By Jan Risbergs Jr

What is the Law of Attraction?

The Law of Attraction is a modern name for an ancient concept.

Approximately 3,000 years ago King Solomon, in the Book of Proverbs, penned the phrase “As a man thinketh in his heart, so he is.”

Around 1854, Ralph Waldo Emerson wrote “We become what we think about all day long.”

Over 100 years ago in 1902, James Allen used Solomon’s quote and wrote one of the first modern self-help books “As a Man Thinketh.” Allen stated “A man is literally what he thinks, his character being the complete sum of all his thoughts.”

In 1936 in the middle of America’s Great Depression, Napoleon Hill wrote the success classic “Think and Grow Rich!” Dr. Hill is known for many sayings, one of the lesser known phrases he used, which describes the Law of Attraction is “Hold a picture of yourself long and steadily enough in your mind’s eye, and you will be drawn toward it.”

Twenty years later, in 1956, radio announcer Earl Nightingale, a follower of Napoleon Hill, started the self-help audio industry with his million selling recording of “The Strangest Secret.” This summed up the Law of Attraction as “You become what you think about.” He said that the irony is that this is neither strange nor is it a secret.

Finally, in 2006, Rhonda Byrne popularized the Law of Attraction in her movie “The Secret.” It was as if the Law of Attraction was discovered after spending 3,000 years in hiding. Byrne paid considerable homage to the previous authors and drew both global success and global skepticism for her work.

So here we are today. Whether you believe in the Law of Attraction in any of its forms or incarnations, or whether you think his is “pseudo-science” or “wishful thinking” really isn’t important. You probably want to know the answer to these three questions:

1) How can I become what I want to be?

2) How can I do what I want to do?

3) How can I get what I want to get?

In a nutshell, the Law of Attraction not only answers these three questions, but offers a step-by-step plan for achieving these desires.

Whether you believe this is a law or whether you believe anything is attracted is not important. To paraphrase Earl Nightingale, the real irony is that you have been using this strangest secret all of your life, whether you know it or not. Everything you are, have done or have is a result of this law – whatever you want to call it.

The real question you should ask is “How can I use this for my benefit?” If I am responsible for all of the good things that have happened to me as well as the bad – how can I have more of the good and less of the bad?

And that is the first step – understanding that you alone are responsible. For everything. This is probably the best news you could ever hear. If you feel great and life is wonderful – just keep doing what you have been doing.

Since you’re reading this article, it’s unlikely you are in this group. If you don’t like where you are, if you don’t have the money or the car or the relationship you want – this is also good news.

You caused the bad, now just change one thing and you cause the good.

All you have to do is change your thoughts. Then your life will change.

If the words of King Solomon and the film footage of Rhonda Byrne agree on one thing – it is this. Change your thoughts – change your life.

Now we can discuss the exact plan you can use to do so.

Jan Risbergs, Jr. has studied the Law of Attraction for over 25 years – well before it was called The Law of Attraction. He creates and produces online multimedia presentations that explain step-by-step how to get the most out of this powerful blueprint for success. His search took 25 years. Yours can take less than 29 Days. An unexpected shocking discovery reveals how two simple exercises provide the key to altering your subconscious programming.

http://JanTheMarketingMan.com

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What One Word Will Instantly Stop the Law of Attraction From Working For You?

By Kristen Howe

Are you doing everything you can to make the Law of Attraction work for you? Have you gone through the steps of the Law of Attraction but you still aren’t attracting the money, success, relationships, happiness and everything else you desire?

You aren’t alone! I’ve said it a million times and I’ll say it a million more. The Law of Attraction is always working…the only question is if it is working for you or against you. If you are reading this article, my guess is that the Law of Attraction is working against you.

If you’re anything like some of my coaching clients, you are further along than most people with The Law of Attraction as far as reading about it and believing in it and making sure you go through the steps of the Law of Attraction.

You are probably clear on what you want and spend time visualizing it and maybe you have a dream board and even take inspired action. Am I right? Does this sound like you?

Luckily, I have the answer and that answer comes in the form of 1 word. This 1 word lies in your unconscious and waits there to judge every move you make toward your desires. This 1 word is all powerful if you don’t flip on the lights and deal with it head on but the good news is, it loses all power once you do face it.

What is the 1 word?

Impossible!

Yup, the word is impossible. If you unconsciously judge everything that you desire and visualize as impossible, it will be just that. And the thing about impossible is that it is just a word.

There is 1 essential step in the practice of the Law of Attraction that you may be missing. You must remove any resistance to what you desire.

Do this…

1. Write down everything you want.

2. Ask yourself if you believe that there is anyone (not you) that could have what you desire.

I guarantee you’ll say ‘yes’.

3. Write down ‘Why’ this person can have what you desire.

What is it about them? What resources do they have? What are their beliefs? What is their personality like? Etc.

Now it’s time to be honest with yourself (or this won’t help you).

4. Ask yourself if you believe what you desire is possible for you?

If you answer ‘yes’ great (but then I’ll challenge you and ask you why you don’t have it yet) but if you answer ‘no’ this is what I want you to do…

You have already described the person you would need to be to attract what you desire.

Use that as a guide…

5. Answer this: To attract what you desire, what do you need to believe? What do you need to be like? What resources do you need to cultivate? How can you start to cultivate them? Even if it seems that you are so far from being that person, take 1 step toward it now.

Want to harness the Law of Attraction to create the life you want, but can’t figure out what you’re doing wrong?

Click Here to Discover exactly how to use the Secret of Deliberate Creation to Attract Money and everything else you desire at http://www.lawofattractionkeyreport.com

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Meet the Future You

By Stuart Goldsmith / Author of The Midas Method

Imagine walking into a room and meeting the ‘you’ of ten years from now. What will you be wearing? Where will you be living? What will your lifestyle be like? What car will you be driving? Will you be running a business? If so, how successful will you be? What will your net worth be?

You really only have three choices here about how the ‘you of the future’ will look, and this is where the power of this exercise lies:

1. Somewhere in between how you are now,
and a depressed, broke and scruffy tramp.

2. An exact clone of how you are now -
absolutely nothing has changed in a decade.

3. A happier, wealthier, healthier version of the ‘you of today.’

Only a suicidal depressive would visualize number one. Number two is effectively saying that nothing will change; you will not grow over the next ten years, you will not get richer, happier, wiser, healthier – anything. The ‘you of tomorrow’ will be indistinguishable from the ‘you of today.’

So that just leaves number three, and if you selected this it remains for you to back this glittering vision of the ‘future you’ with all the force of your imagination.

Having imagined how you will be in the next ten years, here is a really neat trick to help you achieve it.

Ask yourself the fòllowing questions:

“What do I need to achieve in the next 12 months in order to make my future dream a reality?”

“What do I need to do in the next month to start myself on this journey?”

“What can I do by next week to prepare myself for the journey?”

“What can I do right now, today, in order to start this process off?” Do you see how this works?

You need to dream, but this is not enough. Dreams come a size too large so that you can grow into them and this means that dreams are too large to realize all at once. Our minds are finite, and so all large projects must be broken down into bite-sized chunks otherwise we become discouraged by the scale of the endeavor. This is one of the secret keys of successful people. They are undaunted by large projects, because they have the knack of breaking them down into simple steps. Each step is easily manageable, and can be completed in anything from a few hours to a few weeks.

In contrast, it is useful to analyze the situation of people who are stuck, both monetarily and in spirit, if only to allow you to avoid these errors. This is based on my experience of two decades of dealing with both winnèrs and temporary losers in the game of life.

1. They are frightened.

Their lives are dominated by fear. They see the world as a scary, threatening place and crave security, dullness, mediocrity. They long for every day to be the same as the last and become scared and upset if even a small change breaks the monotony of their days.

2. They completely lack visualization ability.

If asked to visualize their future self, they would stare at you blankly. They are not pretending. They do not even understand what you mean by this exercise. If you force them to try, they’ll come back with nothing more than a shrug.

3. Assuming that you could drag some sort of dream out of them

(for example wanting to be worth a million dollars some day) then they would be wholly incapable of working backwards from that point to the present, and suggesting actions they might have to take in order to make this come about. Again, they are not faking. There is now; there is the future; and in between, a yawning, fathomless chasm – a blank.

4. Even if you were to write the steps out, 1-100,

with a check box next to each one, they absolutely lack the discipline even to start on the task, let alone complete the steps. At the first slight downfall, or negative comment from a friènd, they will give up. In any situation which requires a choice between working for a better future, and instant gratification now, they will unfailingly choose instant gratification.

But this is not you, hopefully. If you recognize yourself here, then don’t worry because it is possible to change and get out of this ‘stuck’ pattern you are in.

You need to develop the habits of a winnèr. You want to enjoy today, but have an even better tomorrow waiting for you.

To do this you must model yourself on winnèrs – people who have achieved great things in their lives. I am talking here about ‘winnèrs’ and ‘losers’ but I do not mean the term ‘loser’ in the usual derogatory sense.

By a ‘loser’ I mean someone who, by their own definition of winn*ng and losing, is falling well short of where they want to be. By a ‘winnèr’ I mean someone who, by their own definition of winn*ng and losing, is pulling ahead of the game and achieving that which they set out to achieve.

This is how a winner operates:

1. They are brave.

Like all human beings they feel fear, but have mastered it and are able to rise above it. Whilst they acknowledge that there are frightening people and places in the world, in general they view the world as a benevolent place, full of great opportunities and wonderful people. This is a vital principle. Winnèrs view the world as mainly benevolent with some bad bits. Losers see the world as mainly malevolent with some good bits.

2. They are good visualizers.

They have the ability to imagine the future, often in glorious Technicolor detail. They have high self-esteem, and know that they are worth more than they have at present. Life to them is an exciting adventure to be lived to the full. In contrast, losers view life as a terrible chore to be somehow ‘got through’ with as little pain as possible.

3. They are intelligent, rational and logical.

If they have a dream of the future, they know the secret technique for making this happen. Today’s dreams are tomorrow’s realities. They know that large projects cannot be tackled by finite human minds unless they are broken down into manageable, bite-sized pieces. They are able to work backwards from a future dream to the present day, and to lis*t the logical steps required to make that dream come true.

4. Having written down the steps required to achieve their goals, they know what is required next.

Action. Up until this point, all of their plans amount to little more than ethereal hòt air. It is action which grounds the circuit and allows the current to flow. They know that the journey will be long and hard. Any worthwhile dream will involve hard work, concentrated effort and some suffering to attain. They need one more quality. Discipline. This keeps them going during setbacks, when the li*st seems too long, and when others heap mirth and derision upon their efforts.

Stuart Goldsmith is a British multimilliònaire author and lecturer. He created a 16 milliòn fortune starting from a position of heavy debt, and has taught thousands of others how to get wealthy. Discover how his brèakthrough power strategies can help you achieve your specific goals/dreams.

Learn more by clicking here.

The Single Most Critical Skill for the 21st Century

The World Future Society

The World Future Society supports individuals and organizations by acting as a neutral clearinghouse for forecasts, trends, and ideas about the future. We serve as an early warning system to help people spot problems and opportunities far enough in advance so that they can prepare and make wise decisions.

The Single Most Critical Skill for the 21st Century

In this era of accelerating change, knowledge alone is no longer the key to a prosperous life. Foresight is the critical skill. Knowledge quickly goes out of date, but foresight enables you to navigate change, make good decisions, and take action now to create a better future.

We often think people are successful because of luck, when in fact it was their foresight that made them “lucky.” If you look at any successful person, organization, even a country, you will find a high degree of foresight. That’s why foresight is…

The Secret Ingredient of Success

Foresight is critical to achievement in all areas of your life, including your major life decisions. People who lack foresight are likely to find themselves unemployed when jobs are unexpectedly lost to new technologies, competition from overseas, or shifts in consumer tastes. Foresight is the key to survival in a world of disruptive innovation.

Foresight enables you to see opportunities, avoid threats, and chart the fastest path to your goals. The key to success is seizing opportunity when it arises. But you need to see the opportunity and be prepared to take action. That’s why foresight gives you power and agility to achieve any goal you want to achieve.

7 Ways to Spot Tomorrow’s Trends Today

In the more than 40 years since the World Future Society was founded, futurists have developed a range of techniques to study the future. Here are a few techniques futurist use to spot new opportunities and potential problems. These methods give individuals and organizations an edge to help them succeed in a fast-changing world:

1. Scan the Media to Identify Trends—Futurists often conduct an ongoing and systematic surveys of news media and research institutes.These surveys help spot significant trends and technology breakthroughs. Futurists call this environmental scanning.
2. Analyze and Extrapolate Trends—After the trends are identified, the next step is to plot the trends to show their direction and development into the future.Trend analysis and extrapolation can show the nature, causes, speed, and potential impacts of trends.
3. Develop Scenarios—Futurists often describe the future development
   of a trend, a strategy, or a wild-card event
   in story form.

   These scenarios can paint a vivid picture that can help you visualize possible future developments and show how you can prepare effectively for future risks and opportunities.

   Scenarios help you to blend what you know about the future with imagination about the uncertain.

   Scenarios help you move from dreaming to planning and then to accomplishment.

4. Ask Groups of Experts—Futurists also conduct “Delphi Polls” which are carefully structured surveys of experts.Polling a wide range of experts in a given field can yield accurate forecasts and suggestions for action.
5. Use Computer Modeling—Futurists often use computer models to simulate the behavior of a complex system under a variety of conditions.For example, a model of the U.S. economy might show the effects of a 10 percent increase in taxes.
6. Explore Possibilities with Simulations—Futurists create simulations of a real-world situations by means of humans playing different roles.For example, in war games, generals test out tactics they may later use on the battlefield, or corporate executives can explore the possible results of competitive strategies.

1. Create the Vision—Futurists help organizations and individuals systematically develop visions of a desirable future.Visioning creates the big picture of the possibilities and prepares the way for goal setting and planning.

Each issue of THE FUTURIST brings you the results of the work of leading futurists from around the world.

You can also discover how to use these techniques yourself in your business and personal life. We’ve prepared a special report that shows how to use these and other futurist techniques.

It’s called The Art of Foresight: Preparing for a Changing World.

This report will show you how to use futurist techniques to prepare realistically and creatively for success in your personal and professional life.

You can get a copy FREE with an introductory membership in the World Future Society.

[NOTE: The 2 biggest complaints are 1) Fewer workers consider their jobs to be interesting and 2) Incomes have not kept up with inflation. Just imagine you are able to market your skills and products and business via Internet Marketing. Complaint #2 is always there at the beginning. Complaint #1 disappears if and when you follow the "17 Steps" - Please do not trust me - ask Jim Edwards and Joe Vitale, both are successful today after being broke, despearate and, in Joe's case, homeless. Their job satisfaction is quite high. Your's can be too.]

From: MyWay (AP News Service)

Americans’ job satisfaction falls to record low

Jan 5, 7:34 AM (ET)

By JEANNINE AVERSA

WASHINGTON (AP) – We can’t get no job satisfaction.

Even Americans who are lucky enough to have work in this economy are becoming more unhappy with their jobs, according to a new survey that found only 45 percent of Americans are satisfied with their work.

That was the lowest level ever recorded by the Conference Board research group in more than 22 years of studying the issue. In 2008, 49 percent of those surveyed reported satisfaction with their jobs.

The drop in workers’ happiness can be partly blamed on the worst recession since the 1930s, which made it difficult for some people to find challenging and suitable jobs. But worker dissatisfaction has been on the rise for more than two decades.

“It says something troubling about work in America. It is not about the business cycle or one grumpy generation,” says Linda Barrington, managing director of human capital at the Conference Board, who helped write the report, which was released Tuesday.

Workers have grown steadily more unhappy for a variety of reasons:

- Fewer workers consider their jobs to be interesting.

- Incomes have not kept up with inflation.

- The soaring cost of health insurance has eaten into workers’ take-home pay.

If the job satisfaction trend is not reversed, economists say, it could stifle innovation and hurt America’s competitiveness and productivity. And it could make unhappy older workers less inclined to take the time to share their knowledge and skills with younger workers.

Nate Carrasco, 26, of Odessa, Texas, says he’s been pretty unhappy in most of his jobs, including his current one at an auto parts store.

“There is no sense of teamwork in most places any more,” Carrasco gripes.

When the Conference Board’s first survey was conducted in 1987, most workers – 61 percent – said they were happy in their jobs. The survey of 5,000 households was conducted for the Conference Board by TNS, a global market research company.

One clue that may explain workers’ growing dissatisfaction: Only 51 percent now find their jobs interesting – another low in the survey’s 22 years. In 1987, nearly 70 percent said they were interested in their work.

Workers who find their jobs interesting are more likely to be innovative and to take the calculated risks and the initiative that drive productivity and contribute to economic growth, Barrington says.

“What’s really disturbing about growing job dissatisfaction is the way it can play into the competitive nature of the U.S. work force down the road and on the growth of the U.S. economy – all in a negative way,” says Lynn Franco, another author of the report and director of the Conference Board’s Consumer Research Center.

Conference Board officials and outside economists suggested that weak wage growth helps explain why workers’ unhappiness has been rising for more than 20 years. After growing in the 1980s and 1990s, average household incomes adjusted for inflation have been shrinking since 2000.

Also, compared with 1980, three times as many workers contribute to the cost of their health insurance – and those contributions have gone up. The average employee contribution for single-coverage medical care benefits rose from $48 a month to $76 a month between 1999 and 2006.

Workers under 25 expressed the highest level of dissatisfaction. Roughly 64 percent of workers under 25 say they were unhappy in their jobs. The recession has been especially hard on young workers, who face fewer opportunities now and lower wages, some analysts say.

The most satisfied were those ages 25 to 34, who may see some opportunities for upward mobility as baby boomers retire. Around 47 percent of workers 25 to 34 say they were happy in their jobs.

Some other key findings of the survey:

- Forty-three percent of workers feel secure in their jobs. In 2008, 47 percent said they feel secure in their jobs, while 59 percent felt that way in 1987.

- Fifty-six percent say they like their co-workers, slightly less than the 57 percent who said so last year but down from 68 percent in 1987.

- Fifty-six percent say they are satisfied with their commute to work even as commute times have grown longer over the years. That compares with 54 percent in 2008 and 63 percent in 1987.

- Fifty-one percent say their are satisfied with their boss. That’s down from 55 percent in 2008 and around 60 percent two decades ago.

Carrasco said he wishes his bosses would take time to listen to workers’ ideas – and their difficulties on the job.

“Most of the time they only listen to what their bosses are saying,” he says. “Bosses need to come down to the employee level more and see what actually goes on, versus what their paperwork tells them is happening in the stores.”

It wouldn’t be fair to blame low job satisfaction solely on bad bosses, Barrington says.

“It is two-way responsibility,” she says. “Workers also have to figure out what they should be doing to be the most engaged in their jobs and the most productive.”

5 Ways To Overcome (Blogging) Shyness And Be More Productive

August 26, 2007

Are you by any chance a shy bloogger? I have a sad news for you: you are going to fail as a blogger if you have not already. Blogging is about socialising, and a self-conscious person lacking good social skills can never be a blogger, let alone a good blogger.

Not sure whether you are a shy blogger or not, and what do I mean by that? Here are some clues: You think a hundred times before writing a sentence, you delete whole paragraphs from your posts just because they make you look bad, you are scared to say something foolish, and.. you are too shy in dealing with your readers. If you are shaking your head in horror, you are, sadly, a shy blogger.

Not to worry though, because shyness can be overcome once you know you are unreasonably shy about things others could care less about.

Shyness is characterized by fear, self-doubt, and lack of confidence. A shy person, in real life, would fear mingling with other people, lock himself in his room, and would try his mightiest to hide his shortcomings from others.

Those are all unreasonable fears, and can be eliminated with little effort on your part.

Here are my thoughts about overcoming shyness in blogging, and in real life in general (therefore not too focused on any one of them)

Kill your blogging persona

If you are shy, you probably have created a persona to represent you on your blog. The first step you are going to take is kill that persona. It’s your blog, and your readers look forward to hearing from you, not your persona. If you think you can hide behind a fake representation of yours, you are mistaken. People who are clever enough to be surfing the web and reading weblogs have powerful BS detectors. They’ll know you are being dishonest to them. You are not a good actor.

Reveal your innermost feelings

Are you afraid of darkness? Tell the world you are, don’t put up false bravado. If it’s any consolation to you, many people are afraid of darkness. When you reveal your innermost feelings and your so-called secrets, you’ll be amazed to know that many of your readers share your thoughts, and have experienced the same kind of emotions as you. You aren’t the only king of the embarrassing moments in the world.

Are you afraid of dogs? *cough*

Turn your shyness into excitement

Shyness usually comes with accelerated activity of adrenaline. Imagine the moment just before going to a party, do you feel a rush of anxious excitement in your spine? If you look at the excitement of going to the party positively, you can start looking forward to meeting all those people you dreaded facing (girls baby!) Make use of that adrenaline while it lasts.

And how does it relate to blogging you ask? Look at it this way: While writing a post, you remember a personal anecdote of falling into a street gutter. Your adrenaline starts flowing because of embarrassment and you are forced to craft a fake version of that anecdote. You tell your readers that your friend fell into the gutter while you watched him. Now, since you are kind of feeling guilty about being dishonest to your readers, you’ll probably leave a clue for your readers that your anecdote is all made up (shy people are not very good at being shy) So, why not relate the anecdote as it really happened and let your adrenaline pump some excitement into it? It’s even more exciting to see people laugh at your ‘cuteness’.

Be outspoken

One way to overcome shyness is to somehow gather the courage to call a spade a spade. If you don’t like religion, say so. Be critical. Don’t be afraid of challenging what you think is wrong. People won’t laugh at you, they won’t sneer at you. And if they do, why’d you care as long as you know you are right? Perhaps it would be a good chance to laugh at those goof balls laughing at you. There is no reason to be shy about expressing your real beliefs and opinions. You have as much right to have your own beliefs as anybody else.

Don’t be too appearance conscious

One problem with shy people is that they care too much about their personality and their appearance. Don’t ever write a post and then look at it from different angles with intention to modify it to make you look as good as possible (according to your own twisted standards) The thing is that there is no set standard of goodness. How people judge you varies from person to person. So, it’s time to start being yourself rather than someone else.

OK, above points are not radically different from one another, but they highlight different aspects of one point: Its no good caring too much about how others might perceive you, and it’s counterproductive to be shy.

AND while we are at it, did I tell you that I don’t have a girlfriend?

Ultimate Secrets of Total Self Confidence

Fables of the Reconstruction

Paleoclimatology and the Art of Civilization Maintenance

Before jumping straight to the math stuff, let’s cover some preliminaries.

There is widespread acceptance of the fact that global temperatures rose to some extent in the latter part of the 20th Century, compared to various baseline periods from the 19th through early 20th Century. Let’s not quibble on that point. Whether that fact is worth losing sleep over really depends on how big of an increase it is was; 150 years is a nanosecond on the geological calendar, and a slight temperature rise over a century of two may not be any big whoop if similar (or bigger) increases have occurred the past. What’s needed is a long view — say, 1000 years or more.

That’s where paleoclimatology comes in. Paleoclimatology concerns itself with figuring out what climate was like in the past; e.g., “climate reconstructions.” For the really really distant past, say the Paleozoic Era, climate is inferred from geological records, rock formations, fossils and the like. For more recent past (say the last 1000 years), traditional methods of climate reconstruction used a combination of human historical records (European harvest dates, sea explorer notations of ice floes etc.), plant and animal records (tree rings, the northern geographic spread of plant and insect species), celestial data (e.g. sun spots), and other indicators. They weren’t at a high level of granularity or statistical sophistication, but the traditional reconstructions generally agreed there was a “Medieval Warm Period” between roughly 1000 and 1350 AD, followed by a “Little Ice Age” approximately from 1350 to 1950, from which we are now just emerging.

Over the last 20 years or so, paleoclimatology saw the emergence of a new paradigm in climate reconstruction that utilized relatively sophisticated statistical modeling and computer simulation. Among others, practitioners of the emerging approach included the now -famous Michael Mann, Keith Briffa, and Philip Jones. For sake of brevity I’ll call this group “Mann et al.”

The approach of Mann et al. resulted in temperature reconstructions that looked markedly different from the one previously estimated, and first receive widespread notice in a 1998 Mann paper that appeared in Nature. The new reconstruction estimated a relatively flat historical temperature series until the past hundred years, at which point it began rising dramatically, and accelerating around 1990. This is the celebrated “hockey stick” with which we are all familiar.

Wow! Science-y!

The consequences of this reconstruction are even more dramatic. If one subscribes to the older reconstruction, the recent increase in global temperatures are real but well within the range of temperatures seen over the past millenium (by some estimates Earth is still more than 1 degree cooler than during the Medieval Warm Period). For good or bad, our recent warming can be explained as the result of natural long term climate cycles. In this view, long term temperatures rise and fall, and have a fairly weak association with human population and CO2 production.

But… if the Mann et al. reconstructions are correct, recent temperatures are well beyond the range seen in over the past 1000 years. Foul play is assumed and the hunt is on for a culprit; a natural suspect is man made CO2, which has increased coincidental with temperatures. It’s a small part of overall global greenhouse gases (5.5% if you don’t include water vapor, 0.3% if you do), but maybe — just maybe — the atmosphere is in a delicate, wobbly, equilibrium balance. Even a small increase in human CO2 might push it past a catastrophic tipping point — a conclusion that is bolstered by the hockey stick. Therefore, as this view has it, our survival depends on massive and immediate reductions of human made CO2 emissions.

This is essentially the “settled science” that has been the basis for emergency carbon treaty fests like Kyoto and Copenhagen, and it’s hard to overstate the role that the research of Mann et al. has played in creating them. In the 2001 IPCC report the hockey stick was given a starring role and Mann was lead author of the chapter on observed climate change.

Given the enormous economic stakes involved, you might think the media would have some spent a little time explaining the models underlying the hockey stick. Ha! Silly you. Whether it was a matter of ideological sympathy or J-school stunted math skills, press coverage has generally stuck to the story that there’s an overwhelming scientific consensus supporting AGW. As proven by brainiac scientists with massive supercomputers running programs much too complex for you puny simian mind.

Au contraire! The climate reconstruction models used by Mann, et al. are relatively simple to derive, don’t take a lot of data points, and don’t require any special or expensive software. In fact, anybody with a decent PC can build a replica at home for free. Here’s how:

Stuff you’ll need

1. A computer. Which I assume you already have, because you’re reading this.

2. The illustrative spreadsheet, available as an Open Office Calc document here, or as a Microsoft Excel file here. Total size is about 1mb.

3. A spreadsheet program. I highly encourage you to use Sun’s Open Office suite and its included Calc spreadsheet — it’s free, very user friendly and similar to Excel, and it’s what I used to create the enclosed analysis. You can download and install Open Office here. You can do all of the examples in Excel too, but you’ll also need to download an additional add-on (see 4 below)

4. A spreadsheet add-in or macro for principal components analysis. Open Office Calc has a nice one called OOo Statistics which can be download and installed from here. This is the macro I used for the enclosed analysis. If you’re using Excel, you’ll have to find a similar Excel add-in or macro for principal components analysis. There are several commercial and free versions available.

Okay, ready? Now let’s start reconstructing. Open the illustrative spreadsheet and follow the bouncing ball.

Step 1: Get some instrumental global temperature data

On the first tab of the spreadsheet you’ll find an estimated Northern Hemisphere annual temperature series for the years 1856-2001, along with an associated graph. The source data here were copied from NOAA, and cited to Philip Jones from the University of East Anglia. The values are scaled as degrees celsius difference from the average in 1961-1990.

“A-ha! This is the garbage data that was…” Hold that thought, we’ll get to that later. Let’s assume for the time being that the temperature measures are valid; remember, the goal here is to roughly replicate the Mann et al. method of temperature reconstruction. Assuming the temperatures are valid, the series visually certainly seems to indicate a recent increase in Northern Hemisphere temperatures. But it only goes back around 150 years.(all subsequent pictures can be click-embiggened)

Step 2: get some high frequency proxy data

Although observed temperature measurements prior to 1850 are unavailable, there are a number of natural phenomena that are potentially related to global temperatures, and can be observed retrospectively over 1000 years through various means. Let’s call these “proxy variables” because they are theoretically related to temperature. Some proxies are “low frequency” or “low resolution” meaning they can only be measured in big, multi-year time chunks; for example atmospheric isotopes can be used to infer solar radiation going back more than 1000 years, but only in 5-20 year cycles. Other low frequency proxies include radiocarbon dating of animal or plant populations, and volcano eruptions.

By contrast, some proxy variable are “high frequency” or “high resolution,” meaning they can be measured a long time back at an annual level. Width and density of tree rings are an obvious example, as well as the presence of o-18 isotope in annually striated glacial ice cores. In principle this type of proxy variable is more useful in historic temperature reconstruction because they can be measured more precisely, more frequently, and in different places around the planet.

On the second tab in the spreadsheet are a candidate set of those proxy variables. I downloaded these data from Michael Mann’s Penn State website here, with column descriptions here; long description here.

The column headings in the spreadsheet (row 3) give a brief description of the proxy variables, all of which are either tree ring width, tree ring density, or glacier ice core o-18, and the series extends back to the year 1400 AD. I would have preferred to have used the 1000-year MXD (“Maximum Latewood Density”) proxy variables often used by Mann et al.  but the University of East Anglia site no longer allows downloads of it.

Step 3: Extract the principal components of the proxy variables

Huh what? “Principal components”?

Here’s the deal: now that we’ve got the temperature series and the proxy variables, a tenderfoot statistician is tempted to say, “hey — let’s fit a regression model to predict temperatures from the proxies.” Good intuition, but there are potential problems if the predictor variables (the proxies) are correlated with one another — e.g., imprecise coefficients and overfitting. (and if you’re wondering what a “regression model” is, don’t worry, we’re getting to that soon.)

One way of dealing with the problem of correlated predictors (and the one used by Mann et al.) is through the method of principal components, which “uncorrelates” the predictors by translating them into a new set of variables called principle components. Nothing nefarious about this, it’s a standard statistical technique. If you want further explanation, read the indent; otherwise skip ahead.

In a nutshell principal components analysis (PCA) works like this, in matrix notation:

P = Xw

X here represents a matrix of the original correlated predictor variables, with n rows and k columns (in our example, this is the proxy data).  P represents a matrix of the ‘principal components’ of X, which also has n rows and k columns, and w is a k-row, k-column matrix of weights that translates the original variables into the principle components. The values of w are derived through the method of singular value decomposition.

Unlike the original X data, all of the columns of P are all mutually uncorrelated with each other, and have a mean of 0. The columns of P are ordered, such that the first column has the highest variance, the second column the second highest variance, and so on. Because P is a simple linear transform of X, it contains all the original information in X. Because the columns of P are ordered in descending variance, PCA is often used for data reduction — when the low variance columns are discarded, P still maintains most of the original information in X in a smaller number of columns.

You can find the principal components of the 15 proxy variables on the third tab in the spreadsheet, which I obtained with Open Office’s OOo Stats macro. Starting in column P, there are 15 principal components for the years 1400 to 1980.

You can replicate my PCA by doing the following: go to the second spreadsheet tab, the one containing the proxy variables. From the OOo Stats menu, select “Multivariate Statistics” ==> “Principal Components.”

This will bring up the Principal Components dialog box. For “range” select the rows and columns containing the proxy data, along with the column header row 3 (range B3:P584 — do not use the year column).  For “Output” select “New Sheet” and give it a name in the text box. Now click “OK.”

Depending on the speed of your computer, this may take 2-10 minutes to complete, so go grab a beer. You may see a little warning box pop up with the message “warning: failing to converge,” but just click OK, it will eventually identify the correct principal components (I validated it against other stat software packages). The new principal components output sheet you named should be identical to the one provided.

Step 4: Inner merge the extracted principal components with the instrumental temperature data

Now that we’ve got the 1856-2001 observed temperature series (tab 1) and the 1400-1980 proxy variable principal components (tab 3), let’s match them up by common year. The initial match is in tab 4.

Note that for recent years (1981-2001) we have an observed temperature, but no proxy principle components; for years before 1856, we have proxy principle components but no observed temperature. Lets subset this down to only those years for which we have both temperature and principle components (1856-1980). That subset is on tab 5 of the spreadsheet.

Step 5: For the common years, fit a regression model between instrumental temperature and the proxy variables

Now this is where the analytical rubber meets the road, where we find a functional equation to link temperature to the proxy variables through the principal components. The approach of Mann et al. is to use multiple regression. A basic multiple regression equation looks like this:

Y_i = β₀ + β₁X_1i + β₂X_2i + … + β_kX_ki + ε_i

Where Y is some variable you want to explain / predict, [X₁ ... X_k ] are the variables you want to use as predictors, [β₀ ... β_k] are a set of coefficients, and ε is error. A regression analysis finds the values of [β₀ ... β_k] that minimize the squared error in prediction. In our example case of Mann-style temperature reconstruction,

Temperature_i = β₀ + β₁P_1i + β₂P_2i + … + β₁₅P_15i + ε_i

Where the P’s are the principal components. I ran this multiple regression, and the β coefficient estimates are on tab 6 of the spreadsheet, in the range C129:C144. In the above regression model β₁ is -0.01 (cell C129), β₁ is +0.05 (cell C130), and so on. The Y intercept value β₀ is -0.23 in cell C144.

If you want to replicate how I got these estimates, go to tab 5 of the spreadsheet, containing the matched temperature and proxy principle components data. Select the range containing the data (cells B3:Q128). Now from the OOo Stats menu select “Basic Statistics” ==> “Multiple Regression.”

This will bring up the multiple regression macro dialog box. Under “Output” click “New Sheet” and give it a name, then click “OK.”

This will bring up the regression model specification dialog box. One by one click the values “PC_1,” “PC_2,” etc. under “Model”and they are added in the regression model specification box below it.

Once the all of the PCs are added, click “Fit.” There will soon appear a new regression output worksheet tab with the name you specified, identical to the one included with the original spreadsheet.

Step 6: Evaluate how well the regression model fits the observed temperature data.

If the regression model we specified is any good, it ought to fit the temperature data where we know it. The predicted temperatures can be found by applying the estimated β coefficients to the proxy variable principle components…

Predicted Temperature_i = β₀ + β₁P_1i + β₂P_2i + … + β₁₅P_15i

The regression output computes this prediction for us; you can find it in the regression output tab in column Q. I merged the predictions with the actual temperature for 1856-1980 and put it in tab 7, along with a plot. The blue line is actual, the red line is the prediction from the model. It’s not perfect, but it tracks okay. The correlation between actual and predicted is .71. The squared correlation (r²) is .50, meaning 50% of the variation in actual 1856-1980 temperatures can be accounted for by the principal components (and by inference the original ice core and tree ring proxy variables).

Step 7: Apply the regression coefficients to the years before observed temperatures to reconstruct estimated temperatures

Now that we’ve (weakly) validated the regression model, we are finally at the point where we can compute a historical temperature reconstruction. Remember, in the years 1400 to 1855 we do not have any observed temperatures, but we do know the proxy variable principle components. For these years, we can also use the regression model:

Predicted Temperature_i = β₀ + β₁P_1i + β₂P_2i + … + β₁₅P_15i

Tab 8 of the spreadsheet shows the predicted values for each year from 1400 through 1980, in column C (highlighted in yellow). Predictions of 1981-2001 can’t be computed from the model because the proxy variable series stops at 1980. The formula in cell C23, i.e.

=MMULT(D23:R23; ‘Regression Results’.$C$129:$C$143) + ‘Regression Results’.$C$144

is equivalent to the temperature prediction equation above; it cross multiplies each principle component with its associated β coefficient, adds them up and finally adds in the β₀ intercept value. We can apply this formula to every year for which we have proxy data, which gives us a Northern Hemisphere temperature reconstruction all the way back to 1400 AD.

A time series graph of the reconstructed temperatures (red) overlaid with actual temperatures (blue) appears on tab 9 of the spreadsheet.

Voila, it sure does looks like that famous hockey stick — relatively flat for 500 years, then zooming up like a mo-fo right after MTV debuted. The one thing that is missing here is the error band (the grey zone around the hockey stick line way back up the post). That’s approximated by

Upper 95% confidence band = Predicted Temperature + 1.96 * stdev(ε)

Lower 95% confidence band = Predicted Temperature – 1.96 * stdev(ε)

where stdev(ε) = the standard deviation of the column labeled “residual” on tab 6.

Quick! Get us an NSF grant, stat!

Discussion

Again, all that math-y spreadsheet-y stuff above was not meant to perfectly replicate any specific study done by Mann et al.; those specific studies differ by the choice of instrumental temperature data set, the choice of proxy variables, whether series are smoothed with a filter (Fourier transform etc), and so on. My goal was to provide interested people with a hands-on DIY example of the basic statistical methodology underlying temperature reconstruction, at least as practiced by the leading lights of “Climate Science.”

If you’ve followed all this, it should also give you the important glossary terms that should help you decipher the Climategate emails and methodology discussions. For example “instrumental data” means observed temperature; “reconstructions” are the modeled temperatures from the past; “proxy” means the tree ring, ice core, etc. predictors; “PCs” mean the principal components.

Is there anything wrong with this methodology? Not in principle. In fact there’s a lot to recommend it. There’s a strong reason to believe that high resolution proxy variables like tree rings and ice core o-18 are related to temperature. At the very least it’s a more mathematically rigorous approach than the earlier methods for climate reconstruction, which is probably why the hockey stick / AGW conclusion received a lot of endorsements from academic High Society (including the American Statistical Association).

The devil, as they say is in the details. In each of the steps there is some leeway for, shall we say, intervention. The early criticisms of Mann et al.’s analyses were confined to relatively minor points about the presence of autocorrelated errors, linear specification, etc.  But a funny thing happened on the way to Copenhagen: a couple of Canadian researchers, McIntyre and McKitrick, found that when they ran simulations of “red noise” random principle components data into Mann’s reconstruction model, 99% of the time it produced the same hockey stick pattern. They attributed this to Mann’s method / time frame for selecting of principle components.

To illustrate the nature of that debate through the spreadsheet, try some of the following tests:

Run step 3 through step 7, but only use the proxy data up through 1960 instead of 1980.

Run step 5 through step 7, but only include the first 2 principle components in the regression.

Run step 3 through step 7, but delete the ice core data from the proxy set.

Run step 2 through step 7, but pick out a different proxy data set from NOAA.

Or combinations thereof. What you’ll find is that contrary to Mann’s assertion that the hockey stick is “robust,” you’ll find that the reconstructions tend to be sensitive to the data selection. M&M found, for example, that temperature reconstructions for the 1400s were higher or lower than today, depending on whether bristlecone pine tree rings were included in the proxies.

What the leaked emails reveal, among other things, is some of that bit of principal component sausage making. But more disturbing, they reveal that the actual data going into the reconstruction model — the instrumental temperature data and the proxy variables themselves — were rife for manipulation. In the laughable euphemism of Philip Jones, “value added homogenized data.” The data I provided here was the real, value added global temperature and proxy data, because Phil told me so.  Trust me!

In any case, I’m confident that the real truth will emerge soon, hopefully while Mike and Phil are enjoying their vacations. In the meantime have fun and stay warm.