arXiv subjects

Bjorn Stevens

Publications and source records attributed to Bjorn Stevens.

4 recordsLinked to original sources

arXiv2025

Computing the Full Earth System at 1 km Resolution

We present the first-ever global simulation of the full Earth system at 1.25 km grid spacing, achieving highest time compression with an unseen number of degrees of freedom. Our model captures the flow of energy, water, and carbon through key components of the Earth system: atmosphere, ocean, and land. To achieve this landmark simulation, we harness the power of 8192 GPUs on Alps and 20480 GPUs on JUPITER, two of the world's largest GH200 superchip installations. We use both the Grace CPUs and Hopper GPUs by carefully balancing Earth's components in a heterogeneous setup and optimizing acceleration techniques available in ICON's codebase. We show how separation of concerns can reduce the code complexity by half while increasing performance and portability. Our achieved time compression of 145.7 simulated days per day enables long studies including full interactions in the Earth system and even outperforms earlier atmosphere-only simulations at a similar resolution.

physics.ao-ph↗

arXiv2023

Earth Virtualization Engines -- A Technical Perspective

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change.

physics.ao-ph↗

arXiv2021

On moist potential temperatures and their ability to characterize differences in the properties of air parcels

A framework is introduced to compare moist `potential' temperatures. The equivalent potential temperature, $θ_e,$ the liquid water potential temperature, $θ_\ell,$ and the entropy potential temperature, $θ_s$, are all shown to be potential temperatures, in the sense that they measure the temperatures of certain reference state systems whose entropy is the same as that of the air-parcel. They only differ in the choice of reference state composition: $θ_\ell$ describes the temperature a condensate-free state, $θ_e$ a vapor-free state, and $θ_s$ a water-free state would require to have the same entropy as the given state. Although in this sense $θ_e,$ $θ_\ell,$ and $θ_s$ are all different flavors of the same thing, only $θ_\ell$ satisfies the stricter definition of a `potential temperature', as corresponding to a reference temperature accessible by an isentropic and closed transformation of a system in equilibrium; both $θ_e$ and $θ_\ell$ measure the `relative' enthalpy of an air parcel at their respective reference states; but only $θ_s$ measures air-parcel entropy. None mix linearly, but all do so approximately, and all reduce to the dry potential temperature, $θ$ in the limit as the water mass fraction goes to zero. As is well known, $θ$ does mix linearly and inherits all the favorable (entropic, enthalpic, and potential temperature) properties of its various -- but descriptively less rich -- moist counterparts. All, involve quite complex expressions, but admit relatively simple and useful approximations. Of the three moist `potential' temperatures, $θ_s$ is the least familiar, but the most well mixed in the broader tropics, a property that merits further study as a possible basis for constraining mixing processes.

physics.ao-ph↗

arXiv2020

Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection

Humans excel at detecting interesting patterns in images, for example those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. However, it is often difficult to gather enough data of subjective features for significant analysis. This paper presents an example of how two tools that have recently become accessible to a wide range of researchers, crowd-sourcing and deep learning, can be combined to explore satellite imagery at scale. In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions. Shallow clouds play a large role in the Earth's radiation balance yet are poorly represented in climate models. For this project four subjective patterns of organization were defined: Sugar, Flower, Fish and Gravel. On cloud labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowd-sourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern detection and create global climatologies of the four patterns. Analysis of the geographical distribution and large-scale environmental conditions indicates that the four patterns have some overlap with established modes of organization, such as open and closed cellular convection, but also differ in important ways. The results and dataset from this project suggests promising research questions. Further, this study illustrates that crowd-sourcing and deep learning complement each other well for the exploration of image datasets.

physics.ao-ph↗