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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,203 @@ | ||
| --- | ||
| title: "BCCM Results Over Full Domain" | ||
| author: "Andrew Edwards and Travis Tai" | ||
| output: rmarkdown::html_vignette | ||
| vignette: > | ||
| %\VignetteIndexEntry{BCCM Results Over Full Domain} | ||
| %\VignetteEngine{knitr::rmarkdown} | ||
| %\VignetteEncoding{UTF-8} | ||
| date: "Last rendered on `r format(Sys.time(), '%d %B, %Y')`" | ||
| --- | ||
|
|
||
| ```{r run, echo = FALSE, eval = FALSE} | ||
| rmarkdown::render("bccm_full.Rmd") | ||
| # to build, or click the knit button in RStudio | ||
| ``` | ||
|
|
||
| ```{r setup, include = FALSE} | ||
| knitr::opts_chunk$set( | ||
| collapse = TRUE, | ||
| comment = "#", | ||
| cache = TRUE, | ||
| cache_path = "bccm-full-cache/", | ||
| fig.path = "bccm-full-figs-cache/", | ||
| fig.width = 7.7, | ||
| fig.height = 7 | ||
| ) | ||
| ``` | ||
|
|
||
| # Introduction | ||
|
|
||
| The British Columbia Continental Margin (BCCM) model is a | ||
| physical biogeochemical oceanographic model, implemented using the Regional Ocean Modeling | ||
| System (ROMS; Peña et al., 2019). Results restricted to Canada's Exclusive Economic Zone and mapped | ||
| to a 2 km x 2 km grid onshore and 6 x 6 km grid offshore are | ||
| discussed in the [BCCM Results vignette](bccm.html), which should be looked at | ||
| to understand more about the model. The restrictions and | ||
| varying of the gridsize were done to reduce file sizes. | ||
|
|
||
| Due to several requests from users we have now also mapped the BCCM results for its full domain (extending | ||
| north and south into US waters) onto a 2 km x 2km grid. These requests included: | ||
|
|
||
| - [Issue #49](https://github.com/pbs-assess/pacea/issues/49): extend the BCCM | ||
| results into US waters | ||
| - [Issue #58](https://github.com/pbs-assess/pacea/issues/58): add the results from the | ||
| HOTSSea physical oceanographic model for the Salish Sea (a region not covered by | ||
| the BCCM results, ideally on a grid that overlaps as that used for the BCCM | ||
| - [Issue #48](https://github.com/pbs-assess/pacea/issues/48) and [Issue | ||
| #62](https://github.com/pbs-assess/pacea/issues/62): have an easily exported grid that covers a larger domain | ||
| - [Issue #60](https://github.com/pbs-assess/pacea/issues/60): projecting the Optimally Interpolated Sea Surface Temperature values onto the same grid as the | ||
| BCCM results. | ||
|
|
||
| These requests are now mostly fulfilled, and include the BCCM results on the | ||
| full domain. The results are in the same format as those for the restricted | ||
| domain described in the [BCCM Results vignette](bccm.html) vignette. The | ||
| file sizes are much larger (each variable is about 120 Mb rather than about 30 | ||
| Mb), but only need to be downloaded once. | ||
|
|
||
| We have kept consistent terminology and functionality as for the restricted BCCM | ||
| results. Basically: | ||
| - variable names are appended with `_full` | ||
| - plotting and analysis functions should still work on the `_full` variables. | ||
|
|
||
| ## Available variables | ||
|
|
||
| ```{r packages} | ||
| library(pacea) | ||
| library(dplyr) | ||
| library(sf) | ||
| library(ggplot2) | ||
| ``` | ||
|
|
||
| For the restricted domain, the available variables are given in the data object | ||
| `bccm_data`. For the full domain, the variables are therefore given in | ||
| `bccm_data_full`: | ||
| ```{r bccm_data-Full} | ||
| bccm_data_full | ||
| ``` | ||
| The `bccm_data` object gives the same `r nrow(bccm_data_full)` names, but without | ||
| the `_full`. | ||
|
|
||
| ## Downloading bccm full results | ||
|
|
||
| The 22 BCCM full model results are stored on Zenodo at https://zenodo.org/records/14031460. Each can be downloaded | ||
| individually (if you only want certain variables) as described below. It is | ||
| easiest to just | ||
| just simply download them all to your computer in one go, using | ||
| ```{r download, eval = FALSE} | ||
| bccm_all_variables_full() | ||
| ``` | ||
| This places them into a cache directory on your local machine, given by | ||
| by `paste0(pacea_cache(), "/bccm_full")`. It only took about 10 minutes to | ||
| download them all (the code uses parallel cores on Windows) on a home network, | ||
| but could be longer on a work network. You can check progress in your local | ||
| cache directory, where files should gradually appear. | ||
| Any files that are already | ||
| present in your cache directory are not re-downloaded. See | ||
| `?bccm_all_variables_full` if you have problems. | ||
|
|
||
| ### Using the bccm full results | ||
|
|
||
| As an example, let's look at the estimates of primary production, calling the | ||
| object `pp` locally: | ||
| ```{r pp} | ||
| pp <- bccm_primaryproduction_full() | ||
| ``` | ||
| Note the `()`. The `bccm_primaryproduction_full()` function assigns the | ||
| output to our designated variable simply named `pp`. | ||
|
|
||
| If you have not already previously downloaded the relevant results it | ||
| will first download them into your cache folder (i.e. it will get just the | ||
| specified model results, rather than all of them as in the | ||
| `bccm_all_variables_full()` described above). If you have previously downloaded | ||
| the results the function will simply load them in from your cached folder. | ||
|
|
||
| View the data help for more information | ||
| (e.g. `?bccm_primaryproduction_full`); similar help functions all point to a | ||
| common help file for all `bccm_full` objects. | ||
|
|
||
| So, what does the object look like? | ||
| ```{r pp1} | ||
| pp | ||
| head(pp[, 1:5]) # note that the geometry column is included when selecting columns from | ||
| # an `sf` object. | ||
| ``` | ||
|
|
||
| The object is in wide format, with each column representing a unique year-month | ||
| combination, and is a 'simple features' (`sf`) R object, and so contains a | ||
| `geometry` column. The data also have various attributes, such as the units for the | ||
| data values, and some extra ones to automate some plotting. (This is useful but | ||
| not essential for users to know). | ||
| ```{r} | ||
| names(attributes(pp)) | ||
| attributes(pp)$units | ||
| class(pp) | ||
| ``` | ||
| ## Visualising the bccm full results | ||
|
|
||
| We give some examples here, and then refer users to the [BCCM Results | ||
| vignette](bccm.html) since the examples shown there should work for the `_full` | ||
| results. (Note we have not tested every potential idea you might have, but by | ||
| keeping the structure of the outputs the same, our custom plotting and analysis | ||
| functions should work). | ||
|
|
||
|
|
||
| ### Plotting with `plot()` | ||
|
|
||
| Results can be plotted using the `plot()` function (that we have | ||
| customised). The default settings are to plot results for April, 2018. However, | ||
| users can specify any time period(s) available in the results. Examples are: | ||
| ```{r plot1} | ||
| plot(pp) | ||
| ``` | ||
|
|
||
| To see the estimated primary production for each month of 2018 | ||
| ```{r, ppplot, fig.width = 16, fig.height = 12} | ||
| plot(pp, months = 1:12) | ||
| ``` | ||
|
|
||
| This shows the production ramping up through the spring. | ||
|
|
||
| ### Climatologies and anomalies | ||
|
|
||
| Let's have a quick look at anomalies, say for oxygen at the bottom of the water | ||
| column, adapting the examples from the [BCCM | ||
| vignette](http://htmlpreview.github.io/?https://github.com/pbs-assess/pacea/blob/main/vignettes/bccm.html). Again, | ||
| see that vignette for extra details. | ||
|
|
||
| So let's ask: how do the anomalies of bottom oxygen for | ||
| April and September in 2010 and 2018, look compared to a climatology (for each | ||
| month) from 1993 to 2010 (the full range of years currently available). | ||
|
|
||
| We can easily load the model results and calculate the anomalies for a | ||
| climatology (see `?calc_clim` to obtain the climatology also): | ||
| ```{r, anomalies} | ||
| bottom_oxygen <- bccm_bottom_oxygen_full() | ||
| anom <- calc_anom(bottom_oxygen, | ||
| clim_years = 1993:2010, | ||
| time_period_return = c("Apr", "Sep"), | ||
| years_return = c(2010, 2018)) | ||
| anom | ||
| ``` | ||
| ```{r, anomaliesplot, fig.width = 8, fig.height = 8} | ||
| plot(anom, | ||
| months.plot = c("Apr", "Sep"), | ||
| years.plot = c(2010, 2018), | ||
| eez = FALSE) | ||
| ``` | ||
|
|
||
| So some areas are relatively oxygen rich (green) in these months compared to the | ||
| same areas from 1993 to 2010, whereas others are lower in oxygen (red). This | ||
| varies spatially and by month. Along the coast there looks to be more oxygen | ||
| than normal in April 2010, generally a bit less in September 2010, and even less | ||
| in April and September 2018. | ||
|
|
||
| See the [BCCM | ||
| vignette](http://htmlpreview.github.io/?https://github.com/pbs-assess/pacea/blob/main/vignettes/bccm.html) | ||
| for further example calculations, which hopefully stimulate your own ideas. | ||
|
|
||
| ## Reference | ||
|
|
||
| Peña, M.A., Fine, I. and Callendar, W. 2019. Interannual variability in primary | ||
| production and shelf-offshore transport of nutrients along the northeast Pacific | ||
| Ocean margin. Deep-Sea Research II, doi:10.1016/j.dsr2.2019.104637. |
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