Weather and Climate (1991) 11: 15-26 15 ANALYSIS OF CLIMATIC DATA FOR THE CHATEAU, MT RUAPHEHU (1930-1988), IN RELATION TO CLIMATIC CHANGE Frances West and Terry Healy Department of Earth Sciences, University of Waikato, Private Bag, Hamilton. ABSTRACT Greenhouse-effect-induced climate change in New Zealand will likely manifest itself by changes in general circulation intensity, which will result in changes in frequences of weather patterns, and thus ultimately in climatic parameters. In this study certain climatic parameters at the Chateau, on the northern footslopes of Mt Ruapehu, were analysed for changes during the period of record (1930-1988). Climatic change could be deduced, especially in temperature parameters. The monthly mean temperature increased 0.9 C during the period of record. Changes in summer and winter temperatures have been more consistent than those in autumn and spring. Monthly-precipitation totals appear not to have changed significantly over the period of record, but the overall seasonal-distribution of total precipitation indicates a decrease during summer and an increase in winter and spring. The monthly average number of days with snow has evidently decreased over the period of record (16.8 days). Directional frequencies of winds seem to be changing, with decreases from N, NW and W directions but increases from the SW. Principal-component analysis of twelve variables was undertaken and the major interpretations were that the number of snow days was inversely associated with low temperatures, as would be expected; and that high yearly-precipitation totals were associated with low temperature ranges. INTRODUCTION In the recent past considerable attention has been raised in scientific circles and in the mass media over the effects of possible global warming from an athropogenically induced 'Greenhouse Effect' (Bolin et al., 1986). At the Wellington conference convened by the Ministry for the Environment, potential impacts of the warming on the New Zealand snowlines were raised by Fitzharris (1988) and Healy (1988). In this paper we pursue this theme for Mt Ruapehu. In particular, we aim to investigate aspects of the nature and extent of detectable climatic change in the available records for Mt Ruapehu. CLIMATIC RECORD Monthly data for the Chateau have been published since 1929 (NZ Meteorological Service, 1930, 1933, 1934, 1936-1946, 1947-1986, 1987-1988). The Chateau site climatological enclosure is on the northern side of the mountain at an altitude of 1097m, 39' 12'S, 175' 30'E (NZ Meteorological Service, 1947). In discussing the climatological enclosure at the Chateau, Salinger (1981), repotted the observation records as being suitable for determining temperature trends as they are relatively free of many site inhomogeneities associated with urbanisation, relocation, or vegetation. The major disadvantage is a num-
16 her of missing records for some months in 1930, 1932, 1935, 1937, 1973, 1974, 1975. Salinger (1981), however, did not mention changes in observation procedures. In particular, the recording time has changed with changes in standard time. Variables affected by a change in the time of observation have been excluded from the analyses. The excluded variables are: dry and wet bulb temperatures, humidity, vapour pressure and amount o f cloud. The wind data was thought to have been affected to a lesser extent, and so was included, except for the percentage of calm conditions which appears to have altered with the observer. The variables analysed include: all temperature data except wet and dry bulb temperatures, rainfall, wind direction and the number of snow days. Variables used in analyses Variables are monthly averages o f daily readings unless otherwise stated. Monthly average temperature is found by averaging the monthly average maximum and minimum temperatures ( C). Monthly average o f the daily maximum temperature ( C). Monthly average o f the daily minimum temperature ( C). Month's highest maximum temperature ( C), is the highest temperature recorded for each month. Month's lowest minimum temperature ( C), is the lowest temperature recorded f o r each month. Monthly average of the daily temperature range ( C), is the temperature range between the monthly average maximum and minimum temperatures. Month's greatest daily temperature range ( C), is the temperature range between the month's m a x i m u m a n d m i n i m u m temperatures. Monthly average of the daily grass minimum ( C). Month's lowest grass minimum ( C), is the lowest of the grass minimum temperatures recorded in each month. Monthly total precipitation (mm), i s the total precipitation f r o m a l l types o f precipitation. Total number of days with precipitation in month (days), the number of days in which any type of precipitation fell at the Chateau recording station. Total number of days with snow in month (days), the number of days in which snow fell at the Chateau recording station. This ceased to be published in 1971. Percentage of winds from the: north, northeast, east, south-east, south, south-west, west and north-west. Published in a monthly form until 1968, and from then i n a yearly form. METHODS OF ANALYSIS Data analysis procedures to investigate possible time trends of the parameters defined above included: (i) standardising the complete data set t o metric units. (ii) Seasonal and yearly averages were calculated from the standardised data set for all variables listed. Standard seasons of: summer (December, January, February), autumn (March, April, May), winter (June, July, August) and spring (September, October, November) were used. (iii) Multiple-regression analysis of all variables with dummy variables for months was performed to define the seasonal cycle. Removal of the seasonal cycle gave seasonally adjusted data. There w e r e eleven dummy variables (feb, mar, apr, may, jun, jul, aug, sep, oct, nov, dec) which were assigned the value one f o r data from February, March, April, May, June, July, August, September, October, November, and December, respectively and zero otherwise. The regression results were used t o construct an equation t o remove the annual oscillation f o r each variable. (iv) Time plots and linear-regression analyses were used to provide descriptions of the temporal behaviour of each variable over the period o f record. Linear-regression analysis was performed on seasonallyadjusted monthly averages of daily readings, a g a i n s t t i m e. T h e seasonallyadjusted residuals were used so as not to include serial correlation. Monthly averages were used for all variables except wind for which only yearly averages were available f r o m 1968. Ti m e plots were drawn using yearly averages so as t o reduce the scatter.
(v) Linear regression for each month and each variable (excluding wind variables which were not available in a monthly from after 1968) was performed on the unadjusted monthly data set against time to test whether time trends were consistent throughout the year. (vi) Correlations were performed between all seasonally-adjusted climatic variables and time, to further ascertain the behaviour of variables during the period of record. (vii)principal-component analysis (on yearly averaged data) was used to identify possible variable interrelations. The data set excluded wind data. Varimax rotation was performed to facilitate interpretation as the coefficients are closer to 0 or +1 than unrotated values. Principal-component analysis assumes that the data series is stationary, that its variance also remains stationary, and that the variables are independently randomly distributed. Climate change by definition violates at least one of these assumptions. However, unless the non-stationarity is large, the analysis is believed not to be misleading (Pittock, 1975). monthly mean temperature has shown a significant rise, supporting the concept of warming. The differing rates of change between temperatures recorded in the Stevenson screen 1930 1940 1950 1960 1970 1980 17 1990 RESULTS TIME SERIES PLOTS AND REGRESSION ANALYSES Time trends are illustrated graphically in Figs. 1 a-u and tabulated in Table 1. Temperature Variables Changes in temperature variables over time are not consistent among variables. Monthly average maximum (Fig. la), monthly maximum (Fig. lb), monthly average minimum (Fig. lc) and monthly mean temperatures (Fig. 1d) all increased, whereas monthly minimum temperature (Fig. le) has remained static and both the monthly mean grass minimum and monthly lowest grass minimum temperatures (Figs. if and g) have decreased. With maximum temperatures increasing at a greater rate than minimum temperatures, the monthly mean daily range and monthly extreme daily range have increased (Figs. lh and i). These analyses indicate significant climate changes at the Chateau. Although uniform increases are not shown in all variables, the 1930 1940 1950 1960 1970 1980 1990 1930 1940 1950 1960 1970 1980' 1990 Fig 1. Time plots (a) of yearly average maximum temperature, (b) yearly average of the monthly maximum temperature, (c) yearly average minimum temperature for the Chateau meteorological recording station, Mt Ruapehu.
18 C h a t e a u Climatic Data 9 - -3 - Cl -4 0. R - 5-6 -7 Cl -8 7g. 9, 1930 1940 1950 1960 1970 1980 1990 1930 1940 1950 1960 1970 1980 1990 Cl 0 1940 1950 1960 1970 1980 1990 ot 22-5 2 1 5 20 Cl rz 19-1E 18 17- o - -2-, 1930 1940 1950 1960 1970 1980 1990 7.- 16-, 1930 1 r 1940 1950 1960 1970 ' I 1980 1990 Fig 1 (contd). Time plots of (d) yearly mean temperature, (e) yearly average of the monthly minimum temperature, (f) yearly mean grass minimum temperature for the Chateau meteorological recording station, Mt Ruapehu. Fig 1 (contd). Time plots of (g) yearly average of the monthly lowest grass minimum temperature, (h) yearly mean temperature range, (i) yearly average of the monthly extreme temperature range for the Chateau meteorological recording station, Mt Ruapehu.
19 310-1290- F. S 2 7 0 5: 250- g 230- '2.210 p 190-5 170-25, 150-, 1930 1940 1950 1960 1970 1980 1990 1 9 3 0 1940 1950 1960 1970 1980 1990 21 120 :ral9 f'18-17- 416-15 14 13: 12-11-, 1930 1940 1950 1960 1970 1980 1990 1930 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970.1980 1990 Fig 1 (contd). Time plots of (j) yearly average of the monthly total precipitation, (k) yearly average number of days with precipitation, (1) yearly average number of days with snow (some data missing) for the Chateau meteorological recording station, Mt Ruapehu. Fig 1 (contd). Time plots of (m) yearly average percentage of north winds, (n) yearly average percentage of north-west winds, (o) yearly average percentage of west winds for the Chateau meteorological recording station, Mt Ruapehu.
20 C h a t e a u Climatic Data 14-10 6 4 2 1930 1940 1950 1960 1970 1980 1990 1930 1940 1950 1960 T 1970 1980 1990 19- -g 15 "g 13- tz. 111- al 9-72 1930 1940 1950 1960 1970 1980 1990 32, 1930..., 1940 1950 1960 1970, 1980 1990 Figs. 1 (contd). Time plots of the (s) average percentage of south winds, (t) yearly average percentage of south-west winds for the Chateau meteorological recording station, Mt Ruapehu. and ground minima could be due to a number of factors including an increase in ground frosts, decrease in cloud cover, change in wind patterns and strengths, increase in calm conditions, change in ground vegetation, or cold air funnelling onto the grass minimum thermometer. Decreasing ground minima may have driven the change between maximum and minimum temperatures in the Stevenson screen. 1940 1950 1960 1970 Fig. 1 (contd). Time plots of (p) yearly average percentage of north-east winds, (q) yearly average percentage of east winds, (r) yearly average percentage of south-east winds for the Chateau meteorological recording station, Mt Ruapehu. Precipitation Variables Linear-regression analysis of precipitation variables indicates that no change has occurred to the total precipitation (Fig. 1j) or the number of days with precipitation (Fig. 1k) at the Chateau. The number of days with snow (Fig. 11), however, has shown a severe reduc-
tion over the period of record. Since total precipitation and number of days with precipitation have not changed, there is a possibility that the snow contribution to total precipitation has decreased. The reduction in number of days with snow may be a result of increased mean temperatures. Wind Variables Linear-regression analysis of wind data indicates a decrease in the percentage of northerly (Fig. 1m), north-westerly (Fig. in) and westerly winds (Fig. lo) and no change in north-easterly (Fig. 1p), easterly (Fig. lq), south-easterly (Fig. 1r), southerly (Fig. 1s), or south-westerly (Fig. it). REGRESSION OF MONTHLY AVERAGED DATA Separate linear regressions for each month for each temperature and precipitation variable were calculated to ascertain whether changes shown in Table 1 were consistent throughout the year, or whether certain months have experienced greater or lesser amounts of change. Many of the variables (see Table 2) show significant monthly changes. Temperature Variables Most temperature variables suggest discernible change. Significant changes (expressed as a rate of 'C per century) were as follows (see Table 2): (i) mean temperature increased at rates of 3.65 C in January, 2.00 C in June and 1.74 C in July; (ii) average maximum temperature increased at rates of 4.26 C in January, 3.61"C in February, 2.37 C in March, 3.27 C in June, 2.i2 C in July, 2.03 C in August and 2.37 C in November; (iii) average minimum temperature increased at rates of 2.64 C in January and 1.53 C in September; 21 TABLE 1: SUMMARY OF THE LINEAR REGRESSION ANALYSIS ON SEASONALLY ADJUSTED TEMPERATURE AND PRECIPITATION DATA AND YEARLY WIND DATA. Variables analysed Rate of change L e v e l of per century S i g n i f i c a n c e Monthly average of daily mean grass minimum temperature Month's lowest grass minimum Monthly total number of days with snow Yearly percentage of north-west winds Yearly percentage of west winds Yearly percentage of north winds Monthly average of daily maximum temperature Month's maximum temperature Monthly average of daily minimum temperature Monthly average of the mean daily range Month's extreme daily temperature range Monthly mean temperature Decrease 2.09 C 4.15 C 28 days 1.6% 1.9% 1.3% Increase 2.27 C 1.42'C 0.72 C 1.44 C 1.54 C 1.53 C 99 % 95% 95% 95% No Significant Change Month's minimum temperature Total monthly precipitation Number of days precipitation in month Yearly percentage of south-east winds Yearly percentage of south winds Yearly percentage of east winds Yearly percentage of south-west winds Yearly percentage of north-east winds
22 TABLE 2: REGRESSION ANALYSIS FOR EACH MONTH FOR EACH VARIABLE. THE ANALYSIS WAS AGAINST TIME TO EXAMINE WHETHER VARIABLES BEHAVED CONSISTENTLY DURING THE YEAR, OR WHETHER THE MONTHS ARE BEHAVING DIFFER- ENTLY. ONLY SIGNIFICANT RESULTS HAVE BEEN LISTED. Variable Rate of change Level of per century Significance Monthly Average of the Mean Temperature 'C Janmuary 3.65 99 % June 2.00 July 1.74 Monthly Average of the Daily Maximum Temperature 'C January 4.26 February 3.61 March 2.37 95% June 3.27 July 2.12 August 2.03 95% November 2.37 95% Monthly Average of the Daily Minimum Temperature 'C January 2. 6 4 9 5 % September 1. 5 3 9 5 % Month's Maximum Temperature 'C February 4. 6 9 June 4. 0 0 9 9 % November 4. 2 8 9 9 % Month's Minimum Temperature 'C June - 3. 0 6 9 9 % Monthly Average of the Daily Temperature Range 'C February 3.09 95% April 2.73 June 2.49 August 2.20 95% November 1.76 95% Month's Extreme Temperature Range 'C February 3. 9 1 95(70 June 6.91 November 5.28 Monthly Average of the Daily Grass Minimum Temperature 'C February - 5. 0 4 9 9 (70 April - 4. 3 0 9 9 % May - 3. 2 6 9 5 % June - 3. 1 8 9 5 ( 7 0 Month's Lowest Grass Minimum Temperature 'C February - 5. 2 6 9 5 % May - 6. 6 2 9 9 % June - 1 1. 4 7 9 9 % October - 4. 7 0 9 5 % November - 4. 6 7 9 5 % Monthly Average Total Precipitation (mm) February - 2 0 3. 3 9 5 % April - 1 4 7. 9 9 5 To September 1 7 9. 5 9 5 % Monthly Average of the Number of days with Precipitation (days) January - 7. 2 9 5 % September 1 1. 6 9 9 % Monthly Average of the Number of days with Snow (days) June - 5. 3 8 9 5 % July - 6. 7 5 9 0 % October - 4. 9 1 9 5 % (iv) monthly maximum temperature increased at rates of 4 69 C in February, 4.00 C in June and 4.28 C in November; (v) monthly minimum temperature decreased at a rate of -3.06 C in June; (vi) mean temperature range increased at rates of 3.09 C in February, 2.73 C in April, 2.49 C in June, 2.20 C in August, and 1.76 C in November; (vii) monthly extreme temperature range increased at rates of 3.91 C in February, 6.91 C in June and 5.28 C in November; (viii) mean grass minimum temperature decreased at rates of -5.04 C in February, -4.30 C in April, -3.26 C in May and -3.18 C in June; (ix) monthly lowest grass minimum temperature decreased at rates of -5.26 C in February, -6.62 C in May, -11.47 C in June, -4.70 C in October and -4.67 C in November; These results again show the differing trends between maximum and minimum temperatures and between screen temperatures and grass minima. However, also evident are differing rates of change depending on the month. All temperatures were significantly related to time in either January or February (or both) and in June. Maximum temperatures increased with time. Few screen minima changed significantly so that daily range and mean temperatures each tended to show increases over time. All statistically significant changes in grass minima indicated decreases over time. Precipitation Variables Precipitation variables suggest discernible change. Significant changes (rates per century, see Table 2) were as follows: (i) number of days with rain decreased in January (-7.2 days) and increased in September (+11.6 days); (ii) total precipitation decreased in February (-203 mm) and April (-148 mm) and increased in September (+180 mm); (iii) number of days with snow decreased in June (-5.4 days), July (-6.8 days) and October (-4.9 days). Although linear-regression analysis determined that precipitation has remained static in total, there was a seasonal shift in precipitation with a decrease in late summer and an increase in spring. Numbers of days with snow
have shown substantial decreases in winter and spring. CORRELATIONS OF SEASONALLY AVERAGED DATA Correlations were calculated between seasonally-averaged climatic variables, using standard seasons, and time. Correlations between Seasonal Data and Time For temperature variables (Table 3): (i) Average maximum temperature has increased for all four seasons, maximum temperature has increased in summer and winter. (ii) Average minimum temperature has increased in spring, minimum temperature has decreased in winter and spring. (iii) Mean temperature has increased in summer, autumn and winter. (iv) Mean grass minimum temperature has decreased in autumn and winter, lowest grass minimum temperature has decreased for all four seasons. (v) Mean daily range has increased in summer, autumn and winter, and extreme daily range has shown a significant increase in winter. For precipitation variables (Table 3): (i) Number of days with snow decreased in all seasons. (ii) Number of days with precipitation decreased in summer and increased in spring. (iii) Total precipitation decreased in summer and increased in winter and spring. 23 The above results are consistent with the time regression of monthly averaged data, but indicate significant seasonal influences in temperature and precipitation variables. Temperature variables show non-uniform seasonal change, whereas seasonal changes of precipitation variables indicate that summer is now drier, while winter and spring are wetter with little net annual change. Winters have been rainier, but with fewer snow falls. PRINCIPAL-COMPONENT ANALYSIS Principal-component analysis was undertaken to ascertain possible patterns of interrelationships amongst the annually-averaged data for the years 1930 to 1970, excluding wind data. Four principal components resulted (Table 4): Factor one (explaining 40.3% variance) is a pattern showing an inverse relationship between temperature variables and the number of days with snow. This grouping is expected, as higher temperatures would be expected to be negatively correlated with the number of days with snow. (ii) Factor two (22.2% variance) grouped the monthly mean of the daily temperature range with total precipitation and the number of days with precipitation in a negative relationship between the precipitation variables and the temperature range. This relationship suggests a pattern in the data of an increase in precipitation with a decrease in temperature range. TABLE 3: STATISTICALLY SIGNIFICANT (95% LEVEL) CORRELATION COEFFICIENTS OF SEASONAL AVERAGES VERSUS TIME. POSITIVE VALUES INDICATE THE VARIABLE INCREASES WITH TIME; NEGATIVE VALUES INDICATE A DECREASE WITH TIME. Variable Summer Autumn Winter Spring Mean temperature 0.15 0.23 0.38 Average maximum temperature 0.19 0.22 0.57 Average minimum temperature 0.16 Maximum temperature 0.28 0.31 Minimum temperature -0.14-0.16 Mean daily temperature range 0.20 0.24 0.29 Extreme temperature range 0.20 Mean grass minimum temperature -0.24-0.35 Lowest grass minimum temperature -0.17-0.23-0.44-0.36 Total precipitation -0.16 0.17 0.15 Number of days rain -0.17 0.30 Number of days snow -0.16-0.20-0.39-0.34
24 C h a t e a u Climatic Data TABLE 4: PRINCIPAL-COMPONENT ANALYSIS WITH VARIMAX ROTATION ON T H E D ATA SET EXCLUDING WIND BETWEEN 1930 A N D 1968. A L L LOADINGS WITH A N ABSOLUTE VA L U E GREATER THAN 0.4 W E R E INCLUDED. Factor 1-0.71 Percentage total variance 40.3 DISCUSSION BEHAVIOUR OF THE CLIMATIC PARAMETERS DURING THE PERIOD OF RECORD Temperature Variables The regression analysis results of meteorological variables from the Chateau recording station are similar t o published evidence o f general climatic warming elsewhere in New Zealand during the last 60 years (Salinger and Gunn, 1975; S a l i n g e r, 1 9 7 9, 1 9 8 0, 1 9 8 1, 1987a,b; Pittock and Salinger, 1982). The different rate of change observed for the monthly average of daily maximum and minimum temperatures (Table 1 ) w a s also noted b y Salinger (1976, 1979) i n annual data records for all of New Zealand. Salinger (1976) noted temperature increases of 1.0 C between 1935 and 1970 giving a rate of change during that period of approximately 2.9 C per century. He also found t h a t between 1945 and 1975 the maximum temperatures increased at a rate of Factor 3-0.69 0.45-0.63 0.58 0.97 0.96 0.93 0.86 0.78 0.71 Mean temperature Average maximum temperature Average minimum temperature Maximum temperature Minimum temperature Mean daily temperature range Extreme temperature range Mean grass minimum temperature Lowest grass minimum temperature Total precipitation Number of days rain Number of days snow (iii) Factor three (20.0% variance) is an obvious pattern indicating t h a t w i t h a decrease i n minimum temperature and an increase i n average maximum temperature there is an increase in the mean and extreme daily ranges. (iv) Factor four (17.4% variance) suggests a positive relationship between t h e mean and lowest grass minimum temperatures, indicating t h a t w i t h a lowering o f t h e lowest grass m i n i m u m there i s also a lowering of the mean grass minimum. Factor 2 0.93 0.92 0.86 0.87 22.2 Factor 4 20.0 17.4 2.3 C per century and minimum temperatures increased at a rate of 1.7 C per century. Most temperature variables at the Chateau illustrate increases from 1930 to 1988 suggesting t h a t a n o v e r a l l c l i m a t i c w a r m i n g has occurred. Temperature changes seen in the analyses are not uniform over all variables. Maximum temperatures have increased, and minimum temperatures have decreased or remained unchanged. The number of frosts may have increased causing grass m i n i m a t o decrease. These changes indicate a complex climatic change. Variables have shown non-uniform trends over the seasons with changes in some months greater than others. There has been a greater temperature increase in summer and winter months a n d increased temperature range also in summer and winter. Grass minimum temperatures have mostly decreased in winter. Regressions of monthly-averaged data and correlations between seasonal averages and time a l l indicate patterns of change i n temperature variables which vary throughout the year. Although temperature changes are evident throughout the year, statistically significant changes have occurred i n both w i n t e r and summer months. M a x i m u m and mean temperatures and temperature ranges have increased m o s t l y i n s u m m e r a n d w i n t e r, whereas monthly minimum temperature has decreased particularly in winter. Ground temperatures have decreased mostly i n autumn and winter.
25 The decrease in grass minimum temperatures as seen in the linear-regression analysis and time plot was consistent over the period of record. This trend, which contrasts with the trends seen in temperatures recorded in the Stevenson screen, perhaps indicate a change in temperature gradient between the ground and screen. A change in temperature gradient could be due to a number of factors including an increase in ground frosts, decrease in cloud cover, change in wind patterns and strengths, increase in calm conditions, change in ground vegetation, o r cold a i r funnelling onto the grass minimum thermometer. No simple hypothesis can explain the different behaviour of grass minima and air temperatures, however, the grass minimum temperature is of minor importance to this study as i t measures the microclimate around the thermometer, and does not reflect the more general situation as measured in the screen (Geiger, 1966). Precipitation Variables Total precipitation and number of days with precipitation have n o t detectably changed over the period o f record. This finding i s consistent with that of Pittock and Salinger (1982) who concluded, from comparing nine warm years with nine cool years o f New Zealand weather, that the precipitation in the Ruapehu region was not significantly different. In contrast, regression of monthly averaged data and seasonal correlations with time indicate that the seasonal distribution of precipitation has changed w i t h a decrease i n summer and an increase in winter and spring. Even though the total precipitation has remained unchanged between 1930 and 1988, the number of days with snow significantly decreased between 1930 and 1970. Wind Variables Wind directional frequency changed over time, with an increase in winds from the SW, and a reduction in winds from the N, W and NW. These results differ from the inference made by Salinger (1988) that in the Holocene climate warming airflow over New Zealand was weaker but with a greater proportion of winds from the north-west (Salinger, 1988). General Comment The reduction i n summer precipitation could be associated with increased summer temperatures if these are induced by dry and warm weather patterns. Changes in summer conditions together with warmer and wetter winters suggest an overall increase in intensity of seasonal variations in weather. IMPLICATIONS FOR THE SNOWLINE AT M T RUAPEHU Data analysis from the recording station at the Chateau cannot definitively be used for nferring changes higher u p the mountain. However, the analyses o f Chateau data do show warmer temperatures, a seasonal shift in precipitation with increases in winter and spring and decreases in summer, and a reduction in the number of days with snow. I f the warming t r e n d continues, w i t h increased spring precipitation and a reduction in snow days i t seems likely that the snowline w i l l continue to recede. Napper (1986) and Keys (1988) report that the glaciers at Mt Ruapehu have been receding during this century, presumably due to warmer winter temperatures and reduced snow falls. Napper (1986) also states that the freezing level has risen therefore reducing the total snow accumulation. The snow records used by Napper (1986) consisted of a classification of good, fair or poor snow years, although t h e r e a r e l i m i t a t i o n s i n t h e classification system. A good snow year in the 1930s meant skiing down t o national Park (altitude 800 m), while in the 1960s it meant skiing to the Chateau (altitude 1125 m), and in the 1980s i t meant skiing to the top of the Bruce (altitude 1650 m). So while the number of good snow years has not changed, the definition of a good snow year has. CONCLUSIONS Analysis of the climate o f the Chateau indicates that warming has occurred of similar magnitudes to that reported elsewhere for New Zealand, and is consistent with the concept of global warming. (ii) A trend of decreased mean grass minimum temperatures contrasts with other temperature parameters. The decrease has been greatest in winter. This paradoxical result may be related to thermometer site factors. (iii) Annual precipitation totals have remained static overall, however, the distribution has altered. The 1980s have more precipi-
26 tation in winter and spring and less in summer than the 1930s. (iv) The number of days with snow has decreased markedly over the period of record, and the implication of this, taken with the trend of temperature warming, suggests that snow conditions for skiing will likely decline with continued global warming. ACKNOWLEDGEMENTS The authors are grateful to Dr W. Bolstad (Department of Mathematics, University of Waikato, Hamilton) who assisted with the statistical analyses, Dr W. de Lange and Dr W. E. Bardsley (Department of Earth Sciences, University of Waikato, Hamilton) for reviewing the paper, and Dr H. A. I. Madgwick for his assistance and comments. Salinger, M. J., 1979: New Zealand Climate: The Tempeature Record, Historical Data and Some Agricultural Implications. Climatic Change, 2, 109-126. Salinger, M. J., 1980: New Zealand Climate: 1. Precipitation Patterns. Monthly Weather Review, 108, 1892-1904. Salinger, M. J., 1981: New Zealand Climate: The Instrumental Record. Unpublished PhD Thesis, Victoria University, Wellington, N.Z. Salinger, M. J., 1982: New Zealand Climate: Scenarios for a Warm High-0O2 World. Weather and Climate, 2, 9-15. Salinger, M. J., 1987a: Greenhouse Warming Preparing for Climate Change. Soil and Water, 24(1), 9-12. Salinger, M. J., 1987b: Impact of Climatic Warming on the New Zealand Growing Season. Journal of the Royal Society of New Zealand, 17(4), 363-371. Salinger, M. J., 1988: New Zealand Climate: Past and Present. In Climate Change: The New Zealand Response, N.Z. Ministry for the Environment, p. 17-24. Salinger, M. J., and Gunn, J. M., 1975: Recent Climatic Warming around New Zealand. Nature, 256, 396-398. REFERENCES Bolin, B., Jager, J., and Doos, B. R., (Eds.) 1986: The Greenhouse Effect Climate Change and Ecosystems. Wiley & Sons. 523pp. Fitzharris, B. B., 1988: Climatic Change and the Future of New Zealand's Snow Cover and Snowline. In Climate Change the New Zealand Response, N.Z. Ministry for the Environment, p. 253-254. Geiger, R., 1966: The Climate Near the Ground. Oxford University Press. 611pp. Healy, T. R., 1988: The Future of Mid-Latitude Snowlines in Relation to the Climatic Warming: Implications for Mt Ruapehu. In Climate Change the New Zealand Response, N.Z. Ministry for the Environment, p. 258-264. Keys, H., 1988: 1988 Survey of the Glaciers on Mt Ruapehu, Tongariro National Park A Baseline for Detecting Effects of Climate Change. Department of Conservation Report, No. 24. Napper, J., 1986: Tongariro National Park Snow Level Study. Report for Department of Conservation. 15p. N.Z. Meteorological Service, 1930, 1933, 1934. Supplement to the New Zealand Gazette. N.Z. Meteorological Service, 1936-1946: Meteorological Observations for 1936-1946. New Zealand Meteorological Service Publication. N.Z. Meteorological Service, 1947-1986: Meteorological Observations for 1947-1986. NZ Meteorological Service Miscellaneous Publication, 109. N.Z. Meteorological Service, 1987-1988: Monthly Climate Table. New Zealand Meteorological Service Publication. Pittock, A.B., 1975: Climate Change and the Patterns of Variation in Australian Rainfall. Search, 6(11-12), 498-504. Pittock, A. B., and Salinger, M. J., 1982: Towards Regional Scenarios for a CO2-Warmed Earth. Climatic Change, 4, 23-40. Salinger, M. J., 1976: New Zealand Temperatures Since 1300 AD. Nature, 260, 310-311.