climate

Recent advances in machine learning have revolutionized dynamical modeling, yet AI weather and climate models often suffer from instability and unphysical drift when integrated over long timescales. This talk unifies three complementary works addressing this challenge. First, we present a theoretical eigenanalysis of neural autoregressive models that establishes a semi-empirical framework linking inference-time stability to the spectrum of the model’s Jacobian. This analysis reveals how integration-constrained architectures suppress unstable eigenmodes and enable predictable error growth. Building on this foundation, we identify spectral bias—a universal tendency of deep networks to under-represent high-wavenumber dynamics—as the root cause of instability in AI weather models. We demonstrate how higher-order integration schemes and spectral regularization, implemented in the FouRKS framework, mitigate this bias and produce century-scale stable emulations of turbulent flows. Finally, we translate these theoretical insights into practice with LUCIE-3D, a data-driven climate emulator trained on reanalysis data that captures forced responses to CO₂, reproduces stratospheric cooling and surface warming, and remains computationally efficient. Together, these results chart a rigorous pathway from mathematical theory to physically consistent AI climate models capable of stable, interpretable, and trustworthy long-term Earth-system emulation.

Institutions

• AI for Good

Despite the huge success of foundation models across fields, they still suffer from hallucinations and can produce physically inconsistent outputs. To leverage foundation models for climate science, it is critical to integrate first principles and physical laws to the learning and reasoning of these models.  In this talk, I will discuss our on-going effort to ground foundation models, including diffusion models and large language models for climate science. In particular, I will discuss dynamics-informed diffusion models for emulating complex fluids and an adaptive framework for LLM agents to use scientific tools. I will demonstrate the use cases of our methods on building an autonomous LLM agent as a climate co-scientist.

Learning Objectives:

By the end of this session, participants will be able to:

• Identify the limitations of current foundation models in climate science.
• Understand how integrating first principles and physical laws can ground diffusion models and LLMs for reliable scientific use.
• Recognize emerging applications of autonomous LLM agents as climate co-scientists for simulation, reasoning, and tool use.

Institutions

• AI for Good

Welcome to the 2025 edition of the WarmWorld-ESiWACE3 Summer School, in the old town of Lauenburg near Hamburg!

The Summer School will give an insight into ICON , one of the state-of-the-art weather and climate science models. The students will learn basic meteorology concepts and will be invited to tackle code challenges using intermediate and advanced approaches from software engineering, high-performance computing and data analysis, all under the guidance of experienced lecturers from these various fields.

Important dates

• Apr 15, 2025 – Deadline for travel grants requests
• Apr 30, 2025 – Registration closes
• until May 15, 2025 – Notification of acceptance
• Jul 28 - Aug 7, 2025 – Summer School

Institutions

• IT Center for Science (CSC-IT)
• Deutsches Klimarechenzentrum (DKRZ)
• Deutscher Wetterdienst (DWD)
• Technical University of Munich (TUM)
• University of Cologne , Center for Earth System Observation and Computational Analysis (CESOC)
• Max Planck Institute for Meteorology (MPI-M) 

Willkommen zur ITMC-Conference 2025

In diesem Jahr steht das Thema „Resiliente IT - Resilienz als Schlüssel für ein krisenfestes und zukunftsfähiges Unternehmen im Spannungsfeld von politischen Entwicklungen, Fachkräftemangel, Klimakrise, Mangel an Diversität und wirtschaftlichen Herausforderungen“ im Zentrum der ITMC-Conference.

Die ITMC-Conference ist die ideale Gelegenheit für Studieninteressierte, die sich für den Masterstudiengang IT-Management und -Consulting (ITMC) an der Universität Hamburg begeistern. Am 2. Juni 2025 laden wir Dich in den Lichthof der Staats- und Universitätsbibliothek ein, um einen authentischen Einblick in die Inhalte, Projekte und das Umfeld des Studiengangs zu gewinnen.

Erlebe spannende Keynotes und Vorträge von Expert:innen aus Wissenschaft und Praxis, informiere Dich über aktuelle Trends im IT-Management und Consulting und tausche Dich direkt mit Studierenden, Alumni und Lehrenden aus. Die Konferenz wird von Studierenden des ITMC-Studiengangs organisiert und bietet Dir die perfekte Plattform, um Fragen zu stellen, Kontakte zu knüpfen und herauszufinden, wie das Studium Dich auf eine Karriere in der IT-Welt vorbereitet.

Nutze die Chance, einen der innovativsten IT-Studiengänge kennenzulernen und Teil einer engagierten Community zu werden – wir freuen uns auf Dich!

Climate variability and weather extremes pose profound challenges for prediction, preparedness, and resilience. Traditional approaches often rely on predefined indices or supervised learning methods, which can overlook unexpected patterns or reinforce biases inherent in labeled datasets. This keynote explores how unsupervised learning techniques can uncover hidden patterns in high-dimensional climate data. I will highlight recent innovations that adapt established methods to reveal properties not captured by conventional architectures, offering new perspectives on modes of variability and extreme events. For instance, a knowledge-guided autoencoder can disentangle distinct Pacific climate modes with differing spectral signatures, while a custom hyperparameter search can optimize self-organizing maps to produce smooth, interpretable pathways among weather regimes. Together, these advances help uncover processes and mechanisms that may underlie established climate and weather phenomena. Ultimately, unsupervised learning provides a powerful lens for scientific discovery, with implications for understanding, prediction, and decision-making in a changing climate.

Institutions

• AI for Good

As AI projects gain traction in the humanitarian sector, securing their funding and long-term sustainability remains a critical challenge. This session explores how AI initiatives can align with the SDGs and address pressing climate concerns, while also examining innovative funding models and cross-sector partnerships. From philanthropic investments to public-private collaborations, join us to uncover strategies for ensuring AI projects not only launch successfully but also endure to create lasting, scalable impact in humanitarian efforts. Participants will gain insights into best practices for funding AI projects and explore case studies showcasing successful funding models and partnerships.  

Key Learning Objectives: 

• Identify the key challenges in funding and sustaining AI projects in the humanitarian sector.
• Analyze innovative funding models and cross-sector partnerships for AI projects. 
• Evaluate the long-term sustainability of AI projects through various funding strategies.
• Develop strategies for securing funding and ensuring the scalability of AI projects in humanitarian efforts. 

Target Audience: This event is designed for humanitarian workers at all levels, policymakers, academics, data specialists, communication specialists, and technology experts who are involved in crisis response and interested in the ethical use of AI.  

Prerequisites: No prerequisite knowledge is required. Basic understanding of AI and humanitarian principles is recommended. 

Anselm Fehnker, Senior AI Projektmanager am Artificial Intelligence Center Hamburg (ARIC) e.V.
Louisa Rockstedt, Projektmanagerin am Artificial Intelligence Center Hamburg (ARIC) e.V.

Künstliche Intelligenz gilt als Schlüsseltechnologie der Zukunft. Doch was kann sie beitragen, wenn es um eines unserer wichtigsten und zugleich am stärksten bedrohten Güter, dem Wasser, geht? Und worauf müssen wir achten, um technologische Innovation mit ökologischer Verantwortung zu verbinden?