Alexey Karpechko & Elisa Manzini Diagnosing stratospheric contribution to climate change in the CMIP5 models Alexey Karpechko & Elisa Manzini

Contributions from many people, most importantly: James Anstey, Seok-Woo Son, Paolo Davini, Giuseppe Zappa, Natalia Calvo, Steven Hardiman, and others… 4.12.2018

Stratosphere and climate change The atmosphere will change in response to GHG emissions The stratosphere will change too How will the stratosphere change? And what are the implications of stratospheric changes for tropospheric climate change? 4.12.2018

Arctic polar vortex changes -> ? In the Northern Hemisphere future changes in the Arctic polar vortex remain poorly understood, although a convergence of model results is appearing. Equatorward shift of the polar night jet Seems to be the most common response to doubling CO2 in stratosphere-resolving models but …not reproduced by all models Large decadal variability may mask the forced signal (Butchart et al. 2000) Sigmond et al (2004) 4.12.2018

Arctic polar vortex changes There seems to be a systematic difference between high-top (stratosphere-resolving) and low-top models Low-top models typically simulate strengthening of zonal winds throughout the polar stratosphere but… Not necessarily → a high-top is not needed to simulate weakening of the polar winds: ECHAM5, U, 2xCO2, JFM Scaife et al (2011) 4.12.2018

Arctic polar vortex changes: Impacts on the troposphere Positive vs negative response of the Northern Annular Mode Spread of the results (Shindell et al. 1999; Fyfe et al 1999; Gillett et al. 2003; Sigmond et al. 2008, 2010; Scaife et al. 2011, Karpechko and Manzini 2012) Likely related to changes in polar stratospheric winds (weaken or strengthen) Future weakening of the polar stratospheric winds drives the NAM towards negative phase (But there are other factor influencing future NAM changes) Karpechko and Manzini (2012)

Future Arctic polar vortex changes Equatorward shift of the Arctic polar night jet seems to be the most common response to GHG increases in stratosphere-resolving models Large interannual and decadal variability Lack of understanding of the mechanisms Up-down or down-up influence? Impacts on future surface climate remain unclear 4.12.2018

CMIP5 models What do CMIP5 climate simulations say about future changes in the Arctic wintertime polar vortex and its implications for surface climate change? 4.12.2018

CMIP3/IPCC AR4 models High top models: the lid is above the stratopause Low top models: the lid is below the stratopause Low tops dominate based on Cordero and Forster (2007) 4.12.2018

CMIP5/IPCC AR5 models High tops dominate from Charlton-Perez et al (2013) High tops dominate 4.12.2018

Data 24 CMIP5 models 10 Low-tops, 13 High-tops, 1 intermediate (1 hPa) Historical and rcp 8.5 simulations Monthly mean sea level pressure (SLP), zonal mean U and T 42 simulations for SLP and U 7 models with more than 1 simulation 24 simulations for T One simulation per model only Focus on DJF mean (unless otherwise mentioned) Difference between 2060-2099 mean and 1961-2000 mean 4.12.2018 4.12.2018 11

CMIP5 zonal wind changes Multi-model mean change ~70% models simulate weakening polar stratospheric winds Large spread among individual models Largest spread is around 70°N and 10hPa Why spread? What are its implications for surface climate? Intermodel standard deviation (σ) Define stratospheric winds (SUA) index: (-1∙ΔU) 4.12.2018

CMIP5 vs CMIP3 models the period 101-140 minus the period 1-40 DJF 1%CO2 experiment in CMIP3&CMIP5 the period 101-140 minus the period 1-40 DJF CMIP5: Zonal winds weaken north 70N CMIP3: Zonal winds strengthen through the stratosphere 4.12.2018 4.12.2018 13 13

CMIP5 zonal temeparture changes Multi-model mean change Largest intermodel spreads in: Polar stratosphere (SUA index) Upper tropical troposphere (tropical warming) Lower high latitude troposphere (Arctic amplification) Is the stratospheric spread related to those in the troposphere? Or is it not? Intermodel standard deviation (σ) 4.12.2018

1. Impact of tropical warming on tropospheric dynamics: 30oS EQ 30oN 4.12.2018

1. Impact of tropical warming on tropospheric dynamics: 30oS EQ 30oN STRONGER SUBTROPICAL WESTERLY JET 30oS EQ 30oN 60oN 90oN 4.12.2018

2. Impact of polar amplification on tropospheric dynamics: 30oS EQ 4.12.2018

2. Impact of polar amplification on tropospheric dynamics: 30oS EQ WEAKER WINDS AT HIGH LATS 30oS EQ 30oN 60oN 90oN 4.12.2018

3. A stratospheric pathway? 30oS EQ 30oN 60oN 90oN

3. A stratospheric pathway? 30oS EQ 30oN 60oN 90oN 4.12.2018

3. A stratospheric pathway? 30oS EQ 30oN 60oN 90oN 4.12.2018

3. A stratospheric pathway? 30oS EQ 30oN 60oN 90oN 4.12.2018

1. Separating a stratosphere-congruent signal

Approach Yj (φ,p) – T, U, or SLP change in j-th model between 2060-2099 and 1961-2000 X1j – tropical warming index ( change between 2060-2099 and 1961-2000) X2j – polar amplification index ( change between 2060-2099 and 1961-2000) X3j – stratospheric change index ( change between 2060-2099 and 1961-2000) All indices are normalized and have zero mean and unity standard deviation. Index correlation matrix: Tropics Arctic SUA Indices correlate Tropics Arctic SUA 4.12.2018

Approach A multiple regression is not used because of correlation between indices. A step-by- step regression is applied instead: 1. 2. 3. X2j – is calculated on residuals after the 1st regression X3j– is calculated on residuals after the 2nd regression The stratosphere-congruent part (a3 regression coefficient) does not change between the approaches because the SUA index does not correlate with the other indices. 4.12.2018

Multi-model mean change (Y) Polar amplification (X2) Zonal temperatures Multi-model mean change (Y) Tropical warming (X1) Polar amplification (X2) SUA index (X3) Regressions σ(Y) σ2(ε1)/σ 2(Y) σ2(ε2)/σ 2(Y) σ2(ε)/σ 2(Y)

Multi-model mean change (Y) Polar amplification (X2) Zonal winds Multi-model mean change (Y) Tropical warming (X1) Polar amplification (X2) SUA index (X3) Regressions σ(Y) σ2(ε1)/σ 2(Y) σ2(ε2)/σ 2(Y) σ2(ε)/σ 2(Y)

Multi-model mean change (Y) Polar amplification (X2) Sea level pressure Multi-model mean change (Y) Tropical warming (X1) Polar amplification (X2) SUA index (X3) Regressions σ(Y) σ2(ε1)/σ 2(Y) σ2(ε2)/σ 2(Y) σ2(ε)/σ 2(Y) 4.12.2018

High tops vs low tops Different climate sensitivity in high- tops and low tops => different Arctic SLP response The reason is not clear No evidences that different climate sensitivity is related to different stratospheric parts There are evidences that the difference is NOT related to startospheric parts: Similar climate sensitivity in high/low versions of the same model figure by S.-W. Son 4.12.2018

2. Internal variability? 4.12.2018

Intra- vs intermodel spread ANOVA test for SUA index: Intermodel variance: 18.2 m2/s2 Mean intramodel variance: 3.6 m2/s2 F-statistics: 5.1 Intermodel spread dominates (-1)∙ The intermodel spread in SUA index is unlikely explainable by internal variability 4.12.2018

3. Up-down or down-up influence? 4.12.2018

Lagged correlation between changes at different altitudes NAM changes at the end of winter (February) Correlated with zonal wind changes at different altitudes and in different months Remove the tropical warming signal

Lagged correlations Downward influence NAM in February correlates better with stratospheric wind changes in previous months, rather than with tropospheric ones Downward influence 4.12.2018

Summary (1) ~70% of CMIP5 models simulate weakening of the Arctic stratospheric winds in winter (equatorward shift of the polar night jet) (Almost all CMIP3 models simulate strengthening of the stratospheric winds) Spread of the polar stratospheric wind changes among individual models is large Multiple realizations available for some models show much smaller spread around their respective means This suggests that internal variability unlikely explains the intermodel spread in the Arctic stratospheric wind changes

Summary (2) The intermodel spread in stratospheric wind changes is not related to model climate sensitivity (i.e. tropical warming or polar amplification) There is a strong coupling between zonal wind changes and sea level pressure changes: A considerable inter-model spread in sea level pressure change is associated with the inter-model spread in the Arctic winter stratospheric change Weakening of the Arctic stratospheric winds corresponds to a shift towards more negative NAM (But overall future NAM change depends also on climate sensitivity) Lagged correlations suggest that changes in stratospheric winds influence tropospheric changes (downward influence)