Climate Change & Tropospheric Temperature Trends

(A-9.2)

(A-9.3)

The resulting form of W_FT is shown in Figure 48 alongside of W₂ and W₄. The function peaks at the same location as W₂, but roughly 15 percent larger, and goes negative above 100 hPa. Note that it must do this to remove the stratospheric component in the MSU2 signal (equation A-9.2). When Fu’s team originally published (Fu et al., 2004) this was a widely voiced criticism. Because a real weighting function must be everywhere positive (i.e. - a real microwave emitter is by definition not an absorber), some argued that the negative portion of this weighting function proved that Fu’s team had overcorrected for the stratosphere. In fact, concern about this was voiced at Nature during the review process prior to publication until the methodology was clarified (Seidel, 2004). Yet because W_FT is a modified weighting function with “real” and “virtual” components, negative values are allowed.

MSU Weighting Functions are non-dimensional and approximately independent of time. Therefore, they may be decoupled from T(z) allowing us to express equation A-9.1 in the form,

(A-10)

where,

(A-10.1)

(A-10.2)

(A-10.3)

and T₂ and T₄ are the observed MSU2 and MSU4 Brightness Temperatures as taken directly from MSU digital counts. Free Troposphere trends can now be derived from equation A-10 as,

(A-11)

Fu’s team used global monthly average temperature anomalies from thye surface to the 10 hPa layer derived from the LKS radiosonde dataset (Lanzante et al., 2003) applied to equations A-9 to derive a₀, a₂, and a₄. Then, regional and global average trends for T₂ and T₄ were derived using least squares methods applied to the MSU products of UAH (Christy et al., 2003), RSS (Mears et al., 2003), and Vinnikov and Grody (2004). From these, equation A-11 yields the desired T_FT trend. The later work of Fu and Johanson (2004) used the same method to derive a₀, a₂, and a₄ from the HadRT2.1 radiosonde dataset (Parker et al., 1997) and the SPARC-STTA stratospheric temperature profile dataset of Ramaswamy et al. (2001) as an independent check of the original method. The key to this method’s success is the relative regional stability of form and trend in T₄ during the satellite era. This allows for the meaningful use of least squares methods applied to T₄ as a means of using regional and global stratospheric trends to correct for that layer’s impact on T₂. Some have criticized the Fu et al. method on the ground that this cannot meaningfully be done. This would be true if stratospheric trends were not monotonic, and regionally and temporally consistent with respect to T₂ trends during the satellite era. But to at least first order, they are. The specific criticisms made of the method are dealt with in the main body of this paper.

Footnotes

1. To a lesser extent, rocketsonde and lidar studies have also been done, but these have not proven practical for regional or global monitoring.
2. Ambient pressure generally drops as altitude increases and can therefore be used as a measure of altitude. The International Standard Atmosphere relates altitude with a global average pressure at a fixed temperature. Local atmospheric pressures often vary from that predicted for a given altitude by the International Standard Atmosphere. Since most atmospheric phenomena are sensitive to pressure rather than actual altitude, it is common in climate change literature to speak of altitude in pressures, generally millibars (mb) or hectopascals (hPa) rather than actual distances.
3. In climate change parlance, the term Zonal refers to the East to West direction, along lines of latitude. The term Meridional, or poleward, refers to North to South, along lines of longitude.
4. Lapse rate is the rate of change of temperature with altitude, expressed in degrees per unit of distance. It is customary to discriminate between moist and dry lapse rates, as the moisture content of the atmosphere is important for how lapse rates affect weather phenomena.
5. Dept. of Physics, University of Rochester, NY.
6. University of Virginia, retired. Science and Environmental Policy Project (SEPP).
7. Environmental Sciences Dept., University of Virginia.
8. The NCEP/NCAR Reanalysis is a composite upper-air analysis product containing several meteorological parameters combined in a global spatial grid of 2.5° x 2.5° (latitude x longitude) resolution from the surface up to the 10 hPa level. It uses data from land and ship based measurements of temperature, wind and humidity, weather forecasts, MSU satellite data, and rawinsonde data (that is, data from radiosondes that have been tracked by radar or radio-theodolite to obtain wind speed and direction). These data sources are tied together by an AGCM (Atmospheric General Circulation Model – no ocean coupling) run in a “frozen” state to evaluate upper-air temperature, pressure, wind, and humidity from 1948 to the present. The MSU data are used to provide weekly raw “soundings” for the Reanalysis. They are not actual weighted brightness temperature measurements of the sort used in upper-air MSU products like those of UAH and RSS, and are independent of those products. Because it is heavily dependent on model based extrapolations from global rawinsonde data, the NCEP/NCAR Reanalysis cannot be considered as independent of the radiosonde record.
   The NCEP/NCAR Reanalysis has proven to be a valuable tool in many upper-air studies because of its reliance on multiple datasets and the stability of the AGCM that ties them together, minimizing the impact of flaws in any one dataset. However, like other upper-air products it too has difficulties that limit its usefulness for studies of the troposphere and lower stratosphere. These include changes in synoptic land station and ship observations records, contamination of some of its data by surface snow and sea-ice albedo, problems accounting for some regional weather patterns such as the annual Indian monsoon season, and all the same limitations of coverage and record continuity that plague the radiosonde record. It is also subject to many of the same issues facing AOGCM’s as well, which can be more problematic in that it is being used for a fine detail extrapolation of in situ data, whereas AOGCM’s are typically used only for large scale predictions of regional and global upper-air trends. The particular version used by Douglass et al. (2004) for their intercomparison study is a recent update of the Reanalysis that is based on 2-meter resolution vertical layer rawinsonde readings. The original NCEP/NCAR Reanalysis product is best described in Kalnay et al. (1996), and the 2-meter update used by Douglass et al. (2004) is described in Kanamitsu et al. (2002).

Glossary

AOGCM   -   Oceanic and Atmospheric General Circulation Model.

AMSU   -   Advanced Microwave Sounding Unit.

AVHRR   -   Advanced Very High Resolution Radiometer.