Working with observational data in the context of geophysics can be challenging, since we often have to deal with missing data. This requires imputation techniques in pre-processing to obtain data-mining-ready samples. Here, we present a convolutional neural network approach from the domain of deep learning to reconstruct complete information from sparse inputs. As data, we use various two-dimensional geospatial fields. To have consistent data over a sufficiently long time span, we favor to work with output from control simulations of two Earth System Models, namely the Flexible Ocean and Climate Infrastructure and the Community Earth System Model. Our networks can restore complete information from incomplete input samples with varying rates of missing data. Moreover, we apply a bottom-up sampling strategy to identify the most relevant grid points for each input feature. Choosing the optimal subset of grid points allows us to successfully reconstruct current fields and to predict future fields from ultra sparse inputs. As a proof of concept, we predict El Niño Southern Oscillation and rainfall in the African Sahel region from sea surface temperature and precipitation data, respectively. To quantify uncertainty, we compare corresponding climate indices derived from reconstructed versus complete fields.

In the domain of climate science, machine learning (ML) and in particular deep learning (DL) methods are known to be effective for identifying causally linked modes of climate variability as key to understand the climate system and to improve the predictive skills of forecast systems. To attribute climate events in a data-driven way, we need sufficient training data, which is often limited for real world measurements. The data science community provides standard data sets for many applications. As a new data set, we introduce a consistent and comprehensive collection of climate indices typically used to describe Earth System dynamics. Therefore, we use 1000-year control simulations from Earth System Models. The data set is provided as an open-source framework that can be extended and customized to individual needs. It allows users to develop new ML methodologies and to compare results to existing methods and models as benchmark.

The distribution of passively drifting particles within highly turbulent flows is a classic problem in marine sciences. The use of trajectory clustering on huge amounts of simulated marine trajectory data to identify main pathways of drifting particles has not been widely investigated from a data science perspective yet. In this paper, we propose a fast and computationally light method to efficiently identify main pathways in large amounts of trajectory data. It aims at overcoming some of the issues of probabilistic maps and existing trajectory clustering approaches. Our approach is evaluated against simulated larvae dispersion data based on a real-world model that have been produced as part of work in the marine science domain.