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Page 41
7—
MSU Observations
Introduction
The Microwave Sounding Unit (MSU) is a microwave radiometer that
flies aboard NOAA's polar orbiting weather satellites. Each day,
the MSU observes approximately 80% of the earth's surface, with the
orbit shifting slightly each day so that 100% coverage is achieved
over a three to four day period. To date, nine MSUs have been used
operationally, forming an uninterrupted daily time series from 1979
to the present. One MSU remains in orbit as the last representative
of this series, which in 1998 began to be replaced by the Advanced
Microwave Sounding Unit.
The MSU observes the earth's natural upwelling radiation at four
frequencies between 50 and 60 GHz. The particular channel upon
which this report focuses is channel 2 (53.74 GHz). The radiation
detected by channel 2 comes from the earth's atmosphere
(90–95%) and surface (5–10%). The bulk of this
radiation originates in the troposphere, the layer from the earth's
surface up to about 10 km. The MSU also monitors the lower
stratosphere through its channel 4. The intensity of radiation
observed in these channels is directly proportional to the
temperature of the air; hence, MSU can be used as a satellite
"thermometer" for measuring air temperature.
The air temperature computed directly from channel 2 is
representative of the middle-to-upper troposphere (centered about 7
km above the surface). A small but significant part of this
radiation emanatescontinue
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7—
MSU Observations
Introduction
The Microwave Sounding Unit (MSU) is a microwave radiometer that
flies aboard NOAA's polar orbiting weather satellites. Each day,
the MSU observes approximately 80% of the earth's surface, with the
orbit shifting slightly each day so that 100% coverage is achieved
over a three to four day period. To date, nine MSUs have been used
operationally, forming an uninterrupted daily time series from 1979
to the present. One MSU remains in orbit as the last representative
of this series, which in 1998 began to be replaced by the Advanced
Microwave Sounding Unit.
The MSU observes the earth's natural upwelling radiation at four
frequencies between 50 and 60 GHz. The particular channel upon
which this report focuses is channel 2 (53.74 GHz). The radiation
detected by channel 2 comes from the earth's atmosphere
(90–95%) and surface (5–10%). The bulk of this
radiation originates in the troposphere, the layer from the earth's
surface up to about 10 km. The MSU also monitors the lower
stratosphere through its channel 4. The intensity of radiation
observed in these channels is directly proportional to the
temperature of the air; hence, MSU can be used as a satellite
"thermometer" for measuring air temperature.
The air temperature computed directly from channel 2 is
representative of the middle-to-upper troposphere (centered about 7
km above the surface). A small but significant part of this
radiation emanatescontinue
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Page 42
from the lower stratosphere. This is problematical for detecting
changes related to greenhouse warming, because the warming in the
troposphere is expected to be accompanied by cooling in the lower
stratosphere. Therefore, the blending of the radiation from these
two layers seen by channel 2 partially or completely damps out any
greenhouse warming signal.
There are two approaches to processing the observations to
obtain a pure tropospheric temperature. One approach is to combine
observations from the different channels of the MSU. The other
approach is to exploit observations from the different scan angles
using MSU channel 2 alone. The latter approach is the one discussed
in this report because it is much more mature in terms of the
extent of data-set development and validation. To obtain a
temperature closer to the earth's surface in the latter approach,
observations from different scan angles that the MSU uses to view
the earth's atmosphere are arithmetically combined to reduce the
influence of the upper troposphere and stratosphere (Spencer and
Christy, 1992). The advantage of this technique is that the
resulting lower tropospheric temperature (often referred to as "MSU
2LT") is closer to the earth's surface (centered about 4 km high).
Because the central issue being examined in this study is the
expectation by some that the lower troposphere should exhibit
similar temperature trends as the surface, the panel exclusively
discusses the MSU 2LT product in this report. The drawback of using
the MSU 2LT product is that the retrieval method relies on a
subtraction of adjacent view angles, which (a) increases
measurement noise and (b) doubles the sensitivity of the
measurements to surface emissions (to 10% over oceans, 20% over
land) (Spencer and Christy, 1992). These effects more than double
the error characteristics for MSU 2LT relative to MSU 2.
MSU Temperature Trends
The geographical distribution of MSU lower to mid-tropospheric
temperature trends is shown in Figure 7.1. It is evident that the
regions of rapid surface warming apparent in Figure 6.2 (e.g.,
Western Europe, Eastern Russia) tend to be characterized by rapid
warming aloft, and vice versa. In contrast to the surface data,
which exhibit a warming trend at most locations, the satellite data
show roughly equal areas of warming and cooling. Whereas it is
obvious from a visual inspection of Figure 6.2continue
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that the global-mean surface temperature trend is upward, the
same cannot be said of the trend inferred from the MSU data; the
global-mean trend may be viewed as a small difference between
warming trends over some areas and cooling trends over others.
Figure 7.1
Global lower to mid-tropospheric temperature trends (°C/decade) from the MSU
version D over the 20-year period 1979–98. These ordinary least square trends are
computed from data from Christy et al. (2000).
The time series of seasonally averaged global MSU lower to
mid-tropospheric anomalies is shown in Figure 2.3. The data reveal
major swings in temperature over relatively short periods, which
are largely the result of climate perturbations such as the El
Niño—Southern Oscillation (ENSO) warming events in
1983, 1987, 1991, and 1997; the cooling events in 1985, 1988, 1996,
and 1999; and the volcanic aerosol cooling events in 1982 and 1991
(Christy and McNider, 1994). Over the past 20 years, MSU
observations indicate that the globally averaged lower to
mid-tropospheric temperature has increased at a rate of
approximately 0.05 °C/decade, as computed using the ordinary
least squares statistical method. It is important to note that a
single 20-year period of record iscontinue
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unlikely to be representative of both future rates of change and
rates of change over much longer periods of the historical record.
The reasons for this include the large natural variability
associated with El Niño, together with both long- and
short-term changes in the external factors that can influence
climate, such as volcanic eruptions, the sun, and greenhouse
gases.
Sources of Uncertainty in Trend
Estimates
As with all climate data sets, there are drawbacks and
limitations associated with the MSU temperatures. To begin with,
MSU channels 2 and 4, as well as products derived directly from
these channels, have quite coarse vertical resolution. The MSU
cannot measure the temperature at a specific altitude, as is done
by balloon observations. Rather, it detects a weighted average of
the temperature throughout the atmosphere. This is particularly
problematic for diagnosing the causes of climate change, because
the different atmospheric layers may exhibit different long-term
temperature trends. For example, during the 20-year period of MSU
operation, the evidence is clear that the surface layer has
experienced a warming trend, while significant cooling has occurred
in the stratospheric layers observed by MSU channel 4.
Systematic measurement errors are another problem for the MSU.
Variations in sensor gain (i.e., the ratio of the perceived signal
to the actual signal) are particularly problematic in that these
gain variations can be misinterpreted as trends in air temperature.
In principle, gain variations can be measured (and hence corrected)
by the onboard two-point calibration system, consisting of a warm
load at a known temperature and cold space observations at 2.7
Kelvin. However, in practice the gain cannot be exactly determined
because of the presence of small non-linearities in the MSU
response to incoming radiation. It appears that this non-linear
response was not properly characterized in the pre-launch
thermal-vacuum tests. As a result, the two-point calibration system
is not completely removing gain variations. To correct this
problem, a rather elaborate post-launch analysis relies on the fact
that the radiometer gain varies with the physical temperature of
the front-end radiometer components (Christy et al., 2000).
Systematic measurement differences between different satellites
over many years are correlated with the physical temperature of the
radiometers. A time-hard
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varying gain correction, which is a function of instrument
temperature, is then applied to those MSUs that appear to have
experienced calibration problems. This additional complexity in MSU
data processing, in conjunction with a non-linear gain problem that
is poorly understood, decreases our confidence in the ability of
MSU to measure long-term trends. To the extent that these problems
are random, they can be reduced by averaging the millions of
observations (over 15,000 per day). However, residual systematic
calibration errors not correlated with the radiometer temperatures
cannot be removed in this way.
Systematic measurement errors also impact our ability to
intercalibrate the series of MSUs. The offsets between the MSU
observations from each of the satellites can be readily determined
if the bias is constant, but is more difficult to determine if the
satellite is drifting in orbit (see below). During periods of
satellite overlap, the temperatures measured by two different MSUs
are compared. Typically, a temperature offset of up to ±0.4 °C is found. Given the fact
that the MSUs are nearly identical instruments, it is not entirely
clear what is causing these offsets (perhaps very small
manufacturing differences or satellite altitude differences). In
any case, these inter-satellite offsets are addressed by adding
small bias corrections to data from the various MSUs so that they
are, on average, in agreement during overlap periods. This type of
satellite intercalibration works best for long overlap periods (one
year or greater). Unfortunately, in the case of the NOAA-9 MSU the
overlap period was only 102 days. To bridge the NOAA-9 period,
several different adjustment methods were tested (Christy et al.,
1998). These methods produced a spread in trends of about 0.1
°C/decade. The method that produced the lowest error
characteristics and the most data available for analysis was
chosen. This selected method also had the desirable feature of
producing a decadal trend that was close to the mean of all other
possible methods. Nevertheless, the relatively small number of
observations during overlapping periods in 1986–87, coupled
with uncertainties arising from the choice of method used to
correct inter-satellite biases, introduces a further source of
uncertainty in MSU-based estimates of decadal-scale trends.
Aside from the issue related to the method chosen for merging
the satellite data, there is also an uncertainty associated with
determining each satellite's bias relative to some reference value.
Several tests have been performed in which the biases were
calculated from separate subsections of the overlapping periods,
demonstrating very highcontinue
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reproducibility of the results. Even so, small inter-satellite
bias errors can accumulate in such a way as to introduce errors in
the long-term trend.
Even in the absence of measurement error, the drift of the
satellite orbit has the potential to introduce spurious signals
into the MSU temperature trend. One component of orbit drift is the
decrease in satellite altitude that occurs after launch.
Fortunately, this effect can be precisely modeled using the
satellite orbital data and a relatively simple radiative transfer
model. However, it is worth noting that this particular effect,
known as orbit decay, was not recognized until quite recently
(Wentz and Schabel, 1998), which suggests that there may be other
subtle but important corrections that still need to be applied. The
other component of orbit drift is the change in the local time for
satellite observations. As the satellite slowly drifts in time, it
will observe a warming or cooling trend simply due to the change in
time of day being observed on earth (night is cooler than day). If
no correction is applied, then this diurnal signal will be confused
with an interannual signal because the diurnal drift is on time
scales of the order of 0.5 hr/year. As was the case for the
radiometer gain problem, a complex analysis is required to remove
the diurnal drift signal. In Christy et al. (2000), the effect of
the diurnal drift is estimated from the difference between the left
and right sides of the MSU viewing swath, which represents a
difference in local time ranging from over one hour in the tropics
to several hours at the poles. This procedure attempts to remove
most of the diurnal signal, but some error will remain. The
preceding discussion of problems addresses each individually,
whereas in practice these problems are not necessarily independent
(for example, a bias that is changing with time), increasing the
uncertainty of the corrections.
In light of the aforementioned problems, the obvious question is
how accurately can MSU measure long-term trends. This is a
difficult question to answer. The errors associated with radiometer
gain, inter-satellite calibration, and diurnal drift are difficult
to model, and there is always the possibility of other, yet to be
found, effects.
The most recent version of MSU 2LT, which includes adjustments
for orbital changes, instrument heating, and changes in diurnal
sampling, is referred to as version D, distinguishing it from
earlier versions labeled A, B, and C. Over the entire time series,
the adjustments to version D relative to version C affect the trend
of version C as follows: (1) orbit decay, +0.10 °C/decade; (2)
diurnal drift, -0.03 °C/decade; and (3)continue
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instrument body effect on several instruments, along with the
impact of new NOAA-12 calibration coefficients, -0.04
°C/decade. The net effect therefore is +0.03 °C/decade of
version D versus version C.
The time series of globally averaged temperatures from versions
C and D are compared in Figure 7.2 together with the difference
time series (D minus C), which represents the correction to version
C. During individual seasons, the corrections amount to as much as
a few tenths of a degree C. The spikiness in the difference between
C and D in post 1991 data is due to the erroneous calibration
coefficients used in version C, discovered by Mo (1995), and
corrected in version D. The upward trend in the corrections, which
amounts to +0.03 °C/decade, is evident from a visual inspection
of the difference time series in Figure 7.2. The cumulative effect
of all the corrections that have been made to the MSU data since
the release of version A also amounts to +0.03 °C/decade.
Consistent with the recommendations of this report that
independent processing efforts should be undertaken, a separate MSU
time series has been created by Prabhakara et al. (1998). This data
set is based on a small portion of the MSU channel 2 data, with no
adjustments for the effects described above. In many ways it is
similar to the Spencer/Christy MSU 2 version A. However, the data
set has yet to be completed for 1979–98, and has not been
compared with other data sets in detail for assessment
purposes.
Because there is only one set of observations and a limited
number of processing efforts, there is no rigorous way to
objectively compute the MSU's measurement precision. However,
assessments can be made of the effect of perturbing the
methodology, as well as the assumptions that are used to compute
MSU temperatures. One analysis of this type, which also included
direct radiosonde comparisons, suggests a measurement error of
±0.06 °C/decade14 for the MSU trend (Christy et al,
2000).continue
14 This
particular value was derived from three separate calculations. (1)
The 95% error estimate for each of the three correction procedures
was determined and applied to the data. Then the year of worst
reproducibility was identified (i.e., to create a conservative
estimate). The magnitude of this error was applied to all years
(i.e., each year had an error bar with which the 95% trend range
could be determined.). (2) Using co-located radiosonde and MSU
differences on 2.5-degree grids, error estimates were calculated
for regions and then scaled globally in a conservative manner by
assigning all error to MSU (because these were stable, U.S.
controlled stations). (3) Using two different global radiosonde
data sets, error estimates were derived. The value ±0.06 °C/decade encompasses the 95%
range from these three methods of estimation. However, this
estimate does not include testing either of the sensitivity of the
period analyzed, or of the substantial uncertainty associated with
adjustments to the data from NOAA-9.
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Figure 7.2
Globally averaged time series of lower to mid-tropospheric MSU temperature
anomalies from version C (orange curve; Christy et al., 1998) and version D (red
curve; Christy et al., 2000), as well as the difference between versions D and C (D-C)
(gray curve, bottom) from 1979 to 1998. To highlight the differences between the curves,
the vertical scale has been expanded by 50% relative to the report's other MSU time series figures.
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Others, however, view this analysis as not rigorous enough to
reliably identify measurement error at the precision required for
decadal-scale climate monitoring, and estimate the measurement
error in the MSU trend to be about
±0.1 °C/decade (Hurrell and Trenberth, 1998), or
possibly greater.break
Representative terms from entire chapter:
measurement error
