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Disentangling the Link Between Weather and Climate

Disentangling the Link Between Weather and Climate. Ben Kirtman University of Miami-RSMAS. Noise and Climate Variability. What Do We Mean By “Noise” and Why Should We Care? Multi-Scale Issue How to Examine Noise within Context of a Coupled GCM- Interactive Ensemble

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Disentangling the Link Between Weather and Climate

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  1. Disentangling the Link Between Weather and Climate Ben Kirtman University of Miami-RSMAS

  2. Noise and Climate Variability • What Do We Mean By “Noise” and Why Should We Care? • Multi-Scale Issue • How to Examine Noise within Context of a Coupled GCM- Interactive Ensemble • Typical Climate Resolution (T85, 1x1) • Ex: Atmospheric Noise, Oceanic Noise, ENSO Prediction, Climate Change • Resolution Matters • Noise Aliasing • Quantifying Model Uncertainty (Noise)

  3. Why is Noise an Interesting Question? • Large Scale Climate Provides Environment for Micro- and Macro-Scale Processes • Local Weather and Climate: Impacts, Decision Support • Micro- and Macro-Scale Processes Impact the Large-Scale Climate System • Interactions Among Climate System Components • Justification for High Resolution Climate Modeling • But, this is NOT the Definition of Noise • Noise Occurs on all Space and Time Scales

  4. How Should Noise be Defined? • Use ensemble realizations • Ensemble mean defines “climate signal” • Deviation about ensemble mean defines Noise • Climate signal and noise are not Independent • Examples: • Atmospheric model simulations with prescribed SST • Climate change simulations

  5. Tropical Pacific Rainfall (in box) SST Anomaly JFMA1998 SST Anomaly JFMA1989 Different SST  Different tropical atmospheric mean response Different characteristics of atmos. noise

  6. Modeling Weather & Climate Interactions • Previously, this required ad-hoc assumptions about the weather noise and simplified theoretically motivated models • We adopt a coupled GCM approach • Weather is internally generated • Signal-noise dependence • State-of-the-art physical and dynamical processes  Interactive Ensemble

  7. AGCM1 AGCM2 AGCMN Sfc Fluxes1 Sfc Fluxes2 Sfc FluxesN SST OGCM Ensemble of N AGCMs all receive same OGCM-output SST each day • • • Average N members’ surface fluxes each day average (1, …, N) Ensemble Mean Sfc Fluxes OGCM receives ensemble average of AGCM output fluxes each day Interactive Ensemble Approach

  8. Interactive Ensemble • Ensemble realizations of atmospheric component to isolate “climate signal” Ensemble mean = Signal +  • Ensemble mean surface fluxes coupled to ocean component • Ensemble average only applied at air-sea interface • Ocean “feels” an atmospheric state with reduced weather noise M=1 M=2 M=3 M=4, 5, 6 M = number of atmospheric ensemble members

  9. Control Simulation: CCSM3.0 (T85, 1x1) 300-year (Fixed 1990 Forcing) Interactive Ensemble: CCSM3.0 (6,1,1,1)

  10. Fixed 1990 GHG Full CCSM COLA CCSM-IE run

  11. If all SST variability is forced by weather noise, the ratio of SST variance (IE CGCM)/(Standard CGCM) is expected to be 1/6 and the ratio of standard deviations to be 0.41. Variability Driven by Noise Coupled Feedbacks? Ocean Noise?

  12. Ocean and Atmosphere Interactive Ensemble OGCMn Ensemble Member SST AGCM1 OGCM Ensemble Mean SST ●●● Ensemble Mean Fluxes OGCM1 AGCMN Ensemble Mean SST ●●● OGCMM AGCMn Ensemble Member Flux AGCM Ensemble Mean Flux

  13. Impact of Ocean Internal Dynamics with Coupled Feedbacks SSTA Variability Due to Ocean Internal Dynamics Enhanced Reduced

  14. Climate Change Problem Interactive Ensemble Control Ensemble Interactive Ensemble

  15. Climate of the 20th Century: Interactive - Control Ensemble

  16. Global Mean Temperature Regression Control Ensemble Interactive Ensemble

  17. Local Air-Sea Feedbacks: Point Correlation SST and Latent Heat Flux “Best” Observational Estimate Coupled Model Simulation

  18. Why Does ENSO Extend Too Far To The West? The Weather and Climate Link?

  19. Conceptual Model Atmos → Ocean <HF,(dSST)/dt> <HF,SST> Ocean → Atmos

  20. Conceptual Model Atmos → Ocean • Atmosphere Forcing Ocean: • <HF(t), SST(t) > < 0 • <HF(t), d(SST(t))/dt> <0 <HF,(dSST)/dt> <HF,SST> Ocean → Atmos • Ocean Forcing Atmospere: • <HF(t), SST(t) > > 0 • <HF(t), d(SST(t))/dt> > 0

  21. <HF,SST> Conceptual Model: Ocean →Atmos <HF,dSST> GSSTF2 Observational Estimates Area Averaged Fields Eastern Equatorial Pacific from GCMs Prescribed SST is Reasonable In Eastern Equatorial Pacific

  22. Conceptual Model: Atmos →Ocean <HF,dSST> <HF,SST> GSSTF2 Observational Estimates Area Averaged Fields Central/Western Equatorial Pacific CGCM Variability is too Strongly SST Forced

  23. Western Pacific Problem • Hypothesis: Atmospheric Internal Dynamics (Stochastic Forcing) is Occurring on Space and Time Scales that are Too Coherent  Too Coherent Oceanic Response  Excessive Ocean Forcing Atmosphere  Test:Random Interactive Ensemble

  24. AGCM1 AGCM2 AGCMN Sfc Fluxes1 Sfc Fluxes2 Sfc FluxesN SST OGCM Ensemble of N AGCMs all receive same OGCM-output SST each day • • • Average N members’ surface fluxes each day average (1, …, N) Ensemble Mean Sfc Fluxes OGCM receives ensemble average of AGCM output fluxes each day Interactive Ensemble Approach

  25. AGCM1 AGCM2 AGCMN Sfc Fluxes1 Sfc Fluxes2 Sfc FluxesN SST OGCM Ensemble of N AGCMs all receive same OGCM-output SST each day • • • Randomly select 1 member’s surface fluxes each day rand (1, …, N) Selected Member’s Sfc Fluxes OGCM receives output of single, randomly-selected AGCM each day Random Interactive Ensemble Approach

  26. Nino3.4 Power Spectra Moderate Stochastic Atmospheric Forcing Reduced Stochastic Atmospheric Forcing Period (months) Period (months) Increased Stochastic Atmospheric Forcing Increasing Stochastic Atmospheric Forcing Increase the ENSO Period Period (months)

  27. Random IE Control 4 4 3 3 2 2 1 1 0 0 -1 -1 -2 -2 -3 -3 -4 -4 Nino34 Regression on Equatorial Pacific SSTA

  28. Random IE Control Nino34 Regression on Equatorial Pacific Heat Content

  29. Contemporaneous Latent Heat Flux - SST Correlation Observational Estimates Increased “Randomness” Coupled Model Control Coupled Model Random Interactive Ensemble: Increased the Whiteness of the Atmosphere forcing the Ocean

  30. Noise and Climate Variability • What Do We Mean By “Noise” and Why Should We Care? • Multi-Scale Issue • How to Examine Noise within Context of a Coupled GCM? • Typical Climate Resolution (T85, 1x1) • Atmospheric Noise, Oceanic Noise, Climate Change Problem • Resolution Matters • Noise Aliasing • Quantifying Model Uncertainty (Noise)

  31. Equatorial SSTA Standard Deviation Lower Resolution: IE Control Low Resolution: IE Control

  32. Understanding Loss of Forecast Skill • What is the Overall Limit of Predictability? • What Limits Predictability? • Uncertainty in Initial Conditions: Chaos within Non-Linear Dynamics of the Coupled System • Uncertainty as the System Evolves: External Stochastic Effects • Model Dependence? • Model Error

  33. CFSIE - Reduce Noise Version (interactive ensemble) of CFS

  34. RMS(Obs)*1.4 CFSIE RMSE CFS Spread CFS RMSE CFSIE Spread CFSIE - Reduce Noise Version (interactive ensemble) of CFS

  35. Predictability Estimates Worst Case: Initial Condition Error (A+O) + Model Error Worst Case Best Case Best Case: Initial Condition Error (A) + No Model Error Better Case: Initial Condition Error (A) + Model Error Better Case Best Case

  36. Noise and Climate Variability • What Do We Mean By “Noise” and Why Should We Care? • Multi-Scale Issue • How to Examine Noise within Context of a Coupled GCM? • Typical Climate Resolution (T85, 1x1) • Atmospheric Noise, Oceanic Noise, Climate Change Problem • Resolution Matters • Noise Aliasing • Quantifying Model Uncertainty (Noise)

  37. Multi-Model Approach to Quantifying Uncertainty • Multi-Model Methodologies Are a Practical Approach to Quantifying Forecast Uncertainty Due to Uncertainty in Model Formulation • No Determination of Which Model is Better - Depends on Metric • Taking Advantage of Complementary or Orthogonal “Skill” • Taking Advantage of Orthogonal Systematic Error

  38. Time Mean Equatorial Pacific SST COLA COLA HF+CAM Winds Obs CAM COLA Winds+CAM HF

  39. ENSO Heat Content Anomalies OBS COLA CAM COLA HF + CAM Winds COLA Winds + CAM HF

  40. Noise and Climate Variability What Do We Mean By “Noise” and Why Should We Care? Multi-Scale Issue How to Examine Noise within Context of a Coupled GCM- Interactive Ensemble Typical Climate Resolution (T85, 1x1) Ex: Atmospheric Noise, Oceanic Noise, ENSO Prediction, Climate Change Resolution Matters Noise Aliasing Quantifying Model Uncertainty (Noise)

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