Bonam Posted February 4, 2015 Report Share Posted February 4, 2015 You seem to think you need to be a rocket scientist to figure out what you're saying but any grade school drop-out could do that. Apparently not, since neither you nor cybercoma got the gist, instead replying with reflexive political reactions unrelated to what was actually said. Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 4, 2015 Author Report Share Posted February 4, 2015 We see climate alarmism religion in your text which pretty much sums everything else up at a glance. You seem to think you need to be a rocket scientist to figure out what you're saying but any grade school drop-out could do that. So you just scan for a few words and then base what you think off that? Your last post also contains 'climate alarmism religion' in its text, does that sum up everything else at a glance? Quote Link to comment Share on other sites More sharing options...

eyeball Posted February 4, 2015 Report Share Posted February 4, 2015 Apparently not, since neither you nor cybercoma got the gist, instead replying with reflexive political reactions unrelated to what was actually said. The gist seems pretty simple to me, the value determining the climate sensitivity that's alarming church-goers and threatening the global economy is wrong and ipi apparently knows what the correct value is. So...I just kinda wonder why ipi wouldn't announce something so important that it might reassure an alarmed world and save it's economy in a scientific journal instead of some dim back alley of the Internet. It begs a reflexive mocking reaction. Quote Link to comment Share on other sites More sharing options...

Bonam Posted February 4, 2015 Report Share Posted February 4, 2015 Except of course that he is suggesting that the sensitivity is higher than "consensus estimates", not lower. Meaning that if one was going to be alarmed by increasing temperatures due to CO2, and believes that his calculations are correct, one should be more alarmed, not reassured. Quote Link to comment Share on other sites More sharing options...

eyeball Posted February 4, 2015 Report Share Posted February 4, 2015 So you just scan for a few words and then base what you think off that? Pretty much. But you do seem quite balanced on other files like the ME so... Your last post also contains 'climate alarmism religion' in its text, does that sum up everything else at a glance? Hmmmm. The stupid questions always are the hardest one's to answer but I'll give it a whirl... The answer is... ...4? Quote Link to comment Share on other sites More sharing options...

eyeball Posted February 4, 2015 Report Share Posted February 4, 2015 Except of course that he is suggesting that the sensitivity is higher than "consensus estimates", not lower. Meaning that if one was going to be alarmed by increasing temperatures due to CO2, and believes that his calculations are correct, one should be more alarmed, not reassured. Yes but, apparently this is good news - to the point that he's said we should be spending money to subsidize the production of more CO2 not less. I repeat, when does the rest of the planet get to Hear the Good News? Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 4, 2015 Author Report Share Posted February 4, 2015 Yes but, apparently this is good news - to the point that he's said we should be spending money to subsidize the production of more CO2 not less. You really can't understand a conditional statement, can you? In the past, I said that if the marginal external effect of CO2 emissions on the environment + humanity is positive and significant then CO2 should be subsidized and if it is negative and significant then it should be taxed. The economic theory that many are using to justify a CO2 emission tax is the same theory that suggests a subsidy may be a good idea if the marginal effect is positive. It remains to be demonstrated to me if the marginal effect is negative or positive; though I suspect that initially it is positive and then at some point it becomes negative once CO2 concentrations get high enough. Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 4, 2015 Author Report Share Posted February 4, 2015 (edited) @ TimG - If you liked the Loehle approach/model then you might like this CSALT model since it also tries to approach the question of climate sensitivity via time series data, but the analysis is better and it uses more empirical data. http://contextearth.com/2013/10/26/csalt-model/ Edit: It also gets an equilibrium climate sensitivity of approximately 3 C. Edited February 4, 2015 by -1=e^ipi Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 4, 2015 Author Report Share Posted February 4, 2015 (edited) Except of course that he is suggesting that the sensitivity is higher than "consensus estimates", not lower. Well more accurately, I would say that the consensus estimates are validated. The uncertainty should be large enough such that any difference between an equilibrium climate sensitivity of ~3C and the corrections to Loehle's estimate is statistically insignificant. Of course, I would have to use solar irradiance data to make the correction to Loehle to see if this is the case. Edited February 4, 2015 by -1=e^ipi Quote Link to comment Share on other sites More sharing options...

TimG Posted February 4, 2015 Report Share Posted February 4, 2015 @ TimG - If you liked the Loehle approach/model then you might like this CSALT model since it also tries to approach the question of climate sensitivity via time series data, but the analysis is better and it uses more empirical data.Looks more like a curve fitting exercise than an empirically driven analysis. FWIW, I don't see the Loehle approach as the definitive work on the topic. I see it as a way to determine the a lower bound on sensitivity. Quote Link to comment Share on other sites More sharing options...

Bonam Posted February 4, 2015 Report Share Posted February 4, 2015 Well more accurately, I would say that the consensus estimates are validated. Sorry, should have been more specific... higher than the Loehle paper you reference. Quote Link to comment Share on other sites More sharing options...

TimG Posted February 4, 2015 Report Share Posted February 4, 2015 Sorry, should have been more specific... higher than the Loehle paper you reference.Loehle is a vocal skeptic. He pushes the scientific envelope as far as he can to support the skeptical view. This bias needs to be noted when looking at his work. Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 5, 2015 Author Report Share Posted February 5, 2015 (edited) Looks more like a curve fitting exercise than an empirically driven analysis. Interestingly, 'curve-fitting exercise' is exactly what Cawley et al. said about the Loehle paper. http://www.sciencedirect.com/science/article/pii/S0304380014004876 FWIW, I don't see the Loehle approach as the definitive work on the topic. I see it as a way to determine the a lower bound on sensitivity. But why do you consider it a lower bound? Just because it is lower than what other papers say? It is hard to refute the data, and if the methodology were good and relatively simple then it would make sense to reject the other papers (which have a lot more assumptions that may bias their estimates upwards or underestimate the error of these estimates) and accept the Loehle paper. Also, even if you did consider Loehle a lower estimate, that doesn't suggest you should treat 1.99 C as the lower bound. Rather you should do that for the lower end of the 95% confidence interval (which would be 1.51 C assuming that Cawley is right about Loehle understating the error by a factor of 2). Loehle is a vocal skeptic. He pushes the scientific envelope as far as he can to support the skeptical view. This bias needs to be noted when looking at his work. I think it's better to directly look at the science and the assumptions made. Loehle being a skeptic doesn't mean he is biased or wrong. Rather, the simplifying assumptions make the paper biased and inaccurate as I showed. Edited February 5, 2015 by -1=e^ipi Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 5, 2015 Author Report Share Posted February 5, 2015 As for the CSALT model, I think you miss its relevance here. It shows that the majority of the variability in the climate that isn't explained by the Loehle paper or by my corrections can be explained by things like volcanic activity, variations in solar irradiance, North Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation. Now arguably, Loehle does take into account most of the two ocean oscillations using his fitting technique for 20-year and 60-year sinusoidal waves; but as Cawley pointed out, the 20-year and 60-year periods may not be accurate and as the CSALT model shows, it is probably far better to input the empirical data directly for these oscillations. I do have a few issues with the CSALT model though. The first is that the choice of lags is somewhat arbitrary and does not have much physical justification. The second is that I think that the temperature of the Earth should go into the regression model since the closer you get to the equilibrium temperature, the lower the magnitude of temperature change. The third is that putting in both the length of day variation and the Southern Oscillation Index are very strongly correlated; so I'm a bit skeptical of putting them both in the regression model. Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 5, 2015 Author Report Share Posted February 5, 2015 (edited) Actually, there is a really simple way to combine the approach I did in the original post with the CSALT approach. If we start with the basic assumption that temperature change is proportional to the difference between the equilibrium temperature and the current temperature: dT/dt = k(Te(t) - T(t)), where k is some constant, t is time, Te is the equilibrium temperature, and T is the current temperature. And then we suppose that the equilibrium temperature depends linearly on the logarithm of CO2, volcanic aerosols, solar irradiance, the Atlantic MultiDecadal Oscillation, and the Pacific Decadal Oscillation (so we complicate my model in the original post by adding the other explanatory factors mentioned in CSALT) then we get: Te(t) = A + B*ln(CO2(t)) + C*Irradiance(t) + D*Aerosols(t) + E*AMO(t) + F*PDO(t), where CO2 is the atmospheric CO2, Irradiance is the solar irradiance, Aerosols is the volcanic aerosols, AMO is the Atlantic MultiDecadal Oscillation, PDO is the Pacific Decadal Oscillation, and A, B, C, D, E, and F are all constants. So then you get dT/dt = G + H*ln(CO2(t)) + I*Irradiance(t) + J*Aerosols(t) + K*AMO(t) + L*PDO(t) - kT(t) , where G, H, I, J, K and L are constants. This is basically the CSALT model derived from the Gibbs energy formulation but with temperature added as an explanatory variable. So all you would need to do is perform a simple linear regression to estimate this model. And you can directly get the equilibrium climate sensitivity from this; it is H/k*ln(2). Not to mention the error of this estimate is really easy to obtain. So you don't even need fancy general circulation models that run on super computers, try to directly evaluate the effects of various feedback mechanisms (such as clouds or the lapse rate, which can be difficult), or do crazy differential calculus that I was doing earlier in order to obtain a good estimate of climate sensitivity. Just perform a simple linear regression then divide two numbers and multiply by ln(2). It's so simple! In addition, you could probably use this model to make predictions about the future given an emission scenario. For the natural fluctuations in solar irradiance, the Pacific Decadal Oscillation, or the Atlantic Multidecadal Oscillation, you could just perform fourier analysis on each of these, take note of the peak frequencies, fit sinusoidal waves of these peak frequencies to the natural fluctuations to use as an estimate of future natural fluctuations, and then put that in your model to get future global temperature. Edited February 5, 2015 by -1=e^ipi Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 5, 2015 Author Report Share Posted February 5, 2015 Also, I just realized that I made a mistake by saying that the assumption of constant decay towards equilibrium will result in an overestimation of the equilibrium climate sensitivity (ECS). It would result in an overestimation of earth system sensitivity (ESS) if I interpreted the equilibrium to mean the ESS rather than the ECS. Sorry, I must have confused ECS with ESS. In any case, there is no reason to expect that 2.95 C is an overestimate of ECS due to the assumption of constant decay towards equilibrium. Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 7, 2015 Author Report Share Posted February 7, 2015 Update: I had some time so I got data sets on Temperature, CO2 concentrations, North Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation, El Nino Southern Oscillation, Length of Day, Solar Irradiance and Stratospheric Aerosol Optical Thickness that all covered the years 1876-2014. I performed the regression and got an equilibrium climate sensitivity with a 95% confidence interval of (1.98 +/- 0.92) Celcius. I could probably do some minor tweaking (particularly get better solar irradiance data) to improve the estimate. The estimate of the characteristic decay time is way too low, so the assumption of constant decay might be biasing the climate sensitivity estimate downwards. I might have to start considering different decay times for different mechanisms; maybe I should look at the response functions of GCMs. Quote Link to comment Share on other sites More sharing options...

On Guard for Thee Posted February 8, 2015 Report Share Posted February 8, 2015 Update: I had some time so I got data sets on Temperature, CO2 concentrations, North Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation, El Nino Southern Oscillation, Length of Day, Solar Irradiance and Stratospheric Aerosol Optical Thickness that all covered the years 1876-2014. I performed the regression and got an equilibrium climate sensitivity with a 95% confidence interval of (1.98 +/- 0.92) Celcius. I could probably do some minor tweaking (particularly get better solar irradiance data) to improve the estimate. The estimate of the characteristic decay time is way too low, so the assumption of constant decay might be biasing the climate sensitivity estimate downwards. I might have to start considering different decay times for different mechanisms; maybe I should look at the response functions of GCMs. And it all boils down to telling you GW is actually happening. Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 8, 2015 Author Report Share Posted February 8, 2015 (edited) I figured out a way to estimate a characteristic heat sink temperature of the Earth. If we think about changes in CO2 concentrations over time, there are 4 main reasons why this can occur. Humans, Volcanoes, changes in the temperature of Oceans + Permafrost regions (which I will refer to as the characteristic heat sink of Earth), and other natural factors. Human Emissions, which are available: http://cdiac.ornl.gov/trends/emis/tre_glob.html Volcanic Emissions, which should be roughly proportional to volcanic aerosol emissions. Aerosol data is available here: http://data.giss.nasa.gov/modelforce/strataer/tau.line_2012.12.txt Other natural factors, which we can approximate with a constant term and a linear term proportional to CO2 concentrations. I.e. (dCO2/dt)_natural = A + B*CO2, where A and B are constants. A linear function (or a polynomial of higher order) is needed because we know that the Earth was in rough equilibrium at the end of the little ice age. So if there are no human emissions, no changes in Solar activity, no changes in the temperature of the Earth and its oceans then we get: 0 = dCO2/dt = (dCO2/dt)_natural + (dCO2/dt)_volcanic + (dCO2/dt)_human + (dCO2/dt)_heat_sink = A + B*CO2 + (dCO2/dt)_volcanic + 0 + 0 => A = -B*270 - mean((dCO2/dt)_volcanic) since the Earth had ~270 ppm CO2 in preindustrial times. mean((dCO2/dt)_volcanic) would be the mean CO2 emissions from volcanoes. The tricky part is the CO2 emissions due to the characteristic heat sink of the Earth (oceans + permafrost). Since CO2 solubility in water decreases with temperature, and the the change in global mean temperature since preindustrial times is relatively small (0.8 C), the CO2 emissions due to the characteristic heat sink should be roughly proportional to the change in temperature. So one gets (dCO2/dt)_heat_sink = C*dTs/dt, where C is a constant, and dTs/dt is the change in the characteristic heat sink temperature with respect to time. Furthermore, since the change in temperature of the characteristic heat sink of the earth is primarily due to heat exchange with the surface of the Earth, and it is known that for conduction and convection that the rate of heat transfer is roughly proportional to the temperature difference, we get: (dCO2/dt)_heat_sink = C*D*(T - Ts), where D is a constant, T is the surface temperature, and Ts is the characteristic heat sink temperature. Unfortunately, the characteristic heat sink temperature is not known. Using the fact that total change in CO2 is the sum of the effects of humans, volcanoes, changes in the heat sink and other natural factors, one gets: dCO2/dt - (dCO2/dt)_human - (dCO2/dt)_volcano - A - B*CO2 = C*D*(T - Ts). (1) Alternatively, one can write this as: dCO2/dt - (dCO2/dt)_human - (dCO2/dt)_volcano - A - B*CO2 = C*dTs/dt. (2) Integrating the above equation from time = 0 to time = t gives: CO2(t) - CO2(0) - Integral(0 to t; (dCO2/dt)_human*dt) - Integral(0 to t;(dCO2/dt)_volcano*dt) - At - B*Integral(0 to t;CO2(t)*dt) = C*(Ts(t) - Ts(0)). (3) Edit: I make a very critical mistake here, which I don't realize until post #149. So some of the conclusions I get until post #149 are nonsense. Substituting (3) into (1) gives: dCO2/dt - (dCO2/dt)_human - (dCO2/dt)_volcano - A - B*CO2(t) = D*(CO2(t) - CO2(0) - Integral(0 to t; (dCO2/dt)_human*dt) - Integral(0 to t;(dCO2/dt)_volcano*dt) - At - B*Integral(0 to t;CO2(t)*dt)) Rearranging this gives: dCO2/dt = (A - D*CO2(0)) + (B + D)*CO2(t) + (dCO2/dt)_human + (dCO2/dt)_volcano + D*Integral(0 to t; (dCO2/dt)_human*dt) + D*Integral(0 to t; (dCO2/dt)_volcano*dt) - (AD)*t - (BD)*Integral(0 to t;CO2(t)*dt)) Now the change in CO2 ppm due to Humans is going to be proportional to human emissions. So (dCO2/dt)_human = E*CO2_emissions, where E is a constant. And the change in CO2 ppm due to volcanoes is going to be roughly proportional to volcanic aerosol emissions. So (dCO2/dt)_volcano = F*Volcanic_Aerosols, where F is a constant. So one gets dCO2/dt = (A - D*CO2(0)) + (B + D)*CO2(t) + E*CO2_emissions + F*Volcanic_Aerosols + E*D*Integral(0 to t; CO2_emissions*dt) + F*D*Integral(0 to t; Volcanic_Aerosols*dt) - (AD)*t - (BD)*Integral(0 to t;CO2(t)*dt)) So that means that one can estimate these unknown parameters using a simple linear regression. Note that there are 8 dependent variables but the coefficients are all combinations of A, B, D, E and F. This means that one should do a restricted regression with 3 restrictions. Edited February 12, 2015 by -1=e^ipi Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 8, 2015 Author Report Share Posted February 8, 2015 Nevermind. I can't do a simple linear regression because the restrictions are non-linear. So I would have to solve A, B, D, E, and F that minimize the variance directly from the 5 first order equations. Maybe I'll have time next week to do this. Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 10, 2015 Author Report Share Posted February 10, 2015 Tried to solve for the estimates that minimize the variance from the first order conditions. After a while, I realized that to get the estimates, I would need to solve polynomials of order 5 or higher. Then I remembered that by Abel's impossibility theorem, this isn't possible for arbitrary coefficients. Heck even trying to get estimates for A and B from a simple regression such as Y = AX + BY + ABZ + error is not possible due to Abel's impossibility theorem. http://en.wikipedia.org/wiki/Abel%E2%80%93Ruffini_theorem I guess I need to take a numerical approach. http://en.wikipedia.org/wiki/Non-linear_least_squares Quote Link to comment Share on other sites More sharing options...

On Guard for Thee Posted February 10, 2015 Report Share Posted February 10, 2015 Or maybe just listen to what real scientists are telling us and buy a Chevy Volt. Quote Link to comment Share on other sites More sharing options...

On Guard for Thee Posted February 10, 2015 Report Share Posted February 10, 2015 I figured out a way to estimate a characteristic heat sink temperature of the Earth. If we think about changes in CO2 concentrations over time, there are 4 main reasons why this can occur. Humans, Volcanoes, changes in the temperature of Oceans + Permafrost regions (which I will refer to as the characteristic heat sink of Earth), and other natural factors. Human Emissions, which are available: http://cdiac.ornl.gov/trends/emis/tre_glob.html Volcanic Emissions, which should be roughly proportional to volcanic aerosol emissions. Aerosol data is available here: http://data.giss.nasa.gov/modelforce/strataer/tau.line_2012.12.txt Other natural factors, which we can approximate with a constant term and a linear term proportional to CO2 concentrations. I.e. (dCO2/dt)_natural = A + B*CO2, where A and B are constants. A linear function (or a polynomial of higher order) is needed because we know that the Earth was in rough equilibrium at the end of the little ice age. So if there are no human emissions, no changes in Solar activity, no changes in the temperature of the Earth and its oceans then we get: 0 = dCO2/dt = (dCO2/dt)_natural + (dCO2/dt)_volcanic + (dCO2/dt)_human + (dCO2/dt)_heat_sink = A + B*CO2 + (dCO2/dt)_volcanic + 0 + 0 => A = -B*270 - mean((dCO2/dt)_volcanic) since the Earth had ~270 ppm CO2 in preindustrial times. mean((dCO2/dt)_volcanic) would be the mean CO2 emissions from volcanoes. The tricky part is the CO2 emissions due to the characteristic heat sink of the Earth (oceans + permafrost). Since CO2 solubility in water decreases with temperature, and the the change in global mean temperature since preindustrial times is relatively small (0.8 C), the CO2 emissions due to the characteristic heat sink should be roughly proportional to the change in temperature. So one gets (dCO2/dt)_heat_sink = C*dTs/dt, where C is a constant, and dTs/dt is the change in the characteristic heat sink temperature with respect to time. Furthermore, since the change in temperature of the characteristic heat sink of the earth is primarily due to heat exchange with the surface of the Earth, and it is known that for conduction and convection that the rate of heat transfer is roughly proportional to the temperature difference, we get: (dCO2/dt)_heat_sink = C*D*(T - Ts), where D is a constant, T is the surface temperature, and Ts is the characteristic heat sink temperature. Unfortunately, the characteristic heat sink temperature is not known. Using the fact that total change in CO2 is the sum of the effects of humans, volcanoes, changes in the heat sink and other natural factors, one gets: dCO2/dt - (dCO2/dt)_human - (dCO2/dt)_volcano - A - B*CO2 = C*D*(T - Ts). (1) Alternatively, one can write this as: dCO2/dt - (dCO2/dt)_human - (dCO2/dt)_volcano - A - B*CO2 = C*dTs/dt. (2) Integrating the above equation from time = 0 to time = t gives: CO2(t) - CO2(0) - Integral(0 to t; (dCO2/dt)_human*dt) - Integral(0 to t;(dCO2/dt)_volcano*dt) - At - B*Integral(0 to t;CO2(t)*dt) = C*(Ts(t) - Ts(0)). (3) Substituting (3) into (1) gives: dCO2/dt - (dCO2/dt)_human - (dCO2/dt)_volcano - A - B*CO2(t) = D*(CO2(t) - CO2(0) - Integral(0 to t; (dCO2/dt)_human*dt) - Integral(0 to t;(dCO2/dt)_volcano*dt) - At - B*Integral(0 to t;CO2(t)*dt)) Rearranging this gives: dCO2/dt = (A - D*CO2(0)) + (B + D)*CO2(t) + (dCO2/dt)_human + (dCO2/dt)_volcano + D*Integral(0 to t; (dCO2/dt)_human*dt) + D*Integral(0 to t; (dCO2/dt)_volcano*dt) - (AD)*t - (BD)*Integral(0 to t;CO2(t)*dt)) Now the change in CO2 ppm due to Humans is going to be proportional to human emissions. So (dCO2/dt)_human = E*CO2_emissions, where E is a constant. And the change in CO2 ppm due to volcanoes is going to be roughly proportional to volcanic aerosol emissions. So (dCO2/dt)_volcano = F*Volcanic_Aerosols, where F is a constant. So one gets dCO2/dt = (A - D*CO2(0)) + (B + D)*CO2(t) + E*CO2_emissions + F*Volcanic_Aerosols + E*D*Integral(0 to t; CO2_emissions*dt) + F*D*Integral(0 to t; Volcanic_Aerosols*dt) - (AD)*t - (BD)*Integral(0 to t;CO2(t)*dt)) So that means that one can estimate these unknown parameters using a simple linear regression. Note that there are 8 dependent variables but the coefficients are all combinations of A, B, D, E and F. This means that one should do a restricted regression with 3 restrictions. Human emissions dont alter CO2, remember. Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 10, 2015 Author Report Share Posted February 10, 2015 Okay, after reading up on non-linear regressions I think I got it now. I'll spare you guys the calculations but basically, Perform OLS regression without the 3 restrictions. Modify OLS coefficients to satisfy the 3 restrictions such that the increase in the sum of squares of error is minimized. Apply Guass-Newton method for estimating non-linear regression. Quote Link to comment Share on other sites More sharing options...

-1=e^ipi Posted February 10, 2015 Author Report Share Posted February 10, 2015 (edited) And it all boils down to telling you GW is actually happening. So I guess questions like 'what is climate sensitivity?', 'what is the polar amplification factor?', 'how will jetstreams be affected by increasing atmospheric CO2?', 'How much ocean acidification will there be?', 'how will the CO2 fertilization effect influence things?', 'what is the net-benefit of mitigation policies vs alternatives such as adaptation or geoengineering?', etc. do not matter. In that case I guess we should defund all climate science since the only relevant question is whether or not GW exists. *sarcasm* Or maybe just listen to what real scientists are telling us and buy a Chevy Volt. I can't afford a car nor do I drive. Edited February 11, 2015 by -1=e^ipi Quote Link to comment Share on other sites More sharing options...

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