In pbs-assess/PACea: An R package of Pacific ecosystem information to help facilitate an ecosystem approach to fisheries management

Indices

Climatic and oceanographic indices

Various climate and oceanographic indices are currently included in pacea:

kableExtra::kable(pacea_indices, "latex", booktabs = TRUE) %>%
  kableExtra::kable_styling(latex_options = c("scale_down", "striped"))

Climatic and oceanographic indices

For example, to see a plot of the Oceanic Niño Index (ONI) anomaly, simply type

plot(oni)

This shows the onset of El Niño (positive index) conditions in April 2023.

Climatic and oceanographic indices

The values are readily available for doing your own analyses:

oni
tail(oni)

```{asis, echo = long_talk}

Climatic and oceanographic indices

Each climatic and oceanographic index is saved as a data object, and properly documented, described, and referenced in its help file, for example:

```r
?oni

ONI - Oceanographic Niño Index

Description:

     The Oceanic Niño Index is a monthly index which is one measure of
     the El Niño-Southern Oscillation.

Usage:

     oni

Format:

     A tibble also of class `pacea_index` with columns:

     year: year of value

     month: month (1 to 12) of value

     val: absolute values of three-month averages (preceding, current,
          and next month), deg C; note that recent values may change in
          subsequent updates - see details

     anom: anomalies based on 30-year base periods that are updated
          every 5 years, deg C; note that recent values may change in
          subsequent updates - see details

```{asis, echo = long_talk}

Climatic and oceanographic indices

Continuing the help...

```r
     The Oceanic Niño Index (ONI) is a 3-month running mean of sea
     surface temperature (SST) anomalies in the Niño 3.4 region (5 deg
     N to 5 deg S, 120 deg W to 170 deg W) plotted on the center month.
     The SST anomalies are calculated based on 30-year base periods
     that are updated every 5 years, which accounts for global warming
     and some of the decadal-scale SST variability (as seen in the
     Pacific Decadal Oscillation index). The ONI is provided by the
     NOAA’s National Weather Service National Centers for Environmental
     Prediction CPC:
     http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml

     ...

         Because of the high frequency filter applied to the ERSSTv5 data,
     `ONI values may change up to two months after the initial real
     time value is posted`. Therefore, the most recent ONI values
     should be considered an estimate.

```{asis, echo = more_details}

Climatic and oceanographic indices

So for some analyses you may want to restrict values to a specified time period (for which values should not change in future pacea updates), for example

\

```r
oni_fix <- dplyr::filter(oni,
                         year < 2023 | year == 2023 & month < 3)
tail(oni_fix)       # Will always end with February 2023

```{asis, echo = more_details}

Further plotting options and styles

Each index has a default plotting style (which you can override). For oni it shows the monthly anomalies as colour-code bars:

```r
plot(oni)

```{asis, echo = more_details}

Further plotting options and styles

Another option is as a black line with filled-in colouring:

```r
plot(oni,
     style = "red_blue")

```{asis, echo = more_details}

Further plotting options and styles

To see oni as an annual (not monthly index):

```r
plot(oni,
     smooth_over_year = TRUE,
     lwd = 6)

Further plotting options and styles

Say you want to see if specific events coincide with El Niño (based on a true story, see help for details):

plot(oni,
     event_years = c(1996, 2003, 2004, 2006, 2007, 2008, 2009, 2010, 2014, 2016, 2017, 2019),
     xlim = c(lubridate::dmy(01011995),
              lubridate::dmy(01012024)),
     lwd = 2)

```{asis, echo = more_details}

Technical aside regarding plot()

The object oni has the class pacea_index: \

```{asis, echo = more_details, eval = long_talk}
class(oni)

``{asis, echo = more_details} \ which ensures thatplot(oni)automatically uses our specialised functionplot.pacea_index(oni)`, giving the red and blue colours, tickmarks, axis labelling, etc.

\

So see ?plot.pacea_index for further options. Similarly, check the class of other objects saved in pacea. ```

pbs-assess/PACea documentation built on April 17, 2025, 11:36 p.m.