Earth's Energy Imbalance: Natural Variability and SST patterns - - PowerPoint PPT Presentation

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Earth's Energy Imbalance: Natural Variability and SST patterns - - PowerPoint PPT Presentation

Jul 02, 2023 228 likes •567 views

Earth's Energy Imbalance: Natural Variability and SST patterns Cristian Proistosescu JISAO, University of Washington The Earths Energy Imbalance and its implications Nov 15, 2018 Toulouse, France Collaborators: Yue Dong, Kyle Armour, Robb

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Earth's Energy Imbalance: Natural Variability and SST patterns

Cristian Proistosescu

JISAO, University of Washington The Earth’s Energy Imbalance and its implications Nov 15, 2018 Toulouse, France Collaborators: Yue Dong, Kyle Armour, Robb Wills, David Battisti - University of Washington Malte Stuecker - IBS, Pusan, South Korea

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λ = F − Q T ICS = F2×CO2 λ

Net feedback: Inferred Climate Sensitivity

Q

radiative forcing radiative response heat uptake

Energy Budget at TOA

Q = F + λT R(T) = λT F T

Energy budget and climate sensitivity

Gregory et al 2002

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λ = Fμ + Qμ Tμ Q = 0.71 W/m2

Central estimate based on historical anomalies since pre-industrial

F = 2.33 W/m2

  • 2005-2015 average

WCRP assessment; in prep

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λ = (Fμ+Fσ) − Qμ Tμ Q = 0.71 W/m2

Uncertainty in forcing leads to large uncertainty in inferred sensitivity

F = 2.33±0.5 W/m2

3.05 K (Forcing Only)

  • 2005-2015 average
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λ = (Fμ+Fσ) − (Qμ+Qσ) Tμ+Tσ

Additional uncertainty due to observation error is negligible (1/25)

Q = 0.71±0.1 W/m2 F = 2.33±0.5 W/m2

3.05 K (Forcing Only) 3.25 K (Forcing, EEI, Temp)

  • 2005-2015 average
  • Uncertainties add in quadrature
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λ = (Fμ+Fσ) + (Qμ−Qσ + Qvar) Tμ+Tσ + Tvar

3.05 K (Forcing Only) 3.25 K (Forcing, EEI, Temp)

Account for natural variability using decadal averages across CMIP5 piControl runs

3.28 K (Forcing, EEI, Temp +variability)

F = 2.33±0.5 W/m2 Q = 0.71±0.1±0.08 W/m2

  • 2005-2015 average
  • Uncertainties add in quadrature
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  • Uncertainty in forcing strongly dominates observational uncertainty

and natural variability (uncertainties add in quadrature)

  • Natural variability comes from models:
  • What is its structure?
  • Can we trust it?
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CMIP5 piControl variability

  • Variability in TOA associated with ENSO & PDO
  • On long time scales: very little additional

variability in TOA despite significant low frequency variability in TAS

TAS TOA

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CAM5 AMIP run with prescribed SST and Sea Ice (SIC)

  • Atmospheric concentrations (GHG, aerosols) fixed at 2000 level
  • Changes in EEI at TOA driven by changes in SST & SIC

Q = R = λT F = 0

(anomalous)

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CAM5 AMIP run SST [K]

Decompose TOA into mean response to global temperature changes + variability

Q = λ ⋅ T(t) + Qvar λ = < T > < Q > T(t)

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CAM5 AMIP run exhibits significant variability More outgoing radiation recently - more negative feedbacks

λ ⋅ T(t) Qvar T(t)

SST [K]

More Negative Feedback LOWER ICS

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Historical SSTs drive much higher interdecadal variability than the SSTs produced by coupled models

Qvar λ ⋅ T(t) Qvar

More Negative Feedback LOWER ICS

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  • Uncertainty in forcing strongly dominates observational uncertainty

and natural variability (uncertainties add in quadrature)

  • Natural variability comes from models:
  • What is its structure?
  • Can we trust it? - NO!
  • What causes decadal variability in EEI? (in AMIP runs at least?)
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137 fixedSST runs on CAM4

J = ∂R( ⃗ x 1) ∂SST( ⃗ x 2)

Green’s function approach: TOA radiative response to localized SST anomalies

(Dong, Proistosescu, Armour, Battisti, in prep) (Zhou, Zelinka, Klein 2017)

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Response to representative patches

  • S
  • Dong et al in prep
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Controlled by West Pacific Surface Temp Controlled by local warming

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Global radiative response to localized warming

∂R ∂T( ⃗ x )

(downward)

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Future warming pattern preferentially excites positive feedbacks

(downward)

  • Decreased outgoing radiation
  • More positive feedbacks
  • higher climate sensitivity
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Future warming pattern preferentially excites positive feedbacks

(downward) Feedback W/m2/K year Abrupt4xCO2

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Simulations

Probability

Historical

T2×CO2

Pattern effect explains why historical estimates of ECS are low with respect to long term GCM estimates

Feedback W/m2/K year Abrupt4xCO2

Proistosescu & Huybers 2017 Armour 2017 Andrews and Webb 2018

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Feedbacks increase with time in models, but decrease with time in AMIP runs

Feedback W/m2/K year Abrupt4xCO2

(with respect to pre-industrial level)

AMIP- Historical SSTs Feedback W/m2/K year

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Global radiative response to localized warming

∂R ∂T( ⃗ x )

(downward)

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Weight SST field by Green’s function, and take EOFs

Y = T( ⃗ x , t) ⋅ ∂R ∂T( ⃗ x )

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Two dominant EOFs: ENSO + extended warm pool warming

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Warm pool warming dominates decadal changes in TOA EEI

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Warm pool warming dominates decadal changes in TOA EEI EOF #1 Mean warming pattern

Constant feedback term Green’s function x EOF #1 Green’s function Reconstruction CAM5 output

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Coupled models do not generate sufficient decadal variability in warm pool temperatures

AMIP 1σ piControl 1σ

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Conclusions

  • Observational Uncertainty not a major contributor to ECS uncertainty
  • Do we trust uncertainty estimates?
  • Pattern effect: slowly evolving patterns of SST (multidecadal) are the major

source of uncertainty in linking EEI to ECS.

  • Feedbacks increase with time (higher ECS) in coupled models
  • Decrease with time in AMIP runs (historical ECS) due to relative Warm

Pool warming

  • Coupled models severely underestimate decadal scale variability in EEI
  • Associated with underestimation of West Pacific variability
  • Forced (aerosols?) /Unforced?
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SUPPLEMENTAL SLIDES

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Sanity Check: CAM5 (prescribed SST) reproduces CERES EBAF radiation over PDO switch: 2014:2017 - 2000:2014

CERES EBAF SW CERES EBAF LW CERES EBAF NET CESM1 AMIP LW CESM1 AMIP NET CESM1 AMIP SW W/m2

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T Q Background variability in atmospheric TAS and TOA

  • CAM5 AGCM control:
  • Simulations with climatological SSTs
  • White noise variability in TAS and TOA
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Background variability in atmospheric TAS and TOA + variability driven by changes in SSTs TAS TOA

  • CAM5 AGCM control:
  • Simulations with climatological SSTs
  • White noise variability in TAS and TOA
  • CESM1.2 control:
  • Additional variability in TOA associated with

ENSO & PDO type signals

  • On long time scales: very little additional

variability in TOA despite significant variability in TAS

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Background variability in atmospheric TAS and TOA + variability driven by changes in SSTs

  • CAM5 AGCM control:
  • Simulations with climatological SSTs
  • White noise variability in TAS and TOA
  • CESM1.2 control:
  • Additional variability in TOA associated with

ENSO & PDO type signals

  • On long time scales: very little additional

variability in TOA despite significant variability in TAS

  • CMIP5 piControl
  • Consistent picture

TAS TOA