Systematic differences in bucket sea surface temperatures caused by misclassification of engine room intake measurements

Matlab scripts associated with the paper "Systematic differences in bucket sea surface temperatures caused by misclassification of engine room intake measurements" by Duo Chan and Peter Huybers.

All codes are Matlab .m files. We provide a main script for fast reproduction of Figures and bucket model simulations in the main text. If you are reproducing the full analysis, we also provide codes and step-by-step instructions for running these codes. "Parallel" computations using multiple CPUs are encouraged are some steps, though fast-reproduction is runnable on a laptop.

Dependencies

• distinguishable_colors for generating figures.

• sun_position for running the extended wooden bucket model.

If you have issues implementing these scripts, or identify any deficiencies, please contact Duo Chan (duochan@g.harvard.edu).

Table of Contents

• Fast reproduction of Figures
• Full Analysis
• A. Getting Started
• B. Diurnal Cycles
• C. LME Intercomparison
• D. Merging DA and LME

Fast reproduction of Figures

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After cloning this repository, open a Matlab interface and simply run the following lines of codes to reproduce figures in the main text.

addpath(genpath(pwd));
DA_LME_main

In addition to data for reproducing the standard analysis as introduced in the main text, we also provide results from a variety of sensitivity tests including, prioritizing WMO No.47 metadata when identifying bucket SSTs, excluding inferred bucket measurements from country information, grouping data only accordingly to nation information, and calculating excess diurnal amplitudes relative to a buoy climatology. To explore results in these sensitivity tests, please change values for the variable "revision" in DA_LME_main.m.

Full Analysis

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Files are organized following the figure below.

A. Getting started

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To get started, run DA_LME_init.m, which sets up directories structured following the figure above.

dir_data = 'the directory for outputs';
DA_LME_init(dir_data);

After creating directories, you need to add pre-processed ICOAS3.0 data to $DATA/ICOADS3/ICOADS_QCed/, where data (30G) can be downloaded from here. Codes for downloading and preprocessing ICOADS3.0 data are published here.

Moreover, the analysis also requires ships tracks from Carella et al., (2015). Because these data are not yet publicly available, please obtain a permission from Dr. Elizabeth Kent and forward her message to Duo Chan. Duo will provide .mat files upon seeing Kent's permission.

B. Diurnal Cycles

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In this step, diurnal anomalies of individual ships are extracted, and then binned and averaged for individual SST groups. Outputs are diurnal amplitudes of individual bucket SST groups as a function of region-season combinations and 20-year periods. Output data are placed in $DATA/DIURNAL/DATA_for_figures/.

To get started, first run the following lines to extract diurnal anomalies from individual ships and days,

P.relative = 'mean_SST';
for yr = 1880:2009
    DIURNAL_Step_01_ship_diurnal_signal(yr,P);
end

Computed diurnal anomalies have names IMMA1_R3.0.0_YYYY-MM_Ship_Diurnal_Signal_relative_to_mean_SST.mat and are saved in $DATA/DIURNAL/Step_01_Ship_Signal/. Looping over individual years can take quite a while, thus it would be helpful to use multiple CPUs and "parallel" the computation.

Next, run the following lines to subset diurnal anomalies by SST methods and combine them across years. Note that unlike HadSSTs, we do not treat hull sensor SSTs as ERI measurements.

DA_POST_sum_and_fitting(1,[],[],'bucket');
DA_POST_sum_and_fitting(1,[],[],'ERI');

The combined diurnal anomalies are saved in SUM_METHOD_DA_signals_YYYY_YYYY_Annual_relative_to_mean_SST.mat and placed in $DATA/DIURNAL/DATA_for_figures/.

Finally, run the following lines to compute the averaged diurnal cycles for each group over individual 20-year bins and region-season combinations. The code also fits a sinusoidal function for the amplitude of diurnal cycles.

for yr_start = 1880:1990
    yr_end   = yr_start + 19;
    method   = 'bucket';
    DA_POST_sum_and_fitting(0,yr_start,yr_end,method);
end

for yr_start = 1920:1990
    yr_end   = yr_start + 19;
    method   = 'ERI';
    DA_POST_sum_and_fitting(0,yr_start,yr_end,method);
end

Output files will have names following STATS_METHOD_DA_signals_YYYY_YYYY_REGION_relative_to_mean_SST.mat and are placed in $DATA/DIURNAL/DATA_for_figures/.

C. LME Intercomparison

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Intercomparison of collocated SSTs using linear-mixed-effect (LME) model is the same as here, but we have improved the algorithm such that the code avoids gigantic matrix inversion and runs faster than the previous version. The analysis consists of three steps: 1. pairing SSTs, 2. screening pairs, and 3. run the LME model.

To get started, first run the following code to obtain all available pairs from different groups that are within 300km and 2days in time.

LME_Step_01_Run_Pairs

Computed initial pairs have names IMMA1_R3.0.0_YYYY-MM_All_pairs.mat and are saved in $DATA/LME_intercomparison/Step_01_All_Pairs/. This code picks out not only pairs between buckets groups, but also bucket-ERI and ship-buoy pairs. The code loops over individual years, which takes more than a week to run. Thus it would be wise to use multiple CPUs and "parallel" the computation.

The second step is to screen pairs such that each measurement is used at most once. The current code treats all ERI measurements as a single group and uses measurements that have valid diurnal anomaly estimates. In other words, LME analysis should be performed after the diurnal cycle analysis.

LME_Step_02_Screen_Pairs

Output files are have names IMMA1_R3.0.0_YYYY-MM_Bucket_vs_ERI_in_one_group_diurnal_points_mean_SST.mat and are saved in $DATA/LME_intercomparison/Step_02_Screen_Pairs/.

[IMPORTANT] Before running the final step, please download OI_SST_inter_annual_decorrelation_20180316.mat from here and place it in $DATA/Miscellaneous/. This file is used to compute the uncertainty of physical SSTs between pairs of measurements.

The final step is to run the LME model and estimate relative offsets between groups of SSTs by running,

LME_Step_03_LME_bucket_regional_ERI

This step outputs LME_Bucket_vs_ERI_in_one_group_diurnal_points_mean_SST_YYYY_YYYY_Full_SST_Global.mat files and saves them in $DATA/LME_intercomparison/Step_04_LME_output/. Intermediate outputs, including the concatenated and aggregated pairs, are output to $DATA/LME_intercomparison/Step_03_Binned_Pairs/.

D. Merging DA and LME

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Once step A-C are completed, simply run the following script to match outputs from Diurnal and LME analyses and prepare for generating figures and statistics.

DA_LME_Prepare_Data

The script will output a file named All_lme_offsets_and_diurnal_amplitudes.mat and place it in $DATA/. When running DA_LME_main.m to generate figures, please copy All_lme_offsets_and_diurnal_amplitudes.mat to $CODE/ such that Matlab can find it.

Maintained by Duo Chan (duochan@g.harvard.edu)