Climate Indicators Shiny App

This Shiny web application provides interactive visualization and statistical analysis of multiple climate indicators, including surface and satellite temperature records, ocean heat, solar irradiance, atmospheric CO₂, ENSO variability, and polar sea ice extent.

The app supports theme switching (light/dark via Bootswatch) and color palette selection (default, viridis, Okabe-Ito), making all plots accessible and visually consistent.

---

Features

• Global Surface Temperature

  • Data sources: GISS, HadCRUT5, and Berkeley Earth (BEST).
  • Options for trend fitting (linear, LOESS) with uncertainty/confidence intervals.
  • Displays overall net change since user-defined start year.

• Satellite Temperature Records

  • Data sources: RSS and UAH satellite lower troposphere (TLT) datasets.
  • Annualized anomalies with smoothing and trend estimates.

• Ocean Temperatures

  • Data source: HadSST4 global ocean anomalies.
  • Interactive trends and year-to-year variability.

• Solar Variability

  • Data sources:
    • Sunspot numbers (SIDC).
    • Reconstructed Total Solar Irradiance (TSI):
      • SATIRE-T (Wu+ 2018) back to 1642.
      • SATIRE-S (Yeo, Krivova, Solanki) post-1974.
      • Combined record seamlessly merged at August 1974.
  • Annual mean irradiance plotted with linear/LOESS trends.

• Atmospheric CO₂

  • Data source: NOAA Mauna Loa annual mean CO₂ (ppm).
  • Both linear and quadratic fits supported.
  • Net change since chosen start year displayed.

• ENSO (El Niño–Southern Oscillation)

  • Data source: NOAA ONI (Oceanic Niño Index).
  • Monthly anomalies plotted with neutral, El Niño, and La Niña thresholds.
  • Trend analysis with LOESS smoothing.

• Sea Ice Extent/Area

  • Data sources: NOAA PSL monthly sea ice area (Arctic and Antarctic).
  • Units: million square kilometers.
  • User-selectable months (individual or “All”).
  • Linear/LOESS trend fits plus net change since start year.

---

Technology

• Frontend/Server Framework: Shiny
• Plotting: ggplot2 + Plotly for interactive visualization
• Styling: Bootswatch themes via {bslib}
• Palette Options: Default, Viridis (color-blind friendly), Okabe-Ito
• Data Wrangling: tidyverse, lubridate, readr

---

Installation

1. Clone or download this repository.
2. Make sure you have R (≥ 4.1) installed.
3. Install required packages:

install.packages(c(
  "shiny", "plotly", "tidyverse", "readr",
  "lubridate", "naniar", "bslib", "viridisLite"
))