Suzdal-2007

Международная конференция «50-летие Международного геофизического года

и Электронный геофизический год»

Suzdal-2007

Возможные региональные последствия

глобальных изменений климата И.И. Мохов

Институт физики атмосферы им. А.М. Обухова РАН

Possible regional consequences of global climate changesIgor I. Mokhov

A.M. Obukhov Institute of Atmospheric Physics RAS

[email protected]

Selected references Akperov M.G., M.Yu. Bardin, E.M. Volodin, G.S. Golitsyn, and I.I. Mokhov, 2007: Izvestiya, Atmospheric and Oceanic Physics Arpe, K., L. Bengtsson, G.S. Golitsyn, I.I. Mokhov, V.A. Semenov, and P.V. Sporyshev, 1999: Doklady Earth Sciences Arpe, K., L. Bengtsson, G.S. Golitsyn, I.I. Mokhov, V.A. Semenov, and P.V. Sporyshev, 2000: Geophysical Research Letters Golitsyn, G.S., I.I. Mokhov, and V.Ch. Khon, 2000: In: Ecological Problems of the Caspy Golitsyn, G.S., L.K. Efimova, I.I. Mokhov, V.A. Rumyantsev, N.G. Somova, and V.Ch. Khon, 2002: Water Resources Golitsyn, G.S., L.K. Efimova, I.I. Mokhov, V.A. Tikhonov, and V.Ch. Khon, 2004: Meteorology and Hydrology Golitsyn, G.S., I.I. Mokhov, M.G. Akperov, and M.Yu. Bardin, 2006: Izvestiya, Atmospheric and Oceanic Physics Khon, V.Ch., I.I. Mokhov, E. Roeckner, and V.A. Semenov, 2007: Global and Planetary Change Khon, V.Ch., 2007: British-Russian Conference “Hydrological Impact of Climate Change”, Novosibirsk Meleshko, V.P., G.S. Golitsyn, V.A. Govorkova, P.F. Demchenko, A.V. Eliseev, V.M. Kattsov, V.Ch. Khon, S.P. Malevsky-Malevich, I.I. Mokhov, E.D. Nadyozhina, V.A. Semenov, P.V. Sporyshev, 2004: Meteorology and Hydrology Mokhov, I.I., and V.Ch. Khon, 2002: Doklady Earth Sciences Mokhov, I.I., and V.Ch. Khon, 2002: Meteorology and Hydrology Mokhov, I.I., J.-L. Dufresne, H. Le Treut, V.A. Tikhonov, and A.V. Chernokulsky, 2005: Doklady Earth Sciences Mokhov, I.I., E. Roeckner, V.A. Semenov, and V.Ch. Khon, 2006: Doklady Earth Sciences Mokhov, I.I., E. Roeckner, V.A. Semenov, and V.Ch. Khon, 2006: Water Resources Mokhov, I.I., V.A. Semenov, and V.Ch. Khon, 2003: Izvestiya, Atmospheric and Oceanic Physics Mokhov, I.I., A.V. Chernokulsky, and I.M. Shkolnik, 2006: Doklady Earth Sciences

Mokhov, I.I., V.Ch. Khon, and E. Roeckner, 2006: Doklady Earth Sciences Mokhov, I.I., 2007: British-Russian Conference “Hydrological Impact of Climate Change”, Novosibirsk

Surface air temperature Изменения приповерхностной температуры

Russia

NH

Global

Surface air temperature trends from observations (1975-2004)Annual means

Тренды глобальной приповерхностной температуры для 100-летних скользящих интервалов по данным наблюдений. Вертикальными отрезками отмечены среднеквадратические отклонения. Также приведены соответствующие коэффициенты корреляции (шкала справа).

Global surface temperature trends (for 100-year moving intervals)

Разные модельные оценки 100-летних трендов глобальной приповерхностной температуры: 1 – КМ ИФА РАН А2-GHG, 2 – КМ ИФА РАН B2-GHG, 3 – CCCma A2, 4 – CCCma B2, 5 – CCSRNIES A2, 6 – CCSRNIES B2) в сравнении с оценками по данным наблюдений (черная кривая 7).

Характерные особенности потепления

Увеличение приповерхностной температуры

Изменение режимов осадков, снежного покрова, влагосодержания почвы и речного стока

Уменьшение площади морских льдов в Арктике

Уменьшение распространения вечной мерзлоты

Изменение режимов циклонов и антициклонов в средних и полярных широтах

Изменение режимов засух и пожаров

Global climate simulations are analyzed in comparison with observations for an assessment of regional changes.

Both coupled general circulation models and global model of intermediate complexity are used with different anthropogenic scenarios for the 21st century.

Special attention is given to estimates of possible changes in the Volga, Ob, Yenisei and Lena rivers basins.

Regional climate extremes like droughts and fires are also analyzed with the use of regional model simulations.

Surface air temperature changes in winter (relative to 1981-2000) (7 models ensemble means)

2041-20602041-2060А2А2

2020880-2090-20999A2A2

BB22

B2B2

Surface air temperature increase in summer ((relative to 1981-2000) to 1981-2000) (7 models ensemble means)

2041-20602041-2060А2А2

2020880-2090-20999A2A2

BB22

B2B2

Changes of precipitation (%) relative to (1981-2000) from ensemble-mean (7 models) simulations in winter

2041-20602041-2060SRES-SRES-А2А2

2020880-2090-20999SRES-A2SRES-A2

SRES-BSRES-B22

SRES-B2SRES-B2

Precipitation changes (%) relative to (1981-2000) from ensemble-mean (7 models) simulations in summer

2041-2060SRES-A2

2080-2099SRES-A2SRES-A2

SRES-B2

SRES-B2SRES-B2

Changes of snow mass (кg/m2) at the beginning of Spring ( (MarchMarch) )

2041-2060А2

2080-2099A2

B2

B2

IAP RAS CM simulations

Продолжительность ледового сезона (1980-1999 гг.)Duration of seasons with sea ice (days)

a) Satellite data (SMMR-SSM/I)

b) Observations (HadISST)

c) HadGEM1 Model d) HadCM3 Model

e) GFDL-CM2.0 Model f) GFDL-CM2.1 Model g) CCSM3 Model h) IPSL-CM4 Model

Морской лед в Арктике (Северный морской путь) Arctic Sea Ice (Northern Sea Route)

Changes in time intervals (days) with a potential navigation relative to 1961-1990 from ECHAM5/MPI-OM simulations with SRES-A2 scenario: 1) 2001-2030, 2) 2031-2060, 3) 2061-2090.

Selected watersheds in Russia and contiguous regions

LenaLenaObOb YeniseiYenisei

PechoraPechora

VolgaVolga

BalticBaltic

DneprDnepr

1 – 2041-2060 2041-20602 – 2080-2099 2080-2099

WinterWinter

-10

-5

0

5

10

15

20

25

Dnepr Volga Balt Pechora Ob Enisei Lena

1

2

SummerSummer

Precipitation changes (%) in watersheds, SRES-B2

-10

-5

0

5

10

15

20

25

Dnepr Volga Balt Ob Enisei Lena Pechora

1

2

Changes of annual-mean precipitation (mm/day) in watersheds during the 21st century relative to the end of the 20th century (1981-2000)

SRES-А2 and SRES-В2(7 models)

Pechora & N.DvinaPechora & N.Dvina

Dnepr & DonDnepr & Don

LenaLena

Volga & UralVolga & Ural

95%

95%95%95%

95%

95%

95%

95%

Changes of runoff (km3/yr) in watersheds in the 21st century relative to the end of the 20th century (1981-2000).

SRES-В2

Pechora & N.DvinaPechora & N.Dvina

Dnepr & DonDnepr & Don

LenaLena

Volga & UralVolga & Ural

95%

95%

95%

95%

95%95%

95%

95%

Eurasian rivers annual runoff changes (%, 30-year moving averages) [Volga&Ural (left-upper), Ob (right-upper), Yenisey (left-lower), Lena (right-lower)]

Different scenarios

1900 1950 2000 2050 210080

90

100

110

120

130

Волга и Урал

Rn, %

5

4

3

2

1

годы1900 1950 2000 2050 2100

80

90

100

110

120

130

140

150

5

Rn, %

Обь

4

3

2

1

годы

1900 1950 2000 2050 210080

90

100

110

120

130

140

5

Енисей

Rn, %

4

3

2

1

годы1900 1950 2000 2050 2100

80

90

100

110

120

130

140

150

160

170

ЛенаR

n, %

4

3

2

5

1

годы

1-4 – simulations (IAP RAS global climate model), 5 - observations

Winter

Precipitation changes (%) to the end of the 21st century relative to the end of the 20th century

IPCC-AR4 Simulations (SRES-A1B)(Ensemble Means)

Summer

River Runoff (1961-1990) IPCC-AR4 simulations in comparison with observations

Volga Ob

Yenisei Lena

Volga

Ob

Yenisei Lena

River Runoff Changes (%) to the end of the 21st century relative to the end of the 20th century

IPCC-AR4 Simulations (SRES-A1B)

Trends (%/100 years) of the winter precipitation characteristics in the 21st century as simulated by the ECHAM5/MPI-OM with the use SRES-B1 and SRES-A2

Trends (%/100 years) of the summer precipitation characteristics in the 21st century as

simulated by the ECHAM5/MPI-OM with the use SRES-B1 and SRES-A2

The number of cyclones and anticyclones (the double number of cyclone and anticyclones days) at 20-80 0N for 1952-2000 obtained from NCEP/NCAR

reanalysis and INM model for April-September and October-March. <N> is a mean value for cyclone-day and anticyclone-day.

cyclonesApril-September

years

1960 1980 2000 2020 2040 2060 2080 2100

N/<

N>

0.80

0.85

0.90

0.95

1.00

1.05

1.10

NCEP(<N>=2814)INM(XX)(<N>=2474)INM(A2)(<N>=2364)

cyclonesOctober-March

years

1960 1980 2000 2020 2040 2060 2080 2100

N/<

N>

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15


anticyclonesApril-September

years

1960 1980 2000 2020 2040 2060 2080 2100

N/<

N>

0.80

0.85

0.90

0.95

1.00

1.05

1.10


anticyclonesOctober-March

years

1960 1980 2000 2020 2040 2060 2080 2100

N/<

N>

0.85

0.90

0.95

1.00

1.05

1.10


IPSL-CM2 (with carbon cycle)

SRES-A2

Western Europe

Year

1900 1950 2000 2050 2100

D, %

0

20

40

60

80

100

Eastern Europe

Year

1900 1950 2000 2050 2100

D, %

0

10

20

30

40

50

60

Коэффициенты корреляции биопродуктивности (NPP) с количеством осадков (а) и влагосодержанием почвы (б) в мае-июле для европейской территории России в средних широтах по модельным расчетам для 60-летних скользящих интервалов

ãî äû

1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080

a

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

ãî äû

1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080

á

0.3

0.4

0.5

0.6

0.7

0.8

Coefficients of correlation (60-years running periods) of Net Primary Production (NPP) with precipitation (a) and soil water content (b) in May-July for European part of Russia in mid-latutudes from IPSL-CM2 simulations with SRES-A2 scenario

DYNAMICS OF FIRES NUMBERS AND BURNED AREA IN RUSSIA

Korovin and Zukkert 2003, updated

Index of Potential Forest Fire Danger (IF)MGO Regional Climate Model

(Summer Means for 1991-2000, <IF>)

Forest Fires MGO Regional Climate Model

SRES-A2

[IF(Δt) - IF(1991-2000)] / IF(1991-2000)

Δt: 2041-2050 Δt: 2091-2100

Характерные особенности потепления Увеличение приповерхностной температуры (увеличение

экстремальных температур)

Изменение режимов осадков, снежного покрова, влагосодержания

почвы и речного стока (Увеличение частоты интенсивных осадков)

Уменьшение площади морских льдов в Арктике

Уменьшение площади распространения вечной мерзлоты (сезонно

замерзающей почвы)

Изменение режимов циклонов и антициклонов в средних и полярных

широтах (блокингов, центров действия атмосферы, например общее

ослабление Сибирского зимнего антициклона)

Изменение режимов засух и пожаров (регионы повышенного риска

лесных пожаров, например в Забайкалье)

Тренд Tα, К/10 лет

1970-1999 гг.С-сценарий А-сценарий Е-сценарий

Сибирь(Иркутск)

HadCM3 0.34 (±0.13) 0.32 (±0.09) 0 (±0.08)

КМ ИФА РАН 0.16 (±0.13) 0.29 (±0.12) 0.08 (±0.13)

Аляска(Барроу)

HadCM3 0.51 (±0.18) 0.54 (±0.18) -0.08 (±0.02)

КМ ИФА РАН 0.19 (±0.07) 0.18 (±0.06) -0.07 (±0.05)

Антарктический п-в(Беллинсгаузен)

HadCM3 0.43 (±0.14) 0.34 (±0.13) 0.06 (±0.14)

КМ ИФА РАН 0.12 (±0.07) 0.12 (±0.12) 0 (±0.03)

Температурные тренды для последнего 30-летия ХХ века по расчетам с HadCM3 и КМ ИФА РАН

при разных сценариях (форсингах)

Scenarios

SCENARIOS OF MAIN GREENHOUSE GASES AND AEROSOLS SCENARIOS OF MAIN GREENHOUSE GASES AND AEROSOLS

INCREASES IN 21st CENTURYINCREASES IN 21st CENTURY

SCENARIOS SCENARIOS А2 А2 && В2 В2

NN22OO

CHCH44COCO22

300

400

500

600

700

800

900

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

y e a r s

СО

2 (p

pm)

A2

B2

1500

2000

2500

3000

3500

4000

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

y e a r s

CH

4 (

bpm

)

A2

B2

300

325

350

375

400

425

450

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

y e a r s

N2O

(bp

m)

A2

B2

Аэрозоль Аэрозоль SOSO44

РОСТ КОНЦЕНТРАЦИИ ПАРНИКОВЫХ ГАЗОВВ 21-м СТОЛЕТИИ

СЦЕНАРИИ SRES-А2 и SRES-В2

N2O

CH4CO2

CO2

300

400

500

600

700

800

900

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

г о д ы

СО

2 (м

лн-1

)

A2

B2

1500

2000

2500

3000

3500

4000

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

г о д ы

CH

4 (

мл

рд-1

) A2

B2

300

325

350

375

400

425

450

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

г о д ы

N2O

(м

лрд-1

)

A2

B2

Projected global average warming

Low scenario

Medium scenarioHigh scenario

Warming of about 0.2oC per decade for next two decades for a range of scenarios

1.8oC

2.8oC

3.4oC

Higher emissions lead to more warming later in century.

Further warming of ~0.6oC after concentrations stabilized

Forest Fires MGO Regional Climate Model

SRES-A2

[IF(Δt) - IF(1991-2000)] / IF(1991-2000)

Δt: 2041-2050 Δt: 2091-2100

Changes (%) of soil moisture and runoff relative to relative to (1981-2000)

in spring and summer, SRES В2 (7 models ensemble means)

Spring

Spring Summer

2080-2099

Summer

2041-2060

0.5

1

1.5

1860 1900 1940 1980 2020 2060 2100

a

0

0.5

1

1.5

2

1860 1900 1940 1980 2020 2060 2100

б

Изменения нормированных значений NPP (a) и NEP (б) для европейской части России (в средних широтах) в мае-июле по расчетам с КМОЦ IPSL-CM2 при увеличении антропогенной эмиссии СО2 согласно сценарию SRES-A2 с учетом всех обратных связей (сплошные тонкие кривые) и без антропогенных изменений климата (тонкий пунктир пунктир) нормировались на их соответствующие средние значения в мае-июле для 30-летнего периода 1961-1990 гг. Жирными кривыми отмечены соответствующие 30-летние скользящие средние для NPP и NEP.

Depth increase of melted soil (cm) in August in the 21st century Depth increase of melted soil (cm) in August in the 21st century for regions with permafrostfor regions with permafrost

20208080-209-20999

2041-2060 2041-2060

AA22 BB22

BB22AA22

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

0

20

40

60

80

100


0

20

40

60

80

100


Ï ëî ù àäü î òêðû òî é âî äû , %

0

20

40

60

80

10012345

● Simulations show a general increase of the annual mean precipitation and rain intensity for Russia in the XXI century, but the wet day probability increases only in the northern latitudes. These tendencies are related basically to winter seasons, while in summer the decrease of wet day probability was simulated for the main part of Russia. It is resulted in the decrease of summer precipitation over significant part of Russia, though the rain intensity in summer for Russia generally increases.

● Model results display that the increase of temperature in the XXI century is accompanied in the mid-latitudes over land by the decrease of precipitation in spring-summer and by the increase of drought indices. Drought indices display also the general variability increase in the XXI century.

● Model results display an increase of mean values of regional precipitation and runoff in the Ob, Yenisei, Lena, Volga and Neva rivers basins. Alongside with such a general tendency a remarkable variations with an increase of variance of regional hydrological characteristics have been noted from model simulations. In particular, models show some decrease of the Volga, Ob and Yenisei rivers runoff at the beginning of XXI century.

● Sensitivity of permafrost conditions in the Northern Hemisphere as a whole from model simulations depends on forcing only slightly and agrees with paleoreconstructions.

Droughts and Fires

Different data are used for diagnosis of drought and fire conditions and their changes in the Northern Eurasia regions, in particular daily meteorological observations from the RIHMI-WDC, gridded data from the CRU, reanalyses ERA-40 and NCEP/NCAR data.

Extreme meteorological conditions in spring and summer months (May-June-July) are analyzed for the basic cereals-producing regions in the European (ER) and Asian (AR) mid-latitudinal regions of Russia and contiguous territories during 1891-2006.

Global and regional climate models simulations (SRES-A2, SRES-B2)

Droughts

T

QHTC

1.0Q – precipitationT – surface air temperature higher than 10°C for some time period (month and vegetation season).

Hydrothermal Coefficient (HTC)

Drought conditions can be characterized by the D index with the negative precipitation anomalies δPr (normalized on the long-term mean value for precipitation) larger than -20% and positive temperature anomalies δT larger than 1K. Similar index M characterizes the wet conditions with δPr>20% and δT<-1K. Two additional indices are used: D-M and S=(δT/σδT - δP/σδP), where σδT and σδP are respective standard deviations.

Fires

Different characteristics of fire hazard are used. We used the Nesterov fire frequency index for wildfires and its modifications as a characteristic of fire hazard. The fire hazard index IF was determined from meteorological data according to

IF = Σ(TM - Td)TM .

Here TM is the maximal temperature in оC and Td is the temperature of the dew-point (depending on relative humidity and temperature) in оC. Summation is performed for those days when the daily precipitation P does not exceed 3 mm. At P > 3 mm the IF value turns to zero. Conditions with IF < 300 (I) are not considered hazardous. Conditions in the ranges 300-1000, 1000-4000, 4000-10000, and >10000 are considered as regimes with low (II), moderate (III), high (IV), and extreme (V) level of fire hazard.

Drought Index (D) at the end of the 20th century (left) and its changes (right) to the end of the 21st century

MGO Regional Climate Model (SRES-B2)

DroughtsMGO Regional Climate Model (SRES-B2)

Hydrothermal Coefficient HTC (1991-2000)

HTC (2041-2050)

HTC (2091-2100)

Droughts and Fires

Qufu-2007

Some conclusions

Model regional projections display nonlinear changes for droughts and fires in the 21st century with different anthropogenic scenarios

Remarkable El-Nino-like effects in droughts and fires conditions are displayed in the North Eurasian regions

Regions with the increased risks of fires have been noted, particularly to the east from Baikal Lake

Fires

2007

We used also the Nesterov index IF for the forest fires conditions and its different modifications (Nesterov, 1949; Venevsky et al., 2002). This index was calculated by using daily temperature (at 12 h) at the surface, dew-point temperature and precipitation. The difference between the two temperatures was multiplied by the daily temperature and summed over the number of days since the first day with daily precipitation less than 3 mm. When the daily precipitation exceeds 3 mm, the IF value is defined as zero. The ignition potentials are considered to be moderate, high and extreme ones for IF values between 300 and 1000, between 1000 and 4000 and above 4000, correspondingly. We used also modified index ITF for the forest fires. It is defined as a summary of daily temperatures (at 12 h) over the number of days since the first day with daily precipitation less than 3 mm.

Regional Climate Changes

The index ID of drought conditions can be characterized by negative precipitation anomalies Pr larger than (Pr)cr by absolute value and positive temperature anomalies T larger than (T)cr. These critical values can be proportional to respective standard deviations or equal to fixed values. Droughts in EER and WAR are reasonably described with critical anomalies equal to 20% for precipitation and 1K for surface air temperature (Meshcherskaya and Blazhevich, 1997).

We used also the Nesterov index IF for the forest fires conditions and its different modifications (Nesterov, 1949; Venevsky et al., 2002). This index was calculated by using daily temperature (at 12 h) at the surface, dew-point temperature and precipitation. The difference between the two temperatures was multiplied by the daily temperature and summed over the number of days since the first day with daily precipitation less than 3 mm. When the daily precipitation exceeds 3 mm, the IF value is defined as zero. The ignition potentials are considered to be moderate, high and extreme ones for IF values between 300 and 1000, between 1000 and 4000 and above 4000, correspondingly. We used also modified index ITF for the forest fires. It is defined as a summary of daily temperatures (at 12 h) over the number of days since the first day with daily precipitation less than 3 mm.

Different data are used for diagnosis of drought and forest fire conditions and their changes in regions Northern Eurasia during the second half of the 20th century. In particular, daily station data from the RIHMI (Razuvayev et al., 1993), gridded observational data from the CRU (New et al., 2000), data of the ERA-40 (Simmons et al., 2000) and NCEP/NCAR (Kistler et al., 2001) reanalyses are analyzed (Mokhov et al., 2002; Mokhov, 2005). We analyzed also extremal meteorological conditions in May-July (MJJ) for the basic cereals-producing regions in the eastern European (EER) and western Asian (WAR) mid-latitudinal regions from (Meshcherskaya and Blazhevich, 1997).

Зима

Лето

Tem

per

atu

re

Pre

cip

itat

ion

Win

ter

Win

ter

Su

mm

er

Su

mm

er

Changes of the surface air temperature (К) and precipitation (%) to the end of the 21st century relative the end of the 20th century

Global Climate Model (SRES-A2)

Changes of SAT (К) and precipitation (%) to the end of the 21st century relative the end of the 20th century

Regional Climate Model (SRES-A2)T

emp

erat

ure

Pre

cip

itat

ion

Win

ter

Su

mm

er

Win

ter

Su

mm

er

Droughts

Hydrotermal Coefficient

HTC(1991-2000)

HTC(2041-2050)-HTC(1991-2000)

HTC(2091-2100)-HTC(1991-2000)

SRES-B2

Droughts

D (1991-2000)

D(2091-2100)-(1991-2000)

D(2091-2100)-(1991-2000)

SRES-B2

Fires

Distributions (1961-1990) of the fire index characteristics (IF≥300) in summer (JJA) over Northern Eurasia by data from reanalysis ERA-40: mean intensity (a), probability (b).

Fires

Distributions (1961-1990) of the fire index mean intensity (ITF) in summer (JJA) over Northern Eurasia: RIHMI observations (a), reanalysis ERA-40 (b).

IPSL-CM2

Selected Western and Eastern European regions

Fire Index: Difference between 2041-2050 and 1991-2000

Based on simulations with the MGO regional model (SRES-B2)

Regional Climate Changes

Повторяемость летних дней с индексом, превышающим средний в 2 раза.

(1991-2000)

Повторяемость летних дней с индексом, превышающим средний в 4 раза. (1991-2000)

Mean precipitation (1961-1990) in DJF (left column) and JJA (right column) from observations CRU

(a, b), reanalysis ERA-40 (c, d) and simulations with ECHAM5/MPI-OM (e, f), mm/day

Novosibirsk-2007

a b

c d

e f

Mean precipitation (mm/day) in river basins

from observations (CRU), reanalysis (ERA-40)

and model simulations (ECHAM5/MPI-OM)

1961-1990

Trends (%/100years) in the 20th century

from observations (CRU) and model simulations (ECHAM5/MPI-OM)

ECHAM4/OPYC3

years

1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080

Co

rrela

tio

n c

oe

ffic

ien

t

(6

0-ye

ars

ru

nn

ing

perio

ds

)

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5ObYeniseiLena 99 %

95 %

90 %

95 %

90 %

years

1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080

Co

rrela

tio

n c

oefficien

t

(6

0-y

ea

rs r

un

nin

g p

erio

ds)

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

ObYeniseiLena

95%

90%

90%

99%

Precipitation: NAO

Runoff: NAO

Î áù åå êî ëè÷åñòâî çèì í èõ î ñàäêî â

0

20

40

60

80

100

B1A2

Êàâêàç

Âî ëãàÎ áü

Åí èñåéËåí à

Èí òåí ñèâí î ñòü çì í èõ î ñàäêî â

0

20

40

60

80

B1A2

Êàâêàç

Âî ëãà Î áüÅí èñåé

Ëåí à

Âåðî ÿòí î ñòü çèì í èõ î ñàäêî â

-40

-20

0

20

40

B1A2

Êàâêàç

Âî ëãà Î áüÅí èñåé Ëåí à

Ýêñòðåì àëüí û å çèì í èå î ñàäêè

0

20

40

60

80

B1A2

ÊàâêàçÂî ëãà Î áü

Åí èñåé Ëåí à

Î áù åå êî ëè÷åñòâî ëåòí èõ î ñàäêî â

-100

-80

-60

-40

-20

0

20

B1A2Êàâêàç

Âî ëãà Î áü Åí èñåé Ëåí à

Âåðî ÿòí î ñòü ëåòí èõ î ñàäêî â

-100

-80

-60

-40

-20

0

B1A2

Êàâêàç

Âî ëãà Î áüÅí èñåé Ëåí à

Èí òåí ñèâí î ñòü ëåòí èõ î ñàäêî â

-20

-10

0

10

20

30

40

B1A2

Êàâêàç

Âî ëãà

Î áü Åí èñåé

Ëåí à

Ýêñòðåì àëüí û å ëåòí èå î ñàäêè

-40

-20

0

20

40

60

B1A2

Êàâêàç

Âî ëãà Î áü Åí èñåéËåí à

Тренды региональных характеристик ежесуточных зимних (слева) и летних (справа) осадков (% за 100 лет) в XXI веке (относительно периода 1961-1990 гг.) для разных регионов северной Евразии (Кавказа и бассейнов четырех рек – Волги, Оби, Енисея и Лены) по расчетам с КМОЦ ECHAM5/MPI-OM при двух антропогенных сценариях SRES-B1 и SRES-A2: общего количества, интенсивности, вероятности и экстремальных значений.

IPSL-CM2SRES-A2

IPSL CM2

Year

1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080

NP

P&

SW

C co

rre

latio

n co

effic

ien

t

0.3

0.4

0.5

0.6

0.7

0.8

Year

1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080

NP

P &

Q

co

rre

latio

n co

effic

ien

t

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Cor

rela

tion

coe

ffic

ient

(60

-yea

rs r

unni

ng p

erio

ds)

Global climate simulations are analyzed in comparison with observations for an assessment of changes in regional hydrologic cycle, particularly precipitation and river runoff.

Both coupled general circulation models and global model of intermediate complexity are used with different anthropogenic scenarios for the 21st century.

Special attention is given to estimates of possible changes in the Volga, Ob, Yenisei and Lena rivers basins.

Different characteristics of precipitation including mean precipitation, rain intensity, rain event probability and extreme events are analyzed.

Regional climate extremes like droughts and fires are also analyzed with the use of regional model simulations.

CONCLUSIONSCONCLUSIONS

Hydrological changes are expected to manifest in the 21st century through different patterns in Russia due to its large latitudinal-longitudinal extension.

Hydrological cycle processes undergo significant regional changes dependent on season and level of global and regional warming.

There are still large uncertainties in model simulations and evaluation of regional hydrological characteristics (precipitation, soil water content, runoff, extreme events etc.) and their changes.

Suzdal-2007

Documents

Transcript of Suzdal-2007