Climate diagrams are useful tools which provide a visual summary of average climate conditions for a place over a specified period of time. This guide will explain the different parts of a Walter and Lieth climate diagram, and show you how to create one in R using the "climatol" package. This package provides a really quick and easy way to make good-looking Walter and Lieth climate diagrams. But, this guide will also show you how to create one from scratch (no packages needed!).
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.
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.
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.
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.