- Africa Keystone of Pangea
- American Canyon Earthquake
- Animal Behavior
- Anthropic interglacial
- Asteroid Impacts
- Beggaring Science
- Body Language
- California Drought
- Carbon Cycle
- Carbon Dioxide Loves to Swim
- Carbon Prohibitionists
- Carbon Theology
- Climate and Civilization
- Climate Change
- climate sensitivity
- Cold is the New Hot
- Continental Wander Path
- Cretaceous normal superchron
- Dud PDO
- Energy Budget
- Energy of Photons
- Fourth Law of Thermodynamics
- Geological Evolution of the Western United States
- Global Mean Sea Level
- Global Warming
- Global Warming Denial
- God and Dice
- god of chaos
- Government Industry
- Grand Canyon
- Gravity Anomalies
- Gravity Potato
- Greenhouse Effect
- Greenhouse Spectra
- Half Dome
- hard data
- Having one's head up one's maths
- History of Life
- Hold and Haul
- Human need for Judgdment
- Isotope Integrated Carbon Cycle
- Judge Alsup Questions
- Large Igneous Provinces
- LLSVP's are Doughboys
- Magnetic Reversals
- Manual Prayer
- Microbial Dark Matter
- Mount Whitney
- Ocean Acidification
- Optical Material Properties
- Pacific Triangle
- Paleo Sea Level
- PDA and the Apple of Eden
- Photon Food Fight
- Plate Tectonics
- Pressure Broadening
- Red Blotch Disease
- Relationship of SST and 200hPa Anomalies
- Salvation from Cows
- San Francisco Rainfall
- SARS 2
- Seafloor Isochrons
- Seamount Chains as Incipient Island Arcs
- Seismic Tomography
- Sheepherders and ignoramuses
- Sierra View
- Solar EUV
- the "Pause"
- True Polar Wander
- Unsaturated "black hole" H2O bands
- Virtual Geomagnetic Pole (VGP)
- What's going on here?
In 2017 we became interested in the CERES data, in particular the measured trend of increasing longwave radiation to space. Nobody seemed to register the significance of this, so in 2018 we downloaded the data and produced this graphic:
It showed very clearly that contrary to the greenhouse effect narrative that current warming is caused by increased absorption of long wave (LW) radiation to space by human CO2, that this radiation to space was actually increasing. It further showed that net radiation to space, a value calculated by subtracting the sum of SW and LW outgoing from incoming solar radiation (hence the inverted axis), was controlled by a marked decrease in solar SW radiation being reflected back to space. In other words, it showed that our planet was warmed not by human CO2, but by a decrease in solar SW radiation reflected to space and therefore absorbed by the surface and atmosphere. We presented this argument continuously in social media and blogs, including to such luminaries as Gavin Schmidt, now director of NASA GISS. They could never refute the data, but chose to ignore it, especially since the Trunkmonkey Research Institute has little standing in those circles. In 2020, before the CERES data format was changed, we updated the graphic to show that the trends had continued.
Comes now a fully peer reviewed paper supporting our argument.
They comment: “the root cause for the positive TOA net flux and, hence, for a further accumulation of energy during the last two decades was a declining outgoing shortwave flux and not a retained LW flux.”
The authors have taken advantage surface fluxes, new to the CERES data, to directly measure the greenhouse effect. They do this by subtracting top of atmosphere upward LW flux from the surface upward LW flux in clear sky conditions. They find an attenuation of about 130W/m2 (33%) under clear skies from all the atmosphere except the liquid water and ice from clouds. Under cloudy skies, however, they find their correlations with CO2 and water vapor break down entirely, and now complicated by the absorption liquid water and ice in the clouds, a much reduced attenuation of 33W/m2 (12.6%). They comment:
“the rise of the greenhouse gas concentration from 2001 to 2020 had a measur‐
able effect on the LW flux in the “Clear Sky”, covering about 1/3rd of the Earth surface. In
the cloudy part, about 2/3rd, this effect was much smaller, if significant at all.”
The problem is not that charlatans have duped the public with pseudoscience and misinformation but rather that the expert class and the institutions in which they are embedded has failed to attend to the panoply of public values that are unavoidably implicated in the construction of policy-relevant science. The solution, they argue, is not more research, better science communication, or louder condemnations of science denial. Instead, it is greater cognitive pluralism — both in how we define problems and how we shape solutions — so that both are better able to speak to a broader range of normative postures toward risk.
A seasonal second wave of SARS-2 infections has spread around the Northern Hemisphere since the last post. Generally deaths are far fewer, both because many of the most vulnerable have been exposed and because a younger and healthier group is getting it now.
We digitized the available graphs of deaths for the 1918-19 pandemic. these were available for several US cities and for England.
The three Eastern Seaboard Cities of Boston, D.C., and Baltimore are pretty similar with DC having more of a preamble in July and Baltimore getting off easier on the second wave. San Francisco was more like the Eastern Seaboard but shifted. It had an easier first wave and a rougher second. Augusta had two more equal waves, with the second worse than the first. England got off easy in both waves and its first wave was opposite phase. There were substantial regional variations in phase and severity.
There are considerable phase and severity variations in the current pandemic around the US as well. What is really striking is how we have dragged the current pandemic out so much longer than prior ones.
Data for 1918 uses SF as a proxy, and requires a 50x separate scale. December data for Covid is incomplete and will increase a bit. If we did actually flatten the Covid death curve with our interventions, the unflattened curve would have been impressively steep. I suspect we just delayed the waves.
A joke last winter was that if Covid lasted until summer, we could have corona with Lyme (disease). It did.
Fuel moisture content equilibrates with atmospheric humidity on a scale of hours to weeks depending whether it is dead or alive and how thick it is. There is widespread superstition that drier fuels from climate change can be blamed for the rash of fires recently. Here we explore the relationship between atmospheric temperature and humidity to show that temperatures have not increased enough to reduce fuel moisture more than 3%.
The United States Forest Service has been studying fires for a long time. A graphic from USFS research paper INT-359 below shows the relationship between atmospheric temperature and humidity in an Idaho forest.
It can be seen that the temperature changes about 25 degrees F and humidity changes about 25%. Santa Rosa, CA has a similar diurnal range. We can generalize that the relationship is symmetrical and inversely related at 1:1 degrees F to %humidity.
Santa Rosa and nearby Windsor have lost houses each of the last three years. How much has Santa Rosa’s climate changed? Below is a graphic from Jim Steele showing average high temperatures for Santa Rosa.
It can be seen that average maximum temperatures in Santa Rosa have declined since the 1930’s like most of California and the American West. Maximum temperatures control fuel moisture low points. Atmospheric humidity and fuel moisture must have increased from the 30’s, although they have been decreasing since 1980.
The average temperature of the entire state of California is a fatuous metric for fires, but even if we could come to believe the average somehow controls the many areas like Santa Rosa where temperature has decreased, the increase of 2.5 degrees F from 1930 would only yield a 2.5% decrease in fuel moisture. The dragon’s breath needs more than that.
Furthermore, NASA has come out with a global fire area assessment.
Seems the dragon just likes California.
Last year’s seasonal flu vaccine was judged 45% effective. This is typical effectiveness for a seasonal flu vaccine after half a century of practice. Now let’s say 50% of the people will take the vaccine in line with recent studies. This is multiplicative so the population effectiveness is 22.5%. SARS 2 has an Ro of at least 3, so a vaccine would need to be effective for 70% of the population. 22.5% will help but will not end the pandemic. However, natural immunity from recovery has been building. Unlike vaccine immunity based only on who was given or will take the vaccine, natural immunity is based on the virus finding the most susceptible first. Natural group immunity may require only ~20% of the population to end the pandemic. The extent of natural immunity is currently unknown. The pandemic will end when either the natural or vaccine threshold of group immunity is achieved, or some combination thereof. What is clear is that a significant contribution from natural immunity will be necessary for a vaccine to work.
The time scale of viral evolution is hours. The infectious vectors called virions can each produce thousands of copies of themselves from a single infected cell. Within hours, natural selection will determine which virions will successfully infect another cell/organ/individual; and which will die.
In ecology, the opposing reproductive strategies of wide dispersal and rapid colonization vs. taking a stand and extracting maximal resources are mathematically modeled as r and k selection, respectively. R selection favors being a weed. K selection favors being a giant sequoia.
The hypothesis of seasonal Darwinism attempts to explain the mysterious seasonality of some viruses as near real time r selection, favoring dispersal and transmissivity, during the summer when human populations are better fed, more dispersed, more outside, and more vitamin D enriched; and k selection, favoring virulence, in the winter when these factors are reversed.
SARS 2 is a single stranded positive RNA virus. It must first create a negative copy for replication, making replication and translation at the same time more difficult. The virus must balance these two processes, and the balance struck determines how many copies of itself it makes–its virulence. Selection based on this balancing strategy, with virulence favored when it is easy to find new hosts, and transmission–producing fewer copies for a longer period of time–when finding new hosts becomes more difficult.
One might argue that SARS 2 is not seasonal since a resurgence has taken place over much of the northern hemisphere summer, but this resurgence has clearly been less virulent, with a far lower case fatality ratio. The coming winter will tell.
Credit: These ideas are strongly influenced by Patrick Stewart on his blog, oldwivesandvirologists. He believes strongly in a temperature switch in the viral RNA secondary structure. Whether or not this proves to be the case, natural selection based on conditions of the host population–seasonal Darwinism–can explain much of viral seasonality.
Perusing the state new case curves on https://rt.live/ some patterns become apparent.
Some states with early exposure are on a clear downward trend.
Some southern states with later exposure are on an upward trend, California being an exception by early exposure.
Some early starting midwestern states seem over the hump, DC being eastern with similar shape.
Some later starting states also seem over the hump.
Some northerly late starting states are climbing.
Some states are cruising.
Some show a second wave, and Wyoming which never lifted off.
- There is no consistent relationship between lifting house arrest and new cases.
- There is no indication that seasonality is slowing the spread.
- Below Rt=1 (faintly visible in background) Rt can rise and new cases can still fall. The inverse is also true.
- Washington State has managed a modest decline with Rt>1.
- Utah has had steady Rt>1 for the entire period. A month elapesd before there was appreciable increase in cases, and it experienced a striking rise without any increase in Rt.
The more you know, the more you know you don’t know Jack.
We had been trying to do this ourselves but found this fun model: https://gabgoh.github.io/COVID/index.html
We can attest that the model parameters agree with our own incomplete efforts. It has lots of fun stuff to play with, and you can test the sensitivities of all the variables. We disagree with the commonly assumed R(initial) value ~2.5. The table of calculated values on the site has an average of 4.5. An excellent paper in review, Sanche et al at Los Alamos available on the CDC website finds Ri over 5. This model goes completely off the rails at these values so we set close to 3. We also disagree with a short infectious duration. Symptomatic spread alone is at least 5 days and there are at least 3 days of asymptomatic. Surprisingly, the model is insensitive to infectious duration, merely shifting the timing. Above we set the model to as close as possible to N=1 million so for the US you multiply by 327. The model seems excessively sensitive to initial infections. By limiting to 1 million and 1 initial behavior improved. Hospitalizations are a reasonable match, but deaths are too low. What we like about this is that contrary to the IHME model which predicts a decline that has not materialized, this shows we are going to be dealing with this for a while.
Above we tried to coax the model to replicate data we have for deaths and hospitalizations. We know from the Covid Tracking Project and Worldometers that daily hospitalization accelerated to 79,000 about April 15 and have declined only slightly since ever since, defying the IHME model’s projected decline. Daily deaths reached a high of 2800 about April 20, and have likewise failed to decline according to IHME.
We were able to get a reasonably sensible approximation of actual US hospitalization and death only by removing intervention altogether. This makes no sense as intervention must surely be a factor. The model also takes way too long to reach appropriate values. We are seemingly only two months in.
We have developed some sympathy for the difficulty of modeling, but are forced to conclude that important variables are not yet included.