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2—
Background
Estimates of temperature variations near the earth's surface are
based on thermometer readings taken daily at thousands of land
stations and on board thousands of ships. Dating back into the late
nineteenth century, the data coverage has been dense enough to
reveal the existence of gradual changes in hemispheric- and
global-mean surface temperature. A time series of global-mean
temperature from 1880 to 1998 (Figure 2.1) displays short-term
fluctuations that can be identified with El Niño events and
volcanic eruptions. Superimposed upon these short-term fluctuations
in the time series are more gradual variations that include a
warming of between 0.4 and 0.8 °C over the course of the
century. The exact amount of estimated warming depends upon which
of the existing compilations of the data is used as a basis for the
calculation, the method used to estimate global means on the basis
of irregularly spaced station observations, and the way in which
the data are smoothed in time. Such globally averaged time series
are not necessarily representative of local conditions: for
example, Canada and Siberia have warmed much more rapidly during
the past 20 years than indicated in Figure 2.1, while parts of the
high latitude North Atlantic and North Pacific regions have cooled
slightly. In order to estimate globally averaged temperature
changes with a high degree of accuracy, it is necessary to have a
broad spatial distribution of observations that are made with high
precision.break
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Representative terms from entire chapter:
surface temperature
Page 10
Figure 2.1.
Time series of seasonally averaged global surface temperature (December 1879–August 1999)
based on the Quayle et al. (1999) data set, computed as differences from the 1880–1998 mean.
The time series uses-an area-weighted average of the surface air temperature over land and
the temperature of water at the ocean's surface.
Temperature changes at and just above the earth's surface are of
singular importance from the standpoint of societal and human
impacts, and they are also widely regarded as an important
indicator of human-induced climate change. However, if global
warming is caused by the build-up of greenhouse gases in the
atmosphere, it should be evident not only at the earth's surface,
but also in the lower to mid-troposphere. Temperatures aloft can be
measured in a number of ways, two of which are useful for climate
monitoring: by radiosondes (balloon-borne instrument packages,
including thermometers, released daily or twice daily at a network
of observing stations throughout the world), and by satellite
measurements of microwave radiation emitted by oxygen gas in the
lower to mid-troposphere, taken with an instrument known as the
Microwave Sounding Unit (MSU).5
The balloon measurements are taken at the same Greenwich mean times
each day, whereas the times of day of the satellite measurements
for a given location drift slowly with changes in the satellite
orbits. The radiosonde network has been operative since the late
1940s and substantialcontinue
5 The
Microwave Sounding Unit senses radiation in a number of different
channels, each of which is representative of a different layer of
the atmosphere. The measurements discussed in this report are
derived from channel 2—a channel that senses radiation in the
layer extending from the surface up to about 15 km. To eliminate
the influence of the stratospheric radiation, rather elaborate
processing is required. The processed data are referred to as MSU
2LT (lower to mid-troposphere). Successive, improved versions of
the MSU 2LT data have been produced over the past several years.
The current version (D) was released in early 1999. For further
discussion see chapter 7.
Page 11
only since the mid-1960s that the instrumentation has been
stable enough and sufficiently well documented for these
measurements to be of use for estimating global temperature
changes. Continuous MSU measurements began in 1979.
In the scientific literature on the detection of climate change,
temperatures are commonly expressed in terms of departures from the
local "climate" mean for a specified reference period. In this
report, the reference period is the 20-year period 1979–98.
Such departures from climate means are referred to as "temperature
anomalies." Figure 2.2 shows the patterns of tropospheric
temperature anomalies over the Western Hemisphere, as sensed by the
MSU, during the northern winters (December through February) of
1982–83 and 1997–98, which both correspond to strong El
Niño events in the tropical Pacific, and during the winter
of 1988–89, which corresponds to a La Niña event.
During the El Niño winters, temperatures throughout the
tropics were above the mean of the past 20 years (i.e., the
anomalies were positive), with alternating patches of warm and cold
anomalies at higher latitudes. In contrast, during the La
Niña winter the tropics were colder than the mean for the
20-year period. The patterns in this figure reflect the warmer
global-mean temperatures characteristic of El Niño years, in
contrast to the cooler La Niña years.
Figure 2.3 shows three time series of global-mean temperature
anomalies. The black curve represents surface temperature, and the
colored curves represent the temperature of the lower to
mid-troposphere as inferred from MSU measurements (red) and
radiosonde observations (green). Year-to-year fluctuations are
evident in all three time series, and particularly in the series
for the temperatures aloft. For example, the El Niño years
1983 and 1998 were a few tenths of a degree warmer, while
1992–93 following the eruption of Mt. Pinatubo were a few
tenths of a degree cooler, than the 20-year average. Contrasting
warm El Niño and cold La Niña years show up even more
clearly in the tropical time series shown in Figure 2.4. In both
global and tropical data, the peaks and dips in the satellite and
radiosonde time series correlate quite well. Since these two time
series represent largely independent mean temperature estimates for
the same atmospheric layer, the strong correspondence between them
is further proof that the fluctuations are real. El Niño and
La Niña years are also evident in surface observations for
the tropical belt (Figure 2.4), but they do not show up as clearly
in the global-mean time series (Figure 2.3).break
Page 12
Figure 2.2.
Upper air temperature anomalies over North America and the eastern
Pacific Ocean for three different winters, computed as the average temperature
during December, January, and February. Two of these winters were characterized
by strong El Niños (1982–83 and 1997–98) and one by a strong La Niña (1988–89).
The data are derived from the MSU Channel-2 and represent lower to mid-tropospheric
temperature (the so-called "MSU 2LT"). The contour interval is 1 °C. Warm anomalies
are indicated by orange/red tones and-cold anomalies by blue tones.
Page 13
Figure 2.3.
Seasonal mean time series of global-mean temperature anomalies from 1979 to 1998.
The red curve shows lower to mid-tropospheric temperature from MSU 2LT (Christy
et al., 2000). The green curve shows temperature data from the same layer as measured
by radiosondes (i.e., "simulated 2LT") (Parker et al., 1997). The black curve shows surface
temperature data (a combination of land air and sea based on the Jones et al. (1999) data set).
The light gray line represents the mean of each time series. The first season is March–May 1979,
and the last season is September–November 1999 for the MSU data set and December 1998–
February 1999 for the other two data sets. El Niño, La Niña, and volcanic cooling episodes are
indicated below the figure.
Figure 2.4.
As in Figure 2.3, but-for the tropical domain 20 °S - 20 °N.
Page 14
Upon close inspection, it is evident that the surface
temperature time series in both Figures 2.3 and 2.4 show upward
trends relative to the corresponding tropospheric temperature time
series for the past 20 years. The fit of a trend line to the time
series of global-mean surface temperature (e.g., Figure 2.5)
indicates a warming between 0.25 to 0.4 °C for this 20-year
period, or approximately 0.1 to 0.2 °C per decade,6 depending upon which of the existing
data sets is used to represent the surface temperatures, and
exactly how the fitting is done. In contrast, the tropospheric time
series exhibits a smaller upward temperature trend of about 0.1
°C during this 20-year period. This disparity between the
recent trends in global-mean surface and tropospheric temperature
is the motivation for this report. Since this phenomenon first
became apparent in the early 1990s, the research community has been
seeking to identify and quantify possible sources of errors in the
surface and upper air temperature measurements, and it has been
trying to understand the physical processes that may have caused
surface and upper air temperatures to change relative to one
another. A number of biases in the data sets have been identified
and corrected, and the process of refining the data sets is
continuing.
In considering possible sources of errors in the satellite,
radiosonde, and surface-based temperature measurements, it should
be noted at the outset that none of these measurement systems was
specifically designed for long-term climate monitoring (NRC, 1999).
Changes in instrumentation and station locations have introduced
time-varying biases into all three temperature time series. In
principle, time series can be adjusted to remove these artifacts,
but in practice there is some ambiguity in making such corrections.
Decisions concerning which corrections need to be made, and how to
implement them, are subject to debate. While many adjustments have
been implemented, some quite recently, there will always remain a
possibility of biases in the data that may be beyond the range of
the current formal error estimates based on currently recognized
sources of error. One mitigating factor is thecontinue
6 In the
literature on climate change, rates of change observed during
prescribed intervals such as the past 20 years are conventionally
expressed in units of degrees per decade. Rates of change computed
in this manner are not necessarily applicable to periods of record
outside the interval for which they were estimated. For example,
the rate of warming of surface air temperature observed during the
past 20 years is much greater than that observed during the
previous 20-year interval, 1960–79, and is not necessarily
indicative of the rate of temperature change that will be observed
during the future interval 2000–2019.
Page 15
independence of both the measurement errors and the
uncertainties in satellite, radiosonde, and surface-based
temperature records, which lends greater confidence to an
assessment based on all three measurement categories than to an
assessment based on any one of them in isolation.
Figure 2.5.
Time series of global-mean surface temperature from 1979 to 1998, repeated from
Figure 2.3, shown with a trend line fitted by the method of ordinary least squares.
The numerical value of the 20-year trend based on this particular data set (Jones et
al., 1999) and fitting method is 0. 19 °C/decade.
A concern that has been raised with respect to the surface-based
temperature measurements is the effect of land use changes such as
urbanization. As growing metropolitan areas encroach into the
surroundings of formerly rural observing stations, the temperatures
at these stations rise, particularly at night, in response to the
well-documented "urban heat island effect." Some have suggested
that much of the observed rise in global surface temperature during
the twentieth century might be merely the expression of such local
environmental transformations that are real, but not necessarily a
signature of the global warming predicted to be associated with an
increase in atmospheric greenhouse gas concentrations. These
concerns have been addressed in numerous studies over the years
that have sought to quantify the effect of land use changes and
adjust the estimated global surface temperaturecontinue
Page 16
trends accordingly. There have also been continuing efforts to
document changes in instrumentation and observing practices, and to
make appropriate adjustments in the data to compensate for them.
Documentation of instrumentation and observing practices is also
critical with respect to the radiosonde data. Ongoing efforts are
being made to recover information on the past observing practices
of the various national weather services and to apply adjustments
as appropriate.
The major uncertainties in satellite measurements of upper air
temperature are due to sensor and spacecraft biases and
instabilities, the characteristics of which need to be estimated by
performing satellite intercalibrations during overlapping
intervals. These intervals are designed to be about two years long,
but on two occasions, the overlap was substantially shorter due to
instrument failures. The temperature measurements have recently
been adjusted for gradual changes in satellite orbits that affect
the levels and times of day at which the microwave radiation is
sampled, and for small non-linearities in sensor performance, which
cannot be determined in advance on the basis of laboratory
calibrations. Because there is, in effect, only one satellite-based
temperature record for which most of the processing has been
performed by a single group, efforts to independently verify the
MSU temperature measurements have, of necessity, focused on
comparisons with radiosonde data.
Calculating the global-mean temperature anomaly for a particular
season based on the MSU is straightforward, because the
measurements are densely spaced and global in extent. However, for
radiosonde observations, which are irregularly spaced with large
gaps over the oceans (Figure 2.6), global-mean temperature is
estimated on the basis of those stations operating during the
season in question. Notice, for example, how the radiosonde data
fail to sample the strongest local temperature anomalies over the
subtropical eastern Pacific shown in Figure 2.2. Even in the
absence of any real temperature variation, the global-mean
temperature anomaly computed from radiosonde data could conceivably
change from one season or decade to the next, merely as a result of
stations in one of these poorly sampled regions going into or out
of operation. Surface-based estimates are also subject to similar
discontinuities, but they are not considered as serious a problem
because there are so many more surface stations than there are
radiosonde stations (compare Figures 2.6 and 2.7). In addition,
surface data coverage over the oceans is much better, with the
notable exception of highcontinue
Page 17
latitudes in the Southern Hemisphere. The effects of this uneven
sampling are being investigated and quantified in several ways, for
example by estimating ''true" global-mean temperatures from the
complete fields generated by satellite observations, blends of
satellite and in situ data, or climate models, and then sampling
these fields using the actual (incomplete) observed data coverage
(see chapter 9).
Figure 2.6.
Location of radiosonde stations in the World Meteorological Organization's (WMO)
Global Observing System upper air network. Fewer than half of these 905 stations
report monthly climatic data, and only about two-thirds have reported regularly over
the past few decades. Although most report some daily data, tropical portions of South
America and Africa are often missing. Note the sparseness of the stations over the oceans
and the high latitudes.
Measurement errors and uncertainties are not the whole story.
The possibility that there may have been a real disparity between
trends in surface and tropospheric temperature also needs to be
considered. One way of making such an assessment is to consider
whether simulations of the evolving climate of the past 20 years in
climate models exhibit disparities as large as those that are
observed.break
Page 18
Figure 2.7.
Location of surface temperature observations. The map is a composite of all of the
Global Historical Climatology Network temperature stations (small dots), and the
ship-based and floating and moored buoy observations from a single week used in
the production of the Reynolds and Smith (1994) sea surface temperature data set
(very small dots).
Climate models are tools that can be used to relate changes at
the surface to those in the troposphere. Although today's
state-of-the-art models accurately depict many physical processes,
they are deficient in several respects, owing to difficulties in
representing small-scale processes, such as those associated with
clouds. Moreover, the detailed three-dimensional spatial structure
and the temporal evolution of the many forcings of the climate
system that are used to "drive" the models are poorly known. Model
simulations are helpful in understanding the disparity between the
20-year trends in surface versus tropospheric temperatures, but
they are not sufficiently reliable to provide a definitive
assessment of whether the trends at these two levels are physically
consistent.
Due to the non-deterministic nature of the climate system, an
ensemble7 of simulations run with
the same climate model yields acontinue
7 In such an
ensemble, each individual simulation is run with the same
time-dependent climate forcings (greenhouse gases, aerosols, etc),
but with different, but equally
(footnote continued on the next page)
Page 19
number of different possible scenarios, each with its own
20-year trends at various levels of the atmosphere. It is only by
performing ensembles of simulations with these models that it is
possible to assess whether the observed disparity lies within the
range of what should be regarded as physically plausible. Because
these numerical experiments are computationally intensive, only a
very limited number of them have been run thus far.
It is evident from Figure 2.3 that globally averaged temperature
fluctuations associated with El Niño tend to be larger aloft
than at the surface, and this behavior is well-simulated in
numerical models. These models show evidence of stronger cooling
aloft than at the surface in the wake of major volcanic eruptions
such as Mt. Pinatubo and in the amplitude of temperature variations
induced by fluctuating solar irradiance. The longer the period over
which trends are computed, the more these naturally occurring
fluctuations in the temperature time series tend to average out.
For example, the influence of these phenomena upon the trends
should be much smaller when the trends are estimated for a 20-year
long record compared with a 5-year record. However, model
simulations suggest that such natural variability can still amount
to an appreciable fraction of the observed disparity between the
global-mean temperature trends at the earth's surface and in the
lower to mid-troposphere. Because 20-year trends can be
substantially influenced by just a few single or multi-year "warm"
or "cold" events, they are not necessarily representative of the
true response of the climate system to the more gradual changes in
atmospheric composition that are taking place in response to human
activities.
A number of different human-induced forcings are, in fact,
believed to have contributed to the observed temperature changes
during the past 20 years. The climate system is highly
non-linear8 and relatively little
is known about the effect on temperature changes resulting from
human contributions to the changing three-dimensional distributions
of ozone and aerosols, either or both of which may have been
partially responsible for the observed discrepancy between surface
and lower to mid-tropospheric temperature changes. The aerosol
contribution iscontinue
(footnote continued from the previous
page)
plausible initial conditions. Differences among
the climates in the individual stimulations are interpreted as
being due to the internal (unforced) variability of the climate
system.
8 Highly
non-linear in this context means that there is no guarantee that
the response of the climate system to the sum of these forcings
would be equal to the sum of its responses to the individual
forcings if each of them had occurred in isolation.
Page 20
particularly difficult to estimate because of the limited
understanding of how aerosols affect cloud properties, which affect
the transfer of radiation through the atmosphere. In addition to
changes in atmospheric composition, land use changes can be a
significant factor in causing climate change at the earth's
surface.
Despite the many unresolved issues touched on in this chapter
and discussed in more detail in chapters 5–9, the progress
that has been achieved over the past few years provides a basis for
drawing some tentative conclusions concerning the nature of the
observed differences between surface and upper air temperature
trends, and their implications for the detection and attribution of
global climate change.break