The use of non-speech audio to convey information is known as sonification. One of the earliest and perhaps the most well known applications of sonification in science is the Geiger counter, a device which produces a distinctive clicking sound when it interacts with ionising radiation . Beyond the Geiger counter, sonification is also widely used in monitoring instrumentation. Sonification is appropriate when the information being displayed changes in time, includes warnings, or calls for immediate action. Sonification instrumentation is used in environments where the operator is unable to use a visual display, for instance if the visual system is busy with another task, overtaxed or when factors such as smoke, light or line of sight impact the operator's visual system . Sonification also allows several metrics to be displayed simultaneously using variations in pitch, timbre, volume and period . For these reasons, sonification is widely used in medicine for monitoring crucial metrics of patient health .

Outside of sonification for monitoring purposes, the sonification of data can also be used to produce music. There have been several examples of sonification of climate system data. “Climate symphony” by Disobedient Films is a musical composition performed by strings and piano using observational data from sea ice indices, surface temperature and carbon dioxide concentration. Daniel Crawford's “Planetary bands, warming world” is a string quartet which uses observational data from Northern Hemisphere temperatures. In this piece, each of the four stringed parts represents a different latitude band of the Northern Hemisphere temperature over the time range 1880–2012. Similarly, the Climate Music Project (https://climatemusic.org/, last access: 17 August 2020) is a project which makes original music inspired by climate science. They have produced three pieces which cover a wide range of climatological and demographic data and both observational and simulated data. However, pieces such as those by and often use similar observational temperature and carbon dioxide data sets. Both of these data sets only have monthly data, and approximately one century of data or less are available. In addition, both temperature and carbon dioxide have risen since the start of the observational record. This means that these musical pieces tend to have similar structures and sounds. The pieces are slow, quiet and low pitched at the start of the data set before slowly increasing and building up to a high-pitched conclusion at the present day. It should be noted that all the pieces list here are also accompanied by a video which explains the methodology behind the creation of the music, shows the performance by the artists or shows the data development while the music is played.

An alternative strategy was deployed in the Sounding Coastal Change project . In that work, sound works, music recordings, photography and film produced through the project were geotagged and shared on a sound map. This created a record of the changing social and environmental soundscape of North Norfolk. They used these sounds to create music and explore the ways in which the coast was changing and how people's lives were changing with it.

In addition to its practical applications, sonification is a unique field in which scientific and artistic purposes may coexist . This is especially true when, in addition to being converted into sound, the data are also converted into music. This branch of sonification is called musification. Note that the philosophical distinction between sound and music is beyond the scope of this work. Through the choice of musical scales and chords, tempo, timbre and volume dynamics, the composer can attempt to add emotive meaning to the piece. As such, unlike sonification, musification should be treated as a potentially biased interpretation of the underlying data. It cannot be both musical and a truly objective representation of the data. Furthermore, even though the composer may have made musical and artistic decisions to link the behaviour of the data with a specific emotional response, it may not necessarily be interpreted in the same way by the listener.

With the ever-growing interest from the general public in understanding climate science, it is becoming increasingly important that we present our model results and methods in ways that are accessible and engaging to non-experts. In this work, six musical pieces were procedurally generated using output from a climate model, specifically the first version of the United Kingdom Earth System Model UKESM1;. By using simulated data instead of observational data, we can generate music from time periods outside the recent past, such as the pre-industrial period before 1850 and multiple projections of possible future climates. Similarly, model data allow access to regions and measurements far beyond what can be found in the observational record. The UKESM1 is a current generation computational simulation of the Earth's climate and has been deployed to understand the historical behaviour of the climate system and make projections of the climate in the future. The UKESM1 is described in more detail in Sect. . The methodology used to produce the pieces, and a brief summary of each piece, is shown in Sect. . The aims of the project are outlined below in Sect. .

Each of the six musical pieces was produced alongside a video showing the time series data developing concurrently with the music. These videos were published on the YouTube video hosting service. This work was an early pilot study and has revealed several limitations which we outline in Sect. . We also include some possible extensions, improvements and new directions for future versions of the work.

The UKESM1 is a computational simulation of the Earth system produced by a collaboration between the Hadley Centre Met Office from the United Kingdom and the Natural Environment Research Council NERC;. The UKESM1 represents a major advancement in Earth system modelling, including a new atmospheric circulation model with a well resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; troposphere–stratospheric chemistry that allows the simulation of radiative forcing from ozone, methane and nitrous oxide; a fully featured aerosol model; and an ocean biogeochemistry model with two-way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model.

In this work, we have exclusively used data from the ocean component of the UKESM1. The UKESM1's ocean is subdivided into three component models, namely the Nucleus for European Modelling of the Ocean (NEMO), which simulates the ocean circulation and thermodynamics , the Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification (MEDUSA), which is the sub-model of the marine biogeochemistry , and the Los Alamos Sea Ice Model (CICE), which simulates the growth, melt and movement of sea ice .

The UKESM1 is being used in the UK's contribution to the sixth international Coupled Model Intercomparison Project (CMIP6) . The UKESM1 simulations that were submitted to the CMIP6 were used to generate the musical pieces. These simulations include the pre-industrial control (PI control), several historical simulations and many projections of future climate scenarios. The CMIP6 experiments that were used in these works are listed in Table .

This is not the first time that the UKESM1 has been used to inspire creative projects. In 2017, the UKESM1 participated in a science and poetry project in which a scientist and a writer were paired together to produce poetry. Ben Smith was paired with Lee de Mora and produced several poems inspired by the United Kingdom Earth System Model UKESM;.

In this section, we describe the method used to produce the music and the videos. Figure  illustrates this process. The initial data are UKESM1 model output files, downloaded directly from the United Kingdom's Met Office data storage system (MASS). These native-format UKESM1 data will not be available outside the UKESM collaboration, but selected model variables have been transformed into a standard format and made available on the Earth System Grid Federation (ESGF) via, for example, https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/, last access: 17 August 2020.

The time series data are calculated from the UKESM1 data by the BGC-val model evaluation suite . BGC-val is a software toolkit that was deployed to evaluate the development and performance of the ocean component of the UKESM1. In all six pieces, we use annual average data as the time series data. The data sets that were used in this work are listed in Table .

Each time series data set is used to create an individual Musical Instrument Digital Interface (MIDI) track composed of a series of MIDI notes. The MIDI protocol is a standardised digital way to convey musical performance information. It can be thought of as the instructions that tell a music synthesiser how to perform a piece of music . All six pieces shown here are saved as a single MIDI file which contains one or many MIDI tracks played simultaneously. Each MIDI track is composed of a series of MIDI notes.

Each MIDI note is assigned four parameters. The first two parameters are timing (when the note occurs in the song) and duration (the length of time that the note is held). The timing is the number of beats between this note and the beginning of the song. The duration is positive rational number representing the number of beats for which the note is held. A unity duration is equivalent to a crotchet (quarter note), a duration of two is a minim (half note) and the duration value of a half is a quaver (eighth note).

The third MIDI note parameter is the pitch which, in MIDI, must be an integer between  1 and 127, where 1 is a very low pitch and 127 is a very high pitch. These integer values represent the chromatic scale, and middle C is set to a value of 60. The pitch of the MIDI notes must be an integer as there is no capacity for MIDI notes to sit between values on the chromatic scale. Musically this can be explained, as there are not notes in between the notes on a keyboard in MIDI. The total range of available pitches covers 10.5 octaves; however, we found that pitches below 30 or above 110 started to become unpleasant when performed by TiMidity; other MIDI pianos may have more success. Also note that MIDI's 127 note system extends beyond the standard piano keyboard, which only covers the range 21–108 of the MIDI pitch system. MIDI uses the 12-tone equal temperament tuning system; while this is not the only tuning system, it is the most widely used in Western music.

The fourth MIDI note parameter is the velocity; this indicates the speed with which the key would be struck on a piano and is the relative loudness of the note. In practical terms, velocity is an integer ranging between 1 and 127, where 1 is very quiet and 127 is very loud. The overall tempo of the piece is assigned as a global parameter of the MIDI file in units of the number of beats per minute.

Each model's time series data set is converted into a series of consecutive MIDI notes, which together form a track. For instance, the sea surface temperature (SST) time series could be converted into a series of MIDI notes in the upper range of the keyboard to form a track. For each track, the time series data are converted into musical notes so that the lowest value in the data set is represented by the lowest note pitch available, and the highest value in the data set is represented by the highest note pitch available. The notes in between are assigned proportionally by their data value between the lowest and highest pitched notes. The lowest and highest notes available for each track are predefined in the piece's settings, and they are considered an artistic decision. Each track is given its own customised pitch range so that the tracks may be at a lower pitch, higher pitch or have overlapping pitch ranges relative to other tracks in the piece. The ranges of notes available for the piece “Earth System Allegro” is shown in Fig. . In this figure, the four histograms on the left-hand side show the distributions of the data used in the piece, and the right-hand side includes four standard piano keyboards showing the musical range available in each data set. For instance, the Drake Passage current ranges between 135 and 175 Tg s-1 in these simulations, and we selected a range between MIDI pitches 72 and 96. This means that the lowest Drake Passage current values (135 Tg s-1) would be represented in MIDI with a pitch of 72, and the highest Drake Passage current values (175 Tg s-1) would be assigned a MIDI pitch of 96, which is two octaves higher.

These note pitches are then binned into a scale or a chord. The choice of chord or scale depends on the artistic decisions made by the composer. For instance, the C major chord is composed of the notes C, E and G, which are the zeroth, fourth and seventh notes, respectively, in the 12-note chromatic scale, starting from C at zero. Figure  shows a representation of these notes on a standard piano keyboard. The C major in the zeroth octave is composed of the following set of MIDI pitch integers: 1Cmajor0={0,4,7}.

In the 12-tone equal temperament tuning system, the 12 named notes are repeated, and each distance of 12 notes represents an octave. As shown in Fig. , a chord may also include notes from subsequent octaves. In this figure, the C major chord is highlighted in green, and the zeroth octave is shown in a darker green than the subsequent octaves. As such, the C major chord can be formed from any of the following sets of MIDI pitches: 2Cmajor0,1,2,…={0,4,7,12,16,19,24,28,31…127}.

It then follows that the notes of the C major chord are values between 0 an 127, where the following condition is true: p∈Cmajor0,1,2,… This can be can be written more simply as follows: p%12∈Cmajor0, where p represents the pitch value, namely an integer between the minimum and maximum pitches provided in the settings, and the percent sign (%) represents the remainder operator.

The zeroth octave values for other chords and scales with the same root note can be calculated from their chromatic relation with the root note. For instance: Cminor0={0,3,7}Cmajor07={0,4,7,11}Cminor07={0,3,7,10}… Note that the derivation of these chords and their nomenclature is beyond the scope of this work. For more information on music theory, please consult an introductory guide to music theory such as or .

The zeroth octave values for other keys can be included by appending the root note of the scale (C: 0, C#/Db: 1, D: 2, D#/Eb: 3 and so on) to the relationships in the key of C above. For instance: Cmajor0={0,4,7}C#major0={0,4,7}+1={1,5,8}Dmajor0={0,4,7}+2={2,6,9}D#major0={0,4,7}+3={3,7,10}…

Using these methods, we can combinatorially create a list of all the MIDI pitches in the zeroth octave for all 12 keys for most standard musical chords. From this list, we can convert model data into nearly any choice of chord or scale.

The conversion from model data to musical pitch is performed using the following method. First, the data are translated into the pitch scale but kept as a rational number between the minimum and maximum pitch range assigned by the composer for this data set. As an example, in the piece “Earth System Allegro” the Drake Passage current was assigned a pitch range between 72 and 96, as shown in Fig. . Once the set of possible integer pitches for a given chord or scale has been produced using the methods described above, the in-scale MIDI pitch with this smallest distance to this rational number pitch is used. As mentioned earlier, the pitch of the MIDI notes must be an integer as there is no capacity for MIDI notes to sit between values on the chromatic scale. The choice of scale is provided in the piece's settings and is an artistic choice made by the composer. Furthermore, instead of using a single chord or scale for a piece, it is also possible to use a repeating pattern of chords or a chord progression. The choice of chords, and the order of chords, is different for each piece. In addition, the number of beats between chord changes, and the number of notes per beat, is also assigned in the settings. Furthermore, each track in a given piece may use a different chord progression.

The velocity of notes is determined using a similar method to pitch; the time series data are converted into velocities so that the lowest value in the data set is the quietest value available, and the highest value of the data set is the loudest value available. The notes in between are assigned proportionally by their data value between the quietest and loudest notes. Each track may have its own customised velocity range, such that any given track may be louder or quieter than the other tracks in a piece. The choice of data set used to determine velocity is provided in the settings. We rarely used the same data set for both pitch and velocity. This is because it results in the high-pitch notes being louder and the low-pitch notes being quieter.

After binning the notes into the appropriate scales, all notes are initially the same duration. If the same pitched note is played successively, then the first note's duration is extended and the repeated notes are removed.

A smoothing function may also be applied to the data before the data set is converted into musical notes. Smoothing means that it is more likely that the same pitched note will be played successively, so a track with a larger smoothing window will have fewer notes than a track with a smaller window. From a musical perspective, smoothing slows down the piece by replacing fast short notes with longer slower notes. Smoothing can also be used to slow down the backing parts to highlight a faster moving melody. Nearly all the pieces described here used a smoothing window.

After applying this method to multiple tracks, they are saved together in a single MIDI file using the Python MIDITime library . Having created the MIDI file, the piece is performed by the TiMidity++ digital piano , which converts the MIDI format into a digital audio performance in the MP3 format. In principle, it should be possible to use alternative MIDI instruments, but for this limited study we exclusively used the TiMidity++ digital piano. Where possible, the MIDI files were converted into sheet music portable document format (PDF) files using the MuseScore software . However, it is not possible to produce sheet music for all six pieces as some have too many MIDI tracks to be converted to sheet music by this software.

Each piece has a diverse range of settings and artistic choices made by the composer, including the choice of data sets used to determine pitch and velocity for each track, the pitch and velocity ranges for each track, the piece's tempo and the number of notes per beat, the musical key and chord progression for each track, and the width of the smoothing window. The choice of instrument is also another artistic choice, although in this work only one instrument was used, namely the TiMidity+ piano synthesiser. As a whole, these decisions allow the composer to attempt to define the emotional context of the final piece. For instance, a fast-paced piece in a major progression may sound happy and cheerful to an audience who are used to associating fast-paced songs in major keys with happy and cheerful environments. It should be mentioned that there are no strict rules governing the emotional context of chords, tempo or instrument, and the emotional contexts of harmonies, timbres and tempos differ between cultures. Nevertheless, through exploiting the standard behaviours of Western musical traditions, the composer can attempt to imbue the piece with emotional musical cues that fit the theme of the piece or the behaviour of the underlying climate data.

To create a video, we produced an image for each time step in each piece. These figures show the data once they have been converted and binned into musical notes using units of the original data. A still image from each video is shown in Fig. . The FFmpeg video editing software was used to convert the set of images into a video and to add the MP3 as the soundtrack.

The finished videos were uploaded onto the lead author's YouTube channel

See https://www.youtube.com/c/LeedeMora, last access: 17 August 2020.

Six pieces were composed, generated and published using the methods described here. These pieces and their web addresses are below. Note that each of these videos' last access before this paper was published was 17 August 2020.

“Earth System Allegro”; https://www.youtube.com/watch?v=RxBhLNPH8ls

The main goals of the work were to generate music using climate model data and to use music to illustrate some standard practices in Earth system modelling that might not be widely known outside our community. Beyond these broader goals, each piece had its own unique goal; for example, to demonstrate the principles of sonification using UKESM1 data in the “Earth System Allegro”. The “Pre-industrial Vivace” introduces the concept of a PI control simulation and highlights how an emotional connection can be made between the model output and the sonification of the data. The goal of the “Sea Surface Temperature Aria” is to demonstrate the range of behaviours of the future climate projections. “Ocean Acidification in E minor” aims to show the impact of rising atmospheric CO2 on ocean acidification and also to illustrate how historical runs are branched from the PI control. The “Giant Steps Spin Up” shows the process of spinning up the marine component of the UKESM1, and finally, the “Seven Levels of Climate Change” aims to use the musical principles of jazz harmonisation to distinguish the full set of UKESM1's future scenario simulations.

These six pieces are summarised in Fig.  and Table . Figure  shows the final frame of each of the pieces, and Table  shows the summary of each of the videos, including the publication date and duration, and lists the experiments and data sets used to generate the piece.

We have successfully demonstrated that it is possible to generate music using data from the UK's Earth System Model. We have also shown that we can illustrate some standard practices in Earth system modelling using music. Within the framework of this pilot study, we must also raise some limitations and suggest some possible extensions for future versions of this work.

A significant omission from this study is the measurement of the impact, the reach or the engagement of these works. We did not test whether the audience was composed of laypeople or experts. We did not investigate whether the audience learnt anything about Earth system modelling through these series of videos. We did not monitor the audience reactions or interpretations of the music. Future extensions of this project should include a survey of the audience to investigate their backgrounds and demographics, what they learnt about Earth system models, and their overall impressions of the pieces. This could take the form of an online survey associated with each video or a discussion with the audience at a live performance.

In addition, in this work, we make no effort to monitor or describe the reach of the YouTube videos, track comments, subscriptions or the source of the views. While some tools are available for monitoring the number of videos within YouTube's content creator toolkit, YouTube Studio , a preliminary investigation found that it was not possible to use these tools alone to create a sufficiently detailed analyses of the impact, reach or dissemination of these music creation methods. YouTube Studio currently includes some demographic details, including gender, country of origin, viewership age, and traffic source, but it is not sufficient for an audience survey. This toolkit was built to help content creators monitor and build their audience and to monetise videos using advertisements. It is not fit for the purpose of scientific engagement monitoring. For instance, it was not possible to use YouTube Studio to determine the expertise of the audience, their thoughts on climate change, whether they read the video description section or whether they understood the description. Some of these features could be added to YouTube by Google, but many of them would require the audience survey described above.

Our videos only include the music and a visualisation of the data; they do not include any description about how the music was generated or the Earth system modelling methods used to create the underlying data. The explanations of the science and musification methodologies are given in a description below the video. Furthermore, viewers must expand this box by clicking the “show more” button. Using the tools provided in YouTube studio, it is not currently possible to determine whether the viewers have expanded, read or understood the description section. When we have shown these videos to audiences at scientific meetings and conferences, it has always been associated with a brief explanation of the methods. In future, this explanatory preface to the work could be included in the video itself, or as a separate video, in addition to the text below the video in the description section. This would likely increase the audience's understanding of our music-generation process.

If additional pieces were made, there are several potential ways that the methodology used to create them could be improved relative to the methods used to create the initial set of videos. In future versions of this work, it should be possible to use the ESMValTool to produce the time series data instead of BGC-val. This would make the production of the time series more easily repeatable but would also make it easier for pieces to be composed using data available in the CMIP5 and CMIP6 coupled model intercomparison projects. This broadens the scope of the data by allowing other models, other model domains, including the atmosphere and the land surface, and even observational data sets. For instance, we could make a multi-model intercomparison piece or a piece based on the atmospheric, terrestrial and ocean components of the same model. In addition, using ESMValTool would also make it more straightforward to distribute the source code that was used to make these pieces.

In the reflections on auditory graphics, lists several “Things that work” and “Approaches that do not work”. From the list of things that work, we included four of the five methods that worked: pitch coding of numeric data, the exploitation of temporal resolution of human audition, manipulating loudness changes and using time as time. We were not able to include the selection of distinct timbres to minimise stream confusion. From the list of approaches that do not work, we successfully avoided several of the pitfalls, notably pitch mapping to continuous variables and using loudness changes to represent an important continuous variable. However, we did include one of the approaches that Flowers did not recommend: we simultaneously plotted several variables with similar pitches and timbres. However, it is worth noting that maximising the clarity of the sonification is the goal of , but our focus was to produce and disseminate some relatively listenable pieces of music using UKESM1 data.

The two suggestions by that we failed to address were both related to using the same timbre digital piano synthesiser for all data. Due to the technical limitations of using TiMidity++, we were not able to vary the instruments used, and thus there was very little variability in terms of the timbres. These pieces were all performed by the same instrument, a solo piano, which limits the musical diversity of the set of pieces. In addition, each data set in a given piece was performed by the same instrument, making it difficult to distinguish the different data sets being performed simultaneously. Further extensions of this work could use a fully featured digital audio workstation to access a range of digital instruments beyond the digital piano, such as a string quartet, a horn and woodwind section, a full digital orchestra, electric guitar and bass, percussive instruments, or electronic synthesised instruments. This would comply with the suggestions listed in , allowing the individual data sets to stand out musically from each other in an individual piece, but would also lead to a much more diverse set of musical pieces.

From a musical perspective, there are many ways to improve the performances of the pieces for future versions of this work. As raised in the comments from social media, a human pianist would be able to add a warmth to the performance that is beyond the abilities of MIDI interpreters. A recording of a human performance could also add the hidden artefacts of a live recording, such as room noise, stereo effects and natural reverb. On the other hand, due to the nature of the process used to generate these pieces, it may not be possible for a single human to perform several of the pieces due to the speed, complexity, number of simultaneous notes or the range of these pieces. Alternatively, it may be possible to “humanise” the MIDI by making subtle changes to the timing and velocities of the MIDI notes. This is a recording technique that can take a synthesised, perfectly timed beat and make it sound like it is being played by a human. It does this by moving the individual notes slightly before or after the beat, and adding subtle variations in the velocity . Also, TiMidity++ uses the same piano sample for each pitch. This means that when two tracks of a piece play the same pitch at the same time, exactly the same sample is played twice simultaneously. These two identical sample sound waves are added constructively, and the note jumps out much louder than it would if a human played the part. A fully featured digital piano or a human performance would remove these loud jumps but also be able to add more nuance and warmth to the performance. Finally, the published pieces had no mastering or post-production. Even a basic mastering session by a professional sound engineer would likely improve the overall quality of the sound of these pieces.

In terms of the selection of chords progression, tempo and rhythms, it may be possible to target specific audiences using music based on popular artists or genres. For instance, the reach of a piece might be increased by responding to viral videos or by basing a work on a popular song.

In these works, we have focused on reproducing Western music, both traditional and modern, in order to connect each piece with the associated emotional musical cues. Alternatively, there is a significant diversity in traditional and modern styles of music from other regions around the world; a much wider range of rhythms, timbres, styles and emotional cues could be exploited in future extensions of this work.

With regards to the visual aspect of these videos, it should be straightforward to improve the quality of the graphics used. The current videos only show a simple scalar field as it develops over time. They could be improved by adding animated global maps of the model, interviews or live performances to the video. It may also be a positive addition to preface the videos with a brief explanation of the project and the methods deployed. On the technical side, there may also be some visual glitches and artefacts which arise due to YouTube's compression or streaming algorithms. A different streaming service or alternative video making software might help remove these glitches.

YouTube videos are typically shown in the suggestions queue with a thumbnail image and the video title. The thumbnail is the graphic placeholder that shows the video while it is not playing on YouTube as a suggested video or in Facebook or Twitter feeds. The thumbnail is how viewers first encounter the video, and it is a crucial part of attracting an audience. There are lots of guides helping one to create better thumbnails . Future works should attempt to optimise the video thumbnail to attract a wider audience.

While we did not investigate the reach or dissemination of these pieces in this work, if the goal of future projects was to increase the online audience size then it might be possible to reach a wider audience using a press release, a public screening of the videos, a scheduled publication date or through a collaboration with other musicians or YouTube content creators. It may also be possible to host a live concert, make a live recording or broadcast a YouTube live stream. It is not fully understood how a video can go viral, but it has been shown that view counts can rise exponentially when a single person or organisation with a large audience shares a video . Improvements to the music, the video, the description and the thumbnail make it more likely for such an influencer with large audience to like, share or retweet a piece, which could result in a significant increase in the audience size and view count. The videos in this work were posted online in an ad hoc fashion as soon as they were finished. To maximise the number of views, experts have recommended consistent, scheduled in advance, weekly videos, and it has been advised to publish them late in the week in the afternoons . Finally, it should be possible to increase the reach of this work through paid advertising on YouTube and other social media platforms. This would place the videos higher in the suggested video rankings and on the discovery queues.

In this work, we took data from the first United Kingdom Earth System Model and converted it into six musical pieces and videos. These pieces covered the core principles of climate modelling or ocean modelling, namely PI control runs, the spin-up process, multiple future scenarios, the Drake Passage current, the air–sea flux of CO2 and the Atlantic meridional overturning circulation. While limited to a single instrument, namely the synthesised piano, they included a range of musical styles, including classical, jazz, blues and contemporary styles.

While the wider public are likely to be familiar with climate change, they are less likely to be familiar with our community's methods. In fact, many standard tools in the arsenal of climate modellers may not be widely appreciated outside our small community, even within the scientific community. These six musical pieces open the door on a new, exciting and fun approach to how we engage with the fellow scientists and the wider public.

We have also discussed some ways of improving future iterations of this pilot study. Future works could be performed to a live audience, we could collaborate with musicians and the viewership would likely be increased with improved video graphics, thumbnails, live performances, video diversity, and more frequent upload rates. The scientific content of the videos could be expanded by accessing new data sets, other parts of the UKESM1 Earth System Model, other CMIP models or observational data sets. The quality of the music could be improved by including additional instruments and musical genres, and by making live recordings instead of MIDI performance. The knowledge transfer aspect of the project could be improved upon by appending explanations of the science to the video and by surveying the audience to identify the impact of these works.

Finally, the authors would like to encourage other scientists to think about how their work may be sonified. You may have beautiful and unique music hidden within your data; the methods described in this work would allow it to be made manifest.