The "hot spot" is still missing
John R. Christy, Distinguished Professor, Department of Atmospheric Science, Director Earth System Science Center, The University of Alabama in Huntsville
One important part of climate change research is to document the amount of change that can already be attributed to human activity. In other words we want to know the answer to the question, “How has the climate changed specifically because of the enhancement of the natural greenhouse effect caused by extra emissions due to human progress?” These rising emissions come primarily from energy production using carbon-based fuels which emit, as a by-product, the ubiquitous and life-sustaining greenhouse gas, carbon dioxide (CO2). From about 280 ppm in the 19th century, the current concentration of CO2 has risen to about 400 ppm.
So, what has the extra CO2 and other greenhouse gases done to the climate as of today? Climate model simulations indicate that a prominent and robust response to extra greenhouse gases is the warming of the tropical troposphere, a layer of air from the surface to about 16 km altitude in the region of the globe from 20°S to 20°N. A particularly obvious feature of this expected warming, and is a key focus of this blog post, is that this warming increases with altitude where the rate of warming at 10 km altitude is over twice that of the rate at the surface. This clear model response should be detectible by now (i.e. 2012) which gives us an opportunity to check whether the real world is responding as the models’ simulate for a large-scale, easy-to-compare quantity. This is why we care about the tropical atmospheric temperature.
There are two aspects to this tropical warming that are sometimes confused. One aspect is the simple magnitude of the warming rate, or temperature trend, of the entire troposphere. This metric quantifies the amount of heat that is accumulating in the bulk atmosphere. A well-established result of adding greenhouse gases to the atmosphere is that heat energy (in units of joules) will accumulate in the troposphere which can be detected as a rise in temperature. [The fundamental issue of the effects of greenhouse warming is: how many joules of heat are accumulating in the climate system per year?]
We don’t know at what rate that accumulation might occur as other processes may come into play which reduce or magnify it. For example, with extra greenhouse gases, the rate at which the joules are allowed to escape to space may be reduced by additional responses, causing even more heating. On the other hand, there could be an increase in cloudiness which may limit the number of joules (from the sun) which enter the climate system, thus causing a cooling influence. A reaction of the climate system to extra CO2 that promotes even more accumulation over what would have happened due to CO2 alone is a positive feedback, while one that limits the accumulation of joules is a negative feedback. In the climate system, there are numerous feedbacks of both signs, all interdependent and intertwined.
The second aspect of enhanced temperature change is the amount of amplification the higher altitude layers will experience relative to the surface warming as noted earlier – which is linked to the first aspect and is a feature discussed as a complement to the first aspect. In simple thinking, if enough joules are added to the troposphere to increase its temperature by 1 °C throughout, one would expect a uniform 1 °C warming from the surface to the top of the troposphere. However, as seen in the way the real atmosphere behaves on monthly and yearly time scales, the surface temperature change tends to be less than 1 °C while the upper troposphere warms to more than 1 °C. Since there is a reduction in the expected increase of the surface temperature given the number of joules added, this phenomenon is called a negative lapse-rate feedback to surface temperature (even though the upper air heats up more.) So the models anticipate that there will be a strong amplification of the surface temperature change as one ascends through the troposphere. [So, if someone claims that surface and upper air trends agree in magnitude, then they are also claiming that this is not consistent with the enhanced greenhouse effect since, according to models, the temperature trend of those two levels should not agree.]
Thus, there are two ideas to test in the tropics, (1) the overall magnitude of the layer-average temperature rise and (2) the magnification or amplification of the surface temperature change with height.
Balloons and satellites
Measurements of tropical tropospheric temperature have been performed by balloons that ascend through the air and radio back the atmosphere’s vital statistics, like temperature, humidity, etc. Due to a number of changes in these instruments through the years research organizations have spent a lot of effort to remove such problems and create homogenous or consistent databases of these readings. For this study we shall assume that the average of four major and well-published datasets (known as RATPAC, RAOBCORE, RICH and HadAT2) will serve as the “best guess” of the tropical temperatures at the various elevations (see Christy and Hnilo, 2007, Christy et al. 2010, 2011 for descriptions and earlier results.)
For a layer-average of the tropospheric temperature there are two satellite-based tropospheric datasets (known as UAH and RSS) which have by independent methods combined the readings from several spacecraft carrying microwave instruments into a time series beginning in late 1978. There are dozens of publications which detail the methods used by the various groups to generate both balloon and satellite products. Through the years each group has updated their products as new information has come to light, and we use the latest versions as of June 2013.
The time frame we shall consider here will begin in Jan 1979 and end in Dec 2012 as this is the time we have output from models and from observations, both balloons and satellites. It is also the period for which the greatest amount of accumulation of heat energy (joules) should be evident due to the increasing impact of the rising greenhouse gas concentrations.
To examine the simple magnitude of full-tropospheric trends we look at two layers as represented by what satellites measure which are roughly the average temperature of the surface and to about 10 km (lower troposphere or TLT) and surface to about 17 km (mid-troposphere or TMT). TMT gives more weight to the region between 500 (5.5 km) and 200 hPa (12 km) where the warming is expected to be most pronounced according to models, so the figures will focus on TMT. We can simulate the satellite layer using both balloon data and model output for direct, apples-to-apples comparisons (Fig. 1.)
Figure 1. Time series of the mid-tropospheric temperature (TMT) of 73 CMIP-5 climate models (rcp8.5) compared with observations (circles are averages of the four balloon datasets and squares are averages of the two satellite datasets.) Values are running 5-year averages for all quantities. [There are four basic rcp emission scenarios applied to CMIP-5 models, but their divergence occurs after 2030. Thus, for our comparison which ends in 2012, there are essentially no differences among the rcp scenarios.] The model output for all figures was made available by the KNMI Climate Explorer.
We see that all 73 models anticipated greater warming than actually occurred for the period 1979-2012. Of importance here too is that the balloons and satellites represent two independent observing systems but they display extremely consistent results. This provides a relatively high level of confidence that the observations as depicted here have small errors. The observational trends from both systems are slightly less than +0.06 °C/decade which is a value insignificantly different from zero. The mean TMT model trend is +0.26 °C/decade which is significantly positive in a statistical sense. The observed satellite and balloon TLT trends (not shown) are +0.10 and +0.09 °C/decade respectively, and the mean model TLT trend is +0.28 °C/decade. In a strict hypothesis test, the mean model trend can be shown to be statistically different from that of the observations, so that one can say the model-mean has been falsified (a result stated in a number of publications already for earlier sets of model output.) In other words, the model mean tropical tropospheric temperature trend is warming significantly faster than observations (See Douglass and Christy 2013 for further information.)
Regarding the second aspect of temperature change, we show the vertical structure of those changes in Fig. 2 where we display the temperature trend by vertical height (pressure) as indicated by the four balloon datasets (circles), their average (large circle) and 73 model simulations (lines of various types).
Figure 2 Temperature trends in °C/decade by pressure level with 1000 hPa being the surface and 100 hPa being around 16 km. Circles represent the four observational balloon datasets, the largest circle being their mean. The lines represent 73 CMIP-5 model simulations (identities in Fig. 3) with the non-continuous lines representing models sponsored by the U.S. The large black dashed line is the 73-model mean. The pressure values are very close to linear with respect to mass but logarithmic with respect to altitude, so that 500 hPa is near 5.5 km altitude, 300 hPa near 9 km altitude and 200 hPa about 12 km altitude.
Figure 3 Caption for Fig. 2, identifying model runs and observational datasets.
The models (especially) show increasing trends as altitude increases to 250 hPa (about 10 km) before decreasing toward the stratosphere (~90 hPa). In comparing model simulations with the observations it is clear that between 850 and 200 hPa, all model results are warmer than the average of the balloon observations, a result not unexpected given the information in Fig. 1.
The amount of the amplification of the value of the surface trend with elevation in Fig. 2 is somewhat difficult to discern as each model has its own surface trend magnitude. To better compare the amplification effect, we normalize the pressure-level trend values by the trend of the surface value for each dataset and model simulation.
Figure 4. Value of the 1979-2012 temperature trend at various upper levels divided by the magnitude of the respective surface trend, i.e. the ratio of upper air trends to surface trends. Model simulations are lines with the average of the models as the dotted line. Squares are individual balloon observations (green – RATPAC, gray RAOBCORE, purple – RICH and orange – HadAT2) with the averages of observations the gray circles.
Figure 4 displays the ratio, or amplification factor, that observations and models depict for 1979-2012 in the tropics (see Christy et al. 2010 for further information). The mean observational result indicates the values are between +0.5 and +1.5 through the lower and middle troposphere (850 to 250 hPa). [The observational results tend to have greater variability due to the denominator (surface trend) being relatively small. Viewing Fig. 2 shows that the observations are rather tightly bunched for absolute trends in comparison to the model spread.] The models indicate a systematic increase in the ratio from 1.0 at the surface with amplification factors well above +1.5 from 500 to 200 hPa. What this figure clearly indicates is that the second aspect of this discussion, i.e. namely the rising temperatures with increasing altitude, is also over-done in the climate models. The differences of the means between observations and models are significant.
OverwarmWhile there is much that can be discussed from these results, we wonder simply why the models overwarm the troposphere compared with observations by such large amounts (on average) during a period when we have the best understanding of the processes that cause the temperature to change. During a period when the mid-troposphere warmed by +0.06 °C/decade, why does the model average simulate a warming of +0.26 °C/decade?
Unfortunately, a complete or even satisfactory answer cannot be provided. Each model is constrained by its own sets of equations and assumptions that prevent simple answers, especially when all of the individual processes are tangled together through their unique complex of interactions. The real world also presents some baffling characteristics since it is constrained by the laws of physics which are not fully and accurately known for this wickedly complex system.
An interesting feature of the models is that almost all show greater year-to-year variability than observations (Fig. 1.) The average model annual variance (detrended) of anomalies is 60 percent greater than that of the observational datasets. This is a clue that suggests the models’ atmospheres are more sensitive to forcing than is the real climate system, so that an increase in greenhouse forcing in models will lead to a greater temperature response than experienced by the actual climate system. But saying the climate models are too sensitive only identifies another symptom of the issue, not the cause.
We want to know why the extra joules of energy that increasing CO2 concentrations should be trapping in the climate system are not found in Nature’s atmosphere compared with what the models simulate.
Could the extra joules be absorbed by the deep ocean and prevented from warming the atmosphere (Guemas et al. 2013)? This requires extremely accurate measurements of the deep ocean (better than 0.01 °C precision) which are not now available comprehensively in space and time. Current studies based only on observations suggest this enhanced sequestration of heat is not happening.
Could there be a separate process like enhanced solar reflection by aerosols that is keeping the number of joules available for absorption at a smaller level relative to the past? The interaction of aerosols with the entire array of climate processes is another fundamental area of research that has more questions than answers. How do aerosols affect cloudiness (more?, less?, brighter?, darker?). What is the precise, time-varying distribution of all types of aerosols and what exactly does each type do in terms of affecting the absorption and reflection of the joules in all frequencies? The IPCC typically shows very large error ranges for our knowledge of the aerosol effects, so there is a possibility that models have significant and consistent errors in dealing with them (IPCC 2007 AR4 Fig SPM.2).
Clouds and water vapor
Could there be a complex feedback response in the way the real atmosphere handles water vapor and clouds that acts to enhance the expulsion of joules to space under extra greenhouse forcing so they don’t accumulate very rapidly? Of the many processes that models struggle to represent, none are more difficult than clouds and water vapor. As recently shown by Stevens and Bony (2013) different models driven by an identical, simplified forcing produced very different results for cloudiness. This is my favorite option in terms of explaining the lack of joule-accumulation. As my colleague Roy Spencer reminds us, if you think about it, the atmosphere should have 100 percent humidity because it has an essentially infinite source of water in the oceans. However, precipitation prevents that from happening, so precipitation processes are apparently in control of water vapor concentrations – the greenhouse gas with the largest impact on temperature. This means the way precipitation and clouds behave (both in causing changes or responding to them) when slight changes occur in the environment is key in my view. We have actually measured large temperature swings that were preceded by changes in cloudiness in our global temperature measurements. So a response to the extra CO2 forcing by clouds and water vapor, which have a massive impact on temperature, could be the reason for the rather modest temperature rise we’ve experienced (Spencer and Braswell, 2010).
Or, could there be natural variations that completely overcome small enhancements in greenhouse-joule-trapping? These variations have demonstrated the ability to drive large temperature swings in the past, but we cannot simulate or predict them well at all. For that we need extremely accurate ocean simulations along with accurate representations of clouds, precipitation and water vapor (among other things).
The bottom line is that, while I have some ideas based on some evidence, I don’t know why models are so aggressive at warming the atmosphere over the last 34 years relative to the real world. The complete answer is probably different for each model. To answer that question would take a tremendous model evaluation program run by independent organizations that has yet to be formulated and funded.
What I can say from the standpoint of applying the scientific method to a robust response-feature of models, is that the average model result is inconsistent with the observed rate of change of tropical tropospheric temperature - inconsistent both in absolute magnitude and in vertical structure (Douglass and Christy 2013.) This indicates our ignorance of the climate system is still enormous and, as suggested by Stevens and Bony, this performance by the models indicates we need to go back to the basics. From this statement there is only a short distance to the next - the use of climate models in policy decisions is, in my view, not to be recommended at this time.
J.R. Christy is Distinguished Professor of Atmospheric Science at the University of Alabama in Huntsville and Director of the Earth System Science Center. He is Alabama’s State Climatologist. In 1989 he and Dr. Roy Spencer, then of NASA, published the first global, bulk-atmospheric temperatures from microwave satellite sensors. For this achievement they were recognized with NASA’s Medal for Exceptional Scientific Achievement and the American Meteorology Society’s Special Award for developing climate datasets from satellites. Christy has served on the IPCC panels as Contributor, Key Contributor and Lead Author and has testified before the U.S. Congress, federal court, many state legislatures and regulatory boards on climate issues.
Christy, J. R., W. B. Norris, R. W. Spencer, and J. J. Hnilo. Tropospheric temperature change since 1979 from tropical radiosonde and satellite measurements, J. Geophys. Res., 2007. 112, D06102, doi:10.1029/2005JD006881.
Christy, J.R., B. Herman, R. Pielke, Sr., P. Klotzbach, R.T. McNider, J.J. Hnilo, R.W. Spencer, T. Chase and D. Douglass, (2010): What do observational datasets say about modeled tropospheric temperature trends since 1979? Remote Sens. 2, 2138-2169.
Christy, J.R., R.W. Spencer and W.B. Norris, 2011: The role of remote sensing in monitoring global bulk atmospheric temperatures. Int. J. Remote Sens., 32, 671-685, DOI:10.1080/01431161.2010.517803.
Douglass, D. and J.R. Christy, 2013: Reconciling observations of global temperature change: 2013. Energy and Env., 24 No. 3-4, 414-419.
Guemas, V., F.J. Doblas-Reyes, I. Andrea-Burillo and M. Asif, 2012: Retrospective prediction of global warming slowdown in the past decade. Nature Clim. Ch., 3, 649-653, DOI:10.1038/nclimate1863.
Spencer, R.W. and W.D. Braswell, 2010: On the diagnosis of radiative feedback in the presence of unknown radiative forcing. J. Geophys. Res., 115, DOI:10.1019/2009JD013371.
Stevens, B. and S. Bony, 2013. What Are Climate Models Missing? Science. 31 May 2013. Doi:10.1126/science/1237554.