Risks of Climate Change in the ArcticHelsinki, Finland, 10-13 February 2015 Peter Koltermann Faculty...

Meeting the Challenges and Risks of Climate Change in the

ArcticПроблемы и риски, связанные с изменением климата в Арктике

Klaus Peter Koltermann

The 1st Pan-Eurasian Experiment (PEEX) Science Conference

Helsinki, Finland, 10-13 February 2015

Peter Koltermann Faculty of Geography, MSU, Moscow

Температура 2080-2099 относительно 1980-1999

Изменения зимней температуры в Арктике по данным наблюдений и модельный прогноз на 21 век для 4 сценариев выбросов. Доверительные интервалы показывают разброс результатов по 42 численным моделям.


Лето / Summer Зима/Winter

(Оценочный Доклад, 2014)


Площадь морского льда (млн. кв.км)

Arctic sea ice cover


Russia has the longest coast-line with the Arctic Ocean

• The Arctic Ocean determines the climate of Northern Russia

• The North Atlantic contributes heat to the western Russian Arctic (Murmansk ice-free port,..)

• The climate of Northern Russia is in a subtle balance between Arctic and Atlantic influences :

– We have to understand the small changes which have large implications



Relevant climate drivers:- Temperature- Wind- Sea ice- Snow cover- Floods- Vegetation

Relevant change processes:o local:-Radiation balance, albedo, snow cover, vegetation cover-Water cycle, snow type and cover, precipitation-Permafrosto regional:-Atmospheric and oceanic advection-River and groundwater flow

Population density of the Arctic regions of Russia


Industry of the Arctic regions of Russia


Transport of the Arctic regions of Russia


The socio-economic features of the deltas in Russia. Problems of water use and environmental protection


Ice and temperature regime of the Arctic region

rivers


Global Annual Surface Air Temperature Anomalies, °C

Rates of increase of

annual temperature

for the “globe” (60S

to 90N) and

Northern Eurasia are

0.91 C/130 yr and

1.5C/130yr

respectively (Lugina

et al. 2007, updated).

Global

temperature

anomalies

2010˚C 1998


Wind Climatology over Russia, 1977-2011, Bulygina et al. 2013

(a) Mean annual wind speed, in m (sec)-1

(b) Annual number of days with Wind > 15 m (sec)-1Peter Koltermann Faculty of Geography,

MSU, Moscow

Begin of the no-frost season in Siberia

Dates when daily minimum temperature sustainably crosses 0°C in spring and remains above it Groisman, 2009

1936-2010; dD/dt = -6 days/100yr; R² = 0.14

1966-2010; dD/dt = -17 days/100yr; R² = 0.34

140

145

150

155

160

165

170

1930 1940 1950 1960 1970 1980 1990 2000 2010

Julian days


Annual and winter number of days with thaw over European Russia south of 60°N

dD/dt = 6.5 days/50yrs; R² = 0.18

dD/dt = 11 days/50yrs; R² = 0.35

0

10

20

30

40

50

60

1949 1959 1969 1979 1989 1999 2009

Groisman, 2009


Snow cover extent anomalies over EurasiaA

no

mal

ies

in 1

06

km2

Years

May 1967-2013

Top. Mean values for the 1966–2009 period along the snow surveys in the forested (left) and open (“field,” right) areas.

Mean maximum snow water equivalent, mm

Bottom. Changes in snow water equivalent over Northern Siberia along the “field” snow survey routes (approximately 55–65°N lat. belt).

Increases within this belt are also observed eastward from Moscow (not shown) .Bulygina et al. 2011 Peter Koltermann Faculty of Geography,

MSU, Moscow

Global hydrological cycle: small is not insignificant for extremes

MPI Hamburg


Atlantic Multidecadal Variability impact on Volga river discharge

www.NRAL.orgNatural NRAL Risk Assessment Laboratory

NRAL : Gulev, Semenov, Zolina

Changes of Volga river discharge explain

about 80% of the Caspian Sea level

variability.

An effect of the AMV on the hydrological

cycle over Russia is studied in the

simulations with a climate model forced by

periodically varying heat flux anomalies

corresponding to the AMV.Anomalous annual “AMV”

Q-flux pattern, W/m2

Annual precipitation regression, 0.1 mm/day / 0.1PW

Simulated and observed Volga river discharge, km3/yr

Multidecadal AMV variations have a strong impact on

hydrological cycle in Volga watershed with expected

decrease of the runoff in the first half of the 21st century

and probable Caspian Sea level decline.Peter Koltermann Faculty of Geography,

MSU, Moscow

The arctic deltas of Russia

The largest deltas on the Russian Arctic coast are located in the mouths of

the Severnaya Dvina, Pechora, Ob, Pur, Taz,

Yenisey, Olenek, Lena, Yana, Indigirka and Kolyma rivers

Google earthPeter Koltermann Faculty of Geography, MSU, Moscow

The annual water runoff River

Water runoff, km3/year

LHS UBD MBD

Sev. Dvina 105 107.6 108

Pechora 110 130.8 132

Ob 398 407.0 408

Pur 28.4 32.7 32.9

Taz 33.5 45.6 45.8

Yenisey 587 631.4 633

Olenek 37.2 40.6 40.7

Lena 533 538.0 543

Yana 34.4 35.0 35.9

Indigirka 50.5 53.5 54.1

Kolyma 104 123.6 124

LHS - lowest hydrometrical stationUBD - upper boundaries of the deltaMBD - marine boundary of the delta


Long-term changes of the annual

water runoff

River –

hydrometric stationΔWQ/Δy*

The linear trend coefficient,

km3/1year

1936-2006 1975-2006

Sev.Dvina - Ust-Pinega +5.4%/+15mm +0.119 +0.104

Pechora - Ust-Tsilma +4.2%/+18mm +0.205 +0.347

Ob - Salekhard -0.2%/~0mm +0.180 +0.467

Pur - Samburg +1%/+3mm - -

Yenisey - Igarka +5.3%/+12mm +0.794 +1.984

Olenek - Sukhana +14%/+23mm +0.088 +0.276

Lena - Kyusyur +5.3%/+11mm +0.876 +1.244

Yana – Jiangky/Yubileynaya +8.8%/+12mm +0.070 +0.194

Kolyma - Srednekolymsk -0.2%/~0mm -0.045 +0.094

*change of annual water runoff in 1976-2006 in comparison with the value of

annual water runoff in 1936-1975


The longitudinal changes in average

turbidity and suspended sediments of the

regulated rivers before and after the

creation of the large reservoirs

Yenisey

wikipedia.ru

Kolyma

Yenisey

Ob

Kolyma

wikipedia.ru

Ob

wikipedia.ru


LHS - lowest hydrometrical station, UBD - upper boundary of the delta

1before the period of regulated regime, 2during the period of regulated regime

River

Suspended sediment

runoff, million t/year

Bottom sediment


LHS UBD UBD

Sev. Dvina 3.27 3.33 0.65*

Pechora 5.59 6.43 2.28*

Ob 15.9 16.0 2.89*

Pur 0.707 0.77 0.41*

Taz (0.524) 0.73 0.49*

Yenisey 12.01–4.1212.41–4.52 2.77*

River

Suspended sediment


Bottom sediment


LHS UBD UBD

Olenek 1.16 1.31 1.12*

Lena 21.2 21.4 5.40*

Yana 4.48 4.49 1.46*

Indigirka 11.7 11.8 3.40*

Kolyma 9.94 11.7 4.20*

Sediment runoff of the Arctic rivers and

turbidity of river waters

*according to Alekseevskiy N.I.Peter Koltermann Faculty of Geography,

MSU, Moscow

Time series of average water and suspended sediment

runoff of the large Arctic rivers


Long-term changes of

thermal regime on the lower

reaches of large Arctic rivers

The longitudinal changes in average water

temperature of July before and after the

creation of the Krasnoyarsk reservoir


Long-term dynamics of ice phenomena

-5

0

5

10

1976-2012 2002-2012

freezing

break-up

Sev.Dvina Rv.

Δfr

om

1936-7

5 y

rs (

days)

-5

0

5

10

1976-2012 2002-2012

freezing

break-upLena Rv.

Δfr

om

1936-7

5 y

rs (

days)


Сhanges

of average

sea level

I. Intra-annual changes of average sea level

SeaAmplitude, m* Months with*

mean min/max max. level min. level

Barents 0.35–0.40 0.13/0.62 X–XII IV–V

White 0.15–0.34 – X II

Kara 0.32–0.50 0.14/1.18 VI–VII, X–XII IV–V

Laptev 0.29–0.50 0.17/0.97VI–VII**/

VI–XII***III–V

East Siberian 0.41–0.50 0.16/1.15 VI**/VI–X*** III–V

Chukchi 0.36–0.52 0.19/0.83 X III–V

*according to (Vorobiev et al., 2000; Hydrometeorology and hydrochemistry of the

seas, 1991)

** in mouth nearshore zone and near river mouths

***away from the mouth of large rivers

II. Long-term relative rise of average sea level

SeaVorobiev et al., 2000

Bol’shiyanov et al.,

2013

1950–1995 1950–2010

Barents +0.2 mm/yr +1.2 mm/yr

White – –

Kara +1.5 mm/yr +2.2 mm/yr

Laptev +2.1 mm/yr +2.4 mm/yr

East Siberian +1.5 mm/yr +1.7 mm/yr

Chukchi +2.3 mm/yr +2.2 mm/yrPeter Koltermann Faculty of Geography, MSU, Moscow

Climatic factors

The average air temperature:

The air temperature for months VI-IX:

ΔT=T1976-2008 – T1936-1975

ΔT=+0.60oC

ΔT=+0.55oC

ΔT=+0.63oC

ΔT=+0.49oC

ΔT=+0.04oC

ΔT=-0.41oC

ΔT=+0.31oC

ΔT=+0.28oC


Опасные ледовые явленияFluctuations of freeze-up (А) and break-up (B) dates

A) B) from 1960-1991 to

1893-1960

A) B)

from 1997-2006 to

1961-1990

A) B)

Probable change of freeze-up (A) and break-up (B)dates (days) at air temperature increase on 2°С

Increase in average of air temperature in

April, °C

Change of an averaged month discharge in the spring, %

–50 – 25 0 25 50

1,0 5 3 1 –1 –3

2,0 4 2 0 –2 –4

3,0 (2046–2065 гг.)

(Kislov, 2008)3 0,5 –1,5 –3,5 –5,5

5,5 (2081–2100 гг.)

(Kislov, 2008)0 –2 –4 –6 –8

Possible anomalies (days) of break-

up dates depending on change of air

temperature and a river runoff for

North Dvina

>+10

+6 +10

+1 +5

0 -4

-10 -15

-6 -10

-1 -5

0 +5

+8 +13

+3 +7

0 +2

<-10

-6 -10

-1 -5

0 +2

Mitigation of ice regime has

strongly increased in last

decades


Frolova, 2015

Опасные ледовые явленияFluctuations of freeze-up dates anomalies (∆D)

in lower reaches of the Arctic rivers

-30

-20

-10

0

10

20

1880 1930 1980

Northern Dvina∆D, days

-30

-20

-10

0

10

20

1880 1930 1980

Pechora∆D, days

-30

-20

-10

0

10

20

1880 1930 1980

Ob'∆D, days

-30

-20

-10

0

10

20

1855 1905 1955 2005

Yenisei∆D, days

-30

-20

-10

0

10

20

1880 1930 1980

Lena∆D, days

-30

-20

-10

0

10

20

1880 1930 1980

Indigirka∆D, days

Peter Koltermann Faculty of Geography, MSU, Moscow Frolova, 2015

Опасные ледовые явленияFluctuations of break-up dates anomalies (∆D)

in lower reaches of the Arctic rivers since 1880

-20

-15

-10

-5

0

5

10

15

20

1880 1930 1980

Pechora∆D, days

-20

-15

-10

-5

0

5

10

15

20

1880 1930 1980

Nortern Dvina∆D, days

-20

-15

-10

-5

0

5

10

15

20

1880 1930 1980

∆D, days Ob'

-20

-15

-10

-5

0

5

10

15

20

1880 1930 1980

Yenisei∆D, days

-20

-15

-10

-5

0

5

10

15

20

1880 1930 1980

∆D, days Lena

-20

-15

-10

-5

0

5

10

15

20

1880 1930 1980

Indigirka∆D, days


Heat energy input into the Arctic seas from river runoff

Сток теплоты с территории России в СЛО

78,5∙1016 Кдж/год

11,2•1016 Кдж/год

местный сток67,3•1016 Кдж/год –

сток средних и больших рекPeter Koltermann Faculty of Geography, MSU, Moscow Magritsky, 2014

Time series of

freeze up (a) and

break up dates (b)

from selected

Russian Siberian

rivers

a b


Expected changes of dates of ice formation (а) and ice break up (b) some rivers of the Arctic part of

Russia to 2050 г. (% accordingly base period) in case of 20C air temperature rise

Устье

реки

Аномалии появления льда Аномалии вскрытия рек

2020 г. 2050 г. 2090 г. 2020 г. 2050 г. 2090 г.

∆D,

сут.∆D/σ

∆D,

сут.∆D/σ

∆D,

сут.∆D/σ

∆D,

сут,∆D/σ

∆D,

сут.∆D/σ

∆D,

сут.∆D/σ

Печора,

Северная

Двина

4 0,4 7 0,7 11 1,0 -4 -0,5 -7 -0,9 -13 -1,6

Обь 3 0,4 5 0,7 9 1,3 -3 -0,4 -7 -1,0 -12 -1,8

Енисей 2 0,3 5 0,9 8 1,4 -3 -0,5 -5 -0,9 -9 -1,6

Лена 2 0,4 4 0,8 7 1,4 -3 -0,6 -6 -1,2 -9 -1,8

Возможные

изменения сроков

ледовых явлений на

некоторых

арктических реках

России в XXI в. (по

сравнению с 2000 г.)

(Гинзбург, 2005)

а

b


Продолжительность отопительного периодаМодель ГГО - 2050 г. по отношению к 1990 г. (Катцов, 2011)


Changes in length of heating period for buildings

Изменения вегетационного периода (дни). Модель ГГО - 2050 г. по отношению к 1990 г. (Катцов, 2011)


Change of vegetation period length in days

Synoptic map (29.10.2000.) on the basis of simulation data of the COSMO-CLM

Extreme wind velocity over the Cola Peninsula was formed by northern large-scale flow.

Peter Koltermann Faculty of Geography, MSU, Moscow Kislov, 2015

The focus is on extreme events of wind velocity. The linkages to emphasize are the

following:

1. Observations and empirical distribution functions

2. Geography of extreme values

3. Possibility to use reanalysis data to extreme events assessment

4. Synoptic conditions leading to extreme events occurrence (case studies)

Териберка

Ловозеро

Зимнегорский_Маяк

Кандалакша

Network of station of the Cola Peninsula

Murmansk

Lavozero

Teriberka

Kandalaksha

Umba

Krasnochelie

Zimnegorsky Mayak


Kislov, 2015

Териберка

Ловозеро



18

1212

12

15

29

26

Geography of extreme values during winter season

The quantile U(p=0,99), m/s

For winter conditions U(p=0,99) characterize an event which could be no more often than once per cold season (October to May).


Териберка

Ловозеро



12

98

8

8

15

13

Geography of extreme values during summer season

The quantile U(p=0,99), m/s

For summer conditions U(p=0,95) characterizes an event which could be no more often than once per warm season (June to August (the Arctic summer)).


Териберка

Ловозеро


Кандалакша19

21

26

25

22

Geography of

extreme values

during winter

seasonThe quantile U(p=0,99), m/s, for 1

year records.

U(p=0,99) characterize an event which could be no more often than once per 100 years .


Empirical distribution functions of U

(m/s) for 72 hours time step records

(statistically independent time series)

Observations and empirical distribution functions


y = 2,8426x - 7,1227R² = 0,9789

-6

-4

-2

0

2

4

0 0,5 1 1,5 2 2,5 3 3,5 4

ln(-

ln(1

-F(x

)))

ln(x)

Teriberka20th Century Reanalysis

1966-2013Weibull distribution (general)

Winter k = 2.8A = 0.00085

y = 2,6582x - 5,5414R² = 0,9896

-4

-2

0

2

4

0 0,5 1 1,5 2 2,5 3 3,5

ln(-

ln(1

-F(x

)))

ln(x)


1966-2013Weibull distribution (general)

Summer k = 2.66A = 0.004

-8

-6

-4

-2

0

2 2,5 3 3,5

ln(1

-F(x

))

ln(x)


Pareto distribution (general)Winter

1966-2013 V > 10 …

α = 8.86β = 10.1

y = -9,2469x + 21,219

-6

-4

-2

0

2 2,5 3

ln(1

-F(x

))

ln(x)


Pareto distribution (general)Summer

1966-2013 V > 10 …

α = 9.25β = 10.1

Empirical distribution functions of U (m/s) based on the 20th Century

Reanalysis for 72 hours time step records

Possibility to use reanalysis data to extreme events assessment


0

10

20

30

40

50

0 5 10 15 20 25 30 35 40 45

R

W

Comparison observation (W) - reanalysis (R) data U(p=0,99), U(p=0,95)

The Reanalysis: systematic underestimation of extreme values


COSMO-CLMSynoptic conditions leading to extreme events occurrence (case studies)


Terminology Терминология• Hazard: threat

Опасное явление: угроза

• Risk: how much the threat can affect me

Риск: какова угроза для меня

• Vulnerability: how much am I protected from the hazard ?

Уязвимость: насколько я защищен от опасности?

• Preparedness: what can I do to reduce my vulnerability ?

Готовность: что я могу сделать, чтобы уменьшить мою уязвимость?


Risks in the Arctic• Harsh and fragile environment

– Special protection for people, infrastructure

• Economic development, environmental impact– Protection from anthropogenic impact, balance

• Adequate protection– Estimate the potential to damage, the impact

• Design appropriate infrastructure– Ports, towns, roads, exploration and exploitation sites

• Consider changes due to climate change– Sea ice cover, storms, wave climate, river discharge,

freeze-up, spring break-up, permafrost, snow cover, albedo/radiation balance, gas exchange (methane)


Zemtsov et al, 2014Peter Koltermann Faculty of Geography,

MSU, Moscow

Arctic Environment Laboratory ЛКЭ-ГИА Objectives

• Utilize all available observations and modelling products to quantitatively assess changes in meteorological, oceanographic and environmental variables that directly affect ongoing and future societal well-being and economic development in the coastal areas of the Circumpolar Arctic.

• Quantitatively evaluate the impacts of climatic and environmental changes on the societal well-being and economic development of the Arctic coastal areas. These include fossil fuels and mineral extraction, maritime and land transportation, industrial fishing, and infrastructure development.

• Quantitatively assess the magnitude and the spatial pattern of positive and negative climate-induced changes which have the potential to influence the economic development in the Circumpolar Arctic.

• Prepare a suite of recommendations to mitigate negative climate-induced impacts to achieve a sustainable development that contributes to the highest possible quality of life in the Arctic and benefits both the region and the Arctic nations.


Annual anomalies of the average thickness of seasonally frozen (permafrost) depth in Russia from 1930 to 2000. Each data point represents a composite from 320 stations as compiled by Russian Hydrometeorological Stations (RHM) (upper right inset). The composite was produced by taking the sum of the thickness measurements from each station and dividing the result by the number of stations operating in that year. Although the total number of stations is 320, the number providing data may be different for each year but the minimum was 240. The yearly anomaly was calculated by subtracting the 1971–2000 mean from the composite for each year. The thin lines indicate the 1 standard deviation (1σ) (likely) uncertainty range. The line shows a negative trend of –4.5 cm per decade or a total decrease in the thickness of seasonally frozen ground of 31.9 cm from 1930 to 2000 (Frauenfeld and Zhang, 2011) (reproduced from IPCC AR5 report).


Якутск. Площадь Орджоникидзе. 1996 год

Якутск. Площадь Ленина. Июль 2008 года

В Норильске в результате растепления оснований фундаментов за 10 лет снесено 300 зданий

Norilsk

Инженерные сооружения на мерзлых грунтахPeter Koltermann Faculty of Geography,

MSU, Moscow

Потери территории России:

Разрушение берегов северных морей идет со скоростью до 10 м/год и более

Russian climate science priorities include: Permafrost

Криогенные процессы

Город (поселок) Деформированные

здания, %

Амдерма 40

Диксон 33

Черский 40

Тикси 22

Певек 50

Якутск 27

Статистика деформаций в России

Awareness, preparedness

• After the tsunami:

• Traditional houses destroyed

• Modern fixed structures survived (vertical evacuation shelter)

• Foothills protected

Tohoku tsunami, 11 March 2011Peter Koltermann Faculty of Geography,

MSU, Moscow

Co-operation needed

• We need to improve forecast qualities

• We have to think in a synergetic manner

• We have to incorporate different disciplines

• We have to learn from each other how to work across discipline borders

• - we need good socio-economic data to assess the impact of our environmental results,

• and to target local and regional consequences


Outlook and Conclusions

• Enlarge, maintain and optimize observational networks,

• Identify ecological “hot spots” and their larger impact,

• Co-operate across disciplinary boundaries and freely share data and data sets,

• Closely co-operate from the beginning with the socio-economic community,

• to fully support the sustainable economic development of the Russian Arctic with fact-based decisions criteria


ThanksСпасибо за внимание!


Risks of Climate Change in the ArcticHelsinki, Finland, 10-13 February 2015 Peter Koltermann Faculty...

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