Annual Sea Level Anomalies

Annual Sea Level Anomalies#

../../../_images/319ab380dd40d4f1d04d0995b2cb316a95a767f1eb8ae47ea2b4b05e660ddc03.png

In this notebook, we’ll be taking a look at sea level anomalies (SLA), which can be thought of as signals that differ from the climatology (of a station or region). For these products we’ll focus on yearly anomalies. For a nice overview and animation of daily mean SLAs in the Hawaii and eastern Pacific region, see Pacific Sea Level Monitoring.

Setup#

As with previous sections, we first need to import the necessary libraries, establish our input/output directories, and set up some basic plotting rules.

# import necessary libraries
import numpy as np
import xarray as xr
import datetime as dt
from pathlib import Path
import pandas as pd
import os
import os.path as op
import sys

# data retrieval libraries
import requests
from urllib.request import urlretrieve #used for downloading files
import json
import copernicusmarine

# data processing libraries
from scipy import stats
import seaborn as sns
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt

from myst_nb import glue #used for figure numbering when exporting to LaTeX

sys.path.append("../../../functions")
from data_downloaders import download_oni_index, download_uhslc_data

data_dir = Path('../../../data')
path_figs = "../../../matrix_cc/figures"
data_dir = Path(data_dir,'sea_level')
output_dir = data_dir 

# Create the output directory if it does not exist
output_dir.mkdir(exist_ok=True)
data_dir.mkdir(exist_ok=True)

Time Series Plots#

Now we’ll look at monthly and yearly anomalies at the tide stations through time. We’ll also plot ENSO signals on the same plot, to get a visual feel for any ENSO-driven anomalous effects on sea level.

Show code cell source Hide code cell source

def plot_TG_rsl_anomaly_annual(rid, fig, ax, rsl_yearly_mean, rsl_subset, rsl_anomalies,oni, oniPlot=False):

    ax.set_xlim(1993,2024)
    ax.set_ylim(-0.5, 0.5)
    ax.grid(alpha=0.2, color='lightgray')

    if oniPlot:
        ymin, ymax = ax.get_ylim()
        # Shading El Niño events
        ax.fill_between(oni.index, ymin,ymax, 
                        where=oni['El Nino'] == 1, color='red', alpha=0.1, label='El Niño')
        # Shading La Niña events
        ax.fill_between(oni.index,  ymin,ymax,
                        where=oni['La Nina'] == 1, color='blue', alpha=0.1, label='La Niña')
    
    # get min and max for each year
    rsl_yearly_min = rsl_subset.groupby('storm_time.year').min('storm_time').isel(record_id=rid)
    rsl_yearly_max = rsl_subset.groupby('storm_time.year').max('storm_time').isel(record_id=rid)

    rsl_yearly_min = rsl_yearly_min['sea_level_anomaly_detrended']/1000
    rsl_yearly_max = rsl_yearly_max['sea_level_anomaly_detrended']/1000

    #make a fill from max to min for each month
    ax.fill_between(rsl_yearly_mean.storm_year+0.5, 
                     rsl_yearly_min, rsl_yearly_max, 
                     alpha=0.2, label='Range (Annual SLA Min-Max)')

    ax.plot(rsl_yearly_mean.storm_year+0.5, 
            0.001*rsl_yearly_mean['sea_level_anomaly_detrended'].isel(record_id=rid), 
            label='Annual Mean Sea Level Anomaly (m)',alpha=1,linewidth=2)

    # convert rsl_monthly['time'] to float
    storm_time_float = rsl_monthly['time.year'] + (rsl_monthly['time.month']-5)/12 #subtract 5 months to go with 'storm year' convention (May-April)
    ax.plot(storm_time_float, 
            0.001*rsl_monthly['sea_level_anomaly_detrended'].isel(record_id=rid), 
            label='Monthly Mean Sea Level Anomaly (m)',alpha=1, color='gray',linewidth=0.75)
    ax.set_ylabel(''), ax.set_xlabel('')

    # add rsl_yearly_mean['station_name'].isel(record_id=rid).values to upper center of plot
    nameID = str(rsl_subset['station_name'].isel(record_id=rid).values) + ' (' + str(rsl_subset['record_id'].isel(record_id=rid).values).zfill(3) + ')'
    ax.text(0.5, 0.9, nameID, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes)

First, we’ll plot one gauge

fig,ax = plt.subplots(figsize=(10,5))
# add ENSO to the plots
# import oni.csv
p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
oni = download_oni_index(p_data)

#adjust the index to be float value of fractional year (storm year)
enso_events = detect_enso_events(oni)
enso_events.index = pd.to_datetime(enso_events.index )
enso_events.index = enso_events.index.year + (enso_events.index.month - 1)/12 -5/12

sid = 0
plot_TG_rsl_anomaly_annual(sid, fig, ax,  rsl_yearly_mean, rsl_years, rsl_anomalies,enso_events,oniPlot=True)
ax.legend(loc='upper right', ncol=1, bbox_to_anchor=(1.45, 1.0))

# # Save the plot to a file
# filename = 'sea_level_MMA_annual_' + str(rsl_yearly_mean['station_name'].isel(record_id=sid).values) + '.png'
# plt.savefig(output_dir / filename, dpi=300, bbox_inches='tight')

fig.text(0.05, 0.5, 'Sea Level Anomaly (m)', va='center', rotation='vertical')
fig.text(0.5, 0, 'Storm Year', ha='center')

glue('SL_MMA_annual_station',fig,display=False)
station = str(rsl['station_name'].isel(record_id=sid).values)

glue('station',station,display=False)

p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
oni = download_oni_index(p_data)
enso_events = detect_enso_events(oni)
enso_events

	ONI	ONI Mode	year_storm	El Nino	La Nina
1951-01-01	-0.82	Neutral	1950	False	False
1951-02-01	-0.54	Neutral	1950	False	False
1951-03-01	-0.17	Neutral	1950	False	False
1951-04-01	0.18	Neutral	1950	False	False
1951-05-01	0.36	Neutral	1951	False	False
...	...	...	...	...	...
2026-08-01	NaN	Neutral	2026	False	False
2026-09-01	NaN	Neutral	2026	False	False
2026-10-01	NaN	Neutral	2026	False	False
2026-11-01	NaN	Neutral	2026	False	False
2026-12-01	NaN	Neutral	2026	False	False

912 rows × 5 columns

rsl_yearly_min = rsl_years.groupby('storm_time.year').min('storm_time').isel(record_id=0)
rsl_yearly_max = rsl_years.groupby('storm_time.year').max('storm_time').isel(record_id=0)
rsl_yearly_min = rsl_yearly_min['sea_level_anomaly_detrended']
rsl_yearly_max = rsl_yearly_max['sea_level_anomaly_detrended']
#make a fill from max to min for each month
fig, ax = plt.subplots(figsize=(10,5))
ax.fill_between(rsl_yearly_mean.storm_year+0.5, 
                 rsl_yearly_min, rsl_yearly_max, 
                 alpha=0.2, label='Range (Annual SLA Min-Max)')

<matplotlib.collections.FillBetweenPolyCollection at 0x17e8b0a40>

../../../_images/d9ae46ab18aa1eb02846997478ac341a3bc7c55452dba9b5fa16cf826cb27bff.png

Annual Sea Level Anomalies

Contents

Annual Sea Level Anomalies#

Setup#

Retrieve the Tide Station(s) Data Set(s)#

2. Now we’ll remove the trend (over the entire record) from the station.#

3. Resample to monthly time series of anomalies.#

4. Now sort the subset on “storm year” instead of calendar year for yearly means.#

Absolute Value: satellite (trend removed)#

Plot an area-wide map, with stations#

Area-weighted average trend#

Create a map#

Time Series Plots#