Along these lines: I really like the 'Climate Reanalyzer' project by the Climate Change Institute at the University of Maine [1].
There's so much good stuff there if you click around a bit; you can create custom plots for the surface temperature of different regions for example[2], which quickly shows you that Western Europe has actually warmed a lot more than the global average, and we're closer to +2°C already in that region.
It can also be clearly seen that the 2020 limit on the sulphur content in the fuel oil used on board ships [1] had quite the negative effects when it comes to surface sea temperatures, but I haven't that many climate (and not only) scientists taking responsibility of that act (even though related warnings had been made, I remember reading one just before the measure went in effect).
Cutting sulphur content wasn't about climate. Why would climate scientists be taking responsibility?
Blasting pollution into the air is generally a bad idea. If it becomes necessary in order to fight warming, it should be done deliberately and with due consideration, not by having a bunch of ships burning dirty fuel.
It didn't make them worse. It solved one problem. That made the extent of the other existing problem more apparent.
That was known and expected. We could not continue to put sulfur in the air; it causes acid rain.
The fact that we also cannot afford to put CO2 into the air is a separate problem. That goes beyond temperature: even if additional sulfur would mitigate the temperature increase, it would also make ocean acidification worse.
This March (2026) in Norway was nearly 4 K warmer than the preceding thirty year average for March, and 0.6 K warmer than the previous record set about 10 years ago.
So I could easily believe that we are already at +2 K for the year as whole.
The daily global 5km SSTA product requires a daily climatology to calculate the daily SST anomalies. Daily climatologies (DC) are derived from the monthly mean (MM) climatology via linear interpolation. To achieve this, we assigned the MM value to the 15th day of each corresponding month, with the individual days between these dates being derived using linear interpolation.
We then calculate the SSTA product using: SST_anomaly = SST - DC where the SST is the value for the day in question, and DC is the corresponding daily climatology for that day of the year.
This doesn't give me a clear idea as a layman on how to interpret this information. Is it ok for the layman to believe that may 1st 1985 the variations of -5 to 5 were around 86 mean but in 2025 the same were around 82 mean, if that were to be the case, irrespective of the variations, it would not give me an idea of whether its concerning or not (this is just a random example, don't read too much into my beliefs)
I made something like this (in the VERY broadest sense) 10 years ago - inspired me to revisit and update both visuals and data (a lot has changed in that time).
This looks cool but it's missing a clear legend on the default view to help the viewer understand what they're looking at.
It's not immediately clear if it's just absolute temperatures or relative temperatures or what. You have to look at the color scale to notice that it's from -5 to +5. But relative to what? Over what timescale? Is it a moving average?
I guess I could dig into the data link to figure it out but most people aren't going to do that.
Very emotionally powerful to watch something play out, even if I'm already consciously aware of it. Would love a speed where I can watch the whole dataset play out in about 1 minute.
a) published data tends to see corrections from sensors and methodology which take several years to work out the fine details. (This isn't an attack this is science) Which means always take yesterday's numbers with more scepticism than 2yr ago.
(This is making no statement of any data you're looking at or any trend you claim to see)
b) a field dominated by modelling needs data to back it up, otherwise the conversation would be, "Why is the LHC failing to find strong theory which is absolutely there" vs "I wonder if the modelling is correct based on..."
This is a certain level of maturity that certain sciences are only starting to reach after playing in the ballpark of "let's go model my idea and make a press release which will just so happen to help my funding".
Yes sea level temps are rising, absolute numbers are still difficult to come by though and last UN summary doc I read still put things at 5C global average over a century. (Yes still horrifically catastrophic for the wrong people, but I'm also not in charge)
I doubt it has anything to do with data-quality, I'd be surprised if even 10% of climate denialists have studied the numbers. Remember >20% of US citizens are still creationists, a lot of people aren't emotionally ready to believe scary things, and maybe they never will be.
No, most of these people consciously or otherwise, just want/need to be contrarians. Look at flat Earthers. There is no way any sane person would say the earth is flat.
Please don't bring up another thing started by idiot scientists for a laugh to laugh at stupid people. You have no idea what it's like dealing with the "just open your eyes" and "what else are they hiding" tier of pseudo-intellectualism enabled by nu-media.
There are reasons to be sceptical which are set in reason and it's worth not throwing that out with the bath water. Even if the bath water is full of low iq bitchute comments...
I think you may be reading more into my comment than I wrote. I was only talking about what we are seeing in the Show HN. I have no baseline to compare it to so all I can see is a map of the oceans with some areas red and some areas blue.
For those interested in this type of climate data visualization apps, I have worked on this one in the past, which is actively maintained with a lot of love, and very nice:
Very nice. I had a quick look at the data source and I wonder if the more recent data is more sensitive/better quality since 2020? There's a clear trend of the oceans getting warmer but recently it seems like there's more and more heat retained.
"CRW's first-generation global monitoring products were operational at NOAA until April 30, 2020, when they were officially retired, and succeeded by CRW's next-generation operational daily monitoring products."
I don't quite understand the temperature color scale of -5 to 5, what is the baseline here on -5 to 5, is it relative to global average of that day? Or a period of time?
Serious question. Why are there static (in absolute positional terms) anomalies in the data that seem to be recording at the other end of the spectrum to their immediate surrounding waters?
Also nice to see several shipping lanes crop up when watching it.
[1]: https://climatereanalyzer.org/clim/sst_daily/?dm_id=world2 [2]: https://climatereanalyzer.org/research_tools/monthly_tseries...