Cloud Computing for Climate Modelling: Evaluation, Challenges and Benefits

Cloud computing is a mature technology that has already shown benefits for a wide range of academic research domains that, in turn, utilize a wide range of application design models. In this paper, we discuss the use of cloud computing as a tool to improve the range of resources available for climate science, presenting the evaluation of two different climate models. Each was customized in a different way to run in public cloud computing environments (hereafter cloud computing) provided by three different public vendors: Amazon, Google and Microsoft. The adaptations and procedures necessary to run the models in these environments are described. The computational performance and cost of each model within this new type of environment are discussed, and an assessment is given in qualitative terms. Finally, we discuss how cloud computing can be used for geoscientific modelling, including issues related to the allocation of resources by funding bodies. We also discuss problems related to computing security, reliability and scientific reproducibility.

[1]  Hassan Hajjdiab,et al.  Security and Privacy of AWS S3 and Azure Blob Storage Services , 2019, 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS).

[2]  B. Raoult,et al.  Cloud Computing for the Distribution of Numerical Weather Prediction Outputs , 2016, CloudCom 2016.

[3]  Bryan Lawrence,et al.  Storing and manipulating environmental big data with JASMIN , 2013, 2013 IEEE International Conference on Big Data.

[4]  Jiawei Zhuang,et al.  Enabling High‐Performance Cloud Computing for Earth Science Modeling on Over a Thousand Cores: Application to the GEOS‐Chem Atmospheric Chemistry Model , 2020, Journal of Advances in Modeling Earth Systems.

[5]  Kazi Zunnurhain,et al.  Google cloud platform security , 2019, SEC.

[6]  Rajiv Ranjan,et al.  Cloud Resource Orchestration Programming: Overview, Issues, and Directions , 2015, IEEE Internet Computing.

[7]  Tim Palmer,et al.  Climate forecasting: Build high-resolution global climate models , 2014, Nature.

[8]  M. Allen Do-it-yourself climate prediction , 1999, Nature.

[9]  Juan A. Añel,et al.  The importance of reviewing the code , 2011, Commun. ACM.

[10]  Gordon Bell,et al.  Beyond the Data Deluge , 2009, Science.

[11]  J. Thepaut,et al.  The ERA5 global reanalysis , 2020, Quarterly Journal of the Royal Meteorological Society.

[12]  Jiawei Zhuang,et al.  Enabling Immediate Access to Earth Science Models through Cloud Computing: Application to the GEOS-Chem Model , 2019, Bulletin of the American Meteorological Society.

[13]  H. Suleman,et al.  Design and Implementation of a Cloud Computing Adoption Decision Tool: Generating a Cloud Road , 2015, PloS one.

[14]  C. Brühl,et al.  Multimodel assessment of the upper troposphere and lower stratosphere: Tropics and global trends , 2010 .

[15]  Subhas C. Misra,et al.  Identification of a company's suitability for the adoption of cloud computing and modelling its corresponding Return on Investment , 2011, Math. Comput. Model..

[16]  R. Garcia-Herrera,et al.  The impact of a future solar minimum on climate change projections in the Northern Hemisphere , 2016 .

[17]  Bradley Zavodsky,et al.  Clouds in the Cloud: Weather Forecasts and Applications within Cloud Computing Environments , 2015 .

[18]  Nadia Drake,et al.  Cloud computing beckons scientists , 2014, Nature.

[19]  Tomás F. Pena,et al.  Enabling BOINC in infrastructure as a service cloud system , 2016 .

[20]  Shiyong Lu,et al.  Enabling scalable scientific workflow management in the Cloud , 2015, Future Gener. Comput. Syst..

[21]  Juan A. Añel,et al.  Comment on “Most computational hydrology is not reproducible, so is it really science?” by Christopher Hutton et al. , 2017 .

[22]  Andrew Dawson,et al.  An approach to secure weather and climate models against hardware faults , 2017 .

[23]  Jan O. Korbel,et al.  Data analysis: Create a cloud commons , 2015, Nature.

[24]  L. Wald,et al.  Advancing climate services for the European renewable energy sector through capacity building and user engagement , 2019, Climate Services.

[25]  B. McKenna Dubai Operational Forecasting System in Amazon Cloud , 2016, CloudCom 2016.

[26]  Bojan Spasic,et al.  Security Pattern for Cloud SaaS: from system and data security to privacy , 2018, 2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech).

[27]  David Wallom,et al.  Utilising Amazon web services to provide an on demand urgent computing facility for climateprediction.net , 2016, 2016 IEEE 12th International Conference on e-Science (e-Science).

[28]  Christopher Hutton,et al.  Most computational hydrology is not reproducible, so is it really science? , 2016, Water Resources Research.

[29]  K.,et al.  The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability , 2015 .

[30]  Daniel R. Marsh,et al.  Climate change from 1850 to 2005 simulated in CESM1(WACCM) , 2013 .

[31]  M. Toohey,et al.  Characterizing sampling biases in the trace gas climatologies of the SPARC Data Initiative , 2013 .

[32]  M. Hegglin,et al.  The SPARC Data Initiative: Assessment of stratospheric trace gas and aerosol climatologies from satellite limb sounders , 2017 .

[33]  Xiuhong Chen,et al.  Running climate model on a commercial cloud computing environment: A case study using Community Earth System Model (CESM) on Amazon AWS , 2017, Comput. Geosci..

[34]  James Robertson,et al.  Security in the Cloud: understanding your responsibility , 2019 .

[35]  C. Brühl,et al.  Multimodel assessment of the upper troposphere and lower stratosphere: Extratropics , 2010 .

[36]  Richard G. Jones,et al.  weather@home 2: validation of an improved global–regional climate modelling system , 2016 .

[37]  Simon Wilson,et al.  weather@home—development and validation of a very large ensemble modelling system for probabilistic event attribution , 2015 .

[38]  Stefanie N. Lindstaedt,et al.  Realising the European Open Science Cloud , 2016 .

[39]  Myong H. Kang,et al.  Security and Architectural Issues for National Security Cloud Computing , 2010, 2010 IEEE 30th International Conference on Distributed Computing Systems Workshops.