Update README.md

README.md

@@ -1,6 +1,6 @@
 # Deep learning of systematic sea ice model errors from data assimilation increments
 
-This repository contains code and data relating to work as part of the [M2LInES](https://m2lines.github.io) project, which involves developing climate model parameterizations using machine learning, in order to reduce systematic model biases. Here we use Convolutional Neural Networks to derive a mapping from model state variables to sea ice concentration analysis increments from an ice-ocean data assimilation (see [Zhang et al., 2021](https://journals.ametsoc.org/view/journals/clim/34/6/JCLI-D-20-0469.1.xml) for details on the data assimilation experiment). The model which this study has been applied to is the Geophysical Fluid Dynamics Laboratory (GFDL) Seamless system for Prediction and EArth system Research (SPEAR) model. A manuscript detailing this work is in preparation, and will be submitted to the Journal of Advancements in Model Earth Systems (JAMES) in due course.
+This repository contains code and data relating to the article [Gregory et al., 2023](https://doi.org/10.48550/arXiv.2304.03832), which is part of the larger [M2LInES](https://m2lines.github.io) project. M2LInES involves developing climate model parameterizations using machine learning, in order to reduce systematic model biases. Here we use Convolutional Neural Networks to derive a mapping from model state variables to sea ice concentration analysis increments from an ice-ocean data assimilation experiment. The model which this study has been applied to is the Geophysical Fluid Dynamics Laboratory (GFDL) Seamless system for Prediction and EArth system Research (SPEAR) model.
 
 ### Offline learning