1/ #oversterfte Netherlands: The observed mortality rates from EMA Pharmacovigilance (C19 vax) look bad, but in a range that shouldn't show up in the total mortality. It would be a disaster if it did.
What we need are mortality rates by cause and age (e.g. cardiac...).
2/ NL 15-19 years: nothing to see, except MH17 incident 2014.
3/ NL 20-24 years: nothing to see here, except MH17 incident 2014.
4/ NL 25-29 years: nothing to see here. MH17 incident 2014 still visible above the normal rates.
5/ NL 30-34 years: nothing to see here. MH17 incident 2014 still there.
6/ NL 35-39 years: nothing to see here.
7/ NL 40-44 years: nothing to see here.
8/ NL 45-49 years: nothing to see here.
9/ NL 50-54 years: nothing to see here.
10/ NL 55-59 years: nothing to see here.
11/ NL 60-64 years: nothing to see here. Seasonality starts to get visible in this age group.
12/ NL 65-69 years: seasonality visible. Also the sharp but short peak of the first C19 wave. But not at amplitudes that would justify a general panic.
13/ NL 70-74 years: seasonality visible. Sharp but short peak of the first C19 wave. The 2020 spring peak was higher (but shorter) than the typical flu wave. The total peak area is comparable with the 2018 flu season. The 2nd 2020 autumn wave was longer.
14/ NL 75-79 years: seasonality visible. Sharp but short peak of the first C19 wave. The 2020 spring peak was higher (but shorter) than the typical flu wave. The total peak area is comparable with the 2018 flu season. The 2nd 2020 autumn wave was longer.
15/ NL 80-84 years: seasonality visible. Sharp but short peak of the first C19 wave. The 2020 spring peak was higher (but shorter) than the typical flu wave. The total peak area is comparable with the 2018 flu season. The 2nd 2020 autumn wave was longer.
16/ NL 85-89 years: seasonality visible. Sharp but short peak of the first C19 wave. The 2020 spring peak was higher (but shorter) than the typical flu wave. The total peak area is comparable with the 2018 flu season. The 2nd 2020 autumn wave was longer.
17/ Conclusion for 2021: 1) Seasons start at different times--> little can be said now for 21/22 season. 2) The background is higher (even in the young) than any potential vax signal. It would be a disaster if vax would be visible in total mortality. 3) We need data by cause.
18/ To assess any vax safety issues in detail we need: 4) mortality figures by cause, gender and age bin for the young cohorts(<65), e.g. cardiac events. 5) IC data by cause, gender and age for the young cohorts (<65) e.g. cardiac events and thrombotic events.
They're trolling / insulting. The request was clear: compare ERA5 2km / @meteoblue with @AEMET_CValencia sensor at an hourly level. If they match at night, cloudy days, winter, but the sensor shows higher T in summer clear skies / no wind / day 👉 sensor is heat-biased. So? Go.
Thanks, @meteoblue. Normal conversation can be so easy. If the Spanish gentlemen would now provide access to their hourly station dataset, we can overlay it with the fine-grid ERA5 2km hourly product and see what's going on. Does that sound like a way forward @AEMET_CValencia ?
@meteoblue @AEMET_CValencia He clearly doesn’t understand their response nor my request. At this stage, I just want him to provide THE HOURLY DATA. What the answer actually means is that the 30 km cell is more representative of the region’s climate—yes, it’s better than the station. Well done @ChGefaell 👍.
1/ Such places have no credibility for accurate bias free measurements. It's the opposite of a stable environment and per default a diesel powered urban expedition place. We see how the melting starts around the airport and the town.
2/ Here we see another example (Alaska). Russian high-lat regions are among the worst. It's a deception to take measurements from such places and claim that you've done 'science' while actually just picking up dirt. Why not Everest dirt basecamp next?
3/ It escalated quickly. Similar to @BMcNoldy from Miami, master's student @Daaanvdb also used airport data instead of professional equipment, like what's available at @UNISvalbard.
Let's do better and use proper data from a better looking station.
1/ As mentioned, Europe is too urbanized for climate measurements. Shown below is just the UHI effect. As mentioned, ANY type of urban landscape altering increases surface temperatures as well. The Netherlands and Benelux regions are all fully biased and unfit for climate science
2/ As mentioned previously, North Sweden is the most credible place for climate measurements due to its development, peace, and ability to capture high-quality data. Besides Sweden, only the US provides reliable historic data. All other regions are not credible and biased today.
3/ Source: YCEO Surface Urban Heat Islands: Spatially-Averaged Daytime and Nighttime Intensity for Annual, Summer, and Winter.
It's from 2003. Now it's even more urbanized = worse.
2/ Florida: the gulf area showing up red at the anomaly chart. The buoy shows nominal at average values. 25C versus +26-27C in the SST model. That's a +1C heat bias.
3/ Next - Hawaii. Buoys are below average. SST product is showing heat anomalies there.
14th May: buoy 24.5C vs. 25.5C SST.
+1C heat bias
Interesting. It's apparently too warm, as long as you don't stick a real thermometer into the water to measure and realize: it's cold.
1/ Let's revisit this result from AIRS satellite measurements over 17 years, showing a +0.36W increase in forcing alongside a 40 ppm rise in CO2 concentration.
Does this align with the "observed" (questionable) increase in global temperature anomaly (+0.6C)?
2/The IPCC reports a calculated CO2 forcing of +0.5W, as detailed on the NOAA AGGI page, which you can find here:
The SW calculation overestimates by 40% compared to the +0.36W derived by the AIRS satellite, marking the first significant discrepancy. gml.noaa.gov/aggi/aggi.html
3/ Now we return to Happer's paper, showing that doubling CO2 from 400 --> 800 ppm results in +3W of forcing.
This is consistent with +3.5W reported by the NOAA AGGI (+3.5W).