The roles of aerosols and clouds as radiative forcing is the least well known aspect of the climate change problem. According the the IPCC 2007 Summary for Policymakers, aerosols represent a radiative forcing of approximately -1.2 W/m2, combining the direct effect of aerosols and the cloud albedo effect (also known as the first indirect effect or the Twomey effect). The estimate error in this value is +/- 1.2 W/m2. Other radiative forcings, such as CO2, have a much higher level of scientific understanding. The result is that the total net anthropogenic forcing is estimated at 1.6 W/m2 +/- 0.9 W/m2. The large uncertainty in the net forcing is almost all the result of unknown, or not-well understood, effects from clouds and aerosols.

Kim and Ramanathan (2008) use multiple satellite observing systems, along with ground-based measurements to compare the radiative forcing from aerosols and clouds with model results.
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On Dr. Roger Pielke’s Weblog, Dr. Ben Herman discusses the maximum temperature trends with respect to the HO-83 thermometers, which have been shown to have a warm bias. He states that the “thermometers have… been replaced, but to the best of my knowledge, none of the station data have been corrected for this problem.” This post will take a look at one station’s maximum temperature data. In particular, I’ll be looking at the temperature record from Tucson, station 28815. The data is available here, and I believe it’s freely available.

The main problem is that I don’t know the exact dates when the HO-83 thermometer was used at this station. However, Steve McIntyre states that these thermometers were “introduced in the early 1990s”, and Ben Herman states that they were “were replaced in the mid to late 90’s”. He also states that the “error in the Tucson data was about 2-3 deg F.” From this information, it’s possible to see what effect the HO-83 thermometers may have had on the temperature trends in Tucson.
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Until I get around to making a dedicated section on this site for IDL-related stuff, I have to post this here. Most, if not all, of you will not care about this at all. I’d suggest simply ignoring it.

Apply_hanning.pro
A function that takes a time series and applies a Hanning filter to it. You can actually do this in IDL in one line, but this adds a few checks for errors. It can handle 1D and 2D arrays.

Contour_on_map.pro
A procedure that will take a 2D array of values, with corresponding 1D arrays of latitudes and longitudes and then plot it on a map. This may sound simple, and it is, but it’s trickier than it sounds because IDLs documentation is not so good.

Cumulative_correlation.pro
A procedure that will plot the cumulative correlation of two time series. This makes it easier to see over what temporal, or spatial, frequencies the time series correlate than, for instance, a power spectrum from a Fourier transform.

Interpolate_bad_points
Exactly as it sounds. It finds the “bad” points in a time series and does a linear interpolation to fill them in.

Plot2axis.pro
Plot two time series on the same plot, but with different vertical axis. Allows lots of customization using keywords. Important note: This procedure will not work as currently written unless you have a function called numtostring() which takes a number (integer, float, or double), and converts it into a string variable. This is a function I wrote that I have not released yet. If you wish to use this procedure, just replace all occurrences of “numtostring(” with string(”; without the quotes.

Silence_math_errors.pro
Again, exactly as it sounds. Will make it so IDL doesn’t display those silly math error, like the floating underflow. Note: there really isn’t a good reason to use this; the displaying of the errors doesn’t hurt anything, and it’s better to actually fix the error than just ignore it.

Fully understanding the effects of aerosols on stratocumulus clouds is important in the climate change discussion. Low-level clouds have a significant cooling effect on the planet. In the 1970s Twomey showed that, all things being equal, by adding pollution (aerosols) to these clouds, they have a higher optical depth and thus reflect more of the incoming sunlight. This cools the surface more than the original cloud. However, things aren’t that simple; they rarely are in science. There are other effects that must be taken into account when looking at the effects of aerosols.

The work done by Guillaume S. Mauger and Joel R. Norris in Stratocumulus Sensitivity to Aerosols and Dynamics [May be behind paywall] is such an example. They attempt to find the causation behind the correlation between aerosol optical depth and cloud cover. The do this by looking at the stability of the boundary layer.
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