Comparative analysis of climatic variability characteristics of the Svalbard archipelago and the

North European region based on meteorological stations network data

Daria Vasilyeva (St-Petersburg State University)

Project : “Meteo-glaciological monitoring of mass-heat exchange of glaciers”

Supervisors: Pavel Svyashchennikov (AARI), Jack Kohler (NPI)

THE GOAL:

Studying of the climate change in the Svalbard archipelago and the North European region

Focal points:

To reveal climate change tendencies in 1930 – 2003 and in 1993 – 2003 in different seasons

To analyze spatial climatic variability structure

To estimate long term oscillations contribution in climatic variability

To describe climate regimes in the Atlantic sector of the Arctic

Data

Location of meteorological stations in the study area: •, • - analyzable stations over the period 1930 – 2003

• - additional analyzable stations over the period 1993 – 2003

Methods

positive monthly average surface air temperature sums (PMASATS) negative monthly average surface air temperature sums (NMASATS)

1.

sum of positive monthly average surface air temperatures of one year is sum of all positive monthly average values of year:

(t>0)= ti(>0), where (t>0) – PMASATS,

ti(>0) – monthly average positive temperature

sum of negative monthly average surface air temperatures of one year is sum of all negative monthly average values, beginning from autumn of previous year, i. e. for example the sum of negative temperatures over 1931 is accumulated from negative values ofmonthly temperature from November till December 1930 and from January till May 1931:

(t≤0)= ti(≤0), where(t≤0) – NMASATS,

ti(≤0) – monthly average negative temperature

Methods

Reasons:

Positive air temperature sum can be considered as a value, proportional heat of ice and snow fusion.

Negative air temperature sum can be considered as a value, which determines cold content.

Model results had shown (Makshtas et al, 2003), that ice cover is extremely sensitive to positive temperature changing.

From the point of statistical analysis using such value as temperature sum allows to weaken weather, in this case noise, component (Alekseev, Svyaschennikov, 1991).

Usage of such characteristic is convenient also as variance of values sumequal variances sum of these values, thus if we use temperature sum, then we receive value, having larger variability in comparison with monthly temperature. It is convenient to use more variable characteristic to reveal climate changes.

Methods

2.

Method of cores (or delta-like functions) were used for calculation of probability density functions of PMASATS and NMASATS.

Reason:

This method allows to find trusty assessments of probability density in sufficiently short set of observations. Dispersion of assessment of probability density function is several times shorter than variance of assessment were found with more prevalent histograms method. The histograms method is not correct for short time series (Alekseev, Svyaschennikov, 1991).

Trends of PMASATS during the period 1930 – 2003 (black marked numbers are values (0C) of not statistically significant trends, red marked numbers are values (0C) of significant trends (significance level less 0.05): • - positive trends • - negative trends

• - no trends

Trends of NMASATS during the period 1930 – 2003 (black marked numbers are values (0C) of not statistically significant trends, red marked numbers are values (0C) of significant trends (significance level less 0.05): • - positive trends • - negative trends • - no trends

Trends of PMASATS during the period 1993 – 2003 (black marked numbers are values(0C) of not statistically significant trends, red marked numbers are values (0C) of significant trends (significance level less 0.05): • - positive trends, basic stations • - positive trends, additional stations • - negative trends, basic stations, • - negative trends, additional stations

Trends of NMASATS during the period 1993 – 2003 (black numbers are values(0C) of not statistically significant trends, red numbers are values (0C) of significant trends (significance level less 0.05): • - positive trends, basic stations • - positive trends, additional stations • - negative trends, basic stations • - negative trends, additional stations

Middle of warmest periods (maxima of PMASATS): • - 1935 – beginning of 1940s • - end of 1950s

• - end of 1980s – end1990s

Middle of the warmest periods (Maxima of NMASATS): • - beginning of 1940s • - 1949, 1955 • - 1990s

Contribution of long term (fraction unit) oscillations into variance of PMASATS (oscillation period is more than 12 years) during the

period 1930 – 2003

Contribution of long term (fraction unit) oscillations into variance of NMASATS (oscillation period is more than 12 years) during the period

1930 – 2003

- 1 5 - 1 0 - 5 0 5 1 0 1 5

0

0 . 0 2

0 . 0 4

0 . 0 6

0 . 0 8

0 . 1P (

'

a

- 3 0 - 2 0 - 1 0 0 1 0 2 0 3 0

0

0 . 0 1

0 . 0 2

0 . 0 3

0 . 0 4

0 . 0 5

P (

'

b

Probability density function of: a) PMASATS (Murmansk), b) NMASATS (Bjornoya island)

Spatial distribution of PMASATS probability density functions types: - single-modal distribution

- bimodal distribution

Spatial distribution of NMASATS probability density functions types: - single-modal distribution

- bimodal distribution

Pressure field of July – August in 1953 (warm year)

Pressure field of July – August in 1966 (cold year)

Pressure field of January –February in 1954 (warm year)

Pressure field of January –February in 1966 (cold year)

Conclusions:

As a whole our investigations had shown, that usage of PMASATS and NMASATS was convenient to determine the two seasons in the Arctic, justified and reasonable.

Our results as well as results of other researchers evidence the complex nature of climate change during measurements period in the Atlantic sector of the Arctic and it can not be brought to anthropogenic impact only.

Climatic variability study in measurements period from 1930 to 2003 had shown, that positive tendency of PMASATS predominates in the region in whole. Overall cooling is observed in cold season. However trends are statistically significant far from all. But question of unidirectional tendencies chance for the most station requires more investigation in detail.

Modern time (1993 – 2003) is characterized with warming in general.

Three warming periods over 1930 – 2003 were distinguished, especially in warm period. The means of sums temperatures maxima of the periods turned out very close. These periods are 1935 – beginning of 1940s, the end of 1950s and the end of 1980s – end of 1990s for PMASATS and the beginning of 1940s; 1949,1955; 1990s for NMASATS, that reveal warming beginning in warm season before cold season.

The contribution of long-term oscillations into dispersion of PMASATS decreases in general eastward in the study region. The contribution of long-term oscillations into the dispersion of NMASATS is characterized with values decreasing southward and eastward in the study region.

Nonuniqueness of climate regime of study area investigation had shown, that some stations had single-modal distribution, others had bimodal one. We can interpret such bimodal distribution as two climate regimes presence. Our results of bimodal distribution of probability density function of temperatures sums presence evidences that mean and variance are not sufficient to climate regime description of study area. Information of probability density function requires for the complete climate distribution.

As a whole the obtained results evidence that in spite of global warming Arctic regional climate changes are complex. There are short-term oscillations and internal dynamical factor can be cause of climate change without external factors.

Conclusions: