A Review on the Influence of Solar Variability upon the Climate

1. A Review onthe Influence of Solar Variability upon the Climate Shigeo Yoden Dept. of Geophysics, Kyoto Univ.

2. 0. Recent research trends • T. Nagasima (NIES, Japan): report on IUGG2003 • JSA02: “External forcing on the middle atmosphere and ionosphere” • JSA08: “Effects of solar variability on climate change” • JSM05: “Solar effects in the middle atmosphere and ionosphere” • MI01: “Variation of the solar energy output and its influence on climate” • Recent motivations to study the solar activity-climate relationship • in the past: prejudice due to the accuracy in observations • accumulation of high-quality data mostly from satellite obs. • increase of interest on global climate change • anthropogenic effects, solar effects, volcanic effects, ... • increased ability of climate modeling + computer resources

3. 1. What kinds of solar variability are there? • T. Nagasima (2003) +α (a) variations of the solar radiation • 27-day cycle • 11-year cycle • longer time-scale fluctuations (e.g., Maunder minimum) • direct effects on the radiative balance in the atmosphere • temperature and ozone in the middle atmosphere • amplification and downward influence through coupling with dynamical process ? • temperature in the lower atmosphere and SST, particularly in cloud free areas ?

4. (b) variations of the solar wind (high energy plasma) • similar cycles with the variations of the solar radiation • increased energetic electron precipitation (EEP) • NOx generation in the mesosphere • influence on NOx balance in the stratosphere or else ? (c) solar modulations of galactic cosmic ray (GCR) flux • anti-correlation with the variations of the solar wind • some purportedcorrelations* with total cloud cover • Friis-Christensen and Lassen (1991; Science 254 698-) and following papers • influence on cloud condensation nuclei (CCN) ? * Laut (2003; JASTP 65 801-) “Solar activity and terrestrial climate: an analysis of some purported corellations”

5. 2. Variations of the solar irradiance • Lean (1997; Annual Rev. Astron. Astrophys. 35 33-)

6. Lean (1991; Rev. Geophys.29 505-)

7. Dada from Lean et al. (1997) Estimated solar irradiance change in the UV part during the 11-year solar cycle (Matthes et al. 2003; Papers in Met. And Geophys. 54 71-)

8. 3. Observed correlations with the solar variations • Significant changes in the ionosphere associated with the 11-year cycle (Lean, 1997) b. critical frequency foF2, above which radio waves are lost because of no ionospheric reflection

9. Labitzke and van Loon (1999) “The Stratosphere” • Time series of the solar activity (10.7 cm radio wave) and annual mean of the 30-hPa height at 30N, 150W 30 hPa heights 3-yr running mean of heights Cor.～0.75 Solar activity

10. Correlation between the solar activity and annual mean 30-hPa heights (about 24 km) in the NH Shaded area: statistical significance exceeds 1% (confidence level 99%)

11. pressure • Average temperature differences (oC) for two-months mean between two years in solar maxima and two years in solar minima in four solar cycles

12. Time series of the solar activity and the zonal-mean annual-mean 700 hPa temperature 30-hPa height (20N-40N) 700-hPa temperature (20N-40N) Solar activity [3-yr running means]

13. Annual mean Northern summer Southern summer • Correlation between the solar activity and the TOMS total ozone (1978-1993) Shaded area: statistical significance exceeds 5%

14. NCEP/CPC (1980-1997) SSU (1979-1997) +2.5 K +0.8 K -1 K +0.25 K +1 K WMO (1999) Lon Hood (2002) Matthes et al. (2003)Observed annual mean solar signal in temperature

15. Matthes et al. (2003; update of Kodera (1995))Observed monthly mean solar signal in mean zonal wind Max-Min; NMC Data (1979-1998) poleward-downward movement of large difference (signal) Jan Nov Feb Dec

16. high cloud cover middle cloud cover low cloud cover • Marsh and Svensmark (2000) • “Low cloud properties influenced by cosmic rays” Phys.Rev.Lett.85 5004- • global average of monthly cloud anomalies International Satellite Cloud Climate Project (ISCCP) • July 1983 – June 1994

17. Laut (2003) • “Solar activity and terrestrial climate: an analysis of some purported correlations” J.Atmos.Solar-Terr.Phys.65 801- Marsh and Svensmark (2000) Updated by Kristjansson (2002) Same as (b) but smoothed

18. White, Lean, Cayan, and Dettinger (1997) • “Response of global upper ocean temperature to changing solar irradiance” JGR102 3255 : El Nino Bathythermograph Temperature data; 30S--60N area-veraged Global Ice and Sea Surface Temperature data; 40S--60N area-averaged Solar irradiance anomalies reconstructed from calculations of sunspot darkening (sunspot areas and disk positions) 1874～

19. 4. Numerical experiments with general circulation models (GCMs) • Shindell et al. (1999) “Solar cycle variability, ozone, and climate” Science284 305- • Differences of 30-hPa height in DJF between solar minimum and maximum in GISS GCM runs • Interactive ozone code

20. Assumed ozone variations (solar Max – Min) • calculated off-line with 2D model • applied to GCM background ozone climatology • used for solar induced ozone changes between max-min +3% +3% Annual Mean data from Haigh (1994) Matthes et al. (2003)“SPARC/GRIPS Solar Experiments Intercomparison Project”

21. Matthes et al. (2003)

22. Matthes et al. (2003)

23. Annual mean SW heating rate differences [K/day]:20-yr mean Max exp. – Min.exp.(Matthes et al., 2003)

24. Annual mean temperature differences [K]:20-yr mean Max exp. – Min.exp.(Matthes et al., 2003)

25. Monthly mean zonal mean zonal wind differences [m/s]:20-yr mean Max exp. – Min.exp. (Matthes et al., 2003)

26. early winter anomalies • Kodera and Kuroda (2002) a possible mechanism of the downward influence by planetary wave – mean zonal flow interaction solar Max: larger Ty and U smaller F smaller v* and w* larger T in the equatorial lower stratosphere

27. Meehl et al. (2003)J. Climate16 426-“Solar and GHG forcing and climate response in the 20th century” NCAR PCM (CCM3+LSM+POM) (a) Radiative forcing from the coupled model for each experiment (b) Global annual mean surface air temperature - early century:solar - residual(= solar+GHG+sulfateminus GHG+sulfate) - late century:GHG+sulfates

28. Haigh (2003) “The effects of solar variability on the Earth’s climate”Phil.Trans.R.Soc.Lond.A361 95- Difference in zonal mean temperature between solar max and min calculated using an AGCM in which the spectral composition of the change in irradiance was included in addition to solar-induced changes in ozone S-T dynamical interactions

29. Naito, Y., M. Taguchi and S. Yoden, 2003: J.Atmos.Sci.60 1380- • Naito, Y. and S. Yoden, 2003: IUGG2003 MC05 • Experiments on the QBO Effects on Coupled Variability with an MCM • We impose a “QBO forcing” in the zonal momentum eq.: to assess the atmospheric response to small (or finite) change in the external parameter UQBO by a statistical method.

30. 10,800-day mean fields of zonal-mean zonal wind [m/s] 50m/s 75m/s 55m/s 45m/s 45m/s

31. Temperature [K] Frequency [%] : Time-mean temperature Frequency distributions of zonal-mean temperature [K]at φ=86N, p=2.6hPa for 10,800 days CTRL

32. Frequency distributions of zonal-mean temperature [K] at φ=86N, p =449hPa for 10,800 days E1.0 W1.0 Statistical significanceof the QBO effectscan be estimated witha large sample method A standard normal variable: The probability that Z reaches 40.6 for two samples of the same populations is quite small ( < 10-27 ).

33. 5. Geological records on the variations of solar activity and climate

34. ～ Solar radiation History of East Africa (Drought) Lake depth Salinity  Verschuren et al. (2000) “Rainfall and drought in equatorial east Africa During the past 1,100 years” Nature 403 410 Comparison of the depth and salinity of Crescent Island Crater (Lake Naivasha; Kenya) with atmospheric 14CO2 production (a proxy for solar radiation) and the pre-colonial history of east Africa

35.  Bondet al. (2001) “Persistent solar influence on north Atlantic climate during the holocene” Science 294 2130 Comparison of 10Be and 14C records with time series of a drift-ice record and stacked marine records “1500-yr cycle”

36. atmospheric D14C (x0.1%; proxy for solar activity) d18O(x0.1% VPDB) of Stalagmite H5 from a cave in Oman  Neff et al. (2001) “Strong coherence between solar variability and the monsoon in Oman between 9 and 6 kyr ago”Nature411 290-

37.  Burns et al. (2002) “A 780-year annually resolved record of Indian Ocean monsoon precipitation from a speleothem from south Oman” JGR 107 ArtNo.4434 Comparison of d18O from stalagmite S3 (from Oman) with historical rainfall data (from East Africa and southern Arabia)

38. d18O values (proxy for monsoon rainfall) atmospheric D14C ( proxy for solar activity)  Fleitmannet al. (2003) “Holocene forcing of the Indian monsoon recorded in a stalagmite from Southern Oman”Science300 1737-

39. 6. Concluding remarks • in the past: prejudice due to • the accuracy in observations • limited number (area) of the data • limited length of the data • these days: accumulation of high-quality data from • High-tech observations and large ensemble mean • globally covered satellite observations • high-accuracy geological data

40. increase of interest on global climate change • anthropogenic effects, solar effects, volcanic effects, ... • detection of the response to small fluctuation of external forcing • PDFs • stochastic resonance (multiple stable states) • advanced computer resources • progress in the ability of climate modeling • advanced processing of huge data • new methods on the statistical significance test

41. End of (important) slides. Slides hereafter are extra.

42. Frequency distributions of zonal-mean temperature [K] at φ=86N, p =449hPa for 10,800 days Statistical significanceof the QBO effectscan be estimated witha large sample method. A standard normal variable: W1.0 E1.0 The probability that Z reaches 40.6 for two samples of the same populations is quite small ( < 10-27 ).

43. Summer Winter • Correlation between solar activity and two-month means of the 30-hPa heights on the NH (July 1957-June 1997) Shaded area: statistical significance exceeds 1% (confidence level 99%)