A study by German scientists from Jena and Hamburg, published today in the journal Nature, shows that artificial intelligence (AI) can substantially improve our understanding of the climate and the Earth system. Especially the potential of deep learning has only partially been exhausted so far. In particular, complex dynamic processes such as hurricanes, fire propagation, and vegetation dynamics can be better described with the help of AI. As a result, climate and Earth system models will be improved, with new models combining artificial intelligence and physical modeling.
In the past decades mainly static attributes have been investigated using machine learning approaches, such as the distribution of soil properties from the local to the global scale.
Anomalies in the CO2 exchange on land during El Niño years, based on AI-upscaled FLUXNET data. Radiation anomalies are shown in red, temperature anomalies in green and water anomalies in blue.
Graphik: Martin Jung, MPI-BGC
For some time now, it has been possible to tackle more dynamic processes by using more sophisticated deep learning techniques. This allows for example to quantify the global photosynthesis on land with simultaneous consideration of seasonal and short term variations.
Deducing underlying laws from observation data
“From a plethora of sensors, a deluge of Earth system data has become available, but so far we've been lagging behind in analysis and interpretation,” explains Markus Reichstein, managing director of the Max Planck Institute for Biogeochemistry in Jena, directory board member of the Michael-Stifel-Center Jena (MSCJ) and first author of the publication.
“This is where deep learning techniques become a promising tool, beyond the classical machine learning applications such as image recognition, natural language processing or AlphaGo” adds co-author Joachim Denzler from the Computer Vision Group of the Friedrich Schiller University Jena and member of MSCJ.
Examples for application are extreme events such as fire spreads or hurricanes, which are very complex processes influenced by local conditions but also by their temporal and spatial context. This also applies to atmospheric and ocean transport, soil movement and vegetation dynamics, some of the classic topics of Earth system science.
Artificial intelligence to improve climate and Earth system models
However, deep learning approaches are difficult. All data-driven and statistical approaches do not guarantee physical consistency per se, are highly dependent on data quality, and may experience difficulties with extrapolations. Besides, the requirement for data processing and storage capacity is very high.
The publication discusses all these requirements and obstacles and develops a strategy to efficiently combine machine learning with physical modeling. If both techniques are brought together, so-called hybrid models are created. They can for example be used for modeling the motion of ocean water to predict sea surface temperature.
While the temperatures are modelled physically, the ocean water movement is represented by a machine learning approach. “The idea is to combine the best of two worlds, the consistency of physical models with the versatility of machine learning, to obtain greatly improved models,” Markus Reichstein further explains.
The scientists contend that detection and early warning of extreme events as well as seasonal and long-term prediction and projection of weather and climate will strongly benefit from the discussed deep-learning and hybrid modelling approaches.
Prof. Dr. Markus Reichstein
Tel: +49 (0)3641 57 6200
Deep learning and process understanding for data-driven Earth system science
Reichstein M., Camps-Valls G., Stevens B., Jung M., Denzler J. Cavalhais N., Prabhat (2019)
Nature xx, doi: 10.1038/s41586-019-0912-1
https://www.bgc-jena.mpg.de/bgi/index.php/Main/HomePage Webpage of the Reichstein department at MPI-BGC
http://www.inf-cv.uni-jena.de/denzler Webpage of Prof. Denzler at FSU Jena
Dr. Eberhard Fritz | Max-Planck-Institut für Biogeochemie
Welcome Committee for Comets
19.07.2019 | Technische Universität Braunschweig
Sea level rise: West Antarctic ice collapse may be prevented by snowing ocean water onto it
18.07.2019 | Potsdam-Institut für Klimafolgenforschung
Adjusting the thermal conductivity of materials is one of the challenges nanoscience is currently facing. Together with colleagues from the Netherlands and Spain, researchers from the University of Basel have shown that the atomic vibrations that determine heat generation in nanowires can be controlled through the arrangement of atoms alone. The scientists will publish the results shortly in the journal Nano Letters.
In the electronics and computer industry, components are becoming ever smaller and more powerful. However, there are problems with the heat generation. It is...
Scientists have visualised the electronic structure in a microelectronic device for the first time, opening up opportunities for finely-tuned high performance electronic devices.
Physicists from the University of Warwick and the University of Washington have developed a technique to measure the energy and momentum of electrons in...
Scientists at the University Würzburg and University Hospital of Würzburg found that megakaryocytes act as “bouncers” and thus modulate bone marrow niche properties and cell migration dynamics. The study was published in July in the Journal “Haematologica”.
Hematopoiesis is the process of forming blood cells, which occurs predominantly in the bone marrow. The bone marrow produces all types of blood cells: red...
For some phenomena in quantum many-body physics several competing theories exist. But which of them describes a quantum phenomenon best? A team of researchers from the Technical University of Munich (TUM) and Harvard University in the United States has now successfully deployed artificial neural networks for image analysis of quantum systems.
Is that a dog or a cat? Such a classification is a prime example of machine learning: artificial neural networks can be trained to analyze images by looking...
An international research group led by scientists from the University of Bayreuth has produced a previously unknown material: Rhenium nitride pernitride. Thanks to combining properties that were previously considered incompatible, it looks set to become highly attractive for technological applications. Indeed, it is a super-hard metallic conductor that can withstand extremely high pressures like a diamond. A process now developed in Bayreuth opens up the possibility of producing rhenium nitride pernitride and other technologically interesting materials in sufficiently large quantity for their properties characterisation. The new findings are presented in "Nature Communications".
The possibility of finding a compound that was metallically conductive, super-hard, and ultra-incompressible was long considered unlikely in science. It was...
24.06.2019 | Event News
29.04.2019 | Event News
17.04.2019 | Event News
19.07.2019 | Physics and Astronomy
19.07.2019 | Physics and Astronomy
19.07.2019 | Earth Sciences