All scientific sessions at vEGU21 will be run in the new virtual PICO format (vPICO), modeled on the interactive touchscreen presentations which were used at in-person EGU Assemblys in Vienna. After a brief overview within each session a series of two-minute vPICOs will be presented. Afterwards there will be the opportunity to discuss further with the authors in chats. Workshops are held as virtual one-hour short courses.

For more information, visit the #vEGU21 website:

DKRZ contributions at  #vEGU21

In the session ESSI2.2: Find, access, share and use data across the globe: Infrastructure solutions for Earth System Sciences on April 27 from 11:00-12:30, DKRZ is involved with four vPICOs. Swati Gehlot will present the current status and challenges of the data management plan for PalMod-II in the vPICO PalMod-II Data Management Plan: A FAIR-inspired conceptual framework for data simulation, inter-comparison, sharing and publication which will take place from 11:31-11:13 a.m.

From 11:33-11:35 a.m., Andrea Lammert presents with the vPICO A Standard for the FAIR publication of Atmospheric Model Data developed by the AtMoDat Project the newly developed AtMoDat standard that aims to increase the FAIRness of various atmospheric model data published in repositories.

In the following session part "Tools and Services", Martina Stockhause shows from 11:39-11:41 a.m. in her vPICO CMIP6 data documentation and citation in IPCC's Sixth Assessment Report (AR6) how CMIP6 data usage is documented in IPCC WGI AR6 from three angles: technical implementation, collection of CMIP6 data usage information from the IPCC authors, and a report users’ perspective.

In the fourth contribution to the session, Ivonne Anders reports in her vPICO Generic concepts for organising data management in research projects from 11:49-11:51 a.m. on generic concepts for organizing data management in research projects, with an effort to separate them from general project management tasks.

On April 27 from 1:32-1:34 p.m. Kerstin Fieg will present within session CL1.2: Palaeoclimate modeling: from time-slices and sensitivity experiments to transient simulations into the future with her vPICO From the last interglacial to the future – new insights from modeling the last glacial-interglacial cycle in PalMod new insights from the project PalMod, which aims at filling the long-standing scientific gaps in our understanding of the dynamics and variability of the climate system during the last glacial-interglacial cycle.

The session ESSI3.3 on April 27 is dedicated to The evolving Open and FAIR ecosystem for Solid Earth and Environmental sciences: challenges, opportunities, and other adventures. In this session, Karsten Peters-von Gehlen evaluates from 13:47-13:49 in his vPICO Applying FAIRness evaluation approaches to (meta)data preserved at the World Data Center for Climate (WDCC): results, lessons learned, recommendations the FAIRness of datasets curated at the WDCC using automated and manual approaches and shows that there are very good reasons not to aim for full machine actionability of a repositories’ data holdings at any price, but to apply a combination of automated and manual FAIRness evaluation approaches.

Also in this session, from 13:51 to 13:53, Hannes Thiemann will introduce the NFDI4Earth consortium, which aims to improve data management in Earth system sciences as part of the National Research Data Infrastructure.

On April 28, from 13:30-15:00, the session ESSI3.7: Free and Open Source Software (FOSS) and Cloud-based Technologies to Facilitate Collaborative Science will take place. In the part "Cloud and High-Performance Computing" Marco Kulüke will report from 1:59-2:01 p.m. in the vPICO Transfer Data from NetCDF on Hierarchical Storage to Zarr on Object Storage: CMIP6 Climate Data Use Case about conversion of CMIP data from NetCDF to Zarr and its storage in DKRZ swift storage.

For the visualization of climate data, Marc Rautenhaus (University of Hamburg) and DKRZ visualizers, Michael Böttinger and Niklas Röber, will offer the  SC5.1/SSP5.3 Short Course: Data Visualization in Earth Science on April 29 from 9:00-10:00. With increasing data complexity and growing data volumes, effective and efficient data visualization for data analysis is becoming more important. The course will give an overview of commonly available visualization tools such as ParaView and Met.3D that are especially well suited to analyze earth science data sets.

Within the session AS4.2: High resolution modelling of weather and climate on April 29 from 9:16-9:18 a.m., Julia Duras will present The DYAMOND Winter data collection, which also includes coupled atmosphere-ocean models to resolve ocean eddies, atmospheric storms and their interactions. The DYAMOND project (DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains) is the first initiative for a model intercomparison of global storm resolving (km-scale) climate simulations.

The session CL3.1.3 focusses on the topic of Climate change in the North Atlantic in CMIP6 simulations; within it from 12:09-12:11 p.m. in his vPICO Skip high-volume data transfer and access free computing resources for your CMIP6 multi-model analyses Stephan Kindermann will run Jupyter notebooks directly on the DKRZ` high performance supercomputer - one of the European Network of Earth System modelling (ENES) supercomputers. Working with CMIP6 models, he will show how to load, filter, concatenate, take means, and plot several CMIP6 data to compare their results.

On the final day of vEGU, DKRZ will present two more vPICOs in the session session ITS4.4/AS4.1: Machine learning for Earth system modelling on April 30. Christopher Kadow will kick off the session from 11:05-11:15 a.m. with his 15-minute vPICO Artificial intelligence reconstructs missing climate information, in which he shows how artificial intelligence can fill observational gaps in historical climate datasets when combined with numerical climate model data.

From 1:48-1:50 p.m. Frauke Albrecht presents in her vPICO AI for Fast Atmospheric Chemistry the extent to which the output of an atmospheric chemistry model can be learned and emulated by AI methods - in this case by using artificial neural networks, and how the learned ML methods can be integrated into operational climate models in the future.