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Dataset

 

Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.39 (v20220614)

Update Frequency: Not Planned
Latest Data Update: 2022-06-17
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2022-06-27
DOI Publication Date: 2023-02-08
Download Stats: last 12 months
Dataset Size: 7 Files | 42MB

Abstract

Data for Figure 3.39 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 3.39 shows the observed and simulated Pacific Decadal Variability (PDV).

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How to cite this dataset
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When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:
Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.
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Figure subpanels
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The figure has six panels. Files are not separated according to the panels.

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List of data provided
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pdv.obs.nc contains
- Observed SST anomalies associated with the PDV pattern
- Observed PDV index time series (unfiltered)
- Observed PDV index time series (low-pass filtered)
- Taylor statistics of the observed PDV patterns
- Statistical significance of the observed SST anomalies associated with the PDV pattern

pdv.hist.cmip6.nc contains
- Simulated SST anomalies associated with the PDV pattern
- Simulated PDV index time series (unfiltered)
- Simulated PDV index time series (low-pass filtered)
- Taylor statistics of the simulated PDV patterns
based on CMIP6 historical simulations.

pdv.hist.cmip5.nc contains
- Simulated SST anomalies associated with the PDV pattern
- Simulated PDV index time series (unfiltered)
- Simulated PDV index time series (low-pass filtered)
- Taylor statistics of the simulated PDV patterns
based on CMIP5 historical simulations.

pdv.piControl.cmip6.nc contains
- Simulated SST anomalies associated with the PDV pattern
- Simulated PDV index time series (unfiltered)
- Simulated PDV index time series (low-pass filtered)
- Taylor statistics of the simulated PDV patterns
based on CMIP6 piControl simulations.

pdv.piControl.cmip5.nc contains
- Simulated SST anomalies associated with the PDV pattern
- Simulated PDV index time series (unfiltered)
- Simulated PDV index time series (low-pass filtered)
- Taylor statistics of the simulated PDV patterns
based on CMIP5 piControl simulations.

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Data provided in relation to figure
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Panel a:
- ipo_pattern_obs_ref in pdv.obs.nc: shading
- ipo_pattern_obs_signif (dataset = 1) in pdv.obs.nc: cross markers

Panel b:
- Multimodel ensemble mean of ipo_model_pattern in pdv.hist.cmip6.nc: shading, with their sign agreement for hatching

Panel c:
- tay_stats (stat = 0, 1) in pdv.obs.nc: black dots
- tay_stats (stat = 0, 1) in pdv.hist.cmip6.nc: red crosses, and their multimodel ensemble mean for the red dot
- tay_stats (stat = 0, 1) in pdv.hist.cmip5.nc: blue crosses, and their multimodel ensemble mean for the blue dot

Panel d:
- Lag-1 autocorrelation of tpi in pdv.obs.nc: black horizontal lines in left
. ERSSTv5: dataset = 1
. HadISST: dataset = 2
. COBE-SST2: dataset = 3
- Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.piControl.cmip5.nc: blue open box-whisker in the left
- Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.piControl.cmip6.nc: red open box-whisker in the left
- Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.hist.cmip5.nc: blue filled box-whisker in the left
- Multimodel ensemble mean and percentiles of lag-1 autocorrelation of tpi in pdv.hist.cmip6.nc: red filled box-whisker in the left
- Lag-10 autocorrelation of tpi_lp in pdv.obs.nc: black horizontal lines in right
. ERSSTv5: dataset = 1
. HadISST: dataset = 2
. COBE-SST2: dataset = 3
- Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.piControl.cmip5.nc: blue open box-whisker in the right
- Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.piControl.cmip6.nc: red open box-whisker in the right
- Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.hist.cmip5.nc: blue filled box-whisker in the right
- Multimodel ensemble mean and percentiles of lag-10 autocorrelation of tpi_lp in pdv.hist.cmip6.nc: red filled box-whisker in the right

Panel e:
- Standard deviation of tpi in pdv.obs.nc: black horizontal lines in left
. ERSSTv5: dataset = 1
. HadISST: dataset = 2
. COBE-SST2: dataset = 3
- Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.piControl.cmip5.nc: blue open box-whisker in the left
- Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.piControl.cmip6.nc: red open box-whisker in the left
- Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.hist.cmip5.nc: blue filled box-whisker in the left
- Multimodel ensemble mean and percentiles of standard deviation of tpi in pdv.hist.cmip6.nc: red filled box-whisker in the left
- Standard deviation of tpi_lp in pdv.obs.nc: black horizontal lines in right
. ERSSTv5: dataset = 1
. HadISST: dataset = 2
. COBE-SST2: dataset = 3
- Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.piControl.cmip5.nc: blue open box-whisker in the right
- Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.piControl.cmip6.nc: red open box-whisker in the right
- Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.hist.cmip5.nc: blue filled box-whisker in the right
- Multimodel ensemble mean and percentiles of standard deviation of tpi_lp in pdv.hist.cmip6.nc: red filled box-whisker in the right

Panel f:
- tpi_lp in pdv.obs.nc: black curves
. ERSSTv5: dataset = 1
. HadISST: dataset = 2
. COBE-SST2: dataset = 3
- tpi_lp in pdv.hist.cmip6.nc: 5th-95th percentiles in red shading, multimodel ensemble mean and its 5-95% confidence interval for red curves
- tpi_lp in pdv.hist.cmip5.nc: 5th-95th percentiles in blue shading, multimodel ensemble mean for blue curve

CMIP5 is the fifth phase of the Coupled Model Intercomparison Project.
CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.
SST stands for Sea Surface Temperature.

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Notes on reproducing the figure from the provided data
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Multimodel ensemble means and percentiles of historical simulations of CMIP5 and CMIP6 are calculated after weighting individual members with the inverse of the ensemble size of the same model. ensemble_assign in each file provides the model number to which each ensemble member belongs. This weighting does not apply to the sign agreement calculation.

piControl simulations from CMIP5 and CMIP6 consist of a single member from each model, so the weighting is not applied.

Multimodel ensemble means of the pattern correlation in Taylor statistics in (c) and the autocorrelation of the index in (d) are calculated via Fisher z-transformation and back transformation.

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Sources of additional information
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The following weblinks are provided in the Related Documents section of this catalogue record:
- Link to the report component containing the figure (Chapter 3)
- Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1
- Link to the code for the figure, archived on Zenodo
- Link to the figure on the IPCC AR6 website

Citable as:  Phillips, A.; Kosaka, Y.; Cassou, C.; Kazeroni, R. (2023): Chapter 3 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 3.39 (v20220614). NERC EDS Centre for Environmental Data Analysis, 08 February 2023. doi:10.5285/02006a22c33b42039d96be53d332930a. https://dx.doi.org/10.5285/02006a22c33b42039d96be53d332930a
Abbreviation: Not defined
Keywords: IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis, ENSO teleconnections

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence: http://creativecommons.org/licenses/by/4.0/. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).
Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).

Data Quality:
Data as provided by the IPCC
File Format:
Data are netCDF formatted

Process overview

This dataset was generated by the computation detailed below.
Title

Caption for Figure 3.39 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)

Abstract

Model evaluation of the Pacific Decadal Variability (PDV). (a, b) Sea surface temperature (SST) anomalies (ºC) regressed onto the Tripole Index (TPI; Henley et al., 2015) for 1900–2014 in (a) ERSST version 5 and (b) CMIP6 multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the inverse of the model ensemble size. A 10-year low-pass filter was applied beforehand. Cross marks in (a) represent regions where the anomalies are not significant at the 10% level based on t-test. Diagonal lines in (b) indicate regions where less than 80% of the runs agree in sign. (c) A Taylor diagram summarizing the representation of the PDV pattern in CMIP5 (each a cross in light blue, and the weighted multi-mode mean as a dot in dark blue), CMIP6 (each ensemble member is shown as a cross in red, weighted multi-model mean as a dot in orange) and observations over 40ºS–60ºN and 110ºE–70ºW. The reference pattern is taken from ERSST version 5 and black dots indicate other observational products, Hadley Centre Sea Ice and Sea Surface Temperature data set version 1 (HadISST version 1) and Centennial in situ Observation-Based Estimates of Sea Surface Temperature version 2 (COBE-SST2). (d) Autocorrelation of unfiltered annual TPI at lag one year and 10-year low-pass filtered TPI at lag 10 years for observations over 1900–2014 (horizontal lines) and 115-year chunks of pre-industrial control simulations (open boxes) and individual historical simulations over 1900–2014 (filled boxes) from CMIP5 (blue) and CMIP6 (red). (e) As in (d), but standard deviation of the unfiltered and filtered TPI (ºC). Boxes and whiskers show weighted multi-model mean, interquartile ranges and 5th and 95th percentiles. (f) Time series of the 10-year low-pass filtered TPI (ºC) in ERSST version 5, HadISST version 1 and COBE-SST2 observational estimates (black) and CMIP5 and CMIP6 historical simulations. The thick red and light blue lines are the weighted multi-model mean for the historical simulations in CMIP5 and CMIP6, respectively, and the envelopes represent the 5th–95th percentile range across ensemble members. The 5–95% confidence interval for the CMIP6 multi-model mean is given in thin dashed lines. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Input Description

None

Output Description

None

Software Reference

None

  • var_id: E
  • units: K
  • standard_name: sea_surface_temperature
  • long_name: Sea Surface Temperature
  • var_id: ipo_pattern_obs_signif
  • long_name: Taylor statistics
  • var_id: tay_stats
  • var_id: dataset
  • var_id: ensemble_assign
  • var_id: ipo_model_pattern
  • var_id: stat
  • units: K
  • var_id: tpi
  • long_name: the Tripole Index
  • units: K
  • var_id: tpi_lp
  • long_name: the low-pass filtered Tripole Index

Co-ordinate Variables

  • units: degrees_north
  • standard_name: latitude
  • var_id: lat
  • units: degrees_east
  • standard_name: longitude
  • var_id: lon
  • long_name: time
  • standard_name: time
  • var_id: time
  • units: days
Coverage
Temporal Range
Start time:
1900-01-01T12:00:00
End time:
2014-12-31T12:00:00
Geographic Extent

 
90.0000°
 
-180.0000°
 
180.0000°
 
-90.0000°