A Chronology of Red Blotch

The single strand DNA virus shown to be the causative agent in Red Blotch disease of grapevines will almost certainly become a new genus in the family Geminiviridae. Phylogenetic evidence suggests that GRBaV diverged about 250 million years ago before the breakup of the supercontinent Pangaea.

Lets just say it’s been around a long time.

Dr. Golino has assayed the virus from a leaves collected in 1940 near Geyserville.


The grape was “Burgandy”. A close examination shows some similarities and also some differences from what we are seeing, as expected with at least two generations of rootstocks and clones intervening.

Two possibilities:

1. The virus was widespread in grapevines 1940.

2. This was an unusual occurrence in grapevines.

Vineyards were not widespread in 1940, complicating the issue, but this was the only sample that tested positive. Dr. Olmo obviously found it unusual and interesting enough to warrant saving the leaves. One presumes that an ancient virus, if it did not commonly infect grapevines in 1940, must have been present in the surrounding vegetation and spread to the sample.

Brian Bahder found GRBaV in both wild grapes and blackberries in 2014, although the blackberries tested negative in the winter dormant period.


According to Dr. Jean-Sebastien Reynard, two accessions, Zinfandel A2V13 and Emperor A2V18 imported from UC Davis in 1985 to Switzerland, were found to be infected with GRBaV.

Kari Arnold reports an interesting preliminary analysis of GRBaV infection rates of vines from four planting booms in California.


She uses the latest acronym GRBV in the first column for blotch because Koch’s postulates have been fulfilled and the disease is no longer merely “associated” with the virus. There is no way to know if these infections were latent in the planted stock or subsequently spread to them, but it is interesting that oldest period that spans one hundred years has a lower current incidence than the two fifteen year planting booms that followed. You would expect much higher incidence in the oldest period from accumulated transmission if transmission were efficient.

What appears to be transmission has been measured by qPCR at the UC Davis Oakville Station. Vines that previously tested negative tested positive at a rate of about 4% per year.


We can roughly analyze Kari Arnold’s boom periods on the basis of a 4% transmission rate as follows:

Blotch Transmission

It is quite evident that a 4% transmission rate cannot have taken place even back to 1981 without all of the vines being infected by now.

Summarizing the timeline,

We are dealing with an ancient virus that is first documented in a grapevine in California      in 1940.

Infected stock was shipped from UC Davis from at least 1985 until 2011.

Assuming a 4% annual transmission, all of existing vines planted before 1981 have been exposed to the virus and most of the observed infection since 1996 would be from transmission.

Assuming zero transmission, about half of the vines planted before 2011 were planted infected.

Symptoms were first noticed by growers in Napa in 2008, prompting the “discovery” of the virus in 2012.

What to make of all this?

It does not seem tenable that a virus that has been in grapevines since 1940 and shipped regularly from Davis since at least the mid 1980’s should suddenly show up in 2008 and since become symptomatic and problematic in every vineyard in California without something fundamental having changed.

Steve Thomas of the Kunde Vineyards reports about a 10% GRBV infection rate in certified nursery stock in plantings since 2005. This agrees well enough with Kari Arnold’s 13% current infection in 2011 to 2014 plantings (with a bit of transmission) that 10% seems a reasonable baseline to suppose for historic infected planting.

Using this as a baseline and Kari’s current infection of boom periods we can roughly regress transmission.

Regressed Transmission

It can be seen that either the rate of infected planting or the rate of transmission, or both, was substantially higher in the 1996 to 2010 boom. Something fundamental happened then, but something else fundamental must have happened in about 2008 to bring about the recent explosion of symptoms.




  • GRBaV infection reduced vine’s photosynthetic capability by a range of 27% to 44% as measured by three parameters;
  • GRBaV infection reduced leaf chlorophyll content by about 13% at time of verasion;
  • GRBaV infection reduces soluble solids content and tartaric acid by 10% and 14% respectively, but increased malic acid by 22%;


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Red Blotch Perspective Harvest 2015

The virus seemed different in the vineyard this year. Many vines we had marked as 50% red canopy last year did not meet that threshold this year and some seemed asymptomatic.

We began to notice during our vineyard survey last year that lots of vines had basal leaves that looked like this:


We had lots of that this year as well to the point that you would have been hard pressed to find a single vine in the vineyard that didn’t have at least some of this red leaf spotting.

So what is it? Is it blotch? Insect damage? Fungal colonization at the loci of inset wounds?

My first suspicion was mites. Last year was a bad mite year and we had to use chemical control on part of the vineyard. Mites prompt an anthocyanin response from the plants in the form of “bronzing”.

Based on that suspicion we used stylet oil as the spreader-sticker in a first ever post pruning dormant spray. (Before the blotch field surveys we really had no sense how many vines had been lost to trunk diseases.) We also used stylet oil in our last fungus spray.

Despite being armed with a couple microscopes we found only one mite in the vineyard. It appeared to be a forlorn predatory mite running around. Sticky traps, sticky tape, we expected to find the armies if eriophyid mites Monica Cooper reports finding in Napa. No luck yet. We find mites on the Baccaris outside the vineyard.

Well, either stylet oil is just stunningly effective or mites are not the agents, or both. As previously mentioned, nearly every vine in the vineyard had at least some leaves that looked like this.

Red spots 2015

All of the leaves involved in the beginning of canopy crash in late September looked like this.

Red spots canopy crash

This brings us to Esca. Esca is a poorly understood consortium of fungi that is wreaking havoc in Europe. It involves the genera Phaeoacremonium, Phaeomoniella, Eutypa, Botryosphaeria, Phomopsis, and others. In various combinations these fungi are associated with young vine decline (Petri disease), black goo, Eutypa dieback, and “apoplexy” or sudden vine death.

Esca and blotch

These leaves all show some level of Esca symptoms.

Esca Blotch.png

Up close one can see a “front” of anthocyanins as the leaf apparently mounts an immune response to the growing Esca patch, or possibly mycotoxins from the fungi choking off plant circulation .

So what of blotch and esca? Geminiviridae are known to infect fungi. Could Esca be the vector? Our understanding is so poor that Koch’s postulates are nowhere near being fulfilled for the various fungi even being the causal agents of Esca. Hofstetter et al (2012) report equal measurements of the various fungi in both healthy and unhealthy plants.

One thing for sure is that the conventional insect vector search for blotch dispersal has found nothing. Another thing for sure is that the virus has been around since 1940. If its only symptom were a few red specks on yellow leaves like the one a few shots back, it might easily have gone unnoticed.

Much to learn.

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Going Solar

According to Svalgaard (2015):

  1. Solar EUV, by exciting Oxygen molecules, creates thermal winds in the ionosphere whose dynamo effect changes the earth’s magnetic field.

2. The diurnal change in the earth’s magnetic field as a result of the sun passing from east to west has been measured for a long time.

3. Rudolph Wolf discovered that his relative sunspot numbers correlated extremely well with the range of this diurnal variation.

4. The “East Component” (called Y in the Catresian sense where X is north and Z is vertical) of this diurnal geomagnetic change scales incredibly well with the expected theoretical square root of Solar EUV as measured by the SOHO spacecraft since 1996. The fidelity of this scaling is all the more surprising as its theoretical base is the happenstance that the Pederson and Hall electron densities scale at SQRT F10.7 at the altitude of photochemical neutrality.

5. F10.7 microwave flux is a very good proxy for Solar EUV and it has been measured in Canada and Japan since the late 1940’s and 1950’s, respectively.

6. Thus Relative Sunspot Number=Delta East Component=SQRT Solar EUV=SQRT F10.7 flux.

7. An important observation is made that the Relative Sunspot Number has failed to follow the program for the last two solar cycles, but the Group Number has continued to do so. It is suggested that this is a result of fewer small sunspots.

8. Thus, Group Sunspot Number=Delta East Component=SQRT Solar EUV=SQRT F10.7 flux.

Sea level solar cycle

David Archibald proposes per the graphic above that the Solar Cycle controls sea level. Well, there seems to be a relationship all right but he is using the derivative rate of change for sea level rather than the actual levels and one might conclude that the sea level rate of change leads the solar cycle much of the time.

Lief Sea Level

Using Leif Svalgaard’s data from his paper summarized above and comparing it with global mean sea level produces an interesting but very different result.

I warm my coffee when it has cooled with microwaves rather than a black light for good reason and it is very tempting to look to UV and EUV warming of the oceans as the solar connection.

Getting back to Lief’s equation:

Group Sunspot Number=Delta East Component=SQRT Solar EUV (space)=SQRT F10.7 flux (earth).

One supposes that the sunspot number as a proxy for solar magnetic changes is the driving force of solar EUV flux. The same SQRT scaling applies to sunspots from both 1.5 million kilometers in space on the SOHO and the F10.7 flux measured at the earth surface. The SQRT relationship cannot result from modulation at the ionizing layer at about 105 kilometers. The SQRT relationship between sunspots and EUV, whatever its theory, is fundamental and arises at the sun. Furthermore, the ionization and deflection of the earth’s magnetic field cannot significantly diminish the total EUV energy or it would scale differently from space and at the earth’s surface.

Lief SST Sea level

We can add sea surface temperatures to the graph, but they are dancing to drummers besides the sun. They don’t even relate meaningfully to sea level.

Enough energy from the sun strikes the earth to satisfy human energy needs, including raising living standards in the third world, for the foreseeable future. The difficulty lies in accessing it. The same difficulty seemingly applies to attributing climatic changes to the sun, unless the purely magnetic effects work in ways unrelated to their intensity.


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Sea Level and ENSO

Been puzzling lately over this graphic from the university of Colorado.

Nino Sea level

It shows a very significant relationship between El Nino and global mean sea level. The notes on their website, http://sealevel.colorado.edu/, state that “often” the ENSO index leads sea level and observe that the NINA of 2011 apparently caused global sea level to drop.

The simplistic reasoning usually applied to this goes something like, “Oh yeah, Ninos are hot and Ninas are cold, it’s thermal expansion and contraction.” Well, ENSO indices are measurements of ocean surface temperatures. They do not create energy. The Nino condition allows a lot of warm water that the trade winds have piled up against Indonesia to relax back across the Pacific and replace normally much colder upwelled water off Peru.

Nothing in this process should change the overall temperature or altitude of the global oceans.

So what is going on? To my eye, GMSL leads ENSO about as “often” in their graphic  as the reverse they cite . We don’t know what’s going on and we really need to know if we ever want to understand ENSO.

Since the relationship is clear sea level must now be considered an “index” of ENSO. Accordingly it is included in the graphic below used in the previous post.

ENSO Indicec and GMSL

The graphic says “monthly” but the data were since averaged into yearly and the same was done with the GMSL data. One purpose is to see if the sea level matched any particular ENSO index better than the rest. Nope.

If you look at the Colorado graphic carefully you can see that GMSL dropped sharply during the 1998 Nino, and then rebounded. When this is averaged on a yearly basis it makes for a far less than impressive sea level rise in the “super” Nino that year. It is also interesting that the 2011 sea level drop dramatically overshot the other indices.

All we can really say is that we are looking for an ENSO driver that:

  1. Causes the trade winds to weaken.
  2. Causes sea level to rise.

Wow, nothing really comes to mind…

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How Big a Baby Will the 2015-16 Nino Be?

Lots of bluster about coming storms but what does the data we have tell us?

ENSO Indices Compared

What jumps right out is that the hot whopping 1997 El Nino shows remarkable agreement in phase and strength between all the indices. We really don’t know what causes El Ninos so it is possible the 1997 alignment of indices was the cause of the powerful Nino. It is also possible some unknown force pushed the data into alignment.

Paul Pukite has done some interesting work with QBO. The Quasi Biennial Oscillation (what a mouth-full of hay) is a stratospheric wind that switches direction about every half a year. Paul has shown that the second derivative of QBO correlates well with ENSO. His power spectra are shown below.

Pukite QBO ENSO Power Spectra

Still struggling with whether second derivatives are meaningful. In the first graphic is is clear that the first derivative correlation of QBO with the other indices is not impressive at all. Nevertheless, QBO was “all in” for 1997.

QBO appears to be “all out” this year. It must be noted that the 2015 data goes only through August and has four months to continue growing. QBO might even reverse field, but it will not be phase matched as it was in 1997.

If QBO really is a predictor, the weight of evidence points to a normal baby boy, and not a Super Nino like 1997.

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The banality of California’s ‘1,200-year’ drought


Terrific post. Couldn’t agree more. California drought? What else is new?

Originally posted on California WaterBlog:

The south fork of Lake Oroville, California's second largest reservoir, in September 2014. Photo by Kelly M. Grow/California Department of Water Resources.The south fork of Lake Oroville, California’s second largest reservoir, in September 2014. Photo by Kelly M. Grow/California Department of Water Resources

By Jay Lund

California’s ongoing drought will continue to break records and grab headlines, but it is unlikely to be especially rare from a water policy and management perspective.

Estimates of the current drought’s rarity range from once in 15 years to once in 1,200 years (Griffin and Anchukaitis 2014), depending on the region and indicators used (precipitation, stream runoff, soil moisture or snowpack). In the Middle Ages, large parts of California had droughts far worse than this one, some lasting more than a century (Stine 1994). The probability of California experiencing a once in 1,200-year drought during a short human lifetime is extremely low.

The chance that this dry period is a “new normal” is probably small. Many parts of Australia are paying for expensive desalination plants…

View original 765 more words

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The Day and the Night

I have responded to many of Stephen Mosher’s comments on various sites, but to date he has responded to only one. His response displayed surprising ignorance on many levels but in good spirit I queried if he knew the difference between daytime high and nighttime lows in the BEST data set.

No response, so I had to do it myself. Twenty years is the longest time frame offered for the data. The data is land only for nighttime lows and daytime highs.

Best Monthly Highs vs Monthly Lows

Science is not kind to our preconceptions and I totally thought that CO2 might rule the night. Well, maybe from 1965 to 2000 and change, but the end of the data in 2000 and change (sorry, this is a 20 year average) appears to be another crossover point.

Apparently the highs dominated the warming from 1850 to 1870 even though the lows began the downward drag a few years earlier. The low’s striking drop from 1864 to 1888 barely restrained the highs over the same rough period.The lows lead the charge from 1888 to 1925 when they handed the ball to the highs. The highs were on a roll until 1942 when they ran out of gas. The lows rose to the occasion and carried the ball past the crossover point in 1962 all the way through the high tailspin from 1942 to 1970 to the 2002 crossover.

If you thought this was going to be easy, sorry ’bout that. None of these transition points from daily low to daily high temperature hegemony matches the average temperature transition points. They are on different layers. Average all you like but to get to the bottom of this we are going to have to bear down on many, many layers of input.

My advice: pack a lunch, go to work every day looking for insight.

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