Via CO2science.org
Near the start of the current century, Holland (2001) wrote that with respect to contemporary state-of-the-art global climate models, “some physical processes are absent from the models,” while noting that in light of the coarse-resolution grids employed by the models, “some physical processes are ill resolved” and that others are actually “missing from the simulations,” which facts led him to question, as he put it, “whether the simulations obtained from such models are in fact physically meaningful.”
And so it was that he thus went on to conduct his own analysis of the subject, which he designed to determine the difference in model evolution of sea ice cover using a relatively coarse-resolution grid verses a fine-resolution grid, with specific emphasis placed on the presence and treatment of a mesoscale ocean eddy and its influence on sea ice cover.
Holland’s resolving of the ocean eddy field using the fine-resolution model was found to have a measurable impact on sea ice concentration, implying that a “fine-resolution grid may have a more efficient atmosphere-sea ice-ocean thermodynamic exchange than a coarse one.” Put another way, he reported that the results of his study demonstrated “yet again” that “coarse-resolution coupled climate models are not reaching fine enough resolution in the polar regions of the world ocean to claim that their numerical solutions have reached convergence,” clearly indicating that the models still had a long way to go before their resolution would be fine enough to include (or adequately parameterize) all the important physical processes related to sea ice cover, and possibly those of many other climate phenomena as well.
Two years later, Laxon et al. (2003) used an eight-year time series (1993-2001) of Arctic sea-ice thickness derived from measurements of ice freeboard made by 13.8-GHz radar altimeters carried aboard ERS-1 and 2 satellites to determine the mean thickness and variability of Arctic sea ice between latitudes 65 and 81.5°N, which region covers the entire circumference of the Arctic Ocean, including the Beaufort, Chukchi, East Siberian, Kara, Laptev, Barents and Greenland Seas. This huge but worthy effort revealed that (1) “mean winter sea-ice thickness over the region of coverage was found to be 2.73 meters with a standard deviation of ± 9% of the average, which variability was 50% greater than that predicted by climate models,” that (2) “the inter-annual variability in thickness [9%] compares with a variability in mean annual ice extent of 1.7% during the same period,” that (3) there was “a significant (R2 = 0.924) correlation between the change in the altimeter-derived thickness between consecutive winters and the melt season length during the intervening summer,” which meant that (4) there was an “observed dominant control of summer melt on the inter-annual variability of mean ice thickness,” which was (5) “in sharp contrast with the majority of models,” which suggests that (6) “ice thickness variability in the Arctic Ocean is controlled mainly by wind and ocean forcing,” and that (7) “sea ice mass can change by up to 16% within one year,” which (8) “contrasts with the concept of a slowly dwindling ice pack, produced by greenhouse warming,” which represents still another significant strike against the models.
In summing up their discussion of the subject, therefore, Laxon et al. simply state that their results “show that errors are present in current simulations of Arctic sea ice,” and they thus conclude, in the closing sentence of their paper, that “until models properly reproduce the observed high-frequency, and thermodynamically driven, variability in sea ice thickness, simulations of both recent, and future, changes in Arctic ice cover will be open to question.”
Jumping ahead four years, Eisenman et al. (2007) used two standard thermodynamic models of sea ice to calculate equilibrium Arctic ice thickness based on simulated Arctic cloud cover derived from sixteen different global climate models (GCMs) that were evaluated for the IPCC’s Fourth Assessment Report. This work revealed there was a 40 Wm-2 spread among the sixteen models in terms of their calculated downward longwave radiation, for which both sea ice models calculated an equilibrium ice thickness ranging from one to more than ten meters. However, they noted that the mean 1980-1999 Arctic sea ice thickness simulated by the sixteen GCMs ranged from only 1.0 to 3.9 meters, which is a far smaller inter-model spread. Hence, they said that they were “forced to ask how the GCM simulations produce such similar present-day ice conditions in spite of the differences in simulated downward longwave radiative fluxes.”
Answering their own question, the three researchers stated that “a frequently used approach” to resolving this problem “is to tune the parameters associated with the ice surface albedo” to get a more realistic answer. “In other words,” as they continued, “errors in parameter values are being introduced to the GCM sea ice components to compensate simulation errors in the atmospheric components.” And in light of these machinations, the three researchers concluded that “the thinning of Arctic sea ice over the past half-century can be explained by minuscule changes of the radiative forcing that cannot be detected by current observing systems and require only exceedingly small adjustments of the model-generated radiation fields,” and, therefore, they additionally conclude that “the results of current GCMs cannot be relied upon at face value for credible predictions of future Arctic sea ice.”
One year later, while noting that earth’s polar regions “are expected to provide early signals of a climate change primarily because of the ‘ice-albedo feedback’ which is associated with changes in absorption of solar energy due to changes in the area covered by the highly reflective sea ice,” Comiso and Nishio (2008) set about to provide updated and improved estimates of trends in Arctic and Antarctic sea ice cover for the period extending from November 1978 to December 2006, based on data obtained from the Advanced Microwave Scanning Radiometer (AMSR-E), the Special Scanning Microwave Imager (SSM/I) and the Scanning Multichannel Microwave Radiometer (SMMR), where the data from the last two instruments were adjusted to be consistent with the AMSR-E data.
This work revealed that trends in sea ice extent and area in the Arctic over the period of the two researcher’s analyses were -3.4 ± 0.2 and -4.0 ± 0.2% per decade, respectively; but it also revealed that simultaneous corresponding trends in the Antarctic were +0.9 ± 0.2 and +1.7 ± 0.3% per decade. And, therefore, if it indeed is true that earth’s polar regions should “provide early signals of a climate change,” as many climate alarmists contend they should, it would appear that the Northern and Southern Hemispheres are scheduled to go their own separate ways in response to a continuation of whatever caused them to behave as they did over the prior three decades, during which time the atmosphere’s CO2 concentration rose substantially. But if such were the case, one could not claim that rising atmospheric CO2 concentrations cause global warming.
Another study of interest was that of Kwok (2011), who introduced his work by noting that near the mid-point of the prior decade, simulations of Arctic Ocean sea ice characteristics produced by the climate models included in the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3 (CMIP3) were far from what it might have been hoped they would be. Specifically, he wrote that (1) “Zhang and Walsh (2006) noted that even though the CMIP3 models capture the negative trend in sea ice area, the inter-model scatter is large,” that (2) “Stroeve et al. (2007) show that few models exhibit negative trends that are comparable to observations,” and that (3) “Eisenman et al. (2007) conclude that the results of current CMIP3 models cannot be relied upon for credible projections of sea ice behavior.” And, therefore, in his more recent analysis of the subject — based on the multi-model data set of Meehl et al. (2007) — the Jet Propulsion Laboratory researcher compared CMIP3 model simulations with observations of sea ice motion, export, extent and thickness, along with analyses of fields of sea level pressure and geostrophic wind of the Arctic Ocean.
Kwok’s analysis demonstrated, as he described it, that “the skill of the CMIP3 models (as a group) in simulation of observed Arctic sea ice motion, Fram Strait export, extent and thickness between 1979 and 2008 seems rather poor,” noting that “model-data differences and inter-model scatter of the sea ice parameters in the summarizing statistics are high,” and that “the spatial pattern of Arctic sea ice thickness, a large-scale slowly varying climatic feature of the ice cover, is not reproduced in a majority of the models.” Consequently, he concluded that “the models will not get the main features of natural sea ice variability that may be dominating recent sea ice extent declines, as well as the long-term greenhouse response.”
Therefore, “because the model simulations have difficulties reproducing the mean patterns of Arctic circulation and thickness,” as Kwok writes in his concluding paragraph, ” he says his analysis suggests there are “considerable uncertainties in the projected rates of sea ice decline even though the CMIP3 data set agrees that increased greenhouse gas concentrations will result in a reduction of Arctic sea ice area and volume.” But with all the problems he finds with the models, who really knows how good those latter projections are?
A couple more years down the road, Turner et al. (2013) wrote that “Phase 5 of CMIP (CMIP5) will provide the model output that will form the basis of the Fifth Assessment Report (AR5) of the IPCC,” and they therefore thought it important to determine how well these models represent reality. Thus, they examined “the annual cycle and trends in Antarctic sea ice extent (SIE) for 18 models used in phase 5 of the Coupled Model Intercomparison Project that were run with historical forcing for the 1850s to 2005.” This work revealed that (1) “the majority of models have too small of an SIE at the minimum in February,” that (2) “several of the models have less than two-thirds of the observed SIE at the September maximum,” that (3) “in contrast to the satellite data, which exhibit a slight increase in SIE, the mean SIE of the models over 1979-2005 shows a decrease in each month,” that (4) “the models have very large differences in SIE over 1860-2005,” and that (5) “the negative SIE trends in most of the model runs over 1979-2005 are a continuation of an earlier decline, suggesting that the processes responsible for the observed increase over the last 30 years are not being simulated correctly.” And in light of these findings, Turner et al. state that “as with CMIP3, the models do not simulate the recent increase in Antarctic SIE observed in the satellite data.”
Around this same time, Karlsson and Svensson (2013) wrote that “clouds significantly influence the Arctic surface energy budget and a realistic representation of this impact is a key for proper simulation of the present-day and future climate.” However, they went on to report that “considerable across-model spread in cloud variables remains in the fifth phase of the Coupled Model Intercomparison Project ensemble and partly explains the substantial across-model spread in the surface radiative effect of the clouds,” which further impacts sea-ice extent and albedo. And, therefore, the main focus of their study, as they describe it, was on the question of “how model differences in the parameterization of sea-ice albedo in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) influence the cloud radiative effect on the surface energy budget and the annual cycle of sea-ice concentration.”
In pursuing this course of action, the two researchers report that “the across-model spread in Arctic cloud cover and cloud condensates is substantial, and no improvement is seen from previous model intercomparisons (Karlsson and Svenson, 2011).” And they further note that “this diversity of simulated Arctic clouds in the CMIP5 ensemble contributes to a spread in the models’ cloud influence on the surface energy budget.” Therefore, in the concluding sentence of their paper, the two Swedish scientists state that “the fact that present-day sea-ice albedo is so badly constrained in global climate models impacts the fidelity of future scenario assessments of the Arctic region and should therefore be a concern for the modeling community.” Or in other words, we’re not there yet … and we’ve been stalled in our forward progress in this area for several years.
In yet another pertinent paper from the same year, Mahlstein et al. (2013) state that Lefebvre and Goosse (2008) analyzed the Antarctic sea ice distributions of the CMIP3 climate models and found that “the modelled trends were too negative compared to observations.” Likewise, they say that Turner et al. (2013) also reported “a negative sea ice trend for most CMIP5 models.” And so it was that they decided they would also investigate the subject, to see if things were really as bad as what they appeared to be in these two prior model assessments.
Using historical runs from as many as 25 CMIP5 climate models, Mahlstein et al. compared their hind-casted sea-ice trends for the area around Antarctica against observational data for the period 1980 to 2001, which are archived by the Met Office Hadley Centre (Rayner et al., 2003) and the U.S. National Snow and Ice Data Center (Comiso, 1999, updated 2012). And what did they learn from this endeavor?
Quoting the three researchers, “the representations of Antarctic sea ice in CMIP5 models have not improved compared to CMIP3,” in that “the spread in sea ice area is not reduced compared to the previous models.” Most important of all, however, was their finding that whereas most CMIP5 climate models “simulate a decrease in Antarctic sea ice over the recent past,” real-world data demonstrate that the “average Antarctic sea ice area is not retreating but has slowly increased since satellite measurements began in 1979.” And it is difficult for a climate model to be more wrong than when it hind-casts just the opposite of what has been observed to be happening over the past three and a half decades in the real world, which is what most of the CMIP5 models apparently do.
Last of all, and most recently, Koenigk et al. (2014) — citing Winton (2006) and Serreze et al. (2009) – write that “it seems to be beyond question that the ice-albedo feedback is an important contributor to Arctic temperature amplification and changes in sea ice conditions,” while noting that “the observed Arctic temperature amplification compared to lower latitudes has led to an intensive discussion on the role of the surface albedo,” citing the additional study of Riihela et al. (2013b).
As for their role in broaching the subject, Koenigk et al. say they “used the surface albedo product from the Satellite Application Facility on Climate Monitoring (CM-SAF) clouds, albedo and radiation data set (CLARA-SAL, Ruhela et al., 2013a; Karlsson et al., 2013) and sea ice concentration from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) data set (Eastwood et al., 2010) as comparison for the model data,” which were derived from 21 different CMIP5 models.
In reporting their long list of findings, the three Swedish researchers write that (1) “summer sea ice albedo varies substantially among CMIP5 models,” that (2) “many models show large biases compared to the CLARA-SAL product,” that (3) “single summer months show an extreme spread of ice albedo among models,” that (4) “July values vary between 0.3 and 0.7 for individual models,” that (5) “the CMIP5 ensemble mean … shows too high ice albedo near the ice edges and coasts,” that (6) “in most models, the ice albedo is spatially too uniformly distributed,” that (7) “the summer-to-summer variations seem to be underestimated in many global models,” that (8) “almost no model is able to reproduce the temporal evolution of ice albedo throughout the summer fully,” that (9) “while the satellite observations indicate the lowest ice albedos during August, the models show minimum values in July and substantially higher values in August,” that (10) “June values are often lower in the models than in the satellite observations,” due to (11) “too high surface temperatures in June,” leading to (12) “an early start of the melt season and too cold temperatures in August causing an earlier refreezing in the models,” such that (13) “the impact of the ice albedo on the sea ice conditions in the CMIP5 models is not clearly visible.”
In light of these several findings, Koenigk et al. conclude that “the Arctic climate system can thus not correctly be simulated (other than with compensating errors) if the large-scale atmospheric and oceanic circulation determining the input of mass, heat and momentum into the Arctic is not correctly simulated.” And they also remark that “strong tuning of the albedo in order to achieve realistic Arctic ice and climate conditions in 20th century simulations might lead to unrealistic amplification rates in future simulations.”
So when shopping for Arctic sea ice models when all is said and done, it is not surprising that the word on the street isbuyer beware!
References
Comiso, J. 1999, updated 2012. Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS, edited by NSIDC, 2156-2202. Digital Media, Boulder, Colorado, USA.
Comiso, J.C. and Nishio, F. 2008. Trends in the sea ice cover using enhanced and compatible AMSR-E, SSM/I, and SMMR data. Journal of Geophysical Research 113: 10.1029/2007JC004257.
Eastwood, S., Larsen, K.R., Lavergne, T., Nielsen, E. and Tonboe, R. 2010. Global Sea Ice Concentration Reprocessing: Product User Manual. Product OSI-409, Version1.
Eisenman, I., Untersteiner, N. and Wettlaufer, J.S. 2007. On the reliability of simulated Arctic sea ice in global climate models. Geophysical Research Letters 34: 10.1029/2007GL029914.
Holland, D.M. 2001. An impact of subgrid-scale ice-ocean dynamics on sea-ice cover. Journal of Climate 14: 1585-1601.
Karlsson, K.-G., Riihela, A., Muller, R., Meirink, J.F., Sedlar, J., Stengel, M., Lockhoff, M., Trentmann, J., Kaspar, F., Hollmann, R. and Wolters, E. 2013. CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of global AVHRR data.Atmospheric Chemistry and Physics 13: 5351-5367.
Karlsson, J. and Svensson, G. 2011. The simulation of Arctic clouds and their influence on the winter surface temperature in present-day climate in the CMIP3 multi-model dataset. Climate Dynamics 36: 623-635.
Karlsson, J. and Svensson, G. 2013. Consequences of poor representation of Arctic sea-ice albedo and cloud-radiation interactions in the CMIP5 model ensemble. Geophysical Research Letters 40: 4374-4379.
Koenigk, T., Devasthale, A., Karlsson, K.-G. 2014. Summer Arctic sea ice albedo in CMIP5 models. Atmospheric Chemistry and Physics 14: 1987-1998.
Kwok, R. 2011. Observational assessment of Arctic Ocean sea ice motion, export, and thickness in CMIP3 climate simulations. Journal of Geophysical Research 116: 10.1029/2011JC007004.
Laxon, S., Peacock, N. and Smith, D. 2003. High interannual variability of sea ice thickness in the Arctic region. Nature425: 947-950.
Lefebvre, W. and Goosse, H. 2008. Analysis of the projected regional sea-ice changes in the Southern Ocean during the twenty-first century. Climate Dynamics 30: 59-76.
Mahlstein, I., Gent, P.R. and Solomon, S. 2013. Historical Antarctic mean sea ice area, sea ice trends, and winds in CMIP5 simulations. Journal of Geophysical Research: Atmospheres 118: 5105-5110.
Meehl, G.A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J.F.B., Stouffer, R.J. and Taylor, K.E. 2007. The WCRP CMIP3 multi-model dataset: A new era in climate change research. Bulletin of the American Meteorological Society 88: 1383-1394.
Rayner, N.A., Parker, D.E., Horton, E.B., Folland, C.K., Alexander, L.V., Rowell, D.P., Kent, E.C. and Kaplan, A. 2003. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research 108: 10.1029/2002JD002670.
Riihela, A., Manninen, T., Laine, V., Andersson, K. and Kaspar, F. 2013a. CLARA-SAL: a global 28 yr time series of Earth’s black-sky surface albedo. Atmospheric Chemistry and Physics 13: 3743-3762.
Riihela, A., Manninen, T. and Laine, V. 2013b. Observed changes in the albedo of the Arctic sea-ice zone for the period 1982-2009. Nature Climate Change 3: 895-898
Serreze, M.C., Barrett, A.P., Stroeve, J.C., Kindig, D.N. and Holland, M.M. 2009. The emergence of surface-based Arctic amplification. The Cryosphere 3: 11-19.
Stroeve, J., Holland, M.M., Meier, W., Scambos, T. and Serreze, M. 2007. Arctic sea ice decline: Faster than forecast.Geophysical Research Letters 34: 10.1029/2007GL029703.
Turner, J., Bracegirdle, T.J., Phillips, T., Marshall, G.J. and Hosking, J.S. 2013. An initial assessment of Antarctic sea ice extent in the CMIP5 models. Journal of Climate 26: 1473-1484.
Winton, M. 2006. Amplified Arctic climate change: What does surface albedo feedback have to do with it? Geophysical Research Letters 33: 10.1029/2005GL025244.
Impressive effort to turn everything topsy turvy. It has been clear for a while that models are not able to replicate the observed Arctic sea ice extent loss (let alone volume). See for instance Stroeve et al. (2012): Trends in Arctic sea ice extent from CMIP3, CMIP5 and observations.
That paper is a period piece, published 25 August 2012 on the occasion of the storm-caused record 2012 minimum. Needless to say, that trend has not continued.
The revised manuscript was accepted for publication on 16th July 2012, according to the publication history. That was 3 weeks before the August storm struck. Maybe the models predicted it!
True, the data in the paper go to 2011, not 2012. So the models don’t replicate the observations, everybody agrees.
Climate models are less than inadequate; forget sea ice, due ti the angle of incidence of sunlight, what happens at the poles it pretty unimportant in comparison to what happens in the tropics, as regard to “forcings”. How is that cloud modeling going IPCC?
Here’s a question – how important is albedo in the models? As compared to CO2, I mean?
I get it that albedo/insolation at the poles is a serious amplification, one way or the other. And I get it that their models are not getting albedo right (and I think I am reading they actually measure the albedo).
But how much does that rate? Is it like “well, we havent mastered that bit yet, but that just means we don’t know the exact date of Armageddon. Or are they uttering profanities in private?
Note also the published paper by Scott & Marshall (2010, at http://arctic.synergiesprairies.ca/arctic/index.php/arctic/article/view/971), which states “the passive microwave sea-ice concentrations derived from the NASA Team algorithm were found to underestimate concentration during summer melt by 20.4% to 33.5%”. We’re talking all those sea ice extent maps.
Of course, the modelers, having read these various peer-reviewed articles outlining the shortcomings in the arctic sea-ice modeling in their precious GCMs, have redoubled their efforts over the last decade or more to improve the models
“So when shopping for Arctic sea ice models when all is said and done, it is not surprising that the word on the street is buyer beware!”
Indeed. The models yield only garbage as they begin with incorrect ideas on how the planet’s weather machine works. Start with CO2 drives climate and you will get wrong results every time. (well you can use fudge factors to compensate — they call those “parameters” don’t they?)
“The models yield only garbage…”
I maintain that the models are fit for purpose.
If the purpose is propaganda, then you do have a point.
It seems to me models will be useless unless they can predict the PDO and, even more importantly, the AMO. On the Pacific, Bering Strait side there seems an obvious difference between when the PDO is “warm” and when it is “cold,” and on the Atlantic side we are about to see if the changing AMO has the same effect.
All the talk about “albedo” may sound very logical, but I have never seen that it matters all that much. By the time the ocean is at its minimum ice-cover and can absorb sunlight, the sun is too low. The sea may actually lose more heat when there is less ice.
This year the ice is especially thick on the Fram Strait side of the Pole, and so far is refusing to be good and be flushed south along the east coast of Greenland. In fact the North Pole Camera is around 150 miles north-northwest of where it was at this time last year.
https://sunriseswansong.wordpress.com/2015/06/23/arctic-sea-ice-quirks-of-the-quietude/
“The sea may actually lose more heat when there is less ice.”
This seems hard to doubt.
As a familiar example, ground which is snow and ice covered will not freeze as deeply as uncovered ground, given equal air temps for both situations.
Open water loses heat rapidly in dry air, at night (six months in a row in winter), and when it is extremely frickin’ cold above the water.
Know how easy it is to keep a swimming pool warmer? A thin layer of just about anything will do it…even a layer a few molecules thick of some plastic, or even some other liquid…or even ice.
http://www.bing.com/search?q=liquid%20pool%20cover&qs=n&form=QBRE&pq=liquid%20pool%20cover&sc=8-17&sp=-1&sk=&ghc=1&cvid=471e1260089a458cbb4db5170140f77b
I believe it was about 1998 when massive amounts of multi-year ice was flushed out of the arctic via Fram Strait and at the same time some multi-year ice was lost via the Bering Strait. In subsequent years the loss of multi-year ice caused earlier and more extensive melting than normal. Melting of ice in the Beaufort Sea is largely caused by the outflow of the Mackenzie River and the Chukchi Sea by warm water flowing north through the Bering Strait.
Good points. Things are more complex up there than many assume.
The Laptev Sea is influenced by the Lena River, which is one of the world’s top ten rivers. The thing about the Lena is that only 3% of its yearly flow occurs during a month like January, when pretty much all Siberia is frozen solid. Then the river rises some huge amount (50 feet?) during the time when all the snow melts, and 40% of its yearly flow occurs in August. That huge surge of milder fresh water at first moves over the top of the Laptev Sea and causes melting, but then there is a flip-flop, as it freezes faster in October because it is fresher water. Likely it is a real challenge to map the changes in temperature and salinity during any given year, and then things vary a lot year-to-year as well. As is often the case, the fellows making the computer models have to keep adding more and more variables, the more we learn.
Caleb,. Thank you, I did not know that about the Laptev Sea. Almost exactly the same thing happens in the Canadian Beaufort. The Mackenzie River is one of the worlds great rivers and in the spring fresh water melts the ice in a large area that extends westward off shore of the Alaska coast. In the fall that same area is first to freeze because of the freshened water.
Climate science is turning into a business like any other business – you have to stay relevant to be competitive.
– Scientists have to stay relevant to earn grants and positions.
– Scientific institutions have to stay relevant to attract media attention, funds, candidates.
– Scientific publications have to stay relevant by producing headlines in the mainstream media.
And to compete in this market of attention, you have to produce scary conclusions which can make headlines in the MSM. The scarier the better.
Scientists know it, Institutions know it and the scientific publications know it.
They all play the game, and if you refuse to play along, you will be disregarded.
This is the dynamics which has produced a field of science where all the icons of the field are those who have managed to consistently predict the worst possible outcome: Michael Mann, Rahmstorf, James Hansen, etc.
The media CRAVES catastrophe, and those who are willing to supply catastrophe are the media winners, and those who win in the media win positions for themselves and for their institutions and for their publications.
And around we go again.
The result is a race towards the prediction of catastrophe, where the one who can predict the direst future within the most immediate time frame has won.
The result of this race to the extreme is what we today regard as “consensus science”.
It is science trapped in the logic of the free market, where science is no longer about truth, but about marketing, positioning, job security, personal and institutional prestige, rather than the search for truth.
+97
The media may crave it, but the public will become immune to such warnings if THERE IS NO ACTUAL CATASTROPHE!
Which they have…they just are loathe to discuss it out load, what with all the finger wagging and denier-shaming going on.
It is really like a grown up version of slut-shaming.
Climate sluts!
That’s what they are!
Mods-…discuss it out loud…
-Nick
“the logic of the free market…about marketing, positioning, job security, personal and institutional prestige.”
You’ve described the market for rubbish government work, yet you single out the free market as the bogeyman.
What free market?
These thieves aren’t selling toothpaste or shampoo to willing buyers.
They’re forcing money out of unwilling serfs and then forcing more money out of them to “market” to them how lucky they are that the public service has their backs.
Do not forget those vast sums larded out to their cronies for every project and task they can dream up.
Much of it completely wasted or stolen. Of course, it is not called stealing under these circumstances, but a careful analysis reveals little reason why not.
If I may reiterate: Do climate model projections have any physical meaning?
Software is virtual. Their output is physical, but the meaning of the output is virtually supplied by the wet-wear of the interpreting person(s), as is the development of the software in the first place … i.e., circular reasoning at its ‘best.’
I don’t see how computer models have a chance of being accurate when none of them has found a way to factor clouds and cloud cover into their projections
The models don’t have a chance because they are trying to model a complex, non-linear, chaotic system and Lorenz proved that you cannot accurately model a chaotic system unless you know the starting state and processes to a degree that is not possible with the climate. It is in the nature of chaotic systems that a small difference in one variable can cause wildly different results.
It is a fact that we do not know all the processes that affect climate and we certainly don’t know the initial state of all the processes at any point in the past. We most likely never will. They also do not have a chance because they attempt to model the climate using grids that are far too large (for the sake of computational possibility).
I don’t agree with the fact that the climate system is complex. It is just to give our ignorance a name. This is not because we do not understand much about climatic phenomena that the system is complex. Even if I am provocative, I think it is rather of biblical simplicity, but we do not look at the right place to explain what we observe.
We must not forget that the concept of human-induced warming is based on a fantasy that has given rise to numerous attempts to explain it a posteriori, from pure incantations to the most academic explanations unverifiable in laboratory. Is that obscurantisms are fed by our ignorance in the mechanisms controlling climate variability. Due to their background, climatologists have mainly focused their research on atmospheric phenomena over the past decades whether they refer to human activities or solar cycles, when they should have scrutinized the oceans. Marine scientists working on climate give the oceans a passive role while it has an active role.
The problem is that the functioning of surface currents is very little known and, therefore, we must begin at the beginning, that is to say, to start again with the equations of motion developed in the 30s, solve them with boundary conditions as well as relevant forcing and coupling terms deduced from recent observations. It goes without saying that such work offends the mindset of a few marine scientists, being reluctant to accept new ideas (as it has always happened in the history of science, this is not unique to the oceanology). If we ignore the bad faith of catastrophist climatologists, the idea of involving the oceans as the main driver of climate variability will still take time to be accepted by the scientific community.
This is what prompted me to try to save time by publishing the website:
http://climatorealiste.com/; http://climatorealist.neowordpress.fr/
This is the role attributed to the oceans in climate variability:
1) The tropical oceans produce quasi-stationary baroclinic waves (which store or release heat by oscillation of the thermocline) whose period is 1, 4 or 8 years, which resonate with trade winds and ENSO.
2) The western boundary currents (Gulf Stream, Kuroshio …) carry this succession of warm and cold water to the subtropical gyres. Again a resonance phenomenon occurs (setting the wavelength of the quasi-stationary baroclinic waves so that their natural period coincides with that of forcing). But these waves that I call ‘gyral’ (I had to baptize them) and which wind around the 5 subtropical gyres have incredible property they do not deaden when the period increases (driver = Earth’s rotation + gravity). They have another property, they resonate with the solar and orbital cycles whose periods coincide with their natural periods. These baroclinic gyral waves store or otherwise release heat resulting from changes in solar irradiance (by the oscillation of the thermocline).
3) At mid-latitudes, as a result of baroclinic instabilities of the atmosphere, thermal equilibrium occurs between the sea surface temperature anomalies of the subtropical gyres and thermal anomalies of impacted regions of continents (Western Europe is one), due to cyclones and highs carried by the jet-streams (depending on the sign of anomalies, deficit or excess of latent heat withdrawn from the oceans, then restored by condensation of water vapor).
4) Global temperature anomalies are homogenized from the impacted areas, this resulting from the high specific heat of seawater compared to that of continents.
So, this scheme does not seem to me extraordinarily complicated, less complicated in any case than to cut photons into quarters to try to explain the greenhouse effect. And there, the effect is guaranteed, observations are explained without having to contort too much.
Until the enormous variations that the NSIDC found from 1964 to 66 are explained, everything is just smoke and mirrors…
http://wattsupwiththat.com/2014/09/04/1960s-satellite-imagery-of-polar-ice-discovers-enormous-holes-in-the-sea-ice/
In climate science, the completeness of the models in immaterial. The only important consideration is whether or not the models produce the correct (i.e. desired) results.
Which they do not. And yet they are still with us, and getting fat grants…so it must not be the only important consideration after all.
Perhaps if you modify the comment to read “The only important consideration should be whether or not…”
“Eisenman et al. (2007) used two standard thermodynamic models of sea ice to calculate equilibrium Arctic ice thickness based on simulated Arctic cloud cover derived from sixteen different global climate models (GCMs)”
I thought that the acronym GCM stood for General Circulation Model.
Focus groups have modified that particular acronym. The morphing was a two or three step one over a period of years.
This is “climate science”, baby.
Consistency?
We no need no stinking consistency!
Who needs “buyer beware” when the taxpayers cover your losses.
“This work revealed that trends in sea ice extent and area in the Arctic over the period of the two researcher’s analyses were -3.4 ± 0.2 and -4.0 ± 0.2% per decade, respectively; but it also revealed that simultaneous corresponding trends in the Antarctic were +0.9 ± 0.2 and +1.7 ± 0.3% per decade.”
If albedo is the issue then the relevant info is the sea ice extent. Thickness measurement, even with satellite altimetry, is still in the alchemy phase anyway. I also studied the extent data from 1979 to 2014 and my results and conclusions are somewhat different from the ones reported in the quoted paragraph.
http://tinyurl.com/qjs47jn
If albedo was the major factor that it was warned to be, then how is the Antarctic going to play out, considering that that sea ice has a far larger amount of sunlight impinging on it.
Besides, I think someone pointed out that at that low sun angle, water is almost like a mirror and not much different than ice.
On top of all that, when ice extent got to it’s lowest value, rather than collapsing, it grew back rapidly instead!
But it sure does get tiresome when ice extent, thickness, and total volume trends are sorted through for the one which tells the better CAGW lie de jour.
One thing that has always bothered me about the extent numbers…extent is reported as the area with at least (some percentage…is it 15%?) of coverage. But 1 million sq km with 18% coverage is a much different situation that 1 million sq km with 87% coverage. I am not sure what those numbers mean, except that they are even better at potentially lying than most statistics are.
Extent data should be used in conjunction with concentration data but it turns out that it does not make much of a difference because the average concentration of sea ice within the extent area does not vary much beyone 50%-75%. See http://tinyurl.com/pmzz6ar
Thanks, I will read that later.
“The Arctic is screaming,” said Mark Serreze
I hear it every day!!!!!!!!! Along with the polar bears crying about lack of ice!!!!!
From a SETH BORENSTEIN 2007 article – This week, after reviewing his own new data, NASA climate scientist Jay Zwally said: “At this rate, the Arctic Ocean could be nearly ice-free at the end of summer by 2012, much faster than previous predictions.”
“The Arctic is often cited as the canary in the coal mine for climate warming,” said Zwally, who as a teenager hauled coal. “Now as a sign of climate warming, the canary has died. It is time to start getting out of the coal mines.”
I think Jay Zwally would be better hauling coal again!!!!!!
I remember those “Arctic is screaming” headlines. Yes, there is a circus fringe in climate research and they do make easy targets; but the rhetoric has been toned down considerably. For example compare the description of the Pakistan heat wave of 2015 with the rabid hyperbole of the Russian heat wave of 2010.
Not toning it down none at Huff Po:
https://stevengoddard.wordpress.com/2015/06/26/huffington-post-vying-for-stupidest-article-ever/
From historic maritime observations of Arctic Sea Ice Extents, it’s very likely they’re sinusoidal and tied to PDO and AMO 30-yr warm/cool cycles and NOT CO2 levels…
When satellite sea-ice records began in 1979, the Arctic Ice extent was at its maximum size due to the AMO being at its coldest point in its 30-yr cycle and the PDO just ending its 30-yr cool cycle. The AMO entered its 30-yr warm cycle in 1994, which is when Arctic sea ice started to fall. In 2007, the 30-yr AMO warm cycle reached its peak and has been recovering ever since (with the exception of the 2012 downward spike, which was entirely caused by the strongest and longest Arctic summer cyclone in 50 years).
As the AMO 30-yr warm cycle winds down, and switches to a 30-yr cool cycle around 2022, Arctic sea ice extents will continue to recover and be back near 1980 levels by 2025; perhaps sooner.
This Arctic sea-ice recovery will drive CAGW advocates crazy and I suspect that “new and improved” algorithms to calculate sea-ice extents will be devised to hide the recovering Arctic sea-ice extents for as long as possible.
For some reason not yet understood, Arctic and Antarctic ice extents seem to be inversely correlated, so we may actually see Antarctic sea-ice extents decline as Arctic sea-ice recovers….
And so it goes…until it doesn’t…
Both are now increasing in extent.
What does this mean?
Is it really a good idea to assume the solar cycle shutting down is nothing to worry about?
The filamentary structure of the two main anomalies observed in the Arctic in the north of the Atlantic located between longitudes 30°W-0° and 20°E-40°E, is parallel to the southern limit of the ice, which shows unequivocally that the melting or on the contrary the reconstitution of ice over time is tightly controlled by the ocean (see the videos in http://climatorealist.neowordpress.fr/2015/06/11/climate-change/). This finding suggests that the thermohaline circulation intervenes by imposing temperature conditions at the southern boundary of the ice, stimulating sea water advection below the surface of the pack ice (Polyakov et al., 2010, 2012).
The annual variations of sea ice percentage related to the anomaly 20°E-40°E are in phase with the northernmost thermal anomaly of the North Atlantic that is in contact with the ice, the mean period of which is 8 years.
The concentration of sea ice at the anomaly located between 30°W and 0°, though strongly correlated to the previous one, seems to have a period higher than 8 years. It also shows large variability reflecting specific transfer process at the Denmark Strait, with a significant melting episode during the 1970s and early 1980s.
In the North Pacific, melting is probably the result of a thermal anomaly which exerts its influence through the Bering Strait via the current of Alaska. At longitudes 150°E-160°W the ice is recovering since the mid-2000s.
So, the periodic behavior of annual changes in the ice percentage reflects the leading role of the oceans. The changes in albedo amplifies the phenomenon but I don’t think it is the driver.
“Holland’s resolving of the ocean eddy field using the fine-resolution model was found to have a measurable impact on sea ice concentration, implying that a “fine-resolution grid may have a more efficient atmosphere-sea ice-ocean thermodynamic exchange than a coarse one.” Put another way, he reported that the results of his study demonstrated “yet again” that “coarse-resolution coupled climate models are not reaching fine enough resolution in the polar regions of the world ocean to claim that their numerical solutions have reached convergence”
Wait a second! Holy crap…if I had a hat I would be hanging on to it!
If I am reading this right, he is suggesting that greater resolution and smaller grids leads to more accurate models?
Ahh, go on!
“If I am reading this right, he is suggesting that greater resolution and smaller grids leads to more accurate models?”
Indeed, they do, but unfortunately resolution has to be increased in four dimensions (three spatial dimensions plus time). So cutting the grid size from 100 km (the usual figure) to 10 km means 10^4 = 10,000 more computer power is needed. To go down to 1 km or 100 meters (which is needed to simulate e. g. clouds, thunderstorms, tornados or drifting ice) requires 100,000,000 and 100,000,000,000 times faster computers respectively.
Ain’t gonna happen tomorrow.
Besides for the computer power, is the data collection itself. When are we ever going to have a three dimensional grid work of data recording stations, at any resolution?
Right now they seem to be eliminating surface stations in large numbers.
Why are computer models
The warmists’ Holy Grail?
Doesn’t anyone ask
Why they always fail?
http://rhymeafterrhyme.net/false-predictions/
It should be noted that Arctic/Antarctic sea-ice is an extremely simple and well-understood subsystem from a physical point of view, compared to the entire Atmosphere/Ocean climate system.
If it can’t be modelled realistically by a GCM, that GCM is quite obviously useless.
Those calling the shots will not even report on actual trends in any honest way.
Who could expect them to honestly report a result even if they had a perfect models which told them exactly what is going to happen? Unless the trend is a catastrophe that is.
Although I have never been real clear on what is the big disaster looming if the frozen wastelands of the world become less frozen and wasted?
So, the science is settled. After mere 20 ys praxis modelling sea ice covering the results are inappropriate.
That is an enhancement.
Regards – Hans