Saturday, May 23, 2020

On The Origin Of The Home Of COVID-19 - 3

"This is our home"
I. When

When the characters in Downton Abbey say "this is our home" or "this is where I was born" they do not indicate that they built it or that their reality in their time is the same as it was when Downton Abbey was built some 400 years prior to their current reality.

Likewise, when I refer to "The Origin Of The Home of COVID-19" I do not limit it to the current reality.

II. What

The "home" referred to in this series is the original host of the original symbiotic relationship composed of the "SARS-CoV" virus type, and more specifically the "severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)".

Why I call it "SARS-CoV-2" is because it is "now designated as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses" (Nature-Microbiology).

The popular nickname for SARS-CoV2 is "COVID-19" (ibid).

III. Where

The "where" of the host of the symbiotic relationship, for the purposes of this series, is the host microbe at the time of the symbiotic relationship between the host and once-symbiont but now pathogenic virus now called COVID-19.

That "homie", that home host, then, is not a bat, cow, pig, chicken, etc., rather, it is a microbe, a single celled biotic organism.

Thus, I will diverge from the "host" designation used in the "Nature-Microbiology" paper, i.e. "the CSG proposes to use the following naming convention for individual isolates: SARS-CoV-2/host/location/isolate/date" (ibid, emphasis added).

I am in good company in that divergence:
"...viruses can be considered as symbionts with their hosts. Symbiotic relationships encompass different lifestyles, including antagonistic (or pathogenic, the most well-studied lifestyle for viruses), commensal (probably the most common lifestyle), and mutualistic (important beneficial partners). Symbiotic relationships can shape the evolution of the partners in a holobiont, and placing viruses in this context provides an important framework for understanding virus-host relationships and virus ecology..."
(On The Origin Of The Home Of Covid-19 - 2, quoting from Symbiosis: Viruses as Intimate Partners).

IV. Who

Interestingly enough, we are talking about an original family of multiple host/virus symbiosis:
"... the Coronaviridae. Based on phylogeny, taxonomy and established practice, the CSG recognizes this virus as forming a sister clade to the prototype human and bat severe acute respiratory syndrome coronaviruses (SARS-CoVs) of the species Severe acute respiratory syndrome-related coronavirus ..."
(ibid, emphasis added). More on that in subsequent posts.

V. Why

The why of this series is to show that the current pathogenic phase of COVID-19 is not the original condition, instead it is a result of "the industry" having made war against the host microbe which changed an original symbiont virus convert into a pathogen a la:
"Like pretty much all multi-cellular organisms, humans enjoy the benefits of helpful bacteria. (As you may have heard, there are more [microbial cells] in the human body than [human cells].) These mutualistic microbes live within the body of a larger organism, and, like any good long-term houseguest, help out their hosts, while making a successful life for themselves. It’s a win-win situation for both parties.

Scientists still don’t understand exactly how these relationships began, however. To find out, a team of researchers from the University of California, Riverside, used protein markers to create a detailed phylogenic tree of life for 405 taxa from the Proteobacteria phylum—a diverse group that includes pathogens such as salmonella as well as both mutualistic and free-living species.

Those analyses revealed that mutualism in Proteobacteria independently evolved between 34 to 39 times, the researchers report in the journal Proceedings of the Royal Society B.  The team was a bit surprised to find that this happened so frequently, inferring that evolution apparently views this lifestyle quite favorably.

Their results also show that mutualism most often arises in species that were originally parasites and pathogens."
(Microbial Languages: Rehabilitation of the Unseen--2)., quoting from Smithsonian). Thus, treating what the media calls "a war against the virus" with more "war" will produce more pathogens (more victims of war).

VI. Where Do We Start?

Let's get a clue based on a post in a microbiology-specific source, which urges us to consider that we need to understand the microbiome of bats before we can better understand the various virus/microbe relationships:
"Mammals evolved in a microbial world, and consequently, microbial symbionts have played a role in their evolution. An exciting new subdiscipline of metagenomics considers the ways in which microbes, particularly those found in the gut, have facilitated the ecological and phylogenetic radiation of mammals. However, the vast majority of such studies focus on domestic animals, laboratory models, or charismatic megafauna (e.g., pandas and chimpanzees). The result is a plethora of studies covering few taxa across the mammal tree of life, leaving broad patterns of microbiome function and evolution unclear. Wildlife microbiome research urgently needs a model system in which to test hypotheses about metagenomic involvement in host ecology and evolution. We propose that bats (Order: Chiroptera) represent a model system ideal for comparative microbiome research, affording opportunities to examine host phylogeny, diet, and other natural history characteristics in relation to the evolution of the gut microbiome."
(Bats Are an Untapped System for Understanding Microbiome Evolution in Mammals, emphasis added). Ok, I'm game, let's check out the batosphere.

In the first post of this series we considered the gut microbiota found in domestic animals such as cows, pigs, and chickens.

But, we did not consider those microbiota very deeply, in terms of microbial hosts (but the K-4 kids evidently do).

We will dig deeper into that realm in subsequent posts.

But, since we are now focusing on bats, let's consider the microbiota in bat digestive systems (please excuse a few misuses of "host" in the following sources, which are otherwise quite helpful):

A.
"When you think about bats and what they eat, does the thought of blood come to mind? Only a very small fraction of bats in the world actually survive off of the blood of other animals. The diet for the bat depends on what species you are talking about. Approximately 70% of bats consume insects and small bugs. They are referred to as insectivores. Most of the rest consume fruits and they are called frugivores ... Those that feed on bugs and insects are opportunistic. They will consume anything that they come into contact with ... A bat typically will consume about 1/3 of its own body weight in food per night ... There are two types of insects that bats consume. Most people assume they only eat those that are in the air. Those are called aerial insects and this action can take place with lightening fast speed. They usually use their tail to capture the prey and then they will stop and consume it ... Other types of insects are considered ground dwelling insects. The bats have to swoop down and get them. They often will remain on the ground long enough to consume them and then they continue on again. There are bats that don’t use their tail for catching food though. Instead, they capture it in their teeth. The method that is used depends on the particular species of bat being discussed ... A very small number of bat species also feed on vertebrates. They are said to be the carnivores of the bat world. They consume frogs, lizards, small birds, and also other species of bats. Fish also make great meals for these types of bats. Only the Vampire Bat specifically feeds only on blood for survival."
(BatWorlds). Bats eat insects (which are "the enemy") to kill according to "the insecticide industry" members such as Monsanto, and those "enemies" (a.k.a. bat food) are everywhere:
B.
"Many species [of bats] also inhabit more urban areas like farms, barns, pastures, parks, suburbs, and even cities."
(Animals/Bat). Accordingly, there are many viruses implicated which will aid the ongoing research:

C.
"Bats, which comprise about 20% of the mammal world, have many microbe types which are likely hosts to viruses"
(Bats, Bacteria, and Bat Smell: Sex-Specific Diversity of Microbes in a Sexually Selected Scent Organ).
D.
"The identification of dermatophytic fungi, isolated from the skins of cave-dwelling bat species, is necessary to distinguish pathogenic (disease-causing) microbes from those that are innocuous. This distinction is an essential step for disease diagnoses, early detection of the presence of microbial pathogens prior to symptom development, and for discrimination between microbes that are present on the skins of hibernating bats."
(Discrimination between Pseudogymnoascus destructans, other Dermatophytes of Cave-dwelling Bats, and related innocuous Keratinophilic Fungi based on Electronic-nose/GC Signatures of VOC-Metabolites produced in Culture). Since  microbes are the bona fide hosts of our research, notice this too:

E.
"Microorganisms play a crucial role in maintaining the delicate ecological balance of the earth. They revitalize the soil by recycling the minerals and nutrients of decaying matter, and many are essential to the healthy growth of plants. Microorganisms also affect our lives more directly in the manufacture of such items as food products, detergents, antibiotics and antitumor drugs.

A marvelous symbiosis exists between these organisms and bats. Bacteria in the mammalian intestinal tract aid in the breakdown and digestion of food. These organisms possess enzymes capable of degrading a vast array of substances. Countless microbes are regularly excreted along with waste products, and together with soil organisms, they constitute the microbial population of a bat guano deposit.
"
(Bats, Bacteria and Biotechnology). Looking further into this subject matter, note that some research has hypothesized an interesting, and perhaps unique characteristic of the environment/ecosystem impact on bats:

F.
"Bats may be very susceptible to environmental change -- if they have a transient microbiome, they might not have the most stable defense mechanisms," says Lutz. 'Human-caused disturbances to the environment are a very important issue. Bats may be extra-fragile and more at risk.'"
(Bats don't rely on gut bacteria the way humans do). That article points out the short length of bat intestines compared to other mammals and that they therefore have a more vulnerable gut microbiota population.

VII. Closing Comments

The gravamen of the situation is that bats have a diet that is susceptible to being contaminated by toxins from pesticides and herbicides.

Many of them live in human habitats where anti-biotic, anti-insect, and anti-plant chemicals are consistently sprayed into their habitat, and ours.

This means that we can reasonably hypothesize that these behaviors are anti-host to the point of killing symbiotic relationships and thereby creating pathogenic behavior as symbiotic hosts die and release "homeless" viruses into a strange world (for them).

In other words, the catastrophe of the Anthropogenic era is the ongoing Sixth Mass Extinction which, like the Fifth Mass Extinction, adversely impacts symbiotic relations (What Did The Mass Extinctions Do To Viruses and Microbes?).

The previous post in this series is here.

Thursday, May 21, 2020

On The Origin Of The Home Of COVID-19 - 2

Viruses outnumber stars
I. Which Came First

The logic of cosmology's "Big Bang Hypothesis" (BBH) is clear enough to be understood in several fundamental ways (The New Paradigm: The Physical Universe Is Mostly Machine).

The BBH timeline indicates that there was a "B.C.", that is, there was a time before carbon.

In the B.C. age there was no carbon, and thus no carbon based life forms such as single-celled microbes.

It was the abiotic age:
Dr Clarke said: “There are a lot of fundamental questions about the origins of life and many people think they are questions about biology. But for life to have evolved, you have to have a moment when non-living things become livingeverything up to that point is chemistry.[e.g. atoms, molecules: molecular machines]”
...
“Our cells, and the cells of all organisms, are composed of molecular machines. These machines are built of component parts, each of which contributes a partial function or structural element to the machine. How such sophisticated, multi-component machines could evolve has been somewhat mysterious, and highly controversial.” Professor Lithgow said.
...
Many cellular processes are carried out by molecular ‘machines’ — assemblies of multiple differentiated proteins that physically interact to execute biological functions ... Our experiments show that increased complexity in an essential molecular machine evolved because of simple, high-probability evolutionary processes, without the apparent evolution of novel functions. They point to a plausible mechanism for the evolution of complexity in other multi-paralogue protein complexes.
...
The most complex molecular machines are found within cells.
(Putting A Face On Machine Mutation - 3). Thus, following the logic of the BBH, the carbon-based life-form age would have to have begun after the emergence of carbon, which came about thusly:
"Formation of the carbon atomic nucleus requires a nearly simultaneous triple collision of alpha particles (helium nuclei) within the core of a giant or supergiant star which is known as the triple-alpha process, as the products of further nuclear fusion reactions of helium with hydrogen or another helium nucleus produce lithium-5 and beryllium-8 respectively, both of which are highly unstable and decay almost instantly back into smaller nuclei. This happens in conditions of temperatures over 100 megakelvin and helium concentration that the rapid expansion and cooling of the early universe prohibited, and therefore no significant carbon was created during the Big Bang. Instead, the interiors of stars in the horizontal branch transform three helium nuclei into carbon by means of this triple-alpha process. In order to be available for formation of life as we know it, this carbon must then later be scattered into space as dust, in supernova explosions, as part of the material which later forms second, third-generation star systems which have planets accreted from such dust. The Solar System is one such third-generation star system."
(On the Origin of the Genes of Viruses - 5, quoting Wikipedia & NASA). In one sense this complicates the search for the origin of the "home" of viruses, that is, "where they came from".

But, in another sense it simplifies the origin of symbiotic viruses in terms of the symbiotic relationship between single cell carbon based life forms (microbes) and viruses.

Abiotic viruses emerged first during the abiotic age (pre-carbon), then much later microbes emerged in the biotic age (post-carbon).

Some of the abiotic age machines were 'dynamos' (The Uncertain Gene - 10).

But the saga doesn't stop there, no, viruses "aren't what they used to be" as told in the oldie textbooks:
"Virus infection involves coordination of a series of molecular machines, including entry machines, replication machines, assembly machines, and genome packaging machines, leading to the production of infectious virions.
...
Although viruses had been considered as merely dull, static containers, and protectors of genomes, this false concept was replaced by the realization that viruses are beautiful intricate machines, essential to biological evolution, capable of invading cells, stealthily avoiding the protective barriers of the host, usurping the host's synthetic machinery for their own survival and able to self assemble into complex molecular machines. Indeed it has become apparent that the capabilities of viral machines far exceed those to the simple enzymes first studied in the mid-twentieth century. This book is a partial description of some of the amazing things accomplished by viruses in infecting a host and replicating themselves [the host cell's replication machines replicate viruses]."
(On the Origin of the Genes of Viruses - 7, quoting from "Viral Molecular Machines").

II. Then Came Symbiosis

How the viruses (in the abiotic age's last BC days) reacted to emerging biotic cells as they came on the scene is anybody's guess I suppose.

But most would agree that the relationship would have been incremental, the ultimate relationship eventually developing last (simple to complex), which would be symbiosis.

So, as of now scientists clearly realize that the notion of viruses as enemies of carbon based life forms is archaic and wrong:
"Viruses must establish an intimate relationship with their hosts and vectors in order to infect, replicate, and disseminate; hence, viruses can be considered as symbionts with their hosts. Symbiotic relationships encompass different lifestyles, including antagonistic (or pathogenic, the most well-studied lifestyle for viruses), commensal (probably the most common lifestyle), and mutualistic (important beneficial partners). Symbiotic relationships can shape the evolution of the partners in a holobiont, and placing viruses in this context provides an important framework for understanding virus-host relationships and virus ecology. Although antagonistic relationships are thought to lead to coevolution, this is not always clear in virus-host interactions, and impacts on evolution may be complex. Commensalism implies a hitchhiking role for viruses—selfish elements just along for the ride. Mutualistic relationships have been described in detail in the past decade, and they reveal how important viruses are in considering host ecology. Ultimately, symbiosis can lead to symbiogenesis, or speciation through fusion, and the presence of large amounts of viral sequence in the genomes of everything from bacteria to humans, including some important functional genes, illustrates the significance of viral symbiogenesis in the evolution of all life on Earth."
(Symbiosis: Viruses as Intimate Partners). However, there remains a rancid nomenclature still in use by the commentariat and by many scientists:
"Symbiosis is a concept fraught with misunderstanding, and the literature is full of various definitions. Here we use the original definition of symbiosis as described by Frank and de Bary in the nineteenth century from their studies on lichen. The two critical aspects of this definition are that the entities must be in an intimate relationship, living in or on one another, and that the entities must be dissimilar (1). Symbiotic relationships are not necessarily beneficial; antagonistic symbioses also are common, and for viruses, commensal relationships, where there is no observable cost to the host, are probably the most common. Symbiotic relationships fall on a continuum between mutualistic and antagonistic, where the environment affects the placement of the holobiont on the continuum, a relationship known as conditional mutualism (Figure 1)(2, 3). Although some definitions of symbiosis use the term parasitism instead of antagonism, this further muddies the waters; all viruses, and indeed many other symbiotic microbes, are parasitic, meaning they benefit from their hosts by acquiring nutrients from them. This does not mean that they cannot also be commensal or mutualistic; these distinctions depend on whether or not the benefits outweigh the costs. Finally, mutualism does not necessarily imply symbiosis. For example, just because humans eat fruit and thus are involved in seed distribution, humans and fruiting plants do not live in an intimate relationship (in or on one another), and hence, even though the relationships are mutualistic, they are not symbiotic."
(ibid). The rancid nomenclature mentioned in that quote is typical historical behavior (e.g. Modern Evolutionary Synthesis).

But, as pointed out in the first post of this series, it (understanding the reality of the virus world) is exacerbated to new lows by commercialism.

Just as bad, the term "war" is still being used by the commentariat of the media and the government as it has always been to describe virus/host interaction.

Still, the truth tellers struggle to make reality better known:
"Viruses are the most abundant and diverse biological entities on the planet. Recent biodiversity surveys in desert, ocean, soil, mammalian gut, and plant ecosystems have uncovered an abundance of viruses in every ecosystem and life form examined. These ecological surveys also highlight a common misconception about virus biology: In spite of their ubiquitous incidence, most viruses produce no recognizable symptoms associated with disease. Interactions among viruses and their respective hosts are dynamic and variable and constitute important forces shaping populations."
(ibid, emphasis added). "If it bleeds it leads" is a sacrament of both McTell news and corrupt government.

III. Closing Comments

I have isolated 63,626 scientific papers that focus on viruses in general, then filtered them with the keywords "SARS" and "symbiotic", which "boiled" the scientific papers I am reviewing down to 2,640.

Saying that the "Origin Of The Home Of Covid-19" is "the abiotic age prior to the emergence of carbon based life forms" or "the Earth's ecosystem" is not sufficient to detail the reality.

So, stay tuned as I try to follow the trail, then tell you where it may be leading.

The next post in this series is here, the previous post in this series is here.

Thursday, May 14, 2020

On The Origin Of The Home Of COVID-19

And Now For Some Breaking News
I. Deja Voodoo All Over Again

This post begins a new Dredd Blog series based upon what I suspect is "the industry" response to two previous Dredd Blog posts (On The Origin of the Genes of Viruses - 14, If Cosmology Is "Off," How Can Biology Be "On?" - 2).

The industry response which I suspect is: Why it’s wrong to blame livestock farms for coronavirus.

This is not the first time that "the industry" (MOMCOM) has responded to Dredd Blog posts (Oil-Qaeda & MOMCOM Conspire To Commit Depraved-Heart Murder- 6).

No, not the first time, but it is the same type of response.

The "type of response" I am writing about is diversion and deceit, without any discussion of the gravamen of the situation.

The gravamen of the situation is, in our modern time, where did Covid-19 originate?

I already have written about the general origin of viruses and their genes (On the Origin of the Genes of Viruses, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13; The Uncertain Gene, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11).

This series will be about this specific type of virus, not the general population of viruses.

In this series I will keep in mind that there are far more viruses than there are stars in the visible universe (The Real Dangers With Microbes & Viruses).

So, we have now narrowed the search for an origin, limiting it to a search for the home (host) of the Covid-19 virus (like the title of this series indicates).

II. My Complaint

The venerable American Society for Microbiology (ASM),  an esteemed realm of cognition, expects a lot from our youth.

I say that because the following is what they expect "K-4" (kindergarten thru fourth grade) to contemplate:
"This lesson introduces students to the microbial world and provides insight on the function of microbes by examining bacteria that both help and harm cows. Although multiple bacteria inhabit the cow’s rumen, this lesson focuses on two harmless microbes, Ruminococcus and Selenomonas, which break down cellulose and starch in plant matter, respectively. These bacteria obtain nutrients from the cow’s diet, and the cow gains energy from the products of bacterial metabolism. Therefore, these bacterial species are in a symbiotic relationship with the cow. Other bacterial species can harm cows. Such is the case with Escherichia coli, a non-ruminant bacterium that can cause the udder infection known as mastitis."
(Lesson Plan, Grades K-4, Bacteria That Help and Hurt Cows, emphasis added). I suspect that Dredd Blog readers did not get that lesson presented to them during their K-4 elementary schooling (I certainly didn't).

It doesn't stop there:
"The microbes inhabiting the rumen convert low-quality, fibrous, plant material into useable energy for the host ruminant. Consisting of bacteria, protozoa, fungi, archaea, and viruses, the rumen microbiome composes a sophisticated network of symbiosis essential to maintenance, immune function, and over-all production efficiency of the host ruminant."
(Rumen Microbes In Livestock Production, emphasis added). For those, like me, that did not get tested on "rumen" in grades K-4:  (Medical Definition of rumen : the large first compartment of the stomach of a ruminant from which food is regurgitated for rumination and in which cellulose is broken down by the action of symbiotic microorganisms.)

Anyway, what could go wrong in the rumen? ... good question ... good answer:
"To meet this demand [commercialization / population growth], there are several approaches being in force like antibiotic / antimicrobial / hormonal growth promotors. But these methods also lead to major public health concerns ranging from residues to antimicrobial resistance in human population ... It is a well-known fact that microbiota plays a pivotal role in the gastrointestinal health in ruminant and nonruminant animals."
(Effective Role of Microorganisms in Livestock Development, emphasis added). What could go wrong and has gone wrong is answered by a look at pandemics (The Real Dangers With Microbes & Viruses).

Some of those in "the industry" have found out what could go wrong and has gone wrong:
"For generations, farmers have used antibiotics to improve production of their chicken, pig, and cattle operations. With use of certain antibiotics on the chopping block because of concerns about the rise of resistant organisms, some are turning from anti to pro—probiotics, that is.

Probiotics, live microorganisms that are a staple of the human wellness industry, have the potential to fulfill many of the roles that antibiotics play down on the farm. Research suggests probiotics can help chickens, pigs, and cows quickly put on weight, efficiently digest feed, and withstand the infectious organisms that tend to lurk wherever animals are kept in close quarters ... But farmers have several ways to raise healthy livestock without relying on antibiotics."
(Boosting Farm Animal Health, emphasis added). Why would there be a change after generations of use ... "major public health concerns" ... pandemics?

III. The Accusations

In the accusations set forth in "Why it’s wrong to blame livestock farms for coronavirus" linked to in Section I above, the author of that paper did not mention that she is of the Mansanto persuasion:
"Alison Van Eenennaam, PhD, a cooperative extension specialist in animal genomics and biotechnology at the University of California, Davis, is a leading promoter of genetically engineered animals, crops and the pesticides that accompany them, and an advocate for deregulation. Dr. Van Eenennaam argues GE animals should not be subject to pre-market safety reviews or labels. Dr. Van Eenennaam is a former Monsanto employee [(Wayback Machine Doc)] who opposes requiring safety studies for genetically engineered animals ..."
(USRTK US Right To Know, emphasis added). The "Mansanto persuasion" is to kill any and all microbes that move or exist, it is "anti-" not "pro"- biotics (living things).

IV. The Myths

The news media rightly points out:
"Let’s start at the beginning. As of 17 March, we know that the Sars-CoV-2 virus (a member of the coronavirus family that causes the respiratory illness Covid-19) is the product of natural evolution. A study of its genetic sequence, conducted by infectious disease expert Kristian G Andersen of the Scripps Research Institute in La Jolla, California, and colleagues, rules out the possibility that it could have been manufactured in a lab or otherwise engineered. Puff go the conspiracy theories."
(The Covid-19 pandemic shows we must transform the global food system). That is not quite the beginning, but good enough for gummit work.

But, in the next sentences they jump off track:
"The next step is a little less certain, but it seems likely that the original animal reservoir for the virus was bats. Andersen’s team showed – like the Chinese before them –that the sequence of Sars-CoV-2 is similar to other coronaviruses that infect bats."
(ibid).  Non-microscopic biological entities, "animals" (e.g. bats), are not the host of viruses, contrary to ubiquitous media commentaries (Amazon could be next virus hot zone: scientist).

While the host of Covid-19 is evidently not specifically known, the type of 'animal' is known: microbes (the video at the end of this post will tell you a lot about microbes ... watch it all and it will WOW you).

Microbes, as taught in grades K-4 (according to the ASM quote above in Section II) are for the most part mutualists with their host mammals, including humans.

Those microbes host viruses that are mutualists to them, and by extension are mutualists to the animals they are in, 'human animals' too.

A mutualist virus does not "infect" either its immediate host or meta host.

Instead, they are symbionts to both their immediate (microbe) host and meta host (animal).

When the symbiotic relationship is destroyed by antibiotics and other toxins, the host microbe and its reproductive machinery that reproduce the virus as the microbe reproduces itself, are impacted.

The symbiotic relationship is destroyed, and enmity replaces it.

V. Closing Comments

In the next post of this series we will explore the rehabilitation of the enmity relationship back into a symbiotic relationship.

Just below is a repeat of a previous Dredd Blog post that is about seven years old  (How Microbes Communicate In The Tiniest Language) so that you can get in touch with your inner and outer symbionts:

Microbes
Video Index (time - subject)
00:21 - microbes are oldest life forms on Earth
01:03 - 10 times more microbes than human cells in us
01:31 - 100 times more microbial genes than human genes in us
02:00 - microbes are 99% of our make-up; they keep us alive
02:20 - microbes are vital for keeping us alive and healthy
04:20 - microbes talk with a molecular language
07:50 - quorum sensing (like a census) to know population count
08:20 - Intra species communication (shape of words) dialects
10:50 - microbes communicate with other microbes (multi-lingual)
11:20 - they take a census of all other microbes around them
12:30 - synthetic molecules-words interrupt communication
13:50 - synthetic molecules-words confuse the microbes
15:00 - they have collective, community behaviors
15:20 - microbes made the rules for multi-cellular development
16:00 - microbes invented multi-cellular behavior inside us
17:15 - the team

Dr. Bonnie L. Bassler, Princeton University:



Professor who studies "social intelligence" of microbial entities gives lecture:



The next post in this series is here.

Sunday, May 10, 2020

On Thermal Expansion & Thermal Contraction - 45

Ice Melt Is The Main Cause of Sea Level Change
I. Context

Today, we take a look at actual sea level change (SLC) as measured at hundreds of tide gauge stations around the globe.

With them we can compare the actual in situ measurements taken by instruments at those tide gauge stations located around the world (which have been measuring sea level for hundreds of years in some cases), then analyze the world according to measurements.

In other words, we can then compare those changing values to the actual calculated values for thermal expansion (On Thermal Expansion & Thermal Contraction - 44), as well as the abstract values for thermal expansion previously presented in this series (On Thermal Expansion & Thermal Contraction - 43).

The hypothesis being tested today by so doing is "thermal expansion is the main cause of sea level rise".

II. Source of Sea Level Data

The Permanent Service For Mean Sea Level (PSMSL) records sea level in Revised Local Reference (RLR) format.

For ease of use I convert that RLR millimeter format into simple millimeter format.

But, to satisfy our curiosity I present the values of both formats in the HTML file tables, and in the graphs as well.

So, when you select an HTML table or select a graph for any of the ocean areas listed below in the "Links To Appendices" selection table, you can see both formats and compare them as needed.

The basic difference between RLR and SLC is that RLR values are in multiple thousands, while SLC values are way less than one thousand.

The link to the "RLR format" definition mentioned in the first sentence in this section II explains the reasoning for the use of RLR.

III. Appendices

The HTML Table appendices (A1-E1)) are composed of a list of RLR and SLC values for a particular ocean area over a long span of time.

That span of time is as long as 1880-2019, but in some cases measurements have not been recorded for that long because of the harsh conditions in some regions (or because there is no place to put a tide gauge station ... e.g. the middle of the ocean where there is no land to put one on).

The graph appendices (A2-E2) show the mean averages of those sea level measurement values (RLR-millimeter and millimeter) in graph format.

Links To Appendices
Ocean AreaHtml Tables Graphs
AtlanticAppendix A1Appendix A2
PacificAppendix B1Appendix B2
PolarAppendix C1Appendix C2
EquatorialAppendix D1Appendix D2
IndianAppendix E1Appendix E2

IV. What This Is All About

These Dredd Blog series of posts fall into the category of grey/gray literature.

They concern the long-held hypothesis about what causes SLC, and specifically the size of the part thermal expansion/contraction plays in that SLC.

V. Closing Comments

This series is composed of literature which seeks to draw close attention to the aforesaid hypothesis that "thermal expansion is the main cause of sea level rise".

In this series I have argued that the said hypothesis has been falsified, and no one has responded with any evidence, especially evidence of equal quantity or quality.

Here are the totals for your perusal (NOTE: the 'Total' of the 11 oceans (~5.5 ft,~1690 mm on the last line of the table) is not actual, visual results because the SLC levels out to about the average as the oceans flow together):

Thermosteric tsSLC vs Actual SLC Graphs

Ocean WOD tsSLC (mm) SLC (mm) in situ tsSLC (mm)
North Atlantic 1.69869258.4564.52489
Equatorial Atlantic 2.3308670.430.840493
South Atlantic 1.895548.43111.2491
North Pacific 1.72838130.5643.06685
Equatorial Pacific 2.32835115.1186.4686
South Pacific 1.91171144.1325.02795
North Indian 2.22555-51.5450.563049
Equatorial Indian 2.33282144.0098.16361
South Indian 1.97321468.9546.30896
Southern 0.87936776.95671.98366
Arctic 0.609101377.0290.487493
Average (11 oceans) 1.81032153.6203.51679
Total (11 oceans) 19.91351689.82738.6847

The three right-most columns in the "Thermosteric tsSLC vs Actual SLC Graphs" table compare the results from post 43 ("WOD tsSLC"), this current post 45 ("SLC mm"), and  post 44 ("in situ tsSlc") so as to give a quick look at the contrasts.

The "WOD tsSlc" values were computed using the maximum and mininum values for temperature and salinity presented in the WOD manual as explained in On Thermal Expansion & Thermal Contraction - 43.

The "SLC" values in the middle column are tide gauge station measurements of actual sea levels over time (no temperature and salinity values involved).

The "in situ tsSLC" values were computed using actual temperature and salinity measurements as explained in On Thermal Expansion & Thermal Contraction - 44.

Either way you look at it, the thermal expansion values are not the main cause of sea level rise.

The previous post in this series is here.

Atlantic Appendix A1

This is an appendix to: On Thermal Expansion & Thermal Contraction - 45

Ocean: North Atlantic
WOD Zones:
7606 7605 7604 7603 7602 7601 7600 7506 7505 7504 7503 7502 7501 7500 7407
7406 7405 7404 7403 7402 7401 7400 7309 7308 7307 7306 7305 7304 7303 7302
7301 7300 7209 7208 7207 7206 7205 7204 7203 7202 7201 7200 7108 7107 7106
7105 7104 7103 7102 7101 1600 1601 1500

World Ocean Database Zones



LEGEND for the HTML table below:

Year column: the year when in situ tide gauge measurements were taken.

RLR (mm) column: the average RLR value of the PSMSL database values for all
WOD zones (listed above) and tide gauge stations in those zones (for a particular year).

SLC (mm) column: the sea level change value derived by recording the first
RLR value of the first year, then subtracting it from every subsequent year's RLR value
(including itself to derive 0 for the first year's SLC mm value).



Year RLR (mm)SLC (mm)
18806836.380
18816799.48-36.8918
18826818.61-17.7613
18836811.78-24.5908
18846814.6-21.7708
18856841.515.13224
18866828.22-8.15688
18876816.78-19.5955
18886803.39-32.9869
18896832.72-3.65233
18906845.248.86705
18916847.5211.1439
18926890.1953.8193
18936913.1176.735
18946899.1162.7375
18956881.2644.8809
18966889.4553.0787
18976911.9875.6058
18986976.49140.118
18996990.38154.009
19006937.78101.409
19016901.8165.4303
19026943.4107.02
19036995.7159.322
19046934.8698.4857
19056926.3990.0153
19066939.38103.002
19076920.1383.7499
19086907.6371.2519
19096942.47106.097
19106945.33108.955
19116926.9490.5685
19126947.02110.649
19136931.1694.7816
19146918.0581.6735
19156892.5456.1638
19166957.45121.077
19176941.37104.99
19186938.22101.843
19196947.56111.187
19206917.3580.9761
19216930.5194.1339
19226928.291.8285
19236939.1102.726
19246931.4795.0986
19256937.75101.374
19266932.1195.7392
19276949.48113.103
19286968.11131.732
19296941.27104.89
19306940.11103.739
19316914.7178.3311
19326944.81108.439
19336922.6886.3047
19346896.0759.6897
19356924.7588.3766
19366943.28106.905
19376943.8107.421
19386961.56125.187
19396948.61112.234
19406939.98103.607
19416915.5279.1442
19426945.08108.7
19436978.21141.836
19446950.29113.917
19456980.78144.4
19466974.3137.928
19476983.99147.616
19486992.76156.384
19496974.34137.965
19506965.69129.313
19516980.53144.154
19526985.28148.904
19536975.67139.29
19546975.24138.861
19556984.98148.606
19566962.8126.421
19576979.6143.224
19586986.25149.879
19596964.71128.332
19606991.21154.83
19617004.58168.205
19626965.18128.804
19636951.44115.063
19646946.97110.597
19656964.13127.756
19666988.12151.745
19677006.07169.698
19686982.6146.221
19697004.3167.925
19706999.63163.258
19716992.11155.731
19727015.2178.829
19737020.78184.407
19747014.17177.79
19757006.3169.929
19766987.12150.743
19776997.44161.064
19786998.1161.725
19796997.17160.796
19806983.91147.536
19817006.74170.361
19826999.44163.069
19837044.5208.122
19847014.83178.451
19857015.5179.123
19867018.5182.124
19877015.78179.409
19887016.92180.544
19897014.74178.363
19907030.49194.114
19917016.55180.178
19927018.55182.179
19937022.39186.012
19947022.07185.697
19957028.94192.565
19967021.97185.591
19977048.28211.902
19987054.49218.113
19997034.4198.026
20007030.31193.931
20017018.9182.526
20027040.41204.037
20037039.47203.092
20047039.68203.307
20057045.44209.066
20067053.56217.188
20077042.89206.518
20087057.28220.908
20097061.94225.567
20107062.81226.431
20117064.5228.125
20127053.72217.343
20137058.44222.068
20147079.26242.885
20157070.09233.716
20167087.1250.725
20177098.51262.138
20187094.83258.456
20197165.43329.057


Ocean: South Atlantic
WOD Zones:
5103 5102 5101 5100 3100 3101 5205 5204 5203 5202 5201 5200 3200 3201 5306
5305 5304 5303 5302 5301 5300 3300 3301 3302 5406 5405 5404 5403 5402 5401
5400 3400 3401 3402


LEGEND for the HTML table below:

Year column: the year when in situ tide gauge measurements were taken.

RLR (mm) column: the average RLR value of the PSMSL database values for all
WOD zones (listed above) and tide gauge stations in those zones (for a particular year).

SLC (mm) column: the sea level change value derived by recording the first
RLR value of the first year, then subtracting it from every subsequent year's RLR value
(including itself to derive 0 for the first year's SLC mm value).



Year RLR (mm)SLC (mm)
19057033.330
19066957.83-75.5
19076960.58-72.75
19086932-101.33
19096949.67-83.66
19106894.25-139.08
19116942-91.33
19126992.92-40.41
19136958.83-74.5
19147007.92-25.41
19156960.58-72.75
19166870.17-163.16
19176865.17-168.16
19186907.12-126.205
19196966.92-66.41
19206969.62-63.705
19216958.5-74.83
19226997.8-35.535
19236978.83-54.5
19246949.17-84.16
19256930.54-102.79
19266944.88-88.455
19276946.91-86.415
19286945.54-87.79
19296943.71-89.62
19306958.25-75.08
19316935.5-97.83
19326979.17-54.16
19336946.21-87.12
19346924.38-108.955
19356984.17-49.16
19366950.09-83.245
19376960.54-72.79
19386975.64-57.6933
19396969.75-63.5767
19406976.31-57.0233
19416987.61-45.72
19426990.64-42.6933
19436986.56-46.7733
19446936.29-97.04
19456954.46-78.8725
19466970.48-62.8525
19476967.2-66.1333
19486933.7-99.625
19497001.67-31.66
19506993.3-40.035
19516999.38-33.955
19527020.75-12.58
19536974.16-59.165
19546952.55-80.778
19556930.51-102.823
19566949.64-83.69
19576959.95-73.3771
19586984.23-49.0957
19596993.72-39.6135
19606989.33-43.9983
19617009.47-23.863
19626979.16-54.1674
19636989.43-43.8996
19646940.15-93.1767
19656994.34-38.9865
19666981.88-51.4486
19676970.18-63.1492
19686967.07-66.2613
19696960.91-72.42
19706967.63-65.7035
19716963.75-69.5787
19726987.22-46.1139
19736992.88-40.45
19746979.19-54.1389
19756983.69-49.6411
19767003.11-30.2176
19776979.91-53.416
19786978.49-54.844
19796986.52-46.8094
19807014.24-19.0895
19817002.63-30.6975
19827020.28-13.0457
19837044.2710.9379
19847027.64-5.68684
19856997.01-36.321
19867008.91-24.4221
19877014.36-18.9672
19886972.52-60.8089
19896995.02-38.315
19906999.43-33.9
19916988.69-44.6367
19926999.54-33.7879
19937034.771.43941
19946995.71-37.6159
19957007.72-25.614
19966962.82-70.5117
19977048.815.4677
19986993.95-39.3791
19996998.51-34.8171
20006992.81-40.5245
20017026.25-7.07786
20026981.21-52.1164
20037026.08-7.24692
20046987.53-45.795
20057023.65-9.67727
20067025.38-7.94923
20077041.58.17333
20087006.7-26.625
20097034.381.04643
20107033.460.126429
20117033.970.635
20127052.1718.8355
20137051.9318.596
20147056.3423.0107
20157054.4121.0813
20167082.3849.0492
20177081.7648.4311
20187086.3653.029

Pacific Appendix B1

This is an appendix to: On Thermal Expansion & Thermal Contraction - 45


Ocean: North Pacific
WOD Zones:
1615 1616 1617 7617 7616 7615 7614 7613 7612 7611 7610 1513 1514 1515 1516
1517 7517 7516 7515 7514 7513 7512 1412 1413 1414 1415 1416 1417 7417 7416
7415 7414 7413 7412 1311 1312 1313 1314 1315 1316 1317 7317 7316 7315 7314
7313 7312 7311 1210 1211 1212 1213 1214 1215 1216 1217 7217 7216 7215 7214
7213 7212 7211 7210 1110 1111 1112 1113 1114 1115 1116 1117 7117 7116 7115
7114 7113 7112 7111 7110 7109


World Ocean Database Zones



LEGEND for the HTML table below:

Year column: the year when in situ tide gauge measurements were taken.

RLR (mm) column: the average RLR value of the PSMSL database values for all
WOD zones (listed above) and tide gauge stations in those zones (for a particular year).

SLC (mm) column: the sea level change value derived by recording the first
RLR value of the first year, then subtracting it from every subsequent year's RLR value
(including itself to derive 0 for the first year's SLC mm value).



Year RLR (mm)SLC (mm)
18806935.040
18816951.1716.13
18826911.88-23.16
18836928.38-6.66
18847015.8880.84
18856970.6235.58
18866931.42-3.62
18876902.08-32.96
18886919.96-15.08
18896965.8830.84
18906977.8342.79
18916913.5-21.54
18926880.67-54.37
18936910.67-24.37
18946921.75-13.29
18956961.826.755
18966947.5712.525
18976942.197.15
18986919.58-15.46
18996937.062.01667
19006953.0418
19016869.91-65.125
19026871.66-63.3825
19036859.39-75.655
19046892.18-42.8625
19056897.55-37.492
19066911.33-23.7083
19076916.51-18.5317
19086895.36-39.68
19096953.7418.703
19106940.235.185
19116936.761.718
19126932-3.044
19136951.7416.702
19147002.2967.2455
19156988.8553.8136
19166943.218.16818
19176935.850.811818
19186971.7536.7109
19196974.0238.9775
19206987.4952.4511
19216977.4242.382
19226952.5417.498
19236993.9258.8756
19246936.781.743
19256984.4249.3791
19266964.1529.1092
19276950.8115.768
19286944.379.33231
19296924.74-10.305
19306951.9116.8694
19316951.5216.4789
19326953.3618.3247
19336947.2312.1865
1934696327.9591
19356967.4432.4
19366980.4345.3862
19376985.3550.3104
19386987.1652.1168
19396990.0655.0208
19407013.0377.9859
19417018.9483.902
19426982.8247.777
19436972.9237.8769
19447011.176.0642
19457003.3168.273
19467025.390.2644
19476997.6162.5721
19487005.6570.6078
19496994.0358.9948
19507000.3665.3218
19516987.4952.4494
19526998.4463.3993
19537004.3369.2881
19547004.8869.8391
19556978.4243.3762
19566985.1750.1251
19577009.3874.3423
19587005.0670.0156
19597004.2169.167
19606991.1456.0959
19616988.7553.7135
19626977.6942.6542
19636974.9539.9088
19646987.1652.1157
19656994.2859.236
19666992.7457.6991
19676998.1563.1055
19686983.6248.5772
19697014.979.8563
19706993.758.662
19716990.2555.2125
19727022.0487.0045
19737006.1871.1394
19747011.4376.39
19757007.8472.7993
19767014.9779.9287
19777001.7366.6896
19787002.1467.0984
19797001.4766.4257
19806998.6663.6233
19817015.2880.2435
19826996.8161.7711
19837032.3897.3356
19847001.6266.5838
19856977.3442.2981
19867002.3267.2821
19877017.182.0635
19887010.0174.968
19897023.4988.4467
19907019.3784.3348
19917028.4893.4422
19927039.58104.538
19937015.6580.6127
19947028.2793.2277
19957026.491.3597
19967014.2779.2279
19977041.17106.127
19987044.33109.287
19997041.23106.194
20007026.6891.6388
20017028.6393.5932
20027034.899.7569
20037051.17116.133
20047052.98117.942
20057040.76105.724
20067047.13112.092
20077044.94109.897
20087031.5596.5137
20097041.61106.574
20107067.75132.711
20117051.39116.35
20127074.62139.576
20137046.82111.785
20147068.84133.803
20157067.18132.143
20167097.54162.504
20177075.95140.909
20187065.6130.564
20197000.4965.453


Ocean: South Pacific
WOD Zones:
3113 3114 3115 3116 3117 5117 5116 5115 5114 5113 5112 5111 5110 5109 5108
5107 3214 3215 3216 3217 5217 5216 5215 5214 5213 5212 5211 5210 5209 5208
5207 3315 3316 3317 5317 5316 5315 5314 5313 5312 5311 5310 5309 5308 5307
3415 3416 3417 5417 5416 5415 5414 5413 5412 5411 5410 5409 5408 5407


LEGEND for the HTML table below:

Year column: the year when in situ tide gauge measurements were taken.

RLR (mm) column: the average RLR value of the PSMSL database values for all
WOD zones (listed above) and tide gauge stations in those zones (for a particular year).

SLC (mm) column: the sea level change value derived by recording the first
RLR value of the first year, then subtracting it from every subsequent year's RLR value
(including itself to derive 0 for the first year's SLC mm value).



Year RLR (mm)SLC (mm)
18866918.920
18876934.9216
18886922.753.83
18896955.1736.25
18906955.6736.75
18916957.2138.29
18926965.9647.04
18936987.2968.37
18946941.7922.87
18956948.0429.12
1896694122.08
18976961.9243
18986963.8844.96
18996949.9631.04
19006975.9257
19016944.2125.29
19026919.120.2
190369245.08
19046920.041.12
19056919.120.205
19066898.92-20
19076939.520.58
19086958.9640.04
19096961.0742.145
19106989.0270.1
19117004.6485.715
19126959.6540.725
19136948.3329.41
19146929.7610.8433
19156978.8959.97
19166938.1119.1933
19176978.4959.572
19186959.1840.256
19196943.9325.012
19206955.8736.95
19216955.236.28
19226976.5357.612
19236938.5819.6567
19246980.6461.7233
19256936.617.68
19266936.2717.3486
19276922.033.11
19286925.16.185
19296926.938.01
19306906.8-12.118
19316926.517.58667
19326901.81-17.115
19336915.36-3.55833
19346932.9914.075
19356926.968.035
19366895.08-23.8367
19376929.9811.0587
19386943.0824.1567
1939697455.08
19406925.836.914
19416949.4630.5414
19426959.2740.3467
19436969.0850.155
19446952.7133.79
19456979.5660.6391
19466991.1172.1855
19476964.9346.01
19486971.9353.0143
19496988.1569.2273
19506999.4380.5141
19517027.23108.311
19526996.8777.9547
19536989.570.5806
19546995.0676.1425
19557007.0988.17
19567003.6984.77
19577021.5102.577
19587035.21116.29
19597007.6288.7
19607017.2998.3739
19617019.58100.665
19627024.86105.942
19637014.8695.9354
19647024.98106.058
19657013.8394.9145
19666980.0761.1481
19676978.3859.4569
19686990.9872.0619
19696965.0746.1543
19706989.5270.5983
19716991.2172.2921
19726989.3870.4645
19736974.4355.509
19746996.5677.6386
19756996.0677.1435
19767002.6883.7614
19776980.3861.4647
19786989.6870.7566
19796966.5647.6443
19807000.2881.358
19817011.8792.9471
19827005.1986.2731
19836997.5878.6614
19847004.1285.1986
19856992.0373.1057
19866999.880.8777
19876981.3262.4007
19886999.3380.4078
19897010.1791.2514
19906999.0480.1158
19916989.3170.3926
19926984.2465.3222
19936956.2837.3614
19946960.4641.5369
19956976.4957.5708
19967002.4883.5562
19976983.3764.4454
19986993.0974.1722
19997022.28103.363
20007025.4106.475
20017024.31105.394
20026996.8277.9029
20037003.1484.2213
20046994.575.5829
20056997.9579.0294
20066998.1379.2081
20077001.7182.7853
20087020.84101.919
20097030.63111.712
20107042.83123.911
20117057.7138.78
20127043.43124.506
20137056.46137.542
20147042.07123.147
20157043.48124.556
20167054.64135.723
20177063.11144.191
20187063.05144.132
20197045.29126.368

Polar Appendix C1

This is an appendix to: On Thermal Expansion & Thermal Contraction - 45


Ocean: Arctic
WOD Zones:
1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817
7817 7816 7815 7814 7813 7812 7811 7810 7809 7808 7807 7806 7805 7804 7803
7802 7801 7800 1800 1801 1802 1703 1704 1705 1706 1707 1708 1709 1710 1711
1712 1713 1714 1715 1716 1717 7717 7716 7715 7714 7713 7712 7711 7710 7709
7708 7707 7706 7705 7702 7701 7700 1700 1701 1702 1603 1604 1605 1606


World Ocean Database Zones



LEGEND for the HTML table below:

Year column: the year when in situ tide gauge measurements were taken.

RLR (mm) column: the average RLR value of the PSMSL database values for all
WOD zones (listed above) and tide gauge stations in those zones (for a particular year).

SLC (mm) column: the sea level change value derived by recording the first
RLR value of the first year, then subtracting it from every subsequent year's RLR value
(including itself to derive 0 for the first year's SLC mm value).



Year RLR (mm)SLC (mm)
19066686.670
19076633.33-53.34
19266635.5-51.17
19276632.25-54.42
19286667.25-19.42
19306656.83-29.84
19316833.12146.455
19327049.08362.41
19337016.71330.04
19347001.25314.58
19356975.21288.54
19366802.12115.455
19376752.3465.665
19386827.98141.31
19396828.78142.11
19406715.328.625
19416589.61-97.06
19426554.25-132.42
19436727.7941.1233
19446674.15-12.52
19456564.09-122.585
19466596.38-90.295
19476763.1976.5233
19486980.86294.188
19496913.61226.942
19506976.44289.769
19516990.53303.856
19526929.49242.825
19536994.89308.215
19546997.11310.439
19556949.83263.163
19566897.98211.308
19576935.24248.573
19586926.58239.908
19596988.71302.037
19606931.22244.554
19616955.56268.895
19626956.7270.03
19636971.72285.049
19646987.33300.656
19656920.02233.353
19666938.17251.495
19677054367.327
19686990.29303.617
19696956.23269.559
19706941.62254.947
19716976.63289.956
19726984.03297.359
19736975.59288.916
19746934.69248.017
19757014.16327.494
19766979.4292.732
19776920.09233.419
19786927.77241.1
19796910.51223.844
19806940.15253.485
19816975.75289.075
19826991.47304.795
19837006.7320.028
19846966.45279.781
19856938.81252.139
19866970.26283.595
19876987.51300.836
19886991.93305.256
19897058.78372.108
19907037.15350.478
19917042.65355.982
19927040.46353.787
19937034.8348.125
19947038.1351.434
19957021.6334.926
19967026.95340.278
19977031.19344.517
19987009.83323.156
19997024.17337.497
20007011.62324.949
20017038.93352.265
20027035.43348.76
20037064.74378.073
20047038.8352.129
20057055.42368.75
20067047.63360.96
20077061.88375.21
20087050.16363.489
20097022.13335.457
20106963.54276.867
20117040.08353.414
20127027.74341.07
20136999.74313.069
20147016.54329.871
20157061.96375.293
20167064.43377.759
20177034.98348.312
20187063.7377.029
20196989.3302.63


Ocean: Southern
WOD Zones:
3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517
5517 5516 5515 5514 5513 5512 5511 5510 5509 5508 5507 5506 5505 5504 5503
5502 5501 5500 3500 3501 3502 3603 3604 3605 3606 3607 3608 3609 3610 3611
3612 3613 3614 3615 3616 3617 5617 5616 5615 5614 5613 5612 5611 5610 5609
5608 5607 5606 5605 5604 5603 5602 5601 5600 3600 3601 3602 3706 3707 3708
3709 3710 3711 3716 3717 5717 5716 5715 5714 5713 5712 5711 5710 5709 5708
5707 5706 5705 5704 5703 5702 5701 5700 3700 3701 3702 5816


LEGEND for the HTML table below:

Year column: the year when in situ tide gauge measurements were taken.

RLR (mm) column: the average RLR value of the PSMSL database values for all
WOD zones (listed above) and tide gauge stations in those zones (for a particular year).

SLC (mm) column: the sea level change value derived by recording the first
RLR value of the first year, then subtracting it from every subsequent year's RLR value
(including itself to derive 0 for the first year's SLC mm value).



Year RLR (mm)SLC (mm)
195769570
19586927.86-29.14
19596881.93-75.07
19606926.8-30.205
19617026.2469.24
19627000.7343.7325
19636956.13-0.873333
19647079.81122.807
19657122.45165.445
19667030.9173.9086
19677065.76108.757
19687090.42133.416
19697065.59108.589
19706991.8534.8533
19717026.3169.3125
19727019.8962.885
19737018.4461.44
19747124.43167.43
19756965.848.8425
19766964.417.415
19776963.166.1575
19786957.380.375
19796914.52-42.48
19806920.71-36.2933
19816930.01-26.99
19826918.06-38.94
19836939.41-17.585
19846914.18-42.82
19856966.839.83333
19866960.283.27667
19876928.19-28.805
19886917.29-39.7075
19896964.27.1975
19906999.7442.736
19916955.32-1.68167
19927032.8175.8057
19937073.44116.438
19947074.97117.974
19957066.52109.523
19967064.23107.23
19977042.7685.7587
19987075.55118.547
19997082.84125.838
20007072.59115.59
20017068.06111.061
20027059.87102.869
20037087.57130.573
20047090.99133.988
20057072.9115.899
20067099.77142.771
20077119.24162.24
20087110.03153.033
20097133.68176.682
20107144.7187.698
20117140.11183.106
20127160.63203.634
20137141.45184.448
20147015.6958.6875
20157010.0453.035
20166988.1431.1375
20177033.9676.9567
20187044.887.8033