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Hugues Goosse Université catholique de Louvain, Belgium

Simulating the climate of the last millennium : the role of internal and forced climate variability. Hugues Goosse Université catholique de Louvain, Belgium.

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Hugues Goosse Université catholique de Louvain, Belgium

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  1. Simulating the climate of the last millennium : the role of internal and forced climate variability Hugues Goosse Université catholique de Louvain, Belgium with contributions from C. Ammann, R. Bradley, T. Crowley, M. Delmotte, T. Fichefet, B.K. Khim, M.E. Mann, V. Masson-Delmotte, R. Muscheler, V. Morgan, H. Renssen, B. Stenni, A. Timmermann, T. Van Omnen, E. Zorita

  2. Introduction  Are we able to simulate the climate evolution during the last millennium?  What are the causes of the temperature changes during the last millennium, at hemispheric and regional scale?  What is the role of the natural variability? What is its impact on model-data comparison ?

  3. Outline • Model and forcing  Climate during the last millennia at hemispheric scale •  Climate during the last millennia at regional scale • Combining model and data to study past changes •  Conclusions

  4. ECBILT-CLIO-VECODE ECBILT • Quasi-geostrophic atmospheric model (T21, 3 levels) • Simplified representations of diabatic-heating processes • Explicit hydrological cycle • Prescribed cloudiness • Soil-plus-snow model VECODE • Dynamical terrestrial vegetation model • 3 plant functional types: forest, desert, grass CLIO Sea-ice model Thermodynamics using a 3-layer snow–ice model + leads Dynamics including viscous-plastic rheology Ocean general circulation model • Primitive equations – free surface • Mellor and Yamada’s level-2.5 turbulence-closure scheme • Parameterisation of density-driven downslope flows • 20 vertical levels Horizontal resolution: 3° x 3° + ice sheet, carbon cycle

  5. Natural Forcings Changes in solar irradiance Influence of volcanoes + orbital forcing

  6. Anthropogenic Forcings

  7. Outline Model and forcing  Climate during the last millennia at hemispheric scale  Climate during the last millennia at regional scale Combining model and data to study past changes  Conclusions

  8. Annual mean temperature in the Northern Hemisphere: simulations with 3D models • ECHO-g (ERIK) • ECHO-g (ERIK2) • CCSM • ECBILT-CLIO-VECODE • Jones and Mann (2004) The time series are grouped in 10-year averages

  9. Large-scale temperature in the Northern Hemisphere : role of the forcing Annual mean temperature in the Northern Hemisphere Temperature (ºC) Forcing (W m-2) red: ensemble mean green: forcing at surface (Crowley 2000) Summer temperature in the Northern Hemisphere Forcing (W m-2) Temperature (ºC) Time (yr AD) A 15-year running mean has been applied to the time series

  10. Role of the various forcings Greenhouse gas Aerosols Land-use changes Volcanos Solar irradiance Annual mean temperature in the Northern Hemisphere simulated by ECBILT-CLIO-VECODE using only one forcing at a time A 25-year running mean has been applied to the time series

  11. Uncertainties of the solar forcing Muscheler et al. (2006) Bard et al. (2000) Crowley et al. (2003) Muscheler et al. (2006)different scaling Annual mean temperature in the Northern Hemisphere simulated by ECBILT-CLIO-VECODE using different solar forcings The time series are grouped in 25 year averages

  12. Annual mean temperature in the Southern Hemisphere : hemispheric mean red: ensemble mean grey:2 std. dev. of the ensemble of 25 simulations The time series are grouped in 25-year averages Goosse et al. 2004

  13. Annual mean temperature in the Southern Hemisphere : hemispheric mean Forcing (W m-2) Temperature (ºC) Time (yr AD) red: ensemble mean green: forcing at surface (Crowley 2000) A 15-year running mean has been applied to the time series

  14. Temperature in the Southern Hemisphere : temperatures in the Southern Ocean Annual mean temperature surface temperature in the Southern Ocean Temperature (ºC) grey:5 ensemble members red: ensemble mean Annual mean ice area in the Southern Hemisphere (in 106 km2) during the last 1000 years Ice area (106km2) Time (yr AD) black : ensemble mean grey :2 std. dev.of the ensemble of 10 simulations

  15. Temperature in the Southern Hemisphere : temperatures in the Southern Ocean Annual mean oceanic temperature averaged over the area north of the Antarctic Peninsula Temperature (ºC) Magnetic susceptibility grey:5 ensemble members red: ensemble mean green: magnetic susceptibility data (Khim et al. 2002) Annual mean temperature surface temperature in the Indian sector of the Southern Ocean Temperature (ºC) Deuterium Excess (%0) grey:5 ensemble members red: ensemble mean green: deuterium excess measured in the Law Dome ice core (67 S-113E) Time (yr AD)

  16. Temperature in the Southern Hemisphere : role of propagating anomalies by the ocean Zonal mean of the temperature anomaly in the Atlantic between 1000 and 3500 m in the ensemble mean of 5 simulations Time (yr AD) Latitude Temperature (°C)

  17. Role of propagating anomalies by the ocean : response to idealised forcing Forcing minimum Forcing maximum Response to a sinusoidal forcing with a period of 1000 years Time (years) Zonal mean of the temperature anomaly in the Atlantic between 1000 and 3500 m Latitude Temperature (ºC)

  18. Conclusions (I) • For (nearly-) hemispheric averages, knowing the forced response provides already a large amount of information • The link between the forcing and the response appears simpler in the Northern Hemisphere than in the Southern Hemisphere • Difference between models/simulations: role of the forcing (actually weak in the available GCMs simulations except during the last 150 years)initial conditions model characteristics (climate sensitivity, oceanic heat uptake).

  19. Outline Model and forcing  Climate during the last millennia at hemispheric scale  Climate during the last millennia at regional scale Combining model and data to study past changes  Conclusions

  20. Regional evolution of the temperature • What is the cause of the different temperature changes between different regions ? • Internal processes (land-sea contrast, polar amplification,) • The forcing can have a spatial structure • Response of internal modes of variability Temperature anomaly for the period 1100-1150 averaged over an ensemble of simulations performed with ECBILT-CLIO-VECODE

  21. North Atlantic Oscillation (NAO) Surface Pressure (hpa) Surface Temperature (ºc) James W. Hurrell NCAR

  22. Modes of variability: Responses to volcanic forcing Observed anomalies Model results (Kirchner et al. 1999) Temperature patterns for NH winter (DJF) 1991-1992 following the Mount Pinatubo volcanic eruption Robock 2000

  23. Modes of variability: Responses to solar forcing Shindell et al. 2001 Raw time series Time series filtered to only include time scales > 40 years Regression during the period from 1650 to 1850 between reconstructed solar irradiance and reconstructed annual surface temperatures in °C per -0.32 Wm-2

  24. Geographical influence of the forcing :Land use changes Global fractional cropland (Ramankutty and Foley 1999)

  25. Geographical influence of the forcing :Land use changes Response to historical land use changes in the climate model ECBILT-CLIO-VECODE

  26. Role of the forcing and internal variability Identification of the various contributions to the difference between simulated temperatures (in Kelvin) for the period 1976-2000 and the period 1025-1050. The range associated with the contribution of internal variability is given by two standard deviations of the ensemble of simulations around the ensemble mean. The contributions of the individual forcings are obtained by performing an ensemble of 10 experiments with only one of the 6 forcing studied. Goosse et al., 2006

  27. Regional evolution of the temperature Temperature in two different simulations Northern Hemisphere annual mean temperature Temperature (ºC) Summer temperature in Fennoscandia Temperature (ºC) A 15-year running mean has been applied to the time series Time (yr AD)

  28. Regional evolution of the temperature Northern Hemisphere annual mean temperature red: ensemble mean grey:2 standard deviation of the ensemble Temperature (ºC) Summer temperature in Fennoscandia red: ensemble mean grey:2 standard deviation of the ensemble Temperature (ºC) The time series are grouped in 25-year averages Time (yr AD)

  29. Regional evolution of the temperature Temperature anomaly for two relatively warm periods averaged over an ensemble of simulations performed with ECBILT-CLIO-VECODE 1100-1150 1950-2000

  30. Regional evolution of the temperature Temperature anomaly in a particular simulation for two relatively warm periods 1100-1150 1950-2000

  31. Conclusions (II)  Different temperature changes between different regions at a particular time could be due to the spatial response to a forcing but also to internal variability  The role of internal variability could be particularly large at regional/local scale

  32. Outline Model and forcing  Climate during the last millennia at hemispheric scale  Climate during the last millennia at regional scale Combining model and data to study past changes  Conclusions

  33. Data assimilation Goal: to combine directly model results and proxy records in order to have a reconstruction of past climate changes that is consistent with proxy data, model physics and the forcing. A few techniques have been proposed: Jones and Widmann (2003) and van der Schrier and Barkmeijer (2005) constrain model results to remain close to a reconstruction of the observed atmopsheric circulation. Assimilation of a pattern of winter-mean sea level pressure (mb) corresponding to the period 1790-1820 (van der Schrier and Barkmeijer 2005)

  34. Data assimilation Assimilation of a pattern of winter-mean sea level pressure corresponding to the period 1790-1820 Influence on oceanic temperatures in winter (K) Influence on oceanic surface currents in winter (m/s) van der Schrier and Barkmeijer (2005)

  35. Using paleoclimate proxy-data to select the best realisation in an ensemble Simulation of the climate of the last 1000 years : selecting among a relatively large ensemble of simulations (> 100) the one that is the closest to the observed climate. The experiment selected is the one that minimise a cost function CF for a particular period : Where n is the number of reconstructions used in the model/data comparison. Fobs is the reconstruction of a variable F, while Fmod is the simulated value of the corresponding variable. wi is a weight factor. Goosse et al. 2006

  36. Using paleoclimate proxy-data to select the best realisation in an ensemble Ideally, the selection must occur during the simulationExample using 5 ensemble members and two constraint (observations, in red). The best member selected is displayed in bold while the other ones are dashed Temperature Temperature Time Time But it is much less expansive to use an existing ensemble Goosse et al. 2006

  37. Using paleoclimate proxy-data to select the best realisation in an ensemble Selecting among a relatively large ensemble of simulations the one that is the closest to the observed climate : example Summer temperature in Fennoscandia Temperature in the Arctic The black line corresponds to the mean over the 105 simulations while the grey lines are the ensemble mean plus and minus two standard deviations. The red line is the succession of the states that produce the minimum of the cost function (i.e., the ‘best simulation’). The blue lines are the proxy reconstruction for the region (Briffa et al. 1992, Overpeck et al. 1997). The reference period is the years 1600-1950. Goosse et al. 2006

  38. Using paleoclimate proxy-data to select an optimal realisation: 12 proxy used Selecting among a relatively large ensemble of simulations the one that is the closest to the observed climate : hemispheric mean The dark blue line is the succession of the states that produce the minimum of the cost function (i.e., the ‘best simulation’) (left axis). The red line is the reconstruction of Mann and Jones (2003) (left axis) and the light blue the one of Moberg et al. (2005) (right axis).

  39. Comparison of model results and proxy records in Europe To derive the best pseudo-simulation, 12 long proxy records of European seasonal temperatures are used. Example: Time Time The black line corresponds to the mean over the 125 simulations while the grey lines are the ensemble mean plus and minus two standard deviations of the ensemble at decadal scale. Proxy records are in green (van Engelen et al. 2001). The best pseudo simulation are represented by the red lines.

  40. Comparison of model results and proxy records in Europe Average over Europe: winter mean temperature anomaly (K) Time The red line corresponds to the mean over the 125 simulations while the grey lines are the ensemble mean plus and minus two standard deviations of the ensemble at decadal scale. Proxy records are in green (Luterbacher et al. 2004). The best pseudo simulation are represented by the orange line.

  41. Comparison of model results and proxy records in Europe Average over Europe: summer mean temperature anomaly (K) Time The red line corresponds to the mean over the 125 simulations while the grey lines are the ensemble mean plus and minus two standard deviations of the ensemble at decadal scale. Proxy records are in green (Luterbacher et al. 2004) and blue (Guiot et al. 2005). The best pseudo simulation are represented by the orange line.

  42. Using paleoclimate proxy-data to select the best realisation in an ensemble Normalized winter (DJF) temperature anomaly during the period 1690-1700 AD in the reconstruction of Luterbacher et al. (2004) in the best simulation. Reconstruction Best Simulation Goosse et al. 2006

  43. Using paleoclimate proxy-data to select the best realisation in an ensemble Anomaly of winter (DJF) geopotential height (in dam) in the best simulation during the period 1690-1700 AD Goosse et al. 2006

  44. Using paleoclimate proxy-data to select the best realisation in an ensemble Anomaly in surface ocean current (m/s) during the period 1690-1700 AD in the best simulation. Goosse et al. 2006

  45. Conclusions (III)  Very useful information could be obtained from a combined analysis of model results and proxy-data about the magnitude of the past changes and about the causes of those changes

  46. General Conclusions  The influence of the forcing is clear at hemispheric scale  The response to the forcings could display some characteristic spatial patterns The internal variability could have a large role at regional/local scale  Combining model and data could help in the analysis of past changes

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