Repository for data, code, and figures used by Hancock et al. (2023).
The proxy records in the most recent Holocene Hydroclimate dataset are provided here:
https://lipdverse.org/HoloceneHydroclimate/current_version/
A table listing the proxy records is provided here: https://raw.githack.com/clhancock/HoloceneHydroclimate/main/Figures/Proxy/TableS1/TableS1.html and described by: https://raw.githack.com/clhancock/HoloceneHydroclimate/main/Figures/Proxy/TableS1/TableS1_Key.pdf
A complete list of references for the proxy records used in this study is provided here: https://lipdverse.org/HoloceneHydroclimate/current_version/references.html
Cite: Hancock, C. L., McKay, N. P., Erb, M. P., Kaufman, D. S., Routson, C. R., Ivanovic, R. F., et al. (2023). Global synthesis of regional Holocene hydroclimate variability using proxy and model data. Paleoceanography and Paleoclimatology, 38, e2022PA004597. https://doi.org/10.1029/2022PA004597
Trace/Hadcm:
-NetCDF files (Annual/JJA/DJF) binned to 100-year resolution.
-Each dataset includes temperature, precipitation, evaporation, and P-E.
-Examples of the original file names that these were created from are "trace.01-36.22000BP.cam2.PRECT.22000BP_decavgANN_400BCE.nc" and "deglh.vn1_0.precip_mm_srf.monthly.ANN.010yr_s.nc" respectively.
-Data for each model are provided in the native resolution and a regridded spatial resolution.
-The original files were modified to standardize units/names between different models according to the /Notebooks/1_DataPrep/transientFormatting.py script.
(Dimensions: (lon: 135, lat: 90, age: 121))
These results are shown in Fig. 5
CMIP6:
-NetCDF files of mid-Holocene minus preindustrial anomalies.
-Each dataset includes temperature, precipitation, evaporation, and P-E
-Data for each model are provided in the native resolution and a regridded spatial resolution.
-The original files were modified to standardize units/names between different models according to the /Notebooks/1_DataPrep/ cmip6Formatting.py script.
(Dimensions: (lon: 135, lat: 90, model: 12))
These results are shown in Fig. 4
RegionalTS:
-csv files containing the mean of data binned by IPCC region for pre, tas, and p-e.
-For hadcm/trace, each region is a column and each row an age.
-For cmip6, each row is a model
-Land/all files distinguish if the regional mean includes an ocean mask.
-Data calculated using /Notebooks/1_DataPrep/CalculateModelValuesByRegion.ipynb.
These results are shown in Fig. 6
pseudoProxyCorr_pre.csv & pseudoProxyCorr_pre_byCount.csv:
-csv files of correlations between pseudoproxies and regional mean (RegionalTS data)
-Data calculated using /Notebooks/2_Analysis/pseudoProxyCorrelations.py
These results are shown in the SM
lipdData.rds
-rds file of LiPD extracted to ts objects (list of proxy records and their data)
-Contains hydroclimate and temperature files
-Includes additional standardization / metadata calculation not included in the lipdverse files (such as IPCC region of each site)
-Data downloaded from https://lipdverse.org/ and modified by /Notebooks/1_DataPrep/1_StandardizeLiPDs.Rmd.
These results are shown in Fig. 1
Proxy_MetaData
-csv files containing key metadata which are used for plotting
-Different files for hydroclimate and temp12k
-Unlike lipdData.rds, this file can be opened in R and python.
-Created by /Notebooks/1_DataPrep/1_StandardizeLiPDs.Rmd.
-csv files for hydroclimate and temperature proxy composites for each region
-Hydroclimate composites are standardized anomalies (z-scores). Temperatures are degC anomalies. Both relative to the Holocene mean.
-Each region has a csv file which includes the entire composite ensemble
-'MedianTS_byRegion.csv' list the ensemble median for each region in a single file
-Results of /Notebooks/2_Analysis/Composite.R
-Results of /Notebooks/2_Analysis/CompositeCorrelations.R (zip file)
These results are shown in Fig. 2,3,6,7
1_DataPrep
1_StandardizeLiPDs.Rmd: for loading/standardizing data LiPD data (proxies)
Convert2calAge.R: for converting radiocarbon years to calendar years
cmip6Formatting.py: for standardizing CMIP6 data
transientFormatting.py: for standardizing TraCE/HadCM data
2_Analysis
Composite.R: for compositing the data
CompositeCorrelations.R: for calculating the correlation between HC and T composite ensembles
pseudoProxyCorrelations.py: for testing the correlation between pseudoproxies and the regional mean.
3_Figures
For creating figures in Hancock et al. (2023).
Each script named to indicate which figure it was used to create (Fig1_...)
Figure used in publication are identified in '/Notebooks/3_Figures/README.md'
Figures created by /Notebooks/3_Figures/...