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

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

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. SST OGCM average (1, …, N) Sfc Fluxes 1 AGCM 1 Sfc Fluxes 2 AGCM 2 Sfc Fluxes N AGCM N Ensemble Mean Sfc Fluxes Interactive Ensemble Approach Ensemble of N AGCMs all receive same OGCM-output SST each day OGCM receives ensemble average of AGCM output fluxes each day Average N members’ surface fluxes each day

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=2 M=1 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. Full CCSM COLA CCSM-IE run Fixed 1990 GHG

11. Variability Driven by Noise Coupled Feedbacks? Ocean Noise? 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.

12. Ocean and Atmosphere Interactive Ensemble AGCM 1 AGCM N Ensemble Mean Fluxes OGCM 1 OGCM M Ensemble Mean SST AGCM n Ensemble Member Flux AGCM Ensemble Mean Flux OGCM n Ensemble Member SST OGCM Ensemble Mean SST

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

14. Climate Change Problem Control Ensemble Interactive Ensemble

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

16. Global Mean Temperature Regression Control Ensemble Interactive Ensemble

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

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

20. Conceptual Model Atmos → Ocean Ocean → Atmos

21. Conceptual Model Atmos → Ocean Ocean → Atmos Atmosphere Forcing Ocean: < 0 Ocean Forcing Atmospere: > 0

22. Area Averaged Fields Eastern Equatorial Pacific from GCMs GSSTF2 Observational Estimates Prescribed SST is Reasonable In Eastern Equatorial Pacific Conceptual Model: Ocean →Atmos

23. Area Averaged Fields Central/Western Equatorial Pacific CGCM Variability is too Strongly SST Forced GSSTF2 Observational Estimates Conceptual Model: Atmos →Ocean

24. 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

25. SST OGCM average (1, …, N) Sfc Fluxes 1 AGCM 1 Sfc Fluxes 2 AGCM 2 Sfc Fluxes N AGCM N Ensemble Mean Sfc Fluxes Interactive Ensemble Approach Ensemble of N AGCMs all receive same OGCM-output SST each day OGCM receives ensemble average of AGCM output fluxes each day Average N members’ surface fluxes each day

26. SST OGCM rand (1, …, N) Sfc Fluxes 1 AGCM 1 Sfc Fluxes 2 AGCM 2 Sfc Fluxes N AGCM N Selected Member’s Sfc Fluxes Random Interactive Ensemble Approach Ensemble of N AGCMs all receive same OGCM-output SST each day OGCM receives output of single, randomly-selected AGCM each day Randomly select 1 member’s surface fluxes each day

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

28. ControlRandom IE Nino34 Regression on Equatorial Pacific SSTA 00 1 2 -2 -3 -4 -3 -2 1 2 3 4 3 4

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

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

31. 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)

32. Equatorial SSTA Standard Deviation Low Resolution: IEControl Lower Resolution: IEControl

33. 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

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

35. RMS(Obs)*1.4 CFSIE RMSE CFS Spread CFS RMSE CFSIE Spread

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

37. 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)

38. 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

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

44. ENSO Heat Content Anomalies OBS CAMCOLA COLA HF + CAM WindsCOLA Winds + CAM HF

45. 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)