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Updating project links
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‎_pages/home.md‎

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alt: "Global temperature map"
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title: "ClimateBench"
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excerpt: "A benchmark dataset for the emulation of full-complexity climate models."
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url: "/docs/configuration/"
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url: "/projects/climatebench_app/"
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btn_class: "btn--primary"
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btn_label: "Learn more"
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- image_path: /assets/images/shiptracks_small.jpg
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alt: "Shiptracks"
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title: "Detecting ship tracks"
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excerpt: "Using machine learning to automatically detect the brightening effect that shipping can have on clouds."
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url: "/docs/layouts/"
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url: "/projects/shiptracks"
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btn_class: "btn--primary"
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btn_label: "Learn more"
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- image_path: /assets/images/emulator_schematic.svg
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alt: "Emulator schematic"
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title: "Model Emulation for Calibration"
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excerpt: "Developing climate model emulators for better parameter estimation and calibration."
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url: "/docs/license/"
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url: "/projects/emulation/"
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btn_class: "btn--primary"
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btn_label: "Learn more"
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---

‎projects/_posts/0000-01-01-climatebench_app.md‎

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teaser: "assets/images/climatebench.png"
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# featured_figure:
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# image: "https://www.science.org/cms/10.1126/sciadv.aay4740/asset/5f0263bd-fc33-4dc6-87df-46a19f3ab895/assets/graphic/aay4740-f2.jpeg"
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learn_more: "https://doi.org/10.1126/sciadv.aay4740"
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code: "http://github.com/maikejulie/plottingAEPs"
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data: "http://github.com/maikejulie/plottingAEPs"
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people:
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- name: "Duncan Watson-Parris"
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cal: true
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- name: "Stephanie Dutkiewicz"
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- name: "Christopher Hill"
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- name: "Gael Forget"
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learn_more: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002954
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code: http://github.com/duncanwp/climatebench
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data: https://doi.org/10.5281/zenodo.5196512
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tags:
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- emulation
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- climatebench
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- model
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---
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Easily explore future warming under different emissions scenarios using an online ClimateBench emulator as developed in
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[Watson-Parris et al. 2022](https://doi.org/10.1029/2021MS002954). See the [emulation](emulation) project page for more
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[Watson-Parris et al. 2022](https://doi.org/10.1029/2021MS002954). See the [emulation](/projects/emulation) project page for more
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details on the scientific and societal uses of such emulators.
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<div id="climatebench">
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<iframe src="https://duncanwp-climatebench-app-streamlit-app-gr3ej2.streamlit.app?embedded=true" height="600">
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<iframe src="https://duncanwp-climatebench-app-streamlit-app-6rqkhf.streamlit.app?embedded=true" width="600" height="400">
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<p>Your browser does not support iframes.</p>
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</iframe>
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*Note*, these emulator has been simplified and compressed for easy browsing. Only global total emissions of aerosol
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are considered and it has not been rigourously tested or benchmarked. **Please don't rely on it for any realistic
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estimate of future warming!**
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## Data
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- The climatebench database used to train this emulator be found on [Zenodo](https://doi.org/10.5281/zenodo.5196512)
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‎projects/_posts/0000-01-01-emulation.md‎

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---
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title: Climate model emulation
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header:
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teaser: "assets/images/emulator_schematic.svg"
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code: https://github.com/duncanwp/ESEm
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data: https://doi.org/10.5281/zenodo.3856644
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tags:
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- emulation
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- model
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simulation outputs with associated forcing data processed in to a consistent format from a variety of experiments
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performed for CMIP6. Benchmark models and evaluation criteria are described in the full [publication](https://doi.org/10.1029/2021MS002954).
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*New!* You can now interact with an online ClimateBench emulator [here](climatebench_app)!
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*New!* You can now interact with an online ClimateBench emulator [here](/projects/climatebench_app)!
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## Relevant papers
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- **Watson-Parris, D.**, Rao, Y., Olivié, D., Seland, Ø., ... "ClimateBench v1.0: A benchmark for data-driven climate projections".
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Jarvis, M. , Korenaga, J., Viezzer, E. & Vinko S. M. "Accelerating
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simulations in science with deep neural architecture search".
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*Accepted at Machine Learning: Science and Technology:* <https://arxiv.org/abs/2001.08055>
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## Code
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- [ESEm](https://github.com/duncanwp/ESEm)
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- [ClimateBench](https://github.com/duncanwp/ClimateBench)
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## Data
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- ClimateBench: <https://doi.org/10.5281/zenodo.5196512>
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- Example Perturbed Parameter Ensemble (Black Carbon): <https://doi.org/10.5281/zenodo.3856644>

‎projects/_posts/0000-01-01-pocs.md‎

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---
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title: Pockets of Open Cells
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header:
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teaser: "assets/images/example_poc.png"
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code: https://github.com/climate-processes/poc-detection
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data: https://doi.org/10.5281/zenodo.4451345
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tags:
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- shallow-clouds
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- image-segmentation
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## Relevant papers
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- **Watson-Parris, D**., Sutherland, S. A., Christensen, M. W. &
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- **Watson-Parris, D**., Sutherland, S. A., Christensen, M. W. &
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Stier, P. "A large-scale analysis of pockets of open cells and their
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radiative impact". *Submitted Geophysical Research Letters:*
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<https://doi.org/10.1002/essoar.10501877.1>
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radiative impact". *Geophysical Research Letters, 48:* <https://doi.org/10.1029/2020GL092213>
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- **Watson-Parris, D.**, Sutherland, S., Christensen, M., Caterini,
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A., Sejdinovic, D., Stier, P. "Detecting anthropogenic cloud
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perturbations with deep learning" *Climate Change: How Can AI Help?
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workshop at ICML 2019, Long Beach, California:*
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<https://arxiv.org/abs/1911.13061>
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## Code
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- <https://github.com/climate-processes/poc-detection>
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## Data
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- The hand-labelled tracks and images used for training can be found here: <https://imiracli-data.s3.us-east-2.
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amazonaws.com/public/POC+training.zip>
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- The resulting database of 8,491 POCs can be found here: <https://doi.org/10.5281/zenodo.4451345>

‎projects/_posts/0000-01-01-shiptrack_map.md‎

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title: Ship-track viewer
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header:
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teaser: "assets/images/shiptracks_small.jpg"
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data: https://doi.org/10.5281/zenodo.7038702
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code: https://github.com/duncanwp/shiptrack-detection
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learn_more: https://www.pnas.org/doi/10.1073/pnas.2206885119
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tags:
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- shallow-clouds
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- image-segmentation
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Easily browse all the ship-tracks detected in [Watson-Parris et al. 2022](https://www.pnas.org/doi/10.1073/pnas.2206885119) using our machine learning
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algorithm. Each track as an associated MODIS timestamp so you can easily match with the underlying data. See the
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[ship-track detection](shiptracks) page for more details on their importance and effects.
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[ship-track detection](/projects/shiptracks) page for more details on their importance and effects.
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<div id="map">
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<div id="info-box">
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*Note*, these tracks have been simplified and compressed for easy browsing. They are also not always very obvious in RGB imagery shown above, but the detection algorithm uses a microphysical composite as described in the paper. If you need the exact tracks used in our analysis please see the links below.
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## Data
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- The shiptrack database can be found on [Zenodo](https://doi.org/10.5281/zenodo.7038702)
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- The raw inference masks are much bigger but also freely available [here](https://catalogue.ceda.ac.uk/uuid/0d88dc06fd514e8199cdd653f00a7be0)
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‎projects/_posts/0000-01-01-shiptracks.md‎

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title: Detecting Ship-tracks
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header:
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teaser: "assets/images/shiptracks_small.jpg"
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code: https://github.com/duncanwp/shiptrack-detection
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data: https://catalogue.ceda.ac.uk/uuid/0d88dc06fd514e8199cdd653f00a7be0
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tags:
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- shallow-clouds
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- image-segmentation
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perturbations with deep learning" *Climate Change: How Can AI Help?
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workshop at ICML 2019, Long Beach, California:*
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<https://arxiv.org/abs/1911.13061>
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## Code
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- [Shiptrack detection](https://github.com/duncanwp/shiptrack-detection)
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## Data
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- https://catalogue.ceda.ac.uk/uuid/0d88dc06fd514e8199cdd653f00a7be0

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