Антропогенные изменения климата на европейской территории

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Международная конференция
«50-летие Международного геофизического года
и Электронный геофизический год»
Возможные региональные последствия
глобальных изменений климата
И.И. Мохов
Институт физики атмосферы им. А.М. Обухова РАН
Possible regional consequences of global climate changes
Igor I. Mokhov
A.M. Obukhov Institute of Atmospheric Physics RAS
mokhov@ifaran.ru
Suzdal-2007
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
Global surface temperature trends
(for 100-year moving intervals)
Тренды глобальной приповерхностной температуры для 100-летних скользящих
интервалов по данным наблюдений.
Вертикальными отрезками отмечены среднеквадратические отклонения.
Также приведены соответствующие коэффициенты корреляции (шкала справа).
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)
А2
A2
2041-2060
2080-2099
B2
B2
Surface air temperature increase in summer (relative to 1981-2000)
(7 models ensemble means)
А2
A2
2041-2060
2080-2099
B2
B2
Changes of precipitation (%) relative to (1981-2000)
from ensemble-mean (7 models) simulations in winter
SRES-А2
SRES-A2
2041-2060
2080-2099
SRES-B2
SRES-B2
Precipitation changes (%) relative to (1981-2000)
from ensemble-mean (7 models) simulations in summer
SRES-A2
SRES-A2
2041-2060
SRES-B2
2080-2099
SRES-B2
Changes of snow mass (кg/m2) at the beginning of Spring (March)
А2
2041-2060
B2
A2
2080-2099
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
Baltic
Pechora
Dnepr
Volga
Lena
Ob
Yenisei
Precipitation changes (%) in watersheds, SRES-B2
1 – 2041-2060
2 – 2080-2099
25
1
20
2
15
Winter
10
5
0
Dnepr
Volga
Balt
Ob
Enisei
Lena
Pechora
-5
-10
25
1
20
2
15
10
Summer
5
0
Dnepr
-5
-10
Volga
Balt
Pechora
Ob
Enisei
Lena
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.Dvina
Lena
95%
95%
95%
95%
Dnepr & Don
Volga & Ural
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.Dvina
Lena
95%
95%
95%
95%
Dnepr & Don
Volga & Ural
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
R n, %
R n, %
130
Волга и Урал
150
3
Обь
120
1
5
100
1
130
4
2
110
4
2
120
110
5
100
90
90
80
1900
1950
R n, %
140
2000
2050
80
2100
Rn , %
годы
170
Енисей
130
120
3
160
1
150
4
2
110
1900
1950
2000
2050
2100
годы
Лена
3
1
140
4
2
130
120
5
100
110
5
100
90
80
3
140
90
80
1900
1950
2000
годы
2050
2100
1900
1950
2000
2050
2100
годы
1-4 – simulations (IAP RAS global climate model), 5 - observations
Precipitation changes (%)
to the end of the 21st century relative to the end of the 20th century
IPCC-AR4 Simulations (SRES-A1B)
(Ensemble Means)
Winter
Summer
River Runoff (1961-1990)
IPCC-AR4 simulations in comparison with observations
Volga
Yenisei
Ob
Lena
River Runoff Changes (%)
to the end of the 21st century relative to the end of the 20th century
IPCC-AR4 Simulations (SRES-A1B)
Ob
Volga
Yenisei
Lena
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.
1.10
1.15
cyclones
April-September
cyclones
October-March
1.10
1.05
1.05
N/<N>
N/<N>
1.00
0.95
1.00
0.95
0.90
0.90
0.85
NCEP(<N>=2814)
INM(XX)(<N>=2474)
INM(A2)(<N>=2364)
NCEP(<N>=2374)
INM(XX)(<N>=2086)
INM(A2)(<N>=1996)
0.85
0.80
0.80
1960
1980
2000
2020
2040
2060
2080
2100
1960
1980
2000
years
2020
2040
2060
2080
2100
years
1.10
1.10
anticyclones
October-March
anticyclones
April-September
1.05
1.05
1.00
N/<N>
N/<N>
1.00
0.95
0.95
0.90
0.85
0.90
NCEP(<N>=2152)
INM(XX)(<N>=2122)
INM(A2)(<N>=2012)
0.80
NCEP(<N>=2126)
INM(XX)(<N>=2145)
INM(A2)(<N>=2058)
0.85
1960
1980
2000
2020
years
2040
2060
2080
2100
1960
1980
2000
2020
years
2040
2060
2080
2100
Eastern Europe
60
50
D, %
40
30
20
10
0
1900
2000
1950
Year
IPSL-CM2 (with carbon cycle)
SRES-A2
2050
2100
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
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
a
0.0
1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080
ãî äû
0.8
0.7
0.6
0.5
0.4
á
0.3
1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080
ãî äû
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
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>)
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)
Температурные тренды для последнего 30-летия ХХ века
по расчетам с HadCM3 и КМ ИФА РАН
при разных сценариях (форсингах)
Тренд Tα, К/10 лет
1970-1999 гг.
С-сценарий
А-сценарий
Е-сценарий
Сибирь
HadCM3
0.34 (±0.13)
0.32 (±0.09)
0 (±0.08)
(Иркутск)
КМ ИФА РАН
0.16 (±0.13)
0.29 (±0.12)
0.08 (±0.13)
Аляска
HadCM3
0.51 (±0.18)
0.54 (±0.18)
-0.08 (±0.02)
(Барроу)
КМ ИФА РАН
0.19 (±0.07)
0.18 (±0.06)
-0.07 (±0.05)
Антарктический п-в
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)
(Беллинсгаузен)
Scenarios
SCENARIOS OF MAIN GREENHOUSE GASES AND AEROSOLS
INCREASES IN 21st CENTURY
CO2
900
CH4
4000
A2
B2
A2
B2
3500
700
CH4 (bpm)
СО2 (ppm)
800
SCENARIOS А2 & В2
600
500
3000
2500
400
2000
300
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
1500
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
y e a rs
N2O
450
400
A2
B2
0,9
A2
B2
0,8
SO4 (TgS)
N2 O (bpm)
425
y e a rs
375
0,7
0,6
350
0,5
325
0,4
300
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
0,3
1990
year s
Аэрозоль SO4
2000
2010
2020
2030
2040
2050
y e a rs
2060
2070
2080
2090
210
РОСТ КОНЦЕНТРАЦИИ ПАРНИКОВЫХ ГАЗОВ
В 21-м СТОЛЕТИИ
СЦЕНАРИИ SRES-А2 и SRES-В2
CO2
CO2
3500
-1
700
A2
B2
600
500
3000
A2
B2
2500
400
2000
300
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
1500
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
годы
годы
N2O
450
425
-1
N2O (млрд )
-1
СО2 (млн )
800
CH4
4000
CH4 (млрд )
900
400
A2
B2
375
350
325
300
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
годы
Projected global average warming
High scenario
Medium scenario
Low scenario
Higher emissions lead to more
warming later in century.
3.4oC
2.8oC
1.8oC
Further warming of ~0.6oC after
concentrations stabilized
Warming of about 0.2oC per
decade for next two decades
for a range of scenarios
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)
2041-2060
Spring
Summer
2080-2099
Spring
Summer
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.
1.5
a
1
0.5
1860
2
1900
1940
1980
1900
1940
1980
2020
2060
2100
б
1.5
1
0.5
0
1860
2020
2060
2100
Depth increase of melted soil (cm) in August in the 21st century
for regions with permafrost
2041-2060
A2
B2
2080-2099
A2
B2
Ï ëî ù àäü
î òêðû òî é âî äû , %
100
1
2
3
4
5
80
60
40
20
0
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
100
80
60
40
20
0
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
100
80
60
40
20
0
●
●
●
●
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 RIHMIWDC, 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 cerealsproducing regions in the European (ER) and Asian (AR) midlatitudinal regions of Russia and contiguous territories during
1891-2006.
Global and regional climate models simulations
(SRES-A2, SRES-B2)
Droughts
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.
Hydrothermal Coefficient
(HTC)
HTC 
Q
0.1 T
Q – precipitation
T – surface air temperature higher than 10°C
for some time period (month and vegetation season).
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)
Droughts
MGO Regional Climate Model (SRES-B2)
HTC (2041-2050)
Hydrothermal Coefficient
HTC (1991-2000)
HTC (2091-2100)
Droughts and Fires
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
Qufu-2007
Fires
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.
2007
Regional Climate Changes
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) midlatitudinal regions from (Meshcherskaya and Blazhevich, 1997).
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.
Winter
Precipitation
Summer
Summer
Temperature
Winter
Changes of the surface air temperature (К) and precipitation (%)
to the end of the 21st century relative the end of the 20th century
Зима
Global Climate Model (SRES-A2)
Лето
Winter
Precipitation
Summer
Summer
Temperature
Winter
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)
Droughts
Hydrotermal Coefficient
HTC(1991-2000)
HTC(2041-2050)-HTC(1991-2000)
SRES-B2
HTC(2091-2100)-HTC(1991-2000)
Droughts
D(2091-2100)-(1991-2000)
D (1991-2000)
SRES-B2
D(2091-2100)-(1991-2000)
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
a
b
c
d
e
f
Novosibirsk-2007
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
0.5
Ob
Yenisei
Lena
Correlation coefficient
(60-years running periods)
0.4
Precipitation: NAO
99 %
0.3
95 %
90 %
0.2
0.1
0.0
-0.1
90 %
95 %
-0.2
-0.3
1880
1900
1920
1940
1960
2000
2020
2040
2060
2080
years
0.5
Ob
Yenisei
Lena
0.4
Correlation coefficient
(60-years running periods)
1980
Runoff: NAO
99%
0.3
95%
0.2
90%
0.1
0.0
-0.1
-0.2
90%
-0.3
1880
1900
1920
1940
1960
1980
years
2000
2020
2040
2060
2080
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/MPIOM при двух антропогенных сценариях SRES-B1 и SRES-A2: общего количества, интенсивности,
вероятности и экстремальных значений.
100
Î á ù åå êî ë è ÷åñòâ î
çè ì í è õ î ñàä êî â
B1
A2
20
Åí è ñåé
80
Âî ë ãà
ë åòí è õ î ñàä êî â
0
Ëåí à
Î áü
Î á ù åå êî ë è ÷åñòâ î
-20
Î áü
Âî ë ãà
60
Åí è ñåé
Ëåí à
-40
40
Êàâ êàç
-60
20
-80
-100
0
È í òåí ñè â í î ñòü çì í è õ î ñàä êî â
80
B1
A2
40
Èí òåí ñè â í î ñòü ë åòí è õ î ñàä êî â
B1
A2
30
Î áü
Åí è ñåé
60
Î
Âî ë ãà
áü
40
Åí è ñåé
Ëåí à
Âî ë ãà
20
Ëåí à
Êàâ êàç
B1
A2
Êàâ êàç
10
0
20
Êàâ êàç
-10
-20
0
40
Âåðî ÿòí î ñòü çè ì í è õ î ñàä êî â
B1
A2
Âî ë ãà
20
Î
áü
Åí è ñåé Ëåí à
0
Âåðî ÿòí î ñòü ë åòí è õ î ñàä êî â
-20
Âî ë ãà
Î
áü
Åí è ñåé Ëåí à
-40
0
-60
-20
-80
Êàâ êàç
Êàâ êàç
-40
80
-100
Ýêñòðåì àë üí û å çè ì í è å î ñàä êè
B1
A2
60
Êàâ êàç
Åí è ñåé
Âî ë ãà
Î
60
Ýêñòðåì àë üí û å ë åòí è å î ñàä êè
B1
A2
Ëåí à
40
áü
Âî ë ãà
20
40
0
20
0
B1
A2
-20
-40
Êàâ êàç
Î
á ü Åí è ñåé
Ëåí à
NPP&SWC correlation coefficient
0.7
0.6
0.5
0.4
0.3
1880
1900
1920
1940
1960
1980
2000
2020
2040
2060
2080
2000
2020
2040
2060
2080
Year
0.8
0.7
NPP & Q correlation coefficient
Correlation coefficient (60-years running periods)
0.8
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1880
1900
1920
1940
1960
1980
Year
IPSL-CM2
SRES-A2
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.
CONCLUSIONS
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.
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