Spencer Clark commited on
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Add initial condition and forcing data for 1xCO2 and 3xCO2

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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.nc filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -10,19 +10,17 @@ Ai2 Climate Emulator (ACE) is a family of models designed to simulate atmospheri
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  **Disclaimer: ACE models are research tools and should not be used for operational climate predictions.**
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- ACE2-SOM is the first model we have trained to emulate the sensitivity of climate to substantial increases in carbon dioxide. It consists of ACE2 coupled to a highly simplified physics-based ocean mode, called a slab ocean model, and is trained on equilibrium-climate output from GFDL's SHiELD model coupled to an identical slab ocean with carbon dioxide concentrations of the present-day, double the present-day, and quadruple the present-day.
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  Quick links:
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- - πŸ“ƒ [Paper (TODO: update)]()
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  - πŸ’» [Code](https://github.com/ai2cm/ace)
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  - πŸ’¬ [Docs](https://ai2-climate-emulator.readthedocs.io/en/stable/)
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  - πŸ“‚ [All Models](https://huggingface.co/collections/allenai/ace-67327d822f0f0d8e0e5e6ca4)
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  Briefly, the strengths of ACE2-SOM are:
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- - accurate emulation equilibrium climates with CO2 concentrations ranging from the present-day to four times the present-day concentration, including
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- - the spatial pattern of temperature and precipitation changes between climates
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- - changes in precipitation extremes between climates
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- - realistic emulation non-stratospheric fields with gradually changing CO2 concentration between the present-day and four times the present-day concentration.
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  Some known weaknesses are:
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  - Sea-ice coverage, ocean heat transport, and ocean mixed layer depth are prescribed based on a present-day climatologies and therefore do not respond to changes in CO2. Presribed sea-ice in particular means projections with ACE2-SOM are missing an important feedback mechanism known to amplify warming in the polar regions.
 
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  **Disclaimer: ACE models are research tools and should not be used for operational climate predictions.**
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+ ACE2-SOM is the first model we have trained to emulate the sensitivity of climate to substantial increases in carbon dioxide. It consists of ACE2 coupled to a highly simplified physics-based ocean mode, called a slab ocean model (SOM), and is trained on equilibrium-climate output from GFDL's SHiELD model coupled to an identical slab ocean with carbon dioxide concentrations of the present-day, double the present-day, and quadruple the present-day (1xCO2, 2xCO2, and 4xCO2).
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  Quick links:
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+ - πŸ“ƒ [Paper (pre-print coming soon)]()
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  - πŸ’» [Code](https://github.com/ai2cm/ace)
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  - πŸ’¬ [Docs](https://ai2-climate-emulator.readthedocs.io/en/stable/)
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  - πŸ“‚ [All Models](https://huggingface.co/collections/allenai/ace-67327d822f0f0d8e0e5e6ca4)
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  Briefly, the strengths of ACE2-SOM are:
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+ - accurate emulation of equilibrium climates with CO2 concentrations ranging from 1xCO2 to 4xCO2, including changes in the spatial patterns of temperature and precipitation, and changes in precipitation extremes between climates
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+ - realistic emulation of surface and lower-to-middle atmosphere fields with gradually changing CO2 concentration between 1xCO2 and 4xCO2.
 
 
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  Some known weaknesses are:
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  - Sea-ice coverage, ocean heat transport, and ocean mixed layer depth are prescribed based on a present-day climatologies and therefore do not respond to changes in CO2. Presribed sea-ice in particular means projections with ACE2-SOM are missing an important feedback mechanism known to amplify warming in the polar regions.
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inference_config.yaml ADDED
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+ experiment_dir: /output_directory
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+ n_forward_steps: 400
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+ forward_steps_in_memory: 50
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+ checkpoint_path: /ace2_som_ckpt.tar
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+ logging:
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+ log_to_screen: true
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+ log_to_wandb: false
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+ log_to_file: true
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+ project: ace
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+ initial_condition:
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+ path: /initial_conditions/1xCO2/ic.nc
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+ start_indices:
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+ times:
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+ - "2032-01-01T00:00:00"
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+ forcing_loader:
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+ dataset:
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+ data_path: /forcing_data/1xCO2
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+ n_repeats: 1 # Use this to extend the four years of forcing data to the desired length
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+ num_data_workers: 4
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+ data_writer:
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+ save_prediction_files: true
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+ save_monthly_files: false
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+ names: ["TMP2m", "VGRD10m", "PRATEsfc"]
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