Reporting the results of your climatological review is how you communicate what you have done to others. This could be in the form of a report, on a website, or in an academic paper.

There are many online resources available for advice and information on scientific and academic writing.

Here we focus on three guiding principles you can use, and discuss how they related to reporting the results of a climatological review.

Principles

Thoughtfulness

Throughout the previous steps we encouraged you to be thoughtful in your decision making. This included in choosing your data source and elements, defining your reference period and what anomalies you report (regular or standardised).

We hope that you have documented these decisions, and noted the reasoning behind your decisions. This is a great starting point for your reporting. All of these decisions and reasoning should be included in your reporting. How you present these is discussed in the following principles.

Clarity

Clarity is an important principle to apply through your reporting. Good clarity ensures that readers do not get misled or misinformed by your reporting.

The level of detail needed to ensure you are being clear depends on your audience. For example, for an academic publication you can assume readers are familiar with the field and may need less explanation of the basic steps. Whereas, for a report on a website, there is likely to be a wider range of readers, with varying background and prior knowledge, hence more detail, rather than less, will be important for clarity.

Clarity relates to explaining the decisions you have made in the analysis steps and why you have made them:

  • what are the data sources?
  • what elements have been used?
  • what steps were done to manipulate and analyse the data?

The example report from EUMETSAT for this task does this well. It clearly explains that the length of the background periods are different for the three elements analysed, and explains why this was done.

Details in explanations in EUMETSAT report

Notice that one of the background periods, for land surface temperature, is much less than 30 years, the standard minimum length, which was due to data availability. The report clearly mentions this, and also mentions that possible uncertainty in the results due to this, and provides a caution for comparing with the other elements which have a much longer background period.

For the reader, this now places the results in the correct context, and reduces the chances of being misled. It also gives confidence to the reader when studying the results.

In your own reporting, do not shy away from reporting your decisions, even if you had to do something non-standard. By explaining your reasoning you give the reader the ability to make their own conclusions and avoid misunderstandings.

Clarity is also a critical principle for figures: graphs and maps.

You are likely to present your climatological review as a map, or set of maps. You will want your maps to be visually appealing and attractive to the reader. However, above all, the maps need to clearly communicate the results, without misleading or being difficult to fully interpret.

An article from Better Figures on Picking a colour scale for scientific graphics has a lot of practical advice for clarity in maps.

The critical decision for your map is the choice of colour palette. The kind of data being shown affects the choice of colour palette. Diverging palettes are for data which straddle zero, which is exactly what anomalies are. So a diverging palette will be most appropriate.

Accessibility is also an important consideration to ensure your maps are accessible to those with colour blindness. There are different forms and degrees of colour blindness, but a general rule is to avoid using red and green together. There are now even websites and apps which allow you to see how your visualisation will appear to those with colour blindness.

Although it is common, avoid the "rainbow" colour palette which is problematic for various reasons, including use of red and green, and can present misleading differences.

Reproducibility

Reproducibility has become an increasingly important topic in the scientific community. In academic articles in particular, there is a greater demand from journals for results to be reproducible. Even for non academic reporting, reproducibility is important to allow others to check and critique your work. For reports that are produced regularly, reproducibility ensures reports can be easily updated in the future, and with the confidence that it is consistent with previous reports.

To achieve this, think about what someone needs to have and know, if they wanted to reproduce your results exactly as you have.

You can use the following questions as guidance when thinking about reproducibility in your reporting:

  • What data was used (source, time periods, resolutions) and where is it from? If the data is openly available, provide links to access it. If it is not, you can state this, and provide a contact or name of the data owner and advise readers to contact them to request data access.
  • What manipulations and analysis did you carry out with the data? One way to do this is to provide the scripts you used. This could be via a GitHub repository, for example. If you did not use scripts, you can state the software used, including the version, and which functions you used.
  • How did you produce the outputs (graphs, maps)? Again, this can be shown via scripts, or the functions used in your software.

Exactly where you include the reproducible aspects depends on the type of reporting and the audience. Most readers will likely not be interested in reproducing your analyses, so you do not need to make this the main focus of your reporting. For example, in an academic publication, scripts can often be provided as additional files, or appendices to the paper. For a report, you could put all the files needed at the end of the report.