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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