* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download The impact of Atmospheric and Oceanic Resolution on
Global warming wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Global warming hiatus wikipedia , lookup
Climate engineering wikipedia , lookup
Climate change feedback wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Climate governance wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Solar radiation management wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Climate change and poverty wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Atmospheric model wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Climate sensitivity wikipedia , lookup
IPCC Fourth Assessment Report wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
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 – Typical Climate Resolution (T85, 1x1) – Ex: Atmospheric Noise, Oceanic Noise, ENSO Prediction, Climate Change • Resolution Matters – Noise Aliasing • Quantifying Model Uncertainty (Noise) 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 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 SST Anomaly JFMA1998 SST Anomaly JFMA1989 Tropical Pacific Rainfall (in box) Different SST Different tropical atmospheric mean response Different characteristics of atmos. noise 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 Ensemble of N AGCMs all receive same OGCM-output SST each day AGCM1 AGCM2 Sfc Fluxes1 Sfc Fluxes2 AGCMN ••• Sfc FluxesN average (1, …, N) Average N members’ surface fluxes each day Ensemble Mean Sfc Fluxes OGCM receives ensemble average of AGCM output fluxes each day SST OGCM Interactive Ensemble Approach Interactive Ensemble • Ensemble realizations of atmospheric component to isolate “climate signal” M=1 M=2 Ensemble mean = Signal + • Ensemble mean surface fluxes coupled to ocean component M=3 – Ensemble average only applied at air-sea interface – Ocean “feels” an atmospheric state with reduced weather noise M=4, 5, 6 M = number of atmospheric ensemble members Control Simulation: CCSM3.0 (T85, 1x1) 300-year (Fixed 1990 Forcing) Interactive Ensemble: CCSM3.0 (6,1,1,1) Fixed 1990 GHG COLA CCSM-IE run Full CCSM 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? 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 Impact of Ocean Internal Dynamics with Coupled Feedbacks SSTA Variability Due to Ocean Internal Dynamics Reduced Enhanced Climate Change Problem Interactive Ensemble Control Ensemble Interactive Ensemble Climate of the 20th Century: Interactive - Control Ensemble Global Mean Temperature Regression Control Ensemble Interactive Ensemble Local Air-Sea Feedbacks: Point Correlation SST and Latent Heat Flux “Best” Observational Estimate Coupled Model Simulation Why Does ENSO Extend Too Far To The West? The Weather and Climate Link? Conceptual Model dTa (To Ta ) F dt dTo (Ta To ) To dt dTa (To Ta ) dt dTo (Ta To ) To F dt Atmos → Ocean <HF,(dSST)/dt> <HF,SST> Ocean → Atmos 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 <HF,SST> <HF,dSST> GSSTF2 Observational Estimates Area Averaged Fields Eastern Equatorial Pacific from GCMs Prescribed SST is Reasonable In Eastern Equatorial Pacific Conceptual Model: Ocean →Atmos 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 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 Ensemble of N AGCMs all receive same OGCM-output SST each day AGCM1 AGCM2 Sfc Fluxes1 Sfc Fluxes2 AGCMN ••• Sfc FluxesN average (1, …, N) Average N members’ surface fluxes each day Ensemble Mean Sfc Fluxes OGCM receives ensemble average of AGCM output fluxes each day SST OGCM Interactive Ensemble Approach Ensemble of N AGCMs all receive same OGCM-output SST each day AGCM1 AGCM2 Sfc Fluxes1 Sfc Fluxes2 ••• AGCMN Sfc FluxesN rand (1, …, N) Randomly select 1 member’s surface fluxes each day Selected Member’s Sfc Fluxes OGCM receives output of single, randomly-selected AGCM each day SST OGCM Random Interactive Ensemble Approach Nino3.4 Power Spectra Moderate Stochastic Atmospheric Forcing Period (months) Increased Stochastic Atmospheric Forcing Period (months) Reduced Stochastic Atmospheric Forcing Period (months) Increasing Stochastic Atmospheric Forcing Increase the ENSO Period 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 Random IE Control Nino34 Regression on Equatorial Pacific Heat Content 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 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) Equatorial SSTA Standard Deviation Low Resolution: IE Control Lower Resolution: IE Control 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 CFSIE - Reduce Noise Version (interactive ensemble) of CFS RMS(Obs)*1.4 CFSIE RMSE CFS Spread CFS RMSE CFSIE Spread CFSIE - Reduce Noise Version (interactive ensemble) of CFS 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 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) 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 Time Mean Equatorial Pacific SST COLA COLA HF+CAM Winds Obs CAM COLA Winds+CAM HF ENSO Heat Content Anomalies OBS COLA CAM COLA HF + CAM Winds COLA Winds + CAM HF 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)