Merge pull request #106 from johannag126/master

update sections

content/blog/atmosphere.md

@@ -22,4 +22,4 @@ Much of the uncertainty in climate-model projections for surface precipitation a
 
 ##### Parameterization of the boundary layer
 ###### People involved: Alexander Connolly, Pierre Gentine
-Boundary layer turbulence parameterization remains a major source of uncertainties in climate models, including for low-level clouds. We aim to develop a new approach to the boundary layer parameterization by targeting high-order closure terms in the turbulence representation, leveraging Large-Eddy Simulations and machine learning/symbolic regression. 
+Boundary layer turbulence parameterization remains a major source of uncertainties in climate models, including for low-level clouds. We aim to develop a new approach to the boundary layer parameterization by targeting high-order closure terms in the turbulence representation, leveraging Large-Eddy Simulations and machine learning/symbolic regression.

content/blog/climate.md

@@ -10,8 +10,7 @@ heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg'
 
 Comprehensive climate models typically include **atmosphere, ocean, sea ice, and land components** and the **coupling** between them. These models can also incorporate **land ice, atmospheric chemistry and terrestrial and marine biogeochemistry**, enabling carbon cycle simulations. Early climate models were developed in the 1970s and have increased in complexity over the years, with more process interactions, more sophisticated parameterizations of subgridscale processes, and higher spatial resolution being incorporated over time. These models have been skillful at predicting anthropogenic climate change, and even early models accurately simulated aspects of the spatial pattern of warming. However, there is still considerable uncertainty associated with model structure, and climate models which incorporate different parameterizations can differ greatly in many characteristics of future projected change. Because of this, it is imperative that there are continued developments and improvements of these modeling systems. Bringing new approaches, such as **Machine Learning**, to this challenge has the potential to rapidly accelerate progress.
 
-Studies across M²LInES are using **scientific and interpretable Machine Learning** to gain new insight on parameterization development across the atmosphere, ocean, and sea ice systems. Development and testing of these parameterizations is underway in component model configurations and work is planned to incorporate these into a number of climate models. This includes, among others, improved parameterizations of: 
-* The simulated conductive heat fluxes through sea ice ([Zampieri et al, 2024](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GL106760)), 
-* Ocean mixing processes, 
-* Moist convection in the atmosphere 
-
+Studies across M²LInES are using **scientific and interpretable Machine Learning** to gain new insight on parameterization development across the atmosphere, ocean, and sea ice systems. Development and testing of these parameterizations is underway in component model configurations and work is planned to incorporate these into a number of climate models. This includes, among others, improved parameterizations of:
+* The simulated conductive heat fluxes through sea ice ([Zampieri et al, 2024](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GL106760)),
+* Ocean mixing processes,
+* Moist convection in the atmosphere

content/blog/climateprocess.md

@@ -10,7 +10,7 @@ heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg'
 
 Despite drastic improvements in climate model development, current simulations have difficulty capturing the interactions among different processes in the atmosphere, oceans, and ice and how they affect the Earth’s climate; this can hinder projections of temperature, rainfall, and sea level.
 
-**M²LInES will be focusing on understanding these key climate processes using two types of data:** 
+**M²LInES will be focusing on understanding these key climate processes using two types of data:**
 
 ### High-resolution simulation and observations
 * Atmospheric convection and clouds (O’Gorman, Mooers, Yuval) (see [Atmosphere](/blog/atmosphere))

content/blog/coupledphysics.md

@@ -8,13 +8,13 @@ heroSubHeading: ''
 heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg'
 ---
 
-The earth system is complex with many different distinct subsystems interplaying with each other. Our approach in modeling the earth system is generally to develop separate models for each component and to include additional couplers that model their interactions. For example, Earth System Models typically include models of: 
+The earth system is complex with many different distinct subsystems interplaying with each other. Our approach in modeling the earth system is generally to develop separate models for each component and to include additional couplers that model their interactions. For example, Earth System Models typically include models of:
 * Atmosphere
 * Land
 * Land Ice
 * Ocean
 * River Runoff
-* Sea Ice 
+* Sea Ice
 * Wave
 
 In addition to improving each component independently, it is also important to better understand and model the coupling of components.
@@ -37,9 +37,8 @@ Take the air-sea interaction as an example. The large-scale dynamics of coupled
 * ENSO represents complex feedback between the tropical Pacific Ocean and the atmosphere, which significantly influences global climate patterns.
 
 ### Climate Regulation and Teleconnections:
-* These interactions contribute to teleconnections, where climate changes in one region can affect distant regions. 
-* Large-scale coupling helps balance heat and energy across latitudes, playing a critical role in Earth's climate regulation.  
+* These interactions contribute to teleconnections, where climate changes in one region can affect distant regions.
+* Large-scale coupling helps balance heat and energy across latitudes, playing a critical role in Earth's climate regulation.
 
 
 In M²LInES, there are projects using observational data to better model these air-sea fluxes, in collaboration with scientists from the NSF STC LEAP.  Several other projects also focus on the study of large-scale processes, which enhance our understanding of climate variability and change. This ultimately leads to improvements in climate model accuracy and better predictions for the earth system as a whole.
-

content/blog/projectgoals.md

@@ -14,18 +14,18 @@ heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg'
 
 Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations. Another error source arises from the climate models' numerics, which include spatial and temporal discretizations and numerical dissipation. These errors can accumulate or compensate for each other, making improving climate models intricate and requiring a range of approaches.
 
-To tackle these biases and reduce the potential sources of error, **M²LInES’ strategy is to leverage advances in machine learning & "interrogate” the data to**: 
+To tackle these biases and reduce the potential sources of error, **M²LInES’ strategy is to leverage advances in machine learning & "interrogate” the data to**:
 
 1. Develop data-informed, interpretable & generalizable subgrid physics models (ocean, ice, atm);
 2. Produce error corrections derived from observational products for climate model components.
 
 By improving model physics, this strategy ensures a more faithful representation of feedbacks and sensitivities under different climates.
 
 ### Our vision
-💡 **_Generate new scientific knowledge in climate science_** from innovative use of data and machine learning: e.g., which physics did we overlook that might be important for scale interaction?  
+💡 **_Generate new scientific knowledge in climate science_** from innovative use of data and machine learning: e.g., which physics did we overlook that might be important for scale interaction?
 
-💻 **_Accelerate end-to-end, from development to delivery, for a new generation of climate models_**; this includes learning and testing parameterizations in global frameworks to tackle significant biases in climate models.  
+💻 **_Accelerate end-to-end, from development to delivery, for a new generation of climate models_**; this includes learning and testing parameterizations in global frameworks to tackle significant biases in climate models.
 
-⚙️ **_Drive a change of direction in the field by building models and tools centered around data-driven methods_** for the community to advance climate science discovery.  
+⚙️ **_Drive a change of direction in the field by building models and tools centered around data-driven methods_** for the community to advance climate science discovery.
 
 👩‍🏫 **_Enable a new generation of versatile scientists working at the interface of machine learning, climate science & numerical modeling._**

content/blog/seaice.md

@@ -22,7 +22,7 @@ Although sea ice models are continuously improving, there is still work to do. W
 <img src="/images/research/SPEAR.png" style="width: 30vw; padding-bottom: 30px; padding-top: 0px">
 </center>
 <p style="text-align: left;"><small><b>Figure:</b> Sea ice mean state (1979-2010) of the GFDL SPEAR large ensemble (historical simulation). Thin grey lines represent individual ensemble members and the black line is the ensemble mean. Shown for (top) Arctic, (bottom) Antarctic.</small></p>
-Biases in the sea ice mean state are due to a myriad of factors, and are often difficult to isolate. Biases may originate from missing physics within the sea ice model itself (e.g., absence of a sea ice ridging scheme), or from errors in sea ice model parameters (e.g., sea ice albedo or snow thermal conductivity). Furthermore sea ice is strongly coupled to the atmosphere and ocean, hence biases in either one of these components can imprint on the sea ice.  
+Biases in the sea ice mean state are due to a myriad of factors, and are often difficult to isolate. Biases may originate from missing physics within the sea ice model itself (e.g., absence of a sea ice ridging scheme), or from errors in sea ice model parameters (e.g., sea ice albedo or snow thermal conductivity). Furthermore sea ice is strongly coupled to the atmosphere and ocean, hence biases in either one of these components can imprint on the sea ice.
 <br/><br/>
 M²LInES is working to improve sea ice model biases by developing new data-driven sea ice model parameterization schemes. Recent highlights include work from M²LInES members <b>Lorenzo Zampieri</b> and <b>Marika Holland</b>, who used in-situ data from the recent MOSAiC expedition ( <b>M</b>ultidisciplinary drifting <b>O</b>bservatory for the <b>S</b>tudy of <b>A</b>rctic <b>C</b>limate) to derive a new parametric correction to sea ice and snow conductive heat fluxes within the CICE sea ice model. This work showed that simulations which do not account for local-scale sea ice and snow heterogeneity can under-estimate conductive heat fluxes through sea ice by up to 10% (see this work in <a href="https://doi.org/10.1029/2023GL106760" target="_blank">GRL</a>).
 <br/><br/>

content/jobs/_index.md

@@ -5,13 +5,12 @@ heroSubHeading: ''
 heroBackground: 'images/SouthAtlantic.A2002157.1055.250m.jpg'
 ---
 
-Updated on 01/31/2025 - The links for applications will be updated as they become available.
 
-M²LInES affirms the value of differing perspectives in Sciences. As such, we strongly encourage applications from women, racial and ethnic minorities, and other individuals who are under-represented in the profession, across color, creed, race, ethnic and national origin, physical ability, gender and sexual identity, or any other legally protected basis.
+Updated on 01/31/2024 - The links for applications will be updated as they become available.
+
 
 ### Princeton University/GFDL
 
 * Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning. [Apply here](https://puwebp.princeton.edu/AcadHire/apply/application.xhtml?listingId=37582)
 
 * Postdoctoral researcher or more senior scientist for Ocean Surface Boundary Layer Mixing Parameterizations using Machine Learning. [Apply here](https://puwebp.princeton.edu/AcadHire/apply/application.xhtml?listingId=36662)
-

content/news/2411Shi.md

@@ -9,4 +9,3 @@ images: ['images/news/2411-Shi.png']
 link: 'https://doi.org/10.1038/s41558-024-01958-8'
 ---
 In this [paper](https://doi.org/10.1038/s41558-024-01958-8), Jia-Rui Shi and co-authors present scientific evidence indicating that a human-caused signal has already emerged in the seasonal cycle amplitude of Sea Surface Temperature (SST), distinct from the background noise of natural variability. The geographical patterns of the forced SST seasonal cycle change reveal two notable features: an increase in mid-latitudes of the Northern Hemisphere, which is associated with alterations in mixed-layer depth, and a robust dipole pattern in the Southern Hemisphere mid-latitudes, primarily driven by changes in surface winds. These observed changes in the SST seasonal cycle have significant implications for marine ecosystems.
-

content/news/2411Zampieri.md

@@ -8,4 +8,4 @@ thumbnail:  'images/news/2411-Zampieri.png'
 images: ['images/news/2411-Zampieri.png']
 link: 'https://doi.org/10.5194/tc-18-4687-2024'
 ---
-Lorenzo Zampieri contributed to a [new paper](https://doi.org/10.5194/tc-18-4687-2024), published in the Cryosphere and led by Francesco Cocetta at the CMCC Foundation, where the representation of Arctic sea ice in ocean and sea ice reanalyses is evaluated against observations from different sources. In the upcoming years, these reanalyses will play an important role when used as training datasets from data-driven models of sea ice, which motivates evaluation studies such as this one.
+Lorenzo Zampieri contributed to a [new paper](https://doi.org/10.5194/tc-18-4687-2024), published in the Cryosphere and led by Francesco Cocetta at the CMCC Foundation, where the representation of Arctic sea ice in ocean and sea ice reanalyses is evaluated against observations from different sources. In the upcoming years, these reanalyses will play an important role when used as training datasets from data-driven models of sea ice, which motivates evaluation studies such as this one.

content/news/2412AGU.md

@@ -8,4 +8,4 @@ thumbnail:  'images/newlogo.png'
 images: ['images/newlogo.png']
 link: 'https://mailchi.mp/235bd85ebd4d/m2lines-dec2024'
 ---
-Find all the posters and talks from our team at the AGU 24 conference in our [December newsletter](https://mailchi.mp/235bd85ebd4d/m2lines-dec2024). If you are going, make sure to add them to your schedule.
+Find all the posters and talks from our team at the AGU 24 conference in our [December newsletter](https://mailchi.mp/235bd85ebd4d/m2lines-dec2024). If you are going, make sure to add them to your schedule.

content/news/2502Jupyterbook.md

@@ -14,6 +14,3 @@ Photo (credit: Will Chapman):  students at the Future Earth Research School on d
 
 
 The M²LInES team is proud to share this **[article](https://doi.org/10.21105/jose.00241)** and **[Jupyter Book](https://m2lines.github.io/L96_demo/intro.html)** published in the Journal of Open Science Education (JOSE) and led by Dhruv Balwada. Developed by our team, it aims to introduce Machine Learning (ML) methods to climate scientists and also climate modeling to machine learning experts. The book presents **a wide range of ML applications for climate modeling, focusing on hybrid AI+Physics methods using the Lorenz-96 model**. We hope this book can be used as a pedagogical tool for self-learning, a reference manual, or for teaching modules in an introductory class on ML or hybrid climate modeling.
-
-
-

content/news/2502YTCarlos.md

@@ -8,4 +8,4 @@ thumbnail:  'images/news/2502YTCarlos.png'
 images: ['images/news/2502YTCarlos.png']
 link: 'https://www.youtube.com/@cfgranda/playlists'
 ---
-If you are looking for clear material to understand statistics, probabilities, and mathematical tools for data science, check out the **[Youtube Channel](https://www.youtube.com/@cfgranda/playlists)** of Carlos Fernandez-Granda where he shares recordings of his lectures at NYU!
+If you are looking for clear material to understand statistics, probabilities, and mathematical tools for data science, check out the **[Youtube Channel](https://www.youtube.com/@cfgranda/playlists)** of Carlos Fernandez-Granda where he shares recordings of his lectures at NYU!

content/news/2502Zhang.md

@@ -8,4 +8,4 @@ thumbnail:  'images/news/2502Zhangetal.png'
 images: ['images/news/2502Zhangetal.png']
 link: 'https://doi.org/10.48550/arXiv.2411.01138'
 ---
-This [study](https://doi.org/10.48550/arXiv.2411.01138), led by Cheng Zhang, improves machine-learned models for simulating ocean mesoscale eddies **by addressing artifacts near coastlines caused by out-of-sample predictions using convolutional neural networks (CNN)**. Comparing two different strategies for treating Boundary Conditions (BCs) in CNN models - zero and replicate padding - they show that replicate padding significantly reduces these artifacts, enhancing the accuracy and stability of ocean modeling near complex coastal regions.
+This [study](https://doi.org/10.48550/arXiv.2411.01138), led by Cheng Zhang, improves machine-learned models for simulating ocean mesoscale eddies **by addressing artifacts near coastlines caused by out-of-sample predictions using convolutional neural networks (CNN)**. Comparing two different strategies for treating Boundary Conditions (BCs) in CNN models - zero and replicate padding - they show that replicate padding significantly reduces these artifacts, enhancing the accuracy and stability of ocean modeling near complex coastal regions.

content/pages/code-of-conduct/_index.md

@@ -12,12 +12,12 @@ _____
 
 - This code of conduct applies to all members of M²LInES without exception. We also expect external collaborators to comply with this code when interacting with M²LInES, and we extend guidelines (such as inclusion for authorship) to external collaborators.
 
-- Key aspects of this document: Inclusivity, professionalism and conduct, unethical and/or unacceptable behavior, authorship, scientific integrity, and conflicts of interest.
+- Key aspects of this document: Respect, professionalism and conduct, unethical and/or unacceptable behavior, authorship, scientific integrity, and conflicts of interest.
 
-- Management committee (Laure, Johanna, Alistair, Carlos, Ryan) will deal with any issues arising:
+- Management committee (Laure, Johanna, Alistair) will deal with any issues arising:
 
 
-#### Inclusivity
+#### Respect
 
 - Enjoyable, high-quality research can only be conducted when you **feel safe, secure, and supported**. All group members are thus dedicated to a **harassment-free experience for everyone, regardless of gender identity and expression, sexual orientation, disability, physical appearance, body size, race, age, and/or religion [^1] (or lack thereof), family status or socio-economic status**. The group members also recognize that some of those biases can be unconscious and creep into different aspects of Academic life and research, such as meetings, publications, citations, hiring, etc. Members should strive to consciously combat those biases and bring awareness to others.
 
@@ -41,11 +41,11 @@ _____
 
 - Meetings and interactions:
 
-	+ Be inclusive of all present, listen, invite input, be constructive.
+	+ Be considerate of all present, listen, invite input, be constructive.
 
 	+ Be mindful of dominant personalities, including yourself.
 
-	+ Employ appropriate language inclusive of all backgrounds.
+	+ Employ appropriate language respectful of all backgrounds.
 
 	+ Follow appropriate etiquette:
 		* In virtual meetings, raise hands in your video stream or use the software equivalent.