Oceans are warming dramatically faster, new study warns

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dvdjs said:
I am not sure how the scientists baseline the heat content.
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I don’t know how they do it either, but given that the specific heat of seawater varies by temperature, pressure, and salinity, the calculation must involve a lot of guess… estimation. Even measuring the temperature of the water is at best an educated guess. The question being which is more likely to be the more accurate number: overall temperature change with all of its inherent estimates, or that number times another number containing even more estimates?
 
Ender said:
The question being which is more likely to be the more accurate number: overall temperature change with all of its inherent estimates, or that number times another number containing even more estimates?
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Integration of a noisy function gives a much smoother result.
 
dvdjs said:
But integration smooths out the inaccuracies.
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The energy (heat) added to a system is Q = MC delta T.

In this case M is the mass of the ocean (lots of estimates there)
C is the specific heat of the ocean (lots of estimates involved in this calculation)
Delta T is the rise in temperature of the ocean (yes, lots of estimates here as well).

Where does the integration come in in multiplying all of these terms together, and why would we not expect the term (Q) with three different sets of estimates to be significantly less accurate than the term (Delta T) with only one set?
 
Ender said:
The energy (heat) added to a system is Q = MC delta T.
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You need to elaborate this with integration over spatial variables. Errors in estimates of T, P, salinity, per voxel are smoothed in the integration process
 
dvdjs said:
Simple averaging reduces the noise by N^0.5 where N is the number of samples in the average.
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I expect in this case the models are both using the same data samples as their (name removed by moderator)uts so you aren’t getting more discrete samples (that would reduce error margin). Averaging outputs from different models using the same (name removed by moderator)ut samples gives a false sense of increased certainty.
 
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dvdjs said:
You need to elaborate this with integration over spatial variables. Errors in estimates of T, P, salinity, per voxel are smoothed in the integration process
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I am unwilling to believe that multiplying one number by other numbers containing uncertainties is more accurate than not introducing those uncertainties in the first place. But perhaps not in the science of climate change.
 
Ender said:
I am unwilling to believe that multiplying one number by other numbers containing uncertainties is more accurate than not introducing those uncertainties in the first place. But perhaps not in the science of climate change.
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Mulplication is not the issue. That will scale the error. The important point is integration over spatial variables. Errors in estimates of T, P, salinity, per voxel are smoothed in the integration process
 
Theo520 said:
I expect in this case the models are both using the same data samples
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I am not sure. ISTM that the identification of deep heat sinks would have required new data from deeper voxelsume elemetns . My point is that apart from systematic bias, integrating over the will smooth lumpier data on the state of each element.
 
dvdjs said:
I am not sure. ISTM that the identification of deep heat sinks would have required new data from deeper voxelsume elemetns . My point is that apart from systematic bias, integrating over the will smooth lumpier data on the state of each element.
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Yes, I don’t know how many models were used here. I do know the averaging of the climate models is done to provide a smoothed line and it’s erroneous to assume averaging these models which all use the same data (name removed by moderator)ut somehow reduces the error margins.
 
An entertaining read. It’s worth reading the comments to see the debate about the methodologies used to write this report.
 
Ender said:
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dvdjs:
You need to elaborate this with integration over spatial variables. Errors in estimates of T, P, salinity, per voxel are smoothed in the integration process
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I am unwilling to believe that multiplying one number by other numbers containing uncertainties is more accurate than not introducing those uncertainties in the first place.
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“Not introducing those uncertainties in the first place” means discarding all the measurements with uncertainties, which leaves you with maximum uncertainty, as uncertainty is the lack of knowledge. “Some” uncertainty is better than “maximum” uncertainty.

By the way, why doesn’t anyone ask the scientists who made these claims how they got their estimates? That would be better than letting their enemies define their position and answer in their behalf.
 
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LeafByNiggle said:
“Not introducing those uncertainties in the first place” means discarding all the measurements with uncertainties, which leaves you with maximum uncertainty, as uncertainty is the lack of knowledge. “Some” uncertainty is better than “maximum” uncertainty.
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That’s not actually applicable in this case. There is one number (temperature) that is a combination of measurements and calculations (not quite guesswork, but certainly containing best guess estimates). There is a second number (heat content) that is purely calculation, based on estimates of various constants multiplied by the temperature. Which number contains fewer estimates, the one with X estimates or the one with X + Y estimates? Which number is more likely to be accurate, the one with more uncertainties or the one with less?
 
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