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 [email protected] 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.
