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Decadal Prediction

Decadal Prediction. Roger Lukas OCN/MET666 Fall 2010. Outline. Motivation Mechanisms for decadal variability Assessing predictability Realizing predictability. Motivation for Decadal Prediction. Distinguishing decadal variability vs. anthropogenic climate change: overlapping time scales

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Decadal Prediction

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  1. Decadal Prediction Roger LukasOCN/MET666Fall 2010

  2. Outline • Motivation • Mechanisms for decadal variability • Assessing predictability • Realizing predictability

  3. Motivation for Decadal Prediction • Distinguishing decadal variability vs. anthropogenic climate change: overlapping time scales • Nonstationarity of S-I variability • Background state for ENSO prediction models

  4. Divergence of Climate Projections and Nature Keenlyside et al. (2007) • Global warming projections diverged from nature after ~1998-2000 • Strong La Nina and phase change for PDO • Warming scenarios are radiatively “forced” from a “realistic” 20th century climate • Thermal inertia in the natural system is unitialized in climate projections Red envelope shows range of ensemble members, indicating “intrinsic” variability (in model)

  5. SST trends around global mean:What do spatial patterns tell us? PDO-like pattern AMOC pattern Latif et al. (2006)

  6. SST Change: 1940-1960 minus 1971-1990 not NAO

  7. Anthropogenic Climate Change Impacts Prediction Different scenarios Obs S/N problem

  8. N. Europe winter air temperature(Keenlyside et al., 2007) Different initial conditions, same scenario

  9. Mechanisms for decadal variability • AMOC fluctuations • Which begs the question: What causes those? • Buoyancy forcing • Wind forcing • Time scales? • Forced PDO • External [Meehl et al. (2008)] • Solar cycle (11 and 22 years) • Internal [Latif et al. (2006, 2009)] • Quasi-decadal coupled modes (e.g. PDO, decadal ENSO) • Multi-decadal coupled modes (e.g. AMO) • Stochastic “accumulation” (maybe PDO)

  10. Key Mechanisms • Evidence that multidecadal variations in Atlantic sea surface temperatures are a cause of variations in Atlantic-European climate • SST variations may be a manifestation of variability in the oceanic THC • Atmospheric response to Atlantic SST projects on the NAO pattern • Mechanism involves impact of tropical Atlantic SST anomalies on local precipitation, and a subsequent Rossby wave response

  11. Solar CycleMeehl et al. (2008, J. Climate) • Very small solar forcing (~ 1 W m-2) • Amplified by WISHE in lower limb of Hadley Cells

  12. Meehl et al. (2009, Science)

  13. Solar Cycle and SST

  14. Marshall and Schott Cold, fresh surface stabilized by salinity

  15. Marshall and Schott(200

  16. MOC and deep convection Latif et al. (2007)

  17. Atlantic meridional overturning circulation

  18. SST anomalies associated with interdecadal MOC fluctuations Small Tropical Amplitude

  19. Anomalous poleward heat transport in Atlantic/Arctic associated with MOC maximum Atlantic Heat Transport (1014 Watts) MOC maximum MOC increasing MOC weakening

  20. Latif et al. (2007)

  21. Impact of AMOC on European surface air temperature

  22. JJA Precipitation Anomalies Associated with Maximum MOC Units: cm/day

  23. Elements of Climate Predictability • External forcing • Solar (e.g. Milankovitch cycles) • atmospheric composition • Thermal inertia - persistence • Anomalous heat content • Slow damping, dissipation • Lags • wave propagation • advection

  24. Are decadal variations predictable? At least three factors influence time-evolving regional climate at the decadal timescale: 1) climate change commitment (further warming as the coupled climate system comes into adjustment with increases of greenhouse gases that have already occurred), 2) external forcing, particularly from future increases of greenhouse gases and recovery of the ozone hole, and 3) internally-generated variability.

  25. Meehl et al. (2008)

  26. Initial Conditions vs. Boundary Conditions Initial Value Problem Weekly-Seasonal Decadal Climate Variability And Change Boundary Value Problem Multidecadal to Centennial Climate Change T. DelworthGFDL/NOAA

  27. Assessing Predictability • Perfect models + initialization (Boer, 2001) • Signal variance/ensemble variance provides upper bound for model • Possibly less than potential predictability of nature • Potential predictability from observations (Pohlmann et al., 2004) • Short records, less reliable • Less model dependent • Dynamical variability impact assessment (Park and Latif, 2004) • Hurrell et al. (2009)

  28. Assessing Predictability

  29. Latif et al. (2007)

  30. Potential Predictability

  31. Multidecadal Mode Latif et al. (2007)

  32. Quasi-decadal Mode Latif et al. (2007)

  33. multidecadal mode quasidecadal mode

  34. MOC Streamfunction anomalies

  35. MOC Streamfunction anomalies

  36. Keenlyside et al. (2007) SST initialization Correlation skill for predicting 10-year mean SAT anomalies 10 years ahead (1955-2005) No initialization

  37. Keenlyside et al. (2007)

  38. Smith et al. (2007)

  39. Smith et al. (2007)

  40. Smith et al. (2007)

  41. Smith et al. (2007)

  42. The N. Atl. MOC in the 1860 Control

  43. Ensemble starting at year 1101

  44. Ensemble starting at year 1201

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