The effects of long-term trends

Last updated: 2019-11-06

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File	Version	Author	Date	Message
Rmd	ff0bb95	robwschlegel	2019-11-06	Publish the sub-optimal test vignettes
Rmd	61bf228	robwschlegel	2019-11-06	Prepped trend vignette for site re-build
Rmd	158aa0b	robwschlegel	2019-05-06	Updated project interface
html	158aa0b	robwschlegel	2019-05-06	Updated project interface
html	38559da	robwschlegel	2019-03-19	Build site.
Rmd	970b22c	robwschlegel	2019-03-19	Publish the vignettes from when this was a pkgdown framework
html	fa7fd57	robwschlegel	2019-03-19	Build site.
Rmd	64ac134	robwschlegel	2019-03-19	Publish analysis files

Overview

The last major variable we want to look at that could affect MHW detection is the long-term (secular) trend present in a time series. To do so we will be using the same de-trended data from the previous two vignettes and slowly add in linear trends from 0.01 to 0.30 °C/decade in 0.0.1 °C/decade steps.

# The packages used in this analysis
library(tidyverse)
library(heatwaveR)
library(lubridate)
library(ncdf4)
library(doParallel)

# The custom functions written for the analysis
source("code/functions.R")

# The function used to de-trend the time series:
detrend <- function(df){
  resids <- broom::augment(lm(temp ~ t, df))
  res <- df %>%
    mutate(temp = round((temp - resids$.fitted),2))
  return(res)
}

Analysis

The full analysis on the results, including the functions shown above, is run for all of the tests (time series length, missing data, and long-term trend) all at once to ensure consistency of methodology across tests. For this reason the exact step-by-step process for the long-term trend analysis is not laid out below. To investigate the source code one may go to ‘code/workflow.R’ in the GitHub repository. A link to that site may be found in the top right of the navigation bar for this site (the GitHub icon).

sst_ALL <- rbind(mutate(sst_WA, site = "WA"),
                 mutate(sst_NW_Atl, site = "NW_Atl"),
                 mutate(sst_Med, site = "Med"))
system.time(
  sst_ALL_results <- plyr::ddply(sst_ALL, c("site"), single_analysis, .parallel = T,
                                 full_seq = T, clim_metric = F, count_miss = T, windows = T)
) # 65 seconds

Results

A more thorough explanation of the results may be found in the manuscript. Below we show what the simple results calculated above for the effect of long-term trends on the maximum intensity of the average MHWs look like.

sst_ALL_results %>% 
  filter(test == "trend",
         var == "intensity_max",
         id == "mean_perc") %>% 
  ggplot(aes(x = index_vals, y = val, colour = site)) +
  geom_line() +
  labs(x = "Long-term trend (°C/decade)", y = "Change from control (% max intensity (°C))", colour = "Site")

References

Session information

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/openblas-base/libblas.so.3
LAPACK: /usr/lib/libopenblasp-r0.2.18.so

locale:
 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8   
 [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] doParallel_1.0.15    iterators_1.0.10     foreach_1.4.4       
 [4] ncdf4_1.17           lubridate_1.7.4      heatwaveR_0.4.1.9003
 [7] forcats_0.4.0        stringr_1.4.0        dplyr_0.8.3         
[10] purrr_0.3.3          readr_1.3.1          tidyr_1.0.0         
[13] tibble_2.1.3         ggplot2_3.2.1.9000   tidyverse_1.2.1     

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5  xfun_0.10         haven_2.1.1      
 [4] lattice_0.20-35   colorspace_1.4-1  vctrs_0.2.0      
 [7] generics_0.0.2    viridisLite_0.3.0 htmltools_0.4.0  
[10] yaml_2.2.0        plotly_4.9.0      rlang_0.4.1      
[13] R.oo_1.22.0       pillar_1.4.2      glue_1.3.1       
[16] withr_2.1.2       R.utils_2.7.0     modelr_0.1.5     
[19] readxl_1.3.1      lifecycle_0.1.0   munsell_0.5.0    
[22] gtable_0.3.0      workflowr_1.1.1   cellranger_1.1.0 
[25] rvest_0.3.4       R.methodsS3_1.7.1 htmlwidgets_1.5.1
[28] codetools_0.2-15  evaluate_0.14     labeling_0.3     
[31] knitr_1.25        broom_0.5.2       Rcpp_1.0.2       
[34] backports_1.1.5   scales_1.0.0      jsonlite_1.6     
[37] hms_0.5.1         digest_0.6.22     stringi_1.4.3    
[40] grid_3.6.1        rprojroot_1.3-2   cli_1.1.0        
[43] tools_3.6.1       maps_3.3.0        magrittr_1.5     
[46] lazyeval_0.2.2    crayon_1.3.4      whisker_0.4      
[49] pkgconfig_2.0.3   zeallot_0.1.0     data.table_1.12.6
[52] xml2_1.2.2        assertthat_0.2.1  rmarkdown_1.16   
[55] httr_1.4.1        rstudioapi_0.10   R6_2.4.0         
[58] nlme_3.1-137      git2r_0.23.0      compiler_3.6.1

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The effects of long-term trends

Robert W Schlegel

2019-11-06

Overview

Trending

Analysis

Results

References

Session information