Anomalies in annual mean near surface air temperature over land (1979-2008), averaged over the Northern Hemisphere, from CRUTEM4 (green) and as simulated by an ensemble of atmosphere/land models in which oceanic boundary conditions are prescribed to follow observations.
(P.S this post was corrupted somehow and I had to regenerate it from a copy — but lost a couple of comments. I’ll try to retrieve them.)
As discussed in previous posts, it is interesting to take the atmosphere and land surface components of a climate model and run it over sea surface temperatures (SSTs) and sea ice extents that, in turn, are prescribed to evolve according to observations. In Post #2 I discussed simulations of trend and variability in hurricane frequency in such a model, and Post #21 focused on the vertical structure of temperature trends in the tropical troposphere. A basic feature worth looking at in this kind of model is simply the land temperature – or, more precisely, the near-surface air temperature over land. How well do models simulate temperature variations and trends over land when SSTs and ice are specified? These simulations are referred to as AMIP simulations, and there are quite a few of these in the CMIP5 archive, covering the period 1979-2008.
The figure at the top summarizes the variation in the Northern Hemisphere mean surface air temperature over land in these CMIP5 AMIP runs. (The figures in this post were generated by my colleague Bruce Wyman.) We compute annual and hemispheric means from the monthly averages in the archive. We first average over all available realizations for each of 17 models. (We have left out two of our own models from this ensemble simply because we generated this figure to have something to compare our results with — adding a couple more models would have little impact on this figure.) The observations, in green, are taken from CRUTEM4. The model results are interpolated to the observational grid and the model results treated in the same way as the observations after that point (including discarding model results at grid points where monthly averaged data is missing.) Anomalies are computed, for each model and the observations, from the mean over the same 1979-2008 period. The shading in the figure indicates the middle half –the 25%-75% percentiles — of the resulting ensemble of values. (Sometimes it is important to focus on the model outliers and the full spread, but here we do the opposite and focus on the core of the model distribution.) The land warming trend in these models is about 15% smaller on average than the observed trend over this period. An example of a study that looks at land temperature trends in earlier AMIP simulations (but extending over the full 20th century) in this way is Scaife et al 2009 . I would like to see more work along these lines.
The figure below shows the same result for one of our models, the 50km resolution HiRAM model described in Posts 2 and 21. The shading means something different in this figure. We have three realizations of this model in the archive and the shading shows the spread across these three runs, so it gives you some feeling for the internal variability generated in this statistic by a model with prescribed ocean temperatures and sea ice. This atmospherically-generated internal variability is washed out by averaging over multiple realizations in the figure above. This particular model also underestimates the observed linear warming trend over this period by about 15%. (The grid in this model has the topology of a cube: the “C180″” in the figure indicates that there are 180×180 points on each face of the cube.)
I failed to mention that in these AMIP simulations, in addition to the observed variations in SST and sea ice, one also typically prescribes time-varying “forcing agents”– well-mixed greenhouse gases, aerosols, ozone, solar cycle variations in incoming flux. In some AMIP models aerosol and ozone variations might be predicted, given emissions of precursors, but in the particular model that produces the results above these are all prescribed. (There are no interannual variations in land surface properties such as the type of vegetation in our model at all, and no urban heat island effects.) What happens if you keep all of these forcing agents fixed and vary only the lower boundary condition – the SST and sea ice. The figure below shows what you get from three realizations of this type in the same model. This tells you how much of the land temperature variation and trend is “forced” by the observed changes in ocean boundary conditions versus changes in the forcing agents themselves. In this model, the warming trend over Northern Hemisphere land is reduced by about 30% when holding these forcing agents fixed. Assuming that this is a linear superposition, 70% of the model trend is generated by the communication of the observed oceanic warming to the land.
You have to be a little careful in interpreting this decomposition. Part of the SST and sea ice variation is itself due to the changing forcing, of course. But there are still important things one can learn by comparing this kind of simulation with observed land warming. Suppose that all of of the land warming is just communicated from the ocean, with no direct dependence on forcing agents. Then one can use this fit to analyze what one might call the degree of redundancy of the land temperature record. I use the word redundancy with some reluctance, because it has the connotation of irrelevant whereas I actually mean just the opposite. Redundant climate records are precisely what we need!
On the other hand, to the extent that one can isolate the directly forced component, one can try to use it in attribution studies aimed at seeing whether or not a particular model has, say, the right mix of greenhouse gas and aerosol forcing. For this purpose the hemispheric mean doesn’t give us too much to work with., but there is a lot more information than this in the spatial and seasonal structure of this directly forced component. In particular, one can increase the amplitude of this component by focusing on regions, such as Central Asia, where the oceanic influence is weaker. But it also helps to focus on those regions and times of year when internal (atmospherically-generated) variability is at a minimum (ie summer).
This kind of decomposition of land temperature trends has not received a lot of attention. There are more papers that use AMIP simulations to attribute trends in the atmospheric circulation in this way, such as Deser and Phillips 2009. It would be helpful if this kind of decomposition were available for multiple models in the CMIP5 archive. It would, in particular, be useful to know how robust the spatial and seasonal structure of the fixed “forcing” component is — the more robust this component, the more likely that one can subtract it cleanly from the observed variations and use the remainder to constrain forcing or sensitivity, at least over land.
The value of these AMIP simulations is that one can look in much more detail at the time evolution of the discrepancy between model and observations than is possible when working with a fully coupled model. Consider, for example, the difference between the models’ and CRUTEM4 values in the last few years of this period. Is this due to problems with the land observations, the SSTs and sea ice driving the atmospheric model (we use HADISST), or the models themselves? One interesting point, which I also failed to mention above, is that when one prescribes sea ice in these kinds of AMIP simulations one often just varies ice extent and not thickness, due to the lack of an observational basis for prescribing thickness. ( In contrast, fully-coupled climate models invariably try to simulate thickness variations directly). Could these AMIP models be missing some warming over land due to this deficiency, especially in the last few years of the simulations?
Note added Jan 10: An early paper that introduces the use of AMIP simulations for detection attribution studies that I was not aware of is Folland et al 1998.
[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.]