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 changes Igor I. Mokhov A.M. Obukhov Institute of Atmospheric Physics RAS

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 ( ) 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 21 st 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 ) (7 models ensemble means) А2 А A2 B2 B2 B2 B2 B2

( to ) Surface air temperature increase in summer (relative to ) (7 models ensemble means) А2 А A2 B2 B2 B2 B2 B2

Changes of precipitation (%) relative to ( ) from ensemble-mean (7 models) simulations in winter SRES-А2 SRES-А SRES-A2 SRES-B2 SRES-B2 SRES-B2

Precipitation changes (%) relative to ( ) from ensemble-mean (7 models) simulations in summer SRES-A SRES-A2 SRES-B2 SRES-B2

(March) Changes of snow mass (кg/m 2 ) at the beginning of Spring (March) А A2 B2 B2 B2

IAP RAS CM simulations

Продолжительность ледового сезона ( 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 Modelg) 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 from ECHAM5/MPI-OM simulations with SRES-A2 scenario: 1) , 2) , 3)

Selected watersheds in Russia and contiguous regions Lena Ob Yenisei Pechora Volga Baltic Dnepr

– – Winter Summer Precipitation changes (%) in watersheds, SRES-B2

Changes of annual-mean precipitation (mm/day) in watersheds during the 21 st century relative to the end of the 20 th century ( ) SRES-А2 and SRES-В2 (7 models) Pechora & N.Dvina Pechora & N.Dvina Dnepr & Don Lena Volga & Ural 95%

Changes of runoff (km 3 /yr) in watersheds in the 21 st century relative to the end of the 20 th century ( ). SRES-В2 Pechora & N.Dvina Pechora & N.Dvina Dnepr & Don Lena Volga & Ural 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 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 ( ) IPCC-AR4 simulations in comparison with observations Volga Ob YeniseiLena

Volga Ob Yenisei Lena River Runoff Changes (%) to the end of the 21 st century relative to the end of the 20 th century IPCC-AR4 Simulations (SRES-A1B)

Trends (%/100 years) of the winter precipitation characteristics in the 21 st 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 21 st 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 N for obtained from NCEP/NCAR reanalysis and INM model for April-September and October-March. is a mean value for cyclone-day and anticyclone-day.

IPSL-CM2 (with carbon cycle) SRES-A2

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 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 (I F ) MGO Regional Climate Model (Summer Means for , )

Forest Fires MGO Regional Climate Model SRES-A2 [I F (Δt) - I F ( )] / I F ( ) Δt: Δt:

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 T α, К/10 лет гг. С-сценарийА-сценарийЕ-сценарий Сибирь (Иркутск) HadCM30.34 (±0.13)0.32 (±0.09)0 (±0.08) КМ ИФА РАН0.16 (±0.13)0.29 (±0.12)0.08 (±0.13) Аляска (Барроу) HadCM30.51 (±0.18)0.54 (±0.18)-0.08 (±0.02) КМ ИФА РАН0.19 (±0.07)0.18 (±0.06)-0.07 (±0.05) Антарктический п-в (Беллинсгаузен) HadCM30.43 (±0.14)0.34 (±0.13)0.06 (±0.14) КМ ИФА РАН0.12 (±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 INCREASES IN 21st CENTURY SCENARIOS А2 & В2 N2O N2O N2O N2O CH 4 CO 2 Аэрозоль SO 4

РОСТ КОНЦЕНТРАЦИИ ПАРНИКОВЫХ ГАЗОВ В 21-м СТОЛЕТИИ СЦЕНАРИИ SRES-А2 и SRES-В2 N2O N2O CH 4 CO 2

Projected global average warming Low scenario Medium scenario High scenario Warming of about 0.2 o C per decade for next two decades for a range of scenarios 1.8 o C 2.8 o C 3.4 o C Higher emissions lead to more warming later in century. Further warming of ~0.6 o C after concentrations stabilized

Forest Fires MGO Regional Climate Model SRES-A2 [I F (Δt) - I F ( )] / I F ( ) Δt: Δt:

Changes (%) of soil moisture and runoff relative to relative to ( ) in spring and summer, SRES В2 (7 models ensemble means) Spring Summer 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 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 for regions with permafrost A2A2A2A2 B2B2B2B2 B2B2B2B2 A2A2A2A2

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

Droughts Q – precipitation T – 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 I F was determined from meteorological data according to I F = Σ(T M - T d )T M. Here T M is the maximal temperature in о C and T d is the temperature of the dew-point (depending on relative humidity and temperature) in о C. Summation is performed for those days when the daily precipitation P does not exceed 3 mm. At P > 3 mm the I F value turns to zero. Conditions with I F 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) Hydrothermal Coefficient HTC ( ) HTC ( ) HTC ( )

Droughts and Fires Qufu-2007 Some conclusions Model regional projections display nonlinear changes for droughts and fires in the 21 st 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 Temperature Precipitation Winter Summer 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 21 st century relative the end of the 20 th century Regional Climate Model (SRES-A2) Temperature Precipitation Winter Summer Winter Summer

Droughts Hydrotermal Coefficient HTC( ) HTC( )-HTC( ) HTC( )-HTC( ) SRES-B2

Droughts D ( ) D( )-( ) SRES-B2

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

Fires Distributions ( ) of the fire index mean intensity (I TF ) 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 and Based on simulations with the MGO regional model (SRES-B2)

Regional Climate Changes Повторяемость летних дней с индексом, превышающим средний в 2 раза. ( ) Повторяемость летних дней с индексом, превышающим средний в 4 раза. ( )

Mean precipitation ( ) 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 ab cd ef

Mean precipitation (mm/day) in river basins from observations (CRU), reanalysis (ERA-40) and model simulations (ECHAM5/MPI-OM)

Trends (%/100years) in the 20 th century from observations (CRU) and model simulations (ECHAM5/MPI-OM)

Precipitation: NAO Runoff: NAO

Тренды региональных характеристик ежесуточных зимних (слева) и летних (справа) осадков (% за 100 лет) в XXI веке (относительно периода 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-CM2 SRES-A2 Correlation coefficient (60-years running periods)

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 21 st 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 21 st 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.