Neither do I, that's why I opt for Personal Observation & Common Sense
An article came out the other day which reveals just how rural folks and those native indigenous peoples have a deeper connection to the landscape from a personal hands on observational and practical applications point of view. They have not had any need of Scientific Studies, Computer Models or any stupid worthless debates found on any number combat discussion forums in Cyber Space to understand something is amiss with the weather. Below are a few great photos and I'll post the link here for you to follow the entire story from the Washington Post:
|Photo: Juan Karita, AP|
In this Feb. 14, 2014 photo, farmer and traditional meteorologist Francisco Condori measures rain water with a flow meter in Cutusuma, on Lake Titicaca's southern shore in Bolivia. Condori is a well-heeded font of ancestral knowledge for fellow farmers in these treeless climes frequently punished by frosts, hailstorms and drought.
|Photo: Juan Karita, AP|
This is a photo of farmer and traditional meteorologist Francisco Condori looks at his notes on changes in climate in Cutusuma, Bolivia. Bio-indicators are catalogued in what are known as Pachagrama, registries whose name derives from “Pachamama,” the native Andean word for “Mother Earth.” Communities compile and share the registry information, which is especially crucial from September to November when the dry season ends and farmers need to know how soon to plant, when the rains will begin and how long they will last. Condori says the "bio-indicators" he follows most closely have helped reduce agricultural losses 40 percent in Cutusuma and surrounding communities. Scientists, however, stress there are no empirical data to support the beliefs.
The indicators are catalogued in what are known as Pachagrama, registries whose name derives from "Pachamama," the native Andean word for "Mother Earth." Communities compile and share the registry information, which is especially crucial from September to November when the dry season ends and farmers need to know how soon to plant, when the rains will begin and how long they will last.
My favourite picture is the last one in that series. There are 1000s of these types of simple observations everywhere around the globe, which unfortunately in most developed countries have been lost by a disconnected modern industrial public. Hence debates from ignorance is the new normal. This example below is so simple, even a child could get this.
|Photo: Juan Karita, AP|
This same farmer and traditional meteorologist Francisco Condori measures last year's nest made by a small bird known as quilli quilli, inside his home in Cutusuma, Bolivia. It's in that season they look for guidance to the southern lapwing, a long-legged plover that likes grasslands. If the female drops her eggs on the crest of a furrow, a lot of rain is expected and farmers will plant potatoes rather than quinoa, which requires less water. But if she deposits them inside the furrow, it supposedly will be a dry year. Condori measures the height of the nests from the surface of the lake water determine how much rain is to come. “This year they initially built their nests about 40 centimeters (1.3 feet) above the water level. Then they dismantled them,” Condori says. Twice, in fact, did the birds dismantle nests before finally reweaving them at nearly twice their original height. “We knew it was going to rain a lot,” he says.
The Fallacies of Computer Models
I have to admit that I never been impressed by most of the modern day computer models we hear about for whatever reasons they have been created. I do however tend to have deeper appreciation for historical charts and graphs than these future electronic prediction fortune telling schemes and they are numerous and for varying reasons. But just to focus on the climate change debate, I think these have hurt more than help in the cause of educating the public. How many times since this whole political GW debate mess has started have these scientists continually had to revise these models because of sudden dramatic unexpected change blew former religious assumption and assertions right out of the saddle ? I'm using the term "religious" in the true sense of the word because what we really have here is "blind faith", not actual facts. I have no problem with the word and need for faith. But I like faith based on facts. For example, almost every human on Earth has faith every night that the very next day the sun will rise and do so even if it's a cloudy day. That's because in the experience of single human being the Sun's rising is an every day common occurrence No one ever gives it another thought that day will always without fail follow night. Called, faith based on fact. But again, I hate blind faith because this is where various components of this world's leadership has it's death grip on the majority of mankind.
These Climate Models are made with simplifications and assumptions about the real world. Some aspects are discounted as insignificant while others make it into the computer model based on the personal bias of the particular research in charge of programming. The problem I have observed over the past decade is that every single year since this mess has been debated, they find it necessary to discard what they previously insisted upon as fact and replace it with newer assumptions based mostly on the worsening problems which threw water on their previous climate model campfire. How many of these futuristic fortune-telling prediction models went from how bad things would be at the end of the 21st Century, then downgrading to 2050, 2030, 2020, etc ? Roy Kalawsky, Professor of Systems Engineering at Loughborough University had this to say about the problems associated with computer modeling:
“The model can never be the real thing because there are things you haven't taken into account in the modelling process that could have a tiny but still important impact later on,”
“One of biggest challenges I feel in the modelling process is being able to validate and verify the model you've created," says Roy, "The modelling process doesn't finish when you've made the model, because you've got to compare the outputs from the model or what you observe in the model against what you can see in the real world. If it doesn’t comply then you have to try and understand why that is so you go back and refine the model."
The computer models for the average human being are generally right over their heads and mostly used by politicians and journalists to manipulate some ideologically driven agenda. While some importance could be obtained, mostly they fail. Seriously, read the daily News and nothing around the planet ever seems to improve despite all their self-promoted enlightenment of how things work here on Earth. I'm not talking pilot programs or localized success stories being exaggerated or being embellished to propagandize a cause or obtain further grant funds. I'm talking nothing is improving anywhere from an environmental standpoint. Mostly what I often find disturbingly wrong with the way which much of science attempts communication to the average person is often times they exhibit a total lack of common sense. For all the understanding that is out there archived over the past 100 years about how nature really works, very little of it gets put into any meaningful context in the form of biomimicry when it comes to ideas, solutions or technological innovation. What they need to do is follow the lead of people like Alan Alda who created a program for teaching Scientists to communicate in meaningful terminology by dumping the "Intellect Speak" they are so fond of amongst their peers, but generally alienates the majority of the public. Take a good of one paragraph along with the link below:
"You don't think of knowledge as a curse, but it's a curse if I think you know everything I know and I talk to you in ways [where] you can't understand me," Alda said. "So that's not only the public, that's policy makers like Congress, who have told me over and over again they cannot understand scientists who come in to talk to them."Then there are those science research paper abstracts which mostly are never meant for the Public's eyes, but have you ever wished that peer reviewed publications were written in plain, easy to understand language, without intellect speak jargon and hidden meaning technical terms? Wouldn't it be great if the Scientists were held under the same standard of communication rules as the Plain Writing Act of 2010 requires of Government ? Not that it would make any difference! Then there is this from Adjunct Professor, Matthew Russell of South College in Knoxville Tennessee and his new Venture called Abtract 2.0. He says:
"A majority of published scientific research is federally funded by taxpayer dollars in the U.S. yet most taxpayers have no idea why the research findings from these funds are important or how they contribute to a better society.
What if the article abstracts, laced with big words and jargon, were rewritten to a level where most people could understand; an abstract 2.0 if you will? By reading a short summary of the work, anyone who wanted to know could actually understand the problem studied and the results. Maybe more importantly, the reader would not have to rely on interpretations of the research from popular media sources that have higher priorities than educating the public."This is certainly true not only of climate change, but also other areas of import regarding other topics about of Earth's environment where models and studies are unfortunately incorrect because of personal bias and priori judgements motivated by various biased heartfelt assumptions. This is beautifully illustrated in a post by Prairie Ecologist, Chris Helzer who made an interesting admission about many of his assumptions which later turned out to be incorrect. Here is how it works according to him. Actually, I believe we all have had this experience on and off throughout our lives. I certainly have.
"Unfortunately, observations are inherently biased. When I start to notice a pattern through observation, I construct a theory to explain it. That’s good science. However, once I have a theory in mind, it influences the way I see things – and I tend to interpret my observations based on my theory. That means it’s pretty easy to start telling myself a story that sounds good, but isn’t actually true. Sometimes, I figure out that my story is wrong through repeated observations. More often, however, what causes me to stop and reconsider is cold hard data. Here’s a recent example of my data showing me that I need to reconsider a theory based on observations."Please read the entire article which is actually very interesting and illustrative in how and where things can go wrong in research, although unlike Chris, most researchers are reluctant to backtrack and admit errors. Here is the link to the entire post on Canadian Rye grass.
Here are some further concluding remarks from his post;
"I take two major lessons from this. First, I need to be more careful in my assumptions about how our management is impacting prairies. That’s nothing new – I fall into that trap all the time, and frequently have to remind myself not to overgeneralize. In this case, I had constructed a logical story explaining why Canada wildrye was abundant in our well-established (old) restored prairies but rare in ungrazed plantings such as CRP fields. There are, of course, many possible explanations for that phenomenon (differences in soil types, plant diversity, seeding rates – particularly of warm-season grasses, fire management, etc.) but I grabbed one simple explanation without adequately considering all those other factors."
"The second lesson is that it’s dangerous to rely solely on observations when trying to figure out natural systems. This is not a new lesson either, and it’s why I try to collect as much data as I can. Observations are really important, but are easily biased by what we think is – or should be – happening. It’s natural to see what you expect to see."Admittedly, the Science behind climate change computer models are just as loaded down with persional biased assumptions and assertions. Mainly because the researchers themselves are as equally mistake and error prone just as any other human being on Earth. However, the average person shouldn't really need a Scientist, Politician, Businessman or some Religious Cleric to tell them something is perverted and wrong with our planet's daily functions in the natural world. Watch this video below about the caution which should be taken in trusting any computer models. Most computer models are shackled to the prjudice, biases and flaws of the one inputing the data into them. This is because scientists are humans equal to everyone else on the planet, although admitting this is rarely done.
Can Computer Models accurately predict what the climate will be decades from now? What evidence is there that these models are any more reliable than fortune tellers? Not much. In fact there are many real-world examples of computer modeling failing miserably while working on issues that are much, much less complicated than climate. These indigenous natives peoples from Bolivia are no doubt considered illiterate by today's intellectual standards practiced by the majority of the world's elites. But this same leadership are themselves irrelevant.
What about those Inuit (Eskimo) Elders above the Arctic Circle who for centuries learned to navigate by the stars and geological terrain markings on the horizon, but have noticed the skyline has change, infering something wrong with the Earth's tilt ? Everyone else is going to have to make a decision as to who or what they are going to believe and live with the consequences of that choice. Burying one's head in the global weirding sand can go either way.