Climate changes: Assessment of natural and anthropogenic factors - PowerPoint PPT Presentation

Climate changes: Assessment of natural and anthropogenic factors and cause-and-effect relations Igor I. Mokhov A.M. Obukhov Institute of Atmospheric Physics RAS mokhov@ifaran.ru ENVIROMIS-2010 5 July 2010

Zvenigorod Scientific Station of the IAP RAS (55.7 o N) 260 T e m p e r a t u r e, K 240 220 200 180 1960 1970 1980 1990 2000 2010 Y ¡e ¡a ¡r Temperature changes at the mesopause from spectrophotometric measurements of the hydroxyl emission at the Zvenigorod station (full circles with red line as an approximation) in comparison with observations at different middle-latitude stations: Abastumani (41.8 o N) - hollow circles, Quebec (46.8 o N) and Delaware (42.8 o N) - squares, are Wuppertal (51 o N) - full inverted triangles , Maynooth (53.2 o N) - hollow inverted triangles. Semenov et al.

Zonal-mean atmospheric temperature changes (K) from 1890 to 1999 from model simulations (PCM) with different forcings: a) solar, b) volcanoes, greenhouse gases, d) ozone changes, e) sulfate aerosol, f) sum of all forcings. IPCC-2007

Surface temperature changes Annual anomalies of global land- by GISS data surface air temperature (relative to 1961-1990) from different datasets. Smooth curves show decadal variations.

Surface temperature anomalies (K) in January 2006 and January 2010 (relative to 1951-1980) by GISS data

Surface temperature anomalies (K) in Winter 2009-2010 (relative to 1951-1980) by GISS data

Ratio of the NH blockings action estimates [ energy x time ] for 2xCO 2 and 1xCO 2 regimes from model simulations for different seasons and sectors VII-IX X-XII I-III IV-VI I-XII Summer Autumn Winter Spring Annual Atlantic sector 0.2 1.1 1.2 1.1 1.0 (80 o W-40 o E) Pacific sector 1.0 0.8 1.0 0.9 0.9 (140 o E-100 o W) Continents 0.8 0.3 4.1 4.6 1.8 (40-140 o E, 100-80 o W) Northern Hemisphere 0.7 0.8 1.3 1.4 1.1 Mokhov

Interannual variations of blockings number in the Northern Hemisphere Кол - во блокингов в год Annual 60 50 40 30 20 10 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Winter Winter 16 14 12 10 8 6 4 2 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Summer Summer 14 12 10 8 6 4 2 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 years

Interannual variations of blockings intensity in the Northern Hemisphere Интенсивность ( средн .) Annual 162 142 122 102 82 62 42 22 2 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 интенсивность ( зима ) Winter 60 50 40 30 20 10 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 интенсивность ( лето ) Summer 180 160 140 120 100 80 60 40 20 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 years

Surface temperature anomalies (K) in 2010 (relative to 1951-1980): a) January, b) February, c) March, d) April a) b) c) d) by GISS data

Surface annual-mean temperature anomalies (K) during last decade (2000-2009) (relative to 1951-1980) by GISS data

Surface annual-mean temperature changes (K) between 1990s and 1980s Surface annual-mean temperature changes (K) between 2000s and 1990s by GISS data

Zonal-mean changes of annul-mean surface temperature by GISS data (1990-1999)-(1980-1989) K (2000-2009)-(1990-1999) K

Surface air temperature trends (K/10 years) for 1976-2009 by data from Rosgidromet

Russia K Interannual variations of surface air temperature (relative 1961-1990). Red line – SAT trend (1976-2009). Blue curve – with 11-year averaging. 1976-2009: 0.47 K/10 years (contribution to the variance - 34%) 1976-2008: 0.52 K/10 years (35%) 1976-2007: 0.48 K/10 years (34%) by data from Rosgidromet

Surface air temperature trends (b, K/10 years) in Russian regions during 1976-2009 D – linear trend contribution (%) to the variance Annual Winter Spring Summer Autumn Region Russia European part of Russia Western Siberia Middle Siberia Baikal Lake Region, Transbaikalia Eastern Siberia Primorye & Priamurye by Rosgidromet data

100-year moving trends of global and hemispheric surface temperature CRU GISS by CRU, GISS and NCDC data Mokhov and Karpenko NCDC

Cross-wavelet analysis (local coherency and phase lag) of variations for global surface temperature (by CRU data) and solar irradiance (by different data: a - Lean, b - Hoyt) а Period, years b Period, years Mokhov and Karpenko Time, years

Tung et al.

Tung et al.

Cross-wavelet analysis (local coherency and phase lag) of variations for global surface temperature (by CRU data) and CO2 concentration (by data for Mauna Loa)

Cross-wavelet analysis (local coherency and phase lag) of variations for global surface temperature (by CRU data) and CO2 concentration (by data for Mauna Loa) WTs of TGl (1) & CO2 (2) 80 70 60 P eriod ¡(yr) Period, years 50 Temperature 40 30 20 Wavelet analysis 10 80 70 60 P eriod ¡(yr) 50 CO 2 40 30 20 10 1850 1870 1890 1910 1930 1950 1970 1990 Time, years TGl - CO2 local phase lags (1) coherences (2) lag (yr) posit. lag - CO2 nearest extremum lag behind TGl 80 18 70 12 60 6 P eriod ¡(yr) Phase lag 50 0 40 Period, years -6 30 -12 20 -18 10 -24 Cross-wavelet analysis 80 70 1.00 60 0.80 P eriod ¡(yr) 50 Coherency 0.60 40 -0.60 30 -0.80 20 -1.00 10 1850 1870 1890 1910 1930 1950 1970 1990 Time, years

Granger causality V 0.16 Solar irradiation I , ¡Â ò/ì L -­‑ä àí í û å 2 1367 Volcanic activity 0.12 L - data Wm -2 0.08 1366 0.04 years ãî ä û ãî ä û years 0 1365 1855 1895 1935 1975 2015 1875 1900 1925 1950 1975 2000 I , ¡Â ò/ì H -­‑ä àí í û å 2 Wm -2 1368 H - data 1366 Global temperature Ã Ï Ò T , ¡Ê 0.8 1364 ãî ä û years 0.4 0 1875 1900 1925 1950 1975 -0.4 ãî ä û years -0.8 1875 1900 1925 1950 1975 2000 n 380 Analysis of individual ppmv CO 2 360 340 and joint influence 320 300 years ãî ä û Mokhov & Smirnov 280 1855 1895 1935 1975 2015

Empirical predictive models and Granger causality (prediction improvement) Two series: x and y x t , y t , t = 1, …, N x f ( x , x ,..., x , a ) Individual model = t t 1 t 2 t d − − − 1 [ ] 2 2 Its prediction error ( d ) x f ( x , x ,..., x 1 a , ) ˆ σ = − x 1 t t 1 t 2 t d − − − t x F ( x , x ,..., x , y , y ,..., y , a ) Joint model = t t 1 t 2 t d t 1 t 2 t d − − − − − − 1 2 [ ] 2 2 Its prediction error ( d , d ) x F ( x ,..., x , y ,..., y , a ) ˆ σ = − 1 2 t t 1 t d t 1 t d x y − − − − 1 2 t Prediction improvement of x (when y is incorporated into a model) is a sign of influence y → x 2 2 PI ( d , d ) ( d ) ( d , d ) = σ − σ y x 1 2 x 1 1 2 x y →

Bivariate models (T:I, T:V, T:n) T ( t ) a T ( t 1 ) a T ( t 4 ) b I ( t 1 ) ( t ) = − + − + − + ξ 1 4 I T ( t ) a T ( t 1 ) a T ( t 4 ) b V ( t ) ( t ) = − + − + + ξ 1 4 V T ( t ) a T ( t 1 ) a T ( t 4 ) b n ( t 1 ) b n ( t 2 ) ( t ) = − + − + − + − + ξ 1 4 1 , n 2 , n Joint model (T:I,V,n) T ( t ) a T ( t 1 ) a T ( t 4 ) b I ( t 1 ) b V ( t ) b n ( t 1 ) b n ( t 2 ) ( t ) = − + − + − + + − + − + ξ 1 4 I V 1 , n 2 , n According to the empirical models, the rise in CO 2 concentration determines at least 75% of the GST trend in 1985 − 2005, while the other two factors (forcings) are not the causes of the global warming. In particular, if the CO2 concentration remained at the level of 1856 year, the GST would not rise during the last century. In contrast, variations in solar and volcanic activity would not lead to significant changes in the GST trend. All the influences are detected if the data at least for the interval [1856 − 1940] are used for the model fitting.

The data used: a) GST - global surface temperature (anomalies relative 1961 − 1990); b) solar constant (W/m 2 ); c) volcanic activity (optical depth of volcanic aerosol); d) CO 2 atmospheric content in ppm (parts per million). Solar variations M 2 = Volcanic activity СО 2 content variations (Mokhov & Smirnov, 2009) Bivariate models of GST fitted to different time intervals [1856 − L]: a) models with solar activity; b) models with volcanic activity; c) models with CO 2 atmospheric content. The normalized values of prediction improvement (the thick lines) are indicated on the left y-axes (dimensionless), significance levels (the thin lines) on the right y-axes (dimensionless). The dashed lines show the level of p = 0.05.

Cause-and-effect relations of climatic processes (ENSO, NAO/AO, EAM, Monsoon, AMO, …) North Atlantic Oscillation Indian Monsoon Equatorial Atlantic ENSO ENSO Mode