@ECMWF here's the #ORAS6 version just for May. Doesn't make the outlook any better but does add to the overall confidence in the forecasts
At @ECMWF we have routine monitoring plots of the (old) ocean analysis system #ORAS5*.
These are designed by my office mate and ocean expert Eric de Boisseson.
I asked him about El Niño and he immediately pulled up not maps, but vertical cross sections along the equator.
Plots are openly available here:
https://charts.ecmwf.int/catalogue/packages/oras5/products/oras5_xzmaps_1m?AnomalyMode=Anomaly&Field=Temperature
When you look at things this way, the expectation that we will soon get a surface heat anomaly in the eastern Pacific doesn't need much more explanation.
2/2
*The new system, #ORAS6, is in place but publishing the equivalent plots is not yet there.
New ORAS6 ocean reanalysis supporting forecasting and climate monitoring
https://www.ecmwf.int/en/about/media-centre/news/2026/new-oras6-ocean-reanalysis
ECMWF’s next-generation ocean and sea-ice reanalysis system improves the representation of sea-surface temperature, sea ice, and ocean circulation, supporting better forecasts, climate monitoring, and the upcoming ERA6 reanalysis.
What was a preprint is now fully published: https://tc.copernicus.org/articles/20/3299/2026/
Sea ice data assimilation in #ORAS6
The paper documents our methodology for constraining the sea ice with concentration observations in our new reanalysis and NWP system.
Further papers on the system as a whole, and climate signals from it are planned.
Abstract. Accurate weather and climate forecasting relies heavily on the precise modeling of sea ice, a critical component of the Earth's climate system. Sea ice influences global weather patterns, ocean circulation, and the exchange of heat and moisture between the atmosphere and oceans. Initialisation of the sea ice component of global coupled models relies on data assimilation techniques to incorporate information from observations to constrain the system. This study focuses on the development of sea ice data assimilation for ECMWF's latest Ocean Reanalysis System 6 (ORAS6) that includes a multicategory sea ice model. The research addresses the challenge of appropriately distributing sea ice concentration increments across various thickness categories in the model. Here, we show that using a simple proportional increment splitting method improves the accuracy of sea ice concentration analyses compared to previous approaches. Our findings indicate that adding an additional sea ice-sea water temperature physical relationship brings further performance benefits. These results suggest that the choice of increment distribution strategy significantly impacts the accuracy of sea ice representation in reanalysis systems. The system presented here will form the basis of ECMWF's data assimilation system for numerical weather prediction, as well as the next generation coupled reanalyses.
RE: https://masto.ai/@CopernicusECMWF/116476132021240584
Just in time to be superseded by #ORAS6
Phil Browne