Aerosol‐Cloud Interactions: Overcoming a Barrier to Projecting Near‐Term Climate Evolution and Risk

AGU Advances Wiley 7:1 (2026) e2025AV001872

Authors:

Ulas Im, Bjørn H Samset, Athanasios Nenes, Jennie L Thomas, Harri Kokkola, Oleg Dubovik, Vassilis Amiridis, Antti Arola, Nicolas Bellouin, Angela Benedetti, Merete Bilde, Sara Blichner, Stefano Decesari, Annica ML Ekman, Carlos Pérez García‐Pando, Silke Gross, Edward Gryspeerdt, Otto Hasekamp, Ralph A Kahn, Anton Laakso, Ulrike Lohmann, Louis Marelle, Andreas H Massling, Cathrine Lund Myhre, Philip Stier

Abstract:

Plain Language Summary: Clouds have a big influence on Earth's climate. They affect how much sunlight is reflected or trapped, and how weather patterns form. But understanding clouds is very hard‐especially how they interact with tiny particles in the air called aerosols. These particles come from human activities and sources like wildfires, volcanoes. The way aerosols and clouds affect each other is one of the most uncertain parts of climate science. Because of this uncertainty, it's difficult to make accurate predictions about climate change and to give clear advice to decision‐makers. Scientists have made some progress in understanding aerosol‐cloud interactions, but more work is needed. With better tools, observations, and computer models, we can learn more over the next decade. However, because the climate is changing quickly and impacts are getting worse, we need faster action now. This summary explains the current knowledge on how aerosols and clouds interact, and why it's important to reduce the uncertainty. It also highlights what steps can help improve our understanding‐such as global collaboration and sharing knowledge between researchers, governments, and the public. Making faster progress in this area is key to better climate predictions, stronger climate policies, and lower risks for people and the planet.

Physics-Constrained Reduced-Order Modeling of Collision-Coalescence with Advectable Embeddings: Monotonic Mass Partition Scheme

(2026)

Authors:

Kang-En Huang, Minghuai Wang, Philip Stier, Tobias Bischoff, Tim Reichelt, Yannian Zhu, Daniel Rosenfeld

Advancing Sea Ice Surface Classification by Self-Supervised Contrastive Learning for Radar Altimetry

(2026)

Authors:

Lena Happ, Stefan Hendricks, Conrad M Albrecht, Lars Kaleschke, Sonali Patil, Riccardo Fellegara, Dirk A Lorenz

Treatment of Key Aerosol and Cloud Processes in Earth System Models – Recommendations from the FORCeS Project

Tellus B: Chemical and Physical Meteorology Stockholm University Press 78:1 (2026) 1-66

Authors:

Ilona Riipinen, Sini Talvinen, Anouck Chassaing, Paraskevi Georgakaki, Xinyang Li, Carlos Pérez García-Pando, Tommi Bergman, Snehitha M Kommula, Ulrike Proske, Angelos Gkouvousis, Alexandra P Tsimpidi, Marios Chatziparaschos, Almuth Neuberger, Vlassis A Karydis, Silvia M Calderón, Sami Romakkaniemi, Daniel G Partridge, Théodore Khadir, Lubna Dada, Twan van Noije, Stefano Decesari, Øyvind Seland, Paul Zieger, Frida Bender, Ken Carslaw, Jan Cermak, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Yvette Gramlich, Ove W Haugvaldstad, Eemeli Holopainen, Corinna Hoose, Oriol Jorba, Stylianos Kakavas, Maria Kanakidou, Harri Kokkola, Radovan Krejci, Thomas Kühn, Markku Kulmala, Philippe Le Sager, Risto Makkonen, Stella EI Manavi, Thomas F Mentel, Alexandros Milousis, Stelios Myriokefalitakis, Athanasios Nenes, Tuomo Nieminen, Spyros N Pandis, David Patoulias, Tuukka Petäjä, Johannes Quaas, Leighton Regayre, Susanne MC Scholz, Michael Schulz, Ksakousti Skyllakou, Ruben Sousse, Philip Stier, Manu Anna Thomas, Julie T Villinger, Annele Virtanen, Klaus Wyser, Annica ML Ekman

Calibration of climate model parameterizations using Bayesian experimental design

Machine Learning: Earth IOP Publishing 2:1 (2026) 015003-015003

Authors:

Tim Reichelt, Tom Rainforth, Duncan Watson-Parris