Data Reference:
Ivanova, Detelina P.; McClean, Julie L.; Sprintall, Janet; Chen, Ru (2021). Data from: The Oceanic Barrier Layer in the Eastern Indian Ocean as a Predictor for Rainfall over Indonesia and Australia. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0MC8ZW3

Data Description
This is a collection of data and scripts in support of the manuscript “The Oceanic Barrier Layer in the Eastern Indian Ocean as a Predictor for Rainfall over Indonesia and Australia”. This study is focused on the relationship of the barrier layer in a region West of Sumatra (2.5°S - 4.5°N, and from 89.5E to 98.5°E at south boundary and 89.5°E-94.5°E at the north boundary) and the terrestrial rainfall over Indonesia and Australia. The collection consists of model and observational data including seasonal climatologies and monthly time series of ocean variables such as sea surface temperature and salinity, mixed layer depth, isothermal layer depth, barrier layer thickness, and atmospheric variables such as precipitation, heat fluxes (net, latent and sensible heat fluxes at the surface), outgoing longwave radiation at the top of the atmosphere, the wind stress, winds, the vertically integrated total moisture and moisture transport. The directory structure is organized in sub-folders for each of the manuscript figures. The data are in NetCDF-3 classic format. The scripts are in Matlab (version 2020b) and Ferret (version 6.4). In addition we have used the Climate Data Toolbox for Matlab.

The model data are from high-resolution fully coupled 1850 pre-industrial simulation carried out with the Energy Exascale Earth System Model version 0 (E3SMv0) (McClean et al, 2018). It includes the Community Atmosphere Model version 5 - Spectral Element (CAM5-SE), the Community Land Model version 4 (CLM4), the Los Alamos Parallel Ocean Program version 2 (POP2) and the Los Alamos sea ice model version 4 (CICE4). The nominal horizontal grid resolution is 0.25 in the atmospheric and land components and 0.1 in the ocean and sea ice components. The atmospheric model has 30 vertical hybrid levels. In the ocean model the vertical grid has 42 depth levels with variable thickness from 10 to 50 m in the upper 300 m. The ocean and sea-ice model components were initialized from a two-year spun up state of a global 0.1° POP2/CICE4 simulation forced with interannually-varying corrected Coordinated Ocean-ice Reference Experiments phase II product. The preindustrial simulation was integrated for 131 years, from which years 85-131 were used in the analysis.

The observational data sets include: 
National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM0.25v7) for total precipitation (1986-2016): https://disc.gsfc.nasa.gov/datasets/TRMM_3B43_7/summary?keywords=TRMM_3B43_7.
The Scatterometer Climatology (1999-2007) of Ocean Winds (SCOW) available at: http://agate.coas.oregonstate.edu/scow/index.html
International Argo Program climatology (2004-2017) of temperature and salinity profiles Source: http://sio-argo.ucsd.edu/RG_Climatology.html

Contents of the subfolders:
Fig1:
BLT_argo.WS_NDJ_climo.nc – Netcdf-3 classic format file of NDJ climatology of ARGO BLT
BLT_b1850c5_m2a.pop.h.IO.85-131_NDJ_climo.nc – Netcdf-3 classic format file of NDJ climatology of E3SMv0 model
Fig1_blt_panel2_NDJ.gif – Fig.1 plot
plot_Fig1_BLT_2panel_box1new.jnl – Ferret script to plot Fig.1

Fig2:
Fig2_annual_cycles_left.eps – eps format plot of Fig.2a-d
Fig2_annual_cycles_left.png – png format plot of Fig.2a-d
Fig2_annual_cycles_right.eps – eps format plot of Fig.2e-h
Fig2_annual_cycles_right.png – png format plot of Fig.2e-h
Fig2_leftpanel.m – Matlab script to plot Fig.2a-d
Fig2_rightpanel.m – Matlab script to plot Fig.2e-h
FigS2.m – Matlab script to plot Fig.S2
Input – subfolder with the input data for Figure 2

Subfolders in Input
Input/AtmFluxes content: 
Netcdf files of monthly time series (85-131) of PI E3SMv0 atmospheric fluxes area averaged in the WS box
Fluxes extracted from the atmospheric model output (CAM):
Input/AtmFluxes/FLNS_b1850c5_m2a.cam.h0.IO.85-131.boxes.nc – surface net longwave flux
Input/AtmFluxes/FLUS_b1850c5_m2a.cam.h0.IO.85-131.boxes.nc – surface longwave up flux
Input/AtmFluxes/FLUT_b1850c5_m2a.cam.h0.IO.85-131.boxes.nc – top of the atmosphere longwave up flux
Input/AtmFluxes/FSDS_b1850c5_m2a.cam.h0.IO.85-131.boxes.nc – surface shortwave down flux
Input/AtmFluxes/FSNS_b1850c5_m2a.cam.h0.IO.85-131.boxes.nc – surface net shortwave flux
Input/AtmFluxes/LHFLX_b1850c5_m2a.cam.h0.IO.85-131.boxes.nc – latent heat flux
Input/AtmFluxes/SHFLX_b1850c5_m2a.cam.h0.IO.85-131.boxes.nc– sensible heat flux
Fluxes extracted from the ocean model output (POP):
Input/AtmFluxes/SHF_SFWF_b1850c5_acmev0_highres.pop.h.WS.boxes.nc – surface heat (SHF) and freshwater (SFWF) fluxes 
Input/AtmFluxes/TAUX_TAUY_b1850c5_acmev0_highres.pop.h.WS.boxes.nc – surface wind stress components

Input/OceanVar content:
Input/OceanVar/ARGO/TempPsal/RG_ArgoClim_Psal_WS*.nc – netcdf files of 3-D gridded monthly climatologies netcdf files of the ARGO salinity
Input/OceanVar/ARGO/TempPsal/RG_ArgoClim_Temp_WS*.nc – netcdf files of 3-D gridded monthly climatologies netcdf files of the ARGO potential temperature
Input/OceanVar/ARGO/MLD/MLD_argo_WS_*.nc – netcdf files of monthly climatologies the ARGO mixed layer depth
Input/OceanVar/ARGO/ILD/ILD_argo_WS_*.nc - netcdf files of monthly climatologies the ARGO of isothermal layer depth

Input/OceanVar/Model – netcdf files of monthly time series of PI E3SMv0 model ILD, MLD, BLT, SST and SSS
Input/OceanVar/Model/BLT_b1850c5_m2a.pop.h.IO.boxes.85-131.nc
Input/OceanVar/Model/ILD_b1850c5_m2a.pop.h.IO.boxes.85-131.nc
Input/OceanVar/Model/MLD_b1850c5_m2a.pop.h.IO.boxes.85-131.nc
Input/OceanVar/Model/SST_SSS_b1850c5_m2a.pop.h.IO.boxes.85-131.nc

Fig3:
PRECIP_maps/ 
Fig.3_precip_panel3_DJF.gif – gif format plot of Fig.3abc
Fig.3_precip_panel3_DJF.ps – ps format plot of Fig.3abc
PRECT_b1850c5_m2a.cam.h0.IO.85-132_DJF_climo.nc – DJF precipitation climatology (85-131) of PI E3SMv0 model
landfrac_IO.fv801x1600.nc –E3SMv0 land mask
precip_trmm_1998-2016-DJF_fv801x1600.nc – DJF precipitation climatology (1998-2016) of TRMM satellite data
plot_Fig3abc_precip_3panel_TRMM.jnl – Ferret script to plot the Fig. 3abc – model data comparison of precipitation maps

Partial_regress/
BOX1_SST_BLT_MLD_ENSO34_IOD_PLSregression_monthly_lag1_ext.nc – netcdf file of partial least square regression coefficients
Fig3d2i_plot_partial_regress_box1_monthly.jnl – Ferret script to plot PLS coefficients
landfrac_IO.fv801x1600.nc – E3SMv0 land mask
part_regress_box1_5pr_monthly_lag1_ext_95CL_nboot100_ns239_final.png – Fig3d-I – maps of the PLS regression coefficients
partial_regress_box1_monthly_lagged_ext_5pred_NEWboot100_239.m – Matlab script to calculate PLS regression of the precipitation onto BLT, MLD, SST, IOD, Nino3.4
Input – subfolder with the input for the PLS regression including monthly time series of the E3SMv0 model BLT, SST, MLD, IOD, Nino3.4 and precipitation:
BLT_b1850c5_m2a.pop.h.IO.boxes.85-131.nc
IOD_b1850c5_m2a.pop.h.85-131.nc
MLD_b1850c5_m2a.pop.h.IO.boxes.85-131.nc
NINO34_b1850c5_m2a.pop.h.85-131.nc
PRECT.b1850c5_m2a.cam.h0.IO.85-132.nc
SST_SSS_b1850c5_m2a.pop.h.IO.boxes.85-131.nc

Fig.4:
BLT_b1850c5_m2a.pop.h.IO.boxes.nc– monthly time series of E3SMv0 WS box BLT
BOX1_BLT_linear_mvregression_UQ_VQ_z28_lag1.85-131y.nc – linear regression coefficients 
Fig4_uq_vq_z28_tmq_blt_mvregress_coeff_p2.gif – gif format plot of Fig.4
Fig4_uq_vq_z28_tmq_blt_mvregress_coeff_p2.ps – ps format of Fig.4
TMQ_b1850c5_m2a.cam.h0.IO.85-132_DJF_climo.nc – netcdf file of the E3SMv0 total vertically integrated water content (TMQ)
UQ_b1850c5_m2a.cam.h0.IO.85-132_DJF_climo.nc – netcdf of E3SM zonal moisture transport
VQ_b1850c5_m2a.cam.h0.IO.85-132_DJF_climo.nc – netcdf of E3SMv0 meridional moisture transport
linear_regress_box1_monthly_lagged.m – Matlab script to calculate linear regression of total vertically integrated water content (TMQ) and moisture transport (UQ, VQ) with WS box BLT monthly time series
plot_Fig4_tmq_uq_vq_mvregressed_blt_p2.jnl – Ferret script to plot Figure 4. a) Climatological DJF distribution from the PI E3SMv0 model (years 85-131) of total vertically integrated precipitable water (TMQ, kg/m2) and moisture transport (m/s) at 958 hPa, b) Linear regression of monthly TMQ (color, kg/m2) and moisture transport (vectors, m/s) at 958 hPa with WS Box BLT after a 1-month time lag.

Fig.S1:
FigS1_precip_winds_panel8_all_green.png – png plot of Fig.S1

Seasonal climatologies (100-132) of precipitation (PRECT) and winds (U,V) of E3SMv0 model:
PRECT_b1850c5_m2a.cam.h0.IO.100-132_DJF_climo.nc
PRECT_b1850c5_m2a.cam.h0.IO.100-132_JJA_climo.nc
PRECT_b1850c5_m2a.cam.h0.IO.100-132_MAM_climo.nc
PRECT_b1850c5_m2a.cam.h0.IO.100-132_SON_climo.nc
U_b1850c5_m2a.cam.h0.IO.100-132_DJF_climo.nc
U_b1850c5_m2a.cam.h0.IO.100-132_JJA_climo.nc
U_b1850c5_m2a.cam.h0.IO.100-132_MAM_climo.nc
U_b1850c5_m2a.cam.h0.IO.100-132_SON_climo.nc
V_b1850c5_m2a.cam.h0.IO.100-132_DJF_climo.nc
V_b1850c5_m2a.cam.h0.IO.100-132_JJA_climo.nc
V_b1850c5_m2a.cam.h0.IO.100-132_MAM_climo.nc
V_b1850c5_m2a.cam.h0.IO.100-132_SON_climo.nc

Seasonal climatologies (1998-2016) of TRMM observations for precipitation re-interpolated on the model grid:
precip_trmm_1998-2016-DJF_fv801x1600.nc
precip_trmm_1998-2016-JJA_fv801x1600.nc
precip_trmm_1998-2016-MAM_fv801x1600.nc
precip_trmm_1998-2016-SON_fv801x1600.nc
Seasonal climatologies (1999-2007) of SCOW observations for winds
wind_meridional_monthly_maps.nc
wind_spd_monthly_maps.nc
wind_zonal_monthly_maps.nc

plot_precip_winds_8panel_TRMM_SCOW.jnl – Ferret script to plot Fig.S1 – seasonal climatological maps of precipitation and winds
precip_winds_panel8_all_green.gif – gif plot of Fig.S1

Fig.S2:
FigS2.m – Matlab script to plot Fig.S2. Input files are in Fig2 subfolder
FigS2_WSbox_radiative_fluxes_sensfl.eps – eps plot of Fig.S2 
FigS2_WSbox_radiative_fluxes_sensfl.png – png plot of Fig.S2
readme.FigS2

chadagreene-CDT-3208805: Climate Data Toolbox for Matlab  from: http://chadagreene.com/ClimateDataToolbox.mltbx