Meeting the Challenges and Risks of Climate Change in the Arctic - - PowerPoint PPT Presentation

▶
meeting the challenges and
SMART_READER_LITE
LIVE PREVIEW

Meeting the Challenges and Risks of Climate Change in the Arctic - - PowerPoint PPT Presentation

Dec 22, 2023 243 likes •856 views

Meeting the Challenges and Risks of Climate Change in the Arctic , Klaus Peter Koltermann The 1st Pan-Eurasian Experiment (PEEX) Science

slide-1
SLIDE 1

Meeting the Challenges and Risks of Climate Change in the Arctic

Проблемы 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

slide-2
SLIDE 2

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.

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-3
SLIDE 3

Лето / Summer Зима/Winter (Оценочный Доклад, 2014)

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-4
SLIDE 4

Площадь морского льда (млн. кв.км) Arctic sea ice cover

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-5
SLIDE 5

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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-6
SLIDE 6

Peter Koltermann Faculty of Geography, MSU, Moscow

Relevant climate drivers:

  • Temperature
  • Wind
  • Sea ice
  • Snow cover
  • Floods
  • Vegetation

Relevant change processes:

  • local:
  • Radiation balance, albedo, snow cover, vegetation

cover

  • Water cycle, snow type and cover, precipitation
  • Permafrost
  • regional:
  • Atmospheric and oceanic advection
  • River and groundwater flow
slide-7
SLIDE 7

Population density of the Arctic regions of Russia

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-8
SLIDE 8

Industry of the Arctic regions of Russia

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-9
SLIDE 9

Transport of the Arctic regions of Russia

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-10
SLIDE 10

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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-11
SLIDE 11

Ice and temperature regime of the Arctic region rivers

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-12
SLIDE 12

Global Annual Surface Air Temperature Anomalies, °C

Rates of increase of annual temperature for the “globe” (60S to 90N) and Northern Eurasia are 0.91 C/130 yr and 1.5C/130yr respectively (Lugina et al. 2007, updated).

Global temperature anomalies

2010

˚C

1998

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-13
SLIDE 13

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)-1

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-14
SLIDE 14

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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-15
SLIDE 15

Annual and winter number of days with thaw

  • ver European Russia south of 60°N

dD/dt = 6.5 days/50yrs; R² = 0.18 dD/dt = 11 days/50yrs; R² = 0.35 10 20 30 40 50 60 1949 1959 1969 1979 1989 1999 2009

Groisman, 2009

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-16
SLIDE 16

Snow cover extent anomalies over Eurasia

Anomalies in 106  km2

Years

May 1967-2013

slide-17
SLIDE 17
  • 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

slide-18
SLIDE 18

Global hydrological cycle: small is not insignificant for extremes

MPI Hamburg

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-19
SLIDE 19

Atlantic Multidecadal Variability impact on Volga river discharge

www.NRAL.org

Natural NRAL Risk Assessment Laboratory NRAL : Gulev, Semenov, Zolina

Changes of Volga river discharge explain about 80%

  • f

the Caspian Sea level variability. An effect of the AMV on the hydrological cycle

  • ver

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

slide-20
SLIDE 20

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 earth

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-21
SLIDE 21

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 station UBD - upper boundaries of the delta MBD - marine boundary of the delta

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-22
SLIDE 22

Long-term changes of the annual water runoff

River – hydrometric station ΔWQ/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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-23
SLIDE 23

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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-24
SLIDE 24

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 runoff, million t/year 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

12.01–4.12 12.41–4.52

2.77*

River Suspended sediment runoff, million t/year Bottom sediment runoff, million t/year 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

slide-25
SLIDE 25

Time series of average water and suspended sediment runoff of the large Arctic rivers

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-26
SLIDE 26

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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-27
SLIDE 27

Long-term dynamics of ice phenomena

  • 5

5 10 1976-2012 2002-2012 freezing break-up

Sev.Dvina Rv.

Δ from 1936-75 yrs (days)

  • 5

5 10 1976-2012 2002-2012 freezing break-up

Lena Rv.

Δ from 1936-75 yrs (days)

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-28
SLIDE 28

Сhanges

  • f average

sea level

  • I. Intra-annual changes of average sea level

Sea Amplitude, 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 0.17/0.97 VI–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

Sea Vorobiev 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/yr Peter Koltermann Faculty of Geography, MSU, Moscow

slide-29
SLIDE 29

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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-30
SLIDE 30

Опасные ледовые явления

Fluctuations 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 25 50 1,0 5 3 1 –1 –3 2,0 4 2 –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) –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 Peter Koltermann Faculty of Geography, MSU, Moscow

Frolova, 2015

slide-31
SLIDE 31

Опасные ледовые явления

Fluctuations of freeze-up dates anomalies (∆D) in lower reaches of the Arctic rivers

  • 30
  • 20
  • 10

10 20 1880 1930 1980 Northern Dvina ∆D, days

  • 30
  • 20
  • 10

10 20 1880 1930 1980 Pechora ∆D, days

  • 30
  • 20
  • 10

10 20 1880 1930 1980 Ob' ∆D, days

  • 30
  • 20
  • 10

10 20 1855 1905 1955 2005 Yenisei ∆D, days

  • 30
  • 20
  • 10

10 20 1880 1930 1980 Lena ∆D, days

  • 30
  • 20
  • 10

10 20 1880 1930 1980

Indigirka

∆D, days

Peter Koltermann Faculty of Geography, MSU, Moscow

Frolova, 2015

slide-32
SLIDE 32

Опасные ледовые явления

Fluctuations of break-up dates anomalies (∆D) in lower reaches of the Arctic rivers since 1880

  • 20
  • 15
  • 10
  • 5

5 10 15 20 1880 1930 1980 Pechora ∆D, days

  • 20
  • 15
  • 10
  • 5

5 10 15 20 1880 1930 1980 Nortern Dvina ∆D, days

  • 20
  • 15
  • 10
  • 5

5 10 15 20 1880 1930 1980

∆D, days

Ob'

  • 20
  • 15
  • 10
  • 5

5 10 15 20 1880 1930 1980

Yenisei

∆D, days

  • 20
  • 15
  • 10
  • 5

5 10 15 20 1880 1930 1980

∆D, days

Lena

  • 20
  • 15
  • 10
  • 5

5 10 15 20 1880 1930 1980 Indigirka

∆D, days

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-33
SLIDE 33

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 сток

67,3•1016 Кдж/год – сток средних и больших рек

Peter Koltermann Faculty of Geography, MSU, Moscow

Magritsky, 2014

slide-34
SLIDE 34

Time series of freeze up (a) and break up dates (b) from selected Russian Siberian rivers

a b

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-35
SLIDE 35

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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-36
SLIDE 36

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

Peter Koltermann Faculty of Geography, MSU, Moscow

Changes in length of heating period for buildings

slide-37
SLIDE 37

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

Peter Koltermann Faculty of Geography, MSU, Moscow

Change of vegetation period length in days

slide-38
SLIDE 38

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

slide-39
SLIDE 39

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

Peter Koltermann Faculty of Geography, MSU, Moscow

Kislov, 2015

slide-40
SLIDE 40

Териберка Ловозеро Зимнегорский_Маяк Кандалакша

18 12 12 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).

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-41
SLIDE 41

Териберка Ловозеро Зимнегорский_Маяк Кандалакша

12 9 8 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)).

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-42
SLIDE 42

Териберка Ловозеро Зимнегорский_Маяк Кандалакша

19 21 26 25 22

Geography of extreme values during winter season The 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

  • nce per 100 years .

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-43
SLIDE 43

Empirical distribution functions of U (m/s) for 72 hours time step records (statistically independent time series) Observations and empirical distribution functions

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-44
SLIDE 44

y = 2,8426x - 7,1227 R² = 0,9789

  • 6
  • 4
  • 2

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

ln(-ln(1-F(x)))

ln(x)

Teriberka 20th Century Reanalysis 1966-2013 Weibull distribution (general) Winter k = 2.8 A = 0.00085

y = 2,6582x - 5,5414 R² = 0,9896

  • 4
  • 2

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

ln(-ln(1-F(x)))

ln(x)

Teriberka 20th Century Reanalysis 1966-2013 Weibull distribution (general) Summer k = 2.66 A = 0.004

  • 8
  • 6
  • 4
  • 2

2 2,5 3 3,5

ln(1-F(x)) ln(x)

Teriberka 20th Century Reanalysis Pareto distribution (general) Winter 1966-2013

V > 10 …

α = 8.86 β = 10.1

y = -9,2469x + 21,219

  • 6
  • 4
  • 2

2 2,5 3

ln(1-F(x)) ln(x)

Teriberka 20th Century Reanalysis Pareto distribution (general) Summer 1966-2013

V > 10 …

α = 9.25 β = 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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-45
SLIDE 45

10 20 30 40 50 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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-46
SLIDE 46

COSMO-CLM

Synoptic conditions leading to extreme events occurrence (case studies)

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-47
SLIDE 47

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?

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-48
SLIDE 48

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)

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-49
SLIDE 49

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-50
SLIDE 50

Zemtsov et al, 2014

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-51
SLIDE 51

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
  • n 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.

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-52
SLIDE 52

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).

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-53
SLIDE 53

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 грунтах

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-54
SLIDE 54

Потери территории России:

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

slide-55
SLIDE 55

Russian climate science priorities include:  Permafrost

Криогенные процессы

slide-56
SLIDE 56

Город (поселок) Деформированные здания, % Амдерма 40 Диксон 33 Черский 40 Тикси 22 Певек 50 Якутск 27

Статистика деформаций в России

slide-57
SLIDE 57

Awareness, preparedness

  • After the

tsunami:

  • Traditional

houses destroyed

  • Modern fixed

structures survived (vertical evacuation shelter)

  • Foothills

protected

Tohoku tsunami, 11 March 2011

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-58
SLIDE 58

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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-59
SLIDE 59

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

Peter Koltermann Faculty of Geography, MSU, Moscow

slide-60
SLIDE 60

Thanks Спасибо за внимание!

Peter Koltermann Faculty of Geography, MSU, Moscow