#global-temperature — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #global-temperature, aggregated by home.social.
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https://www.europesays.com/ie/525344/ Scientists Say All Life on Earth May Follow the Same Temperature Rule #AmbientTemperature #Éire #GlobalTemperature #GlobalTemperatures #IE #Ireland #LifeOnEarth #performance #Science #temperature #temperatures #TemperaturesRise
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https://www.europesays.com/people/60050/ Bill Gates makes major climate change reversal after years of doomerism: ‘People will be able to live and thrive’ #Bill&MelindaGatesFoundation #BillGates #ClimateActivists #ClimateChange #ClimateDisaster #GlobalTemperature #GlobalTemperatures #GreenhouseGasEmissions #PoorestCountries
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Bill Gates makes major climate change reversal after years of doomerism: ‘People will be able to live and thrive’ https://www.byteseu.com/1996463/ #Bill&MelindaGatesFoundation #BillGates #Climate #ClimateActivists #ClimateChange #ClimateDisaster #GlobalTemperature #GlobalTemperatures #GlobalWarming #GreenhouseGasEmissions #PoorestCountries
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Hurdles to a hobby: How climate change and ‘runfluencer’ culture impact our daily jog
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Hurdles to a hobby: How climate change and ‘runfluencer’ culture impact our daily jog
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Hurdles to a hobby: How climate change and ‘runfluencer’ culture impact our daily jog
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Hurdles to a hobby: How climate change and ‘runfluencer’ culture impact our daily jog
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Hurdles to a hobby: How climate change and ‘runfluencer’ culture impact our daily jog
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#Berkeley #BerkeleyEarth #GlobalTemperature #RobertRohde #ZekeHausfather
February 2026 nominally 2nd warmest February on record
Average
- global temperature 1.55 ± 0.12 °C (2.78 ± 0.22 °F)
- land temperatures 2.55 ± 0.30 °C (4.59 ± 0.55 °F)
- ocean temperatures 1.04 ± 0.17 °C (1.87 ± 0.30 °F)
above 1850-1900 averagehttps://berkeleyearth.org/february-2026-temperature-update/
#climate #ClimateScience #climatechange #ClimateCrisis #ClimateBreakdown #ClimateDisruption #globalWarming #globalHeating #ExtremeWeather #polycrisis
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#Berkeley #BerkeleyEarth #GlobalTemperature #RobertRohde #ZekeHausfather
February 2026 nominally 2nd warmest February on record
Average
- global temperature 1.55 ± 0.12 °C (2.78 ± 0.22 °F)
- land temperatures 2.55 ± 0.30 °C (4.59 ± 0.55 °F)
- ocean temperatures 1.04 ± 0.17 °C (1.87 ± 0.30 °F)
above 1850-1900 averagehttps://berkeleyearth.org/february-2026-temperature-update/
#climate #ClimateScience #climatechange #ClimateCrisis #ClimateBreakdown #ClimateDisruption #globalWarming #globalHeating #ExtremeWeather #polycrisis
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#Berkeley #BerkeleyEarth #GlobalTemperature #RobertRohde #ZekeHausfather
February 2026 nominally 2nd warmest February on record
Average
- global temperature 1.55 ± 0.12 °C (2.78 ± 0.22 °F)
- land temperatures 2.55 ± 0.30 °C (4.59 ± 0.55 °F)
- ocean temperatures 1.04 ± 0.17 °C (1.87 ± 0.30 °F)
above 1850-1900 averagehttps://berkeleyearth.org/february-2026-temperature-update/
#climate #ClimateScience #climatechange #ClimateCrisis #ClimateBreakdown #ClimateDisruption #globalWarming #globalHeating #ExtremeWeather #polycrisis
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#Berkeley #BerkeleyEarth #GlobalTemperature #RobertRohde #ZekeHausfather
February 2026 nominally 2nd warmest February on record
Average
- global temperature 1.55 ± 0.12 °C (2.78 ± 0.22 °F)
- land temperatures 2.55 ± 0.30 °C (4.59 ± 0.55 °F)
- ocean temperatures 1.04 ± 0.17 °C (1.87 ± 0.30 °F)
above 1850-1900 averagehttps://berkeleyearth.org/february-2026-temperature-update/
#climate #ClimateScience #climatechange #ClimateCrisis #ClimateBreakdown #ClimateDisruption #globalWarming #globalHeating #ExtremeWeather #polycrisis
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#Berkeley #BerkeleyEarth #GlobalTemperature #RobertRohde #ZekeHausfather
February 2026 nominally 2nd warmest February on record
Average
- global temperature 1.55 ± 0.12 °C (2.78 ± 0.22 °F)
- land temperatures 2.55 ± 0.30 °C (4.59 ± 0.55 °F)
- ocean temperatures 1.04 ± 0.17 °C (1.87 ± 0.30 °F)
above 1850-1900 averagehttps://berkeleyearth.org/february-2026-temperature-update/
#climate #ClimateScience #climatechange #ClimateCrisis #ClimateBreakdown #ClimateDisruption #globalWarming #globalHeating #ExtremeWeather #polycrisis
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Paleoclimatology: Earth's Past Explains Unprecedented Modern Warming Trend
#climatechange #climateforecast #CopernicusClimateChangeService #Globaltemperature #paleoclimatology
https://blazetrends.com/paleoclimatology-earths-past-explains-unprecedented-modern-warming-trend/?fsp_sid=156174 -
Paleoclimatology: Earth's Past Explains Unprecedented Modern Warming Trend
#climatechange #climateforecast #CopernicusClimateChangeService #Globaltemperature #paleoclimatology
https://blazetrends.com/paleoclimatology-earths-past-explains-unprecedented-modern-warming-trend/?fsp_sid=156174 -
Paleoclimatology: Earth's Past Explains Unprecedented Modern Warming Trend
#climatechange #climateforecast #CopernicusClimateChangeService #Globaltemperature #paleoclimatology
https://blazetrends.com/paleoclimatology-earths-past-explains-unprecedented-modern-warming-trend/?fsp_sid=156174 -
Paleoclimatology: Earth's Past Explains Unprecedented Modern Warming Trend
#climatechange #climateforecast #CopernicusClimateChangeService #Globaltemperature #paleoclimatology
https://blazetrends.com/paleoclimatology-earths-past-explains-unprecedented-modern-warming-trend/?fsp_sid=156174 -
‘Changing faster than we ever imagined’
Many of the world’s top climate scientists are sounding the alarm over a number of rapid changes currently…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Science #Antarctica #BritishAntarcticSurvey #globaltemperature #globaltemperatures #MarineEcosystems #MichaelMeredith #rapidchanges #Sealevelrise
https://www.newsbeep.com/us/236390/ -
‘Changing faster than we ever imagined’
Many of the world’s top climate scientists are sounding the alarm over a number of rapid changes currently…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Science #Antarctica #BritishAntarcticSurvey #globaltemperature #globaltemperatures #MarineEcosystems #MichaelMeredith #rapidchanges #Sealevelrise
https://www.newsbeep.com/us/236390/ -
The remaining glaciers of California’s Sierra Nevada are vanishing
For as long as there have been people in what is now California, the granite peaks of the…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Environment #andrewjones #California #glacier #globaltemperature #ICE #largeglacier #late1800 #researcher #Rock #Science #Scientist #sierranevada #stream #study #year #yosemite
https://www.newsbeep.com/us/195802/ -
The remaining glaciers of California’s Sierra Nevada are vanishing
For as long as there have been people in what is now California, the granite peaks of the…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Environment #andrewjones #California #glacier #globaltemperature #ICE #largeglacier #late1800 #researcher #Rock #Science #Scientist #sierranevada #stream #study #year #yosemite
https://www.newsbeep.com/us/195802/ -
Scientists make alarming discovery after attaching GoPro cameras to polar bears in the Arctic — here’s what’s happening
The large amounts of melting sea ice due to rising global temperatures are impacting polar bears’ ability to…
#NewsBeep #News #Wildlife #globaltemperature #globaltemperatures #meltingseaice #polarbears #risingsealevels #Science #UK #UnitedKingdom
https://www.newsbeep.com/uk/27062/ -
Scientists make alarming discovery after attaching GoPro cameras to polar bears in the Arctic — here’s what’s happening
The large amounts of melting sea ice due to rising global temperatures are impacting polar bears’ ability to…
#NewsBeep #News #Wildlife #CA #Canada #globaltemperature #globaltemperatures #meltingseaice #polarbears #risingsealevels #Science
https://www.newsbeep.com/ca/27696/ -
https://www.europesays.com/uk/296734/ Scientists make alarming discovery after attaching GoPro cameras to polar bears in the Arctic — here’s what’s happening #GlobalTemperature #GlobalTemperatures #MeltingSeaIce #PolarBears #RisingSeaLevels #Science #UK #UnitedKingdom #wildlife
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The world is on track for between 1.9 and 3.7°C of warming by 2100
https://www.newscientist.com/article/2465472-the-world-is-on-track-for-between-1-9-and-3-7c-of-warming-by-2100/#climatechange #climatecollapse #greenhouseeffect #extinctionrisk #anthropogenicclimatechange #stopai #extinction #globaltemperature
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The world is on track for between 1.9 and 3.7°C of warming by 2100
https://www.newscientist.com/article/2465472-the-world-is-on-track-for-between-1-9-and-3-7c-of-warming-by-2100/#climatechange #climatecollapse #greenhouseeffect #extinctionrisk #anthropogenicclimatechange #stopai #extinction #globaltemperature
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The world is on track for between 1.9 and 3.7°C of warming by 2100
https://www.newscientist.com/article/2465472-the-world-is-on-track-for-between-1-9-and-3-7c-of-warming-by-2100/#climatechange #climatecollapse #greenhouseeffect #extinctionrisk #anthropogenicclimatechange #stopai #extinction #globaltemperature
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The world is on track for between 1.9 and 3.7°C of warming by 2100
https://www.newscientist.com/article/2465472-the-world-is-on-track-for-between-1-9-and-3-7c-of-warming-by-2100/#climatechange #climatecollapse #greenhouseeffect #extinctionrisk #anthropogenicclimatechange #stopai #extinction #globaltemperature
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The world is on track for between 1.9 and 3.7°C of warming by 2100
https://www.newscientist.com/article/2465472-the-world-is-on-track-for-between-1-9-and-3-7c-of-warming-by-2100/#climatechange #climatecollapse #greenhouseeffect #extinctionrisk #anthropogenicclimatechange #stopai #extinction #globaltemperature
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And now, the moment you’ve all been waiting for…
Previously on “merging global temperatures”, we looked at different ways of hierarchically grouping global temperature datasets to get a reasonable best estimate and uncertainty range. There were three principle ways to do that grouping: by SST data set, by LSAT dataset, and by INERTPolation method (the errant capitalisation will have a purposed later1).
I put these different groupings through my code for calculating an ensemble of ensembles and got the following summary statistics. I show here the annual means and a lowess smoothed series to highlight any differences in shorter and longer term behaviour.
First plot shows the means of the three ensembles and, as you can see, there is very little difference between them, so I won’t analyse this in detail.
Next up the standard deviation of the ensemble. There are some small differences here which are interesting.
The SST ensemble has a higher standard deviation in the 1880-1915 window possibly reflective of differences between marine datasets around this period. The LSAT ensemble has a larger standard deviation in the post 1930 period. Even though the differences are visible, they are still relatively small. The rule of thumb is that uncertainties in uncertainties are usually worse than 10% so we’re in that fuzzy zone where we can maybe explain why we see differences, but at the same time, we maybe don’t need to worry about them too much.
So, after all that, not much difference. This is a good thing though. It suggests that we’re not overly sensitive to reasonable choices about how to split up the ensemble.
We can also compare to what would happen if we just treated each dataset equally, as if they were all independent.
It doesn’t make much difference to the mean, but the uncertainty…
There’s a big difference there, with equal weighting generally coming in with a lower estimate of the uncertainty vs all the other combinations. This partly comes from burying DCENT in a mound of datasets that, for all their differences, are quite similar particularly in the early 20th century. I think this is a vote in favour of a more complex weighting in so far as we believe the edges of the distribution rather than mere weight of numbers.
-fin-
- The errant spelling was wholly unintentional though. Leaving it here as a reminder. ↩︎
-
And now, the moment you’ve all been waiting for…
Previously on “merging global temperatures”, we looked at different ways of hierarchically grouping global temperature datasets to get a reasonable best estimate and uncertainty range. There were three principle ways to do that grouping: by SST data set, by LSAT dataset, and by INERTPolation method (the errant capitalisation will have a purposed later).
I put these different groupings through my code for calculating an ensemble of ensembles and got the following summary statistics. I show here the annual means and a lowess smoothed series to highlight any differences in shorter and longer term behaviour.
First plot shows the means of the three ensembles and, as you can see, there is very little difference between them, so I won’t analyse this in detail.
Next up the standard deviation of the ensemble. There are some small differences here which are interesting.
The SST ensemble has a higher standard deviation in the 1880-1915 window possibly reflective of differences between marine datasets around this period. The LSAT ensemble has a larger standard deviation in the post 1930 period. Even though the differences are visible, they are still relatively small. The rule of thumb is that uncertainties in uncertainties are usually worse than 10% so we’re in that fuzzy zone where we can maybe explain why we see differences, but at the same time, we maybe don’t need to worry about them too much.
So, after all that, not much difference. This is a good thing though. It suggests that we’re not overly sensitive to reasonable choices about how to split up the ensemble.
We can also compare to what would happen if we just treated each dataset equally, as if they were all independent.
It doesn’t make much difference to the mean, but the uncertainty…
There’s a big difference there, with equal weighting generally coming in with a lower estimate of the uncertainty vs all the other combinations. This partly comes from burying DCENT in a mound of datasets that, for all their differences, are quite similar particularly in the early 20th century. I think this is a vote in favour of a more complex weighting.
-fin-
-
And now, the moment you’ve all been waiting for…
Previously on “merging global temperatures”, we looked at different ways of hierarchically grouping global temperature datasets to get a reasonable best estimate and uncertainty range. There were three principle ways to do that grouping: by SST data set, by LSAT dataset, and by INERTPolation method (the errant capitalisation will have a purposed later).
I put these different groupings through my code for calculating an ensemble of ensembles and got the following summary statistics. I show here the annual means and a lowess smoothed series to highlight any differences in shorter and longer term behaviour.
First plot shows the means of the three ensembles and, as you can see, there is very little difference between them, so I won’t analyse this in detail.
Next up the standard deviation of the ensemble. There are some small differences here which are interesting.
The SST ensemble has a higher standard deviation in the 1880-1915 window possibly reflective of differences between marine datasets around this period. The LSAT ensemble has a larger standard deviation in the post 1930 period. Even though the differences are visible, they are still relatively small. The rule of thumb is that uncertainties in uncertainties are usually worse than 10% so we’re in that fuzzy zone where we can maybe explain why we see differences, but at the same time, we maybe don’t need to worry about them too much.
So, after all that, not much difference. This is a good thing though. It suggests that we’re not overly sensitive to reasonable choices about how to split up the ensemble.
We can also compare to what would happen if we just treated each dataset equally, as if they were all independent.
It doesn’t make much difference to the mean, but the uncertainty…
There’s a big difference there, with equal weighting generally coming in with a lower estimate of the uncertainty vs all the other combinations. This partly comes from burying DCENT in a mound of datasets that, for all their differences, are quite similar particularly in the early 20th century. I think this is a vote in favour of a more complex weighting.
-fin-
-
And now, the moment you’ve all been waiting for…
Previously on “merging global temperatures”, we looked at different ways of hierarchically grouping global temperature datasets to get a reasonable best estimate and uncertainty range. There were three principle ways to do that grouping: by SST data set, by LSAT dataset, and by INERTPolation method (the errant capitalisation will have a purposed later1).
I put these different groupings through my code for calculating an ensemble of ensembles and got the following summary statistics. I show here the annual means and a lowess smoothed series to highlight any differences in shorter and longer term behaviour.
First plot shows the means of the three ensembles and, as you can see, there is very little difference between them, so I won’t analyse this in detail.
Next up the standard deviation of the ensemble. There are some small differences here which are interesting.
The SST ensemble has a higher standard deviation in the 1880-1915 window possibly reflective of differences between marine datasets around this period. The LSAT ensemble has a larger standard deviation in the post 1930 period. Even though the differences are visible, they are still relatively small. The rule of thumb is that uncertainties in uncertainties are usually worse than 10% so we’re in that fuzzy zone where we can maybe explain why we see differences, but at the same time, we maybe don’t need to worry about them too much.
So, after all that, not much difference. This is a good thing though. It suggests that we’re not overly sensitive to reasonable choices about how to split up the ensemble.
We can also compare to what would happen if we just treated each dataset equally, as if they were all independent.
It doesn’t make much difference to the mean, but the uncertainty…
There’s a big difference there, with equal weighting generally coming in with a lower estimate of the uncertainty vs all the other combinations. This partly comes from burying DCENT in a mound of datasets that, for all their differences, are quite similar particularly in the early 20th century. I think this is a vote in favour of a more complex weighting in so far as we believe the edges of the distribution rather than mere weight of numbers.
-fin-
- The errant spelling was wholly unintentional though. Leaving it here as a reminder. ↩︎
-
Bear with
It started with a trifling dissatisfaction with how the IPCC arrived at their composite global temperature series which then developed as new datasets came out. Or perhaps even before then, with a similarly trifling dissatisfaction on the very same topic. My blog doesn’t get a lot of comments, but the two more recent posts have had a lot of very interesting and technical comments from Bruce Calvert (Thanks Bruce) on how to formalise some of the ideas. My latest post on the topic largely ignored the formalisms because I have a preference for simple methods (and a small brain).
What both are trying to do is satisfy a bunch of criteria. We have a set of different global temperature datasets, but what we want is:
- A single dataset…
- That integrates all of the information that the individual datasets provide
- Also, integrating all the knowledge we have that isn’t necessarily tied up in those datasets
- With a reasonable central estimate
- And an uncertainty range that represents our uncertainty
- which can be used to generate samples that are representative of uncertainty at all time scales
- and are representative of actual global temperature variability
These criteria would make a useful dataset with broad utility.
My method (as it has developed) provides 1, 4, 5, and 6, but falls short on 2, 3 and 7 by throwing out some information and mixing together datasets that represent somewhat different things. One could quibble about 4, 5, and 6 of course.
The Guttorp and Craigmile method (see also) provides 1, 4, 6, and 7, but does less well (in my assessment, see the links above) on 2, 3 and 5. In places their central estimate is likely compromised by poor dataset choices and they ignore information that is available in the datasets. These issues could be remedied.
Is it reasonable? Well, it includes some older datasets (e.g. GETQUOCS) that have old bias adjustments because they have a nice uncertainty analysis. One might even argue that with the publication of DCENT, all other datasets are questionable. I would counter that by noting that the major compelling improvements from DCENT really affect the early 20th century warming, but prior to that it just widens the uncertainty range.
Does it really represent our uncertainty? Again, it’s hard to say. We have an ensemble of opportunity and rather a poor one at that. The hierarchical grouping I suggested is healthier than it was when I first suggested it. We now have DCENT and COBE-STEMP3, which broaden the range of estimates, but we are still trying to estimate a broad distribution with a handful of samples. My method is only as broad as the range of the datasets we have but this is partly by design. Another thing missing is the fact that we know that mixing and matching the land and ocean components of NOAAGlobalTemp and HadCRUT would widen the spread.
Does it use all the information? No. The hierarchy tries to encode the major covariances that define the structural uncertainties, assuming these come from the choice of SST (or marine temperature) dataset. We know that datasets use similar land temperature datasets and largely the same sea ice datasets. I also don’t use uncertainty ranges if they’re not represented by an ensemble. This is partly in order to avoid having to make assumptions about the correlation structures of the errors and partly because I don’t know what those structures are. I’m also missing information from the NOAAGlobalTemp ensemble. That would be a very useful addition. The Vaccaro dataset also has an ensemble and an interestingly different interpolation approach. And now there is a new dataset in preprint, GloSAT, which combines marine air temperatures with land air temperatures to give a completely new beast.
How to do better?
One obvious way is to get those missing ensembles.
Another is to employ the more formal statistical approach
Sticking with my simplistic approach, Bruce came up with an interestingly objective way to weight datasets using the estimated covariances between them. This would rely on expert judgement and it seems like this would be a difficult issue. There’s not a single covariance between datasets. Say two datasets use the same SST dataset, but different interpolation methods and land temperatures. At any time step, the two datasets will effectively give the SST dataset different weights and those weights will change over time. That means the covariance will change over time too. The temporal structure will also vary with time. It’s complex but we could come up with reasonable approximations. We could weight land and ocean as 30:70 representing the ratio, or have some simple smoothed representation. We could develop a hierarchy of hierarchies. We could take a survey of experts, asking them to make their covariance estimates. etc.
So, a first minimal extension is to include GloSAT and Vaccaro ensembles, because the data are just there begging to be used. I rearranged the hierarchy to put Vaccaro and GETQUOCS in the same category and separated them from the HadCRUT5 datasets. I also jacked the ensemble up to 50,000 members because I can and I want to make matplotlib explode.
The shape of the uncertainty curve might look odd, but it’s just a consequence of using 1850-1900 as a baseline. Uncertainty is generally smaller during the baseline period because each ensemble member is forced to average to zero during that period. It increases afterwards because there is a lot of uncertainty in the early 20th century.
Till next time…
-
Bear with
It started with a trifling dissatisfaction with how the IPCC arrived at their composite global temperature series which then developed as new datasets came out. Or perhaps even before then, with a similarly trifling dissatisfaction on the very same topic. My blog doesn’t get a lot of comments, but the two more recent posts have had a lot of very interesting and technical comments from Bruce Calvert (Thanks Bruce) on how to formalise some of the ideas. My latest post on the topic largely ignored the formalisms because I have a preference for simple methods (and a small brain).
What both are trying to do is satisfy a bunch of criteria. We have a set of different global temperature datasets, but what we want is:
- A single dataset…
- That integrates all of the information that the individual datasets provide
- Also, integrating all the knowledge we have that isn’t necessarily tied up in those datasets
- With a reasonable central estimate
- And an uncertainty range that represents our uncertainty
- which can be used to generate samples that are representative of uncertainty at all time scales
- and are representative of actual global temperature variability
These criteria would make a useful dataset with broad utility.
My method (as it has developed) provides 1, 4, 5, and 6, but falls short on 2, 3 and 7 by throwing out some information and mixing together datasets that represent somewhat different things. One could quibble about 4, 5, and 6 of course.
The Guttorp and Craigmile method (see also) provides 1, 4, 6, and 7, but does less well (in my assessment, see the links above) on 2, 3 and 5. In places their central estimate is likely compromised by poor dataset choices and they ignore information that is available in the datasets. These issues could be remedied.
Is it reasonable? Well, it includes some older datasets (e.g. GETQUOCS) that have old bias adjustments because they have a nice uncertainty analysis. One might even argue that with the publication of DCENT, all other datasets are questionable. I would counter that by noting that the major compelling improvements from DCENT really affect the early 20th century warming, but prior to that it just widens the uncertainty range.
Does it really represent our uncertainty? Again, it’s hard to say. We have an ensemble of opportunity and rather a poor one at that. The hierarchical grouping I suggested is healthier than it was when I first suggested it. We now have DCENT and COBE-STEMP3, which broaden the range of estimates, but we are still trying to estimate a broad distribution with a handful of samples. My method is only as broad as the range of the datasets we have but this is partly by design. Another thing missing is the fact that we know that mixing and matching the land and ocean components of NOAAGlobalTemp and HadCRUT would widen the spread.
Does it use all the information? No. The hierarchy tries to encode the major covariances that define the structural uncertainties, assuming these come from the choice of SST (or marine temperature) dataset. We know that datasets use similar land temperature datasets and largely the same sea ice datasets. I also don’t use uncertainty ranges if they’re not represented by an ensemble. This is partly in order to avoid having to make assumptions about the correlation structures of the errors and partly because I don’t know what those structures are. I’m also missing information from the NOAAGlobalTemp ensemble. That would be a very useful addition. The Vaccaro dataset also has an ensemble and an interestingly different interpolation approach. And now there is a new dataset in preprint, GloSAT, which combines marine air temperatures with land air temperatures to give a completely new beast.
How to do better?
One obvious way is to get those missing ensembles.
Another is to employ the more formal statistical approach
Sticking with my simplistic approach, Bruce came up with an interestingly objective way to weight datasets using the estimated covariances between them. This would rely on expert judgement and it seems like this would be a difficult issue. There’s not a single covariance between datasets. Say two datasets use the same SST dataset, but different interpolation methods and land temperatures. At any time step, the two datasets will effectively give the SST dataset different weights and those weights will change over time. That means the covariance will change over time too. The temporal structure will also vary with time. It’s complex but we could come up with reasonable approximations. We could weight land and ocean as 30:70 representing the ratio, or have some simple smoothed representation. We could develop a hierarchy of hierarchies. We could take a survey of experts, asking them to make their covariance estimates. etc.
So, a first minimal extension is to include GloSAT and Vaccaro ensembles, because the data are just there begging to be used. I rearranged the hierarchy to put Vaccaro and GETQUOCS in the same category and separated them from the HadCRUT5 datasets. I also jacked the ensemble up to 50,000 members because I can and I want to make matplotlib explode.
The shape of the uncertainty curve might look odd, but it’s just a consequence of using 1850-1900 as a baseline. Uncertainty is generally smaller during the baseline period because each ensemble member is forced to average to zero during that period. It increases afterwards because there is a lot of uncertainty in the early 20th century.
Till next time…
-
Bear with
It started with a trifling dissatisfaction with how the IPCC arrived at their composite global temperature series which then developed as new datasets came out. Or perhaps even before then, with a similarly trifling dissatisfaction on the very same topic. My blog doesn’t get a lot of comments, but the two more recent posts have had a lot of very interesting and technical comments from Bruce Calvert (Thanks Bruce) on how to formalise some of the ideas. My latest post on the topic largely ignored the formalisms because I have a preference for simple methods (and a small brain).
What both are trying to do is satisfy a bunch of criteria. We have a set of different global temperature datasets, but what we want is:
- A single dataset…
- That integrates all of the information that the individual datasets provide
- Also, integrating all the knowledge we have that isn’t necessarily tied up in those datasets
- With a reasonable central estimate
- And an uncertainty range that represents our uncertainty
- which can be used to generate samples that are representative of uncertainty at all time scales
- and are representative of actual global temperature variability
These criteria would make a useful dataset with broad utility.
My method (as it has developed) provides 1, 4, 5, and 6, but falls short on 2, 3 and 7 by throwing out some information and mixing together datasets that represent somewhat different things. One could quibble about 4, 5, and 6 of course.
The Guttorp and Craigmile method (see also) provides 1, 4, 6, and 7, but does less well (in my assessment, see the links above) on 2, 3 and 5. In places their central estimate is likely compromised by poor dataset choices and they ignore information that is available in the datasets. These issues could be remedied.
Is it reasonable? Well, it includes some older datasets (e.g. GETQUOCS) that have old bias adjustments because they have a nice uncertainty analysis. One might even argue that with the publication of DCENT, all other datasets are questionable. I would counter that by noting that the major compelling improvements from DCENT really affect the early 20th century warming, but prior to that it just widens the uncertainty range.
Does it really represent our uncertainty? Again, it’s hard to say. We have an ensemble of opportunity and rather a poor one at that. The hierarchical grouping I suggested is healthier than it was when I first suggested it. We now have DCENT and COBE-STEMP3, which broaden the range of estimates, but we are still trying to estimate a broad distribution with a handful of samples. My method is only as broad as the range of the datasets we have but this is partly by design. Another thing missing is the fact that we know that mixing and matching the land and ocean components of NOAAGlobalTemp and HadCRUT would widen the spread.
Does it use all the information? No. The hierarchy tries to encode the major covariances that define the structural uncertainties, assuming these come from the choice of SST (or marine temperature) dataset. We know that datasets use similar land temperature datasets and largely the same sea ice datasets. I also don’t use uncertainty ranges if they’re not represented by an ensemble. This is partly in order to avoid having to make assumptions about the correlation structures of the errors and partly because I don’t know what those structures are. I’m also missing information from the NOAAGlobalTemp ensemble. That would be a very useful addition. The Vaccaro dataset also has an ensemble and an interestingly different interpolation approach. And now there is a new dataset in preprint, GloSAT, which combines marine air temperatures with land air temperatures to give a completely new beast.
How to do better?
One obvious way is to get those missing ensembles.
Another is to employ the more formal statistical approach
Sticking with my simplistic approach, Bruce came up with an interestingly objective way to weight datasets using the estimated covariances between them. This would rely on expert judgement and it seems like this would be a difficult issue. There’s not a single covariance between datasets. Say two datasets use the same SST dataset, but different interpolation methods and land temperatures. At any time step, the two datasets will effectively give the SST dataset different weights and those weights will change over time. That means the covariance will change over time too. The temporal structure will also vary with time. It’s complex but we could come up with reasonable approximations. We could weight land and ocean as 30:70 representing the ratio, or have some simple smoothed representation. We could develop a hierarchy of hierarchies. We could take a survey of experts, asking them to make their covariance estimates. etc.
So, a first minimal extension is to include GloSAT and Vaccaro ensembles, because the data are just there begging to be used. I rearranged the hierarchy to put Vaccaro and GETQUOCS in the same category and separated them from the HadCRUT5 datasets. I also jacked the ensemble up to 50,000 members because I can and I want to make matplotlib explode.
The shape of the uncertainty curve might look odd, but it’s just a consequence of using 1850-1900 as a baseline. Uncertainty is generally smaller during the baseline period because each ensemble member is forced to average to zero during that period. It increases afterwards because there is a lot of uncertainty in the early 20th century.
Till next time…
-
Bear with
It started with a trifling dissatisfaction with how the IPCC arrived at their composite global temperature series which then developed as new datasets came out. Or perhaps even before then, with a similarly trifling dissatisfaction on the very same topic. My blog doesn’t get a lot of comments, but the two more recent posts have had a lot of very interesting and technical comments from Bruce Calvert (Thanks Bruce) on how to formalise some of the ideas. My latest post on the topic largely ignored the formalisms because I have a preference for simple methods (and a small brain).
What both are trying to do is satisfy a bunch of criteria. We have a set of different global temperature datasets, but what we want is:
- A single dataset…
- That integrates all of the information that the individual datasets provide
- Also, integrating all the knowledge we have that isn’t necessarily tied up in those datasets
- With a reasonable central estimate
- And an uncertainty range that represents our uncertainty
- which can be used to generate samples that are representative of uncertainty at all time scales
- and are representative of actual global temperature variability
These criteria would make a useful dataset with broad utility.
My method (as it has developed) provides 1, 4, 5, and 6, but falls short on 2, 3 and 7 by throwing out some information and mixing together datasets that represent somewhat different things. One could quibble about 4, 5, and 6 of course.
The Guttorp and Craigmile method (see also) provides 1, 4, 6, and 7, but does less well (in my assessment, see the links above) on 2, 3 and 5. In places their central estimate is likely compromised by poor dataset choices and they ignore information that is available in the datasets. These issues could be remedied.
Is it reasonable? Well, it includes some older datasets (e.g. GETQUOCS) that have old bias adjustments because they have a nice uncertainty analysis. One might even argue that with the publication of DCENT, all other datasets are questionable. I would counter that by noting that the major compelling improvements from DCENT really affect the early 20th century warming, but prior to that it just widens the uncertainty range.
Does it really represent our uncertainty? Again, it’s hard to say. We have an ensemble of opportunity and rather a poor one at that. The hierarchical grouping I suggested is healthier than it was when I first suggested it. We now have DCENT and COBE-STEMP3, which broaden the range of estimates, but we are still trying to estimate a broad distribution with a handful of samples. My method is only as broad as the range of the datasets we have but this is partly by design. Another thing missing is the fact that we know that mixing and matching the land and ocean components of NOAAGlobalTemp and HadCRUT would widen the spread.
Does it use all the information? No. The hierarchy tries to encode the major covariances that define the structural uncertainties, assuming these come from the choice of SST (or marine temperature) dataset. We know that datasets use similar land temperature datasets and largely the same sea ice datasets. I also don’t use uncertainty ranges if they’re not represented by an ensemble. This is partly in order to avoid having to make assumptions about the correlation structures of the errors and partly because I don’t know what those structures are. I’m also missing information from the NOAAGlobalTemp ensemble. That would be a very useful addition. The Vaccaro dataset also has an ensemble and an interestingly different interpolation approach. And now there is a new dataset in preprint, GloSAT, which combines marine air temperatures with land air temperatures to give a completely new beast.
How to do better?
One obvious way is to get those missing ensembles.
Another is to employ the more formal statistical approach
Sticking with my simplistic approach, Bruce came up with an interestingly objective way to weight datasets using the estimated covariances between them. This would rely on expert judgement and it seems like this would be a difficult issue. There’s not a single covariance between datasets. Say two datasets use the same SST dataset, but different interpolation methods and land temperatures. At any time step, the two datasets will effectively give the SST dataset different weights and those weights will change over time. That means the covariance will change over time too. The temporal structure will also vary with time. It’s complex but we could come up with reasonable approximations. We could weight land and ocean as 30:70 representing the ratio, or have some simple smoothed representation. We could develop a hierarchy of hierarchies. We could take a survey of experts, asking them to make their covariance estimates. etc.
So, a first minimal extension is to include GloSAT and Vaccaro ensembles, because the data are just there begging to be used. I rearranged the hierarchy to put Vaccaro and GETQUOCS in the same category and separated them from the HadCRUT5 datasets. I also jacked the ensemble up to 50,000 members because I can and I want to make matplotlib explode.
The shape of the uncertainty curve might look odd, but it’s just a consequence of using 1850-1900 as a baseline. Uncertainty is generally smaller during the baseline period because each ensemble member is forced to average to zero during that period. It increases afterwards because there is a lot of uncertainty in the early 20th century.
Till next time…
-
All things counter, original, spare, strange
Yesterday, I wrote a short post on Kadow et al. but I think it’s interesting to look at a bunch of different datasets to see how decisions about input data, QC and infiling affect what the dataset looks like.
I’ve taken nine datasets:
- HadCRUT5 non-infilled – this is the basic gridded data. It’s bias-corrected and quality controlled, but gaps aren’t filled and there are still some obvious data issues associated with measurement errors.
- HadCRUT5 analysis – this is the analysis version of the HadCRUT5 dataset. It’s infilled using gaussian process magic, which uses the error covariances. The analysis doesn’t just fill the gaps, but also makes an improved estimate of what’s in each gridbox based on the available info.
- Kadow – this is based on HadCRUT5 non-infilled, but the gaps are filled using a neural net.
- Calvert 2024 – based on HadCRUT5-non-infilled. It uses something kriging like, but with spatially and seasonally varying variance. It also accounts for the climatological difference between open water and sea ice. It also includes a spatial pattern representing ENSO variability (mostly).
- Berkeley Earth – kriging based estimate, using HadSST4 for the ocen.
- DCENT – non-infilled but using a different approach to homogenising land and ocean data.
- NOAAGlobalTemp v6 – this is based on a completely different system to HadCRUT. Data for land and sea are quality controlled, gridded and bias corrected differently across the board. Data are infilled using neural networks for the land and a low-frequency smoother plus local patterns of variability over the ocean.
- Vaccaro – uses GraphEM to fill gaps in the HadCRUT4 dataset. This method uses spatially varying local patterns of variability to fill gaps.
- GETQUOCS – uses multi-resolution lattice kriging, which is fairly self-descriptive. Based on HadCRUT4
It’s loaded with HadCRUT-based datasets because I have lots of them and it reduces the effects of other considerations (without removing them completely). Berkeley, DCENT and NOAAGlobalTemp are all quite different though. I’ve also shown the “central estimate” for each dataset. Let’s look at one month.
Temperature anomalies from nine datasets. Temperature scale runs from -3C (very blue) to +3C (very red).The month shown here is August 1877. There’s an El Nino in full swing. Positive temperature anomalies over southern Europe and colder-than-average temperatures further north. There’s some sort of Indian Ocean Dipole (IOD) thing going on (negative SST anomalies in the east and positive in the west; I forget which phase of the IOD that is).
You can also see where there are data and aren’t data (Robert Rohde reminds me that, you can see where there are and aren’t data in the HadCRUT and DCENT datasets, other datasets have more). There’s very little data in Africa (a few stations on the coasts, but not much in the interior), the Amazon, Canada, western Australia, large areas of Asia and, of course, nothing for Antarctica. Ships are largely confined to a few regular shipping routes, but there are exceptions. The Southern Ocean, Pacific and southwest Indian Ocean are sparsely observed. Some datasets choose to infill everywhere, others (HadCRUT, Berkeley) use limited interpolation (or none of course).
One thing to note is the “blobbiness” of the kriging based datasets – HadCRUT, Calvert, Berkeley, GETCUOCS – which is related to their use of local covariance functions (or similar). These tend to match (or come close) to the available observations, but in the gaps, the methods tend back to their background estimates. You can see this in the structure of the El Nino. Berkeley and GETCUOCS have warmer blobs associated with the available observations, but they don’t have a well-developed El Nino warm tongue like you see in Calvert (which uses an ENSO-related pattern) or in Kadow (neural nets), NOAAGlobalTemp (local patterns) and Vaccaro (local patterns).
The kriging estimates are also “smooth” which is a characteristic of these kinds of estimates. It’s also, partly, a result of showing the central estimate. Some of these datasets have an ensemble associated with them (HadCRUT and GETCUOCS) which provide samples from the posterior distribution of the analysis. These samples have more realistic variability in so far as the estimated covariances and uncertainties are realistic1.
In this early period, with extensive data gaps, there can be large differences between datasets even where there are data. The addition or removal of one station can make quite a difference. In areas with absolutely no observations, the different methods can give very different answers. Also note how each dataset deals with the sea ice edge, particularly around Antarctica.
Another example month – December 1926 – shows some of the interesting differences that can occur locally due to how uncertainty and structure in the SST fields are handled. As both measurement error and actual changes in SST can affect the variance of the field, any infilling algorithm is essentially trying to put the variability it sees into one of those two bins.
HadCRUT non-infilled shows a streak of positive anomalies in the South Atlantic. You can see the same in DCENT. Now, it could be that it’s a real feature. At the same time, those observations are very different from their near-neighbours and follow an elongated path that suggests they all came from one ship. In the HadCRUT error model, each identifiable ship is assumed to be biased by some amount (imagine a miscalibrated thermometer that, in this case, always reads 2C too high). In addition to the per-ship bias, each individual observation is assumed to have an independent measurement error. When all the ships and observations are averaged onto a grid, this combination leads to complicated structures in the errors. The way other datasets use the HadCRUT error model or build their own error models changes how those errors – and their correlations – are represented.
Each infilled dataset also makes assumptions about the structure of the actual temperature anomaly field. The kriged estimates largely assume that locations that are close together are more likely to have similar anomalies and locations that are far apart will essentially be independent. Some of the kriged estimates (Calvert, HadCRUT, Berkeley) also include some kind of global average or other large scale pattern(s)2. NOAAGlobalTemp has a low-frequency component which effectively averages over large areas and longer time periods, but also fits local patterns of variability to the data. These have local structure but are relatively short range. Vaccaro has “local” patterns too, where local is defined in terms of how closely related locations’ anomalies are. Kadow is a neural net, so god knows what’s going on in there; some combination of local and large scale structure, no doubt.
The warm South Atlantic feature is more or less absent in HadCRUT’s infilled analysis. This is likely because it identified those observations as coming from a single ship and so down weighted them relative to independent information from other nearby grid cells. There’s still some effect, but the anomalies are scaled down. Calvert does likewise. In contrast, Berkeley Earth and GETQUOCS do pick up the feature more strongly. NOAAGobalTemp has a feature aligned with the ship track (Assuming that’s what it is) but it’s balanced by cooler anomalies in the wider vicinity. None of the patterns in NOAAGlobalTemp or Vaccaro quite match to the feature, so it doesn’t have a clear effect.
In other cases, the response isn’t so clear. Sometimes one or another dataset will react more strongly to a particular “feature”. Sometimes quality control in one analysis will miss something that another analysis caught and rejected. Even when “ship tracks” and other artificial features aren’t obvious to the eye, they’re still there, but hiding in the general noisiness. None of the datasets is perfect, and each will respond in different ways to what’s in the input data, which can lead to differences between datasets even in relatively well observed periods. These correlated errors might be relatively small at a local level, but when aggregated into a global or regional mean, they become relatively more important.
Anyway, that’s enough of that. Enjoy the movie.
-fin-
- UPDATE 2024-12-07: It’s also worth thinking about resolution and what that might mean. Most of these datasets are on 5°x5° latitude-longitude grids but Berkeley Earth is 1°x1°. However, when we’re thinking about the information they provide, it’s also worth thinking about feature resolution, which is (loosely speaking) the smallest level of realistic detail that the dataset can represent. Obviously a 5° datasets can’t resolve anything smaller than it’s gridcells (about 500km square at the equator, but smaller longitudinally at higher latitudes) but feature resolution is also related to the “smoothness” of the kriged estimates. The smoothness of the fields depends on the “function” used to estimate covariance between data points and also on the assumptions about uncertainty in the data. The pattern based methods don’t tend to smooth things out so much. On the other hand, they assume that those patterns remain unchanged, or that the actual pattern can be recreated by adding together a bunch of other patterns. The neural network method should, in principle be able to resolve any size of feature (down to the grid scale, of course), but what that means in areas without data is an interesting question. Watch what NOAAGlobalTemp and Kadow do in Antarctica pre 1958 for example. ↩︎
- “Global average” isn’t much of a pattern, but it is a sort of pattern. ↩︎
#climate #climateChange #climateMonitoring #globalTemperature
-
All things counter, original, spare, strange
Yesterday, I wrote a short post on Kadow et al. but I think it’s interesting to look at a bunch of different datasets to see how decisions about input data, QC and infiling affect what the dataset looks like.
I’ve taken nine datasets:
- HadCRUT5 non-infilled – this is the basic gridded data. It’s bias-corrected and quality controlled, but gaps aren’t filled and there are still some obvious data issues associated with measurement errors.
- HadCRUT5 analysis – this is the analysis version of the HadCRUT5 dataset. It’s infilled using gaussian process magic, which uses the error covariances. The analysis doesn’t just fill the gaps, but also makes an improved estimate of what’s in each gridbox based on the available info.
- Kadow – this is based on HadCRUT5 non-infilled, but the gaps are filled using a neural net.
- Calvert 2024 – based on HadCRUT5-non-infilled. It uses something kriging like, but with spatially and seasonally varying variance. It also accounts for the climatological difference between open water and sea ice. It also includes a spatial pattern representing ENSO variability (mostly).
- Berkeley Earth – kriging based estimate, using HadSST4 for the ocen.
- DCENT – non-infilled but using a different approach to homogenising land and ocean data.
- NOAAGlobalTemp v6 – this is based on a completely different system to HadCRUT. Data for land and sea are quality controlled, gridded and bias corrected differently across the board. Data are infilled using neural networks for the land and a low-frequency smoother plus local patterns of variability over the ocean.
- Vaccaro – uses GraphEM to fill gaps in the HadCRUT4 dataset. This method uses spatially varying local patterns of variability to fill gaps.
- GETQUOCS – uses multi-resolution lattice kriging, which is fairly self-descriptive. Based on HadCRUT4
It’s loaded with HadCRUT-based datasets because I have lots of them and it reduces the effects of other considerations (without removing them completely). Berkeley, DCENT and NOAAGlobalTemp are all quite different though. I’ve also shown the “central estimate” for each dataset. Let’s look at one month.
Temperature anomalies from nine datasets. Temperature scale runs from -3C (very blue) to +3C (very red).The month shown here is August 1877. There’s an El Nino in full swing. Positive temperature anomalies over southern Europe and colder-than-average temperatures further north. There’s some sort of Indian Ocean Dipole (IOD) thing going on (negative SST anomalies in the east and positive in the west; I forget which phase of the IOD that is).
You can also see where there are data and aren’t data (Robert Rohde reminds me that, you can see where there are and aren’t data in the HadCRUT and DCENT datasets, other datasets have more). There’s very little data in Africa (a few stations on the coasts, but not much in the interior), the Amazon, Canada, western Australia, large areas of Asia and, of course, nothing for Antarctica. Ships are largely confined to a few regular shipping routes, but there are exceptions. The Southern Ocean, Pacific and southwest Indian Ocean are sparsely observed. Some datasets choose to infill everywhere, others (HadCRUT, Berkeley) use limited interpolation (or none of course).
One thing to note is the “blobbiness” of the kriging based datasets – HadCRUT, Calvert, Berkeley, GETCUOCS – which is related to their use of local covariance functions (or similar). These tend to match (or come close) to the available observations, but in the gaps, the methods tend back to their background estimates. You can see this in the structure of the El Nino. Berkeley and GETCUOCS have warmer blobs associated with the available observations, but they don’t have a well-developed El Nino warm tongue like you see in Calvert (which uses an ENSO-related pattern) or in Kadow (neural nets), NOAAGlobalTemp (local patterns) and Vaccaro (local patterns).
The kriging estimates are also “smooth” which is a characteristic of these kinds of estimates. It’s also, partly, a result of showing the central estimate. Some of these datasets have an ensemble associated with them (HadCRUT and GETCUOCS) which provide samples from the posterior distribution of the analysis. These samples have more realistic variability in so far as the estimated covariances and uncertainties are realistic1.
In this early period, with extensive data gaps, there can be large differences between datasets even where there are data. The addition or removal of one station can make quite a difference. In areas with absolutely no observations, the different methods can give very different answers. Also note how each dataset deals with the sea ice edge, particularly around Antarctica.
Another example month – December 1926 – shows some of the interesting differences that can occur locally due to how uncertainty and structure in the SST fields are handled. As both measurement error and actual changes in SST can affect the variance of the field, any infilling algorithm is essentially trying to put the variability it sees into one of those two bins.
HadCRUT non-infilled shows a streak of positive anomalies in the South Atlantic. You can see the same in DCENT. Now, it could be that it’s a real feature. At the same time, those observations are very different from their near-neighbours and follow an elongated path that suggests they all came from one ship. In the HadCRUT error model, each identifiable ship is assumed to be biased by some amount (imagine a miscalibrated thermometer that, in this case, always reads 2C too high). In addition to the per-ship bias, each individual observation is assumed to have an independent measurement error. When all the ships and observations are averaged onto a grid, this combination leads to complicated structures in the errors. The way other datasets use the HadCRUT error model or build their own error models changes how those errors – and their correlations – are represented.
Each infilled dataset also makes assumptions about the structure of the actual temperature anomaly field. The kriged estimates largely assume that locations that are close together are more likely to have similar anomalies and locations that are far apart will essentially be independent. Some of the kriged estimates (Calvert, HadCRUT, Berkeley) also include some kind of global average or other large scale pattern(s)2. NOAAGlobalTemp has a low-frequency component which effectively averages over large areas and longer time periods, but also fits local patterns of variability to the data. These have local structure but are relatively short range. Vaccaro has “local” patterns too, where local is defined in terms of how closely related locations’ anomalies are. Kadow is a neural net, so god knows what’s going on in there; some combination of local and large scale structure, no doubt.
The warm South Atlantic feature is more or less absent in HadCRUT’s infilled analysis. This is likely because it identified those observations as coming from a single ship and so down weighted them relative to independent information from other nearby grid cells. There’s still some effect, but the anomalies are scaled down. Calvert does likewise. In contrast, Berkeley Earth and GETQUOCS do pick up the feature more strongly. NOAAGobalTemp has a feature aligned with the ship track (Assuming that’s what it is) but it’s balanced by cooler anomalies in the wider vicinity. None of the patterns in NOAAGlobalTemp or Vaccaro quite match to the feature, so it doesn’t have a clear effect.
In other cases, the response isn’t so clear. Sometimes one or another dataset will react more strongly to a particular “feature”. Sometimes quality control in one analysis will miss something that another analysis caught and rejected. Even when “ship tracks” and other artificial features aren’t obvious to the eye, they’re still there, but hiding in the general noisiness. None of the datasets is perfect, and each will respond in different ways to what’s in the input data, which can lead to differences between datasets even in relatively well observed periods. These correlated errors might be relatively small at a local level, but when aggregated into a global or regional mean, they become relatively more important.
Anyway, that’s enough of that. Enjoy the movie.
-fin-
- UPDATE 2024-12-07: It’s also worth thinking about resolution and what that might mean. Most of these datasets are on 5°x5° latitude-longitude grids but Berkeley Earth is 1°x1°. However, when we’re thinking about the information they provide, it’s also worth thinking about feature resolution, which is (loosely speaking) the smallest level of realistic detail that the dataset can represent. Obviously a 5° datasets can’t resolve anything smaller than it’s gridcells (about 500km square at the equator, but smaller longitudinally at higher latitudes) but feature resolution is also related to the “smoothness” of the kriged estimates. The smoothness of the fields depends on the “function” used to estimate covariance between data points and also on the assumptions about uncertainty in the data. The pattern based methods don’t tend to smooth things out so much. On the other hand, they assume that those patterns remain unchanged, or that the actual pattern can be recreated by adding together a bunch of other patterns. The neural network method should, in principle be able to resolve any size of feature (down to the grid scale, of course), but what that means in areas without data is an interesting question. Watch what NOAAGlobalTemp and Kadow do in Antarctica pre 1958 for example. ↩︎
- “Global average” isn’t much of a pattern, but it is a sort of pattern. ↩︎
#climate #climateChange #climateMonitoring #globalTemperature
-
Researchers point out that the rapid increase in global temperature might be due to reduced planetary albedo.
https://newstainmentora.blogspot.com/2024/12/researchers-point-out-that-rapid.html
#GlobalWarming #globaltemperature #Earth #planet #newstainmentora -
Researchers point out that the rapid increase in global temperature might be due to reduced planetary albedo.
https://newstainmentora.blogspot.com/2024/12/researchers-point-out-that-rapid.html
#GlobalWarming #globaltemperature #Earth #planet #newstainmentora -
Researchers point out that the rapid increase in global temperature might be due to reduced planetary albedo.
https://newstainmentora.blogspot.com/2024/12/researchers-point-out-that-rapid.html
#GlobalWarming #globaltemperature #Earth #planet #newstainmentora -
Researchers point out that the rapid increase in global temperature might be due to reduced planetary albedo.
https://newstainmentora.blogspot.com/2024/12/researchers-point-out-that-rapid.html
#GlobalWarming #globaltemperature #Earth #planet #newstainmentora -
Sunday Monday and Tuesday this week all exceeded Global Daily Average Temperature Record set in July 2023. Welcome to the Anthropocene.
“Monday 22 July revised to 17.16C, as Tuesday comes in at 17.15C. Both break the Sunday global temperature record and all break the global temperature record set last year
17.15C 23 July 2024
17.16C 22 July 2024
17.09C 21 July 2024
17.08C 6 July 2023 “ - 🇦🇺climatologist Andrew Watkins
#GlobalTemperature #climatecrisis
https://pulse.climate.copernicus.eu/ -
Sunday Monday and Tuesday this week all exceeded Global Daily Average Temperature Record set in July 2023. Welcome to the Anthropocene.
“Monday 22 July revised to 17.16C, as Tuesday comes in at 17.15C. Both break the Sunday global temperature record and all break the global temperature record set last year
17.15C 23 July 2024
17.16C 22 July 2024
17.09C 21 July 2024
17.08C 6 July 2023 “ - 🇦🇺climatologist Andrew Watkins
#GlobalTemperature #climatecrisis
https://pulse.climate.copernicus.eu/ -
Sunday Monday and Tuesday this week all exceeded Global Daily Average Temperature Record set in July 2023. Welcome to the Anthropocene.
“Monday 22 July revised to 17.16C, as Tuesday comes in at 17.15C. Both break the Sunday global temperature record and all break the global temperature record set last year
17.15C 23 July 2024
17.16C 22 July 2024
17.09C 21 July 2024
17.08C 6 July 2023 “ - 🇦🇺climatologist Andrew Watkins
#GlobalTemperature #climatecrisis
https://pulse.climate.copernicus.eu/ -
Sunday Monday and Tuesday this week all exceeded Global Daily Average Temperature Record set in July 2023. Welcome to the Anthropocene.
“Monday 22 July revised to 17.16C, as Tuesday comes in at 17.15C. Both break the Sunday global temperature record and all break the global temperature record set last year
17.15C 23 July 2024
17.16C 22 July 2024
17.09C 21 July 2024
17.08C 6 July 2023 “ - 🇦🇺climatologist Andrew Watkins
#GlobalTemperature #climatecrisis
https://pulse.climate.copernicus.eu/ -
Sunday Monday and Tuesday this week all exceeded Global Daily Average Temperature Record set in July 2023. Welcome to the Anthropocene.
“Monday 22 July revised to 17.16C, as Tuesday comes in at 17.15C. Both break the Sunday global temperature record and all break the global temperature record set last year
17.15C 23 July 2024
17.16C 22 July 2024
17.09C 21 July 2024
17.08C 6 July 2023 “ - 🇦🇺climatologist Andrew Watkins
#GlobalTemperature #climatecrisis
https://pulse.climate.copernicus.eu/ -
#GlobalTemperature Anomalies from 1880 to 2023
From the #NASA Scientific Visualisation StudioFrom blue, to yellow, to burnt orange in 140 years
https://svs.gsfc.nasa.gov/5207/courtesy Gerald Kutney
-
#GlobalTemperature Anomalies from 1880 to 2023
From the #NASA Scientific Visualisation StudioFrom blue, to yellow, to burnt orange in 140 years
https://svs.gsfc.nasa.gov/5207/courtesy Gerald Kutney
-
#GlobalTemperature Anomalies from 1880 to 2023
From the #NASA Scientific Visualisation StudioFrom blue, to yellow, to burnt orange in 140 years
https://svs.gsfc.nasa.gov/5207/courtesy Gerald Kutney
-
#GlobalTemperature Anomalies from 1880 to 2023
From the #NASA Scientific Visualisation StudioFrom blue, to yellow, to burnt orange in 140 years
https://svs.gsfc.nasa.gov/5207/courtesy Gerald Kutney
-
#GlobalTemperature Anomalies from 1880 to 2023
From the #NASA Scientific Visualisation StudioFrom blue, to yellow, to burnt orange in 140 years
https://svs.gsfc.nasa.gov/5207/courtesy Gerald Kutney
-
Global 2m surface temperatures spiked to 1.98°C above the 1850-1900 IPCC baseline on Nov 17 according to Prof Eliot Jacobson at the birdsite.
Only one day since 1940 has been more extreme: Feb. 28, 2016, with an anomaly of 1.99°C.
Update: The global 2m temperature on Nov. 18 was 2.01°C.
Based on the first 17 days of November, this month is heading towards a new global heat record.
#ClimateCrisis #GlobalTemperature
https://twitter.com/EliotJacobson/status/1725849894941024543
-
Global 2m surface temperatures spiked to 1.98°C above the 1850-1900 IPCC baseline on Nov 17 according to Prof Eliot Jacobson at the birdsite.
Only one day since 1940 has been more extreme: Feb. 28, 2016, with an anomaly of 1.99°C.
Update: The global 2m temperature on Nov. 18 was 2.01°C.
Based on the first 17 days of November, this month is heading towards a new global heat record.
#ClimateCrisis #GlobalTemperature
https://twitter.com/EliotJacobson/status/1725849894941024543
-
Global 2m surface temperatures spiked to 1.98°C above the 1850-1900 IPCC baseline on Nov 17 according to Prof Eliot Jacobson at the birdsite.
Only one day since 1940 has been more extreme: Feb. 28, 2016, with an anomaly of 1.99°C.
Update: The global 2m temperature on Nov. 18 was 2.01°C.
Based on the first 17 days of November, this month is heading towards a new global heat record.
#ClimateCrisis #GlobalTemperature
https://twitter.com/EliotJacobson/status/1725849894941024543
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Global 2m surface temperatures spiked to 1.98°C above the 1850-1900 IPCC baseline on Nov 17 according to Prof Eliot Jacobson at the birdsite.
Only one day since 1940 has been more extreme: Feb. 28, 2016, with an anomaly of 1.99°C.
Update: The global 2m temperature on Nov. 18 was 2.01°C.
Based on the first 17 days of November, this month is heading towards a new global heat record.
#ClimateCrisis #GlobalTemperature
https://twitter.com/EliotJacobson/status/1725849894941024543
-
Global 2m surface temperatures spiked to 1.98°C above the 1850-1900 IPCC baseline on Nov 17 according to Prof Eliot Jacobson at the birdsite.
Only one day since 1940 has been more extreme: Feb. 28, 2016, with an anomaly of 1.99°C.
Update: The global 2m temperature on Nov. 18 was 2.01°C.
Based on the first 17 days of November, this month is heading towards a new global heat record.
#ClimateCrisis #GlobalTemperature
https://twitter.com/EliotJacobson/status/1725849894941024543
