Merge pull request #580 from PCMDI/issue578_durack1_DYAMOND3TitleTweak

update page title (#578)

@kristinchang3 @mzelinka ping

research/DYAMOND3/index.md

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-title: DYAMOND3 prototype Homepage
+title: PCMDI - DYAMOND3
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 # DYAMOND3
 # Cess-Potter Uniform +4K Experiments
 <img src="images/250114_durack1-940x529-png8-AR_CA60_zoom2880x1440_starField3.png" alt="SCREAM_CA60_zoom">
 
 ## Synopsis
-Earth system model projections of the future vary widely owing to uncertainty in radiative forcing and equilibrium climate sensitivity (ECS)  –  the steady-state change in temperature in response to a sustained doubling of atmospheric carbon dioxide.  Reducing uncertainty in ECS is an abiding goal of climate science, as many aspects of the global climate response to warming are tied to the overall response of global temperature. The root cause of uncertainty in ECS is uncertainty in radiative feedbacks that modulate the ability of the Earth to shed heat to space as it warms. Cloud feedback is the dominant component of this uncertainty because of the significant leverage that clouds have on the Earth’s energy budget, the wide diversity of cloud types in the atmosphere, and the fact that their radiative properties are controlled by both macroscale and microscale processes, most of which are crudely represented in global models. Given the resultant disagreement among global climate models in cloud feedback and ECS (which has persisted for decades and has recently grown), recent efforts have instead attempted to constrain climate sensitivity through synthesizing other lines of evidence (i.e., historical record, paleoclimate evidence, and process-level studies), with promising results. Nevertheless, numerical simulations of present and future climate at the global scale remain a unique and essential tool for climate science, not only for exploring and improving our understanding of Earth’s climate in a physically consistent framework, but also for providing information on future climate at space and time scales that are relevant to a growing spectrum of societal needs.<br/><br/>
+Earth system model projections of the future vary widely owing to uncertainty in radiative forcing and equilibrium climate sensitivity (ECS) – the steady-state change in temperature in response to a sustained doubling of atmospheric carbon dioxide.  Reducing uncertainty in ECS is an abiding goal of climate science, as many aspects of the global climate response to warming are tied to the overall response of global temperature. The root cause of uncertainty in ECS is uncertainty in radiative feedbacks that modulate the ability of the Earth to shed heat to space as it warms. Cloud feedback is the dominant component of this uncertainty because of the significant leverage that clouds have on the Earth’s energy budget, the wide diversity of cloud types in the atmosphere, and the fact that their radiative properties are controlled by both macroscale and microscale processes, most of which are crudely represented in global models. Given the resultant disagreement among global climate models in cloud feedback and ECS (which has persisted for decades and has recently grown), recent efforts have instead attempted to constrain climate sensitivity through synthesizing other lines of evidence (i.e., historical record, paleoclimate evidence, and process-level studies), with promising results. Nevertheless, numerical simulations of present and future climate at the global scale remain a unique and essential tool for climate science, not only for exploring and improving our understanding of Earth’s climate in a physically consistent framework, but also for providing information on future climate at space and time scales that are relevant to a growing spectrum of societal needs.<br/><br/>
 
 In this context, the latest generation of high resolution (<5k km horizontal grid spacing) global storm-resolving models (GSRMs) serves as a potential game changer. This is because they explicitly simulate more small-scale processes, thereby requiring fewer subgrid-scale parameterizations than their coarse-resolution counterparts, with the anticipation that this will allow them to simulate climate more reliably. Indeed, GSRMs have been shown to simulate cloud and precipitation characteristics with much greater fidelity than coarse resolution models ([Caldwell et al 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002544)), and explicitly simulate impactful weather events like tropical cyclones and mesoscale convective systems that cannot be resolved by coarse resolution models ([Judt et al 2021](https://www.jstage.jst.go.jp/article/jmsj/99/3/99_2021-029/_html/-char/en); [Feng et al 2018](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018MS001305)). Despite these improvements, they still struggle with simulating clouds that are formed from sub-kilometer-scale motions or that depend sensitively on representation of cloud microphysics, which remain unresolved. The DYAMOND (DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains) Project was established to facilitate intercomparison of GSRM representations of the atmospheric circulation at timescales of weeks to decades and to explore the ability of such models to better represent the atmospheric general circulation relative to traditional global climate models. The [first](https://www.esiwace.eu/the-project/past-phases/dyamond-initiative/services-dyamond-summer) and [second](https://www.esiwace.eu/the-project/past-phases/dyamond-initiative/services-dyamond-winter) phases of DYAMOND were successful in uniting and invigorating a growing community of global storm-resolving modelers to simulate and examine 40-day simulation campaigns in northern-hemisphere summer and winter seasons (respectively).<br/><br/>