This interesting essay by Dr. Spencer is reposted from his blog, link here:
Global Warming Causing Carbon Dioxide Increases: A Simple Model
May 11th, 2009 by Roy W. Spencer, Ph. D.
Global warming theory assumes that the increasing carbon dioxide concentration in the atmosphere comes entirely from anthropogenic sources, and it is that CO2 increase which is causing global warming.
But it is indisputable that the amount of extra CO2 showing up at the monitoring station at Mauna Loa, Hawaii each year (first graph below) is strongly affected by sea surface temperature (SST) variations (second graph below), which are in turn mostly a function of El Nino and La Nina conditions (third graph below):
Click for larger images
Click for larger image
During a warm El Nino year, more CO2 is released by the ocean into the atmosphere (and less is taken up by the ocean from the atmosphere), while during cool La Nina years just the opposite happens. (A graph similar to the first graph also appeared in the IPCC report, so this is not new). Just how much of the Mauna Loa Variations in the first graph are due to the “Coke-fizz” effect is not clear because there is now strong evidence that biological activity also plays a major (possibly dominant) role (Behrenfeld et al., 2006).
The direction of causation is obvious since the CO2 variations lag the sea surface temperature variations by an average of six months, as shown in the following graph:
So, I keep coming back to the question: If warming of the oceans causes an increase in atmospheric CO2 on a year-to-year basis, is it possible that long-term warming of the oceans (say, due to a natural change in cloud cover) might be causing some portion of the long-term increase in atmospheric CO2?
I decided to run a simple model in which the change in atmospheric CO2 with time is a function of sea surface temperature anomaly. The model equation looks like this:
delta[CO2]/delta[t] = a*SST + b*Anthro
Which simply says that the change in atmospheric CO2 with time is proportional to some combination of the SST anomaly and the anthropogenic (manmade) CO2 source. I then ran the model in an Excel spreadsheet and adjusted an “a” and “b” coefficients until the model response looked like the observed record of yearly CO2 accumulation rate at Mauna Loa.
It didn’t take long to find a model that did a pretty good job (a = 4.6 ppm/yr per deg. C; b=0.1), as the following graph shows:
Click for larger image
The best fit (shown) assumed only 10% of the atmospheric CO2 increase is due to human emissions (b=0.1), while the other 90% is simple due to changes in sea surface temperature. The peak correlation between the modeled and observed CO2 fluctuation is now at zero month time lag, supporting the model’s realism. The model explained 50% of the variance of the Mauna Loa observations.
The best model fit assumes that the temperature anomaly at which the ocean switches between a sink and a source of CO2 for the atmosphere is -0.2 deg. C, indicated by the bold line in the SST graph, seen in the second graph in this article. In the context of longer-term changes, it would mean that the ocean became a net source of more atmospheric CO2 around 1930.
A graph of the resulting model versus observed CO2 concentration as a function of time is shown next:
If I increase the anthropogenic portion to 20%, the following graph shows somewhat less agreement:
Click for larger images
There will, of course, be vehement objections to this admittedly simple model. One will be that “we know the atmospheric CO2 increase is manmade because the C13 carbon isotope concentration in the atmosphere is decreasing, which is consistent with a fossil fuel source.” But has been discussed elsewhere, a change in ocean biological activity (or vegetation on land) has a similar signature…so the C13 change is not a unique signature of fossil fuel source.
My primary purpose in presenting all of this is simply to stimulate debate. Are we really sure that ALL of the atmospheric increase in CO2 is from humanity’s emissions? After all, the natural sources and sinks of CO2 are about 20 times the anthropogenic source, so all it would take is a small imbalance in the natural flows to rival the anthropogenic source. And it is clear that there are natural imbalances of that magnitude on a year-to-year basis, as shown in the first graph.
What could be causing long-term warming of the oceans? My first choice for a mechanism would be a slight decrease in oceanic cloud cover. There is no way to rule this out observationally because our measurements of global cloud cover over the last 50 to 100 years are nowhere near good enough.
And just how strenuous and vehement the resulting objections are to what I have presented above will be a good indication of how politicized the science of global warming has become.
REFERENCES
Michael J. Behrenfeld et al., “Climate-Driven Trends in Contemporary Ocean Productivity,” Nature 444 (2006): 752-755.
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
Anthony [replying to Smokey (19:03:41)]: “What Smokey said. Joel, please be my guest, go to CA and make the same claim. And while I’m at it let me add that MciNtyre does things. He investigates, analyses, publishes, makes FOI requests to agancies that aren’t cooperative in sharing data, and suffers fools gladly. Other than whine and complain about others here, what have you done of merit?”
Comments:
1) I like reading what Joel has to say – it helps me learn the “standard arguments”.
However,
2) I back Smokey & Anthony regarding the “gimmick” attack — here’s part of why: I’ve done these engineering calculations – they are rigorous — and engineers use common sense like I’ve rarely seen in the 6 other disciplines I’ve visited during the past 2 decades.
– –
Joel Shore (18:49:35) “[…] and while there are certainly uncertainties with regards to clouds, noone has yet demonstrated a model with a significant negative cloud feedback that can successfully reproduce the basics of the current climatology as well as the current models can.”
Interesting.
George E. Smith:
“But increases or any changes in global temperature (which I said we can’t reliably measure anyway) have not risen to the point where they can be reliably measured.”
If you don’t think global temperatures can be measured reliably, then logically you have no idea whether they have risen, fallen or stayed the same.
Your presumptuous ‘we’ does not include people actually doing the measurements – such as Roy Spencer, who seems to find no problem with the concept of reliable measurements of global temperatures, and reports an upward trend of +0.13&dec;C/decade since 1979.
RE: Joel Shore (18:49:35) :
Gerald Machnee says:
**The IPCC number is based on much more than the original Charney estimate. It is based on a lot of observational data, such as the difference in temperature between the last glacial maximum (LGM) and now, the response of the global temperature to the Mt Pinatubo eruption, and the 20th century temperature record (although the latter does not provide very strong constraints because of the uncertainty in the climate forcing).**
They make assumptions which are not working too well now in the temperature forecasts.
**In addition to this is the fact that the climate models containing our current understanding of all of the relevant physics seem to converge to the same range of values as these observational data give … and while there are certainly uncertainties with regards to clouds, noone has yet demonstrated a model with a significant negative cloud feedback that can successfully reproduce the basics of the current climatology as well as the current models can.**
The current models are not better, but have been massaged to reflect the current results. The cloud and moisture is not adequately modelled. The positive feedbacks are assumed not measured. The errors are starting to show, but the modellers are using 30 years or so to verify and mask the problem.
**What McIntyre’s gimmick amounts to is a statement that the climate sensitivity is not easy to calculate in any direct and simple way. Well, yes, that is true. But that hardly means that we should ignore the best evidence from the observational data and from modeling of the climate system in favor of hopes that it is much lower.**
That is not a gimmick but a legitimate question. The IPCC has their head buried in the sand. McIntyre asked for more information when he was doing reviews and the Chair Susan Solomak responded byt telling him he would be kicked off the panel if he asked such questions. That is how they get their consensus. One on the senior people at IPCC, a British Meteorologist conveniently “lost or discarded” his notes which are supposed to be archived.
If climate sensitivity is nor easy to calculate maybe they should allocate some funds to that instead of using the same tired data to claim the “hockey stick” is still correct.
What best evidence are you referring to??
Smokey (16:32:24) :
Here’s a graph [by Bill Illis] showing a 5-month lag: click. There are lots of different cycles where CO2 lags temperature. The 800± year lag is only one of them
I’ve just realised how these lags are derived thanks to Paul Vaughan.
People are looking at monthly derived figures and differentiating them. They are then picking up the small seasonal variation of CO2. Doing similar things with temperature then pointing out a 6 month lag. Temperature variation is seasonally most predominant in NH and so an average global temp will show perturbations relating to NH seasons. See http://cdiac.ornl.gov/trends/co2/sio-keel.html the further south (in general) you go the less the seasonal variation 15 ppm in canada (alert) and 0 (ish) in NZ
South Pole, Antarctica 2ppm
New Zealand Baring Head ~1ppm
Kermadec Islands ~1ppm
Cape Matatula, Samoa ~1ppm
Christmas Island 4ppm
Cape Kumukahi 11ppm
Mauna Loa, Hawaii 9ppm
Baja California Sur, Mexico ~8ppm
La Jolla Pier 13ppm
Barrow, Alaska 20ppm
Alert, NWT, Canada 15ppm
A seasonal variation will affect CO2, with a Lag as is to be expected. BUT this is not the continual rise in CO2 shown by all measuring stations referenced in Pauls post
http://cdiac.ornl.gov/trends/co2/sio-keel.html
It is interesting to note that Antarctica has a greater seasonal variation than NZ. perhaps one needs to check the phase relation ship of CO2 between the various locations. Is Nz’s seasonally produced CO2 nulling the NH change. Is antarctica showing a SH or NH variation. Is SH CO2 seasonal changes 6 months antiphase with NH?
So I would expect there to be a lag between differntial of T and differential of CO2 on seasonal scale. BUT there is no way( ?) of determining the delay +ve or -ve between the slow rise of CO2 and Temperature.
Bill Illis’s graph does not state if the plot of CO2 is differential or standard. I would expect the former
Very clear, and passes the common sense test. Thank you, and may cooler heads prevail. I realize it is beyond the scope of the essay, but I’m interested in what may cause changes in cloud cover. Are there cyclical inputs over large time scales (years) which affect the percentage of cloud cover?
It’s interesting doing a comparison of a few of the CO2 levels from CDIAC:
http://img190.imageshack.us/img190/4250/co2x4.jpg
Antarctica = SH CO2 seasonal changes
NZ = mix of SH(dominant) and NH
Xmas Is and Mauna Loa NH
Somewhat OT.
But, whatever happened to the group that was showing that CO2 was maybe not so well mixed in the Atmosphere?
Should have plotted a few more northerly points:
http://img190.imageshack.us/img190/1068/co2x7.jpg
My graphs were all based on anomalies from the average seasonal cycle.
regarding the realism of models that have tried negative cloud feedback and could not replicate the average behavior of the climate system (which I have never heard before):
1) I doubt that any one has tried that hard, and
2) For global warming we are interested in the feedbacks…not replicating the average behavior of the climate system…which all of the models have difficulty doing anyway.
The CERES team leaders (Wielicki, and now Norman Loeb) have both mentioned that they can not get the modelers to do any more than cursory, basic comparisons with the satellite radiation budget data. I can’t help but wonder why…
The weird thing about these NH plots is the dip. It does not look biological it looks as if a switch has been flipped (or full wave rectified – for the electronic people).
at the beginnin of july it is racing down by the beginning of august it is going north at the same ppm rate.
Is there daily data for CO2 – does this show an even spikier spike?
Another strange thing is that from 1978 to 2007 the plot of barrow has exactly the same shape – the peak is in may the min is in August
the fall from may to august is 14.55ppm and in 2007 is 17.22
Please note that my comment above (bill (08:58:20) : Your comment is awaiting moderation) is a month out 2000.0 = january / 2000.5 = july
You would have thought that if temperature were causing CO2 to change then the sink would beginearlier each year and the dip would change by less (or more) than the 20% it does now. In fact with warming seas shouldn’t this dip be getting less not more as less CO2 gets absorbed.
Or is the flora. But then wouldnt the dip start earlier than May? And wouldn’t the dip be more rounded and continue into October?
Hhhhhhhhmmmmmm!!!! Strange!
bill, I see you’ve posted a multi-site CO2 timeplot:
http://img190.imageshack.us/img190/1068/co2x7.jpg
Have you looked at an analogous plot for dCO2/dt? It is [a great deal] more telling.
For example, note the Alert winter dip [which I mentioned at Paul Vaughan (13:08:57), responding to Lucy Skywalker (01:06:41)].
You indicated curiosity regarding spikiness at daily resolution. I would push that to sub-daily; 3 hour resolution should be sufficient. Without the finer resolution, it would be difficult (perhaps impossible) to explore the nuances of diurnal & polar-day/night interaction with polar (& latitudinal more generally) continental distribution – as seasons pass. (Think of alternate continental & north-south symmetry/asymmetry configurations to get a handle on what we should be digging for in analyses.)
Thanks to Dr. Spencer & WUWT, I see how ABSOLUTELY RIDICULOUS it is to claim Mauna Loa CO2 represents global CO2 (without applying appropriate heavy qualification – in the form of paragraphs – [not just adjectives]) — stunningly ridiculous. [Before this thread I had been trusting in the “well-mixed” dogma – but, fortunately, another layer of innocence & ignorance has been ripped away in the pursuit of true wisdom.]
bill, your graph illustrates exactly how folks came to the flawed conclusion; they IGNORED the fundamental importance of spatiotemporal variation – perhaps accidentally, but it’s a very serious error, so they need to get past it.
[Enlightened] ecologists & physical geographers have been railing against this very serious error for nearly 3 decades now. [See the literature for an absolutely nauseating number of papers hammering this important & painful lesson (which still does not get through to everyone, despite the intense effort).] The efforts have been motivated by an acute awareness that scientific literature is plagued with erroneous analyses, based on FALSE assumptions (for example about uniformity & symmetry).
We should also be listening to the common sense of engineers, who are forced (by the nature of their work) to remain practical. They are not (so easily) deluded into excessively-linear abstraction by notions that means are of dominating importance; rather, they have a healthy appreciation for – for example – minima, maxima, & ranges. Statisticians can work with the latter summaries no problem, but it is up to those who are consulting with them (who have subject-area expertise) to stress the importance of not making false assumptions – for example about independence & distribution-tails.
Statistical consulting work easily slips deep into communication failure. A skilled consultant knows what questions to ask, but the consultee must also take some responsibility – the ethics of not just truthful, but also full disclosure of relevant details apply.
It remains clear that many bright folks have some very serious catching up to do when it comes to sampling & data analysis in the presence of multi-scale spatiotemporal heterogeneity. In the meantime, these bright folks must NOT be allowed to influence policy with FLAWED analyses and MISLEADING summaries. (Their errors may be innocent, but the consequences?… I could tell you stories…)
Roy W. Spencer (08:05:55) :
regarding the realism of models that have tried negative cloud feedback and could not replicate the average behavior of the climate system (which I have never heard before):
The absence of “theory” is a limiting quality eg Ramanathan 2008
“It is remarkable that general circulation climate models (GCMs) are able to explain
the observed temperature variations during the last century solely through variations in greenhouse gases, volcanoes and solar constant. This implies that the cloud contribution to the planetary albedo due to feedbacks with natural and forced climate changes has not changed during the last 100 years by more than ±0.3%; i.e, the cloud forcing has remained constant within ±1 Wm–2. If indeed, the global cloud properties and their influence on the albedo are this stable (as asserted by GCMs), scientists need to validate this prediction and develop a theory to account for the stability.”
Om the other hand the other UNEP scientific assessment committee (ozone) has a different perspective.eg.
“The Sun’s output is not constant over time, and solar UV-C radiation changes significantly over the 11-year solar cycle. This UV-C does not penetrate as far as the Earth’s surface, but changes in UV-C cause ozone changes of ~3%. Counter- intuitively, the UV-B received at the surface is therefore expected to be a minimum when the solar output is a maximum. However, there may be other climatic impacts of solar variability as well (e.g. changes in cloud cover), …
Cloud Effects
Cloud effects are important. The mean attenuation of UV-B by clouds is typically in the range 15-30%. There have been improvements in the measurement of clouds from automated imagers at the Earth’s surface. Progress has been made relating these cloud images to satellite-derived cloud patterns and to the UV radiation received at the Earth’s surface.37 There is evidence for long-term changes in cloud in some regions, as discussed later….
Biospherical Feedbacks
Other feedbacks can involve the biosphere (Chapters 4 and 5).9, 84, 85 For example, increasing UV can reduce the productivity of oceanic phytoplankton. This can produce two feedbacks. Firstly, it reduces the oceanic sink for carbon in atmosphere (production of carbonates which fall to the sea floor). Secondly, it can influence the production of dimethyl sulphide (DMS), which is an important source of condensation nuclei.85 This, in turn affects cloud-droplet size, cloud reflectivity, and hence planetary albedo.
Paul Vaughan (14:37:04) :
http://img190.imageshack.us/img190/1068/co2×7.jpg
Have you looked at an analogous plot for dCO2/dt? It is [a great deal] more telling.
No
You indicated curiosity regarding spikiness at daily resolution. I would push that to sub-daily; 3 hour resolution should be sufficient. Without the finer resolution, it would be difficult (perhaps impossible) to explore the nuances of diurnal & polar-day/night interaction …
methinks you are getting carried away!
I see how ABSOLUTELY RIDICULOUS it is to claim Mauna Loa CO2 represents global CO2 … stunningly ridiculous.
You are looking at the minutiae, not at the important stuff which is over a longer period. I find it reassuring that the average CO2 for such diverse places is actually very similar antarctica is within a few ppm of actric.
bill, your graph illustrates exactly how folks came to the flawed conclusion; they IGNORED the fundamental importance of spatiotemporal variation – perhaps accidentally, but it’s a very serious error, so they need to get past it.
So what effect are you proposing that the spatiotemporal variation will have on peoples views?
I am proposintg that the rate of change which Roy Spencer has plotted is not relevant neither to man nor beast, as the change is occurring because of flora or sea fauna and is negated a couple of months later. Each cycle is getting to higher levels of CO2 and is showing no reductions.
..these bright folks must NOT be allowed to influence policy with FLAWED analyses and MISLEADING summaries.
VERY true neither should the anti-AGW’s be allowed endanger the future. What is needed is a computer with no agenda, infinite knowledge, capable of weighing up all current climate data. –
(DEEP THOUGHT: The Great Hyperlobic Omnicognate Neutron-Wrangler can talk all four legs off an Arcturan Mega-Donkey but only I can persuade it to go for a walk afterwards.)
This from Piers Corbyn should be taken in to account also :
“Extra plant transpiration-cooling negates all extra co2 heating (i.e. 3.8 W/m2 – 3.8 W/m2 = 0).”
~~Piers Corbyn,
-solar physisist,
-ARCS, FRAS, FRMetS,
-WeatherAction.com, the Long Range Forecasters,
-developer, SWT (Solar Weather Technique),
-Award of AMEME Hopley Shield 2008
PDF page 7
http://junkscience.com/mar08/What_Does-Does_Not_Drive_Climate_Change.pdf
Mr. Spencer,
If you have time, do you have any thoughts on Miklos Zagoni’s finding that there can be no runaway greenhouse effect that is outlined in this video :
bill (06:40:56) “[…] a Lag as is to be expected.”
It is important to note here (for anyone following along) that the lags we are seeing in these data seem [far] more likely to be related to anti-phase than to delayed-response (although we cannot be absolutely certain based just on these data alone).
From what I can see there are 2 (main) cycles of differing amplitudes – & they are in anti-phase. This comes as no surprise, as this is what is seen in many other geophysical time series (on our north-south asymmetric Earth).
– – –
maksimovich (14:52:40) “[…] Ramanathan 2008 […] “This implies that the cloud contribution to the planetary albedo due to feedbacks with natural and forced climate changes has not changed during the last 100 years” […]”
Note the use of the word “implies”; this is another example of the flawed logic that many applied above (in this thread) in interpreting the 90/10 split in Dr. Spencer’s demonstrative-presentation. It stems from a deep misunderstanding of decomposition (& shared variance).
bill (15:36:33) “methinks you are getting carried away!”
How much photosynthesis do you think happens at night (relative to during the day)? ..and in the polar regions during the polar-day versus during the polar-night (i.e. summer vs. winter)?
–
bill (15:36:33) “What is needed is a computer […]”
Computers are certainly useful tools, but no computer is going to be able to substitute satisfactorily for sound human judgement in the foreseeable future.
–
bill (15:36:33) “You are looking at the minutiae, not at the important stuff […] I am proposintg that the rate of change which Roy Spencer has plotted is not relevant neither to man nor beast […] VERY true neither should the anti-AGW’s be allowed endanger the future.”
Do you think a boreal forest carbon-modeler would agree with your suggestion that we ignore seasonal variations?
I think you might seriously misunderstand my motivation (& the motivation of others).
My interest is in understanding nature, including nature’s nuances. As indicated above, prior to this thread I did not find CO2 to be a very interesting variable to study because all it shows (at annual resolution – & upon superficial inspection) in the modern 1958+ records is dull, monotone increase (which leaves little over which to puzzle).
I’m neither “anti-AGW” nor “pro-AGW”. I’m a student of nature [which includes humans], an advocate of careful analysis, and a proponent of balance.
–
bill (15:36:33) “So what effect are you proposing that the spatiotemporal variation will have on peoples views?”
The take-home message for the general public here might be something like this:
Thinking of CO2 equilibria-chain balances only in terms of a “global annual average” is far too simple.
—
Final comments:
No one (sensible) is trying to say CO2 is not increasing; (sensible) people are saying, “Let’s understand nature, including water.” I see no sensible reason for introducing biases about what spatiotemporal scales are worth understanding. We can draw no definitive conclusions until we understand the whole picture (if that’s even possible).
Paul Vaughan (18:29:02) :
Thanks for the response.
I admire your interest in these things.
I think that CO2 is measured at a 2 weekly rate so you will find it difficult to find shorter period with long term records.
I would still like to know how sharp the NH dip is and what feature of the ecosystem can respond in such a rapid way!
bill
bill (19:12:15)
“I think that CO2 is measured at a 2 weekly rate so you will find it difficult to find shorter period with long term records.
I would still like to know how sharp the NH dip is and what feature of the ecosystem can respond in such a rapid way!”
All you have to do to see the dip is difference the series (& then plot). [It is as simple as d(i+1) = CO2(i+1) – CO2(i). There are advantages to this approach (over making assumptions about annual structure, which will mess up attempts to go after subtle nuances).]
I too was hoping someone would share the “standard” 2-sentence overview of the Alert winter dip – to tide me over until I have time to venture into another massive branch of the research jungle.
I rarely (fully) trust any conclusions I see in any publication without performing analyses myself. Researchers have all sorts of formal training in (abstract, theoretical) statistics (including heavily-mathematical statistics, complete with loads of proofs & derivations), but the education system – which has other priorities – lacks focus on fundamental concepts, data analysis, and the application of sound judgement in data analysis.
My guess is that there will be plenty of datasets & publications, including a good number focused on short timescales. Publicly-available text-format data-websites are (arguably) essential in a knowledge society. Although I’m confident shorter timescale datasets exist, I suspect you’re right that they will not be long-term and I anticipate tediously-inefficient bureaucratic access-hoops (in most/many cases).
–
bill (19:12:15) “I admire your interest in these things.”
Likewise – & same regarding co-participants in the evolving confluence of science & democracy that occurs in these valuable forums.
Misleading the Public? – Or Just an “Accident”?
If one googles “carbon dioxide wiki”, one is led to:
Wikipedia – Carbon dioxide
http://en.wikipedia.org/wiki/Carbon_dioxide
Focusing further on CO2 “in the Earth’s atmosphere” within that article leads to:
Wikipedia – Carbon dioxide in the Earth’s atmosphere
http://en.wikipedia.org/wiki/Carbon_dioxide_in_the_Earth%27s_atmosphere
The caption of the first graph one sees there links to:
Wikipedia – Keeling Curve
http://en.wikipedia.org/wiki/Keeling_curve
If one digs around for a link to “data” on that page, one finds a link to CO2 data labeled:
“Globally averaged marine surface monthly mean data.”
…but the link actually leads to the Mauna Loa Observatory (elevation more than 3km) data:
ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt
…and note that nowhere on that webpage can one find “mauna loa” — that clue is found elsewhere in the directory – by adjusting the url …which is also how one can find the “globally-averaged” data.
Maybe it was just a linking accident? – even if so, the error is well worth pointing out, particularly considering the insights (into spatial variation) that arose in this thread.
– – –
Worthy of note for albedo/cloud enthusiasts:
“[…] a decline in the coccolithophores may have secondary effects on climate change, by decreasing the earth’s albedo via their effects on oceanic cloud cover.”
Wikipedia – Ocean Acidification
http://en.wikipedia.org/wiki/Ocean_acidification
[a rather “alarmist” article – well worth assessing for political bias]
Wikipedia – Carbon sink
http://en.wikipedia.org/wiki/Carbon_dioxide_sinks
“Because the effect of periodic small scale phytoplankton blooms on ocean ecosystems is unclear, more studies would be helpful. Phytoplankton have a complex effect on cloud formation via the release of substances such as dimethyl sulfide (DMS) that are converted to sulfate aerosols in the atmosphere, providing cloud condensation nuclei, or CCN. But the effect of small scale plankton blooms on overall DMS production is unknown.”
Daily CO2 data:
http://scrippsco2.ucsd.edu/data/data.html
useful crash course:
http://www.ferdinand-engelbeen.be/klimaat/co2_measurements.html
Paul Vaughan (20:51:52) :
Worthy of note for albedo/cloud enthusiasts:
“[…] a decline in the coccolithophores may have secondary effects on climate change, by decreasing the earth’s albedo via their effects on oceanic cloud cover.”
Wikipedia – Ocean Acidificationhttp://en.wikipedia.org/wiki/Ocean_acidification
[a rather “alarmist” article – well worth assessing for political bias]
GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 15, No. 2, PAGES 507-516, JUNE 2001
Decreasing marine biogenic calcification: A negative feedback on rising atmospheric pCo2
Ingrid Zondervan, Richard E. Zeebe1, Björn Rost, and Ulf Riebesell
Alfred Wegener Institute for Polar and Marine Research Bremerhaven, Germany
Abstract. In laboratory experiments with the coccolithophore species Emiliania huxleyi and Gephyrocapsa oceanica, the ratio of particulate inorganic carbon (PIC) to particulate organic carbon (POC) production decreased with increasing CO2 concentration ([CO2]). This was due to both reduced PIC and enhanced POC production at elevated [CO2]. Carbon dioxide concentrations covered a range from a preindustrial level to a value predicted for 2100 according to a “business as usual” anthropogenic CO2 emission scenario. The laboratory results were used to employ a model in which the immediate effect of a decrease in global marine calcification relative to POC production on the potential capacity for oceanic CO2 uptake was simulated. Assuming that overall marine biogenic calcification shows a similar response as obtained for E. huxleyi or G. oceanica in the present study, the model reveals a negative feedback on increasing atmospheric CO2 concentrations owing to a decrease in the PIC/POC ratio
Paul Vaughan (23:00:02)
Because the effect of periodic small scale phytoplankton blooms on ocean ecosystems is unclear, more studies would be helpful. Phytoplankton have a complex effect on cloud formation via the release of substances such as dimethyl sulfide (DMS) that are converted to sulfate aerosols in the atmosphere, providing cloud condensation nuclei, or CCN. But the effect of small scale plankton blooms on overall DMS production is unknown.”
Variability of atmospheric dimethylsulphide over the southern Indian
Ocean due to changes in ultraviolet radiation
D. R. Kniveton,1 M. C. Todd,2 J. Sciare,3 and N. Mihalopoulos4
Received 13 January 2003; revised 16 May 2003; accepted 11 August 2003; published 10 October 2003.
[1] Dimethylsulphide (DMS) is a climatically important component of global
biogeochemical cycles, through its role in the sulphur cycle. Changes in ultraviolet
radiation (UV) exhibit both positive and negative forcings on the dynamics of production and turnover of DMS and its precursor dimethylsulphoniopropionate (DMSP). In this study we investigate the net forcing of UV on atmospheric DMS. The work is based on a 10-year record of observed DMS at Amsterdam Island in the southern Indian Ocean, and satellite-based retrievals of surface UVand photosynthetically active radiation (PAR).The results show an inverse relationship between UV radiation and atmospheric DMS associated with extreme changes (defined as the greatest 5%n daily UV, independent of changes in wind speed, sea surface temperature, and PAR.
Testing the relationship between the solar radiation dose and surface DMS
concentrations using high resolution in situ data
C. J. Miles, T. G. Bell, and T. M. Lenton 2009
Abstract
We tested the recently proposed, strong positive relationship between dimethylsulphide (DMS) concentrations and the solar radiation dose (SRD) received into the surface ocean. We utilised in situ daily data sampled concurrently with DMS concentrations 5 from the Atlantic Meridional Transect (AMT) programme for the component variables of the SRD; mixed layer depth (MLD), surface insolation (I0) and a light attenuation coefficient (k), to calculate SRDin situ. We find a significant correlation (_=0.53) but the slope of the relationship is approximately half that previously proposed. The correlation is improved (_=0.76) by replacing the in situ data with an estimated I0 (which assumes 10 a constant 50% removal of the top of atmosphere value; 0.5×TOA), a MLD climatology and a fixed value for k following a previously described methodology. Equally significant, but non-linear relationships are also found between DMS and both in situ MLD (_=0.73) and the estimated I0 (_=0.76) alone. The DMS data shows an interesting relationship to an approximated UV attenuation depth profile. Using a cloud adjusted, 15 satellite climatology of surface UVA irradiance to calculate a UV radiation dose (UVRD)provides an equivalent correlation (_=0.73) to DMS. With this data, MLD appears the dominant control upon DMS concentrations and remains a useful shorthand to prediction without fully resolving the biological processes involved. However, the implied
relationship between incident solar/ultraviolet radiation dose and sea surface DMS con20 centrations (modulated by MLD) is critical for closing a climate feedback loop.
Also recommend Paul Crutzen.
Crutzen PJ 2002. Analysis of the Gaia hypothesis as
a model for climate/biosphere interactions. GAIA
2/2002, 96–103.
Also an interesting article here
(PhysOrg.com) — Groundbreaking Victoria University research shows that ocean acidification may have no negative effect on tropical corals and local sea anemones – in fact it may improve photosynthesis.
http://www.physorg.com/news161877580.html