Understanding the 1979-2014 Southern Ocean trends in...
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Understanding historical Southern Ocean trends in observations
and simulations
Yavor Kostov (Oxford) John Marshall (MIT), Kyle Armour (UW), David Ferreira (Reading)
Models Observations
1979-2014 1979-2014
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Understanding historical Southern Ocean trends in observations and simulations
1979-2014 Observations: HadISST and HadSLP2r
1979-2014 CMIP5 Historical Simulations
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Emphasis on the seasonality
1979-2014 Observations: HadISST and HadSLP2r
1979-2014 CMIP5 Historical Simulations
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We reconstruct the SO SST trends from CMIP5 historical simulations.
We use linear convolutions of SO step response functions with: 1) Seasonal SAM forcing;
2) Greenhouse gas (GHG) forcing;
3) SAM trend due to greenhouse gas forcing (overlap).
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Given a history of observed forcing, we can estimate the SAM contribution to SST anomalies via the convolution
𝑆𝑆𝑇ℎ𝑖𝑠𝑡 𝑡 = 𝑆(𝜏)𝑑𝑆𝐴𝑀ℎ𝑖𝑠𝑡
𝑑𝑡 (𝑡−𝜏)
𝑑𝜏
𝑡
0
where 𝑆(𝜏) is the SO SST step response to SAM
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Example: Model CCSM4
– SAM – SST 55S-70S
We obtain the SO SST step-responses to SAM from control runs as in Kostov et al. (2017, published)
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Stronger fast response to DJF SAM (summer)
Weaker fast cooling JJA SAM (winter)
Consistent with
Purich et al. (2016)
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CMIP5 Step Responses to Greenhouse Gas Forcing
• Based on CMIP5 abrupt CO2 quadrupling experiments • Monotonic Southern Ocean warming but slower than the global signal
(delayed Southern Ocean warming as in Armour et al., 2016) • Constitute step responses functions of SO SST to TOA radiative forcing.
smoothed
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Impact of GHG forcing on the SAM
• Based on CMIP5 abrupt CO2 quadrupling experiments; • Constitute step responses functions of SAM to TOA radiative forcing.
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Combined GHG and SAM Convolutions (using seasonal SAM forcing & responses)
Ensemble mean
• Reproduces the ensemble mean response; • Captures the intermodel spread.
Observations
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Combined GHG and SAM Convolutions (using only DJF and MAM SAM forcing & responses)
Ensemble mean
• The summer and fall SAM have a major contribution. • Consistent with the seasonal signature of the ozone hole.
Observations
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Reconstructing the observed SST Trends using CMIP5 step responses convolved with
SAM from historical simulations and GHG trends
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Reconstructing the observed SST Trends using CMIP5 step responses convolved with
observed SAM and GHG trends
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Possible combinations of GHG and SAM responses
• The GHG and SAM convolutions across the ensemble are uncorrelated.
• We consider all possible re-combinations of plausible GHG and SAM convolutions with simulated or with observed SAM.
• 19 models 193 possible combinations.
• Using observed SAM in the convolutions removes some of the bias.
ob
serv
atio
ns
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Conclusions
• Historical Southern Ocean SST trends: both seasonal SAM and greenhouse gas (GHG) forcing play important roles.
• In CMIP5 the response to GHG forcing is monotonic warming.
• Many coupled GCMs exhibit a two-timescale response of the Southern Ocean to SAM. The fast cooling regime can transition to a slow warming response.
• Seasonal SAM – consistent with the impact of the ozone hole.
• Poor representation of historical SAM trends biased response.
• To reproduce the observed Southern Ocean cooling trends, models need both realistic SAM anomalies and a realistic ocean climatology.