One of the most complex (and, for some, controversial) aspects of climate change studies is that many are based on models. Models are mathematical tools that basically spit out results that are based on the assumptions and data that you feed them. Feed them different assumptions or data, and they may spit out different results. The vast majority of climate change-related studies have come up with similar results despite different assumptions and data fed to them. Nevertheless, many people feel that models are just that: something abstract and not real, and full of uncertainties.
Statistics and polls, on the other hand, have a way of grabbing people's attention, not only because most people actually do understand what they say (95 percent chance of this, 60 percent of people think that, etc.) but because they are based on actual data, real facts. The consequence of that "reality" is that people will likely believe statistics more than models, simply because statistics are based on actual things that have actually happened.
So, the new study by James Hansen where he uses statistics (not models) to connect extreme weather events to climate change should come as a more believable, more reliable way for us to actually understand what is happening with our world. Hansen states in his Washington Post op-ed that "this is not a climate model or a prediction but actual observations of weather events and temperatures that have happened." A piece right here at Huff Post does a great job of summarizing the study's findings and the reaction from both sides of the climate arena.
An opinion piece by Michael Mann commenting on Hansen's study states that:
Over the past decade, records for daily maximum high temperatures in the U.S. have been broken at twice the rate we would expect from chance alone. Think of this as rolling double sixes twice as often as you'd expect -- something you would readily notice in a high stakes game of dice. Thus far this year, that ratio is close to 10 to 1. That's double sixes coming up ten times as often as you expect.
We all understand that language, and we all instinctively know that when things start happening consistently above chance, something fishy is going on (yeah, the dice is loaded, or the cards are marked). If that happened at a casino, most everyone would cry foul. Why aren't we crying foul now, when there is so much more at stake than money?
Follow Astrid Caldas on Twitter: www.twitter.com/@climategeek
A. Siegel: Fascinated by Olympic Record-breaking? What About Record-breaking Climate Change?
http://articles.latimes.com/2012/jun/13/nation/la-na-romney-energy-20120613
REGULATIONS ARE BAD = is certainly useful if you're a bank derivatizing household debt and overselling it to unsuspecting Mainstreeters as AAA-rated investments. Also useful to any bank setting the LIBOR rate to make billions off unsuspecting Mainstreeters seeking loans. Also, if you are part of the global 1% hiding an estimated $20-30 trillion in offshore accounts. So, this meme gets enormous funding.
NO TAXES = much more useful to the 1% than to rank-and-file Republicans. Indeed, the rank and file wouldn't even notice it if the Bush tax cuts expired. So this meme also has to be funded and repeated in rightwing media.
GLOBAL WARMING IS A HOAX = if you were in the top 5 U.S. oil corporations, and were making $14 million AN HOUR in profit off selling your poison (oops, product), what would YOU do? All evidence to the contrary, this meme gets enormous funding, and is repeated in rightwing media as a fundamental matter of identity for all 'right thinking' Republicans.
But Mioffe below tries to discredit climate modeling by citing uncertainties via sub-grid parametrization of processes occurring at scales smaller than the models can resolve explicitly.
One test is how well models reproduce paleo climates:
"the PMIP-2 LGM simulations confirm current AOGCMs are able to simulate the broad-scale spatial patterns of regional climate change recorded by palaeodata in response to the radiative forcing and continental ice sheets of the LGM, and thus indicate that they adequately represent the primary feedbacks that determine the climate sensitivity of this past climate state to these changes.
http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch6s6-4-1-3.html
Indeed James Annan, et al., showed that the standard methodology for evaluating climate models is in itself faulty.
".. the popular evaluation of ensemble spread, based on a direct comparison with a pdf based on observational constraints, can be highly misleading. Disagreement between these two pdfs is virtually certain, but such an analysis does not directly address the fundamental question of whether the ensemble provides reliable predictions. .. An alternative approach, which directly addresses the question of predictive performance, enables us to evaluate to what extent the observations support or contradict the ensemble. In many
cases, we expect that this approach will result in the conclusion that observations actually enhance our confidence in the models."
http://www.jamstec.go.jp/frsgc/research/d5/jdannan/Annan_Geophys.%20Res.%20Lett._2011.pdf
of global climate change.
"What is climate?
The climate of a place, a region, or the Earth as a whole, is the
average over time of the meteorological condition that occurs
there—the average weather. For example, in the month of November
between 1971 and 2000, the average daily high temperature in
Washington, DC was 14ºC, the average daily low was 1ºC and
0.3 cm of precipitation fell. These average values, along with
averages of other meteorological quantities such as humidity, wind
speed, cloudiness, and snow and ice coverage, define the November
climate of Washington over this period. p 7.
..the average daily high temperature,
daily low temperature, precipitation, averages of
meteorological quantities such as humidity, wind speed,
cloudiness, and snow and ice coverage... Exactly these averages
scientists are putting into computers.
If you will put in a
computer model “the average daily high temperature, the average
daily low temperature, precipitation, averages of other meteorological
quantities such as humidity, wind speed, cloudiness, and snow and ice
coverage…”Please show me that exactly CO2 is responsible for
the claim that the Earth’s surface must warm.
Here we have
another possibility. Knowing the amount of CO2 that was
increased, perhaps from 280 ppm to 350 ppm, we could put
the forcing factor 350/280=1.25, but it is up to the scientist who
did that to say that the increasing of GHG is a forcing factor
for climate change.
That homework is months late, Mr F.
So, this is really for other curious readers.
Long-established, well-verified (by many chemical physicists) the CO2 spectral absorption data from literature shows precisely how to compute the thermal radiative absorption by additional CO2 over any given time period.
The additional CO2 increment may be obtained from CO2 measurements from Mauna Loa Observeratory and other sites, or by ground-up computation from annual man-made carbon inventories, geologic weathering data, and biosphere sub-models.
Anyway, modelers then add the annual CO2 rise to their atmospheric/oceanic CGCM model's radiative transfer module, which typically compute the impact of additional CO2 and other greenhouse gases via a pencil beam calculation of thermal absorption in each spectral line. It then sums the computed incoming minus outgoing radiative flux, line by line, layer by layer, then azimuthally and zenithally (hemispherically) to obtain atmospheric and land/sea heating/cooling rates from the bottom of the ocean's well-mixed layer up to the top of our atmosphere at each horizontal grid point location.
These heating/cooling rates then feed air/water transport modules, yielding their impact on general circulation. The model then steps forward one time increment and iterates, eventually yielding regional/global climates.
Radiative transfer is computationally intensive, requiring five nested integrations and a large fraction of total model runtime.
If I understand correct these methods will calculate how much energy trapped along rays of pencil beam (or something close).
If water vapor will constantly bring UP parcel of air, and evaporation from ocean (land) level will be constant by amount during all period of measurement in average yours tools will "obtain atmospheric and land/sea heating/cooling rates from the bottom of the ocean's well-mixed layer up to the top of our atmosphere at each horizontal grid point location."
At the same time if all parcels of air will constantly move UP how your tools will calculate movement of energy by buoyancy forces?