Climate Change & Tropospheric Temperature Trends

Since then, many important strides have been made toward these goals. AOGCM’s have improved considerably since the NRC report. The latest and best ones do far better than their predecessors at reproducing observed 20th century climate changes. Many have done so without resorting to flux corrections. The UAH and RSS teams continue the painstaking work of refining their own MSU/AMSU troposphere and stratosphere products. Slowly, but surely, their analyses are growing in accuracy as more uncertainties are discovered and corrected for and with each passing day their datasets chip away at the short record length that has plagued satellite dataset analyses of global change. Radiosonde datasets are improving also. At the time of this writing, the U.K. Met Office has released Version 2.3/2.3s of their HadRT product and are working on an even newer product designated HadAT. HadAT will be more spacio-temporally consistent that HadRT products and will rely on near-neighbor station comparisons rather than MSU comparisons for the detection of anomalous record changes. As such, it will be independent of the MSU record. The NOAA Air Resources and Geophysical Fluid Dynamics Laboratories continue their work expanding the original LKS radiosonde analysis (Lanzante et al., 2003). The new product, designated the Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) has to date extended the original product to 2003 using a new semi-automated first difference method to append 1997-present radiosonde data to data from the original 81 station network. The method avoids the tedious, labor intensive work of the original and is designed to provide optimal use in estimating long-term small trends, which is exactly what is needed for global change studies.

Proposals have also been made that would allow the use of new data that was not previously suitable for long-term upper-air studies. MSU Channel 1 for instance, views the lower troposphere and could provide much needed data about the surface to 850 hPa layer. To date, it has not been used for these studies due to the high degree of contamination it receives from surface emissivity and the problems encountered in correcting for it. There are also plans to develop improved characterizations of surface emissivity that would make accurate surface corrections possible for this channel and allow it to be used. This would result in valuable ancillary data as well as improved surface corrections for MSU2 and MSU TLT. Likewise the High Resolution Infrared Sounder (HIRS) which operates in the infrared, is also more sensitive to the lower troposphere than MSU2 and would also be usable if accurate surface emissivity corrections were available. This device would also provide high resolution detection of clouds, and thus better characterizations of their impact on incoming and outgoing long-wavelength radiation (Karl et al., 2002). As these proposals are implemented and the length of upper-air datasets grows, so will our understanding of the troposphere and our ability to detect any and all anthropogenic “fingerprints. As these methods are refined and our knowledge of troposphere and stratosphere dynamics grows, the remaining uncertainties should diminish. Given the independent evidence for an anthropogenic fingerprint on global surface temperature trends, we have every reason to believe that the remaining questions about the resulting impact on the troposphere will be resolved as well.

Appendix I - The Fu et al. Method

The Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU) packages carried by NOAA’s Polar Orbiting Environmental Satellites (POES) measure upwelling atmospheric radiation at specific microwave frequencies and use these to derive a bulk brightness temperature for the atmospheric “column” being viewed at any given moment. The total amount of radiation received is determined by surface temperature, atmospheric temperature, and the optical depth of the atmosphere at the frequency being monitored (which is actually quite large at the microwave frequencies used by MSU detectors, and therefore not likely to be a significant factor in the weighting function profile). For each MSU observation, the intensity measured will be given by the sum total of these emissions, the atmospheric contribution increasing with altitude until a peak is reached and falling off to zero from there. Thus, after correction for surface effects, the total intensity measured will correspond to a bulk Brightness Temperature T_b of this vertical layer with the lion’s share of the signal corresponding to the altitude of peak emission. For a perfect blackbody emitting at Frequency ν with no dispersion, Brightness Temperature T_b is defined as,

I_ν = B_ν(T_b) (A-1)

Where I_ν is the Specific Intensity of the detected radiation at Frequency ν, given in J sec^-1 m^-2 ster^-1 Hz^-1 and B_ν(T) is the corresponding Blackbody Intensity as determined from Planck’s Law. For a perfect signal with no dispersion, this is given by,

(A-2)

Where h is the Planck Constant, k is the Boltzmann Constant, and c is the Speed of Light. In reality, MSU and AMSU radiometers detect radiation with nominal beam widths of 7.5 deg (MSU) and 3.3 degrees (AMSU) at full width half maximum power (FWHM). In addition, they also experience input from side lobe emissions, reflectance off of spacecraft surfaces, and occasional “light pollution” from the moon. These are corrected out so that after calibration against the hot target and deep space temperatures, I_ν measured as raw “digital counts” will correlate directly to a T_b value.

Because the measured I_ν is the sum total of upwelling radiation emissions from broad layers of the atmosphere, T_b will be given by the integrated sum of the actual vertical temperature of the atmosphere factored by a weighting function that reflects both the emissive strengths at the frequency being measured and the optical depth of the atmosphere to that frequency. Thus, the observed brightness temperature corresponding to MSU/AMSU observation at nadir is given by,

(A-3)

where W_S is the Surface Emissivity factor, T_S is Surface Temperature, W(z) is the Weighting Function giving emitted intensity at Frequency ν as a function of Altitude z, and T(z) is the actual Vertical Temperature Profile of the atmospheric column within the MSU/AMSU beam view. As a weighting, or “emission density” function of altitude, W(z) must also meet the requirement of,

(A-4)

The specific atmospheric layer corresponding toT_b will be dependent on the frequency being detected and its associated weighting function. MSU Channel 2 (MSU2/AMSU5) detects at a frequency of 53.74 GHz and receives most of its input from the lower and middle troposphere. MSU Channel 4 (MSU4/AMSU9) detects at 57.95 GHz and receives mainly from the lower stratosphere. Figure 7 shows the Weighting Functions for MSU2 and MSU4, respectively designated as W₂(z) and W₄(z). It can be seen that whereas MSU2 receives most of its signal from the lower and middle troposphere, it still receives significant input from above 300-100 hPa, which corresponds globally to the tropopause. During the satellite era stratospheric trends have been quite different than those of the troposphere. Because MSU2 sees these differences above 300-100 hPa, it will alias those trends into the troposphere signal and will therefore not accurately reflect tropospheric trends.

With regard to comparisons of surface and troposphere temperature trends, the layer that is of most interest is the 850-300 hPa layer, often referred to as the “Free Troposphere”. To effectively measure trends for this layer using MSU2 we need to remove the signal contributions it receives from the surface and the atmosphere above 300 hPa. Because MSU4 receives nearly all of its signal from above 200 hPa and has a temperature trend history that is relatively stable compared to that of the troposphere, it can be used to do this. The Brightness temperature T₂ observed by MSU2 is derived from equation A-3 as,

(A-5)

Above the surface affected layer, this can be separated into vertical layer components above and below the tropopause giving,

(A-6)

where 200 hPa is taken to be the global average tropopause altitude. It is evident from Figures 7 and 62 that both MSU2 and MSU4 receive significant portions of their signal from the 300-100 hPa layer, and MSU2 is still receiving non-negligible contributions from above it. MSU4 however, receives virtually all of its signal from above 300 hPa and in fact peaks near 100 hPa where stratospheric brightness temperatures and trends will be clearest. Because of this, if the MSU2 signal above 300 hPa is expressed as a function of MSU4, it will be possible to account explicitly for both the temperature and trend impacts on MSU2 and explicitly separate them from the desired free troposphere trend. We now define the Free Troposphere Brightness Temperature T_FT for the 850-300 hPa layer as,

(A-7)

This can in turn be expressed in terms of a free troposphere Weighting Function W_FT in the usual manner as,

(A-8)

Taking the 200 hPa level as the global average tropopause height, we may express W_FT in terms of the MSU2 and MSU4 signals by respectively defining the Channel Correction Functions α₂(z) and α₄(z) with the following conditions,

(A-9.1)