1Supplemental Materials for2
3“Spurious Indo-Pacific Connections to Internal Atlantic Multidecadal Variability

4

Introduced by the Global Temperature Residual Method”

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Clara Deser* and Adam S. Phillips

7National Center for Atmospheric Research, Boulder, CO

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9Submitted 23 July 2022; Revised 30 October 2022

10Geophys. Res. Lett.

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12* Corresponding author: Clara Deser (cdeser@ucar.edu)

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14

Contents: This document contains a list of references, 4 Tables (Tables S1-S4) and 15 Figures

15(Figs. S1 – S15).

16References

17

Danabasoglu, G., S. G. Yeager, Y. -O. Kwon, J. J. Tribbia, A. S. Phillips, and J. W. Hurrell,

18

2012. Variability of the Atlantic Meridional Overturning Circulation in CCSM4. J. Climate, 25,

195153-5172, doi: 10.1175/JCLI-D-11-00463.1.

20
21

Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J.-H., Dunne, K. A., et al. (2020).

22

SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction

23and projection. Journal of Advances in Modeling Earth Systems, 12, e2019MS001895.

24https://doi.org/10.1029/2019MS001895.

25
26Kay, J. E., C. Deser, A. Phillips, A. Mai, C. Hannay, G. Strand, J. Arblaster, S. Bates, G.

27

Danabasoglu, J. Edwards, M. Holland, P. Kushner, J. -F. Lamarque, D. Lawrence, K. Lindsay,

28

A. Middleton, E. Munoz, R. Neale, K. Oleson, L. Polvani, and M. Vertenstein, 2015: The

29

Community Earth System Model (CESM) Large Ensemble Project: A community resource for30

studying climate change in the presence of internal climate variability. Bull. Amer. Met. Soc., 96,

31

1333–1349, doi: 10.1175/BAMS-D-13-00255.1.

3233

Kirchmeier-Young, M. C., Zwiers, F. W. and Gillett, N. P. , 2017: Attribution of extreme events

34in Arctic Sea ice extent. J. Climate 30, 553–571.

3536

Maher, N., Milinski, S., Suarez-Gutierrez, L., Botzet, M., Dobrynin, M., Kornblueh, L., et al.

37(2019). The Max Planck Institute Grand Ensemble: Enabling the exploration of climate system38

variability. J. Adv. Mod. Earth Sys., 11, 2050– 2069. https://doi.org/10.1029/2019MS001639

3940

Rodgers, K. B., Lee, S.-S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C.,

41

Edwards, J., Kim, J.-E., Simpson, I., Stein, K., Stuecker, M. F., Yamaguchi, R., Bodai, T.,

42

Chung, E.-S., Huang, L., Kim, W., Lamarque, J.-F., Lombardozzi, D., Wieder, W. R., and43

Yeager, S. G., 2021: Ubiquity of human-induced changes in climate variability, Earth Syst.

44

Dynam, https://doi.org/10.5194/esd-2021-50, 2021.

45
46

Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J.,

47

Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C.,

48

Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The

49

Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823– 137

504873, https://doi.org/10.5194/gmd-12-4823-2019, 2019.

51
52

Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M.,

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Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki,

54

T., Yoshimura, K., Takata, K., O'Ishi, R., ... Kimoto, M. (2019). Description and basic evaluation

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of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific

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Model Development, 12(7), 2727-2765. https://doi.org/10.5194/gmd-12-2727-2019.

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Table S1. Model Large Ensembles used in this study.
Modeling
Center
CCCma
MPI
NCAR
CCCma
GFDL
MIROC
NCAR

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ModelResolution(atm/ocn)~2.8°x2.8°/
CanESM2~1.4°x0.9°
MPI~1.9°x1.9°/
ESM-LRnominal
1.5°
CESM1- ~1.3°x0.9°/
CAM5
nominal1.0°~2.8°x2.8°/
CanESM5~1.4°x0.9°
SPEAR_50km/
MED
nominal 1°~1.4°x1.4°/
MIROC6
nominal 1°~1.3°x0.9°/
CESM2
nominal1.0°
Model
VersionNumberof
Forcing
Members
historical,
195050
rcp85
2100

Years

Reference
Kirchmeier -Young etal. (2017)

18502100

100

historical,
rcp85

Maher et al. (2019)

1920210040

historical,
rcp85

Kay et al. (2015)

18502100
19212100
18502014
18502100

503050100historical,
ssp5-8.5
historical,
ssp5-8.5
historical,
ssp5-8.5
historical,
ssp3-7.0

Swart et al. (2019)
Delworth et al. (2020)
Tatebe et al. (2019)Rodgers et al. (2021)

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Table S2. Observational data sets used in this study.

Name
NOAA Extended Reconstruction
SSTs Version 5
Global Precipitation Climatology
Centre
ECMWF Reanalysis 5thGeneration
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Acronym

Variable

SpatialResolution

Years

ERSSTv5

SST

2°x2°

1854-2022

GPCC

PR

1°x1°

1891-2022

ERA5

PSL

0.25° x 0.25° 1950-2022

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Table S3. Glossary of selected acronyms used in this study.

Acronym

MeaningiAMVInternal component of AMV

G, iG, fG

Total, internal and forced components of global-mean SST
Internal component of SST, PSL and PR obtained by removing theensemble-mean component
Internal component of SST, PSL and PR obtained by removing theensemble-mean component

SST, iPSL, iPR
iSST, iPSL, iPR

63gresSST, gresPSL, gresPR

SST, PSL and PR after removing component associated with G

fgresSST, fgresPSL, fgresPR

SST, PSL and PR after removing component associated with fGNA, iNA

Total and internal components of North Atlantic SST

gresNA, fgresNA

NA after removing the component associated with G, fG

iGNA; iG*Component of iG congruent with iNA; iG-iGNA

iAMVtruthNA

Regression maps of iSST, iPSL and iPR onto iNA

iAMVtruthNA-G

Regression maps of iSST, iPSL and iPR onto iNA-iG[iAMVtruthNA], [iAMVtruthNA-G]

Ensemble-means of iAMVtruthNA , iAMVtruthNA-G

iAMVgresRegression maps of gresSST, gresPSL and gresPR onto gresNA

iAMVfgres

Regression maps of fgresSST, fgresPSL and fgresPR onto fgresNA[iAMVgres], [iAMVfgres]

Ensemble-means of iAMVgres , iAMVfgres

MMLE

Multi-model Large Ensemble

SST_Obs, G_Obs

Observed SST, Observed global-mean SSTgresSST_Obs

SST_Obs after removing component associated with G_Obs

fgresSST_Obs

SST_Obs after removing component associated with models’ fG

NA_ObsObserved NA SST

NAgres_Obs, NAfgres_Obs

NA_Obs after removing component associated with G_Obs, models’ fG

iAMVgres_Obs

Regression map of gresSST_Obs onto gresNA_Obs

iAMVfgres_Obs

Regression map of fgresSST_Obs onto fgresNA_Obs

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Table S4. Global pattern correlations between: (a) [iAMVgres] and [iAMVtruthNA-G]; (b)65[iAMVfgres] and[iAMVtruthNA]; and (c) ([iAMVgres] minus [iAMVfgres]) and ([iAMVtruthNA-66

G67

the forward slash are based on the period 1950-2020 and 2030-2100, respectively. See main text68

for details and Table S3 for a glossary of terms.

] minus [iAMVtruthNA]) for each model LE and the MMLE. Numbers to the left and right of(a)

(b)

(c)

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MPI
SST 1.0/1.0
PSL 1.0/1.0
PR 1.0/1.0
SST 1.0/.99
PSL .99/.99
PR .99/.99
SST 1.0/1.0
PSL 1.0/1.0
PR 1.0/1.0

CESM1.95/.89.96/.93.95/.86.94/.94.95/.97.94/.94.99/1.0.99/.99
1.0/.99

CanESM2.93/.99.96/.99.92/.99.94/.99.95/.99.95/.99.98/.99.99/1.0
1.0/1.0

MIROC6.97/.97.99/.99.99/.98.98/.97.97/.99.97/.97.99/.99
1.0/1.0
1.0/1.0

CanESM5.91/.85.90/.86.88/.85.92/.89.85/.88.85/.80.98/.98
1.0/.99
1.0/.99

SPEAR.96/.88.94/.93.96/.94.98/.85.89/.90.92/.84.97/.99.98/.99.99/.99

CESM2.95/.93.95/.93.94/.92.97/.91.98/.93.97/.88.99/.99.99/1.0
1.0/1.0

MMLE.97/.98.98/.99.97/.98.98/.98.98/.99.96/.96.99/1.0
1.0/1.0
1.0/1.0

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Figure S1. Ensemble-mean iAMV regression maps for SST (color shading) and PSL (contours;negative values dashed) based on 1950-2020 for each model Large Ensemble using the following
methods: (top row) [iAMVgres]; (middle row) [iAMVfgres]; and (bottom row) top minus middle.
See main text for explanation.

Figure S2. Ensemble-mean iAMV regression maps for SST (color shading) and PSL (contours;negative values dashed) based on 1950-2020 for each model Large Ensemble using the following
methods: (top row) [iAMVtruthNA-G]; (middle row) [iAMVtruthNA]; and (bottom row) top minus
middle. See main text for explanation.

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Figure S3. As in Fig. S1 but for PR.

Figure S4. As in Fig. S2 but for PR.

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Figure S5. Multi-Model Large Ensemble iAMV regression maps for SST (color shading) and PSL(contours; negative values dashed) based on 2030-2100 using the following methods: (a)[iAMVgres]; (b) [iAMVtruthNA-G]; (c) [iAMVfgres]; (d) [iAMVtruthNA]; (e) a-c; f) b-d. See main
text for explanation.

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Figure S6. As in Fig. S5 but for PR.

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Figure S7. Comparison of iAMV SST regression maps from (left) preindustrial control (pictl)and (right) historical (1950-2020) Large Ensemble simulations for MPI, CESM1, MIROC6,CanESM5 and CESM2 (pictl simulations are not available for CanESM2 and GFDLSPEAR_MED). The MME panel shows the average of the regression maps across the 5 models.
For the pictl simulations, the top and middle panels show regressions onto the NA-G and NA SST
indices (denoted iAMVNA-G and iAMVNA-G, respectively); the bottom panel shows the difference
between the top and middle panels. For the LE simulations, the top and middle panels show
[iAMVtruthNA-G] and [iAMVtruthNA], respectively, and the bottom panel shows the difference
between the top and middle panels. SSTs from the pictl simulations were detrended and low-passfiltered using a 10-yr Butterworth filter. The length of the pictl simulations are as follows: MPI(2001 years); CESM1 (1800 years); MIROC6 (800 years); CanESM5 (1000 years); and CESM2(1900 years). The numbers in the upper right of the pictl panels indicate the global (60ºN-60ºS)
spatial correlation with the adjacent Truth panel. For example, the spatial correlation between
iAMVNA-G and [iAMVtruthNA-G] is 0.96 for MPI and 0.98 for MME.

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Figure S8. Distribution of iAMV SST pattern correlations between each ensemble member andthe ensemble-mean (gray dots) for each model LE based on 1950-2020 using the following
methods: (a,b) iAMVgres; (c,d) iAMVfgres; and (e,f) iAMVgres minus iAMVfgres. Boxes outline
the 25th-to-75th percentile range, and whiskers show the 5th-to-95th percentile range. Black dots
indicate correlations between observations and the model ensemble-means. Left column (a,c,e):Atlantic (80°W-30°E); Right column (b,d,f): Indo-Pacific (defined as the region outside theAtlantic).

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Figure S9. Global-mean (60ºN-60ºS) low-pass filtered SST anomaly timeseries (ºC) based on
observations (black curve) and the MMLE ensemble-mean (blue curve).

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Figure S10. Distribution of temporal correlations between IPV and the difference between thegresNA and fgresNA SST indices for each ensemble member of each model LE based on 19502020 (gray dots) at (a) zero-lag and (b) IPV leading by 1 yr. Boxes outline the 25th-to-75th percentile
range, and whiskers show the 5th-to-95th percentile range. The horizontal black line indicates theobserved correlation value.

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Figure S11. Observed iAMV regression maps for SST (color shading) and PSL (contours;negative values dashed) based on 1950-2020 using the following methods: (a) iAMVgres; (b)iAMVfgres; and (c) iAMVgres minus iAMVfgres. See main text for explanation.

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Figure S12. Observed iAMV SST (left) and PR (right) regression maps based on 1900-2020 using
the following methods: (a,b) iAMVgres; (c,d) iAMVfgres; and (e,f) iAMVgres minus iAMVfgres.
See main text for explanation.

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Figure S13. Sampling variability of [iAMVfgres] SST regression patterns based on (a) 15member averages and (b) 20-member averages for the period 1950-2020. For each model LE, weconstruct 15-member and 20-member averages of the SST iAMV patterns obtained with the fGRes method by randomly selecting (with replacement) from the full set of members. We repeat
this procedure 30 times for each model LE, and then compute the global pattern correlations
between the full-member average and each of the thirty 15-member and 20-member averages foreach model LE (gray dots). Boxes outline the 25th-to-75th percentile range, and whiskers show the5th-to-95th percentile range.

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Figure S14. Sampling variability of iAMVfgres_Obs SST regression pattern for the period 19502020. We compute 6-member averages of G(t) by randomly sampling (with replacement) fromthe full set of 420 simulations across the 7 model LEs, and repeat this procedure 100 times. We
compute pattern correlations between iAMVfgres_Obs based on the full 420-member average todefine fG(t) with that based on each of the 100 6-member averages to define fG(t) (gray dots) for
the global, Atlantic and Indo-Pacific domains. Boxes outline the 25th-to-75th percentile range, andwhiskers show the 5th-to-95th percentile range.

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Figure S15. Multi-Model Large Ensemble ensemble-mean regression maps for SST (colorshading) and PSL (contours; negative values dashed) based on 1950-2020 using the Truth method
and the following normalized indices: (a) North Atlantic (NA) SST; (b) Atlantic Meridional
Overturning Circulation (AMOC) leading by 4 years, where AMOC is defined as the leading
principal component timeseries of oceanic meridional mass transport in the Atlantic sector
between 33°S-90°N following Danabasoglu et al. (2012); and (c) their difference. See main text
for explanation. The MIROC6 LE is excluded from all calculations due to lack of AMOC data.