With permission, I am reposting two very interesting posts by Troy Ca on his weblog Troy’s Scratchpad which was motivated by Bob Tisdale and my post
Yes, I am yet another software engineer with a climate science blog. My hope here is to keep the focus technical and away from the drama we find on other climate sites in the blogosphere. While that drama can be entertaining in its own right, there are plenty of other places to find it.
Besides — and I know I’m not the first person to say this — it’s a lot easier to publish a general comment attacking politics, motivations, or even a broad scientific topic, than it is to make a specific and coherent technical argument.
His excellent two posts are presented below:
I saw a post over at Roger Pielke Sr.’s a few days ago with some preliminary analysis of the sea surface trends by latitude, the primary point of which seemed to be that the water vapor feedback may be overestimated, as the warmer waters in the topics – which contribute more to evaporation – were actually heating at a slower rate than other regions. I was skeptical of a few aspects of this, particularly the analysis with the short period (2003-2011) and the fact that the width of the latitude bands showing the trends seemed to arbitrary. Furthermore, what it showed was a general increase in temperature trend the further north it went over the period 1982-2011, which I seemed to recall was similar in the GFDL CM2.1 model run.
Thus, I decided to compare the monthly NOAA SST anomaly trends by latitude to several of the GFDL CM2.1 model run trends by latitude over the same period (1982-2010 was the overlap here). I used those GFDL CM2.1 runs for CMIP5 (rather than the CMIP3 runs I had used before), so these should have used historical forcings up until 2005. I ultimately downloaded 5 of the 10 runs to get a decent sense of variability over that period. Intermediate data and code for this post is available here…you’ll see that I used 15 degree latitude bands to cut down on some of the noise.
Anyhow, when it came to reproducing the absolute temperatures by latitude, GFDL CM2.1 did quite well, although perhaps this was to be expected:
However, I was somewhat surprised to see that the spatial pattern of the warming was quite different:
While the model seems to overestimate the SST trends, this was perhaps common knowledge already. Instead, what is more interesting is that despite the variability in the model runs, almost all of them seem to show the greatest rate of warming near the equator, thus producing the general shape we see in the blue line (the mean from the 5 runs). Compare it to the NOAA SST, and there appears to be a discrepancy.
So, what affect will this have on the TOA radiation and the water vapor feedback? I’m not sure. My next step, if I find time, will be to compare a GFDL CMIP5 AMIP run (which has the atmospheric component tied to the actual SST observations up till 2008) to that of these coupled runs, perhaps using the GFDL water vapor kernels to convert the atmospheric specific humidity outputs in each into the global TOA radiative effect. Or perhaps this work is already out there, and I need to search the existing publications.
Sensitivity of the water vapor feedback to locations of SST trends [August 15 2012]
With a new computer that has the necessary disk space and memory, I was finally able to do some of the analysis I had talked about doing in the previous post, and the preliminary results seem quite interesting. To recap, I wanted to see what effect the discrepancy in the rates of sea surface warming at different latitudes would have on the TOA radiation budget that could specifically be attributed to water vapor. This was originally to check out Dr. Pielke Sr.’s hypothesis that perhaps the different rates of evaporation in these areas would have an impact on the water vapor feedback.
Data and Methods
Here, I use the same 5 CMIP5 historical runs from the GFDL CM2.1 model as I did in that last post, in particular the specific humidity and surface air temperature values. I estimated the TOA radiative impact specifically attributed to water vapor for each month by using the longwave water vapor radiative kernel derived from GFDL CM2.1. I then compared this against the 3 GFDL HIRAM C180 AMIP runs available on the GFDL data portal, again converting specific humidity to the corresponding radiative anomaly. Since the primary difference between the CMIP and AMIP runs is that the AMIP runs have the atmospheric model responding to actual sea surface temperature observations, if we assume that the atmospheric model in GFDL’s CM2.1 is very similar to GFDL’s HIRAM C180, we are thus essentially able to isolate the different water vapor responses specifically to the discrepancy in the location of SST trends. The code and intermediate data for this post can be found here.
In the figure above you can see the trend in the outgoing longwave radiation (OLR) over time in the coupled vs. atmosphere-only model runs. The lower the OLR anomaly, the more heat that is “trapped” due to water vapor. It is interesting to note that the AMIP runs, bounded as they are by actual SST observations, see a trend that has a much smaller magnitude than the fully coupled models (and, unsurprisingly, the runs show much less variation). Clearly the discrepancy in SST trends has had a large impact on the TOA balance, at least with respect to water vapor. However, if we’re estimating a global factor for the water vapor feedback, this in itself would not necessarily indicate an overestimation of the feedback (dR/dT) in the CM2.1 coupled model, since perhaps the denominator for the coupled model (the temperature trend) has been overestimated just as much as dR (one may argue that an overestimation in temperature trend could indirectly indicate an overestimate of some positive feedback, but it is by no means straight-foward).
The figure below shows the temperature trend in the coupled runs vs. GISS. I should mention that it is my understanding that the long term water vapor feedback will be primarily driven by ocean temperatures, rather than including land surface air temperatures. However, given that feedbacks and sensitivity estimates are typically calculated with respect to surface air temperature, I’ve framed the results in this way to be able to make meaningful comparisons to other results. As we can see, the trends tend to be higher in the coupled runs, but the difference in trends is not as drastic as in the OLR anomalies.
Additionally, there are are few different ways we might calculate the feedback ratio, dR/dT. Soden and Held (2006) take the mean from the last 10 years and subtract the mean from the first 10 years when calculating the feedback from models, but they had 110 years to work with; since we only have 28 years here, I’ve employed a similar method (method #2 in the figure below) using the mean from the last 5 years (2004-2008) and the first 5 years (1981-1985). Another method I’ve employed (method #1), in order to use all years, is the ratio of linear trends in the wv-induced OLR changes and temperature changes, or (dR/dt) / (dT/dt) (such a method is used, for example, when determining trend amplification in land vs. ocean vs. lower troposphere). These methods, I believe, are more likely to highlight the long-term (climate scale) radiative variable response relevant to climate sensitivity.
The final method (method #3) I show is regressing R_wv (yearly anomalies) directly against temperatures. This method has been used in several papers where the satellite observations are limited (to ~10 years). However, in this approach, you tend to be measuring the response to ENSO-induced interannual variability, unless the trends are strong enough to negate this…and as I’ve discussed before, there appears to be little evidence to suggest that the global radiative response to ENSO-induced temperature changes correlates well with the long-term response, and it certainly doesn’t in most models.
And thus we have a boxplot of the estimated water vapor feedback:
Conclusions and Limitations
A simple glance would suggest that the water vapor feedback is significantly lower (~40%) in those runs bounded by SST observations than the coupled runs, which would have potentially large impact on the estimated sensitivity (for example, all else being equal and assuming a an average water vapor feedback of 1.8 W/m^2/K in models, this sort of reduction in the WV feedback would lower the climate sensitivity estimate from 3.0 K to 1.9 K). However, there are a couple of large caveats here, the first of which is whether a similar warming pattern of the sea surface (lower in the tropics) would be expected with the continual increase in CO2, or whether some sort of natural variability is impacting this pattern. This goes for land vs. SST as well, where it appears the discrepancy between the SST and land rates is greater in observations than in the coupled model. Additionally, it is quite possible that some of the lessening of the positive water vapor feedback may be counteracted by a decrease in the strength of the negative lapse rate feedback which could mitigate the effect on overall sensitivity.
Another thing that should raise some question marks is that all of the methods above during this time period seem to underestimate the 100 year water vapor feedback for GFDL CM2.1, at least according to Soden and Held (2006), where it is listed as 1.97 W/m^2/K. Also, the all-sky radiative kernel should not be entirely relied upon, as it is based on the models that do not accurately represent the cloud distribution (see errors in all-sky outgoing radiation). One may also wonder why we don’t use available reanalysis. Apart from the fact that we want to isolate the differences simply to water vapor concentrations resulting from observed sea surface temperatures, reanalysis products have also been shown to be inadequate for the task of reproducing long-term trends in water vapor [John et al., 2009 ; Serreze et al., 2012].
So, with regards to the original question about whether the discrepancy in the spatial distribution of sea surface temperature trends may affect the water vapor feedback specifically because of the different rates of evaporation, the last two posts at least provide some support for the hypothesis. I could not say evaporation is the key here for sure, but neither do I see any evidence to dispute it.
I am a bit curious as to whether this method has been employed before (that is, combining AMIP models with radiative kernels for comparison against coupled runs for feedback analysis), as it seems straightforward and could provide a more extended period of analysis than is available in the satellite record. It seems like it could be an interesting avenue to explore.
My Comment: This is a very informative analysis, with issues that need exploration. It further documents, however, that the water cycle radiative feedback in the climate models is not accurately representing the real-world feedback as also can be inferred from the paper
Sun, D.-Z., Y. Yu, and T. Zhang, 2009: Tropical Water Vapor and Cloud Feedbacks in Climate Models: A Further Assessment Using Coupled Simulations. J. Climate, 22, 1287-1304
that I posted on in
The Sun et al article concluded that
“The extended calculation using coupled runs confirms the earlier inference from the AMIP runs that underestimating the negative feedback from cloud albedo and overestimating the positive feedback from the greenhouse effect of water vapor over the tropical Pacific during ENSO is a prevalent problem of climate models.