We’ve all heard the terminology. An extreme event happens — a flood or heat wave — and soon after it is characterized as a “1,000-year event” (or it doesn’t have to be 1,000, it could be any number). This week I watched one of the world’s most visible climate scientists, Michael E. Mann, go on national TV and in process show that he had no idea what the concept actually means.
Let’s start by correcting that climate scientist who expressed a popular misconception (about which climate scientists should know better). A 1,000-year flood does not refer to a level of flooding that comes around every 1,000 years.
This is an update of my CO2 budget model that explains yearly Mauna Loa atmospheric CO2 concentrations since 1959 with three main processes:
an anthropogenic source term, primarily from burning of fossil fuels
a constant yearly CO2 sink (removal) rate of 2.05% of the atmospheric “excess” over 295 ppm
an ENSO term that increases atmospheric CO2 during El Nino years and decreases it during La Nina years
The CO2 Budget Model
I described the CO2 budget model here. The most important new insight gained was that the model showed that the CO2 sink rate has not been declining as has been claimed by carbon cycle modelers after one adjusts for the history of El Nino and La Nina activity.
If the sink rate was really declining, that means the climate system is becoming less able to remove “excess” CO2 from the atmosphere, and future climate change will be (of course) worse than we thought. But I showed the declining sink rate was just an artifact of the history of El Nino and La Nina activity, as shown in the following figure (updated through 2022).
This is a continuation of previous papers (1) and (2) on Cloud Reduction. Further analysis of cloud data has revealed four new observations:
Mount Pinatubo ash in the atmosphere and Amazonia deforestation may be seen in the cloud data.
A correlation of measured “Temperature – Dew point Temperature”, T-Td, to Cloud Cover was found.
The Temperature – Dew point Temperature variable suggests Cloud Reduction has been going on before 1975.
A simple model shows that Clouds either by reduced Cloud Fraction, decreased Cloud Albedo (lower reflectivity), or both can account for most of the observed Radiation and the associated Global Warming, GW.
CO2 is innocent but Clouds are guilty.
Climate change leaves a multi variable data finger print in the Atmosphere that is useful in drawing conclusions and testing theories. The first of these finger prints is shown in Figure 1 where Cloud Cover, Temperature, Specific Humidity, and Relative Humidity (ground and 850mb) are shown on the same time scale. None of Figure 1 graphs is a flat line any theory on GW should account for all these observations. Figure 1 is NOAA data from “NOAA Physical Science Laboratory”, (3) average Northern and Southern Hemisphere. In Figure 1 note that relative humidity at 1000mb is much less sensitive than the relative humidity at 850mb(where cumulus cloud are). Cloud Data is from Climate Explorer, (11)
Another data finger-print data set is shown in Figure 2 from “Met Office Climate Dashboard” (“HadISDH” data), (4) (station and buoy data). Note that the Met Data has a much better relative humidity correlation. The relative humidity is significant variable in the Dew Point temperature calculation, Figure 2 (e).
La géologie, une science plus que passionnante … et diverse