Ian McHarg Medal Lecture by Mikhail Kanevski & ESSI Division Outstanding ECS Award Lecture by Christopher Kadow


Ian McHarg Medal Lecture by Mikhail Kanevski & ESSI Division Outstanding ECS Award Lecture by Christopher Kadow
Convener: Jens Klump | Co-conveners: Jane Hart, Federico Amato, Lesley Wyborn
| Mon, 23 May, 19:00–20:00 (CEST)
Room 0.31/32

Session assets

Presentations: Mon, 23 May | Room 0.31/32

Chairpersons: Kerstin Lehnert, Lesley Wyborn, Kaori Otsu
ESSI Division Outstanding ECS Award Lecture
On-site presentation
Christopher Kadow, David M. Hall, Uwe Ulbrich, Igor Kröner, Sebastian Illing, and Ulrich Cubasch

Today's climate science is being driven by IT more than ever. Earth system models on high-performance computers (HPC) are common tools for researching the past and projecting it into the future. In addition to that, statistical modelling is reborn thanks to modern computer architectures equipped with artificial intelligence (from ensemble to deep learning). Future advances in machine learning will also shape climate research through analysis tools, prediction techniques, signal and event classification, post-processing, Model Output Statistics (MOS), evaluation and verification, etc. This presentation will look at nowadays research about the future (part one) and the past (part two) of our climate system using AI/ML ideas and technologies in combination with numerical climate models - from two publications accordingly. A special focus will be on the importance of climate science, where the needs are, and how to choose the AI/ML hammer wisely:

(1) FUTURE: Derived from machine (ensemble) learning and bagging, a new hybrid climate prediction technique called 'Ensemble Dispersion Filter' is developed. It exploits two important climate prediction paradigms: the ocean's heat capacity and the advantage of the ensemble mean. The Ensemble Dispersion Filter averages the ocean temperatures of the ensemble members every three months, uses this ensemble mean as a restart condition for each member, and further executes the prediction. The evaluation  shows that the Ensemble Dispersion Filter results in a significant improvement in the predictive skill compared to the unfiltered reference system. Even in comparison with prediction systems of a larger ensemble size and higher resolution, the Ensemble Dispersion Filter system performs better. In particular, the prediction of the global average temperature of the forecast years 2 to 5 shows a significant skill improvement.

Kadow, C., Illing, S., Kröner, I., Ulbrich, U., and Cubasch, U. (2017), Decadal climate predictions improved by ocean ensemble dispersion filtering, J. Adv. Model. Earth Syst., 9, 11381149, doi:10.1002/2016MS000787. 

(2) PAST: Nowadays climate change research relies on climate information of the past. Historic climate records of temperature observations form global gridded datasets like HadCRUT4, which is investigated e.g. in the IPCC reports. However, record combining data-sets are sparse in the past. Even today they contain missing values. Here we show that artificial intelligence (AI) technology can be applied to reconstruct these missing climate values. We found that recently successful image inpainting technologies, using partial convolutions in a CUDA accelerated deep neural network, can be trained by 20CR reanalysis and CMIP5 experiments. The derived AI networks are capable to independently reconstruct artificially trimmed versions of 20CR and CMIP5 in grid space for every given month using the HadCRUT4 missing value mask. The evaluation reaches high temporal correlations and low errors for the global mean temperature.

Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408–413 (2020).

How to cite: Kadow, C., Hall, D. M., Ulbrich, U., Kröner, I., Illing, S., and Cubasch, U.: Artificial Intelligence and Earth System Modeling - revisiting Research of the Past and Future, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12895,, 2022.

Ian McHarg Medal Lecture
Presentation form not yet defined
Mikhail Kanevski

Geo- and environmental sciences produce a wide variety and numerous data which are extensively used both in fundamental research on Earth processes and in important real-life decision-making. Most natural phenomena are non-linear, multivariate, highly variable and correlated at many spatio-temporal scales. Analysis and treatment of such complex data and their integration/assimilation with science-based models is a difficult problem. Contemporary machine learning (ML) proposes an important set of effective approaches to address this problem at all phases of the study.

Nowadays, Geosciences are one of the major customers of ML ideas and technologies. To a large degree, it is connected to the local and global challenges facing humanity: sustainable development, biodiversity, social and natural hazards and risks, meteo- and climate forecasting, remote sensing Earth observation, etc. Despite being theoretically a universal modelling tool, the success of ML applications significantly depends on the problem formulation, quantity and quality of data and objectives of the study. Therefore, an efficient application of ML demands a good knowledge of the phenomena under study and a profound understanding of learning algorithms which can be achieved in close collaboration between experts in the corresponding domains.

In the current presentation, the study of geo- and environmental data using different machine learning algorithms is reviewed. A problem-oriented approach, which follows a generic data-driven methodology, is applied. The methodology consists of several important steps, in particular, optimization of monitoring and data collection, comprehensive exploratory data analysis and visualization, feature engineering and relevant variables selection, modelling with careful validation and testing, explanation and communication of the results. Advanced experimentation with data by using different supervised and unsupervised ML algorithms helps in better understanding of original data and constructed input feature space, obtaining more reliable and robust results and making intelligent decisions. The presentation is accompanied by simulated and real data case studies from natural hazards (avalanches, forest fires, landslides), environmental risks (pollution) and renewable energy assessment. In conclusion, some general remarks and future perspectives are discussed.


How to cite: Kanevski, M.: On Machine Learning from Environmental Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6482,, 2022.