Skip to content
Projects
Groups
Snippets
Help
Loading...
Help
Support
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
B
bjerknes_feedback_symmetry
Project overview
Project overview
Details
Activity
Releases
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Labels
Merge Requests
0
Merge Requests
0
Analytics
Analytics
Repository
Value Stream
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Commits
Open sidebar
open-source-code
bjerknes_feedback_symmetry
Commits
f025487e
Commit
f025487e
authored
Feb 07, 2019
by
Claas Faber
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Delete README.txt
parent
0b367588
Changes
1
Hide whitespace changes
Inline
Side-by-side
Showing
1 changed file
with
0 additions
and
177 deletions
+0
-177
README.txt
README.txt
+0
-177
No files found.
README.txt
deleted
100644 → 0
View file @
0b367588
####################################################################
# #
# ------------------------------------------------------------ #
# A COMPARISON OF THE ATLANTIC AND PACIFIC BJERKNES FEEDBACKS: #
# SEASONALITY, SYMMETRY, AND STATIONARITY #
# ------------------------------------------------------------ #
# #
# T. DIPPE, J. F. LÜBBECKE, R. J. GREATBATCH #
# #
# in #
# #
# JOURNAL OF GEOPHYSICAL RESEARCH: OCEANS #
# #
# #
# Readme for using the Matlab scripts #
# #
####################################################################
# --------- #
# TECHNICAL #
# --------- #
Matlab version used for all analysis: Matlab 2018
# ------------- #
# USED DATASETS #
# ------------- #
- AVISO-SSH: https://www.aviso.altimetry.fr/en/home.html
- ERSST: https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5
- ERA-Interim USTR: https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim
- ERA-40 USTR: https://apps.ecmwf.int/datasets/data/era40-moda/
- ORAS4 data: http://icdc.cen.uni-hamburg.de/projekte/easy-init/easy-init-ocean.html
# ------- #
# GENERAL #
# ------- #
# ANALYSIS USING A BUNDLE OF SCRIPTS
# ++++++++++++++++++++++++++++++++++
Many scripts will be made up of a group of three to four scripts. These are:
- <name>.m: The main script that does the principal calculations
- <name>_settings.m: Prescribing all kinds of settings, including paths and
various plotting options
- <name>_plots.m or similar: Plots
- <name>_wrapper.m: A wrapper around the main script that allows looping over,
for example, feedback elements or basins
For a given script <name>, I will refer to the bundle as <name>*.m
# ZONAL ANALYSIS VERSUS INDIVIDUAL INDICES
# ++++++++++++++++++++++++++++++++++++++++
All analyses are based on indices. However, I distinguish between "zonal
analyses" and "individual indices". Datasets for the zonal analyses contain a
matrix that holds time series of indices that correspond to a sliding index
along the equator. The zonal analysis is set up in
zonal_analysis_region_definition.m (for the published paper: 4 deg lon x
4 deg lat boxes that slide along the equator from a fixed western boundary
towards the eastern boundary), an the collection of sliding indices is
calculated with calculate_equatorial_indices*.m. These datasets should be stored
in the <path where data for zonal analysis is stored>.
In contrast, individual indices refer to just a single index. Single indices are
calculated with the function calcIndexAveraged() and should be stored in the
<path were individual indices are stored>.
Additionally, raw SST data might be required, such as ERSST. These datasets
should be stored in the <path where SST data is stored>.
# --------------- #
# LIST OF SCRIPTS #
# --------------- #
# FUNCTIONS
# +++++++++
- bootstrapFeedbackStrength() and bootstrapFeedbackStrengthDiff(): Significance
test for feedback strengths and their differences.
- calcAnomalies(): Calculates the anomalies, and allows for variable detrending
- calcIndexAveraged(): Calculates an index, requires a file that describes the
grids that the variables are given on, including the
spatial extent of each grid cell to apply the correct
spatial averaging. (The grid files are stored in my case
in the file modelGrids.mat, which contains a structure
for each grid, which in turn stores information such as
box_grid_area.)
- calculateFeedback(): Perform a robust regression to estimate the strength of a
given feedback element.
- diagnoseLag(): Diagnose the lag at which the feedback strength between two
variables is maximum.
- eventCharacteristics(): Identifies events in a time series and returns various
statistical measures about them. This function is the
backbone of the SST event analysis in the Discussion.
- testValue(): Given a distribution, a significance level and a test value,
discern whether the test value sits on the "significant" tails of
the distribution.
# (More or less) PROJECT-WIDE SETTINGS
# ++++++++++++++++++++++++++++++++++++
- atlantic_asymmetric_bjf_plot_settings.m contains general settings
for plots that will be called for a number of analysis scripts. Customize the
colorbar handles in this script. However, the script is not used uniformly
across all analysis scripts. Sorry for the inconvenience.
- zonal_analysis_region_definition.m defines the properties of the zonal
analysis, i.e. the total zonal extent of the analysis region, as well as the
width of zonal bins that will be used to calculate the equatorial indices.
# PREPARING THE DATA: Binning data into sliding indices along the equator
# ++++++++++++++++++
- calculate_equatorial_indices*.m: Calculate indices for each zonal bin defined
in zonal_analysis_region_definition.m. These datasets are the basis for most
of the analysis. They should be stored in the <path where data for zonal
analysis is stored>.
Date vectors are expected to be Matlab date vectors (see
https://de.mathworks.com/help/matlab/ref/datevec.html).
# ANALYSIS
# ++++++++
Fig. 1: compare_oras4_ersst*.m
Fig. 2: diagnose_zonal_feedback_lags.*
<Fig. 3: Created in MacOS's Keynote>
Fig. 4-6: lagged_feedback_strength*.m to diagnose the strengths of the feedback
elements, and diagnose_feedback_strength_difference_significance*.m to
test the significance
Fig. 7: total_bjerknes_feedback_from_regression*.m
Fig. 8: compare_aviso_oras4_strengths_Atl3_Nino34*.m
Figs. 9-11: decadal_feedback_variations*.m
Fig. 12: zonal_enso_properties*.m
Fig. 13: decadal_sst_event_variations*.m
Figs. A1-A2: calculate_instantaneous_feedback*.m, and
compare_lagged_instantaneous_feedback_strengths*.m
Figs. A3-A5: total_bjerknes_feedback_from_regression*.m
# ------- #
# CONTACT #
# ------- #
Contact me via tina.dippe@gmail.com if you have questions.
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment