia and North America, with considerable regional anomalies.

Mountainous areas, elevation generally being more than 1000 m, are characterized by late snowmelt, large variability or both. A tentative categorization is shown. It should be noted, however, that temporal variability may be aliased as spatial variability, or vice versa, because of the nature of the data set used.

Remaining problems

We got information of snow depth from existing literature, because the NOAA/NESDIS data do not tell it. We consider we should examine the distribution of snow mass more throughly by combining microwave and ground-based data. An attempt in this direction was made by our colleague, Igarashi (1992).

We should have discussed the cause of the patterns, but we are not able to do it. The results of this study suggest that following subjects should be more clarified.

Surface heat balance
Development of seasonal mean atmospheric pressure system
Development and movement of cyclones
Effects of mountain ranges on the distribution of snowfall amount
Behavior of sea ice and its interactions with snow cover
Behavior of permafrost and its interactions with snow cover.

Discussion of the signals of interannual variations is also an interesting matter that is left to future studies.

Acknowledgements

We are greateful to many persons who encouraged this work.

The original data which we used for this HTML version are from Data Support Section, National Center for Atmospheric Research, USA (for 1966 -- 1991) and from CPC (for 1988 -- 1998). Dr. Wataru Morishima (at Tokyo Institute of Technology) combined these two series and made them available to us.

The data used for the version published in 1993 were from Satellite Data Services Division (SDSD) of NOAA/NESDIS. We acquired the data for 1973 - 1987 from SDSD. Dr. Akio Kitoh (Meteorological Research Institute, Japan Meteorological Agency) provided the data for 1988 and 1989 which he obtained from SDSD.

References Cited

Anonymous (1960): Atlas Sel'skogo Hozyaistva SSSR (Atlas of Agriculture of the USSR), Glavnoe Upravlenie Geodezii i Kartografii, Moskva, 308 p. ***
Bryson, R. A. and Hare, F. K. (eds.) (1974): Climates of North America. World Survey of Climatology, 11, Elsevier, 420 p.
Canada Department of Transport, Meteorological Branch (1967): Climatic Charts. Climatological Division, Toronto. (cited by Bryson and Hare, 1974.)
Dewey, K.F. and Heim, R.H. Jr. (1982): A digital archive of northern hemisphere snow cover, November 1966 through December 1980. Bull. Amer. Meteorol. Soc., 63, 1132 - 1141.
Gates, W.L. and Nelson, A.B. (1975): A New (Revised) Tabulation of the Scripps Topography on a 1^o Global Grid. Part 1: Terrain Heights. Rand Corporation, R-1276-1-ARPA, 132 p.
Hahn, D.G. and Shukla, J. (1976): An apparent relationship between Eurasian snow cover and Indian monsoon rainfall. J. Atmos. Sci., 33, 2461 - 2462.
Hastings, D.A. and Dunbar, P.K. (1999): Global Land One-kilometer Base Elevation (GLOBE): (Digital Elevation Model Version 1.0; Documentation Version 1.0). NGDC Key to Geophysical Records Documentation No. 34, 138 p. National Geophysical Data Center, Boulder, Colorado, USA.
Igarashi, H. (1992): Yurasia Tairiku ni okeru Sekisetu no Eisei Kikogaku (Satellite Climatology of Snow Cover over the Eurasian Continent), Master's Thesis, Graduate Course of Geoscience, Univ. of Tsukuba, 76 p. **
Kotlyakov, V. M. (1968): Snezhnyuy Pokrov Zemli i Ledniki (Snow cover on the Earth and Glaciers). Gidrometeoizdat, Leningrad, 479 p. (cited by Lydolph, 1971). ***
Lydolph, P. E. (1977): Climates of the Soviet Union. World Survey of Climatology, 7, Elsevier, 443 p.
Masuda, K., Morinaga, Y., Numaguti, A. and Ouchi, A. (1989): Kita-hankyu sekisetu hihuku no kisetu henka (Seasonal variation of snow cover over the northern hemisphere). In Masuda, K. (ed.) Tairiku Kibo no Tihyomen Zyotai no Kisetu Henka no Kaiseki (Analysis of Seasonal Variation of Land Surface Conditions in Continental Scales), Report for Grant-in-aid for Scientific Research FY 1988 No. 62540301, 12 - 35. *
Masuda, K. and Morinaga, Y. (1990): Mesh data sets of snow cover of the northern hemisphere. J. Geography (Tokyo), 99, 695 - 703. **
Matson, M., Ropelewski, C.F. and Varnadore, M.S. (1986): An Atlas of Satellite-derived Northern Hemispheric Snow Cover Frequency. NOAA Atlas NOAA/NESDIS/NWS, 74 p.
Morinaga, Y. and Yasunari, T. (1993): Koiki sekisetu ni okeru taiki seppyo sogo sayo (Atmosphere-cryosphere interactions of large scale snow cover), Kisyo Kenkyu Noto (Meteorological Research Notes), No. 177, 41 - 129. *
Mosteller, F. and Tukey, J.W. (1977): Data Analysis and Regression. Addison-Wesley, 588 p.
Parkinson, C.L. and Cavalieri, D.J., 1989: Arctic sea ice, 1973 - 1987: Seasonal, regional and interannual variability. J. Geophys. Res., 84, 14499 - 14523.
Pewe, T.L. (1983): Alpine permafrost in the contiguous United States: A review. Arctic and Alpine Research, 15, 145 - 156.
Potter, J.G. (1965): Snow cover. Canada Department of Transport, Meteorological Branch, Toronto, Climatol. Stud., 3, 69 p. (cited by Bryson and Hare, 1974).
Wiesnet, D.R., Ropelewski, C.F., Kukla, G.J. and Robinson, D.A. (1987): A discussion of the accuracy of NOAA satellite-derived global snow cover measurements. In Goodison, B.E., Barry, R.G. and Dozier, J. (eds.) "Large Scale Effects of Seasonal Snow Cover", IAHS Publications, 166, 291 - 304.
Yasunari, T. (1991): The Monsoon year - A new concept of the climatic year in the tropics. Bull. Amer. Meteorol. Soc., 72, 1331 - 1338.

(*: in Japanese; **: in Japanese with English abstract; ***: in Russian)

[List of Figures]

HTML version updated 2001-02-09, 2003-06-25
Kooiti MASUDA
Frontier Research System for Global Change, Yokohama 236-0001, Japan

_[Top of this text] [Index page of Masuda]