Current status on the need for improved accessibility to climate models code

Añel, Juan Antonio; García-Rodríguez, Michael; Rodeiro, Javier

doi:10.5194/gmd-14-923-2021

Cited by 12 publications

(9 citation statements)

References 35 publications

(38 reference statements)

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“…Algorithm developers in the satellite realm are perhaps more used to specifying their assumptions through the Algorithm Theoretical Basis Documents (ATBD) but a full comparison between the physics and empirical values behind both algorithms and parameterizations is much needed to advance the field. On that note, it is clear that better access to climate models code would contribute to address scientific gaps in climate models and to improve their reliability (Añel et al, 2021). It would be also highly desirable that scientists not only specify the parameterizations they have used, but also the assumptions and empirical values they have actually selected within these.…”

Section: Discussionmentioning

confidence: 99%

Empirical values and assumptions in the convection of numerical models

Villalba-Pradas

Tapiador

2021

Preprint

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Abstract. Convection influences climate and weather events over a wide range of spatial and temporal scales. Therefore, accurate predictions of the time and location of convection and its development into severe weather are of great importance. Convection has to be parameterized in Numerical Weather Prediction models, Global Climate Models, and Earth System Models (NWPs, GCMs, and ESMs) as the key physical processes occur at scales much lower than the model grid size. The convection schemes described in the literature represent the physics by simplified models that require assumptions about the processes and the use of a number of parameters based on empirical values. The present paper examines these choices and their impacts on model outputs and emphasizes the importance of observations to improve our current understanding of the physics of convection.

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Section: Discussionmentioning

confidence: 99%

Empirical values and assumptions in the convection of numerical models

Villalba-Pradas

Tapiador

2021

Preprint

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“…Software sharing is still not very established and can be challenging [25]. Specifically, sharing of model code is not common practice across institutions in climate science [26] due to intricate licensing frameworks and non-trivial code adaptations necessary to apply it on different computing infrastructures.…”

Section: Data and Software (Re)usementioning

confidence: 99%

“…The model code is often not freely available and is subject to strict licensing and usage agreements. Only a few models are freely accessible in their source code ( [26]) and can thus really be declared as FDO.…”

Section: Critical View On the Use Of Fdos In Simulation Based Climate...mentioning

confidence: 99%

Canonical Workflows in Simulation-based Climate Sciences

2022

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In this paper we present the derivation of Canonical Workflow Modules from current workflows in simulation-based climate science in support of the elaboration of a corresponding framework for simulation-based research. We first identified the different users and user groups in simulation-based climate science based on their reasons for using the resources provided at the German Climate Computing Center (DKRZ). What is special about this is that the DKRZ provides the climate science community with resources like high performance computing (HPC), data storage and specialised services, and hosts the World Data Center for Climate (WDCC). Therefore, users can perform their entire research workflows up to the publication of the data on the same infrastructure. Our analysis shows, that the resources are used by two primary user types: those who require the HPC-system to perform resource intensive simulations to subsequently analyse them and those who reuse, build-on and analyse existing data. We then further subdivided these top-level user categories based on their specific goals and analysed their typical, idealised workflows applied to achieve the respective project goals. We find that due to the subdivision and further granulation of the user groups, the workflows show apparent differences. Nevertheless, similar “Canonical Workflow Modules” can be clearly made out. These modules are “Data and Software (Re)use”, “Compute”, “Data and Software Storing”, “Data and Software Publication”, “Generating Knowledge” and in their entirety form the basis for a Canonical Workflow Framework for Research (CWFR). It is desirable that parts of the workflows in a CWFR act as FDOs, but we view this aspect critically. Also, we reflect on the question whether the derivation of Canonical Workflow modules from the analysis of current user behaviour still holds for future systems and work processes.

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“…* (Hutton et al, 2016) Comment on "Most computational hydrology is not reproducible, so is it really science?" (Melsen et al, 2017) Towards a more reproducible ecology* (Borregaard and Hart, 2016) Elevating The Status of Code in Ecology* (Mislan et al, 2016) Using R in hydrology: a review of recent developments and future directions* (Slater et al, 2019) Hydrogeological conceptual model building and testing: A review* (Enemark et al, 2019) Open science, reproducibility, and transparency in ecology* (Powers and Hampton, 2019) On doing large-scale hydrology with Lions: Realising the value of perceptual models and knowledge accumulation** (Wagener et al, 2020) Current status on the need for improved accessibility to climate models code* (Añel et al, 2021)…”

Section: Open Publishingmentioning

confidence: 99%

A Hydrologist's Guide to Open Science

Hall

Saia

Popp

et al. 2021

Preprint

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Abstract. Open, accessible, reusable, and reproducible hydrologic research can have a significant impact on the scientific community and broader society. While more individuals and organizations within the hydrology community are embracing open science practices, technical (e.g., limited coding experience), resource (e.g., open access fees), and social (e.g., fear of being scooped) challenges remain. Furthermore, there are a growing number of constantly evolving open science tools, resources, and initiatives that can seem overwhelming. These challenges and the ever-evolving nature of the open science landscape may seem insurmountable for hydrologists interested in pursuing open science. Therefore, we propose general Open Hydrology Principles to guide individual and community progress toward open science for research and education and the Open Hydrology Practical Guide to improve the accessibility of currently available tools and approaches. We aim to inform and empower hydrologists as they transition to open, accessible, reusable, and reproducible research. We discuss the benefits as well as common open science challenges and how hydrologists can overcome them. The Open Hydrology Principles and Open Hydrology Practical Guide reflect our knowledge of the current state of open hydrology; we recognize that recommendations and suggestions will evolve and expand with emerging open science infrastructures, workflows, and research experiences. Therefore, we encourage hydrologists all over the globe to join in and help advance open science by contributing to the living version of this document and by sharing open hydrology resources in the community-supported repository (https://open-hydrology.github.io).

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Current status on the need for improved accessibility to climate models code

Cited by 12 publications

References 35 publications

Empirical values and assumptions in the convection of numerical models

Empirical values and assumptions in the convection of numerical models

Canonical Workflows in Simulation-based Climate Sciences

A Hydrologist's Guide to Open Science

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