Un court article #Wikipédia sur Akio Arakawa, l'un des auteurs du rapport Charney de 1979 (voir pouets précédents), et l'auteur réputé de contributions aux modèles de circulation générale de l'atmosphère.

Si des spécialistes peuvent relire et améliorer la description de ses contributions scientifiques, ce n'est pas de refus ! #ClimateModeling #climatechange

https://fr.wikipedia.org/wiki/Akio_Arakawa

Okay... so... I gave my #NASAGISS talk about the #HISTORY of #Climate #ClimateModeling at #NASA GISS for its 60th anniversary.

I am a passable story teller after all.

I’ll give a text-version here... though not all at once… 40-minutes of the first 20 yrs.

____

In the beginning... climate ‘prediction' used Farmer's Almanacs... these were pretty awesome resources in knowing when to plant what crops.

They were pretty awesome, that is, until humans intervened…

1/ <many TBD>

" By 2050, summers are projected to become as turbulent as 1950 winters and autumns... For every 1 °C of global near-surface warming, autumn, winter, spring, and summer are projected to have an average of 14%, 9%, 9%, and 14% more moderate CAT, respectively. Our results confirm that the aviation sector should prepare for a more turbulent future."

One way and/or another, more turbulence ahead.

#ClimateScience
#ClimateModeling
#ClimateFuture
#Aviation

https://link.springer.com/article/10.1007/s00382-023-06694-x

Fewer logic gates and less memory for same effective work: less juice.

Not that climate model operation is a salient problem compared to others, such as buying cheap jet tickets to wherever-for-a-weekend, by the millions of seats and weekends and loads of external costs of lift-a-drink-in-a-different-country imposed on others.

But still: more efficient is better!

#ClimateModeling
#ClimateResearch

https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.4435?af=R

Fascinating today at #NASA GISS w visitors from #MIT highlighting #MachineLearning and #ClimateModeling.

They demo’d fast & eddy resolving oceananigans.jl, http://ferrari.mit.edu/eapsdb/ramadhanetal20/ , a simplified ocean model.

#julialang benchmarking v C/FORTRAN (most climate models including me) had been equivocal. But the demo of julialang opening up faster, high res simulations with easy *gpu* coding is a whole new ballgame.

Sign me up. I need to learn not dabble.

@julialang

To add some tags to my #introduction, I'm interested in:
#climate, #policy, #adaptation, #ClimateJustice, #DEI

#climatemodeling, #drought, #teleconnections, #regionalclimate, #ENSO,

#carbon, #CDR, #DAC, #sequestiation

#ClimateIntervention, #ClimateEngineering, #Geoengineering, #SAI, #MCB, #clouds

#LosAngeles, #California, #UCLA, #bikes, #cats

Interesting summary article on the move to #climatemodeling in Julia by MIT - https://www.theregister.com/2022/04/13/climate_mit_fortran/ Julia seems particularly well suited to the kind of conceptual model that treats interactions and feedbacks between climate components, but is also getting serious traction in Earth system modelling via the Clima project.