Editing Climate model (section)

== Issues ==

=== Electricity consumption ===
Cloud-resolving climate models are nowadays run on high intensity [[Supercomputer|super-computers]] which have a high power consumption and thus cause CO<sub>2</sub> emissions.<ref name=":0">{{Cite journal |last=Loft |first=Richard |date=2020 |title=Earth System Modeling Must Become More Energy Efficient |url=https://eos.org/opinions/earth-system-modeling-must-become-more-energy-efficient |journal=Eos |issue=101 |doi=10.1029/2020EO147051 |issn=2324-9250|doi-access=free }}</ref> They require [[exascale computing]] (billion billion – i.e., a quintillion – calculations per second). For example, the [[Frontier (supercomputer)|Frontier exascale supercomputer]] consumes 29 MW.<ref>{{Cite web |last=Trader |first=Tiffany |date=2021 |title=Frontier to Meet 20MW Exascale Power Target Set by DARPA in 2008 |url=https://www.hpcwire.com/2021/07/14/frontier-to-meet-20mw-exascale-power-target-set-by-darpa-in-2008/ |access-date=2023-12-08 |website=HPCwire |language=en-US}}</ref> It can simulate a year’s worth of climate at cloud resolving scales in a day.<ref>{{Cite web |title=Cloud-resolving climate model meets world’s fastest supercomputer |url=https://www.sandia.gov/labnews/2023/04/06/cloud-resolving-climate-model-meets-worlds-fastest-supercomputer/ |access-date=2023-12-08 |website=LabNews |language=en-US}}</ref>

Techniques that could lead to energy savings, include for example: "reducing floating point precision computation; developing machine learning algorithms to avoid unnecessary computations; and creating a new generation of scalable numerical algorithms that would enable higher throughput in terms of simulated years per wall clock day."<ref name=":0" />

=== Parametrization ===
{{excerpt|Parametrization (climate modeling)}}