Cirrus clouds are key modulators of Earth's climate. Their dependencies on meteorological and aerosol conditions are among the largest uncertainties in global climate models. This work uses three years of satellite and reanalysis data to study the link between cirrus drivers and cloud properties. We use a gradient-boosted machine learning model and a Long Short-Term Memory (LSTM) network with an attention layer to predict the ice water content and ice crystal number concentration. The models show that meteorological and aerosol conditions can predict cirrus properties with $R^2 = 0.49$. Feature attributions are calculated with SHapley Additive exPlanations (SHAP) to quantify the link between meteorological and aerosol conditions and cirrus properties. For instance, the minimum concentration of supermicron-sized dust particles required to cause a decrease in ice crystal number concentration predictions is $2 \times 10^{-4}$ mg m\textsuperscript{-3}. The last 15 hours before the observation predict all cirrus properties.

Artificial intelligence holds vast potential to help solve a number of challenging problems in Earth system science, from improving prediction of severe weather events to increasing the efficiency of climate models. But as in all AI applications, the use of machine learning and other techniques in environmental science has the potential to introduce biases that could deepen inequities. The authors of a new paper published in the journal Environmental Data Science argue that researchers must develop ethical, responsible, and trustworthy approaches to applying AI in Earth system science to ensure that unintentional consequences do not worsen environmental and climate injustice. "It's really exciting to see all the ways researchers are finding to creatively apply artificial intelligence in weather, climate, and other environmental science research," said David John Gagne, a scientist at the National Center for Atmospheric Research (NCAR) and a paper co-author. "But we have a responsibility to ensure that we don't cause more harm than good."

The TAIAO project (Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science) will advance the state-of-the-art in environmental data science by developing new machine learning methods for time series and data streams that are able to deal with large quantities of big data in real time, which are tailored to deal with data collected on the New Zealand environment. We will build a new open source framework to implement machine learning on time series data, provide an open available repository with datasets to improve reproducibility in environmental data science, and build capability in fundamental and applied data science, accessible to all New Zealanders. This programme is a new collaboration between the Universities of Waikato, Auckland and Canterbury, Beca and MetService and includes world-leading data scientists, data engineers, and environmental scientists. We will work with regional councils, iwi and co-governance entities to implement the methods we develop to support governance and management decisions with analyses based on large volumes of data that they cannot currently process. We will also make use of our existing strong international collaborations to grow our own data science capabilities and attract top international researchers to work with us on challenging data science problems.