Guest Post By Steve Fitzpatrick
Introduction
Projections of climate warming from global circulation models (GCM’s) are based on high sensitivity for the Earth’s climate to radiative forcing from well mixed greenhouse gases (WMGG’s). This high sensitivity depends mainly on three assumptions:
1. Slow heat accumulation in the world’s oceans delays the appearance of the full effect of greenhouse forcing by many (eg. >20) years.
2. Aerosols (mostly from combustion of carbon based fuels) increase the Earth’s total albedo, and have partially hidden the ‘true’ warming effect of WMGG increases. Presumably, aerosols will not increase in the future in proportion to increases in WMGG’s, so the net increase in radiative forcing will be larger for future emissions than for past emissions.
3. Radiative forcing from WMGG’s is amplified by strong positive feedbacks due to increases in atmospheric water vapor and high cirrus clouds; in the GCM’s, these positive feedbacks approximately double the expected sensitivity to radiative forcing.
However, there is doubt about each of the above three assumptions.
1. Heat accumulation in the top 700 meters of ocean, as measured by 3000+ Argo floats, stopped between 2003 and 2008 (1, 2, 3), very shortly after average global surface temperature changed from rising, as it did through most of the 1990’s, to roughly flat after ~2001. This indicates that a) ocean heat content does not lag many years behind the surface temperature, b) global average temperature and heat accumulation in the top 700 meters of ocean are closely tied, and c) the Hansen et al (4) projection in 2005 of substantial future warming ‘already in the pipeline’ is not supported by recent ocean and surface temperature measurements. While there is no doubt a very slow accumulation of heat in the deep ocean below 700 meters, this represents only a small fraction of the accumulation expected for the top 700 meters, and should have little or no immediate (century or less) effect on surface temperatures. The heat content in the top 700 meters of ocean and global average surface temperature appear closely linked. Short ocean heat lags are consistent with relatively low climate sensitivity, and preclude very high sensitivity.
2. Aerosol effects remain (according to the IPCC) the most poorly defined of the man-made climate forcings. There is no solid evidence of aerosol driven increases in Earth’s albedo, and whatever the effect of aerosols on albedo, there is no evidence that the effects are likely to change significantly in the future. Considering the large uncertainties in aerosol effects, it is not even clear if the net effect, including black carbon, which reduces rather than increases albedo, is significantly different from zero.
3. Amplification of radiative forcing by clouds and atmospheric humidity remain poorly defined. Climate models do not explicitly include the behavior of clouds, which are orders of magnitude smaller than the scale of the models, but instead handle clouds using ‘parameters’ that are adjusted to approximate the expected behavior of clouds. Adjustable parameters can of course also be tuned to make a model to predict whatever warming is expected or desired. Measured tropospheric warming in the tropics (the infamous ‘hot spot’) caused by increases in atmospheric water content, falls far short of the warming in this part of the atmosphere projected by most GCM’s. This casts doubt on the amplification assumed by the CGM’s due to increased water vapor.
Many people, including this author, do not believe the large temperature increases (up to 5+ C for doubling of CO2) projected by GCM’s are credible. A new paper by Lindzen and Choi (described at WUWT on August 23, 2009) reports that the total outgoing radiation (visible plus infrared) above the tropical ocean increases when the ocean surface warms, which suggests the climate feedback (at least in these tropical ocean areas) is negative, rather than positive as the CGM’s all assume.
In spite of the many problems and doubts with GCM’s:
1) It is reasonable to expect that positive forcing, from whatever source, will increase the average temperature of the Earth’s surface.
2) Basic physics shows that increasing infrared absorbing gases in the atmosphere like CO2, methane, N2O, ozone, and chloro-fluorocarbons, inhibits the escape of infrared radiation to space, and so does provide a positive forcing.
3) There has in fact been significant global warming since the start of the industrial revolution (beginning a little before 1800), concurrent with significant increases in WMGG emissions from human activities.
There really should be an increase in average surface temperature due to forcing from increases in infrared absorbing gases. This is not to say that there are no other plausible explanations for some or even most of the increases in global temperatures over the past 100+ years. For example, Lean (5) concluded that there may have been an increase of about 2 watts per square meter in average solar intensity (arriving at the top of the Earth’s atmosphere) between the little ice age and the late 20th century, which could account for a significant fraction of the observed warming. But regardless of other possible contributions, it is impossible to refute that greenhouse gases should lead to increased global average temperatures. What matters is not that the earth will warm from increases WMGG’s, but how much it will warm and over what period. The uncertainties and dubious assumptions in the GCM’s make them not terribly helpful in making reasonable projections of potential warming, if you assume the worst case that WMGG’s are the principle cause for warming.
Climate Sensitivity
If we knew the true climate sensitivity of the Earth (expressed as degrees increase per watt/square meter forcing) and we knew the true radiative forcing due to WMGG’s, then we could directly calculate the expected temperature rise for any assumed increases in WMGG’s. Fortunately, the radiative forcing effects for WMGG’s are pretty accurately known, and these can be used in evaluating climate sensitivity. An approximate value for climate sensitivity in the absence of any feedbacks, positive or negative, can be estimated from the change in blackbody emission temperature that is required to balance a 1 watt per square meter increase in heat input, using the Stefan-Boltzman Law. Assuming solar intensity is 1366 watts/M^2, and assuming the Earth’s average albedo is ~0.3, the net solar intensity is ~239 watts/M^2, requiring a blackbody temperature of 254.802 K to balance incoming heat. With 1 watt/M^2 more input, the required blackbody emission temperature increases to 255.069, so the expected climate sensitivity is (255.069 – 254.802) = 0.267 degree increase for one watt per square meter of added heat.
But solar intensity and the blackbody emission temperature of the earth both change with latitude, yielding higher emission temperature and much greater heat loss near the equator than near the poles. The infrared heat loss to space goes as the fourth power of the emission temperature, so the net climate sensitivity will depend on the T^4 weighted contributions from all areas of the Earth. Feedbacks within the climate system, both positive and negative, including different amounts and types of clouds, water vapor, changes in albedo, and potentially many others, add much uncertainty.
Measuring Earth’s Sensitivity
The only way to accurately determine the Earth’s climate sensitivity is with data.
Bill Illis produced an outstanding guest post on WUWT November 25, 2008, where he presented the results of a simple curve-fit model of the Earth’s average surface temperature based on only three parameters: 1) the Atlantic multi-decadal oscillation index (AMO), 2) values of the Nino 3.4 ENSO index, and 3) the log of the ratio of atmospheric CO2 concentration to the starting CO2 concentration. Bill showed that the best estimate linear fit of these parameters to the global mean temperature data could account for a large majority of the observed temperature variation from 1871 to 2008. He also showed that the AMO index and the Nino 3.4 index contributed little to the overall increase in temperature during that period, but did account for much of the variation around the overall temperature trend. The overall trend correlated well with the log of the CO2 ratio. In other words, the AMO and Nino3.4 indexes could hind cast much of the observed variation around the overall trend, and that overall trend could be accurately hind cast by the log of the CO2 ratio.
There are a few implicit assumptions in Bill’s model. First, the model assumes that all historical warming can be attributed to radiative forcing. This is a worst case scenario, since other potential causes for warming are not even considered (long term solar effects, long term natural climate variability, etc.). The climate sensitivity calculated by the model would be lowered if other causes account for some of the measured warming.
Second, the model assumes the global average temperature changes linearly with radiative forcing. While this is almost certainly not correct for Earth’s climate, it is probably not a bad approximation over a relatively small range of temperatures and total forcings. That is, a change of a few watts per square meter is small compared to the average solar flux reaching the Earth, and a change of a few degrees in average temperature is small compared to Earth’s average emissive (blackbody) temperature. So while the response of the average temperature to radiative forcing is not linear, a linear representation should not be a bad approximation over relatively small changes in forcing and temperature.
Third, the model assumes that the combined WMGG forcings can be accurately represented by a constant multiplied by the log of the ratio of CO2 to starting CO2. While this may be a reasonable approximation for some gases, like N2O and methane (at least until ~1995), it is not a good approximation for others, like chloro-fluorocarbons, which did not begin contributing significantly to radiative forcing until after 1950, and which are present in the atmosphere at such low concentration that they absorb linearly (rather than logarithmically) with concentration. In addition, chloro-fluorocarbon concentrations will decrease in the future rather than increase, since most long lived CFC’s are no longer produced (due to the Montreal Protocol), and what is already in the atmosphere is slowly degrading.
To make Bill’s model more physically accurate, I made the following changes:
1. Each of the major WMGG’s is separated and treated individually: CO2, N2O, methane, chloro-fluorocarbons, and tropospheric ozone.
2. Concentrations of each of the above gases are converted to net forcings, using the IPCC’s radiation equations for CO2, methane, N2O, and CFC’s (6), and an estimated radiative contribution from ozone inceases.
3. The change in solar intensity with the solar cycle is included as a separate forcing, assuming that measured intensity variations for the last three solar cycles (about 1 watt per square meter variation over a base of 1365 watts per square meter) are representative of earlier solar cycles, and assuming that sunspot number can be used to estimate how solar intensity varied in the past.
4. The grand total forcing (including the solar cycle contribution), a 2-year trailing average of the AMO index, and the Nino 3.4 index are correlated against the Hadley Crut3V global average temperature data.
This yields a curve fit model which can be used to estimate future warming by setting the Nino 3.4 and AMO indexes to zero (close to their historical averages) and estimating future changes in atmospheric concentrations for each of the infrared absorbing gases.
To find the best estimate of lag in the climate (mainly from ocean heat accumulation), the model constants were calculated for different trailing averages of the total radiative forcing. The best fit to the data (highest R^2) was for a two year trailing average of the total radiative forcing, which gave a net climate sensitivity of 0.270 (+/-0.021) C per watt/M^2 (+/-2 sigma). All longer trailing average periods yielded somewhat lower R^2 values and produced somewhat higher estimates of climate sensitivity. A 5-year trailing average yields a sensitivity of 0.277 (+/- 0.021) C per watt/M^2, a 10 year trailing average yields a sensitivity of 0.289 (+/- 0.022) C per watt/M^2, and a 20 year trailing average yields a sensitivity of 0.318 (+/- 0.025) C per watt/M^2, ~18% higher than a two year trailing average. As discussed above, very long lags (eg. 10-20+ years) appear inconsistent with recent trends in ocean heat content and average surface temperatures.
Oscillation in the radiative forcing curve (the green curve in Figure 1) is due to solar intensity variation over the sunspot cycle. The assumed total variation in solar intensity at the top of the atmosphere is 1 watt per square meter (approximately the average variation measured over the last three solar cycles) for a change in sunspot number of 140. Assuming a minimum solar intensity of 1365 watts per square meter and Earth’s albedo at 30%, the average solar intensity over the entire Earth surface at zero sunspots is (1365/4) * 0.7 = 238.875 watts per square meter, while at a sunspot number of 140, the average intensity increases to 239.05 watts per square meter, or an increase of 0.175 watt per square meter. The expected change in radiative forcing (a “sunspot constant”) is therefore 0.175/140 = 0.00125 watt per square meter per sunspot. When different values for this constant are tried in the model, the best fit to the data (maximum R^2) is for ~0.0012 watt/M^2 per sunspot, close to the above calculated value of 0.00125 watt/M^2 per sunspot.
Regional Sensitivities
Amplification of sensitivity is the ratio of the actual climate sensitivity to the sensitivity expected for a blackbody emitter. The sensitivity from the model is 0.270 C per watt/M^2, while the expected blackbody sensitivity is 0.267 C per watt/M^2, so the amplification is 1.011. An amplification very close to 1 suggests that all the negative and positive feed-backs within the climate system are roughly balanced, and that the average surface temperature of the Earth increases or decreases approximately as would a blackbody emitter subjected to small variations around the average solar intensity of ~239 watts/M^2 (that is, as a blackbody would vary in temperature around ~255 K). This does not preclude a range of sensitivities within the climate system that average out to ~0.270 C per watt/M^2; sensitivity may vary based on season, latitude, local geography, albedo/land use, weather patterns, and other factors. The temperature increase due to WMGG’s may have, and indeed, should have, significant regional and temporal differences, so the importance of warming driven by WMGG’s should also have significant regional and temporal differences.
Credibility of Model Projections
Some may argue that any curve fit model based on historical data is likely to fail in making accurate predictions, since the conditions that applied during the hind cast period may be significantly different from those in the future. But if the curve fit model includes all important variables, then it ought to make reasonable predictions, at least until/unless important new variables are encountered in the future. Examples of important new climate variables are a major volcanic eruption or a significant change in ocean circulation. The probability of encountering important new variables increases with the length of the forecast, of course. So while a curve-fit climate model’s predictions will have considerable uncertainty far in the future (eg 100 years or more), forecasts of shorter periods are likely to be more accurate.
To demonstrate this, the model constants were calculated using temperature, WMGG forcings, AMO, and Nino3.4 data for 1871 to 1971, but then applied to all the 1871 to 2008 data (Figure 4). The model’s calculated temperatures represent a ‘forecast’ from 1972 through 2008, or 36 years. Since the model constants came only from pre-1972 data, the model has no ‘knowledge’ of the temperature history after 1971, and the 1972 to 2008 forecast is a legitimate test of the model’s performance. The model’s 1972 to 2008 forecast performance is reasonably good, with very similar deviations between the model and the historical temperature record in the hind cast and forecast periods.
The model fit to the temperature data in the forecast period is no worse than in the hind cast period. The climate sensitivity calculated using only 1871 to 1971 data is similar to that calculated using the entire data set: 0.255 C per watt/M^2 versus 0.270 C per watt/M^2. A model forecast starting in 2009 will not be perfect, but the 1972 to 2008 forecast performance suggests that it should be reasonably close to correct over the next 36+ years.
Emissions Scenarios
The model projections in Figure 1 (2009 to 2060) are based on the following assumptions:
a) The year on year increase in CO2 concentration in the atmosphere rises to 2.6 PPM per year by 2015 (or about 25% higher than recent rates of increase), and then remains at 2.6 PPM per year through 2060. Atmospheric concentration reaches ~518 PPM by 2060.
b) N2O concentration increases in proportion to the increase in CO2.
c) CFC’s decrease by 0.25% per year. The actual rate of decline ought to be faster than this, but large increases in releases of short-lived refrigerants like R-134a and non-regulated fluorinated compounds may offset a large portion of the decline in regulated CFC’s.
d) The concentration of methane, which has been constant for the last ~7 years at ~1,800 parts per billion, increases by 10 PPB per year, reaching ~2,370 PPB by 2060.
e) Tropospheric ozone (which forms in part from volatile organic compounds, VOC’s) increases in proportion to increases in atmospheric CO2.
The above represent pretty much a “business as usual” scenario, with fossil fuel consumption in 2060 more than 70% higher than in 2008, and with no new controls placed on other WMGG’s. The projected temperature increase from 2008 to 2060 is 0.6834 C, or 0.131 C per decade. This assumes of course that WMGG’s are responsible for all (or nearly all) the warming since 1871; if a significant amount of the warming since 1871 had other causes, then future warming driven by WMGG’s will be less.
Separation of the different contributions to radiative forcing allows projections of future average temperatures under different scenarios for reductions in the growth of fossil fuel usage, with separate efforts to control emissions of methane, N2O, and VOC’s (leading to tropospheric ozone).
One such scenario can be called the “Efficient Controls” scenario. The year on year increase in CO2 in the atmosphere rises to 2.6 PPM by 2014, and then declines starting in 2015 by 0.5% per year (that is, 2.6 PPM increase in 2014, 2.587 PPM increase in 2015, 2.574 PPM increase in 2016, etc.), methane concentrations are maintained at current levels via controls installed on known sources, CFC concentration falls by 0.5% per year due to new restrictions on currently non-regulated compounds, and N2O and tropospheric ozone increases are proportional to the (somewhat lower) CO2 increases. These are far from small changes, but probably could be achieved without great economic cost by shifting most electric power production to nuclear (or non-fossil alternatives where economically viable), and simultaneously taxing CO2 emissions worldwide at an initially low but gradually increasing rate to promote worldwide improvements in energy efficiency. Under these conditions, the predicted temperature anomaly in 2060 is 0.91 degree (versus 0.34 degree in 2008), or a rise of 0.109 degree per decade. Atmospheric CO2 would reach ~507 PPM by 2060, and CO2 emissions in 2060 would be about 50% above 2008 emissions. By comparison, the “business as usual” case produces a projected increase of 0.131 C per decade through 2060, and atmospheric CO2 reaches ~519 PPM by 2060. So at (relatively) low cost, warming through 2060 could be reduced by a little over 0.11 C compared to business as usual.
A “Draconian Controls” scenario, with new controls on fluorinated compounds, methane and VOC’s, and with the rate of atmospheric CO2 increase declining by 2% each year, starting in 2015, shows the expected results of a very aggressive worldwide program to control CO2 emissions. The temperature anomaly in 2060 is projected at 0.8 C, for a rate of temperature rise through 2060 of 0.088 degree per decade, or ~0.11 C lower temperature in 2060 than for the “Efficient Controls” scenario. Under this scenario, the concentration of CO2 in the atmosphere would reach ~480 PPM by 2060, but would rise only ~25 PPM more between 2060 and 2100. Total CO2 emissions in 2060 would be ~15% above 2008 emissions, but would have to decline to the 2008 level by 2100. Whether the potentially large economic costs of draconian emissions reductions are justified by a ~0.11C temperature reduction in 2060 is a political question that should be carefully weighed.
Conclusions
The model shows that the climate sensitivity to radiative forcing is approximately 0.27 degree per watt/M^2, based on the assumption that radiative forcing from WMGG’s has caused all or nearly all the measured temperature increase since ~1871. This corresponds to response of ~1C for a doubling of CO2 (with other WMGG’s remaining constant). Much higher climate sensitivities (eg. 0.5 to >1.0 C per watt/M^2, or 1.85 C to >3.71 C for a doubling of CO2) appear to be inconsistent with the historical record of temperature and measured increases in WMGG’s.
Assuming no significant changes in the growth pattern of fossil fuels, and no additional controls on other WMGG’s, the average temperature in 2060 may reach ~0.68C higher than the 2008 average. Modest steps to control non-CO2 emissions and gradually reduce the rate of increase in the concentration of CO2 in the atmosphere could yield a reduction in WMGG driven warming between 2008 and 2060 of ~15% compared to no action. A rapid reduction in the rate of growth of atmospheric CO2 would be required to reduce WMGG driven warming between 2008 and 2060 by ~30% compared to no action.
tallbloke (08:41:07) :
I thought you said there was still much work to do on the C19th, which is why I offered to help with the digitisation of the records. Why not put the data and methodology up for discussion in a post so we can all discuss it properly on a separate thread?
I may not have been clear. It is not that there are holes that are not covered. The HMF B can be determined from only a few stations and the correction to the sunspot numbers also can be done with only a few stations [because all stations basically show the same]. My goal is to digitize ALL stations anyway, so that the data does not disappear [as it is beginning to do: yearbooks crumble, libraries burn or are flooded, or old books just thrown out].
tallbloke (08:41:07) :
Why not put the data and methodology up for discussion in a post so we can all discuss it properly on a separate thread?
There are two separate issues: HMF B and the Sunspot number.
The HMF B methodology is described here:
http://www.leif.org/research/The%20IDV%20index%20-%20its%20derivation%20and%20use.pdf
The sunspot methodology was described by Rudolf Wolf in the 1850s. A modern version is here:
http://www.leif.org/research/Napa%20Solar%20Cycle%2024.pdf
The data are very voluminous. Some are in public archives, others only in my 5 Gigabyte database. You can see some of the original data here: http://www.leif.org/research/todo/
The problem is not only in entering or OCRing the data, but also in correcting them [yes] and interpreting them correctly. As an example of the subtle issues, see http://www.leif.org/research/todo/api_1905_11_h.PDF
look at row 15 over to the right, where the value 596 look like an error, but really isn’t. The secret is in the 0.356 near the top just under Tagesm. It means that the real data value is 0.356+the table entries/100,000, except for row 15 it should be interpreted as 0.35+the table entry/100,000.
Leif Svalgaard (08:39:09) :
1875-1930 1365.78
1930-2009 1365.94
Well since the sunspot number averages went from 42 to 73 over the same periods, maybe TSI doesn’t match the sunspot numbers too well after all. There again, your TSI numbers are a back extrapolation, whereas the sunspot numbers are the sunspot numbers. If you’re right, it’s amazing that such a small increase in TSI could cause such an acceleration in the thermal expansion of the oceans, and would imply a truly remarkable sensitivity of the Earth to small changes in insolation. It would certainly put co2’s claimed sensitivity in the shade. I’ll run some calcs on your figures and see what it implies.
To be continued on your own thread I hope.
Leif, sorry, not a back extrapolation, a reconstruction using the magnetic field measurements as a proxy. I’d very much like to learn more about the interaction of the Earth’s magnetic field with the solar fields so I can better understand your methodology.
I agree the old records need preserving. I’ll email you soon about my offer of help.
Jacob, as you can see, discussions are best kept to the thread topic and narrowly focused. Your last post was a jumbled mess when judged against more successful debating techniques. Just pick one thing from your post, one component of AGW that you feel strongly about. Keep it tied to the thread which has to do with how sensitive the earth’s natural climate is to small changes. CO2 is a very small change. Pick something about the warming cycle of CO2. And we will go from there. Bear in mind that we like to stick with observed and measured data, not modeled scenarios. However, modeled scenarios can be compared to measured data. There are sources for clouds, vapor, CO2, LWR, SWR, SST, ENSO, you name it, that can be compared to the modeled scenarios. To remind you, the least amount of temperature rise in the scenarios provided by Hansen, IPCC, and the like assumed strict controlled reduction of CO2, which of course has not occured. So the model we will compare to will be the one that is based on and closest to the current estimated CO2 level.
tallbloke (08:49:26) :
“Leif’s law: if data agree with your view they are good.”
There are a couple of different ways that comment can be understood.
I think not. What I have found [empirically] is that people [like you] would gladly use a dataset [even if dubious] if the data agree with their own pet theory, and tend to spread FUD on other data.
I have even come across the following argument: ‘since it is obvious that variation of the Sun is the main [perhaps, sole] driver of climate, the fact that there is climate change proves that the Sun changes accordingly’. From your posts one can only conclude that you wholeheartedly subscribe to that argument, e.g.: “This gives the false impression that the climate is insensitive to changes in insolation levels. Nothing could be farther from the truth.”
BTW, you used the weasel word ‘insolation levels’. Solar insolation drives glaciations, so you are correct about that straw man, but it is irrelevant, because the discussion was not about the large changes in insolation, but about the minute changes in irradiance.
tallbloke (09:14:32) :
Well since the sunspot number averages went from 42 to 73 over the same periods, maybe TSI doesn’t match the sunspot numbers too well after all.
When the sunspot number changes by 150, TSI changes by 1.3 W/m2, so 1 spot means a change of 0.009 W/m2, so 73-42 = 31 spots equates to a change of 0.27 W/m2 [but I also think that the SSN should be 50 not 42]. I had 0.16, Preminger has 0.22 W/m2 [to 2004, so a tad smaller if to 2009] based on Greenwich Sunspot Area.
So tiny tiny numbers.
If you’re right, it’s amazing that such a small increase in TSI could cause such an acceleration in the thermal expansion of the oceans, and would imply a truly remarkable sensitivity of the Earth to small changes in insolation
A simpler and much likely explanation [without truly remarkable amazement] is that there is no causal relation [apart at the hundredth of a degree level] between irradiance [don’t use insolation as that changes a lot ~20W/m2 during a year] and temperatures.
“”” Jacob Mack (19:43:19) :
Please see below, regarding the higher volume of salt water due to its higher density. (D=M/V) Again the differences in water’s physical characteristics due to salinity cannot be ignored.
“In a paper titled “The Melting of Floating Ice will Raise the Ocean Level” submitted to Geophysical Journal International, Noerdlinger demonstrates that melt water from sea ice and floating ice shelves could add 2.6% more water to the ocean than the water displaced by the ice, or the equivalent of approximately 4 centimeters (1.57 inches) of sea-level rise.
The common misconception that floating ice won’t increase sea level when it melts occurs because the difference in density between fresh water and salt water is not taken into consideration. Archimedes’ Principle states that an object immersed in a fluid is buoyed up by a force equal to the weight of the fluid it displaces. However, Noerdlinger notes that because freshwater is not as dense as saltwater, freshwater actually has greater volume than an equivalent weight of saltwater. Thus, when freshwater ice melts in the ocean, it contributes a greater volume of melt water than it originally displaced.” “””
Well somehow, I don’t think your predictions will come about. Sea water is certainly denser than fresh water, which in turn is denser than ice. But there’s that slight problem of cooling. Each gram of ice that melts extracts 80 calories from the surrounding ocean water (remember most of the floating ice is actually submerged and surrounded by sea water, so a huge volume of sea water is cooled as that ice melts.
If you mix equal masses of zero deg C ice, and 80 deg C hot water (both fresh), then entire mass of water will reach zero deg C when all the ice is melted.
Also the interior of the sea ice (that initially froze out of the sea) contains pockets of salty brines, so as the ice melts a lot of salt is added to the solution, so ti doesn’t stay fresh very long.
So long as the salinity remains above 2.47% (normal is about 3.5%), the sea water has a positive temperature coefficient of expansion so cooling it reduces the volume, and the sea level will actually go down when the floating sea ice melts; not up.
I made such a prediction in mid 2004, which was published in Jan 2005. In mid 2006 a British Dutch team using a European polar satellite reported on ten years of measurements of the Arctic ocean sea levels; and they reported that it was dropping at a rate of 2 mm per year. they also said they didn’t know why; but they were very coinfident of their numbers.
So now you know why; that period was a period of Arctic sea Ice retreat, and the sea level dutifully declined.
Jacob, maybe you were confusing ice sheets on land that melt into the sea? Greenland ice sheets and Antarctic ice sheets would raise sea levels if they melted completely off and into the sea. The stuff that is floating, be it land-attached floating ice sheets, ice bergs from glaciers, or sea ice, just does not have the potential for catastrophic sea level rise. And of the two, Greenland is the only one historically that has the greater potential of actually melting.
The pretty colored global long wave radiation map at the top of this essay covers a range from 100 to 350 W/m^2.
This is a bit weird since the NOAA official earth energy budget says the average for the globe is 390 w/m^2 corresponding to a +15 deg C BB temperature number.
Actually the 100 to 350 range corresponds to BB temperatures from 204.9 K up to 280.3 K; -68.2 deg C up to a whopping +7.2 deg C
If you actually cover the entire observed earth surface temperature range from about -90 deg C to +60 deg C then the LWIR emission would be more like 63.8 W/m^2 up to 698.5W/m^2 for the extremes; which is about an 11 to 1 range.
It would be nice if they could measure and report the extreme values, instead of some homogenized average values. That would be good; if for no other reason than the actual spectral peak wavelength changes by a factor of 1.82 over that temperature extreme range, which has a decided influence on the impact that CO2 has.
If they do enough averaging; pretty soon nothing at all ever changes.
Jacob Mack (21:20:39) :
It is honestly difficult for me to follow what this post was trying to say, except that you appear to believe the results of all the different climate models at the same time (even though those results are substantially different from each other), and everything the IPCC says as well. Let’s try to narrow it down to a couple of issues at a time.
1. Let’s start with ocean heat accumulation. Do you believe the multiple publications showing essentially flat to very slightly falling heat in the top 700 meters of ocean from 2003 to 2008 are not correct? If so, please explain why you think that. If you accept the results of these studies, then do you agree that ocean heat accumulation is the most accurate way to measure global warming over any specified period of time? If you do not think so, then please explain why not and what other metric you think might be a more reliable gauge of warming.
2. Now please consider aerosol effects. Do you believe the multiple studies that have shown a significant reduction in atmospheric aerosols since about 1993? If you do not believe these studies, then please explain why not. If you accept that there has in fact been a significant “global brightening” since the early 1990’s (net intensity of full sunlight reaching the Earth’s surface has increased), then does this not suggest that, whatever the net “canceling” of aerosols on radiative forcing may have been in the early 1990’s, that canceling effect has already declined significantly? If not, please explain why.
If we can limit discussion to at most a few topics at once, it will be easier to make progress. There is no reason to not cover a wide range of topics, but it is almost not possible to jointly address all at once. More efficient to explore specific differences in understanding and try to understand from where those difference arise.
Leif Svalgaard (10:11:09) :
A simpler and much likely explanation [without truly remarkable amazement] is that there is no causal relation [apart at the hundredth of a degree level] between irradiance [don’t use insolation as that changes a lot ~20W/m2 during a year] and temperatures.
Well I already explained why surface temperature isn’t such a good indicator. Let’s stick with oceanic thermal expansion for a while, because there’s no argument about what causes that.
When I last had the calculator hot on this I seem to remember I summed that the ocean retained something like 2.5% of the insolation over the 1993-2003 period. So, if you are right, there is a pretty big terrestrial factor to be accounted for. Nir Shaviv suspects decadal changes in cloud cover, and the ISCCP data hints at multidecadal changes too. There seems to be scope for an accomadation for both our data and theories.
One of the best proxies for warming would be how much SWR gets in, how much LWR gets out and what the Net Radiation Budget is. I would caution here to get actual values, not anomolies. And don’t compare to a mean. Just use actual data.
Here are the combinations:
1. If more SWR gets in and less LWR gets out we get warmer.
2. If more SWR gets in and more LWR gets out we might stay the same.
3. If less SWR gets in and less SWR gets out we might stay the same.
4. If less SWR gets in and more LWR gets out we get colder.
What are the entities that influence how much gets in and how much gets out? That depends on whether it is short wave or long wave. I have started the list. By the way, did you know that taken as a whole, clouds have a greater cooling affect by a magnitude of 3 times greater than a doubling of CO2 concentration has? Think what it would be if clouds were doubled.
“The latest results from ERBE indicate that in the global mean, clouds reduce the radiative heating of the planet. This cooling is a function of season and ranges from approximately -13 to -21 Wm-2. While these values may seem small, they should be compared with the 4 Wm-2 heating predicted by a doubling of carbon dioxide concentration.”
A. SWR
1. reflecting particles such as water droplets in clouds
B. LWR
2. absorbing substances such as GHG’s, with water vapor being one
Leif Svalgaard (09:53:09) :
What I have found [empirically] is that people [like you] would gladly use a dataset [even if dubious] if the data agree with their own pet theory, and tend to spread FUD on other data.
People like you say to-mate-o
I say to-mart-o
Let’s agree to disagree rather than resort to incivilities and accusations about motivation for which you have not one jot of evidence. We are both seeking scientific truth, I would hope.
the discussion was not about the large changes in insolation, but about the minute changes in irradiance.
The way I thought it went was that irradiance was what arrived at the top of the atmosphere, and insolation was what hit the weasel on the ground, whatever the timescale.
By the way, how do you tell the difference between a weasel and a stoat?
Reply: A weasel’s weaselly recognized. A stoat’s stoatally different. ~ ctm
Here is the html address that I took the quote above from. I couldn’t get the pdf address to work.
http://74.125.47.132/search?q=cache:Tq_M95uf-x8J:asd-www.larc.nasa.gov/~tak/wong/f20m.pdf+DECADAL+VARIABILITY+OF+EARTH+RADIATION+BUDGET+DEDUCED+FROM+SATELLITE+ALTITUDE+CORRECTED+ERBE/ERBS+NONSCANNER+DATA&cd=3&hl=en&ct=clnk&gl=us&client=firefox-a
@tallbloke
Instead of looking for a link to TSI, perhaps worth your while trying to quantify how the proportion of the different wavelengrths that make up TSI, change from solar max to min effect ocean heating.
High energy UV increases at max penetrates the ocean quite deeply. I think X-ray and other highly energetic frequencies may also be worth a look?
Pamela Gray (12:19:39) :
1. If more SWR gets in and less LWR gets out we get warmer.
What are the entities that influence how much gets in and how much gets out?
Sound analysis Pamela.
Looking at the graphs produced by Bob Tisdale of the way Outgoing LWR plunges by 70W/m^2 in the tropics when big el nino’s occur, it looks to me like water vapour is the major player in town.
OOPS!!!! The quote came from here:
http://cimss.ssec.wisc.edu/wxwise/homerbe.html
If anomalies are the only way to go, there is a tremendous seasonal influence in the Net Radiative Budget so 3 month data sets, like that used with ONI SST measures, would be appropriate. For example JFM, FMA, MAM, AMJ, MJJ, JJA, etc. That is because the Earth does not care to adhere to the 1st through the 30th dates. Overlapping 3-month averages does a better job of taking into account that mother Earth isn’t on a strict 30 day calendar. If all the data sets could thus be arranged, this would make them comparable across sets. Wonder who we could get to do that for the temperature series and all the other series we like to talk about. The thing that makes this valuable is that the results don’t care how much the Sun puts out or varies. The analysis cares about how much of the Sun’s rays we actually get at the surface where it counts.
By the way, the wrong reference I put in my above post 12:27:47 is a VERY interesting read on how satellites can screw up in more than just their drift.
To further clarify terminology in case we want to discuss this:
Positive Net Radiative Budget: Less LWR than SWR.
Negative Net Radiative Budget: More LWR than SWR.
However, the overall forcing should be measured as well as the difference. That is because if two negative budgets have the same difference but are greater (or lesser) in absolute value, that might make a difference in just how warm or cold we get. This kind of measure would also be useful as a double check of surface station test results. If we can ever get the eyes in the skies to do it right. When measuring LWR and SWR, the angle of the device as well as its drift makes a difference in terms of what it measures and introduces artifact that has to be calculated out of the resultant data being beamed back to Earth.
I am on a learning curve here so if you see something I have posted that is in error, please correct me.
The premise I believe regarding anthropogenic CO2 warming is that less LWR will escape from GHG’s. That means that while SWR may have a free pass, LWR will never have a free pass. There seems an opportunity to clearly and finally refute the idea that according to the AGW theory, we should be seeing less and less LWR escaping out to space in lock step with proxied CO2 increases. Can we get a graph going of CO2 (anthropogenic only if possible please) increases and LWR measures since the satellite record began?
Steve, some bodies of water are cooling while others are not. The magnitude of cooling previosuly reported is far higher than it has now been found. Ocean cooling is normal and was predicted many years ago. The Earth is still warming rigtht now. More concise and to the point.
My proposal to compare LWR with CO2 increase has been done. We need to replicate since the author is a controversial one. LWR has gone up, not down, during the study period. Let’s see if we can critique this study.
http://scienceandpublicpolicy.org/images/stories/papers/originals/mar_09_co2_report.pdf
Jacob Mack (13:46:36) :
“Steve, some bodies of water are cooling while others are not. The magnitude of cooling previosuly reported is far higher than it has now been found. Ocean cooling is normal and was predicted many years ago. The Earth is still warming rigtht now. More concise and to the point.”
So do you think the reported slight net cooling from 2003 to 2008 actually happened or not? How can the Earth be warming if the oceans are not accumulating heat? On what basis can you make that claim?