Tootfinder

Der Mai 2025 war global der zweitwärmste Mai seit Beginn der Aufzeichnungen.
Laut ERA5-Daten lag die durchschnittliche #Oberflächentemperatur 0,53 °C über dem Mittel von 1991–2020 und 1,40 °C über dem vorindustriellen Niveau.
Nur der Mai 2024 war wärmer. In den letzten 23 Monaten überschritt die globale Temperatur 14-mal deutlich die 1,5-Grad-Marke.

Machine learning-based correlation analysis of decadal cyclone intensity with sea surface temperature: data and tutorial
Jingyang Wu, Rohitash Chandra
https://arxiv.org/abs/2506.09254

Machine learning-based correlation analysis of decadal cyclone intensity with sea surface temperature: data and tutorial
The rising number of extreme climate events in the past decades has motivated the need for a thorough consideration of tropical cyclone genesis and intensity, given the sea-surface temperature (SST). In this paper, we present an analysis of the relationship between the increasing global SST with cyclone genesis using linear regression machine learning models. We extract and curate a dataset of tropical cyclones across selected ocean basins with their associated SST over the past 40 years. We pr…

Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation
Ruiqi Shu, Yuan Gao, Hao Wu, Ruijian Gou, Yanfei Xiang, Fan Xu, Qingsong Wen, Xian Wu, Xiaomeng Huang
https://arxiv.org/abs/2505.22071

Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation
This work focuses on the end-to-end forecast of global extreme marine heatwaves (MHWs), which are unusually warm sea surface temperature events with profound impacts on marine ecosystems. Accurate prediction of extreme MHWs has significant scientific and financial worth. However, existing methods still have certain limitations, especially in the most extreme MHWs. In this study, to address these issues, based on the physical nature of MHWs, we created a novel hybrid data-driven and numerical MH…

This https://arxiv.org/abs/2412.03795 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Samudra: An AI Global Ocean Emulator for Climate
AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state-of-the-art climate model. We emulate key ocean variables, sea surface height, horizontal velocities, temperature, and salinity, across their…

This https://arxiv.org/abs/2505.08742 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model
Green's functions are a useful technique for interpreting atmospheric state responses to changes in the spatial pattern of sea surface temperature (SST). Here we train version 2 of the Ai2 Climate Emulator (ACE2) on reference historical SST simulations of the US Department of Energy's EAMv3 global atmosphere model. We compare how well the SST Green's functions generated by ACE2 match those of EAMv3, following the protocol of the Green's Function Model Intercomparison Project (GFMIP). The spatia…