Unveiling Hidden Patterns: Clustering Analysis with ISMIP6 Models

A PCA and Clustering Analysis

Introduction

How do different Global Climate Models (GCMs) impact the projections of ice sheet behavior in ISMIP6 simulations? What is the influence of ocean forcing on the projections of ice sheet dynamics in ISMIP6 models? How do different Representative Concentration Pathways (RCPs) affect the projected outcomes of ice sheet models in ISMIP6? These are questions that have been asked within the ice sheet community in the recent years as new data, methods, and ideas have submerged.

The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) aims to address these questions by providing a framework for evaluating and comparing the responses of ice sheet models to various climate scenarios. ISMIP6 integrates multiple GCMs, RCPs, and ocean forcing datasets to project the future behavior of the Greenland and Antarctic ice sheets.

This study focuses on analyzing the RCP experiments and looking at one key variable Surface Mass Balance flux. Principal Component Analysis (PCA) and K-means clustering will be used to analyze the variables, aiming to uncover patterns and groupings in the data. This approach will help to understand the variations in ice sheet response under various scenarios, RCP 8.5 and 2.6. Hypothesis: By implementing PCA and Clustering methods,it is possible to uncover hidden patterns in the ISMIP6 simulation data, enabling more efficient data reduction and grouping of similar data points for further analysis. This approach will enhance our ability to identify key trends and relationships in ice sheet responses, leading to more accurate predictions and insights.

Materials and methods

[~ 200 words]

Narrative: Clear narrative description of the data sources and methods. Includes data from at least two sources that were integrated / merged in R.

Code: The code associated with the project is well organized and easy to follow. Demonstrates mastery of R graphics and functions.

Data:

The data used in this study was sourced from the Center for Computational Research (CCR) at the University at Buffalo. The ISMIP6 dataset was previously downloaded from the CCR repository, focusing on the experiment and variable types needed for the RCP experiment group. This dataset includes projections of land ice thickness different Representative Concentration Pathways (RCPs).

# loading needed packages 
# install.packages("tidyverse")
library(tidyverse)
library(leaflet)
library(kableExtra)
library(htmlwidgets)
library(widgetframe)
library(lattice)
library(ncdf4)
library(stringr)
library(dplyr)
library(curl)
# install.packages("corrr")
library('corrr')
# install.packages("FactoMineR")
library("FactoMineR")
# install.packages("ggcorrplot")
library(ggcorrplot)
knitr::opts_chunk$set(widgetframe_widgets_dir = 'widgets' ) 
knitr::opts_chunk$set(cache=TRUE)  # cache the results for quick compiling

Download and clean all required data

Add any additional processing steps here.

Methods

Using the PCA and the K-means Clustering

Results

[~200 words]

Tables and figures (maps and other graphics) are carefully planned to convey the results of your analysis. Intense exploration and evidence of many trials and failures. The author looked at the data in many different ways before coming to the final presentation of the data.

Show tables, plots, etc. and describe them.

Conclusions

[~200 words]

Clear summary adequately describing the results and putting them in context. Discussion of further questions and ways to continue investigation.

References

All sources are cited in a consistent manner