It was obvious that she had never intended to keep her promise The time when I was given some bad advice The computer is more of a menace than a blessing. What do you think?
With regards to the new Marvel et al paper, I find that: First, the fact that global climatemodels project more positive climate feedbacks in the future does not in any way prove that the models are correct in doing so.
Secondly, the more detailed explanation in the paper itself supports the statement with several different, mainly invalid, arguments: The evidence for argument a is weak.
The second is relevant to increases in CO2 concentration from a doubling to a quadrupling, but its findings are fully explicable by the fact that CO2 forcing increases very slightly faster than logarithmically with concentration.
In these models the pattern of SST warming changes over time after forcing is applied, and on average the feedbacks applying to the later warming pattern are more positive.
However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. But, as I will show, a better interpretation is that estimating ECS by using changes over a twenty-six year period is unwise.
They used the well-known energy-balance estimation formula: They reported that simply subtracting the first decade from the last yielded similar results.
Given the wide spread between CMIP5 models in, inter alia, the level of aerosol forcing, and in estimated ERF from CO2, this will likely cause considerable inaccuracy when using equation 1 to estimate ECS for individual models.
Averaged over all models, the inaccuracy will be smaller. However, post the changes in aerosol ERF are relatively small, so there may be little downwards bias. Figure 1 shows the resulting ECS estimates Marvel et al.
Models are ordered by increasing estimated long-term ECS. Reproduced from Figure 1 of the Supporting Information for Marvel et al. The period is particularly unsuitable for ECS estimation since strong negative volcanic forcing arose during its first half, but not thereafter.
There is evidence including from Marvel et al. It is simple enough to investigate the effect on short-period ECS estimation of avoiding significant influence from volcanism. I do so by using historical simulation data from the almost identical period and Marvel et al.
I made up the base ten years by combining the volcanic-free and periods. I took average changes from the base ten years to the final decade,which is also free of eruptions.
Doing so avoided the El Chichon and Mount Pinatubo eruptions and the main parts of the recoveries from each of them. Figure 2 shows the resulting ECS estimates, upon applying equation 1. Where several historical simulation runs were carried out by a model, the ECS estimates using mean values from its ensemble of runs red triangles are less wild.
But the interesting point shown in Figure 2 is that, across all models, the median of the long-term ECS estimates blue line: ECS estimated from non-volcanic periods in recent historical simulations. Blue triangles show estimated long-term ECS. The red and blue lines which overlap show the multimodel-ensemble medians of respectively ensemble-mean ECS estimates and long-term ECS estimates.
Long-term ECS was estimated using the same method as Marvel et al. It is not possible to find a long period in historical simulations that avoids both significant volcanic activity and a large change in aerosol forcing.
If that is taken as a proxy for the effect of internal variability on ECS estimation, it is not too bad given that this estimate is based only on data spanning a thirty year period, and on averaging over single decades.
For observationally-based energy-balance climate sensitivity estimation, where concern about model aerosol ERF strength is not a concern, one would normally use a much earlier and typically rather longer base period, thereby achieving a higher signal-to-noise ratio.
If the full historical period to date is used to estimate model ECS values from simulation data, better precision is achievable. On that basis, uncertainty in observationally-based ECS estimation arising from internal variability is minor compared with other uncertainties.
The AMIP runs, which generally spanare too short to tell one much about the underlying cause, but in this case I think the lower ECS estimates for models are probably primarily genuine, rather than artefacts arising from use of a period with unbalanced volcanism.
This is a reflection of Marvel et al. An alternative explanation for the models as a group misestimating the actual temporal evolution of SST change patterns is that the models as a group are imperfect.
To my mind that should be the null hypothesis, rather than that internal variability over the last few decades results in an unusually low estimate of ECS. Indeed, the fact that internal variability linked to the Atlantic multidecadal oscillation is thought to have boosted warming over  makes it seem even less likely that in the real climate system ECS estimates based on this period would be biased low.
Moreover, internal variability sufficient to produce a year excursion of the magnitude required to account for the CMIP5 model average difference in N between AMIP and historical simulations does not appear to occurred in any of the 13, odd overlapping 20 year segments of their preindustrial control simulations.
Moreover, it is not the case that low model ECS estimates when driven by observed evolving SST patterns are limited to the last few decades. For now I will refrain from further discussion of this interesting area, which is a focus of current research activity, as this article is already overlong.
Endnotes and References  The paper itself is pay-walled, but the Supporting Information is not. Implications for climate sensitivity from the response to individual forcings.Prepare for the GMAT essay section with our essay grading service.
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