This readme.txt file was generated on 2021-06-28 by Allison Hogikyan

GENERAL INFORMATION

1. Title of Dataset: Why is El Nino warm?

2. Author Information
	A. Principal Investigator Contact Information
		Name: Allison Hogikyan
		Institution: Princeton University
		Address: Guyot Hall, Princeton University, Princeton NJ 08540
		Email: hogikyan@princeton.edu

	B. Contact Information for individual responsible for simulation
		Name: Wenchang Yang
		Institution: Princeton University
		Address: Guyot Hall, Princeton University, Princeton NJ 08540
		Email: wenchang@princeton.edu


3. Date of model run: 2019; Date of retrieval and processing to version presented here: 2021-02

4. Model run on Princeton University tigercpu machine in Princeton NJ


SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: N/A

2. Links to publications that cite or use the data: Hogikyan, A., L. Resplandy, S. Fueglistaler. (2021). Why is El Nino Warm?  Geophysical Research Letters. Submitted. 

3. Links/relationships to ancillary data sets: Contact Dr. Yang for full experiment.

4. Was data derived from another source? Yes: GFDL-FLOR pi-control experiment run by Wenchang Yang on tigercpu

5. Recommended citation for this dataset: Hogikyan, A., L. Resplandy, W. Yang, S Fueglistaler (2021). Why is El Nino warm? Princeton DataSpace. https://doi.org/10.34770/g7fe-hs07


DATA & FILE OVERVIEW

1. File List: warmest_FLOR_figs.nc

2. Relationship between files, if important: N/A

3. Additional related data collected that was not included in the current data package: Contact Dr. Yang for full experiment.

4. Are there multiple versions of the dataset? No


METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation of data: 
We use data from a GFDL-FLOR preindustrial control experiment, as published in Yang et al. (2019). In this experiment, the concentration of radiatively active gases and aerosols is specified at preindustrial levels (1860). The atmosphere and ocean are fully coupled. GFDL-FLOR uses the lower resolution ocean in GFDL-CM2.1 (Delworth et al. 2006) but the higher resolution atmosphere from GFDL-CM2.5 (Delworth et al. 2012) (Vecchi et al. 2014).

2. Methods for processing the data: 
All data are tropics and ocean-only (30S:30N) monthly-means. Variables in percentiles are sorted by sea surface temperature at each timestep and averaged into 100 equal-area bins. The Nino3.4 index is calculated as: (1) SSTs within (5S:5N, 170:120W) are averaged, (2) the seasonal cycle is removed, (3) the time series is normalized by its standard deviation and (4) the time series is smoothed with a 5-month rectangular filter  (https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni).

3. Instrument- or software-specific information needed to interpret the data: 
NetCDF can be read by many languages including Matlab and Python (https://www.unidata.ucar.edu/software/netcdf/). The variables and metadata can be printed with the unix command 'ncdump -h <filename.nc> '.

4. Standards and calibration information, if appropriate: N/A

5. Environmental/experimental conditions: GFDL-FLOR was run in the Princeton University tigercpu high performance computing environment

6. Describe any quality-assurance procedures performed on the data: N/A

7. People involved with sample collection, processing, analysis and/or submission: Wenchang Yang (wenchang@princeton.edu) is responsible for running the model which produced the raw data, Allison Hogikyan (hogikyan@princeton.edu) is responsible for data processing and submission, Matt Chandler (mchandler@princeton.edu) helped greatly with data submission


DATA-SPECIFIC INFORMATION FOR FILE: warmest_FLOR_figs.nc

1. Number of variables: 6

2. Number of cases/rows: variable dependent, see below. Time dimension is 12 months x 100 years. Percentile 0 is the coldest and percentile 99 is the warmest.

3. Variable List: 
lh_percentile = latent heat flux in SST percentiles, units W/m2         , shape 1200 time x 100 percentiles
dq_percentile = q*(SST) - q(2m)  ,                   unitless           , shape 1200 time x 100 percentiles
w_percentile = wind speed for flux calculations including u, v, u_gust  , shape 1200 time x 100 percentiles
t_percentile = sea surface temperature,              units K            , shape 1200 time x 100 percentiles
dsstq_percentile = Qsfc / (rho cp H_ML) ,            units K            , shape 1200 time x 100 percentiles

Nino34,                                              unitless           , shape 1200 time

4. Missing data codes: NaN

5. Specialized formats or other abbreviations used: See above for discussion of percentiles.