#climatemonitoring — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #climatemonitoring, aggregated by home.social.
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Slovakia Sets Central Europe’s Highest Recorded Temperature at 42.2°C | Ukraine news https://www.byteseu.com/2259077/ #422°cSlovakia #CentralEuropeHeatwave #ClimateMonitoring #ExtremeHeat #News #Slovakia #SlovakiaTemperatureRecord
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Slovakia Sets Central Europe’s Highest Recorded Temperature at 42.2°C | Ukraine news
A record-breaking heatwave is reshaping weather expectations across the region, with meteorologists warning that extreme temperatures may soon…
#Slovakia #SK #Europe #Europa #EU #42.2°cslovakia #centraleuropeheatwave #Climatemonitoring #extremeheat #News #slovakia #slovakiatemperaturerecord #Slovensko #Správy
https://www.europesays.com/3181668/ -
🌍 🛰️ New five-year agreement signed between GÉANT and #EUMETSAT
We are proud to announce the signature of a five-year agreement between GÉANT and EUMETSAT, advancing our long-standing collaboration on #network and #connectivity services to support the global distribution of meteorological #satellite #data for weather forecasting, #climate monitoring, and environmental #research.
#EUMETcast #satelliteData #Meteorology #EnvironmentalResearch #ClimateMonitoring
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🌍 🛰️ New five-year agreement signed between GÉANT and #EUMETSAT
We are proud to announce the signature of a five-year agreement between GÉANT and EUMETSAT, advancing our long-standing collaboration on #network and #connectivity services to support the global distribution of meteorological #satellite #data for weather forecasting, #climate monitoring, and environmental #research.
#EUMETcast #satelliteData #Meteorology #EnvironmentalResearch #ClimateMonitoring
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🌍 🛰️ New five-year agreement signed between GÉANT and #EUMETSAT
We are proud to announce the signature of a five-year agreement between GÉANT and EUMETSAT, advancing our long-standing collaboration on #network and #connectivity services to support the global distribution of meteorological #satellite #data for weather forecasting, #climate monitoring, and environmental #research.
#EUMETcast #satelliteData #Meteorology #EnvironmentalResearch #ClimateMonitoring
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🌍 🛰️ New five-year agreement signed between GÉANT and #EUMETSAT
We are proud to announce the signature of a five-year agreement between GÉANT and EUMETSAT, advancing our long-standing collaboration on #network and #connectivity services to support the global distribution of meteorological #satellite #data for weather forecasting, #climate monitoring, and environmental #research.
#EUMETcast #satelliteData #Meteorology #EnvironmentalResearch #ClimateMonitoring
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🌍 🛰️ New five-year agreement signed between GÉANT and #EUMETSAT
We are proud to announce the signature of a five-year agreement between GÉANT and EUMETSAT, advancing our long-standing collaboration on #network and #connectivity services to support the global distribution of meteorological #satellite #data for weather forecasting, #climate monitoring, and environmental #research.
#EUMETcast #satelliteData #Meteorology #EnvironmentalResearch #ClimateMonitoring
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Sentinel 6B launched by NASA satellite to boost climate monitoring | https://www.byteseu.com/1606748/ #Climate #ClimateChange #ClimateMonitoring #EnvironmentalMonitoringSatellite #GlobalWarming #NASA #SeaLevelRise #Sentinel6B
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Department of Geodesy (Hochschule Bochum) and 52°North held an interactive #KomMonitor workshop at the recent AGIT Conference. Participants explored the monitoring capabilities of the KomMonitor Web Application, analyzing heat exposure, the distribution of vulnerable population groups, and the accessibility of green spaces and drinking water infrastructure.
Sebastian Drost provides details in his blog post
👉 https://blog.52north.org/2025/07/22/kommonitor-workshop-agit-2025/ -
Department of Geodesy (Hochschule Bochum) and 52°North held an interactive #KomMonitor workshop at the recent AGIT Conference. Participants explored the monitoring capabilities of the KomMonitor Web Application, analyzing heat exposure, the distribution of vulnerable population groups, and the accessibility of green spaces and drinking water infrastructure.
Sebastian Drost provides details in his blog post
👉 https://blog.52north.org/2025/07/22/kommonitor-workshop-agit-2025/ -
Department of Geodesy (Hochschule Bochum) and 52°North held an interactive #KomMonitor workshop at the recent AGIT Conference. Participants explored the monitoring capabilities of the KomMonitor Web Application, analyzing heat exposure, the distribution of vulnerable population groups, and the accessibility of green spaces and drinking water infrastructure.
Sebastian Drost provides details in his blog post
👉 https://blog.52north.org/2025/07/22/kommonitor-workshop-agit-2025/ -
Department of Geodesy (Hochschule Bochum) and 52°North held an interactive #KomMonitor workshop at the recent AGIT Conference. Participants explored the monitoring capabilities of the KomMonitor Web Application, analyzing heat exposure, the distribution of vulnerable population groups, and the accessibility of green spaces and drinking water infrastructure.
Sebastian Drost provides details in his blog post
👉 https://blog.52north.org/2025/07/22/kommonitor-workshop-agit-2025/ -
🌐❄️ 𝗧𝗵𝗲 𝗻𝗲𝘄 𝗣𝗼𝗹𝗮𝗿 𝗣𝗼𝗿𝘁𝗮𝗹 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝗶𝘀 𝗹𝗶𝘃𝗲!
We’re excited to launch the updated version of 𝗣𝗼𝗹𝗮𝗿𝗽𝗼𝗿𝘁𝗮𝗹.𝗱𝗸 – your gateway to 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗼𝗻 𝗔𝗿𝗰𝘁𝗶𝗰 𝘀𝗲𝗮 𝗶𝗰𝗲 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗚𝗿𝗲𝗲𝗻𝗹𝗮𝗻𝗱 𝗜𝗰𝗲 𝗦𝗵𝗲𝗲𝘁.
📊 On 𝗣𝗼𝗹𝗮𝗿𝗽𝗼𝗿𝘁𝗮𝗹.𝗱𝗸, Danish research institutions share up-to-date monitoring and expert knowledge on the state of the Arctic cryosphere.
𝗛𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗳𝗶𝗻𝗱 𝗱𝗮𝘁𝗮, 𝗮𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘂𝗽𝗱𝗮𝘁𝗲𝘀 𝗼𝗻:
• Surface conditions on the Greenland Ice Sheet and its major outlet glaciers
• Total mass balance of the Greenland Ice Sheet and its contribution to global sea-level
• Sea ice extent, thickness and volume in the Arctic
• Temperature anomalies of sea ice and the open sea in the Arctic
• Icebergs around GreenlandThe portal is powered by the Danish research institutions #DMI, #GEUS, #DTUSpace and #DTUByg and supported by data from #Asiaq - Misissueqqarnerit - Grønlands Forundersøgelser and #PROMICE (https://promice.org/).
🔗 Visit the new site: https://polarportal.dk/en/
#PolarPortal #Greenland #Arctic #ClimateMonitoring #Cryosphere #IceSheet #Seaice #ClimateData #NCKF #climatechange
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🌐❄️ 𝗧𝗵𝗲 𝗻𝗲𝘄 𝗣𝗼𝗹𝗮𝗿 𝗣𝗼𝗿𝘁𝗮𝗹 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝗶𝘀 𝗹𝗶𝘃𝗲!
We’re excited to launch the updated version of 𝗣𝗼𝗹𝗮𝗿𝗽𝗼𝗿𝘁𝗮𝗹.𝗱𝗸 – your gateway to 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗼𝗻 𝗔𝗿𝗰𝘁𝗶𝗰 𝘀𝗲𝗮 𝗶𝗰𝗲 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗚𝗿𝗲𝗲𝗻𝗹𝗮𝗻𝗱 𝗜𝗰𝗲 𝗦𝗵𝗲𝗲𝘁.
📊 On 𝗣𝗼𝗹𝗮𝗿𝗽𝗼𝗿𝘁𝗮𝗹.𝗱𝗸, Danish research institutions share up-to-date monitoring and expert knowledge on the state of the Arctic cryosphere.
𝗛𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗳𝗶𝗻𝗱 𝗱𝗮𝘁𝗮, 𝗮𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘂𝗽𝗱𝗮𝘁𝗲𝘀 𝗼𝗻:
• Surface conditions on the Greenland Ice Sheet and its major outlet glaciers
• Total mass balance of the Greenland Ice Sheet and its contribution to global sea-level
• Sea ice extent, thickness and volume in the Arctic
• Temperature anomalies of sea ice and the open sea in the Arctic
• Icebergs around GreenlandThe portal is powered by the Danish research institutions #DMI, #GEUS, #DTUSpace and #DTUByg and supported by data from #Asiaq - Misissueqqarnerit - Grønlands Forundersøgelser and #PROMICE (https://promice.org/).
🔗 Visit the new site: https://polarportal.dk/en/
#PolarPortal #Greenland #Arctic #ClimateMonitoring #Cryosphere #IceSheet #Seaice #ClimateData #NCKF #climatechange
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🌐❄️ 𝗧𝗵𝗲 𝗻𝗲𝘄 𝗣𝗼𝗹𝗮𝗿 𝗣𝗼𝗿𝘁𝗮𝗹 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝗶𝘀 𝗹𝗶𝘃𝗲!
We’re excited to launch the updated version of 𝗣𝗼𝗹𝗮𝗿𝗽𝗼𝗿𝘁𝗮𝗹.𝗱𝗸 – your gateway to 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗼𝗻 𝗔𝗿𝗰𝘁𝗶𝗰 𝘀𝗲𝗮 𝗶𝗰𝗲 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗚𝗿𝗲𝗲𝗻𝗹𝗮𝗻𝗱 𝗜𝗰𝗲 𝗦𝗵𝗲𝗲𝘁.
📊 On 𝗣𝗼𝗹𝗮𝗿𝗽𝗼𝗿𝘁𝗮𝗹.𝗱𝗸, Danish research institutions share up-to-date monitoring and expert knowledge on the state of the Arctic cryosphere.
𝗛𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗳𝗶𝗻𝗱 𝗱𝗮𝘁𝗮, 𝗮𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘂𝗽𝗱𝗮𝘁𝗲𝘀 𝗼𝗻:
• Surface conditions on the Greenland Ice Sheet and its major outlet glaciers
• Total mass balance of the Greenland Ice Sheet and its contribution to global sea-level
• Sea ice extent, thickness and volume in the Arctic
• Temperature anomalies of sea ice and the open sea in the Arctic
• Icebergs around GreenlandThe portal is powered by the Danish research institutions #DMI, #GEUS, #DTUSpace and #DTUByg and supported by data from #Asiaq - Misissueqqarnerit - Grønlands Forundersøgelser and #PROMICE (https://promice.org/).
🔗 Visit the new site: https://polarportal.dk/en/
#PolarPortal #Greenland #Arctic #ClimateMonitoring #Cryosphere #IceSheet #Seaice #ClimateData #NCKF #climatechange
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🌐❄️ 𝗧𝗵𝗲 𝗻𝗲𝘄 𝗣𝗼𝗹𝗮𝗿 𝗣𝗼𝗿𝘁𝗮𝗹 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝗶𝘀 𝗹𝗶𝘃𝗲!
We’re excited to launch the updated version of 𝗣𝗼𝗹𝗮𝗿𝗽𝗼𝗿𝘁𝗮𝗹.𝗱𝗸 – your gateway to 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗼𝗻 𝗔𝗿𝗰𝘁𝗶𝗰 𝘀𝗲𝗮 𝗶𝗰𝗲 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗚𝗿𝗲𝗲𝗻𝗹𝗮𝗻𝗱 𝗜𝗰𝗲 𝗦𝗵𝗲𝗲𝘁.
📊 On 𝗣𝗼𝗹𝗮𝗿𝗽𝗼𝗿𝘁𝗮𝗹.𝗱𝗸, Danish research institutions share up-to-date monitoring and expert knowledge on the state of the Arctic cryosphere.
𝗛𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗳𝗶𝗻𝗱 𝗱𝗮𝘁𝗮, 𝗮𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘂𝗽𝗱𝗮𝘁𝗲𝘀 𝗼𝗻:
• Surface conditions on the Greenland Ice Sheet and its major outlet glaciers
• Total mass balance of the Greenland Ice Sheet and its contribution to global sea-level
• Sea ice extent, thickness and volume in the Arctic
• Temperature anomalies of sea ice and the open sea in the Arctic
• Icebergs around GreenlandThe portal is powered by the Danish research institutions #DMI, #GEUS, #DTUSpace and #DTUByg and supported by data from #Asiaq - Misissueqqarnerit - Grønlands Forundersøgelser and #PROMICE (https://promice.org/).
🔗 Visit the new site: https://polarportal.dk/en/
#PolarPortal #Greenland #Arctic #ClimateMonitoring #Cryosphere #IceSheet #Seaice #ClimateData #NCKF #climatechange
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🌐❄️ 𝗧𝗵𝗲 𝗻𝗲𝘄 𝗣𝗼𝗹𝗮𝗿 𝗣𝗼𝗿𝘁𝗮𝗹 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝗶𝘀 𝗹𝗶𝘃𝗲!
We’re excited to launch the updated version of 𝗣𝗼𝗹𝗮𝗿𝗽𝗼𝗿𝘁𝗮𝗹.𝗱𝗸 – your gateway to 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗼𝗻 𝗔𝗿𝗰𝘁𝗶𝗰 𝘀𝗲𝗮 𝗶𝗰𝗲 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗚𝗿𝗲𝗲𝗻𝗹𝗮𝗻𝗱 𝗜𝗰𝗲 𝗦𝗵𝗲𝗲𝘁.
📊 On 𝗣𝗼𝗹𝗮𝗿𝗽𝗼𝗿𝘁𝗮𝗹.𝗱𝗸, Danish research institutions share up-to-date monitoring and expert knowledge on the state of the Arctic cryosphere.
𝗛𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗳𝗶𝗻𝗱 𝗱𝗮𝘁𝗮, 𝗮𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘂𝗽𝗱𝗮𝘁𝗲𝘀 𝗼𝗻:
• Surface conditions on the Greenland Ice Sheet and its major outlet glaciers
• Total mass balance of the Greenland Ice Sheet and its contribution to global sea-level
• Sea ice extent, thickness and volume in the Arctic
• Temperature anomalies of sea ice and the open sea in the Arctic
• Icebergs around GreenlandThe portal is powered by the Danish research institutions #DMI, #GEUS, #DTUSpace and #DTUByg and supported by data from #Asiaq - Misissueqqarnerit - Grønlands Forundersøgelser and #PROMICE (https://promice.org/).
🔗 Visit the new site: https://polarportal.dk/en/
#PolarPortal #Greenland #Arctic #ClimateMonitoring #Cryosphere #IceSheet #Seaice #ClimateData #NCKF #climatechange
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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
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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
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For skies of couple-colour as a brinded cow
Some time ago1, I posted on a global temperature dataset by Kadow et al. – Artificial intelligence reconstructs missing climate information – which was created using neural nets to infill data gaps. It was used in the last IPCC report alongside the more traditional datasets: HadCRUT5, Berkeley Earth, and NOAAGlobalTemp. The other three datasets are reasonably mature at this point2, which means that their foibles are reasonably well understood. New foibles are being added all the time, but they get a lot of scrutiny.
I was making films of different datasets this week and one interesting foible of Kadow et al. is quite apparent. If you watch the video (don’t worry, you don’t need to watch the whole 17 minutes), you can quite clearly see a discontinuity at the Greenwich meridian.
The discontinuity is a consequence of the underlying neural network and is mentioned in the paper. The neural net was designed to reconstruct gaps in photographs which are, of course, rectangular. However, the Earth (according to current theories) is not. If you run off one end of the map, you don’t fall off the end of the world, you just appear at the other side.
In pictures, the left and right hand sides of the image are not usually strongly related to each other. Discontinuities are fine. Not so on the Earth. It’s most obvious at high latitudes where variability is high, data are sparse and the distances between gridboxes are much smaller than they are at the equator.
Every dataset has something weird like this buried away in it. The Kadow dataset was a sort of proof of concept (as I understand it) so its not surprising that there is such an obvious weirdness. It’s the kind of thing users should be aware of. It affects the correlations and RMSE errors (see Extended figures from the paper), which are quite different in Western parts of North Africa from areas to the east of the meridian.
For assessing global mean temperature and uncertainty in global temperatures, it’s probably fine. A discontinuity isn’t ideal, but the reconstruction looks to do a good job either side of it. It’s just that it does a different good job on each side3. Silver lining: differences across the divide are probably a reasonable measure of uncertainty. How it affects other measures is something to bear in mind when using it.
I find it interesting to watch datasets like this. While making HadSST4, I must have watched hundreds of hours of videos like this looping slowly through each and every component of the dataset – uncertainties, covariances, multiple reconstructions, bias estimates, gridded anomalies, number of observations, numbers of super observations – to see what I could see. Although the final datasets is only about 2.4 Gbytes – and the bit that most people use is around 9 Mbytes4 – the full processing cycle produced hundreds of gigabytes of intermediate information5.
Anyway, watch movies of the datasets you use and love. It’s great fun.
-fin-
- Really quite a long time ago now. The paper came out in 2020. ↩︎
- Maturity is one of those words that looks like a puddle, but, as anyone jumping into it will rapidly discover, it has no obvious bottom. The framework used to assess maturity applies to data stewardship rather than to a particular dataset per se, but the two things are usually treated synonymously. A dataset is the outcome of a process, but maturity covers the whole process – governance, life cycle, production, archiving, documentation, code, uncertainty, usage – and not just the dataset itself. ↩︎
- In the Icelandic village Hofsós there’s a house that was inherited by two brothers. They had very different ideas about what constituted good upkeep, so each took care (very much more or less care according to their tastes) of their half. The village also has a geothermally heated outdoor swimming pool with a view out across the desolate waters of Skagafjörður. For some reason the wikipedia page doesn’t mention this… ↩︎
- Or 13 kbytes if they’re only using the time series files. ↩︎
- This includes things like global error covariances for every identifiable ship for every month in the dataset, and several different reconstructions for each month and their associated error covariances. ↩︎
#ai #artificialIntelligence #climate #climateChange #climateMonitoring #dataScience #machineLearning #technology
-
For skies of couple-colour as a brinded cow
Some time ago1, I posted on a global temperature dataset by Kadow et al. – Artificial intelligence reconstructs missing climate information – which was created using neural nets to infill data gaps. It was used in the last IPCC report alongside the more traditional datasets: HadCRUT5, Berkeley Earth, and NOAAGlobalTemp. The other three datasets are reasonably mature at this point2, which means that their foibles are reasonably well understood. New foibles are being added all the time, but they get a lot of scrutiny.
I was making films of different datasets this week and one interesting foible of Kadow et al. is quite apparent. If you watch the video (don’t worry, you don’t need to watch the whole 17 minutes), you can quite clearly see a discontinuity at the Greenwich meridian.
The discontinuity is a consequence of the underlying neural network and is mentioned in the paper. The neural net was designed to reconstruct gaps in photographs which are, of course, rectangular. However, the Earth (according to current theories) is not. If you run off one end of the map, you don’t fall off the end of the world, you just appear at the other side.
In pictures, the left and right hand sides of the image are not usually strongly related to each other. Discontinuities are fine. Not so on the Earth. It’s most obvious at high latitudes where variability is high, data are sparse and the distances between gridboxes are much smaller than they are at the equator.
Every dataset has something weird like this buried away in it. The Kadow dataset was a sort of proof of concept (as I understand it) so its not surprising that there is such an obvious weirdness. It’s the kind of thing users should be aware of. It affects the correlations and RMSE errors (see Extended figures from the paper), which are quite different in Western parts of North Africa from areas to the east of the meridian.
For assessing global mean temperature and uncertainty in global temperatures, it’s probably fine. A discontinuity isn’t ideal, but the reconstruction looks to do a good job either side of it. It’s just that it does a different good job on each side3. Silver lining: differences across the divide are probably a reasonable measure of uncertainty. How it affects other measures is something to bear in mind when using it.
I find it interesting to watch datasets like this. While making HadSST4, I must have watched hundreds of hours of videos like this looping slowly through each and every component of the dataset – uncertainties, covariances, multiple reconstructions, bias estimates, gridded anomalies, number of observations, numbers of super observations – to see what I could see. Although the final datasets is only about 2.4 Gbytes – and the bit that most people use is around 9 Mbytes4 – the full processing cycle produced hundreds of gigabytes of intermediate information5.
Anyway, watch movies of the datasets you use and love. It’s great fun.
-fin-
- Really quite a long time ago now. The paper came out in 2020. ↩︎
- Maturity is one of those words that looks like a puddle, but, as anyone jumping into it will rapidly discover, it has no obvious bottom. The framework used to assess maturity applies to data stewardship rather than to a particular dataset per se, but the two things are usually treated synonymously. A dataset is the outcome of a process, but maturity covers the whole process – governance, life cycle, production, archiving, documentation, code, uncertainty, usage – and not just the dataset itself. ↩︎
- In the Icelandic village Hofsós there’s a house that was inherited by two brothers. They had very different ideas about what constituted good upkeep, so each took care (very much more or less care according to their tastes) of their half. The village also has a geothermally heated outdoor swimming pool with a view out across the desolate waters of Skagafjörður. For some reason the wikipedia page doesn’t mention this… ↩︎
- Or 13 kbytes if they’re only using the time series files. ↩︎
- This includes things like global error covariances for every identifiable ship for every month in the dataset, and several different reconstructions for each month and their associated error covariances. ↩︎
#ai #artificialIntelligence #climate #climateChange #climateMonitoring #dataScience #machineLearning #technology
-
Planet Earth was not only record warm July through September 2023 but was also unusually dry. Globally, this was the lowest July-September precipitation since 2000. #Australia and #Malaysia, #Brazil, eastern #Europe and parts of Africa standout as unusually dry. Southwest US and eastern Mediterranean well above average precipitation. Data from ERA5 courtesy of ECMWF/Copernicus. #Climate #ClimateMonitoring @ZLabe @Climatologist49
*Inadvertently attached wrong graphic. Now with correct graphic. -
Planet Earth was not only record warm July through September 2023 but was also unusually dry. Globally, this was the lowest July-September precipitation since 2000. #Australia and #Malaysia, #Brazil, eastern #Europe and parts of Africa standout as unusually dry. Southwest US and eastern Mediterranean well above average precipitation. Data from ERA5 courtesy of ECMWF/Copernicus. #Climate #ClimateMonitoring @ZLabe @Climatologist49
*Inadvertently attached wrong graphic. Now with correct graphic. -
Planet Earth was not only record warm July through September 2023 but was also unusually dry. Globally, this was the lowest July-September precipitation since 2000. #Australia and #Malaysia, #Brazil, eastern #Europe and parts of Africa standout as unusually dry. Southwest US and eastern Mediterranean well above average precipitation. Data from ERA5 courtesy of ECMWF/Copernicus. #Climate #ClimateMonitoring @ZLabe @Climatologist49
*Inadvertently attached wrong graphic. Now with correct graphic. -
Planet Earth was not only record warm July through September 2023 but was also unusually dry. Globally, this was the lowest July-September precipitation since 2000. #Australia and #Malaysia, #Brazil, eastern #Europe and parts of Africa standout as unusually dry. Southwest US and eastern Mediterranean well above average precipitation. Data from ERA5 courtesy of ECMWF/Copernicus. #Climate #ClimateMonitoring @ZLabe @Climatologist49
*Inadvertently attached wrong graphic. Now with correct graphic. -
Planet Earth was not only record warm July through September 2023 but was also unusually dry. Globally, this was the lowest July-September precipitation since 2000. #Australia and #Malaysia, #Brazil, eastern #Europe and parts of Africa standout as unusually dry. Southwest US and eastern Mediterranean well above average precipitation. Data from ERA5 courtesy of ECMWF/Copernicus. #Climate #ClimateMonitoring @ZLabe @Climatologist49
*Inadvertently attached wrong graphic. Now with correct graphic. -
August average temperatures were warmer than normal almost everywhere in and around Alaska, with the eastern Interior and adjacent areas in the Yukon especially warm. Eagle had the warmest August on record, Tok second warmest, Fairbanks and Gulkana third warmest. Southeast was again mild, with Sitka airport having second warmest August. #akwx #ytwx #Summer2023 #ClimateMonitoring
@Climatologist49 @pat_wx -
August average temperatures were warmer than normal almost everywhere in and around Alaska, with the eastern Interior and adjacent areas in the Yukon especially warm. Eagle had the warmest August on record, Tok second warmest, Fairbanks and Gulkana third warmest. Southeast was again mild, with Sitka airport having second warmest August. #akwx #ytwx #Summer2023 #ClimateMonitoring
@Climatologist49 @pat_wx -
August average temperatures were warmer than normal almost everywhere in and around Alaska, with the eastern Interior and adjacent areas in the Yukon especially warm. Eagle had the warmest August on record, Tok second warmest, Fairbanks and Gulkana third warmest. Southeast was again mild, with Sitka airport having second warmest August. #akwx #ytwx #Summer2023 #ClimateMonitoring
@Climatologist49 @pat_wx -
August average temperatures were warmer than normal almost everywhere in and around Alaska, with the eastern Interior and adjacent areas in the Yukon especially warm. Eagle had the warmest August on record, Tok second warmest, Fairbanks and Gulkana third warmest. Southeast was again mild, with Sitka airport having second warmest August. #akwx #ytwx #Summer2023 #ClimateMonitoring
@Climatologist49 @pat_wx -
August average temperatures were warmer than normal almost everywhere in and around Alaska, with the eastern Interior and adjacent areas in the Yukon especially warm. Eagle had the warmest August on record, Tok second warmest, Fairbanks and Gulkana third warmest. Southeast was again mild, with Sitka airport having second warmest August. #akwx #ytwx #Summer2023 #ClimateMonitoring
@Climatologist49 @pat_wx -
August rainfall relative to normal around Alaska showed considerable variability, but except for portions of the eastern Interior was mostly above normal, and in portions of Southcentral and western Alaska rainfall was way above normal. This same basic pattern prevailed in June and July too. #akwx #Summer2023. #ClimateMonitoring
-
August rainfall relative to normal around Alaska showed considerable variability, but except for portions of the eastern Interior was mostly above normal, and in portions of Southcentral and western Alaska rainfall was way above normal. This same basic pattern prevailed in June and July too. #akwx #Summer2023. #ClimateMonitoring
-
August rainfall relative to normal around Alaska showed considerable variability, but except for portions of the eastern Interior was mostly above normal, and in portions of Southcentral and western Alaska rainfall was way above normal. This same basic pattern prevailed in June and July too. #akwx #Summer2023. #ClimateMonitoring
-
August rainfall relative to normal around Alaska showed considerable variability, but except for portions of the eastern Interior was mostly above normal, and in portions of Southcentral and western Alaska rainfall was way above normal. This same basic pattern prevailed in June and July too. #akwx #Summer2023. #ClimateMonitoring
-
August rainfall relative to normal around Alaska showed considerable variability, but except for portions of the eastern Interior was mostly above normal, and in portions of Southcentral and western Alaska rainfall was way above normal. This same basic pattern prevailed in June and July too. #akwx #Summer2023. #ClimateMonitoring
-
Third installment of the July 2023 Alaska and Arctic #climate summary is now posted. For the #Arctic as a whole, this was the second warmest July on record. #akwx #ytwx #Summer2023 #ClimateMonitoring
https://alaskaclimate.substack.com/p/july-2023-temperature-and-precipitation
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Third installment of the July 2023 Alaska and Arctic #climate summary is now posted. For the #Arctic as a whole, this was the second warmest July on record. #akwx #ytwx #Summer2023 #ClimateMonitoring
https://alaskaclimate.substack.com/p/july-2023-temperature-and-precipitation
-
Third installment of the July 2023 Alaska and Arctic #climate summary is now posted. For the #Arctic as a whole, this was the second warmest July on record. #akwx #ytwx #Summer2023 #ClimateMonitoring
https://alaskaclimate.substack.com/p/july-2023-temperature-and-precipitation
-
Third installment of the July 2023 Alaska and Arctic #climate summary is now posted. For the #Arctic as a whole, this was the second warmest July on record. #akwx #ytwx #Summer2023 #ClimateMonitoring
https://alaskaclimate.substack.com/p/july-2023-temperature-and-precipitation
-
Third installment of the July 2023 Alaska and Arctic #climate summary is now posted. For the #Arctic as a whole, this was the second warmest July on record. #akwx #ytwx #Summer2023 #ClimateMonitoring
https://alaskaclimate.substack.com/p/july-2023-temperature-and-precipitation
-
Dramatic contrast in July precipitation in and around Alaska in ERA5 courtesy of ECMWF/Copernicus using a normals 1991-2020 baseline. Parts of western and Southcentral #Alaska much wetter than usual, with some areas with twice the normal rainfall. Eastern mainland Alaska, Southeast and most of the Yukon Territory and Northwest Territories were considerably drier than average. #akwx #ytwx #ntwx #ClimateMonitoring #Summer2023
@Climatologist49 @McYukon -
Dramatic contrast in July precipitation in and around Alaska in ERA5 courtesy of ECMWF/Copernicus using a normals 1991-2020 baseline. Parts of western and Southcentral #Alaska much wetter than usual, with some areas with twice the normal rainfall. Eastern mainland Alaska, Southeast and most of the Yukon Territory and Northwest Territories were considerably drier than average. #akwx #ytwx #ntwx #ClimateMonitoring #Summer2023
@Climatologist49 @McYukon -
Dramatic contrast in July precipitation in and around Alaska in ERA5 courtesy of ECMWF/Copernicus using a normals 1991-2020 baseline. Parts of western and Southcentral #Alaska much wetter than usual, with some areas with twice the normal rainfall. Eastern mainland Alaska, Southeast and most of the Yukon Territory and Northwest Territories were considerably drier than average. #akwx #ytwx #ntwx #ClimateMonitoring #Summer2023
@Climatologist49 @McYukon -
Dramatic contrast in July precipitation in and around Alaska in ERA5 courtesy of ECMWF/Copernicus using a normals 1991-2020 baseline. Parts of western and Southcentral #Alaska much wetter than usual, with some areas with twice the normal rainfall. Eastern mainland Alaska, Southeast and most of the Yukon Territory and Northwest Territories were considerably drier than average. #akwx #ytwx #ntwx #ClimateMonitoring #Summer2023
@Climatologist49 @McYukon -
Dramatic contrast in July precipitation in and around Alaska in ERA5 courtesy of ECMWF/Copernicus using a normals 1991-2020 baseline. Parts of western and Southcentral #Alaska much wetter than usual, with some areas with twice the normal rainfall. Eastern mainland Alaska, Southeast and most of the Yukon Territory and Northwest Territories were considerably drier than average. #akwx #ytwx #ntwx #ClimateMonitoring #Summer2023
@Climatologist49 @McYukon -
Pan-Arctic July 2023 temperature departures from the 1991-2020 average from ERA5 courtesy of ECMWF/Copernicus. Most of the Greenland Ice Sheet, northwest Canada and northern Alaska were exceptionally warm. Cooler than average July northeast Canada and most of the Scandinavian #Arctic. July temperature departures are always small over the central Arctic Ocean because heat goes into melting snow and sea ice.
#akwx #ytwx #ntwx #ClimateMonitoring #Summer2023
@Climatologist49 -
Pan-Arctic July 2023 temperature departures from the 1991-2020 average from ERA5 courtesy of ECMWF/Copernicus. Most of the Greenland Ice Sheet, northwest Canada and northern Alaska were exceptionally warm. Cooler than average July northeast Canada and most of the Scandinavian #Arctic. July temperature departures are always small over the central Arctic Ocean because heat goes into melting snow and sea ice.
#akwx #ytwx #ntwx #ClimateMonitoring #Summer2023
@Climatologist49 -
Pan-Arctic July 2023 temperature departures from the 1991-2020 average from ERA5 courtesy of ECMWF/Copernicus. Most of the Greenland Ice Sheet, northwest Canada and northern Alaska were exceptionally warm. Cooler than average July northeast Canada and most of the Scandinavian #Arctic. July temperature departures are always small over the central Arctic Ocean because heat goes into melting snow and sea ice.
#akwx #ytwx #ntwx #ClimateMonitoring #Summer2023
@Climatologist49 -
Pan-Arctic July 2023 temperature departures from the 1991-2020 average from ERA5 courtesy of ECMWF/Copernicus. Most of the Greenland Ice Sheet, northwest Canada and northern Alaska were exceptionally warm. Cooler than average July northeast Canada and most of the Scandinavian #Arctic. July temperature departures are always small over the central Arctic Ocean because heat goes into melting snow and sea ice.
#akwx #ytwx #ntwx #ClimateMonitoring #Summer2023
@Climatologist49 -
Pan-Arctic July 2023 temperature departures from the 1991-2020 average from ERA5 courtesy of ECMWF/Copernicus. Most of the Greenland Ice Sheet, northwest Canada and northern Alaska were exceptionally warm. Cooler than average July northeast Canada and most of the Scandinavian #Arctic. July temperature departures are always small over the central Arctic Ocean because heat goes into melting snow and sea ice.
#akwx #ytwx #ntwx #ClimateMonitoring #Summer2023
@Climatologist49 -
Alaska and Arctic July Wildfire and Oceans #climate summary now posted:
https://alaskaclimate.substack.com/p/july-2023-wildfire-and-oceans-summary
#akwx #ytwx #Arctic #ClimateMonitoring #Summer2023
@Climatologist49 -
Alaska and Arctic July Wildfire and Oceans #climate summary now posted:
https://alaskaclimate.substack.com/p/july-2023-wildfire-and-oceans-summary
#akwx #ytwx #Arctic #ClimateMonitoring #Summer2023
@Climatologist49 -
Alaska and Arctic July Wildfire and Oceans #climate summary now posted:
https://alaskaclimate.substack.com/p/july-2023-wildfire-and-oceans-summary
#akwx #ytwx #Arctic #ClimateMonitoring #Summer2023
@Climatologist49 -
Alaska and Arctic July Wildfire and Oceans #climate summary now posted:
https://alaskaclimate.substack.com/p/july-2023-wildfire-and-oceans-summary
#akwx #ytwx #Arctic #ClimateMonitoring #Summer2023
@Climatologist49 -
Alaska and Arctic July Wildfire and Oceans #climate summary now posted:
https://alaskaclimate.substack.com/p/july-2023-wildfire-and-oceans-summary
#akwx #ytwx #Arctic #ClimateMonitoring #Summer2023
@Climatologist49 -
There is so much going on with #Alaska and #Arctic #climate that it's going to take a while to sift through it all. In the meantime, new post up with some of the Alaska highlights from July. #akwx #ClimateMonitoring #Summer2023
https://alaskaclimate.substack.com/p/july-2023-alaska-climate-highlights
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There is so much going on with #Alaska and #Arctic #climate that it's going to take a while to sift through it all. In the meantime, new post up with some of the Alaska highlights from July. #akwx #ClimateMonitoring #Summer2023
https://alaskaclimate.substack.com/p/july-2023-alaska-climate-highlights
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There is so much going on with #Alaska and #Arctic #climate that it's going to take a while to sift through it all. In the meantime, new post up with some of the Alaska highlights from July. #akwx #ClimateMonitoring #Summer2023
https://alaskaclimate.substack.com/p/july-2023-alaska-climate-highlights
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There is so much going on with #Alaska and #Arctic #climate that it's going to take a while to sift through it all. In the meantime, new post up with some of the Alaska highlights from July. #akwx #ClimateMonitoring #Summer2023
https://alaskaclimate.substack.com/p/july-2023-alaska-climate-highlights