TOS HL, LL, & Global for Hector

1 Set Up

library(data.table)

Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
data.table 1.14.2 using 1 threads (see ?getDTthreads).  Latest news: r-datatable.com
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This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
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Attaching package: ‘data.table’

The following objects are masked from ‘package:dplyr’:

    between, first, last

library(dplyr)
library(tidyr)

Attaching package: ‘tidyr’

The following object is masked from ‘package:magrittr’:

    extract

library(ggplot2)

DIR <- here::here("tos", "HL_LL_global")

dfile <- file.path(DIR, "HL_LL_global_tos.csv")
if (!file.exists(dfile)){ stop("missing ", dfile, " must run 1.postprocessing.R")}

data <- read.csv(dfile, stringsAsFactors = FALSE)

theme_set(theme_bw())

1.1 Objective

Take a look at tos data processed from pangeo, note that we’ve only processed a fraction of the historical data from 1850 to 1900.

2 TOS data

data %>%
    ggplot(aes(year, value, color = model)) +
    geom_point(alpha = 0.5) +
    facet_wrap("area", scales = "free") +
    theme_bw() +
    theme(legend.position = "none") +
    labs(title = "TOS data")

# First calculate the mean value for the models over the ensembles
data %>%
    group_by(year, model, area) %>%
    summarise(value = mean(value)) %>%
    ungroup ->
    model_ensemble_mean

`summarise()` has grouped output by 'year', 'model'. You can override using the `.groups` argument.

model_ensemble_mean %>%
    ggplot(aes(year, value, color = model)) +
    geom_point(alpha = 0.5) +
    facet_wrap("area", scales = "free") +
    theme_bw() +
    theme(legend.position = "none") +
    labs(title = "Model Mean")

# Get the PI mean defined as 1850 - 1860
model_ensemble_mean %>%
    filter(year %in% 1850:1860) %>%
    group_by(model, area) %>%
    summarise(value = mean(value)) %>%
    ungroup ->
    model_means

`summarise()` has grouped output by 'model'. You can override using the `.groups` argument.

# Calculate the multi-model average.
model_means %>%
    group_by(area) %>%
    summarise(mean = mean(value),
              min = min(value),
              max = max(value),
              sd = sd(value)) ->
    tos_values

model_means %>%
    ggplot(aes(area, value, color = model)) +
    geom_point(alpha = 0.5, position = "jitter") +
    geom_point(data = tos_values, aes(area, mean), color = "black", size = 2, alpha = 0.7) +
    theme_bw() +
    theme(legend.position = "none") +
    labs(title = "Model Averages", y = "deg C", x = NULL)

2.1 New data vs Hector data