Geophysical Research Letters
Supporting Information for
Changes in the geopotential height at 500 hPa under the influence of external
climatic forcings
Nikolaos Christidis and Peter A Stott
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, United Kingdom
Contents of this file
Text S1
Table S1
Figure S1
Introduction
The supporting information provides details on the model data used in the study. It lists
the experiments used and describes how control simulations are used in optimal
fingerprinting analyses. It also provides a figure that illustrates the results for the optimal
fingerprinting analysis for DJF with the global mean removed, similar to Figure 4c in the
main article,
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Text S1.
We use annual and seasonal mean values of Z500 from experiments with the seven
CMIP5 models listed in Table S1. The ALL experiment includes anthropogenic forcings
(changes in well-mixed greenhouse gases, aerosols, tropospheric and stratospheric
ozone and changes in land use) and natural forcings (changes in volcanic aerosols and
solar irradiance). The NAT experiment includes natural forcings only. From all models
that contributed data to CMIP5, we selected only those that provide ensembles of at
least three simulations for the ALL and NAT experiments, as well as at least 500 years
of a control experiment of the unforced climate. Details on the forcings prescribed in the
model simulations can be found in Table 12.1 in:
Collins, M. R. Knutti, J. Arblaster, J.-L. Dufrense, T. Fichefet, P. Friedlingstein, X. Gao,
W. J. Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A. J. Weaver, and M.
Wehner (2013), Long-term climate change: projections, commitments and irreversibility.
Climate change 2013: the physical science basis. Sticker T. F. et al. (eds), Cambridge
university Press, Cambridge, united Kingdom and New York, NY, USA.
Optimal fingerprinting uses the control simulations to represent the effect of internal
variability. We extract 161 independent segments from the control experiments of the
same length as the reference period (1979-2012), apply spatial smoothing using
spherical harmonics and construct the mean Z500 pattern in consecutive 5-year time
slices. Half of the processed segments are subsequently used to estimate the variancecovariance matrix of the noise terms of the fingerprinting regression and construct the
basis of the empirical orthogonal functions of internal variability. The remaining
segments are used to derive the uncertainty in the scaling factors and check whether the
regression residual is consistent with the variability in the control experiments. For
details of the methodology the reader can refer to Allen and Stott (2003). Examples of its
application are referenced in the attribution chapter of the IPCC reports of Working
Group I (e.g. Bindoff et al., 2013).
Model
HadGEM2-ES
CanESM2
CNRM-CM5
CSIRO-Mk3.6.0
GISS-E2-H
GISS-E2-R
IPSL-CM5A-LR
Total:
ALL ensemble size
4
5
10
10
5
5
4
43
NAT ensemble size
4
5
6
5
5
5
3
33
Control length (yrs)
830
995
850
500
780
850
1000
5805
Table S1. The CMIP5 models used in the study, the number of simulations in
experiments with (ALL) and without (NAT) the effect of human influence and the length
of the control simulations.
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Figure S1. Results from optimal fingerprinting analyses of recent changes in the DJF
mean Z500 with different reanalysis datasets. Scaling factors and their 5-95%
uncertainty range corresponding to anthropogenic and natural fingerprints are shown in
orange and blue respectively. Solid lines represent results of the original analyses (same
as Figure 4c in the main article). Dotted lines are results from new analyses that focus
on dynamical changes by removing the global mean values of Z500 from the spatial
patterns. As the dynamic effect is weaker, the uncertainty in the scaling factors is larger.
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