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Arctic climate change – structure and mechanisms

Arctic climate change – structure and mechanisms. Nils Gunnar Kvamstø, Input from: Øyvind Byrkjedal, Igor Ezau, Asgeir Sorteberg, Ivar Seierstad and David Stephenson. Arctic zonal temperature anomalies (within 60º-90ºN latitudinal zone). Winter, summer, and annual anomalies, 1881-2003 period

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Arctic climate change – structure and mechanisms

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  1. Arctic climate change – structure and mechanisms Nils Gunnar Kvamstø, Input from: Øyvind Byrkjedal, Igor Ezau, Asgeir Sorteberg, Ivar Seierstad and David Stephenson

  2. Arctic zonal temperature anomalies(within 60º-90ºN latitudinal zone) • Winter, summer, and annual anomalies, 1881-2003 period • All linear trends significant at the 0.01 level • (available from CDIAC, Lugina et al. 2003, updated). Courtesy P.Groisman

  3. Northern Hemisphere temperature anomalies • Winter, summer, and annual anomalies, 1881-2003 period • All linear trends significant at the 0.01 level • (available from CDIAC, Lugina et al. 2003, updated). Courtesy P.Groisman

  4. Arctic vs. Global Change DJF Zonal mean Ts anomalies Johannessen et al. 2003

  5. ∆Ts DJF MAM SON JJA

  6. Vertical structure Hartman (1994)

  7. Seasonal cycle of Arctic temperature profiles Inversion Hartman (1994)

  8. Vertical structure of recent Arctic warming DJF MAM JJA SON Graversen et al 2008, Nature

  9. Cross-section cold air outbreak, arctic front, Shapiro & Fedor 1989 Isentropes height Sea Ice

  10. Vertical structure Hartmann and Wendler J. Clim (2003)

  11. SAT is heavily sensitive to the relative strengths of surface inversions Change in mean winter temperature from 1957-58 to 2003-04 for decoupled (left) and coupled (right) PBL cases. After Hartmann and Wendler (2003).

  12. POLAR AMPLIFICATION • GHG forcing considered to be quite uniform, why polar amplification? • Ice-albedo feedback • Cloud feedback • ”Dynamic feedback”

  13. Fixed albedo experiment –> Albedo feedback Hall (2004)

  14. Fixed cloud experiment -> Cloud feedback Vavrus (2004)

  15. Ghost forcing -> Dynamical feedback Alexeev, Langen, Bates (2005)

  16. Ghost forcing -> Dynamical feedback Alexeev, Langen, Bates (2005)

  17. 2 Projected changes SRES A1B (CO2 ENDS AT 700 ppm) ___ENSEMBLE MEAN 4-10ºC ºC 0 2 4 6 8 10 1920 1940 1960 1980 2000 2020 2040 2060 2080 CHANGES IN ARCTIC TEMPERATURES FROM 15 CLIMATE MODELS Sorteberg and Kvamstø (2006)

  18. Why is the spread so large? • Insufficient formulation of processes in GCMs? • Internal atmospheric variability? • Differences in external forcing (GHG, aerosols)?

  19. LARGE DIFFERENCES IN PROJECTED CLIMATE CHANGE EVEN WHEN SAME FORCING IS USED: 19 CMIP2 MODELS : ZONAL TRENDS IN T2m YEAR 31-60 (ºC/DECADE) Is this spread entirely due to different models? Sorteberg and Kvamstø (2006)

  20. BCM SPREAD vs MULTIMODEL SPREAD ANNUAL 5 MEMBER ENSEMBLE MEANT2m CHANGE YEAR 1-30 (C) Sorteberg and Kvamstø (2006)

  21. BCM ENSEMBLE SPREAD IN ANNUAL T2m ZONAL MEAN TEMPERATURE CHANGE RELATIVE TO MULTIMODEL SPREAD (%) YEAR 1-30 60% 40% 20% Sorteberg and Kvamstø (2006)

  22. Role of internal variability w.r.t. multi model spread Temperature Precipitation Sorteberg and Kvamstø (2006)

  23. Ensemble mean change Year 61-80 <∆T> <∆P> Sorteberg and Kvamstø (2006)

  24. Ensemble spread Year 61-80 σ∆T σ∆P Sorteberg and Kvamstø (2006)

  25. Signal to noise ratio Year 61-80 S/N; T S/N; P Sorteberg and Kvamstø (2006)

  26. Spreads dependence on ensemble size 95% confidence in annual means: <ΔT>±0.2K <ΔP>±0.1mm/day What contributes to the large Arctic T variability? Sorteberg and Kvamstø (2006)

  27. CHANGE IN ICELANDIC LOW AT 2CO2 DJF: ARCTIC TEMP CHANGE DJF

  28. THE ICELANDIC LOW: A MAJOR PLAYER ATMOSPHERIC HEAT TRANSPORT INTO THE ARCTIC

  29. Surface air temperature change (AR4) DJF (1954 – 2003) A2 B2 Kattsov, Walsh

  30. Can we trust projected changes? (even with large ensemble sizes) • Generally too cold troposphere • Too warm SAT • Underestimation of precipitation • Systematic biases in surface pressure distribution (Beaufort high) • Model problems connected to poles (Randall et al. BAMS, 1999)

  31. T2m is a heavily used climate parameter. How is the ABL represented in GCMs? HIRLAM and ARPEGE comparison with Sodankylä Data http://netfam.fmi.fi/ • Models are missing cold events – model SAT is too warm • Climate variability, diurnal cycle and blocking events are underpredicted

  32. Mixing profiles in NERSC LES (dashed) and ARPEGE – large discrepancy in shallow Arctic PBLs

  33. A model resolution problem An analysis of observations and LES data shows that the standard closure type in todays GCMs e.g. are not applicable on vertical resolutions > 10-50m H: If implemented correctly it should work well

  34. Test of H hPa • 90L: • 90 vertical layers • 70 layers increased resolution from 600hPa and below • 10m resolution in the lowest 60 m • 31L: • 31 vercikal layers (standard) • lowest layer at ca 70m Far too costly – Alt: use analytical functions

  35. Simulated vertical temperature profile vs observed data (SHEBA) 90L 31L Obs

  36. Response in Surface Temperature by season (90L-31L) djf mam Moderate improvement. Local processes important, butlarge-scale dynamics is playinga significant role as well! jja son

  37. Data analysis Daily SLP anomalies in Bergen Highpass filtered SLP variance (2-10d) Monthly storminess Y: where GLM: Predictors: Seasonality Local SLP + 9 leading PCs Seierstad et al (2007)

  38. Can teleconnection patterns provide additional explanation for variations in storminess? Yes! But, restricted to local, mostly high latitude areas. ΔY (%) due to 1σ change in predictors Seierstad et al (2007)

  39. Given limited resources, modellershave to make priorities

  40. Response Surface flux for DJF (90L-31L) latent sensible

  41. Complexity of the Arctic Climate System • Winds (days – weeks) • Ocean Currents (years to decades) • Rivers (years to decades) • Terrestrial cryosphere (centuries and longer) • This is a highly non-linear coupled system Macdonald et al., 2003

  42. Thank you for your attention!

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