Posted on January 1st, 2012 in Isaac Held's Blog
Lower tropospheric MSU monthly mean anomalies, averaged over 20S to 20N, as estimated by Remote Sensing Systems – RSS (red) and the corresponding result from three realizations of the GFDL HiRAMC180 model (black) using HadISST1 ocean temperatures and sea ice coverage. Linear trends also shown. (Details in the post.)
Motivated by the previous post and Fu et al 2011 I decided to look in a bit more detail at the vertical structure of the tropical temperature trends in a model that I have been studying and how they compare to the trends in the MSU/AMSU data. The model is an atmosphere/land model using as boundary condition the time-evolving sea surface temperatures and sea ice coverage from HadISST1. It is identical to the model that generates the tropical cyclones discussed in Post #2 (and the animation of outgoing infrared radiation in Post #1). It has the relatively high horizontal resolution, for global climate models, of about 50km. Three realizations of this model, starting with different initial conditions, for the period covering 1979-2008, have been provided to the CMIP5 database, and it is these three runs that I will use in this discussion. The model also has prescribed time-evolving well-mixed greenhouse gases, aerosols (including stratospheric volcanic aerosols), solar cycle, and ozone. The atmospheric and land states are otherwise predicted.
The MSU data, as gridded monthly mean anomalies, were downloaded from RSS. The weights for the channels referred to here are included in a figure at the bottom of this post — thanks to Qiang Fu and Celeste Johanson for help in this regard. All of the model results are monthly mean anomalies from the model’s seasonal cycle defined as the time mean for each month over the 30 year period Jan 1979- Dec 2008. Observations are plotted as anomalies from a time average over the same period. And all model and observed linear trends are computed over the same time interval as well (unless otherwise stated). I’ll only discuss averages over the deep tropics from 20S to 20N.
Analyzing an atmosphere/land model running over prescribed oceanic boundary conditions has advantages and disadvantages as compared to analyzing a model fully coupled with the ocean. The advantage is that one avoids conflating disagreements between model and observations regarding the variation in sea surface temperature (SST), on the one hand, with problems that the atmospheric model may have in coupling SST variations to the troposphere and land surface, on the other. And one can compare in much more detail the time evolution of quantities of interest — even if one’s coupled model is perfect, its El-Ninos will resemble reality only in their statistics.
The disadvantage is that one might be doing some damage to the atmosphere by disallowing two-way interactions with the oceans. The significance of this distortion is very much problem specific and can be subtle. For example, tropical cyclone intensity is presumably affected by running over prescribed SSTs, by not allowing the oceanic mixing generated by the storm to affect its intensity. If tropical cyclone intensity, in turn, affects the tropical lapse rate trends this would be a problem. I don’t, at present, see this or other related possibilities as significant for this lapse rate issue, but its something to be alert for.
Let’s start with the lower tropospheric channel referred to as T2LT or TLT. The red line in the figure at the top is the RSS MSU time series, while the shading spans the results from the 3 model realizations. (The smallness of this spread shows how tightly the tropical lower troposphere is coupled to the ocean surface in the model. This spread would be much larger in a fully coupled model). Trend lines are shown for both the observations and the three model runs (it is hard to see the 3 distinct model trend lines because of overlap). I get 0.130 C/decade for the RSS trend and 0.148 for the mean of the 3 model runs — with 0.154, 0.137, and 0.152 for the individual runs). If I drop the first two years, 1979 and 1980, the mean model trend drops to 0.143 and the RSS T2LT rises to 0.149. (It might be a consequence of how I plotted this, but this early period does seems to be a major source of the discrepancy. You can think of this as cherry-picking or as a very crude way of judging whether this difference is plausibly significant.)
Moving on to the deeper tropospheric average provided by T2 (also referred to as TMT), we get a very similar looking plot:
The model trends are now (0.138, 0.125, 0.129) with a mean of 0.131, with the RSS trend over this period is 0.102. These trend are smaller than the T2LT trends, in both the model and the observations, despite the fact that T2 weights the lower troposphere less strongly that T2LT. The model trends actually increase with height through the troposphere. The problem, long appreciated, is that T2 has significant weight in the stratosphere, where there is a cooling trend in both model and observations as indicated here by the T4 time series:
The warming due to absorption by El Chichon and Pinatubo aerosols is superposed on an uneven cooling trend. The El Chichon signal is relatively weak in the model, contributing to the underestimate of the cooling trend. (The model is also missing substantial internal variability — it does not simulate a realistic Quasi-Biennial Oscillation, but his does not appear to be the dominant signal in this missing variability). Here I follow Fu et al 2011 and use T24 = 1.1*T2- 0.1*T2LT (oops — I meant T24 = 1.1*T2- 0.1*T4; June 8, 2012) to reduce the influence of the stratosphere on T2. A plot of T24 would look a lot like the that for T2 above, but the mean model trend is increased to 0.168, while the RSS T24 trend is 0.143. The model-0bs difference here is smaller than for T2 itself because the model’s cooling trend in T4 is smaller than that observed.
The actual model trends as a function of height are shown here, along with the trends using the T2LT and T24 weighting functions. To try to capture the model’s upper tropospheric warming better, I have defined T2UT = 2*T24 – T2LT to get something that follows the upper troposphere a little more closely (see the weights at the bottom of the post — you want to avoid negative weights while keeping the integral of the weights unchanged). (I have arbitrarily plotted the satellite channel trends at the pressure levels at which model versions agree with the model trend: T2LT = 700mb, T24 = 550mb, T2UT = 450mb.) The red dots are the RSS values. Also shown at 1000mb in magenta is the trend in SST and, by the three black dots, the land+ocean mean surface trends in the 3 realizations — all over the 20N-20S region.
There is substantial spread in the land warming, associated (I think) mostly with rainfall variability in semi-arid regions — I doubt that the effects of this variability propagate upwards beyond 700mb or so.
For fun, I also generated the same figure after dropping the first two years from the analysis, with this result:
The difference between these two plots is not small. A bias of the sort seen in the first plot, with the tropics evidently being destabilized as compared to the model, would have substantial consequences for tropical meteorology if extrapolated into the future.
Let be the ratio of the trend in T2UT to the trend in T2LT (the ratio of T24 to T2LT, discussed in Fu et al is just ). The three model realizations give (1.29, 1.33, 1.22) compared to 1.19 for RSS. If, once again, we repeat the calculation omitting the first two years, we get (1.25,1.34,1.18) for the model and 1.19 (once again) for RSS. There is a hint that this ratio is more robust to the period considered than the trends themselves.
This is just one model and one observational analysis. (See Thorne et al, 2011 for a recent discussion of differences between alternative analysis of the MSU data, and inconsistencies between radiosondes and MSU.) Accepting this comparison at face value, it is still not clear to me if there is a significant model bias or not, when the SSTs are specified. The differences seem subtle, but small differences in lapse rate can have important effects on tropical meteorology.
I would like to encourage more analysis of these prescribed SST (“AMIP”) simulations in this context. Most of the recent model-data comparisons of tropospheric lapse rate trends focus on coupled models. Especially in the tropics, biases in forcing or climate sensitivity make themselves felt to a large extent through the SSTs. Superposing the bias due to SST differences on any biases due to the internal atmospheric dynamics controlling tropical lapse rates can be confusing. Normalizing tropospheric trends by surface ocean trends can help in this regard, but this assumes that the tropical mean SST is the only thing that matters, which need not be the case.
It is also nice to be able to focus on specific time periods in a way that would not be possible in free-running coupled models generating their own ENSOs, the detailed time histories providing potential insights into the data sets as well as the models. Are the early years in this record (1979-1980 roughly) also the source of model-data disagreement when other AMIP models are examined? Can we determine whether these differences are due to problems in the MSU data, the SST input into the models, or model biases?
[The views expressed on this blog are in no sense official positions of the Geophysical Fluid Dynamics Laboratory, the National Oceanic and Atmospheric Administration, or the Department of Commerce.]