nõges&nõges 2013. weak trends in ice phenology

EUROPEAN LARGE LAKES III

Weak trends in ice phenology of Estonian large lakes despitesignificant warming trends

Peeter Noges • Tiina Noges

Received: 29 December 2012 / Accepted: 26 May 2013 / Published online: 7 June 2013

� Springer Science+Business Media Dordrecht 2013

Abstract We studied changes in air temperature

(AT) in Tartu, Estonia, since 1866; ice phenology in

two Estonian large lakes since the 1920s; and daily

surface water temperatures (SWT) in these lakes since

the 1940s. The Mann–Kendall test showed increasing

AT trends in all seasons with biggest changes in

spring. The strongest increase in SWT occurred in

April and August. The temperature increase has

accelerated since 1961. Despite significant trends in

the seasonal AT and SWT of Estonian large lakes,

trends in ice phenology were weak or absent, implying

that the processes governing ice phenology are more

complex than those governing lake SWT. Greater

snowfall was associated with later ice breakup, longer

duration of ice cover, and greater ice thickness, while

the relationship between winter rainfall and these ice

parameters was the opposite. In the deeper Lake

Peipsi, ice-on occurred later and ice-off earlier than in

the shallower Vortsjarv. The dates of both ice-on and

ice-off responded more sensitively to AT in the case of

Peipsi than in the case of Vortsjarv. An increase of the

average November–March AT by 2�C would presum-

ably halve the ice cover duration in Peipsi but shorten

it only by about 20% in Vortsjarv.

Keywords Air temperature � Surface water

temperature � Freezing date � Ice breakup date �Lake morphometry

Introduction

Water has a high heat absorption capacity and can

absorb or lose large amounts of energy without

undergoing large changes in temperature. Water

temperature tends thus to respond more to long-term

patterns rather than to short-term fluctuations in AT,

and is likely to be affected by climate warming

(Livingstone & Dokulil, 2001). As a result, changes in

lakes’ heat balance, temperature profiles and vertical

mixing can be expected and these, in turn, can affect

vertical fluxes of nutrients and dissolved oxygen and

hence the productivity and composition of lake

plankton (Jankowski et al., 2006). Thermal conditions

of lakes are of socio-economic relevance, for instance

for tourism, fisheries and water supply. Water tem-

perature is thus an important indicator for the heat

balance of lakes, which can affect the lake ecosystem

and its services. Although lakes and reservoirs make

up only a small percentage of the Earth’s surface, they

act as sentinels that reflect the influence of climate

change in their much broader catchments (Adrian

Guest editors: D. Straile, D. Gerdeaux, D. M. Livingstone,

P. Noges, F. Peeters & K.-O. Rothhaupt / European Large

Lakes III. Large lakes under changing environmental

conditions

P. Noges (&) � T. Noges

Centre for Limnology, Institute of Agricultural and

Environmental Sciences, Estonian University of Life

Sciences, 61117 Rannu, Tartu County, Estonia

e-mail: [email protected]

123

Hydrobiologia (2014) 731:5–18

DOI 10.1007/s10750-013-1572-z

et al., 2009; Williamson et al., 2009). Inland waters are

not just passively subjected to the impacts of global

climate change, but being the deposition sites of

terrestrially derived carbon and outgassing of green-

house gases, they become hotspots for carbon cycling

and regulating climate change (Cole et al., 2007). Thus

the understanding of the resistance, resilience, and

responses of lakes to environmental change is crucial

to their effective management (Williamson et al.,

2008, 2009).

Over the last 100 years (1906–2005), the global

mean surface AT has risen at a mean rate of

0.07 ± 0.02�C per decade when estimated by a linear

trend, and the rate of warming over the last 50 years is

almost double that (0.13 ± 0.03�C, IPCC, 2007). In

the Baltic Sea basin the annual mean near-surface AT

has increased at almost the same rate over the period

1871–2004—by 0.1�C per decade in the north ([60�N)

and by 0.07�C per decade in the south (\60�N,

including Estonia)—with the strongest warming

occurring in spring and winter (BACC, 2008). Changes

in the surface water temperature (SWT) of lakes

generally follow the changes in AT, while a strong

regional and supra-regional coherence is provided

through atmospheric teleconnections such as the North

Atlantic Oscillation (Livingstone et al., 2010b).

In areas experiencing winter ice cover on lakes, its

duration varies from some days in northern Spain,

northern Italy and Scotland to more than 200 days in

northern Europe and high alpine regions (Noges et al.,

2009). Ice-on and ice-off are complex phenomena

controlled by many interacting meteorological and

some non-meteorological forcing factors. Air temper-

ature is the key driver determining the timing of ice

breakup, with both long-term trends and short-term

variability in AT being reflected in the timing of ice

breakup (Palecki & Barry, 1986; Assel & Robertson,

1995; Weyhenmeyer et al., 2004). Among meteoro-

logical factors, the amount of precipitation (Vavrus

et al., 1996; Beltaos, 2004), wind speed, wind

direction, and cloud cover (Palecki & Barry, 1986)

also have certain effects. The amount of winter

precipitation can exert a positive feedback both on

the duration of ice cover and ice break-up date. If

warmer winters still have more precipitation in the

form of snow, the thicker snow cover could last longer

and avoid snow-melt and even the effects of an AT

increase due to high albedo. And also vice versa—on

the basis of observations made at the Tartu-Toravere

Actinometric Station (Tooming, 1996), surface albedo

between 1953 and 1994 decreased substantially,

especially in March (-21%) and April (-13%) due

to the reduced snow cover. This reduction coincided

with an increase in temperature over the same period,

mainly observed in winter (up to 4.0�C in March). As

temperature and albedo exert a positive feedback, the

seasonal decrease of albedo intensified the absorption

of radiation at the surface and thereby contributed to a

rise in AT.

Our previous studies covering the period

1961–2004 (Noges, 2009) showed the existence of

significant increasing trends in AT in Estonia (Janu-

ary–April, July–August) and in the water temperature

of Estonian large lakes (April, August). Parn (2006),

who analysed the timing of ice break-up on Peipsi and

the dates when water temperature passed through 5

and 10�C, found that over the period 1947–2000, ice

break-up shifted more than 1 week earlier (P = 0.02),

while there was no trend in the 5 and 10�C temperature

crossing dates.

Based on the most comprehensive available data

covering nearly 150 years of AT and precipitation

measurements, 90 years of ice phenology records and

65 years of SWT and ice thickness measurements, our

study aimed to reveal long-term and more recent

trends in these variables and to analyse the sensitivity

of the water temperature and the ice regime in two

large, shallow, Estonian lakes, Vortsjarv and Peipsi, to

climatic forcing. Vortsjarv (270 km2, mean depth

2.8 m) is the largest lake located entirely within the

borders of Estonia, whereas Peipsi, situated *65 km

from Vortsjarv, is a transboundary lake shared

between Estonia and Russia (Fig. 1). The data

analysed in this paper characterize only the largest

of the three parts of the lake, Peipsi sensu stricto

(2,611 km2, mean depth 8.3 m).

In view of the reported increasing trends in AT and

the shortening of the duration of ice cover in the

northern hemisphere during the last century (e.g.,

Magnuson et al., 2000; Weyhenmeyer et al., 2004,

2011; Dibike et al., 2011, 2012; Bernhardt et al.,

2012), we expected a similar phenomenon in lakes

Vortsjarv and Peipsi. These lakes are large and

shallow and therefore their water temperature should

respond highly sensitively to changes in AT. We

hypothesized that increasing snowfall could exert a

positive effect, and increasing winter rainfall a neg-

ative effect, on ice duration.

6 Hydrobiologia (2014) 731:5–18

123

Materials and methods

We studied long-term data on AT and precipitation in

Tartu, Estonia (monthly averages available since 1866,

daily since 1881); dates of ice cover formation (ice-on)

and ice melt (ice-off) on two Estonian large lakes,

Peipsi (Mustvee station) and Vortsjarv (Rannu Joesuu

station), available since the 1920s; and data on ice

thickness and daily water temperatures in these lakes,

available since the 1940s (Tables 1, 2). All these data

were provided by the Estonian Meteorological and

Hydrological Institute. The ice-on date was defined as

the starting date of a stable ice cover (no open water

visible from the observation station). To be considered

stable, the ice cover had to last for at least 20 days. The

start of the ice drift in spring was considered the end

date of the ice cover and registered as the ice-off date.

Ice thickness was measured at a drill hole at a distance

from the shore (1 km in Vortsjarv and 2 km in Peipsi)

every 10 days during stable ice cover. We split the

December–March precipitation into snowfall and

rainfall depending on the average daily AT (\0 or

[0�C). We used the Jones et al. (1997) NAO index for

winter (NAOWi, XII–III average) as a climate variation

proxy for the whole period. The time-series of NAOWi,

updated to the winter of 2011/2012, was downloaded

from http://www.cru.uea.ac.uk/*timo/datapages/naoi.

htm (last accessed on 20 December 2012).

We applied a non-parametric Mann–Kendall test to

detect gradual trends and a parametric cumulative

deviation test to detect step changes in mean val-

ues (eWater toolkit http://www.toolkit.net.au/Tools/

TREND, last accessed on 20 Dec 2012). Details of

these methods are described by Kundzewicz & Robson

(2004). Besides the full periods determined by data

availability, tests for trends and step changes were also

conducted for the period 1961–2011, which is likely to

have larger anthropogenic trends embedded in the

climate data (IPCC, 2007).

Results

Trends in air and water temperatures

The Mann–Kendall trend analysis revealed several

significant increasing trends in air and water temper-

ature in Estonia and in lakes Peipsi and Vortsjarv

(Table 1; Fig. 2), but no significant decreasing trend.

The annual mean AT in Tartu increased by 0.09�C per

decade since 1866 and by 0.44�C per decade since

Fig. 1 Location map of lakes Vortsjarv and Peipsi. Diamonds show the locations of measurement stations

Hydrobiologia (2014) 731:5–18 7

123

http://www.cru.uea.ac.uk/~timo/datapages/naoi.htm

http://www.cru.uea.ac.uk/~timo/datapages/naoi.htm

http://www.toolkit.net.au/Tools/TREND

http://www.toolkit.net.au/Tools/TREND

1961. Seasonally the biggest change in AT occurred in

spring (III–V, 0.14�C per decade) for the whole period

but shifted to winter (XII–II, 0.59�C per decade) for

the period beginning in 1961.

Trends in the SWT of the lakes during the ice-free

period were always smaller than the corresponding

trends in AT. Since the 1940s, the SWT of Lake Peipsi

increased by 0.27�C per decade in April and by 0.19�C

per decade in August. Over the last 50 years, the

increase has accelerated, reaching 0.48�C per decade

in April and 0.40�C per decade in August. Also in

Vortsjarv the SWT increase in August has accelerated

since 1961, whereas the increase for April (0.28�C per

decade) was similar for both periods analysed. Despite

the large difference in the size of the lakes, the average

SWT did not differ significantly (Table 3).

Table 1 Trends in AT in central Estonia (Tartu) and the SWT of the large lakes Peipsi (Pe) and Vortsjarv (Vo) during the whole

study period and since 1961

Series Period Mean ± SD (�C) Significance

of Mann–Kendall

trend

Linear change

per decade

± SE (�C)

Significance

of linear

regression

Year

of step

change

Significance

of step

change

ATYr 1866–2011 4.9 ± 1.1 P \ 0.01 0.09 ± 0.02 P \ 0.01 1987 P \ 0.01

1961–2011 5.3 ± 1.1 P \ 0.01 0.44 ± 0.09 P \ 0.01 1987 P \ 0.01

ATWi (XII–II) 1867–2011 -5.6 ± 2.7 P \ 0.05 0.12 ± 0.05 P \ 0.05 1987 P \ 0.1

1961–2011 -5.1 ± 3.1 P \ 0.1 0.59 ± 0.28 P \ 0.05 1987 P \ 0.01

ATSp (III–V) 1866–2011 4.0 ± 1.8 P \ 0.01 0.14 ± 0.03 P \ 0.01 1981 P \ 0.01

1961–2011 4.7 ± 1.5 P \ 0.01 0.52 ± 0.12 P \ 0.1 1981 P \ 0.01

ATSu (VI–VIII) 1866–2011 15.9 ± 1.1 n.s. 0.03 ± 0.02 P = 0.11 1987 P \ 0.1

1961–2011 16.0 ± 1.1 P \ 0.01 0.37 ± 0.09 P \ 0.01 1993 P \ 0.01

ATAu (IX–XI) 1866–2011 5.4 ± 1.3 P \ 0.01 0.07 ± 0.02 P \ 0.01 1922 P \ 0.01

1961–2011 5.7 ± 1.3 P \ 0.1 0.21 ± 0.12 P = 0.1 1998 P \ 0.05

SWTIV Pe 1945–2011 3.1 ± 1.8 P \ 0.05 0.27 ± 0.11 P \ 0.05 1988 P \ 0.01

1961–2011 3.3 ± 1.8 P \ 0.01 0.48 ± 0.16 P \ 0.01 1988 P \ 0.01

SWTVIII Pe 1945–2011 18.4 ± 1.5 P \ 0.05 0.19 ± 0.10 P \ 0.1 1993 P \ 0.05

1961–2011 18.4 ± 1.2 P \ 0.01 0.40 ± 0.15 P \ 0.01 1989 P \ 0.05

SWTSp Pe 1945–2011 6.2 ± 1.8 P \ 0.01 0.23 ± 0.11 P \ 0.05 1982 P \ 0.01

1961–2011 6.3 ± 1.6 P \ 0.01 0.42 ± 0.14 P \ 0.01 1982 P \ 0.01

SWTSu Pe 1945–2011 18.5 ± 1.2 P \ 0.05 0.19 ± 0.07 P \ 0.1 1987 P \ 0.1

1961–2011 18.6 ± 1.3 P \ 0.05 0.26 ± 0.11 P \ 0.05 1987 P \ 0.1

SWTAu Pe 1945–2011 6.9 ± 1.1 n.s. 0.05 ± 0.06 n.s. no

1961–2011 6.9 ± 1.0 n.s. 0.10 ± 0.09 n.s. no

SWTIV Vo 1947–2011 4.4 ± 1.3 P \ 0.01 0.28 ± 0.08 P \ 0.01 1988 P \ 0.01

1961–2011 4.5 ± 1.3 P \ 0.1 0.28 ± 0.12 P \ 0.05 1988 P \ 0.01

SWTVIII Vo 1947–2011 18.4 ± 1.5 P \ 0.01 0.25 ± 0.08 P \ 0.01 1989 P \ 0.01

1961–2011 18.4 ± 1.6 P \ 0.01 0.39 ± 0.10 P \ 0.01 1989 P \ 0.01

SWTSp Vo 1947–2011 6.1 ± 0.9 P \ 0.05 0.17 ± 0.06 P \ 0.01 1988 P \ 0.05

1961–2011 6.3 ± 0.8 P \ 0.05 0.15 ± 0.08 P \ 0.1 1988 P \ 0.05

SWTSu Vo 1947–2011 18.4 ± 1.0 P \ 0.01 0.21 ± 0.06 P \ 0.01 1987 P \ 0.01

1961–2011 18.5 ± 1.1 P \ 0.05 0.28 ± 0.09 P \ 0.01 1993 P \ 0.05

SWTAu Vo 1947–2011 7.2 ± 1.0 n.s. 0.09 ± 0.06 n.s. 1994 P \ 0.1

1961–2011 7.2 ± 1.0 P \ 0.1 0.16 ± 0.09 P \ 0.1 1994 P \ 0.05

Data for Peipsi are from Mustvee. The subscript index shows the month number (Roman numerals) or season (Sp spring, Su summer,

Au autumn, Wi winter, Yr whole year)

n.s. Not significant


123

Table 2 Trends in ice phenology on lakes Vortsjarv (Vo) and Peipsi (Pe) and in the critical AT influencing it

Series Period Mean ± SD Significance

of Mann–

Kendall trend

Significance

of linear

regression

Change

per decade

± SE

Year

of step

change

Significance

of step

change

Vo ice-on date 1924–2011 29.11 ± 16 days n.s. n.s. No

1961–2011 27.11 ± 14 days n.s. n.s. No

Vo ice-off date 1924–2011 09.04 ± 13 days n.s. n.s. No

1961–2011 08.04 ± 15 days n.s. n.s. No

Vo ice cover

duration (days)

1924–2011 131 ± 22 n.s. n.s. No

1961–2011 130 ± 21 n.s. n.s. No

Vo max. ice

thickness (mm)

1946–2011 53 ± 10 n.a. n.s. n.a.

1961–2011 52 ± 10 n.a. n.s. n.a.

Pe ice-on date 1922–2011 14.12 ± 22 days n.s. n.s. No

1961–2011 12.12 ± 19 days n.s. n.s. No

Pe ice-off date 1922–2011 04.04 ± 23 days n.s. P \ 0.05 -2.4 ± 0.9 days 1970 P \ 0.05

1961–2011 29.03 ± 29 days n.s. n.s. No

Pe ice cover

duration (days)

1922–2011 111 ± 35 n.s. n.s. No

1961–2011 107 ± 37 n.s. n.s. No

Pe max. ice

thickness (mm)

1948–2011 58 ± 17 n.a. n.s. n.a.

1961–2011 56 ± 16 n.a. n.s. n.a.

ATI (�C) 1922–2011 -6.1 ± 4.3 P \ 0.05 P \ 0.1 0.32 ± 0.17 1987 P \ 0.05

1961–2011 -5.7 ± 4.5 P \ 0.05 P \ 0.05 1.00 ± 0.41 1987 P \ 0.01

ATII (�C) 1922–2011 -6.4 ± 4.2 n.s. n.s. 1986 P \ 0.1

1961–2011 -6.0 ± 4.2 n.s. n.s. 1986 P \ 0.1

ATIII (�C) 1922–2011 -2.5 ± 3.2 P \ 0.05 P \ 0.05 0.30 ± 0.13 1987 P \ 0.05

1961–2011 -1.9 ± 3.2 P \ 0.1 P \ 0.05 0.69 ± 0.29 1987 P \ 0.05

ATIV (�C) 1922–2011 4.4 ± 2.0 P \ 0.01 P \ 0.01 0.31 ± 0.07 1982 P \ 0.01

1961–2011 4.9 ± 1.7 P \ 0.01 P \ 0.01 0.64 ± 0.14 1988 P \ 0.01

ATXI (�C) 1922–2011 0.5 ± 2.2 n.s. n.s. No

1961–2011 0.4 ± 2.4 n.s. n.s. No

ATXII (�C) 1922–2011 -3.6 ± 3.1 n.s. n.s. No

1961–2011 -3.7 ± 3.2 P \ 0.05 P \ 0.1 0.60 ± 0.30 No

ATII–III (�C) 1922–2011 -4.5 ± 3.1 P \ 0.1 P \ 0.05 0.26 ± 0.12 1987 P \ 0.05

1961–2011 -4.0 ± 3.1 P \ 0.1 P \ 0.1 0.54 ± 0.29 1987 P \ 0.05

ATXI–XII (�C) 1922–2011 -1.6 ± 2.0 n.s. n.s. No

1961–2011 -1.6 ± 2.0 P \ 0.05 P \ 0.05 0.41 ± 0.19 2002 P \ 0.1

ATI–IV (�C) 1922–2011 -2.7 ± 2.5 P \ 0.05 P \ 0.01 0.29 ± 0.10 1987 P \ 0.01

1961–2011 -2.2 ± 2.5 P \ 0.01 P \ 0.01 0.68 ± 0.12 1987 P \ 0.01

N of days with

AT \ 0�C

1922–2011 106 ± 21 P \ 0.01 P \ 0.01 -2.3 ± 0.8 1970 P \ 0.05

1961–2011 102 ± 20 P \ 0.05 P \ 0.05 -4.2 ± 1.9 1988 P \ 0.05

Winter snowfall (mm) 1922–2011 80 ± 32 n.s. n.s. No

1961–2011 79 ± 33 P \ 0.01 P \ 0.01 8.5 ± 1.9 1997 P \ 0.05

Winter rainfall (mm) 1922–2011 52 ± 34 P \ 0.01 P \ 0.01 5.6 ± 1.3 1982 P \ 0.01

1961–2011 61 ± 38 P \ 0.01 P \ 0.01 13.0 ± 3.2 1988 P \ 0.01

Total winter

precipitation (mm)

1922–2011 132 ± 39 P \ 0.01 P \ 0.01 6.2 ± 1.5 1987 P \ 0.01

1961–2011 140 ± 44 P \ 0.01 P \ 0.01 20.6 ± 3.1 1989 P \ 0.01

Peipsi data are from Mustvee. Winter is defined as December–March

n.s. Not significant, n.a. not available


123

Among the 30 temperature time-series analysed in

Table 1, 17 showed a significant step change in the

mean value during 1987–1989, five during 1993–1994

and four during 1981–1982. Conspicuously, abrupt

shifts in AT (in 1981, 1987 and 1993) either coincided

with abrupt shifts in water temperature or (mostly)

preceded those by 1 or 2 years.

In relation to ice phenology, AT trends for the cold

half year (XI–III) were studied in more detail

(Table 2). Since 1922, almost equal increasing trends

(0.30–0.32�C/decade) occurred in January, March and

April. In the second half of this period (1961–2011),

the slope of the trend almost tripled for January

(1.00�C/decade) but only doubled for March (0.69�C/

decade) and April (0.65�C/decade). In neither period

was the AT trend for February or November

significant, whereas for December the trend became

significant only in the later period starting in 1961.

The winter (December–March) NAO index (NAOWi)

had a marginally significant (P = 0.05) decreasing trend

since 1866 but no trend since 1961 (not shown).

Trends in ice parameters

There were clear differences in ice phenology between

the two large lakes (Table 3). On the larger Lake Peipsi,

ice cover formed on average 2 weeks later (P \ 0.001)

and disappeared 1 week earlier (P \ 0.05) than on

Vortsjarv. Despite a significantly (P \ 0.01) shorter ice

cover duration on Peipsi, the maximum ice thickness on

the two lakes was almost equal (56 ± 16 cm for Peipsi

and 52 ± 10 cm for Vortsjarv).

Fig. 2 Decadal changes in AT and in the SWT of lakes Peipsi

and Vortsjarv (numerical data presented in Table 1). The

subscripts denote the month number or season (Sp spring, Su

summer, Au autumn, Wi winter, Yr whole year) followed by the

lake name (Pe Peipsi, Vo Vortsjarv) and the beginning year of

the relevant period (all of which end in 2011). n.s. Mann–

Kendall trend non-significant. Seasonally different patterns are

used for better visualization

Table 3 Lake

morphometry and

differences in SWT and ice

parameters for the lakes

studied based on the period

1961–2011

n.s. Not significanta Only the large part of the

lake, the so-called Peipsi

sensu stricto

Variable Vortsjarv Peipsia P

Surface area (km2) 270 2,611

Mean depth (m) 2.8 8.3

SWTSp (�C) 6.3 ± 0.8 6.3 ± 1.6 n.s.

SWTSu (�C) 18.5 ± 1.1 18.6 ± 1.3 n.s.

SWTAu (�C) 7.2 ± 1.0 6.9 ± 1.0 n.s.

SWTIV (�C) 4.5 ± 1.3 3.3 ± 1.8 \0.001

SWTVIII (�C) 18.4 ± 1.6 18.4 ± 1.2 n.s.

Ice-on date 29 Nov. ± 14 days 12 Dec. ± 19 days \0.001

Ice-off date 08 Apr. ± 15 days 29 Mar. ± 28 days \0.05

Ice duration (days) 130 ± 21 107 ± 37 \0.001

Max. ice thickness (cm) 52 56 n.s.


123

Among ice-on and ice-off dates, the only significant

change was the shift of ice-off in Peipsi to an earlier

date since the 1920s (2.4 days per decade, Table 2).

The resulting shortening of ice cover duration was not

statistically significant.

The total amount of precipitation in winter, as well

as the amount of winter rainfall, increased in both of the

periods analysed, but showed 2–3 times steeper slopes

for the period beginning in 1961. The number of days

with negative AT during winter decreased significantly

since 1922, while the average daily amount of precip-

itation (= snowfall) during these days increased. The

total amount of snowfall had no trend during the period

1922–2011, but showed a strong increase during the

period 1961–2011 (Table 2). Curiously, the average

total amounts of winter snowfall for the two periods

were still almost equal.

Correlations between ice phenology, meteorology

and NAO

The ice-on dates in both lakes correlated significantly

with AT in November and December (Fig. 3). In

shallower Vortsjarv, which commonly freezes up at

the end of November, about 2 weeks earlier than

Peipsi (Table 2), the ice-on date correlated most

strongly with November AT. The AT in November

and December taken separately had a much weaker

impact on the freezing date of Peipsi than did the

November–December mean value. The winter mean

AT (XII–II) and NAOWi (XII–III) were positively

correlated with the ice-on date in Peipsi but not in

Vortsjarv.

The ice-off dates for both lakes showed almost

equally strong negative correlations with mean AT in

Fig. 3 Correlations of ice

phenology in lakes Peipsi

(Pe) and Vortsjarv (Vo) with

AT and the winter NAO

index. Months are indicated

using Roman numerals.

Only correlations that are

physically reasonable from a

cause and effect point of

view are shown


123

January–April, January–March and February–March.

Among single months, the strongest relationship with

AT occurred in February. Most of the correlations

between AT and ice-off dates were stronger for

smaller and shallower Vortsjarv than for Peipsi.

The character of relationships between AT and ice

parameters were clearly nonlinear and could be better

described by a second-order polynomial (Fig. 4). The

relationship shows that the sensitivity of the ice-on and

ice-off dates to AT in the corresponding key periods

(XI–XII, I–IV) increases with increasing mean AT

(Fig. 4a). In Peipsi both dates showed higher temper-

ature sensitivity than in Vortsjarv. The variability of

the ice-on dates was larger than that of the ice-off

dates. Fitting the arc cosine model of Weyhenmeyer

et al. (2004), which relates ice-off dates of Swedish

lakes with annual mean AT, to our data yielded

determination coefficients that were little more than

half those obtained from the polynomial model

(Fig. 5).

Both ice cover duration and maximum ice thickness

were most strongly correlated with the average AT

during the cold half year (November–March or

November–April, Fig. 3). For these ice parameters

most relationships with AT were stronger for the

larger Lake Peipsi than for Vortsjarv. Given the

nonlinear temperature dependence of the ice-on and

ice-off dates, the resulting changing rate in ice

duration was highly sensitive to increasing tempera-

tures and showed clear differences between the lakes

(Fig. 4b). According to the extended polynomial fits,

an increase of the average November–March AT from

-2�C (the average for 1961–2011) to 0�C will halve

the ice cover duration in Peipsi but shorten it only by

about 20% in Vortsjarv. An increase in AT by another

2�C would result in Peipsi being mostly ice-free while

Vortsjarv would remain ice-covered for more than

2 months on average.

The relationship between November–March AT

and maximum ice thickness in the two lakes (Fig. 4c)

showed a thicker ice cover in Peipsi during cold

winters and a converging character of the relationship

with increasing temperature.

The total amount of precipitation in winter (XII–II)

was negatively correlated with maximum ice thick-

ness and ice cover duration as well as with ice-off

dates in both lakes (Fig. 6). However, when the

amount of precipitation was divided into snowfall and

rainfall, the former showed positive and the latter

strong negative correlations with the ice parameters

studied.

In winters with a high NAO index, the duration of

ice cover was significantly shorter, mostly due to

earlier ice breakup, and the maximum ice thickness

remained smaller, than in years with a low NAO index

(Fig. 3).

Discussion

Similarly to our findings, time-series of lake temper-

atures reported in the literature reaching back to the

1900s (Korhonen, 2002), 1920s (Livingstone & Do-

kulil, 2001) or later years (Livingstone, 2003; Da-

browski et al., 2004; Pernaraviciute, 2004; Anneville

et al., 2005; Arvola et al., 2010; Schneider & Hook,

2010; Izmest’eva et al., 2011) show either positive

trends or no trends, while negative trends are practi-

cally lacking. The only significant negative trend has

been reported by Arvola et al. (2010) for the Galten

Basin of Lake Malaren for the period 1965–1995. The

reported positive trends for datasets of various lengths

are in the range of 0.05–1.0�C per decade.

Of the four major meteorological drivers of lake

surface temperature (AT, cloud cover, relative humid-

ity and wind speed), only AT is highly spatially

coherent (Livingstone et al., 2010b). Almost one-third

of the inter-annual variation in the northern hemisphere

AT can be associated with the NAO (Hurrell, 1996).

The influence of the NAO on AT is particularly strong

in winter, when most of the northern lakes are ice-

covered. Among Sweden’s largest lakes, the effect of

the winter NAO on SWT was observed only during a

short-time window in spring when the lakes were more

or less ice-free. After May, no significant relationship

between the NAO winter index and SWT could be

established (Weyhenmeyer, 2004). Arvola et al. (2010)

suggested that because the winter ice cover effectively

insulates lakes from climatic forcing, significant trends

are found in the SWT of very few lakes in northern

Europe compared to ice-free lakes situated in central

and western Europe, which are strongly influenced by

the movement of weather systems across the Atlantic,

and consequently show the strongest shifts in their

surface temperature in both summer and winter. A lack

of clear water temperature trends in northern lakes was

noted also by Magnuson et al. (2006), who analysed

SWT in Wisconsin lakes between 1981 and 2001. Still,


123

Fig. 4 Relationships

between AT and ice

parameters in lakes Peipsi

(1922–2011) and Vortsjarv

(1924–2011). Julian day of

ice cover formation and ice

break-up (a), ice cover

duration (b) and maximum

ice thickness (c) plotted

against mean AT in key

periods. Solid lines show the

polynomial fits to the data

and dashed lines show their

extrapolations towards

higher temperatures


123

similar trends in SWT to those found by us in the

Estonian lakes have also been observed in some other

winter ice-covered lakes within the region both to the

north and to the south of Estonia. According to

Korhonen (2002), the August water temperature of

Lake Saimaa, Finland, increased by more than 1�C

over the last century. In Lithuania, the most marked

mean SWT increase between the 1980s and 1990s

(0.8�C per decade) was recorded in April and August

(Pernaraviciute, 2004).

In line with our observations, an abrupt increase in

AT and SWT in the 1980s has been reported from

many areas in the northern hemisphere (Kilkus &

Valiuskevicius, 2001; Arvola et al., 2010; Schneider &

Hook, 2010). Arvola et al. (2010) described a coherent

upward jump in the winter water temperature in some

lakes in Finland, Sweden, the UK and Switzerland in

1987–1988 that occurred synchronously with changes

in winter AT. The authors found the coherence of the

change somewhat surprising given that the northern

lakes were ice-covered in winter whereas the western

lakes were not, and suggested that perhaps different

causal mechanisms were underlying the observed step

changes in winter SWT in these two regions. Kilkus &

Valiuskevicius (2001) suggested that the rise in SWT

in all Lithuanian lakes began approximately in

1981–1985. Investigation of the long-term SWT

dynamics of 7 Lithuanian lakes during the ice-free

period in 1981–2000 (Pernaraviciute, 2004) revealed

that in all of them, SWT started to increase rapidly in

1987–1988, coinciding with the most pronounced

change in our temperature data. A worldwide study of

the SWT of 167 large inland water bodies for the

periods July–September and January–March based on

night-time thermal infrared imagery showed rapid

surface warming since 1985 (Schneider & Hook,

2010). The data showed far greater warming in the mid

and high latitudes of the northern hemisphere than in

the lower latitudes of the northern hemisphere and in

the southern hemisphere. In some regions (e.g., around

the Great Lakes and in northern Europe) the water

bodies have warmed faster than regional AT. Austin &

Colman (2007) described this phenomenon in Lake

Fig. 5 Fit of the arc cosine model relating ice breakup dates in

Swedish lakes with lake-specific annual mean temperature, Tm

(Weyhenmeyer et al., 2004) to data from Estonian large lakes for

the period 1922–2011

Fig. 6 Correlations of ice

phenology in lakes Peipsi

(Pe) and Vortsjarv (Vo) with

the amount of precipitation

in winter. n.s. Non-

significant correlations


123

Superior based on in situ measurements and associated

it with the earlier start of positive thermal stratification

caused by earlier ice melt. The lack of trends in the

date of ice-off in Estonian lakes obviously explains

why trends in SWT did not exceed trends in AT in

Estonia. Intuitively, one might also relate the more

rapid increase in lake SWT, as compared to AT, to the

browning trend described for lakes in the northern

hemisphere (e.g. Roulet & Moore, 2006), as coloured

lakes absorb more light energy per unit volume in the

surface layers than clear lakes, which should cause

their greater heating during the day. However, as

shown by Houser (2006), the rate of heating during the

day did not compensate for the higher rate of heat loss

at night in the coloured lakes and as a consequence, the

epilimnion temperature was inversely correlated with

water colour.

As a result of changing winter climate, trends in ice

phenology have been reported from all regions of the

globe experiencing winter ice cover on lakes

(reviewed for example by Magnuson et al., 2000;

Livingstone et al., 2010a). The indicators include

freezing date, breakup date, duration of ice cover or

number of ice days, maximum ice thickness and the

spatial extent of ice cover in a region or on individual

water bodies.

Despite the occurrence of several significant trends

in seasonal AT and the water temperatures of the

Estonian large lakes, trends in ice phenology were

weak or absent, implying that the processes governing

ice phenology are more complex than those governing

lake surface temperature.

Air temperature, especially in the period

1–3 months before freeze-up and breakup, has been

considered the primary factor determining ice phenol-

ogy (e.g. Palecki & Barry, 1986; Robertson et al.,

1992; Livingstone, 1997, 2000; Duguay et al., 2006).

Various studies suggest a linear relationship between

AT and the freeze-up and breakup dates (e.g. Palecki

& Barry, 1986; Skinner, 1993; Assel & Robertson,

1995; Williams et al., 2004). A number of recent

studies (Weyhenmeyer et al., 2004, 2011; Livingstone

& Adrian, 2009; Livingstone et al., 2010a) have shown

that the relationship between AT and ice phenology is

inherently nonlinear. Based on comprehensive histor-

ical ice phenology data from Sweden, Weyhenmeyer

et al. (2004) showed that the relationship between the

timing of ice-off and AT can be well described by an

arc cosine function. Because AT varies sinusoidally

during the year, the duration of the period (D) with AT

below 0�C is an arc cosine function of the smoothed

AT. D responds to changes in annual mean temper-

ature approximately linearly within a range of

3–9 months, but the relationship deviates increasingly

from linearity as D approaches 0 or 12 months.

Our results confirmed the nonlinear character of the

relationship between AT and the key dates in ice

phenology. Although the duration of ice cover in the

Estonian large lakes still exceeds 3 months on aver-

age, it has been considerably shorter in several years,

especially in Peipsi where permanent ice cover has

repeatedly lasted less than 1 month. The Weyhen-

meyer et al. (2004) arc cosine model for predicting ice

breakup dates based on annual mean AT did not fit our

data well for several reasons. Firstly, it was based on

regional temperature patterns in Sweden (relationships

between means, amplitudes and phase shifts in

temperature cycles in air and lake surface water).

Secondly, the model used aggregated data over a

number of years or a number of lakes, whereas our

data represented single lake-year combinations that

increased the scatter. Thirdly, a systematic difference

between our data and the Swedish data could be

caused by different criteria used for defining the

calendar date of ice breakup, which was the end of ice

cover in our case but could be the end of all ice

phenomena in the case of the Swedish data. As the arc

cosine model used annual mean temperature and did

not account for lake-specific features of the relation-

ship, we preferred to use AT for the key periods which

had strongest correlations with ice phenology. We

found the quadratic fit sufficiently good to predict the

further divergence of the ice regime of the two large

lakes, as they captured the nonlinearity of the AT–ice

relationships and described 50% or more of the

variation in the long-term ice phenology.

As there were no significant trends in November

and November–December average AT, i.e. in the key

period for ice formation, the lack of trends in ice-on

dates is easily explainable. There is, however, no good

explanation for the lack of trend in the ice breakup

dates in Vortsjarv and in ice cover duration for both

large lakes. Vavrus et al. (1996) showed that increased

snowfall causes a monotonic delay in the breakup

date, whereas decreased snowfall nonlinearly hastens

ice decay. Although our data showed that there has

been a significant increase in winter snowfall, this

increase can hardly explain the lack of trends in the ice


123

breakup dates because the amount of winter rainfall

increased even more. Given the strong negative

correlation between winter rainfall and the ice param-

eters, this should rather have amplified the effect of

increasing AT in March and April.

The generally similar climatic forcing acting on the

two lakes, which are located in a flat landscape about

65 km from each other, and the similar structure of the

available data, presented a good opportunity to study

the effect of lake-specific differences on their temper-

ature and ice regimes. Differences in ice phenology

between Vortsjarv and Peipsi were in line with the

observations and model results of Bernhardt et al.

(2012), according to which deeper and clearer lakes

have later ice-on and earlier ice-off dates, resulting in

shorter periods of ice cover than in the case of shallow

and more turbid lakes. There seems to be a general

consensus among investigators regarding the later

freezing of deeper lakes compared to shallower ones.

Due to the high specific heat content of water, the

deeper the lake, the longer the periods with AT below

0�C that are needed to cool down the lake so that ice

can form on its surface (Stewart & Haugen, 1990;

Korhonen, 2006; Sporka et al., 2006; Benson et al.,

2012). The later freezing of deeper lakes shortens the

duration of their ice cover, and, as a result of the

nonlinear relationship between ice duration and AT

discussed earlier, increases the sensitivity of these

lakes to AT changes. Differences in the sensitivity of

the ice cover on Vortsjarv and Peipsi to AT were

clearly revealed in our data (Fig. 4). The largest lake in

Europe, Lake Ladoga in Russia (mean depth 51 m), is

likely to respond quite rapidly to future warming,

since this lake is, on average, completely covered with

ice only in February (Karetnikov & Naumenko, 2008).

The large lakes in Sweden are also becoming increas-

ingly ice-free (Weyhenmeyer et al., 2008). Upon

losing their ice cover, the large lakes in Europe may

shift from a dimictic to a monomictic type in the near

future, which would have important ramifications with

respect to their chemistry and phytoplankton.

As shown by several authors (Korhonen, 2006;

Sporka et al., 2006; Adrian et al., 2009; Benson et al.,

2012), individual physical lake properties influence

freezing processes much more strongly than thawing

processes, which are therefore much more clearly

regulated by external meteorological forcing. Spatial

analysis of ice phenology trends across the Laurentian

Great Lakes region during a recent warming period

(Jensen et al., 2007) showed stronger trends toward

later freeze date in larger lakes. Significant differences

also occurred in the ice-off dates of Vortsjarv and

Peipsi, with the larger lake opening earlier and being

more sensitive to temperature forcing. We hypothesize

that this difference might result from the longer wind

fetch on Peipsi, which often causes strong ice drift and

piling up of the ice on the shore (Jaani, 2001).

The effect of lake surface area on ice phenology is

more debatable, and despite rather strong correlations

found in some datasets between surface area and mean

ice breakup date (Wynne et al., 1996), Stewart &

Haugen (1990) concluded that lakes with large surface

areas do not necessarily freeze later than lakes with

small surface areas.

Conclusions

Air temperature data from Tartu since 1866 showed

increasing trends in all seasons, with the biggest

changes occurring in spring. In the larger Lake Peipsi,

with a shorter ice cover, the biggest changes in water

temperature since the 1940s also occurred in spring,

whereas in the smaller Vortsjarv they occurred in

summer.

Increases in air and water temperatures have

accelerated during the last 50 years, with step changes

occurring mostly in the 1980s.

Despite several significant trends in seasonal AT and

in the SWT of Estonian large lakes, trends in ice

phenology were weak or absent, implying that the

processes governing ice phenology are more complex

than those governing lake surface temperature. Greater

snowfall was associated with later ice breakup, longer

duration and greater thickness of ice, while the

relationship between winter rainfall and these ice

parameters was the opposite.

Our data confirmed the nonlinearity of the rela-

tionship between AT and the freeze-up and breakup

dates and showed that this relationship can be

described well by a quadratic function. However,

there are also notable differences between the two

large lakes that are attributable to differences in

morphometry. The deeper Lake Peipsi had later ice-on

and earlier ice-off, whereas both dates showed higher

temperature sensitivity than in Vortsjarv. As a conse-

quence, an increase of the average November–March

AT by 2�C would presumably halve the ice cover


123

duration in Peipsi, but would shorten it only by about

20% in Vortsjarv.

Acknowledgments The study was supported by the Estonian

target funding project SF 0170011508, by grants 8729 and 9102

from the Estonian Science Foundation, by 7th EU Framework

Programme, Theme 6 (Environment including Climate Change)

project REFRESH (Adaptive strategies to Mitigate the Impacts

of Climate Change on European Freshwater Ecosystems,

Contract No. 244121) and by EU Regional Development

Foundation, Environmental Conservation and Environmental

Technology R&D Programme project VeeOBS (3.2.0802.11-

0043). We wish to thank Dr. David Livingstone for advice and

for checking the English language of our manuscript.

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