{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"title: 'Breaching 1.5°C: Give me the odds'\n",
"subtitle: 'A companion notebook'\n",
"author:\n",
" - name: J. Eduardo Vera-Valdés\n",
" email: eduardo@math.aau.dk\n",
" url: 'https://everval.github.io'\n",
" affiliations:\n",
" - name: Aalborg University\n",
" department: Department of Mathematical Sciences\n",
" country: Denmark\n",
" orcid: 0000-0002-0337-8055\n",
" corresponding: true\n",
" - name: Olivia Kvist\n",
" email: omtk@math.aau.dk\n",
" affiliations:\n",
" - name: Aalborg University\n",
" department: Department of Mathematical Sciences\n",
" country: Denmark\n",
"abstract: |\n",
" This is a companion notebook to the article \"Breaching 1.5°C: Give me the odds\" by J. Eduardo Vera-Valdés and Olivia Kvist. It contains the code for the additional exercise considering the GISTEMP dataset. The code is written in Julia and it is organized into sections follow those of the replication notebook.\n",
"format:\n",
" html:\n",
" mainfont: Georgia, serif\n",
" html-math-method: katex\n",
" theme: litera\n",
" code-fold: true\n",
" title-block-banner: \"#eef7ee\"\n",
"# typst:\n",
"# mainfont: Computer Modern\n",
"# fontsize: 12pt\n",
"# papersize: a4\n",
"execute:\n",
" enabled: true\n",
" cache: true\n",
" freeze: auto\n",
"engine: julia\n",
"#jupyter: julia-1.11\n",
"bibliography: library.bib\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0. Load Packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m "
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"project at `~/Library/CloudStorage/OneDrive-AalborgUniversitet/Research/CLIMATE/Paris Goal/Odds-of-breaching-1.5C`\n"
]
}
],
"source": [
"#| label: load-packages\n",
"#| echo: false\n",
"#| output: false\n",
"#| warning: false\n",
"#| include: false\n",
"\n",
"using Pkg\n",
"Pkg.activate(pwd())\n",
"using Plots, Dates, CSV, DataFrames, LongMemory, Statistics, MarSwitching, Random\n",
"include(\"TrendEstimators.jl\")\n",
"using .TrendEstimators\n",
"\n",
"Random.seed!(123456)\n",
"theme(:ggplot2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Load Data\n",
"\n",
"### 1.1 Temperature"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"| 1 | 1880-01-01 | -0.2 |
| 2 | 1880-02-01 | -0.26 |
| 3 | 1880-03-01 | -0.09 |
| 4 | 1880-04-01 | -0.17 |
| 5 | 1880-05-01 | -0.1 |
| 1 | 1871 | 2 | 25.59 | 26.03 | -0.45 | -0.26 | N |
| 2 | 1871 | 3 | 26.66 | 26.55 | 0.11 | 0.08 | N |
| 3 | 1871 | 4 | 27.57 | 26.99 | 0.58 | 0.31 | N |
| 4 | 1871 | 5 | 27.4 | 27.16 | 0.24 | 0.36 | N |
| 5 | 1871 | 6 | 27.17 | 26.93 | 0.24 | 0.26 | N |
| 1 | 2024-05-01 | 0.4 | 1.16 |
| 2 | 2024-06-01 | 0.15 | 1.24 |
| 3 | 2024-07-01 | 0.04 | 1.2 |
| 4 | 2024-08-01 | -0.11 | 1.3 |
| 5 | 2024-09-01 | -0.22 | 1.23 |
| 1 | Above 0%, 20-years avg. | 2027-05-01 | 2047-09-01 |
| 2 | Above 50%, 20-years avg. | 2033-06-01 | 2060-01-01 |
| 3 | Above 99%, 20-years avg. | 2043-12-01 | 2071-02-01 |
| 1 | Above 0%, 20-years avg. | 2027-05-01 | 2047-09-01 |
| 2 | Above 50%, 20-years avg. | 2033-06-01 | 2060-01-01 |
| 3 | Above 99%, 20-years avg. | 2043-12-01 | 2071-02-01 |
| 4 | Above 0%, 30-years avg. | 2033-02-01 | 2052-09-01 |
| 5 | Above 50%, 30-years avg. | 2039-06-01 | 2065-01-01 |
| 6 | Above 99%, 30-years avg. | 2048-10-01 | 2075-12-01 |
| 1 | Above 0%, 20-years avg. | 2027-05-01 | 2047-09-01 |
| 2 | Above 50%, 20-years avg. | 2033-06-01 | 2060-01-01 |
| 3 | Above 99%, 20-years avg. | 2043-12-01 | 2071-02-01 |
| 4 | Above 0%, 30-years avg. | 2033-02-01 | 2052-09-01 |
| 5 | Above 50%, 30-years avg. | 2039-06-01 | 2065-01-01 |
| 6 | Above 99%, 30-years avg. | 2048-10-01 | 2075-12-01 |