Earth’s energy budget in the presence of internal climate variability

*Yu Kosaka (RCAST, University of Tokyo)

Shang-Ping Xie (Scripps Institution of Oceanography, UCSD)

Yuko M Okumura (University of Texas at Austin)

WCRP Grand Challenge Workshop: Earth’s Climate Sensitivities Schloss Ringberg, Germany

Mar 23, 2015

The global-warming hiatus and internal variability

•  GMST has been flat for the past ~17 yrs •  Slowdown of radiative forcing increase

o  Solar cycle, volcano, anthropogenic aerosol, stratospheric water vapor, …

•  Internal climate variability o  Tropical Pacific decadal variability

GMST anom (ref: 1970-1999)

Radiative forcing only (historical + RCP4.5)

HadCRUT4

Radiative forcing +

Tropical Pacific variability

The global-warming hiatus and internal variability

•  GMST has been flat for the past ~17 yrs •  Slowdown of radiative forcing increase

o  Solar cycle, volcano, anthropogenic aerosol, stratospheric water vapor, …

•  Internal climate variability o  Tropical Pacific decadal variability

Radiative forcing only (historical + RCP4.5)

HadCRUT4

Radiative forcing +

Tropical Pacific variability

Mostly explains the recent hiatus!

GMST anom (ref: 1970-1999)

•  If N = F – λΔT N increase would have accelerated during hiatus

•  Observed OHC increase does not show such acceleration

Is the radiative forcing slowdown the key? … Need an energy equation considering internal variability!

Energy budget during the hiatus?

hiatus

ΔTF

ΔT

ΔTI

F – λΔTF

F – λΔT

hiatus t t

hiatus hiatus

OHC anom [×1022 J] dOHC/dt [W m–2]

Based on top 700m

OHC

ΔT = ΔTF + ΔTI

Radiation imbalance associated w/ internal variability

•  CMIP5 piControl runs by 22 models ( λ values by Forster et al. 2013) •  Detrended, 10-yr low-pass filtered (i.e. decadal-multidecadal variability)

ΔT = ΔTI , N = NI

NI = –λI ΔTI e–iθ

•  λI / λ ~ 0. 5, θ ~ 45º - 90º

•  If F ≠ 0, N = F – λΔTF – λI ΔTI e–iθ

ΔTI (MME)

NI (MME)

NI (MME)

–λ

Lag correlation with ΔTI Lag regression against ΔTI [W/m2/ºC]

NI (each model)

NI (each model)

MME mean±1σ

inco

min

g ou

tgoi

ng

Radiation imbalance associated w/ internal variability

•  High correlation for each of shortwave and longwave radiation o  Longwave radiation peaks at lag 0 → negative feedback o  Shortwave radiation leads GMST → atmospheric stochastic forcing

•  Due to a large cancellation, the correlation for net radiation is not high

(only ~ –0.4 at peak)

→ NI is not a major constraint on ΔTI

ΔTI (MME)

NI (MME)

Lag correlation with ΔTI Lag correlation with ΔTI

NI (each model)

net shortwave

–OLR

inco

min

g ou

tgoi

ng

Time scale dependence: interannual variability

•  Interannual variability: 2-10 yr band-pass filtered •  Again θ ~ 45º - 90º •  Peak correlation (~ –0.65) is higher than in decadal variability

•  Peak NI /ΔTI exceeds the (forced) feedback parameter λ (For internal variability, λI has a strong time-scale dependence)

→ Care needed in estimating λ from short observations

ΔTI (MME)

NI (MME) NI (MME)

–λ

Lag correlation with ΔTI Lag regression against ΔTI [W/m2/ºC]

NI (each model)

NI (each model)

MME mean±1σ

inco

min

g ou

tgoi

ng

Dynamical ocean vs slab ocean

•  Large interannual variability in CCSM4 ← ENSO

•  Comparable power of GMST variability for timescales > 30 yrs

o  Ocean dynamics increases GMST variance by 5% for >30 yrs

→ Ocean dynamics (and therefore subsurface ocean heat uptake) is secondary for decadal hiatus and accelerated GW (≳15 yrs)

Power spectrum of ΔTI •  CCSM4 vs •  CAM4 with a slab-ocean model

(CAM4-SOM)

•  A common AGCM (CAM4) •  For SOM, “Q-flux” and mixed-layer

depth are based on CCSM4 climatology

•  500-year piControl runs

CCSM4

CAM4-SOM

Structure of decadal GMST variability

•  Internal decadal GMST variability is structured to an IPO-like pattern with warming in the polar regions

•  Tropical Pacific decadal variability arises from thermodynamic coupling (Clement et al. 2011, Okumura 2013)

→ Applicable to internal GMST variability

•  GMST variability arises from atmospheric stochastic forcing and thermodynamic air-sea feedback

SAT (shading) & SLP (contours) anom regressed against ΔTI (10-yr low-pass filtered)

[ºC]

CCSM4 CAM4-SOM

Decadal GMST and TOA radiation variability

• Decadal variability (10-yr low-pass filtered)

•  CAM4-SOM:

→ NI = 0 at lag 0, θ = 90º No ocean dynamics (and vertical heat redistribution), but still the correlation is low

•  CCSM4: Dynamical ocean redistributes heat vertically, dynamically inducing ΔTI and perturbing net TOA radiation to damp ΔTI

→ NI < 0 at lag 0, θ = 45º - 90º (from a coherence analysis)

c dΔTIdt

≈ NI

ΔTI

NI

net shortwave

–OLR

ΔTI

NI

CCSM4 CAM4-SOM

c: global-mean heat capacity of the ocean mixed layer

Cor

rela

tion

GMST-TOA radiation variability in the two models

•  NI power decreases with time scale, but ΔTI power increases → λI decreases as time scale gets longer

•  NI is much weaker in CAM4-SOM despite comparable GMST variability → Again NI is not a major constraint on GMST

Power spectrum of ΔTI

CCSM4

CAM4-SOM CAM4-SOM

CCSM4

Power spectrum of NI

Energy budget in the presence of internal variability

• N is smaller than pure forced case (F – λΔTF) during hiatus • Global OHC increase slows down during hiatus (statistically) • Consistent with the OHC observations (despite the loose GMST-TOA rad relation)

hiatus

ΔTF

ΔT

ΔTI

F – λΔTF

hiatus t t

F – λΔTF + NI

NI

hiatus hiatus

OHC anom [×1022 J] dOHC/dt [W/m2]

Based on top 700m OHC

Assuming θ = 90º for simplicity

Conclusions

•  GMST-TOA radiation relationship is distinct b/w forced change and internal variability (time lag b/w GMST and TOA radiation)

•  During hiatus, net incoming energy decreases instead of accelerates (the traditional energy equation predicts the latter)

Observed OHC tendency is consistent with the internal variability hypothesis of the current hiatus

•  Internal GMST variability is only loosely correlated with net TOA radiation

Net TOA radiation is not a major constraint on internal GMST variability

•  Net TOA radiation perturbation change associated with internal GMST change strongly depends on time scale

Care needs to be taken in estimating (forced) feedback parameter from short observations