Ameeriklased

Varem ma imestasin, et miks Dawkins sellist sõda kreatsionistide vastu peab - kirjutab pidevalt evolutsiooniteemadel raamatuid, sõidab mööda Ameerikat ringi ja esineb saadetes. Miks selline pingutus nii lihtsa teema nimel.



Aga sellepärast, et ta vaeseke on vähemuses:

According to a 2001 Gallup poll,^[101] about 45% of Americans believe that "God created human beings pretty much in their present form at one time within the last 10,000 years or so." Another 37% believe that "human beings have developed over millions of years from less advanced forms of life, but God guided this process",^[102] and 14% believe that "human beings have developed over millions of years from less advanced forms of life, but God had no part in this process".^[101]

Belief in creationism is inversely correlated to education; of those with postgraduate degrees, 74% accept evolution.^[103][104] In 1987, Newsweek reported: "By one count there are some 700 scientists with respectable academic credentials (out of a total of 480,000 U.S. earth and life scientists) who give credence to creation-science, the general theory that complex life forms did not evolve but appeared 'abruptly.'"^[105][106]

A 2000 poll for People for the American Way found 70% of the American public felt that evolution was compatible with a belief in God.^[107]

According to a study published in Science, between 1985 and 2005 the number of adult Americans who accept evolution declined from 45% to 40%, the number of adults who reject evolution declined from 48% to 39% and the number of people who were unsure increased from 7% to 21%. Besides the United States the study also compared data from 32 European countries, Turkey, and Japan. The only country where acceptance of evolution was lower than in the United States was Turkey (25%).

Kliimaga on täpselt sama lugu ja ennem kui ei toimu murret evolutsioonimõistmisel ameeriklaste ajudes, ei suuda nad ka mõista seda "kuidas Jumala poolt loodud maailma saab inimene muuta".

Ja kui sellised ajud on antud rahahunniku otsas:

US Wealth Distribution

One way to ‘see’ the distribution of wealth in the U.S. is to imagine a group of 100 people who have a $100 between them. Evenly distributed each would have one dollar of wealth. Alas, that is far from the actual distribution. According to the most recent study, Currents and Undercurrents, by the Survey of Consumer Finance (Federal Reserve, Department of Treasury, 2006) wealth is distributed accordingly: 50 individuals at the bottom have a nickel. ($0.05 times 50 = $2.50) The next 40 each have $0.70 of wealth (40 times $0.70 - $28.00). The next 9 each have $4.00 of wealth (nine times $4.00 = $36.00) The last richest individual has $33.40 (one time $33.40). Combined, you have $100.00.

...siis...



Evolutsionism vs Kreatsionism ülejäänud maailmas

Smokey Joe’ Barton: Global Warming ‘Is A Net Benefit To Mankind’

Kloun Texasest:

Gavin Schmidt

RealClimate`it lugedes on minust saanud Gavin Schmidti fänn. Jõuluvanalt tellisin ka tema raamatu.

Siin on järjekordne "väitlus":

Short and simple arguments for why climate can be predicted

Sometimes, I encounter arguments suggesting that since we cannot predict the weather beyond a couple of weeks, then it must be impossible to predict the climate in 100 years. Such statements tend to present themselves as a kind of revelation, often in social settings and parties after I have revealed for some of the guests that I’m a climatologist (if I say I work for the Meteorological Institute, I almost always get the question “so, what’s the weather going to be like tomorrow?”). Such occasions also tend to be times when I’m not too inclined to indulge in deep scientific or technical explanations. Or when talking to a journalist who wants an easy answer. In those cases I try to provide a short and simple, but convincing, explanation that is easy for most people to understand why climate can be predicted despite the chaotic nature of the weather (a more theoretical discussion is provided in the earlier post Chaos and Climate). One approach is to try to relate the topic to something with which they are familiar, such as to point to empirical observations which most accept (I suppose with hindsight it could be similar to the researchers in the early 20th century trying to convince that nuclear reactions were possible – just look at the Sun, and there is the proof! Or before that, the debate about whether atoms were real or not – just look at the blue sky, and you look at the proof…). I like to emphasised the words ‘weather‘ and ‘climate‘ above, because they mean different things.

It is true that we cannot predict the weather indefinetely (or even beyond a couple of weeks), because of the chaotic nature and infinitesimally small uncertainties in the state as we know to day, will affect how the weather evolves in a few weeks (the ‘chaos effect’). But, still I say that I know with certainty that there is a very high probability that the temperature in 6 months will be lower than now – when winter has arrived (it’s summer on the northern hemisphere at the present). In fact, the seasonal variation in temperature and rainfall (wet and dry seasons in the tropics) tends to be highly predictable: the winters at high latitudes are cold and summers mild (if anyone doubts, read on here); the southeast Asian Monsoon usually starts over India in the first days of June. I don’t usually bring with me maps and figures to social events, but it would be nice to show a picture such as the one in Fig. 1 to illustrate. If the person is not convinced, I may continue with other arguments for why the climate is predictable: take the latitude for instance – the poles are cold and tropics warm. Furthermore, maritime climates at higher latitudes with wet and mild (small day-to-day or season-to-season temperature variations) are distinct to continental climates far away from the sea (dry with great temperature variations). It is well-established that high-altitude places tend to have lower temperatures and greater temperature variations. Most hikers and mountaineers have experienced that. These are local climatic properties that we can predict if we know the geography, even if we cannot predict the weather on an exact day far in the future. To convince further, I may add that empirical evidence suggesting that (local) climate is not unpredictable, but rather systematically influenced by external factors (boundary conditions) is that Northern Europe enjoys a mild climate: Oslo is roughly on the same latitude as the southern tip of Greenland. There is a reason for that – Oslo has a considerably warmer climate because of the effects of oceanic heat transport/capacity and prevailing winds. I also remind that people really have known for centuries that there are systematic factors influencing the local climate, it’s just that this fact sometimes gets forgotten by those who claim that we cannot predict climate. Isn’t it silly? I may ask if there is any reason to think that the predictability stops at the seasonal and geographical variations.

I may continue with in a hand-wavy manner: In a similar fashion as seasonal and geographical effects, changes in Earth’s orbit around the Sun alters the planetary climate by modifying the amount of energy received from our star (but because of terrestrial response, the atmospheric composition is modified as well, enhancing the effect even further), and changes in the atmospheric composition affects the climate because grenhouse gases absorb heat that otherwise would escape into space – greenhouse gases are transparent to sunlight, but opaque to infrared light due to their molecular properties and their ability to absorb energy (if I say it’s quantum physics, people tend to understand it’s getting a bit technical). I stress that the greenhouse effect is also beyond doubt – without it, the energy balance between total energy Earth intercepts from the Sun and the energy lost through black body radiation implies that Earth’s surface on average would be about 30K cooler than we know it. Volcanoes also affect our climate, and we have theories explaining why. Furthermore, looking to other planets, the observation that Venus has higher surface temperature than Mercury, despite being further away from the Sun, can only be explained as a result of different absorbing properties of their respective atmospheres (a strong greenhouse effect at Venus).

So, my question is, do you think people get the message that I try to convey this way? Is it too simple or too complicated? Somebody who knows of every-day examples demonstrating the central principles? Any suggestions on how to explain for laypersons not connected to the Internet?

Butterflies, tornadoes and climate modelling

Many of you will have seen the obituaries (MIT, NYT) for Ed Lorenz, who died a short time ago. Lorenz is most famous scientifically for discovering the exquisite sensitivity to initial conditions (i.e. chaos) in a simple model of fluid convection, which serves as an archetype for the weather prediction problem. He is most famous outside science for the ‘The Butterfly Effect’ described in his 1972 paper “Predictability: Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?”. Lorenz’s contributions to both atmospheric science and the mathematics of dynamical systems were wide ranging and seminal. He also directly touched the lives of many of us here at RealClimate, and both his wisdom, and quiet personal charm will be sorely missed.

Ed Lorenz had a reputation of being shy and quiet, and this was indeed the impression he gave on first meeting. Indeed raypierre was interviewed by Ed at MIT in 1979 for his first faculty job — and remembers having to ask most of the questions as well as answer them. But he also remembers a lot of timely support from Ed that helped smooth over the somewhat rocky transition from basic turbulence theory to atmospheric science. The longer you were around Ed, the more you came to appreciate his warmth and sense of humor. He was an avid hiker, and many in the community (our own Mike Mann included) have recollections of time on the trail with him around the hills of Boulder and elsewhere.

Lorenz launched the modern era of the study of chaotic systems, which has profound implications both within and beyond atmospheric science. We’ll say more about that in a bit, but the monumental work on chaos should not leave Lorenz’s other contributions to atmospheric science completely in its shadow. For example, in a 1956 MIT technical report, Ed introduced the notion of “empirical orthogonal functions” to atmospheric science, and this technique now plays a central role in diagnostic studies of the atmosphere-ocean system. He also pioneered the study of angular momentum transport in the atmosphere, and of atmospheric energetics. Among other things, he introduced the important notion of “available potential energy,” which quantifies the fact that not all of the potential energy can be tapped by allowable rearrangements of the atmosphere. Later, he pioneered the concept of resonant triad instability of atmospheric waves, an idea that has repercussions for the sources of atmospheric low frequency variability. As if that weren’t enough Ed also introduced the concept of the “slow manifold” — a special subset of solutions to a nonlinear system which evolve more slowly than most solutions. The atmospheric equations support a lot of very quickly changing solutions, like sound waves and gravity waves, but on the whole what we think of as “weather” or “climate” involves more ponderous motions evolving on time scales of days to years. Ed’s work on this subject launched the study of how such slowly evolving solutions can exist, and how to initialize a numerical model so as to minimize the generation of the fast transients. This is now part and parcel of the whole apparatus of data assimilation and numerical weather forecasting.

Ed was not a user of general circulation models. His essential approach was to crystallize profound phenomena into very small sets of equations for how a handful of variables change with time. He left behind him a dozen or so such models, each of which would repay many lifetimes of study. He was indeed a master of “seeing the world in a grain of sand.” You can read about some of these models in the talk raypierre gave at the 1987 Lorenz ‘retirement’ symposium — not that this slowed him down!

Now let’s take a closer look at that butterfly effect. Despite the fact that there are no butterflies or tornadoes in climate models, Lorenz’s discoveries and their implications played a central role in climate modelling efforts and in the most recent IPCC report.

The notion of the butterfly effect itself was drawn from a simple but astute observation of the way the solutions of certain nonlinear equations behave when they are solved using a computer. Start with a greatly simplified representation of thermal convection, first formulated by Barry Saltzman using a technique called “low order modelling.” If you run a simulation using these equations and then try and replicate it using starting values that only differ in the last decimal place, you will find that the simulations quickly diverge from one another – and by quickly, it means that the differences grow exponentially fast. Lorenz found this phenomenon by accident, but quickly recognised the profound implications. If the real weather system displayed the same behaviour, it meant that since however well one knew the initial conditions of the atmosphere, there would always be some uncertainty, that uncertainty would be quickly magnified, rendering weather forecasts useless after a few exponential doubling times. The practical implication is that – even if you had a perfect model – for every halving of the error in the initial conditions you only get one extra time period of useful forecast. Given this time period is only a few hours in many cases, the practicality of true weather forecasts for periods longer than two weeks or so, is vanishingly small.

The mathematically inclined reader who takes a look at Ed’s early papers on what is now called the “Lorenz Attractor” will be astonished at the depth and modernity of his ideas about chaos. This line of work was no mere remark on a numerical exercise. Lorenz actually teased out the geometry of chaos — the many-leaved structure of the attractor — realizing that it was no simple geometric entity like a sphere or a folded sheet of paper. It was indeed “strange” in a sense which he made geometrically precise. This is why the work had such lasting impact on the area of pure mathematics known as dynamical systems theory. He went beyond that to develop or apply many fundamental concepts in chaotic systems, quantitatively formulating various measures of predictability and connecting the Lyapunov exponent — a certain precise mathematical characterization of chaos — with the structure of strange attractors. But that’s for the mathematicians. What makes Lorenz’s work interesting to the entity on the Clapham omnibus is the notion of sensitive dependence on initial conditions. Some have even seen in this deterministic chaos the resolution to the problem of free will!

The idea that small causes can have large and disproportionate effects is not at all new of course. The poem: “For the want of a nail, the battle was lost” (medieval in origin) encapsulates that well, and popular culture is full of such examples, “It’s a wonderful life” (1946) and Ray Bradbury’s “A Sound of Thunder” (1952) for instance. Curiously, Bradbury’s story also involves a butterfly, and since it predates Lorenz’s coining of the phrase by a decade or so, people have speculated that there was a connection to Lorenz’s choice of metaphor (he started off with a seagull in his 1963 Trans. N.Y. Ac. Sci. paper). But that doesn’t appear to be the case (see here for a history). It’s worth adding that all of Lorenz’s papers were exceptional in their clarity and are well worth tracking down as an example of science writing at its best.

However, the difference between the long-standing popular conception and Lorenz’s work is that he demonstrated this effect in a completely deterministic system with no random component. That is, even in a perfect model situation, useful predictability can be strongly limited. Strictly speaking, Poincaré first described this effect in the classic three-body problem in the early 1900s, but it was only with the onset of electronic computers, as used by Lorenz, that this became more widely recognised. To throw in another popular culture reference, Tom Stoppard’s Arcadia has a character, Septimus, who drives himself mad trying to calculate chaotic solutions to the logistic map by hand.

So what does this have to do with the IPCC?

Even though the model used by Lorenz was very simple (just three variables and three equations), the same sensitivity to initial conditions is seen in all weather and climate models and is a ubiquitous phenomenon in many complex non-linear flows. It is therefore usually assumed that the real atmosphere also has this property. However, as Lorenz himself acknowledged in 1972, this is not directly provable (and indeed, at least one meteorologist doesn’t think it does even though most everyone else does). Its existence in climate models is nonetheless easily demonstratable.

But how can climate be predictable if weather is chaotic? The trick lies in the statistics. In those same models that demonstrate the extreme sensitivity to initial conditions, it turns out that the long term means and other moments are stable. This is equivalent to the ‘butterfly’ pattern seen in the figure above being statistically independent of how you started the calculation. The lobes and their relative position don’t change if you run the model long enough. Climate change then is equivalent seeing how the structure changes, while not being too concerned about the specific trajectory you are on.

Another way of saying it is that for the climate problem, the weather (or the individual trajectory) is the noise. If you are trying to find the common signal that is a signature of a particular forcing then averaging over a number of simulations with different weather works rather well. (There is a long standing quote in science – “one person’s noise is another person’s signal” which is certainly apropos here. Climate modellers don’t average over ensemble members because they think that weather isn’t important, they do it because it gives robust estimates of the signal they are usually looking for.)

The ensemble approach, and indeed the multi-model ensemble approach, used in IPCC then derives directly from Lorenz’s insights into his serendipitous numerical problem.

KOMMENTAARID - hea liblikaanalüüs

Stern Science

Late last year, the UK Treasury’s Stern Review of the Economics of Climate Change was released to rapturous reception from all sides of the UK political spectrum (i.e. left and right). Since then it has been subject to significant criticism and debate (for a good listing see Rabbett Run). Much of that discussion has revolved around the economic (and ethical) issues associated with ‘discounting’ (how you weight welfare in the future against welfare today) – particularly Nordhaus’s review. We are not qualified to address those issues, and so have not previously commented.

However, as exemplified by interviews on a recent Radio 4 program (including with our own William Connolley), some questions have involved the science that underlies the economics. We will try and address those.

Unlike an earlier report by the House of Lords, Stern spends no time quibbling, and essentially takes the science from the IPCC report, though somewhat updated by more recent work. Most of the science is flipped through fairly quickly within chapter one, and casual readers familiar with the IPCC report will find little to surprise them with sections including statements such as “An overwhelming body of scientific evidence indicates that the Earth’s climate is rapidly changing, predominantly as a result of increases in greenhouse gases caused by human activities” etc. However, the scientific possibilities in Stern are weighted slightly differently than in the IPCC reports since, as he states, “policymakers need to take into account the risks of greater dangers, as well as central expectations, because the consequences if these risks were to materialise would be very serious” (Stern reply to Byatt et al).

There are three strands to the science in Stern: the climate sensitivity, future emissions of greenhouse gases and the impacts of any particular level of change (scaled to the global mean temperature anomaly for convenience).

The climate sensitivity (as discussed here previously) was given a likely range of 1.5 – 4.5 C in IPCC TAR, and with a range of 2 – 5 C in the models used in that report. However, the probability of higher values plays a significant role in the report. Specifically, Meinshausen (2006) that there is “between a 2% and 20% chance that climate sensitivity is greater than 5C” but in the key message section of chapter 1 this is distilled as: “Several new studies suggest up to a 20% chance that warming could be greater than 5C”. This is true, but the report neglects to mention other new studies (Annan and Hargreaves; Hegerl et al) that suggest a negligible probability of CS greater than 5 C.

Uncertainty about future warming is not just the uncertainty about sensitivity, but also about the future greenhouse gas levels (GHG). There is a wide range of scenarios and estimates of future GHG levels that are used in the IPCC reports. The scenario used by the Review is the A2 one, but in this scenario GHG in the latter part of the 21st century is higher than in say, the A1b scenario. The point here is not that A2 is less sound than the A1b scenario, but simply that the Review chooses to work with one of the “high emission” scenarios. Additionally, the report also acknowledges the highly uncertain (but not clearly quantifiable) the possibilities of positive feedbacks in natural CO2 and CH4 emissions.

For impacts of climate change the story is similar: many of the impacts mentioned possible but their likelihood is debatable. For example, the weakening of the THC under 1 degree of warming, a risk of collapse for 3 degrees, risk of irreversible melting of the Greenland Ice sheet at 2 degrees warming, sea level changes of 5 – 12 meters over several centuries, – these eventualities are debatable, and should certainly be viewed as the “adverse tail” of possible impacts.

In conclusion: Stern gets the climate science largely right, though he strays on the high side of various estimates and picks the high side to talk about in the summary. This high-end bias lends the Review open to charges of “alarmism”. The report does make the fair point that the damages and their cost grows disproportionally with increasing temperature change and so, given that asymmetry, policymakers are correct in taking note of them. However, it looks like the major criticism of his work will be directed (in other fora) at the economics.

NB. Rather predictably, some of the usual contrarian suspects have also attacked the science in Stern. It is, however, a measure of their fundamental lack of seriousness that when there really are important uncertainties (i.e. the likelihood that climate sensitivity is higher than generally thought), they ignore them in favour of making the same repetitive uninteresting and incorrect claims they always make.

KOMMENTAARID

Dr. Jane Lubchenco esinemine

Merebioloog Dr. Jane Lubchenco ettekanne koos demonstratsiooniga kuidas CO2 muudab ookeanid happeliseks ning millised on tagajärjed vees elavatele koorikloomadele. Kooriklased omakorda on läbi toiduahela seotud inimesega (toidust turisminduseni).