Sources of climate data

In my recent guide series looking at extracting, analysing and plotting climate data using R, I focused on two sources of climate data: CRU and WorldClim. These datasets provide all kinds of climate data, at varying temporal and spatial resolutions. But, as mentioned in the last post, there are many more climate datasets available, with data for even more climate variables.

Unless you know exactly what you're looking for, finding the data you're after can be a bit of a struggle. This is where the Climate Data Guide from NCAR comes in! Described as: Data discovery guided by experts, this website lists almost 200 different climate datasets, providing information about the dataset, metadata, the strengths and weaknesses of the data, example figures, and links to download the data. An overview of the Climate Data Guide can be read on the about page or in this research paper

Here's an example from their guide for the CRU gridded datasets (Available from https://climatedataguide.ucar.edu/climate-data/cru-ts-gridded-precipitation-and-other-meteorological-variables-1901)

Visit the Climate Data Guide for a look at more datasets.

Global climate datasets are not the only source of climate data. Raw station data is often available. Click read more to find out about climate station data available from NOAA, the Met Office, and the Environment Agency.


Extracting data and making climate maps using WorldClim datasets

In this guide, i'll show you another way in which you can get climate data, by using WorldClim global climate datasets. This guide will take you through all the steps for downloading, opening, extracting, and plotting the data using R.

I'll show you how you can make some good looking climate maps using WorldClim data, for anywhere in the world. And, the guide will also look at some of the differences between the WorldClim datasets and CRU datasets (we looked at this in earlier guides), and why you might choose to use one or the other.


Working with extracted CRU climate data

In my previous guide, I showed you how to download and extract precipitation and temperature climate data from CRU datasets. Now that you have the raw data - i'll guide you through some of the ways in which you can work with, and manipulate this data.

From grouping your sample sites, to calculating annual data or seasonal data, to defining a climate period and then calculating changes to conditions since this period to observe climate change trends. I'll also show you how to plot the data to create climate graphs for your sites.


Getting Climate Data

Have you ever needed weather information or climate data for a project you are working on, such as a dissertation or thesis? Depending on which part of the world you need data for, sometimes it can prove very difficult to obtain good reliable climate datasets. While some agencies make weather station data freely accessible e.g. NOAA, others may restrict access. You might also want to look at climate data on large or global scales, where obtaining data from individual agencies can become more complex.

Luckily, there are several global climate datasets available, which have done all the hard work of collating and processing the climate data into an easily accessible and consistent format.

In part one of this guide, I will show you how to obtain climate data from the Climate Research Unit (CRU) and extract only the data you need for your project using R. In part two of this guide (coming soon!), I will show you how you can manipulate this data, while part three will show you how to extract climate data from WorldClim datasets.



Getting Started with R

Ever wanted to try R, but wasn't sure where to start? Or perhaps you are unsure of what R is and what it can do? This guide is designed to get you started with R and teach you the basics - showing you where to obtain R and introducing you to the R environment. It gives a quick overview of data structures and basic commands, and shows you where to get more help to develop your R skills.

The goal is to introduce you to the R environment, so you can become familiar with R, and can then start to experiment and perform your own data analysis with confidence.


Welcome!

Welcome to my blog! Who am I and what is it about? I have recently completed a PhD in physical geography with a background in Quaternary Palynology and geochemical techniques – basically looking at climate and environment change. I am interested in how our environment has changed over time, and how this is related to climate – and the impacts that climate has on the environment. My research was focused on the environment of Morocco, looking specifically at Atlas Cedar trees in the Middle Atlas. You can read more about this on my publications and about me pages.

So what is this blog about? Throughout my PhD I spent a lot of time analysing data using R – R is a free open-source program for statistical analysis, and can also be used to create good-looking figures and maps. I used R for all of my analysis, creating maps and extracting climate data. R is really easy to use, although may appear daunting for newcomers.

This blog will have a focus on how to use R, with tutorials and guides for performing analysis related to environmental and climate science, which will be updated regularly. Other content will relate more generally to all aspects of environmental science, lab work, and guides for using LaTeX (for thesis writing), and other useful study tips I picked up along the way.

To kick off, I have written an introductory guide to using R, explaining the basics. Future guides will move on to more advanced topics, such as performing climate interpolation, but for now, if you have never used R, then check out my first guide to get started!