scholarly journals Earth System Music: the methodology and reach of music generated from the United Kingdom Earth System Model

2020 ◽  
Author(s):  
Lee de Mora ◽  
Alistair A. Sellar ◽  
Andrew Yool ◽  
Julien Palmieri ◽  
Robin S. Smith ◽  
...  
Keyword(s):  
United Kingdom ◽  
Time Series Data ◽  
Climate Science ◽  
Practical Aspect ◽  
Scientific Data ◽  
Earth System Model ◽  
System Model ◽  
Series Data ◽  
Earth System ◽  
Science Outreach

Abstract. Scientific data is almost always represented graphically either in figures or in videos. With the ever-growing interest from the general public towards understanding climate science, it is becoming increasingly important that we present this information in ways accessible to non-experts. In this pilot study, we use time series data from the first United Kingdom Earth System model (UKESM1) to create six procedurally generated musical pieces and use them to test whether we can use music to engage with the wider community. Each of these pieces is based around a unique part of UKESM1's ocean component model, either in terms of a scientific principle or a practical aspect of modelling. In addition, each piece is arranged using a different musical progression, style and tempo. These pieces were performed by the digital piano synthesizer, TiMidity++, and were published on the lead author's YouTube channel. The videos all show the time progression of the data in time with the music and a brief description of the methodology is posted below the video. To disseminate these works, a link to each piece was published on the lead authors personal and professional social media accounts. The reach of these works was analysed using YouTube's channel monitoring toolkit for content creators, YouTube studio. In the first ninety days after the first video was published, the six pieces reached at least 251 unique viewers, and have 553 total views. We found that most of the views occurred in the fourteen days immediately after each video was published. In effect, once the concept had been demonstrated to an audience, there was reduced enthusiasm from that audience to return to it immediately. This suggests that to use music effectively as an science outreach tool, the works needs to reach new audiences or new and unique content needs to be delivered to a returning audience.

scholarly journals Earth system music: music generated from the United Kingdom Earth System Model (UKESM1)

2020 ◽  
Vol 3 (2) ◽  
pp. 263-278 ◽  
Author(s):  
Lee de Mora ◽  
Alistair A. Sellar ◽  
Andrew Yool ◽  
Julien Palmieri ◽  
Robin S. Smith ◽  
...  
Keyword(s):  
United Kingdom ◽  
Pilot Study ◽  
Time Series Data ◽  
Musical Instrument ◽  
Practical Aspect ◽  
Scientific Data ◽  
Earth System Model ◽  
System Model ◽  
Series Data ◽  
Earth System

Abstract. Scientific data are almost always represented graphically in figures or in videos. With the ever-growing interest from the general public in understanding climate sciences, it is becoming increasingly important that scientists present this information in ways that are both accessible and engaging to non-experts. In this pilot study, we use time series data from the first United Kingdom Earth System Model (UKESM1) to create six procedurally generated musical pieces. Each of these pieces presents a unique aspect of the ocean component of the UKESM1, either in terms of a scientific principle or a practical aspect of modelling. In addition, each piece is arranged using a different musical progression, style and tempo. These pieces were created in the Musical Instrument Digital Interface (MIDI) format and then performed by a digital piano synthesiser. An associated video showing the time development of the data in time with the music was also created. The music and video were published on the lead author's YouTube channel. A brief description of the methodology was also posted alongside the video. We also discuss the limitations of this pilot study and describe several approaches to extend and expand upon this work.

Earth System Music: music generated from the first United Kingdom Earth System model

2020 ◽  
Author(s):  
Lee de Mora ◽  
Alistair Sellar ◽  
Andrew Yool ◽  
Julien Palmieri ◽  
Robin S. Smith ◽  
...  
Keyword(s):  
United Kingdom ◽  
Time Series Data ◽  
Scientific Data ◽  
Earth System Model ◽  
System Model ◽  
Series Data ◽  
Historical Period ◽  
System Modelling ◽  
Earth System ◽  
Model Data

<p>With the ever-growing interest from the general public towards understanding climate science, it is becoming increasingly important that we present this information in ways accessible to non-experts. In this pilot study, we use time series data from the first United Kingdom Earth System model (UKESM1) to create six procedurally generated musical pieces and use them to explain the process of modelling the earth system and to engage with the wider community. </p><p>Scientific data is almost always represented graphically either in figures or in videos. By adding audio to the visualisation of model data, the combination of music and imagery provides additional contextual clues to aid in the interpretation. Furthermore, the audiolisation of model data can be employed to generate interesting and captivating music, which can not  only reach a wider audience, but also hold the attention of the listeners for extended periods of time.</p><p>Each of the six pieces presented in this work was themed around either a scientific principle or a practical aspect of earth system modelling. These pieces demonstrate the concepts of a spin up, a pre-industrial control run, multiple historical experiments, and the use of several future climate scenarios to a wider audience. They also show the ocean acidification over the historical period, the changes in circulation, the natural variability of the pre-industrial simulations, and the expected rise in sea surface temperature over the 20th century. </p><p>Each of these pieces were arranged using different musical progression, style and tempo. All six pieces were performed by the digital piano synthesizer, TiMidity++, and were published on the lead author's YouTube channel. The videos all show the progression of the data in time with the music and a brief description of the methodology is posted alongside the video. </p><p>To disseminate these works, links to each piece were published on the lead author's personal and professional social media accounts. The reach of these works was also analysed using YouTube's channel monitoring toolkit for content creators, YouTube studio.</p>

scholarly journals The infrastructure MESSy submodels GRID (v1.0) and IMPORT (v1.0)

2015 ◽  
Vol 8 (10) ◽  
pp. 8607-8633 ◽  
Author(s):  
A. Kerkweg ◽  
P. Jöckel
Keyword(s):  
Input Data ◽  
Time Series Data ◽  
Computing Time ◽  
Earth System Model ◽  
System Model ◽  
Series Data ◽  
Model Integration ◽  
Earth System ◽  
Model Resolution ◽  
Model Components

Abstract. The coupling of Earth system model components, which work on different grids, into an Earth System Model (ESM) provokes the necessity to transfer data from one grid to another. Additionally, each of these model components might require data import onto its specific grid. Usually, one of two approaches is used: Either all input data is preprocessed to the employed grid, or the imported data is interpolated on-line, i.e. during model integration to the required grid. For the former, each change in the model resolution requires the re-preprocessing of all data. The latter option implies that in each model integration computing time is required for the grid mapping. If all components of an ESM use only one single point of import and the same mapping software, only one software package needs to be changed for code optimisation, inclusion of additional interpolation methods or the implementation of new data formats. As the Modular Earth Submodel System (MESSy) is mainly used for research purposes which require frequent changes of the model setup including the model resolution or the application of different sets of input data (e.g., different emission scenarios), the idea of a common procedure for data import was implemented in MESSy in form of the infrastructure submodel IMPORT. Currently, IMPORT consists of two submodels: IMPORT_TS for reading and processing abstract time series data and IMPORT_GRID, utilising the infrastructure submodel GRID which provides procedures for grid transformations using the remapping software packages NREGRID (Jöckel, 2006) and SCRIP (Jones, 1999). Grid information is stored in a standardised structure as geo-hybrid grids. Based on this unified definition a standardised interface for the grid transformations is provided, thus simplifying the implemention of grid transformations in the model code. This article describes the main functionalities of the two MESSy infrastructure submodels GRID and IMPORT. The Supplement of this article contains stand-alone tools of both IMPORT subsubmodels, IMPORT_TS and IMPORT_GRID. Their handling is explained in detail in the IMPORT User Manual which is also part of the Supplement.

scholarly journals Evaluating Lossy Data Compression on Climate Simulation Data within a Large Ensemble

2016 ◽  
Author(s):  
Allison H. Baker ◽  
Dorit M. Hammerling ◽  
Sheri A. Mickleson ◽  
Haiying Xu ◽  
Martin B. Stolpe ◽  
...  
Keyword(s):  
Data Compression ◽  
Climate Science ◽  
Original Data ◽  
Earth System Model ◽  
System Model ◽  
Climate Simulation ◽  
Earth System ◽  
Climate Data ◽  
Large Ensemble ◽  
Simulation Data

Abstract. High-resolution earth system model simulations generate enormous data volumes, and retaining the data from these simulations often strains institutional storage resources. Further, these exceedingly large storage requirements negatively impact science objectives by forcing reductions in data output frequency, simulation length, or ensemble size, for example. To lessen data volumes from the Community Earth System Model (CESM), we advocate the use of lossy data compression techniques. While lossy data compression does not exactly preserve the original data (as lossless compression does), lossy techniques have an advantage in terms of smaller storage requirements. To preserve the integrity of the scientific simulation data, the effects of lossy data compression on the original data should, at a minimum, not be statistically distinguishable from the natural variability of the climate system, and previous preliminary work with data from CESM has shown this goal to be attainable. However, to ultimately convince climate scientists that it is acceptable to use lossy data compression, we provide climate scientists with access to publicly available climate data that has undergone lossy data compression. In particular, we report on the results of a lossy data compression experiment with output from the CESM Large Ensemble (CESM-LE) Community Project, in which we challenge climate scientists to examine features of the data relevant to their interests, and attempt to identify which of the ensemble members have been compressed and reconstructed. We find that while detecting distinguishing features is certainly possible, the compression effects noticeable in these features are often unimportant or disappear in post-processing analyses. In addition, we perform several analyses that directly compare the original data to the reconstructed data to investigate the preservation, or lack thereof, of specific features critical to climate science. Overall, we conclude that applying lossy data compression to climate simulation data is both advantageous in terms of data reduction and generally acceptable in terms of effects on scientific results.

scholarly journals Evaluating lossy data compression on climate simulation data within a large ensemble

2016 ◽  
Vol 9 (12) ◽  
pp. 4381-4403 ◽  
Author(s):  
Allison H. Baker ◽  
Dorit M. Hammerling ◽  
Sheri A. Mickelson ◽  
Haiying Xu ◽  
Martin B. Stolpe ◽  
...  
Keyword(s):  
Data Compression ◽  
Climate Science ◽  
Original Data ◽  
Earth System Model ◽  
System Model ◽  
Climate Simulation ◽  
Earth System ◽  
Climate Data ◽  
Large Ensemble ◽  
Simulation Data

Abstract. High-resolution Earth system model simulations generate enormous data volumes, and retaining the data from these simulations often strains institutional storage resources. Further, these exceedingly large storage requirements negatively impact science objectives, for example, by forcing reductions in data output frequency, simulation length, or ensemble size. To lessen data volumes from the Community Earth System Model (CESM), we advocate the use of lossy data compression techniques. While lossy data compression does not exactly preserve the original data (as lossless compression does), lossy techniques have an advantage in terms of smaller storage requirements. To preserve the integrity of the scientific simulation data, the effects of lossy data compression on the original data should, at a minimum, not be statistically distinguishable from the natural variability of the climate system, and previous preliminary work with data from CESM has shown this goal to be attainable. However, to ultimately convince climate scientists that it is acceptable to use lossy data compression, we provide climate scientists with access to publicly available climate data that have undergone lossy data compression. In particular, we report on the results of a lossy data compression experiment with output from the CESM Large Ensemble (CESM-LE) Community Project, in which we challenge climate scientists to examine features of the data relevant to their interests, and attempt to identify which of the ensemble members have been compressed and reconstructed. We find that while detecting distinguishing features is certainly possible, the compression effects noticeable in these features are often unimportant or disappear in post-processing analyses. In addition, we perform several analyses that directly compare the original data to the reconstructed data to investigate the preservation, or lack thereof, of specific features critical to climate science. Overall, we conclude that applying lossy data compression to climate simulation data is both advantageous in terms of data reduction and generally acceptable in terms of effects on scientific results.

scholarly journals Evaluation of SO<sub>2</sub>, SO<sub>4</sub><sup>2−</sup> and an updated SO<sub>2</sub> dry deposition parameterization in the United Kingdom Earth System Model

2021 ◽  
Vol 21 (24) ◽  
pp. 18465-18497
Author(s):  
Catherine Hardacre ◽  
Jane P. Mulcahy ◽  
Richard J. Pope ◽  
Colin G. Jones ◽  
Steven T. Rumbold ◽  
...  
Keyword(s):  
United Kingdom ◽  
Dry Deposition ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
The United Kingdom ◽  
The Usa ◽  
Simulated Surface ◽  
So2 Dry Deposition ◽  
Total Column

Abstract. In this study we evaluate simulated surface SO2 and sulfate (SO42-) concentrations from the United Kingdom Earth System Model (UKESM1) against observations from ground-based measurement networks in the USA and Europe for the period 1987–2014. We find that UKESM1 captures the historical trend for decreasing concentrations of atmospheric SO2 and SO42- in both Europe and the USA over the period 1987–2014. However, in the polluted regions of the eastern USA and Europe, UKESM1 over-predicts surface SO2 concentrations by a factor of 3 while under-predicting surface SO42- concentrations by 25 %–35 %. In the cleaner western USA, the model over-predicts both surface SO2 and SO42- concentrations by factors of 12 and 1.5 respectively. We find that UKESM1’s bias in surface SO2 and SO42- concentrations is variable according to region and season. We also evaluate UKESM1 against total column SO2 from the Ozone Monitoring Instrument (OMI) using an updated data product. This comparison provides information about the model's global performance, finding that UKESM1 over-predicts total column SO2 over much of the globe, including the large source regions of India, China, the USA, and Europe as well as over outflow regions. Finally, we assess the impact of a more realistic treatment of the model's SO2 dry deposition parameterization. This change increases SO2 dry deposition to the land and ocean surfaces, thus reducing the atmospheric loading of SO2 and SO42-. In comparison with the ground-based and satellite observations, we find that the modified parameterization reduces the model's over-prediction of surface SO2 concentrations and total column SO2. Relative to the ground-based observations, the simulated surface SO42- concentrations are also reduced, while the simulated SO2 dry deposition fluxes increase.

Korea Institute of Ocean Science and Technology Earth System Model and Its Simulation Characteristics

2021 ◽  
Author(s):  
Gyundo Pak ◽  
Yign Noh ◽  
Myong-In Lee ◽  
Sang-Wook Yeh ◽  
Daehyun Kim ◽  
...  
Keyword(s):  
Science And Technology ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Ocean Science

scholarly journals Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs Concentration Pathways

2021 ◽  
Author(s):  
Hyun Min Sung ◽  
Jisun Kim ◽  
Sungbo Shim ◽  
Jeong-byn Seo ◽  
Sang-Hoon Kwon ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Changes ◽  
Scientific Information ◽  
Earth System Model ◽  
First Century ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Future Projections ◽  
Twenty First Century

AbstractThe National Institute of Meteorological Sciences-Korea Meteorological Administration (NIMS-KMA) has participated in the Coupled Model Inter-comparison Project (CMIP) and provided long-term simulations using the coupled climate model. The NIMS-KMA produces new future projections using the ensemble mean of KMA Advanced Community Earth system model (K-ACE) and UK Earth System Model version1 (UKESM1) simulations to provide scientific information of future climate changes. In this study, we analyze four experiments those conducted following the new shared socioeconomic pathway (SSP) based scenarios to examine projected climate change in the twenty-first century. Present day (PD) simulations show high performance skill in both climate mean and variability, which provide a reliability of the climate models and reduces the uncertainty in response to future forcing. In future projections, global temperature increases from 1.92 °C to 5.20 °C relative to the PD level (1995–2014). Global mean precipitation increases from 5.1% to 10.1% and sea ice extent decreases from 19% to 62% in the Arctic and from 18% to 54% in the Antarctic. In addition, climate changes are accelerating toward the late twenty-first century. Our CMIP6 simulations are released to the public through the Earth System Grid Federation (ESGF) international data sharing portal and are used to support the establishment of the national adaptation plan for climate change in South Korea.

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