#sea-surface-temperature — Public Fediverse posts

Globale Ozeantemperaturen im April 2026 mit bisher zweithöchstem Wert für einen April seit 1979: https://www.wetterkontor.de/de/wetternews.asp

#Wetter #Klima #Ozeantemperaturen #Erde #climate #climatechange #seasurfacetemperature

Globale Ozeantemperaturen im April 2026 mit bisher zweithöchstem Wert für einen April seit 1979: https://www.wetterkontor.de/de/wetternews.asp

#Wetter #Klima #Ozeantemperaturen #Erde #climate #climatechange #seasurfacetemperature

Globale Ozeantemperaturen im April 2026 mit bisher zweithöchstem Wert für einen April seit 1979: https://www.wetterkontor.de/de/wetternews.asp

#Wetter #Klima #Ozeantemperaturen #Erde #climate #climatechange #seasurfacetemperature

Meeresoberflächentemperatur rund um Europa im Jahr 2025 mit neuen Rekordwerten: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #2025 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen #Europa #Rekorde

Meeresoberflächentemperatur rund um Europa im Jahr 2025 mit neuen Rekordwerten: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #2025 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen #Europa #Rekorde

Meeresoberflächentemperatur rund um Europa im Jahr 2025 mit neuen Rekordwerten: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #2025 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen #Europa #Rekorde

Meeresoberflächentemperatur rund um Europa im Jahr 2025 mit neuen Rekordwerten: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #2025 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen #Europa #Rekorde

Meeresoberflächentemperatur rund um Europa im Jahr 2025 mit neuen Rekordwerten: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #2025 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen #Europa #Rekorde

Globale Meeresoberflächentemperatur auch im April 2026 auf Rekordniveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #April #2026 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen

Globale Meeresoberflächentemperatur auch im April 2026 auf Rekordniveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #April #2026 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen

Globale Meeresoberflächentemperatur auch im April 2026 auf Rekordniveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #April #2026 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen

Globale Meeresoberflächentemperatur auch im April 2026 auf Rekordniveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #April #2026 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen

Globale Meeresoberflächentemperatur auch im April 2026 auf Rekordniveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #April #2026 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im März 2026 auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #März #2026 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im März 2026 auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #März #2026 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im März 2026 auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #März #2026 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im März 2026 auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #März #2026 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im März 2026 auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #März #2026 #Nachrichten #Klima #Copernicus #seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im Februar 2026 weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Februar #2026 #Nachrichten #Klima #Copernicus #Seasurfacetemperature

Globale Ozeantemperaturen auch im Februar 2026 weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Februar #2026 #Nachrichten #Klima #Copernicus #Seasurfacetemperature

Globale Ozeantemperaturen auch im Februar 2026 weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Februar #2026 #Nachrichten #Klima #Copernicus #Seasurfacetemperature

Globale Ozeantemperaturen auch im Februar 2026 weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Februar #2026 #Nachrichten #Klima #Copernicus #Seasurfacetemperature

Globale Ozeantemperaturen auch im Februar 2026 weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Februar #2026 #Nachrichten #Klima #Copernicus #Seasurfacetemperature

23 Years of Sea Surface Temperature (SST) - 2002-2025

#Exoplanet #ExoplanetAtmospheres #Oceans #Spectrum #seasurfacetemperature

⏩ 2 new pictures and 2 new videos from NASA (SVS) https://commons.wikimedia.org/wiki/Special:ListFiles?limit=14&user=OptimusPrimeBot&ilshowall=1&offset=20260127203610

23 Years of Sea Surface Temperature (SST) - 2002-2025

#Exoplanet #ExoplanetAtmospheres #Oceans #Spectrum #seasurfacetemperature

⏩ 2 new pictures and 2 new videos from NASA (SVS) https://commons.wikimedia.org/wiki/Special:ListFiles?limit=14&user=OptimusPrimeBot&ilshowall=1&offset=20260127203610

23 Years of Sea Surface Temperature (SST) - 2002-2025

#Exoplanet #ExoplanetAtmospheres #Oceans #Spectrum #seasurfacetemperature

⏩ 2 new pictures and 2 new videos from NASA (SVS) https://commons.wikimedia.org/wiki/Special:ListFiles?limit=14&user=OptimusPrimeBot&ilshowall=1&offset=20260127203610

23 Years of Sea Surface Temperature (SST) - 2002-2025

#Exoplanet #ExoplanetAtmospheres #Oceans #Spectrum #seasurfacetemperature

⏩ 2 new pictures and 2 new videos from NASA (SVS) https://commons.wikimedia.org/wiki/Special:ListFiles?limit=14&user=OptimusPrimeBot&ilshowall=1&offset=20260127203610

Ozeantemperaturen im Jahr 2025 global gesehen mit bisher dritthöchstem Wert seit 1979: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Seasurfacetemperature #2025 #Nachrichten #Klima #Copernicus #Klimawandel #Meerestemperaturen

Ozeantemperaturen im Jahr 2025 global gesehen mit bisher dritthöchstem Wert seit 1979: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Seasurfacetemperature #2025 #Nachrichten #Klima #Copernicus #Klimawandel #Meerestemperaturen

Ozeantemperaturen im Jahr 2025 global gesehen mit bisher dritthöchstem Wert seit 1979: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Seasurfacetemperature #2025 #Nachrichten #Klima #Copernicus #Klimawandel #Meerestemperaturen

Ozeantemperaturen im Jahr 2025 global gesehen mit bisher dritthöchstem Wert seit 1979: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Seasurfacetemperature #2025 #Nachrichten #Klima #Copernicus #Klimawandel #Meerestemperaturen

Globale Ozeantemperaturen auch im November weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Dezember #2025 #Nachrichten #Klima #Copernicus #Klimawandel #Seasurfacetemperature #Wassertemperaturen

Globale Ozeantemperaturen auch im November weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Dezember #2025 #Nachrichten #Klima #Copernicus #Klimawandel #Seasurfacetemperature #Wassertemperaturen

Globale Ozeantemperaturen auch im November weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Dezember #2025 #Nachrichten #Klima #Copernicus #Klimawandel #Seasurfacetemperature #Wassertemperaturen

Globale Ozeantemperaturen auch im November weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Dezember #2025 #Nachrichten #Klima #Copernicus #Klimawandel #Seasurfacetemperature #Wassertemperaturen

Northern Hemisphere Wintertime Teleconnections from the 2023–24 El Niño Offset by Background SST Trends
--
https://doi.org/10.1175/JCLI-D-25-0227.1 <-- shared paper
--
https://doi.org/10.1029/2024GL108946 <-- shared paper
--
#USA #Canada #NorthAmerica #Mexico #NorthernEurope #PacificOcean #ElNiño #ElNino #pluvial #precipitation #rainfall #snow #weather #climate #humanimpacts #model #modeling #spatial #spatialanalysis #spatiotemporal #atmosphere #extremeweather #CONUS #California #atmosphericriver #AtlanticOcean #IndianOcean #tropical #ENSO #winter #ocean #trend #teleconnections #seasurfacetemperature #SST #atmospheric #climatechange #natural #anthropogenic #noaa #NSF
#NationalCenterforAtmosphericResearch

Northern Hemisphere Wintertime Teleconnections from the 2023–24 El Niño Offset by Background SST Trends
--
https://doi.org/10.1175/JCLI-D-25-0227.1 <-- shared paper
--
https://doi.org/10.1029/2024GL108946 <-- shared paper
--
#USA #Canada #NorthAmerica #Mexico #NorthernEurope #PacificOcean #ElNiño #ElNino #pluvial #precipitation #rainfall #snow #weather #climate #humanimpacts #model #modeling #spatial #spatialanalysis #spatiotemporal #atmosphere #extremeweather #CONUS #California #atmosphericriver #AtlanticOcean #IndianOcean #tropical #ENSO #winter #ocean #trend #teleconnections #seasurfacetemperature #SST #atmospheric #climatechange #natural #anthropogenic #noaa #NSF
#NationalCenterforAtmosphericResearch

Northern Hemisphere Wintertime Teleconnections from the 2023–24 El Niño Offset by Background SST Trends
--
https://doi.org/10.1175/JCLI-D-25-0227.1 <-- shared paper
--
https://doi.org/10.1029/2024GL108946 <-- shared paper
--
#USA #Canada #NorthAmerica #Mexico #NorthernEurope #PacificOcean #ElNiño #ElNino #pluvial #precipitation #rainfall #snow #weather #climate #humanimpacts #model #modeling #spatial #spatialanalysis #spatiotemporal #atmosphere #extremeweather #CONUS #California #atmosphericriver #AtlanticOcean #IndianOcean #tropical #ENSO #winter #ocean #trend #teleconnections #seasurfacetemperature #SST #atmospheric #climatechange #natural #anthropogenic #noaa #NSF
#NationalCenterforAtmosphericResearch

Northern Hemisphere Wintertime Teleconnections from the 2023–24 El Niño Offset by Background SST Trends
--
https://doi.org/10.1175/JCLI-D-25-0227.1 <-- shared paper
--
https://doi.org/10.1029/2024GL108946 <-- shared paper
--
#USA #Canada #NorthAmerica #Mexico #NorthernEurope #PacificOcean #ElNiño #ElNino #pluvial #precipitation #rainfall #snow #weather #climate #humanimpacts #model #modeling #spatial #spatialanalysis #spatiotemporal #atmosphere #extremeweather #CONUS #California #atmosphericriver #AtlanticOcean #IndianOcean #tropical #ENSO #winter #ocean #trend #teleconnections #seasurfacetemperature #SST #atmospheric #climatechange #natural #anthropogenic #noaa #NSF
#NationalCenterforAtmosphericResearch

Northern Hemisphere Wintertime Teleconnections from the 2023–24 El Niño Offset by Background SST Trends
--
https://doi.org/10.1175/JCLI-D-25-0227.1 <-- shared paper
--
https://doi.org/10.1029/2024GL108946 <-- shared paper
--
#USA #Canada #NorthAmerica #Mexico #NorthernEurope #PacificOcean #ElNiño #ElNino #pluvial #precipitation #rainfall #snow #weather #climate #humanimpacts #model #modeling #spatial #spatialanalysis #spatiotemporal #atmosphere #extremeweather #CONUS #California #atmosphericriver #AtlanticOcean #IndianOcean #tropical #ENSO #winter #ocean #trend #teleconnections #seasurfacetemperature #SST #atmospheric #climatechange #natural #anthropogenic #noaa #NSF
#NationalCenterforAtmosphericResearch

Globale Ozeantemperaturen auch im November weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #November #Nachrichten #Klima #Seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im November weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #November #Nachrichten #Klima #Seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im November weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #November #Nachrichten #Klima #Seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im November weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #November #Nachrichten #Klima #Seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im November weiterhin auf sehr hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #November #Nachrichten #Klima #Seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im Oktober weiterhin auf hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Oktober #2025 #Nachrichten #Klima #Copernicus
#Seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im Oktober weiterhin auf hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Oktober #2025 #Nachrichten #Klima #Copernicus
#Seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im Oktober weiterhin auf hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Oktober #2025 #Nachrichten #Klima #Copernicus
#Seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im Oktober weiterhin auf hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Oktober #2025 #Nachrichten #Klima #Copernicus
#Seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im Oktober weiterhin auf hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #Oktober #2025 #Nachrichten #Klima #Copernicus
#Seasurfacetemperature #Ozeantemperaturen

Globale Ozeantemperaturen auch im September 2025 weiterhin auf hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #September #Nachrichten #Klima #Ozeantemperaturen #Seasurfacetemperature

Globale Ozeantemperaturen auch im September 2025 weiterhin auf hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #September #Nachrichten #Klima #Ozeantemperaturen #Seasurfacetemperature

Globale Ozeantemperaturen auch im September 2025 weiterhin auf hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #September #Nachrichten #Klima #Ozeantemperaturen #Seasurfacetemperature

Globale Ozeantemperaturen auch im September 2025 weiterhin auf hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #September #Nachrichten #Klima #Ozeantemperaturen #Seasurfacetemperature

Globale Ozeantemperaturen auch im September 2025 weiterhin auf hohem Niveau: https://www.wetterkontor.de/de/wetternews.asp

#wetter #météo #weather #climate #climatechange #September #Nachrichten #Klima #Ozeantemperaturen #Seasurfacetemperature

Open Ocean #11

After a mathematical interlude, I got back to looking at the data. I was concerned with interpolated fields like this. The problem is subtle, but I’ll give you a chance to spot it.

These graphs show one month of data (September 1981). From top left, these show the basic gridded field, the estimated grid cell uncertainties, the number of observations, then on the second row, the interpolated field, the individual ship biases, the deck biases, then third row, the spherical harmonic component, global mean and local interpolation component.

There were quite a few months like this. This was one of the worst, but far from the only one with issues. Something strange was clearly happening. It turned out to be several something stranges. A sequence of them in fact.

One of those things was that the interpolated field had uniform variance, which means that even in areas usually covered by ice, the temperature field had the same variance as it did in the Gulf Stream, or the El Nino regions. This was unrealistic. I “fixed” this by calculating a more realistic prior variance for the field using SST CCI data. I let this vary by month.

The standard deviation of the field varies from place to place and with the seasons, but it’s always quite low nearer the poles (darker colours) where there is sea ice. I kept the length scales I’d used previously, but modified the variance of the gaussian process to vary from grid cell to grid cell. That meant that the local interpolation was less apt to go wild in the polar oceans although some of the marginal ice areas had high variance (lighter colours).

Unfortunately, even though the local interpolation wasn’t going to go wild, it meant everything else instead. The example I showed above indicates that the spherical harmonic component I added to the interpolation was doing what the local interpolation had previously done and taking extreme values to match a small number of observations at higher latitudes. I tuned down their variance, which sort of fixed things, but not entirely.

In looking through the fields in detail, I noticed that some of the fields had anomalies but no corresponding uncertainty. In such cases, the error covariance would be zero so the interpolation would be constrained to pass exactly through that value at the grid box centre. If two grid cells had zero uncertainty right next to each other and the local interpolation was constrained to have a very small variance, the only thing the interpolation could do was to use a different model component to fit the two grid cell values. If these differed by a lot then the only option would be the spherical harmonics and as these have quasi hemi-demi-spheric length scales, we get the alarming pattern shown above.

I looked at the observations in the area – there shouldn’t be observations without uncertainties – and it turned out that some of the IDs were coming through as NaNs rather than empty strings so these were being quietly ignored by the uncertainty calculation (which uses the IDs to group ships together) but not by the anomaly gridder which didn’t use the ID information. Changing the NaNs to a valid string fixed the missing uncertainty problem.

But uncovered another.

At high latitudes, there were some platforms reporting sea surface temperatures ranging from -20C to +20C. On plotting some of these out, I discovered that they were buoys from the International Arctic Buoy Programme. The measurements were not sea surface temperatures, but surface temperatures. While this might have been the SST when the buoys were floating freely, they certainly weren’t SSTs if and when they got frozen into the sea ice. Either way, they were reported in ICOADS as SST. Winkling these out was a little awkward because they aren’t listed as a separate deck or source in ICOADS. Most of them came from general decks – NCEP GTS BUFR data or similar – which includes a range of different sources including ships and drifters. The platform types were also set to drifting buoy in the cases I looked at. ICOADS has a flag for ice buoys, but the documentation notes that the flag is (currently unused) and it wasn’t being used in this case. By googling the IDs of some of the buoys, I found a web page with a comprehensive list of IABP deployments. I scraped the list, passed it through Excel and made a list of IDs to exclude from the processing then reran everything.

After all these steps, the output looks a lot more sensible.

That same month now looks a lot calmer.

It’s still a ways off what I’d like and the ice still presents a problem even if it is much diminished. In principle, the variance of the local interpolation could be scaled according to the sea ice concentration, which would make sure that nothing too strange occurs at the ice edge. The global mean component still affects the ice covered areas though. An alternative might be to only reconstruct the SST over the ice free area or to have a climatology that depends on the ice concentration. There are lots of possibilities, I guess.

#books #climate #climateChange #climateData #observations #science #seaSurfaceTemperature #spirituality #writing

Open Ocean #8

The next step is to extend the time series back in time. To do this, I need a different data source to IQUAM. IQUAM looks like it has been discontinued as of May 2025 so it couldn’t be used for updates either. This is a great shame because it’s a useful dataset1 and there’s nothing else out there that’s quite like it.

The main repository of historical marine in situ data is ICOADS, the International Comprehensive Ocean Atmosphere DataSet. I used the version from NCAR. It contains marine meteorological reports from the past 300+ years. Typically, SST datasets start around 1850 because there are very few SST data before then2. There are other observation, including air temperature measurements, which were used in the GloSAT dataset to push global temperature estimates back to the 18th century (1781).

The reasons I didn’t previously use ICOADS is that it’s in an awful format: IMMA. I mean, it’s a great archival format in that it contains everything and the kitchen sink, but decoding it is a real pain. There is official Fortran code for reading the files, but I’m not working in Fortran. There are NetCDF files which contain a subset of the data, but, alas! for my needs, the wrong subset. Fortunately, Philip Brohan has written a nice python reader, pyIMMA which will decode IMMA.

Even with a decoder in hand the downside of ICOADS is that it takes forever to read the data. To mitigate this, my workflow includes a step where the data are read in and then the elements I need3 are written back out in csv files. I like csv because I can look at the data4 and because it can be read much more efficiently. It’s not necessary to do this reading-rewriting step, I guess, but makes it much easier to rerun the later stages of processing which is something I generally do a lot5. Also, ICOADS is weirdly heterogeneous and sometimes looking at the data is the best way to troubleshoot a particular problem.

Of course, the upside of ICOADS is that now we have lots of nice metadata to play with. Additional metadata can be appended to the ICOADS files via the unique ID added to each record. One major shortcoming common to both HadSSTs 3 and 4 was that the error covariances were incomplete because some of the reports in ICOADS don’t have a meaningful ship IDs, or have a generic ID like “SHIP” or “MASK6“. These reports were excluded from the error covariance calculation7, so the uncertainty would generally be an underestimate. How much of an underestimate varies through time. Since HadSST4 was developed, a method for “tracking” individual ships has been published8. It would be cool to be able to combine the two but that’s for later. Right now, I’m just trying to get ICOADS plumbed in.

One thing I did want to do during the plumbing was write in some functionality that let me add bits to the error covariance depending on arbitrary labels in the data. Previously, correlated errors were of two kinds: (1) correlated for all measurements from one ship and (2) correlated for all observations everywhere. Adding correlations based on arbitrary labels means I can estimate biases associated with measurement method, decks of data, or countries. The way to do this isn’t any different from doing it by ship ID: group the observations based on the label and then add up the components for each unique label9.

So… For June 1855, the SST anomalies gridded at 5 degrees look like this.

The 1850s were cool relative to the 1991-2020 climatology I’m using, but there are still some warm features in various places. The “deck” biases, which are the biases associated with individual “decks” of data in ICOADS look like this:

The biases are relatively modest amounting to a few tenths of a degree. The individual ship biases are larger and correspond to some of the features we saw in the gridded data.

When the data are sparse like this, it’s much easier to see how some individual ships affect the estimated field and the extent to which the interpolation deals with (or fails to deal with) examples of measurement error. The warm and cold biased ships in the South Atlantic really stand out, but there are also contrasts between ship tracks in the Pacific and Indian Oceans, though they’re more subtle. The data gridded at 1 degree resolution highlight some of these biases more clearly.

At 1×1 resolution, it’s much easier to see the correlate nature of some of these features. You can also see that even where ships do have a bias, there can still be variation in the anomaly along the ships path that is informative about changes in the local SST anomaly field.

The interpolated field looks like this:

Not only is the field smooth and less noisy, but it also balances potential errors in individual ships (and decks, though that’s much harder to see) against independent information from other ships and areas. This is most obvious where there are strongly biased ships in the basic gridded field. The interpolation seems to ignore those while picking up the background features. Of coures, it doesn’t perfectly discriminate, but the partitioning of the variance does at least take into account the possibility of measurement error.

One problem with these early SST anomaly fields is that the mean anomaly is rather negative. We are a long way from the climatology. An anomaly of zero is the prior estimate of what the SST field should be and this causes problems because the interpolated field is pulled towards the prior10 particularly if there are terms in the measurement error covariance with large spatial correlations. There are such terms (and more than one of them) so the interpolation will tend to be too warm.

In the period from 1980 on, we would have the opposite problem – the SSTs at the end of the series are much higher than the climatology – were it not for the drifting buoys. They are assumed to be unbiased, so they provide a strong observational constraint which means the posterior ends up centred on the observations with little influence from the prior. How to do this in the 1850s?

• We could bias adjust the data. The problem are the large correlated errors, so if we can correct for those by adjustment then the errors will be reduced and the error terms remaining in the covariance will be smaller and less strongly correlated.
• We know that the data are biased, so we could remove the global correlation term from the covariance matrix.
• Knowing that the data are biased, we could also modify the mean of the prior using some kind of temporally averaged global mean, or low-frequency background field. We could also increase the variance assigned to the global mean in the prior11.
• We could build in some kind of time dependence. While one month of data might not be able to pull the interpolation away from the prior, a sequence of months might. However, bias errors are also correlated in time, so we might just end up with the same problem.
• We could find a benchmark dataset. ERSST used marine air temperature. DCENT used land stations. HadSST4 used marine air temperatures and oceanographic data.
• We could use ERSST or DCENT to provide our first guess somehow. This feels a little like cheating but it could provide the scaffolding necessary to build everything else.

It really depends on what we want to achieve and in what order. My inclination is to calculate a time series from the un-interpolated data, smooth it in time and use that as a first guess. In that way, I would have a dataset that while biased at a global scale, has much reduced biases at a local scale. That might be an interesting thing to look at, but I need to think more about it.

-fin-

1. When updating a dataset, it can help to have more than one source of data in case – as inevitably happens – your major source goes down just when you need it. ↩︎
2. There are times after that too which aren’t well observed. The 1860s have very poor coverage in the Pacific and more generally (recession, civil war, recession in the US), so another logical starting date is the 1870s. ↩︎
3. Of course, I don’t always know exactly what I will need until I need it, so I might end up having to run the extraction multiple times. In the end, I don’t want to be reading masses more data than I strictly need. ↩︎
4. And even open it in Excel. I find Excel useful for some tasks as it can easily do things that are tedious to do in Python or a Jupyter notebook. ↩︎
5. I like to rerun things over and over again and then sit there looking at the output over and over again. There are around 2000 months between 1850 and the 2020s, and generally the code outputs several different diagnostics per month, so during dataset development, I would look at tens of thousands of images over and over again. It’s a fun process trying to puzzle out what’s going on in each image, why it is the way it is, if it’s the way you expected and if not, why not. After repeating this process many time, I end up with something I hate but can’t see how to improve*. ↩︎
6. Some ships hide or “mask” their callsign when reporting on the GTS – Global Telecommunication System. There are various reasons given for this including safety and commercial concerns. It’s annoying for someone working with the data, but there are workarounds. ↩︎
7. HadSST3 bodged them back in with a correction factor for certain time series, but it was impossible to implement in a general sense so for most applications, there was still a bit missing from the uncertainty. ↩︎
8. The math of the tracking is fairly simple, but making best use of the ICOADS data within the context of the maths is a big part of the work. Working with data is 90% data wrangling and 10% everything else. ↩︎
9. You can also look at the components at a per-label level: individual ships, individual decks etc. It won’t always be informative because in areas where there are lots of overlapping covariances, the variance will be partitioned proportionately between them, while the posterior covariance (the uncertainty) will remain large. That’s useful information too of course, but the quantity of information can become overwhelming. When developing HadISST2, I created covariance matrices for every single ship for every single month. Even stored efficiently, that’s a large amount of data: thousands of months, hundreds of ships per month, each of which has a covariance matrix. If I did it again, I’d try to avoid writing any of that out ↩︎
10. Or not pulled away from it, I guess. ↩︎
11. In the HadCRUT interpolation, the global mean is treated differently to what I do here, leading to a different set of interpolation equations. My hazy recollection is that the global mean term has a very elastic prior so this is less of an issue. ↩︎

* Short of going in there and fixing things by hand. I don’t like that way of doing things because it’s not exactly reproducible. Sure, you can enshrine it in code, but at the same time, it feels unsatisfactory, because the best you can say is “it looked funny”. I think there’s space for that kind of approach – we are good at spotting inarticulable oddness – but it doesn’t scale. There are billions of data points in ICOADS, so a bulk approach is the only feasible one. Consequently, most manual exclusions happen at a level where the number of entities is still manageable, i.e. whole decks of data.

#climate #climateChange #data #icoads #observations #seaSurfaceTemperature

Open Ocean #7

Previously on Open Ocean. I did some pattern estimation using an iterative algorithm that worked like magic. But before that, I made a simple Gaussian Process interpolator.

One thing we can use the Gaussian Process interpolation for is to estimate biases in the ship data at a global and ship-by-ship scale. The process is to first grid up the ship and drifter data separately and then sequentially assimilate the buoy and ship observations. The drifter data provides an initial unbiased estimate of the global field. The ship data are then assimilated and the resulting field is effectively bias adjusted.

Jupyter notebook here.

Ship data

First I grid up the ship data. I’m using July 2008 because those are the random numbers my fingers hit. I add a constant term to the error covariance to represent a global bias common to all ships. The uncertainty associated with this is set to 0.2K, which is larger than the bias is likely to be but is roughly the right order of magnitude.

The map of gridded anomalies from ships shows some interesting features. There is a short El Nino warm tongue in the eastern Pacific. The North Atlantic looks to be largely warmer than the average. There is a strong warm anomaly in the western North Pacific with cooler anomalies in the east. In the southeast Pacific there is a lonely ship track with a very positive anomaly. This is likely erroneous.

Drifter data

I gridded the drifter data separately, but in this case I haven’t added a constant to the error covariance reflecting the fact (my belief anyway) that drifters are unbiased. If you’re worried about that, we can take an operational view and just say that the “true” SST is whatever drifting buoys measure on average. The error covariances still allow for biases in individual buoys, but the mean bias is assumed to be zero.

While the coverage of drifting buoy observations is more complete than for ships, there are still significant gaps, including in the tropical Pacific. This is why it can be important to use ship data despite the fact that it is less reliable than drifter data: it still contains useful information. In this case, ships provide information which will help to reconstruct the El Nino. On the other hand, the drifter anomalies in the Atlantic look to be cooler, on average, than the ship data so the drifters will help there (and elsewhere).

Interpolation step 1

I set up the Gaussian Process using the default HadCRUT5 parameters and then interpolate the drifter data. As with the ship data, I add a constant term to the prior covariance for the reconstruction, which represents a “global mean” temperature. Once the interpolation is done, I mask the land areas out for plotting. The first step interpolation looks like this.

The interpolated drifter data have most of the same features as the basic gridded drifter data. They’re just a bit smoother and there are no gaps except where there’s land.

Interpolation step 2 and bias adjustment

Next, I subtract the first interpolation from the ship data. I then use the posterior distribution from the first step of interpolation as a prior for the second step. I then do the interpolation of the ship data and add the interpolated ship data back onto the first interpolation step.

The SST anomalies from the second interpolation step aren’t that different from the first suggesting that the ship data are not adding a great deal of information. However, they are providing some information in the tropical Pacific. There are other minor changes too (we’ll come back to the Arctic).

Estimated ship errors

In principle, we can explicitly estimate each component of the ship errors: large scale bias, ship by ship bias, measurement error and sampling error. Each one is just a component of the overall variance. Mathematically, their covariance matrices are no different from the covariances describing the actual SST field, so, just as we can estimate the SST field using this technique, we can estimate the errors. However, I haven’t done that. I’ve just taken the difference between the gridded ship data and the second step reconstruction. It’s shown below.

First off, you can see the pervasive warm bias. There’s noise because a lot of the error terms are noisy. On the other hand, the ship track joining the Panama Canal to Australia and New Zealand shows up as a swath of positive biases. To the south of that, as expected, the solitary ship in the southeast Pacific has a clear warm bias, but you can also see a cold biased ship which track from the southern tip of South America, slightly west of North up to Central America.

A similar result can be had by just taking the difference between the gridded ship and drifter data. However, the field is less complete, noisier because it includes noise from the drifter data too, and therefore we’re not making full use of the available information.

Doing it properly

OK, so, I calculated the bias components of the ship error on their own. It looks like this ⬇️. I narrowed the colour scale (it now runs from -1 to +1 rather than -3 to +3) to better show off the features. With the noise from uncorrelated measurement and sampling errors removed, you can clearly see the ship tracks criss-crossing the various ocean basins. The pale pink background is associated with the constant term in the error covariance and represents the “global ship bias”.

This still doesn’t quite have the rich spectrum of biases that exists in the real world. We know, for example, that errors correlate at the level of individual data collections (in ICOADS these were called “decks” meaning decks of punched cards on which the data were stored) or country of registration. I don’t have that information in IQUAM, so I can’t deal with that at the moment. Also, SST biases can be subdivided into biases associated with different measurement methods, but IQUAM doesn’t have that metadata either. Even within one measurement method there might be different kinds of bias behaviour. Some ships making measurements in their engine rooms have biases that are anticorrelated with the SST (the bias is bigger when the water is colder). It’s easy enough to add these terms to the covariance if the appropriate metadata can be found.

Bias adjusted time series

By repeating the 2-step interpolation for every month it’s possible to build up a time series that integrates information from the drifting buoys and ships in some kind of “optimal” way1. One nice outcome of this is that the data are bias adjusted in the process.

The interpolated data uses both ship and drifter data, but unlike the simple gridded combination of the two, the series now tends to follow the drifting buoy data throughout. Gridding them together without compensating for the bias in the ship data, leads to an artificial cooling in the combined dataset, but that should now be gone.

Of course, we might have other problems instead. In the 1980s, the coverage of the drifting buoys is very far from global and those buoys that there are take fewer and noisier measurements. Ideally, we would use a different benchmark dataset to continue adjusting the data prior to the drifter era (ERSST uses marine air temperatures, HadSST4 uses oceanographic measurements). Also, I haven’t yet included the moored buoy data in the interpolation and the Arctic Ocean has an unrealistically warm anomaly so I will need to do something about ice. There’s ice up there which moderates the anomalies by pinning them close to whatever the freezing point of sea water happens to be.

Comparison to HadSST4

We can compare this series to that from HadSST4. Previously, I compared the combined series to the unadjusted HadSST4, but here I’m going to compare to the adjusted version because we should have a series that is effectively homogenised. So, let’s see…

That’s surprisingly close, I would say.

There are a couple of things to note. First, the coverage of the two data sets is different because HadSST4 is not interpolated so we might still expect some differences. Second, prior to 1995 or thereabouts, the two diverge. That’s because HadSST4 uses subsurface data in addition to drifter data to benchmark the bias adjustments. In principle, HadSST4 is doing a better job. Third, the two are still referenced to different baselines. I’ve removed the average offset here, but not accounted for any seasonal effects.

For all that though, and despite being less sophisticated, this simple series looks to be doing quite a good job (since the mid 1990s anyway).

-fin-

1. I put the prophylactic bunny ears on “optimal” because the whole thing requires fine tuning and it’s only optimal in the sense that we got everything right, which is to say it’s actually suboptimal. ↩︎

#climate #climateChange #gaussianProcess #interpolation #observations #seaSurfaceTemperature

#FYI #WesternMediterranean #SeaSurfaceTemperature

"The exceptional mediterranean heatwave continues with anomalies close to 8°C above average."
"This is around 9 standard deviations above normal. 5 is considered an exceptional, once in a several thousand year event. This is EXTREME."

https://bsky.app/profile/leonsimons.bsky.social/post/3ltceslxhwc2j

#climate #ClimateScience #climatechange #ClimateEmergency #ClimateCrisis #ClimateBreakdown #climatecatastrophe #globalWarming #globalHeating #ExtremeWeather

#FYI #WesternMediterranean #SeaSurfaceTemperature

"The exceptional mediterranean heatwave continues with anomalies close to 8°C above average."
"This is around 9 standard deviations above normal. 5 is considered an exceptional, once in a several thousand year event. This is EXTREME."

https://bsky.app/profile/leonsimons.bsky.social/post/3ltceslxhwc2j

#climate #ClimateScience #climatechange #ClimateEmergency #ClimateCrisis #ClimateBreakdown #climatecatastrophe #globalWarming #globalHeating #ExtremeWeather