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Power Spectrum Sensitivity Analysis of the Global Mean Surface Temperature Fluctuations Simulated in a Two-Box Stochastic Energy Balance Model Cover

Power Spectrum Sensitivity Analysis of the Global Mean Surface Temperature Fluctuations Simulated in a Two-Box Stochastic Energy Balance Model

Open Access
|Mar 2022

Figures & Tables

Table 1

Values of EBM parameters and their multi-model mean and standard deviation given for the 16-model ensemble (all parameter values, with the exception of the climate feedback factor, are taken from Geoffroy et al., 2013).

MODELPARAMETER
C(W yr m–2K–1)CD(W yr m–2K–1)γ(W m–2K–1)λ(W m–2K–1)f
1BCC-CSM1-17.6530.671.210.64
2BNU-ESM7.4900.530.930.72
3CanESM27.3710.591.030.69
4CCSM46.1690.931.240.63
5CNRM-CM58.4990.501.110.67
6CSIRO-Mk3.6.06.0690.880.610.82
7FGOALS-s27.01270.760.880.74
8GFDL-ESM2M8.11050.901.340.60
9GISS-E2-R4.71261.161.700.49
10HadGEM2-ES6.5820.550.650.81
11INM-CM48.63170.651.510.55
12IPSL-CM5A-LR7.7950.590.790.76
13MIROC58.31450.761.580.53
14MPI-ESM-LR7.3710.721.140.66
15MRI-CGCM38.5640.661.260.62
16NorESM1-M8.01050.881.110.67
Mean7.31060.731.130.66
STD1.1620.180.310.09
Table 2

Base values of model parameters and their ranges of change.

PARAMETER (p)MEAN VALUE (pavg)RANGE(pmin≤ p≤ pmax)
C,W yr m–2K–17.344.7 ≤ C ≤ 8.6
CD,W yr m–2K–1105.553 ≤ CD ≤ 145
λ,W m–2K–11.130.61 ≤ λ ≤ 1.70
γ,W m–2K–10.730.50 ≤ γ ≤ 1.16
σs,W m–20.260.16 ≤ σs ≤ 0.40
f0.660.49 ≤ f ≤ 0.82
Figure 1

Power spectra of the global mean surface temperature (GMST) fluctuations derived from historical (200 years) runs of 16 CMIP5 models: (a) the ensemble average power spectrum (red) and the characteristic 1/ν0.82 slope (blue); (b) the thin colored lines correspond to the individual ensemble members, while the thick black curve shows the ensemble average power spectrum, and the red lines show the characteristic 1/ν0.40 slope for frequencies less than 10–1 1/yr and the characteristic 1/ν1.53 for frequencies more than 10–1 1/yr.

Figure 2

Power spectra of the global mean surface temperature fluctuations derived from the one- and two-box EBMs for different values of feedback factor f listed in Table 2. The orange dashed line shows the characteristic 1/ν2 slope.

Figure 3

Power spectra of the global mean surface temperature fluctuations derived from the two-box EBM with 16 parameter sets listed in Table 1. The thin colored lines correspond to the individual ensemble members.

Figure 4

The ensemble average power spectrum of the global mean surface temperature fluctuations (red curve) derived from the two-box EBM with 16 sets of the parameters listed in Table 1. Grey shading shows the 95%confidence interval calculated from model spread. The blue line shows the characteristic 1/ν0.30 slope.

Figure 5

The box-and-whisker plots showing the temporal changes in power spectra across an ensemble of 16 EBMs.

Figure 6

Absolute sensitivity functions ψλ(1) and ψC(1) for the one-box EBM power spectral density with respect to parameters λ and C calculated for the highest (fmax), lowest (fmin) and average (favd) values of feedback factor f listed in Table 2.

Figure 7

Absolute sensitivity functions ψλ, ψC, ψγ and ψCD for the two-box EBM power spectral density with respect to parameters λ, C, γ and CD, respectively, calculated for the highest (fmax), lowest (fmin) and average (favd) values of feedback factor f listed in Table 2.

Figure 8

Relative sensitivity functions ψλR(1) and ψCR(1) for the one-box EBM power spectral density with respect to parameters λ and C calculated for the highest (fmax), lowest (fmin) and average (favd) values of feedback factor f listed in Table 2.

Figure 9

Relative sensitivity functions ψλR, ψCR, ψγR and ψCDR for the two-box EBM power spectral density with respect to parameters λ, C, γ and CD calculated for the highest (fmax), lowest (fmin) and average (favd) values of feedback factor f listed in see Table 2.

Table 3

The modulus of absolute and relative sensitivity functions with respect to the two-box EMB parameters, and the corresponding absolute δ(ST) (K2yr) and relative [δ(ST)/ST] (%) uncertainties in power spectrum caused by one-sigma uncertainty in model parameters.

PARAMETERλ (W m–2K–1)C(W yr m–2K–1)CD(W yr m–2K–1)γ (W m–2K–1)
One-sigma parameter uncertainties from GCMs±0.31±1.10±62.60±0.18
Period of GMST fluctuations T = 2 yr
|ψα|2.79 × 10–51.09 × 10–55.28 × 10–112.95 × 10–7
|ψαR|0.0081.991.38 × 10–40.005
δ(ST) (K2yr)±8.66 × 10–8±1.20 × 10–5±3.30 × 10–9±5.30 × 10–8
[δ(ST)/ST] × 100%±0.2±29.8±0.008±0.1
Period of GMST fluctuations T = 10 yr
|ψα|1.13 × 10–42.03 × 10–42.44 × 10–81.37 × 10–4
|ψαR|0.171.720.0030.12
δ(ST) (K2yr)±4.03 × 10–5±2.23 × 10–4±1.53 × 10–6±2.47 × 10–5
[δ(ST)/ST] × 100%±4.7±25.7±0.2±2.8
Period of GMST fluctuations T = 30 yr
|ψα|2.30 × 10–34.05 × 10–44.06 × 10–72.42 × 10–3
|ψαR|0.710.810.010.48
δ(ST) (K2yr)±7.14 × 10–4±4.46 × 10–4±2.54 × 10–5±4.36 × 10–4
[δ(ST)/ST] × 100%±19.6±12.2±0.7±11.9
Period of GMST fluctuations T = 102 yr
|ψα|5.76 × 10–31.06 × 10–42.92 × 10–75.92 × 10–3
|ψαR|1.120.130.0050.75
δ(ST) (K2yr)±1.793 × 10–3±1.17 × 10–4±1.83 × 10–5±1.06 × 10–3
[δ(ST)/ST] × 100%±30.9±2.0±0.3±18.4
Period of GMST fluctuations T = 103 yr
|ψα|1.19 × 10–22.09 × 10–54.19 × 10–59.83 × 10–4
|ψαR|1.440.020.470.08
δ(ST) (K2yr)±3.69 × 10–32.30 × 10–5±2.59 × 10–31.77 × 10–4
[δ(ST)/ST] × 100%±39.4±0.2±28.0±1.9
Figure 10

Relative sensitivity functions of the power spectral density of the global mean surface temperature fluctuations with respect to (a) one- and (b) two-box EBMs parameters calculated for the average value of feedback factor favd.

DOI: https://doi.org/10.16993/tellusa.40 | Journal eISSN: 3035-9554
Language: English
Page range: 68 - 84
Submitted on: Feb 21, 2022
Accepted on: Feb 21, 2022
Published on: Mar 22, 2022
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2022 Sergei A. Soldatenko, Robert A. Colman, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.