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  • View in gallery
    Fig. 1.

    HIRS-12 sensitivity functions derived from a zonal mean 1865 HADCM2 atmosphere. The sensitivity functions are normalized in log pressure.

  • View in gallery
    Fig. 2.

    The dependence of HIRS-12 brightness temperature on upper-tropospheric humidity as predicted by the Soden and Bretherton relation.

  • View in gallery
    Fig. 3.

    HIRS-12 yearly mean brightness temperature anomalies, ΔD(x, t), in K.

  • View in gallery
    Fig. 4.

    Temporal standard deviations of HIRS-12 yearly mean brightness temperature data, in K.

  • View in gallery
    Fig. 5.

    Linear trends in HIRS-12 data between 1979 and 1995. Shaded areas indicate trends of greater than 90% significance.

  • View in gallery
    Fig. 6.

    Comparison of TB changes obtained from yearly mean profiles (ΔTB) and those correctly generated using individual profiles (ΔT*B). Superimposed is a least squares linear fit to the data (a = −0.04 K; b = 0.59).

  • View in gallery
    Fig. 7.

    Changes between 1865 and 2045 in the model data: (a) HIRS-12 brightness temperature, (b) upper-tropospheric humidity (UTH), and (c) upper-tropospheric temperature (UTT).

  • View in gallery
    Fig. 8.

    Model data for 1865: (a) HIRS-12 brightness temperatures, (b) UTH, and (c) UTT.

  • View in gallery
    Fig. 9.

    The effect of a change in relative humidity (ΔUTH) on brightness temperatures (ΔM). Scatterplots for (a) 30°N–30°S, (b) 30°–70°N, and 30°–70°S. Superimposed are least squares linear fits to the data.

  • View in gallery
    Fig. 10.

    Pattern similarity statistics R(t) and C(t). Also plotted are the linear least squares fits.

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Spatial Patterns of Climate Variability in Upper-Tropospheric Water Vapor Radiances from Satellite Data and Climate Model Simulations

A. J. GeerSpace and Atmospheric Physics, Imperial College, London, United Kingdom

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J. E. HarriesSpace and Atmospheric Physics, Imperial College, London, United Kingdom

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H. E. BrindleySpace and Atmospheric Physics, Imperial College, London, United Kingdom

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Abstract

The use of multivariate fingerprints and spatial pattern correlation in the detection and attribution of climate change has concentrated on radiosonde temperature fields. However, the large body of radiance data from satellite-borne instruments includes contiguous datasets of up to 17 yr in length and in future years will present the most well-calibrated and large-scale data archive available for climate change studies. Here the authors give an example of the spatial correlation technique used to analyze satellite radiance data. They examine yearly mean brightness temperatures from High Resolution Infrared Spectrometer (HIRS) channel 12, sensitive to upper-tropospheric water vapor and temperature. Atmospheric profiles from a climate change run of the Hadley Centre GCM (HADCM2) are used to simulate the pattern of brightness temperature change for comparison to the satellite data. Investigation shows that strong regional brightness temperature changes are predicted in the Tropics and are dominated by changes in relative humidity in the upper troposphere. At midlatitudes only small changes are predicted, partly due to a compensation between the effects of temperature and relative humidity. The observational data showed some significant regional changes, especially at 60°S, where there was a trend toward lower brightness temperatures. The pattern similarity statistics revealed a small trend between 1979 and 1995 toward the predicted climate change patterns but this was not significant. The detection of any trend is complicated by the high natural variability of HIRS-12 radiances, which is partly associated with the El Niño–Southern Oscillation.

Corresponding author address: Dr. A. J. Geer, Department of Physics, The Blackett Laboratory, Imperial College, London SW7 2BZ, United Kingdom.

Email: a.geer@ic.ac.uk

Abstract

The use of multivariate fingerprints and spatial pattern correlation in the detection and attribution of climate change has concentrated on radiosonde temperature fields. However, the large body of radiance data from satellite-borne instruments includes contiguous datasets of up to 17 yr in length and in future years will present the most well-calibrated and large-scale data archive available for climate change studies. Here the authors give an example of the spatial correlation technique used to analyze satellite radiance data. They examine yearly mean brightness temperatures from High Resolution Infrared Spectrometer (HIRS) channel 12, sensitive to upper-tropospheric water vapor and temperature. Atmospheric profiles from a climate change run of the Hadley Centre GCM (HADCM2) are used to simulate the pattern of brightness temperature change for comparison to the satellite data. Investigation shows that strong regional brightness temperature changes are predicted in the Tropics and are dominated by changes in relative humidity in the upper troposphere. At midlatitudes only small changes are predicted, partly due to a compensation between the effects of temperature and relative humidity. The observational data showed some significant regional changes, especially at 60°S, where there was a trend toward lower brightness temperatures. The pattern similarity statistics revealed a small trend between 1979 and 1995 toward the predicted climate change patterns but this was not significant. The detection of any trend is complicated by the high natural variability of HIRS-12 radiances, which is partly associated with the El Niño–Southern Oscillation.

Corresponding author address: Dr. A. J. Geer, Department of Physics, The Blackett Laboratory, Imperial College, London SW7 2BZ, United Kingdom.

Email: a.geer@ic.ac.uk

1. Introduction

The problem of the detection and attribution of climate change can be approached from two directions. Global climate models (GCMs) are run initially with anthropogenic forcing to predict future changes in climate, known as the “signal,” and without the forcing to estimate the natural fluctuations or “noise.” In combination with these predictions, reliable and long-term climate data must be examined for an emerging signal of any climate change. The changes observed must be attributed to anthropogenic effects rather than natural climate fluctuations. Although surface temperatures are sometimes used in isolation, it has long been suggested that studies looking at many different variables simultaneously, the “fingerprint” approach, will offer a better chance of distinguishing anthropogenic change from natural noise (e.g., Barnett and Schlesinger 1987). Typically, fingerprint studies use spatial pattern matching techniques to compare simulations to observed data. Santer et al. (1996) and Tett et al. (1996) have used this technique to examine the three-dimensional temperature structure of the atmosphere. They have found that the pattern similarity between observational data and model predictions increases with time, suggesting that there might be an influence of anthropogenic effects on climate although many uncertainties remain.

Most fingerprint studies have concentrated on atmospheric temperature data from radiosonde since records extend back for many years, although most studies use only the well-calibrated part of that data. For example, the data used by Santer et al. (1996) extended back to 1963. However, satellite observations are an underutilized but vast source of data. With this in mind, it has been proposed (Kiehl 1983; Goody et al. 1996) to search for a “spectral signature” of climate change in the radiance fields measured by satellites. Since the spectrally resolved outgoing infrared radiation of the earth contains information on clouds, constituent gases, and the temperature and humidity structure of the atmosphere, it is a promising place to look for an unambiguous fingerprint of anthropogenic change. Satellite data also offers the advantage over radiosonde data of extremely good spatial and temporal sampling and a sensitivity to deep layers in the atmosphere. Slingo and Webb (1997) have simulated the spectral signature of the atmosphere’s response to climate change (i.e., ignoring the direct radiative forcing of the increases in greenhouse gases) and they show effects over large parts of the infrared spectrum. As a first step toward a multivariate approach using existing long-term satellite radiance datasets, this paper describes a practical example using data from a single satellite channel. The spatial pattern-matching techniques previously applied to radiosonde data will be applied to satellite radiance data. One aim is to demonstrate areas in which current modelling studies may be improved and the ways that satellite data might best be exploited.

To examine climate change from space we require long-term satellite radiance data with no time-dependent bias. Only the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites and the various geostationary meteorological satellites (GOES, METEOSAT, etc.) have been operating long enough to be used for climate studies. NOAA satellites (Kidwell 1995) have been operating almost continuously for 18 years, which is short in comparison to radiosonde records but long enough to resolve shorter-period climate variability such as the El Niño–Southern Oscillation (ENSO). Clearly the raw radiance data must be processed to reduce data volume to manageable levels. The biases resulting from calibration drifts, intersatellite differences, and temporal sampling must also be removed. Very few such datasets exist despite the large amount of raw satellite data available. In this study we make use of a processed and intercalibrated 17-yr dataset (Bates et al. 1996) from channel 12 of the High Resolution Infrared Spectrometer (HIRS-12), part of the Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) instrument onboard the NOAA satellites. HIRS-12 is located at 6.7 μm in the 6.3-μm vibration–rotation band of water vapor and is sensitive to both the relative humidity and to a lesser extent the temperature of the upper troposphere between about 200 and 500 mb (Wu et al. 1993). One of the few other intercalibrated radiance datasets available is that of Spencer and Christy (1990), whose brightness temperature anomalies from the Microwave Sounding Unit (also part of TOVS) would be suitable for studies using the techniques demonstrated here.

Water vapor is the most important greenhouse gas and despite the small amounts in the upper troposphere it has a large effect on the radiation budget of the earth (Sinha and Harries 1995; Soden and Fu 1995). The relative humidity has been expected to remain fairly constant during climate change, despite temperature increases that result in large increases in specific humidity (e.g., Manabe and Wetherald 1967). The enhanced absorption of outgoing radiation by the increased atmospheric water vapor is responsible for a feedback effect as it further warms the atmosphere, leading to further specific humidity increases. This effect depends upon the reliable prediction of the effect of climate change on tropospheric humidities, and there is still some debate as to the accuracy and even the direction of this feedback (e.g., Lindzen 1990; Sun and Held 1996; Rind et al. 1991). Though the consensus shows positive water vapor feedback, there has been much work to evaluate the accuracy of upper-tropospheric water vapor within GCMs (e.g., Schmetz and van de Berg 1994; Soden and Bretherton 1994; Salathe et al. 1995) and in observational studies of the greenhouse effect of water vapor (e.g., Soden and Fu 1995). In the 6.3-μm band of water vapor this feedback effect is apparent if we assume constant relative humidity. Little increased radiation to space is possible if the relative humidity remains constant, because of the dominating influence of changes in upper-tropospheric relative humidity over temperature changes. This is investigated later. Slingo and Webb (1997) examined this and described it in terms of a compensation between the radiative effects of changes in temperature and specific humidity. Despite the compensation effect, they did identify a latitudinal structure in the effect of climate change on radiances in the 6.3-μm band of water vapor, which was associated with changes in upper-tropospheric humidity.

In this paper we wish to examine the spatial character of predicted changes in HIRS-12, since this and other 6.3-μm water vapor satellite channels are sensitive to the dynamics of the atmosphere via the upper-tropospheric humidity. A global map of HIRS-12 brightness temperatures illustrates both the mean circulation of the atmosphere and the passage of individual weather systems. Hence changes in the dynamics of the atmosphere, as well as any changes in mean relative humidity, should be detectable with HIRS-12. The effects of changes in atmospheric circulation are also demonstrated by Bates et al. (1996) and Soden and Bretherton (1996), who both show that the largest variations in the upper-tropospheric water vapor fields are associated with ENSO. It is demonstrated later, however, that this high natural variability creates problems in the detection of climate change.

Observational studies of tropical tropospheric water vapor from radiosonde observations (Hense et al. 1988;Gutzler 1992) have shown small increasing trends in both precipitable water and relative humidity in the troposphere over recent decades. Depending on altitude and location, some relative humidity trends are statistically significant, though many are not. Ross and Elliott (1996) found significant increases between 1973 and 1993 in lower to midtropospheric relative humidity over the southern United States and also negative trends over Canada. These studies give added motivation to the idea that there might be satellite-detectable relative humidity trends in the upper troposphere, at least on a regional scale.

In this study, predictions are taken from a transient climate change integration of the Hadley Centre climate model, referred to as HADCM2, which included the effects of increasing greenhouse gases and tropospheric aerosols (Mitchell et al. 1995). Decadal mean atmospheres centered on the model years 1865 and 2045 are used to simulate changes in HIRS-12 brightness temperatures. The observational HIRS-12 data is examined for long-term trends, and pattern similarity statistics are used to look for an emerging fingerprint of climate change within this data.

In section 2 we discuss the statistical approach used. The nature of HIRS-12 is discussed and the trends in the observational data are examined in section 3. Section 4 describes the climate model and the simulation of predicted brightness temperatures for comparison to the observational data. The dependence of HIRS-12 brightness temperature on the relative humidity and temperature of the upper troposphere is examined. Section 5 presents the results of the pattern similarity statistics, and conclusions are made in section 6.

2. Pattern similarity statistics

This section describes the statistics that are used to give a quantitative measure of the similarity between predicted climate change and observational data. Pattern similarity statistics were used extensively in the studies of Santer et al. (1995, 1996) and Tett et al. (1996), and the same statistical approach is used here.

The basis of these statistics is comparing the patterns of predicted and observed changes, as opposed to comparing absolute magnitudes. The predicted change in HIRS-12 is the difference between the 2045 and 1865 brightness temperatures, and it is assumed that one may see a similar pattern emerging in the observations with time. The observed change must be determined relative to a reference state, so the observed data will be in the form of anomalies. If there is increasing similarity between the changes seen in the HIRS-12 data and the predicted changes, this could, dependent on the degree of natural variability and statistical significance, indicate the presence of climate change. Though the HADCM2 data come from a transient simulation, for simplicity we ignore the time evolution of the predicted changes and simply look for an emerging pattern in the observations.

The observational data from HIRS-12 consist of yearly means D = D(x, t), where t refers to the year and x is an index running over each latitude–longitude point in the array. The anomaly of this data is defined as ΔD(x, t) = D(x, t) − D0(x), where D0(x) was chosen as the 17-yr mean of the data. This reference period was chosen as shorter periods could be influenced quite strongly by only one or two years and might be biased toward either ENSO or non-ENSO conditions.

The simulated HIRS-12 brightness temperatures from the model for 1865 and 2045 are labelled M1(x) and M2(x), respectively. The model predicted change is defined as
MxM2xM1x
The statistics then compare ΔM(x) and ΔD(x, t), rather than absolute values of the model and data fields.
The uncentered pattern similarity statistic C(t) can then be defined as
i1520-0442-12-7-1940-e2
where W(x) is an area weighting factor proportional to the size of the grid box. It is defined as
i1520-0442-12-7-1940-e3
where θ(x) is the latitude at the point x and c is a normalizing constant. The definition applies specifically to the 5° × 5° grid used in this experiment. Statistic C(t) is not bounded by ±1 but C(t) = +1 corresponds to ΔM(x) = ΔD(x, t), and an increasing C(t) with time indicates an increasing similarity between ΔM(x) and ΔD(x, t).
The pattern correlation R(t) is defined as
i1520-0442-12-7-1940-e4
where Δ(t) is a spatial mean as defined below with Δ defined similarly:
i1520-0442-12-7-1940-e5
Here sD and sM are the spatial standard deviations of the anomalies with sD defined as below and sM similarly:
i1520-0442-12-7-1940-e6
R(t) is bounded by ±1 so that as the pattern of ΔD(x, t) − Δ(t) approaches identical to ΔM(x) − ΔM̂, R(t) approaches +1.

Santer et al. (1993) give a mathematical explanation of these statistics. They show that C(t) contains information on the changes in the spatial mean of the data as well as changes in the pattern of anomalies. Here R(t) is only influenced by the pattern of anomalies. If the global mean change predicted by the model, ΔM̂, is large, then the C(t) statistic will be completely dominated by the correlation between the mean change in the data and the mean change predicted by the model. Both statistics are useful as they give complementary information on the match of model predictions to data.

3. Satellite data

This section describes the satellite radiance data and the processing necessary to produce yearly mean data on the experimental grid. We look at the interannual variability of HIRS-12 brightness temperatures and examine the data for local trends. Throughout this work we use brightness temperatures (TB) as the measure of the satellite-observed radiance.

a. The nature of HIRS-12

The HIRS instrument is part of the TIROS-N Vertical Sounder (TOVS) flown on NOAA operational polar-orbiting satellites. Channel 12 is centered at 6.7 μm in the 6.3-μm vibration–rotation band of water vapor. HIRS-12 is sensitive to humidity and temperature over a broad layer in the upper troposphere. The exact height of this layer is determined by the humidity and temperature profile. Lower relative humidities will result in the emission coming from lower in the troposphere where temperatures are higher. Thus a lower relative humidity in the upper troposphere will result in higher brightness temperatures, and vice versa. Salathe and Smith (1996) have shown that the effects of absorbers other than H2O are small in the 6.3-μm region. Gases O2 and CH4 combined are responsible for a uniform decrease in HIRS-12 brightness temperatures of less than 0.5 K. The effects of other absorbers are negligible. There are still some uncertainties in simulating HIRS-12 brightness temperatures due to the possible influences of cirrus clouds and uncertainties in the water vapor continuum (Salathe and Smith 1996).

Soden and Bretherton (1993, 1996) have shown that the relative humidity in the upper troposphere, when weighted by the sensitivity of HIRS-12 TB to relative humidity perturbations, is strongly correlated to HIRS-12 TB. This weighted upper-tropospheric humidity is referred to here as UTH. The sensitivity is defined as the response of HIRS-12 TB to small perturbations in relative humidity at levels equally spaced in log pressure coordinates. It gives a measure qualitatively similar to the weighting function. Using zonally meaned 1865 and 2045 atmospheres from the model, the sensitivity profiles of HIRS-12 were derived as a function of latitude and log pressure. The sensitivity functions for 1865 are shown in Fig. 1, normalized in log pressure coordinates. It is clear that the HIRS-12 instrument is sensitive to a broad layer in the atmosphere, located between 200 and 550 mb at the equator and lower in the atmosphere at higher latitudes. The peak sensitivity is at about 325 mb at the equator and drops to about 450 mb at high latitudes. The true sensitivity function for a particular location will vary vertically in the atmosphere dependent upon the humidity profile so there is some uncertainty involved using a zonal mean sensitivity function.

Soden and Bretherton (1993, 1996) show that HIRS-12 brightness temperature TB has a logarithmic dependence on UTH. At a zenith angle of 0°, the relationship they derived becomes
abTBP0
Here a and b are empirically determined constants. The only dependence of TB on temperature in this relation is given by P0 where P0 = P240/300 mb and P240 is the climatological mean pressure at which the temperature is 240 K. This essentially accounts for the zonal and seasonal variations of temperature in the upper troposphere. The dependence of TB on UTH in the Tropics (where P0 ≈ 1) is plotted in Fig. 2, using constants determined from the model data. Clearly a change in UTH occurring at low UTH will have a greater influence on TB than an identical change occurring at high UTH.

One feature of the HIRS-12 channel is that the effects of temperature variations on TB are very low. Typical variations in atmospheric relative humidity have 5–8 times the influence of typical variations in temperature at fixed relative humidity (Soden and Bretherton 1993), although the difference is lower at high latitudes.

b. Observational data

The processed and intercalibrated data used in this experiment (Bates et al. 1996) were obtained from Darren Jackson (University of Colorado) and John Bates (NOAA/Environmental Research Laboratory Climate Diagnostics Center). Monthly means of HIRS-12 brightness temperature (TB) on a 2.5° × 2.5° grid have been produced for the years 1979–95, excluding January 1979. The data has been cloud cleared and “limb-corrected” to produce the equivalent nadir brightness temperatures. The data from different satellites has been intercalibrated as described below.

Since the data comes from instruments on successive NOAA satellites that are not totally identical, the weighting functions change slightly from satellite to satellite. A second problem with the HIRS-12 data is that of biases arising from the sampling times of the sun-synchronous orbits, which capture different aspects of the diurnal cycle. The orbits also tend to drift over the lifetime of a satellite and this might cause spurious trends as a result of the interaction with diurnal variability. Problems also occur where only one satellite is available, an instrument malfunctions, or the calibration drifts over time. These differences are crucial to the accuracy of a long-term climate dataset, so the data must in some way be intercalibrated.

Bates et al. (1996) describe the procedure used to intercalibrate the HIRS-12 data. Because the response of a radiometer channel is in general highly nonlinear, it cannot be assumed that there is a fixed bias between measurements from different satellites. Instead, they matched statistical distributions of measurements during periods of satellite overlap using “empirical distribution functions” (Weinreb et al. 1989). With this technique the response of one satellite may be normalized to that of another. They chose NOAA-7 as the baseline satellite and the brightness temperatures from other satellites were adjusted to match the response of this instrument. Brightness temperatures from different daily sampling times were adjusted to that of the evening orbit. Unfortunately, there was no overlap between NOAA-8 and NOAA-9 so Bates et al. (1996) used an ad hoc linear adjustment for brightness temperatures from NOAA-9 and later satellites.

It must be noted that the adjustments did not, however, eliminate the possible effect of drifts in sensor calibration or orbital sampling time over the lifetime of a single instrument. Second, the intercalibrated dataset is self-consistent but does not have an absolute calibration. Radiosonde measurements of upper-tropospheric moisture are poor (Elliot and Gaffen 1991), so they cannot be used to validate the absolute calibration of the instrument. Since the pattern similarity statistics compare relative changes rather than absolute magnitudes, this should not be a problem.

Only data between 70°N and 70°S were used, since HIRS-12 data from polar regions is very sparse due to persistent low clouds, snow and ice cover, and strong inversions that prevent the derivation of cloud-cleared radiances (Wu et al. 1993). Wu et al. (1993) estimated the error on a single HIRS-12 measurement due to instrument noise, angular correction, and errors in the cloud clearing algorithm to be 2.48 K. The standard error s on a mean of HIRS-12 data is given by s = 2.48/(N)1/2, where N is the number of observations used in the mean. The number of measurements used in the 5° × 5° HIRS-12 yearly means is on average 2044, which results in an estimate of the standard error of 0.05 K. This estimate does not include the systematic error resulting from undersampling in cloudy areas, which results in a slight positive bias in the data estimated to be of the order of 0.7 K (Soden and Lanzante 1996). If changes in cloud cover are seen over time, an effect on satellite sampling could have some influence on the HIRS-12 trends.

A small amount of missing data needed to be estimated by bilinear interpolation before the data could be used to produce yearly mean brightness temperatures on a 5° × 5° grid for comparison to the HADCM2 data. Data for January 1979 were not available and May 1986 had 72% missing data. Apart from these periods, there were 1% missing data points per month. A monthly mean climatology was created for each grid point using the good data over the 17-yr period. For May 1986 equal weight was given to the climatology and the results of a bilinear interpolation from the data available. January 1979 was filled with the climatology values, since any inaccuracies arising should be outweighed by the benefits of an extra year of data. It must, however, be remembered that the yearly means in 1986 and 1979 are biased slightly toward the 17-yr mean. Yearly means of the anomalies, ΔD(x, t), were created from the monthly mean HIRS-12 data and a selection is plotted in Fig. 3. The spatial correlation statistics essentially evaluate the similarity of the pattern of these fields to the predicted model changes ΔM(x) (Fig. 7a).

The interannual variability of the HIRS-12 brightness temperatures is exemplified by the variations in Fig. 3. The anomalies are strongly affected by the ENSO events in 1983, 1987, and 1992–93. Bates et al. (1996) show that an ENSO event results in lower brightness temperatures (upper-tropospheric moistening) over the central and eastern Pacific, with a corresponding increase (upper-tropospheric drying) in the brightness temperatures over Indonesia, the northern half of South America, and in the subtropics in the central Pacific. The anomalies in 1983 show this pattern more clearly than the other ENSO years, but the pattern of equatorial moistening and subtropical drying in the central Pacific is common to all. The year 1989 shows some evidence of anomalies in the Pacific of opposite sign to those seen in ENSO years. There is also much interannual variability that cannot be linked to ENSO; for example, the contrast between 1979 and 1986 is quite marked.

Figure 4 shows the temporal standard deviation of the yearly mean HIRS-12 brightness temperatures. A complex structure is apparent, with generally higher standard deviations in the Tropics and subtropics than the midlatitudes. The Tropics and subtropics show standard deviations mostly in the range 0.4–0.8 K but in much of the Pacific they are higher than this, peaking at 1.55 K at 20°N in the eastern Pacific. ENSO is largely responsible for the high variability in the Pacific.

A least squares fit was used to obtain the 17-yr linear trend in the HIRS-12 anomalies at each grid point. The statistical significances of these gridpoint trends were calculated, and a description of the method is given in the appendix. Figure 5 shows the trends with areas of 90% statistical significance shaded. The figure shows some tropical moistening (TB decrease) and subtropical drying (TB increase), but these trends are mostly below the significance level so it would be unwise to draw any conclusions. Some drying trends at 30°S are, however, significant. In the Tropics and subtropics trends will in general be hard to identify because of the high interannual variability, especially over the Pacific.

At 60°S there is a significant general decrease in brightness temperature of approximately 0.025 K yr−1. Given the high latitude, this could be a result of either upper-tropospheric cooling or moistening. There are some areas at 60°N that also show a statistically significant decrease in TB, but at this northern latitude most areas show no real trend. A moistening trend off the equatorial east coast of Africa is significant but a similar-sized trend in the east Pacific is not significant, which is again most likely due to the high interannual variability in the Pacific region.

Peak trends equate to about 1 K brightness temperature over 17 yr. At fixed temperature, this equates to a drop in the relative humidity by a factor of roughly 0.12; that is, at 50% relative humidity, the change will be approximately −6%. These trends are relatively small compared to the high interannual variability in some areas. What Fig. 5 does indicate, however, is that the relative humidity of the upper troposphere does change significantly over this timescale at a regional level. In contrast, the area weighted 70°N–70°S mean change of TB over the 17 yr of HIRS-12 data is only 0.02 K. The small magnitude indicates the difficulties inherent in searching for climate change using only changes in the spatial mean quantities.

4. Model simulated TB changes

a. Climate model and simulation of TB

The data used for the brightness temperature simulations are taken from a transient climate change integration of the Hadley Centre Climate Model, referred to as HADCM2, which included the effects of increasing greenhouse gases and tropospheric aerosols (Mitchell et al. 1995). The model is a coupled ocean–atmosphere general circulation model using a model grid of 2.5° lat × 3.75° long with 19 atmospheric levels and 20 levels in the ocean (Johns et al. 1997). Comparisons with historical surface temperatures in Mitchell et al. (1995) and atmospheric temperatures (1961–95) from radiosonde in Tett et al. (1996) show reasonable agreement with HADCM2 simulations, giving confidence in the model. However, the HADCM2 integration used here does not include the effects of ozone changes. The incorporation of representative ozone scenarios improved agreement with observed atmospheric temperature changes in Tett et al. (1996). The main improvements were seen in the stratosphere and upper troposphere, where increased cooling was seen. The model also uses the “equivalent CO2” approximation where enhanced CO2 concentrations are used to represent the effects of the greenhouse gases methane, nitrous oxide, and the halocarbons. This causes an unrealistically large decrease in stratospheric temperatures, but it has only a minimal effect on the troposphere via downward flux from the stratosphere. The data used here are decadal means centered around 1865 and 2045 taken from the transient run. They were linearly interpolated onto the 5° × 5° grid used in this experiment. When 1865 and 2045 are used to describe the model data, we are referring to the decadal means.

The outgoing nadir radiances were simulated from the decadal-mean model profiles at each grid point using the MODTRAN radiation code (Wang et al. 1996). MODTRAN was used at 1 cm−1 resolution between 1380 and 1565 cm−1 with the effects of H2O, O2, and CH4 included. The satellite-observed radiance was calculated by integrating the product of the simulated radiance data and the normalised NOAA-7 HIRS-12 spectral response. Bates et al. (1996) have removed the effect of variations in the instrument’s spectral response on the observed data by standardizing to that of the instrument on board NOAA-7, so this was used in all calculations. The satellite-observed radiance was then converted to brightness temperature using the Planck function.

One problem arises from the nonlinear dependence of HIRS-12 brightness temperatures on the upper-tropospheric humidity (Fig. 2). At low UTH there is a much stronger dependency of TB on UTH than at higher UTH, so the brightness temperature simulated from a mean atmospheric profile will not be the same as the mean brightness temperature simulated from daily profiles. We examine the effect that using decadal-mean profiles will have on the predicted changes in TB.

Consider a series of atmospheric profiles, Pi=1,N, with a mean profile, P. The brightness temperature simulated from this mean profile will differ from the correctly simulated mean brightness temperature by a certain error, E:
i1520-0442-12-7-1940-e8
In general for HIRS-12, TB(P) will be lower than the correct mean TB, since the drier (higher TB) profiles will have relatively more influence on the mean when simulated individually. The greater the variability of UTH in the series of profiles, and the lower the mean UTH, the larger this difference will be. We examine only the change of brightness temperature in this study, so it is the difference in the error, E, between the 1865 and 2045 mean atmospheres that will affect the model-simulated change.

To evaluate the difference between a change in brightness temperature simulated from mean atmospheres (ΔTB) and that simulated correctly from the individual profiles (ΔT*B), we use a sample of points from the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses for 1997. Fifteen points were chosen, equally spaced between 70°N and 70°S, along a line of longitude at 35°E, giving a sample of the Tropics, subtropics, and midlatitudes. This line intersects the Egyptian dry region (Fig. 8b), which shows very high brightness temperatures (i.e., very low relative humidities) during the summer and where a large meaning error E might be expected. Atmospheric profiles at 0000 and 1200 UTC were extracted for the whole of 1997. To justify the use of only a year of data, rather than a decade of data, we observe that the intra-annual variability of UTH is much larger than the interannual variability. Since it is the variability in UTH that causes the meaning error, the greatest contribution to this will come from the intra-annual variability. In terms of HIRS-12 brightness temperature, the intra-annual standard deviation in the ECMWF samples ranged from 3.5 to 6 K. In contrast, the interannual standard deviation of HIRS-12 TB peaks at 1.55 K but is generally much lower (Fig. 4).

Brightness temperatures were simulated over 1997 for each point and a mean was calculated (i.e., TB). For each point the yearly mean atmospheric profiles were derived and from this another brightness temperature was calculated [i.e., TB(P)]. Although the water vapor variable used here is always relative humidity, the means in the ECMWF profiles were calculated using specific humidities, since the HADCM2 mean atmospheres were also created this way. We found that meaning in relative humidity leads to similar conclusions to those below. The relative humidities in the troposphere (below 200 mb) in the ECMWF profiles were then changed by values representative of the model-predicted changes (Fig. 7b) and brightness temperatures were again simulated. Using these humidity changes, we derived a set of true mean changes in brightness temperature (ΔT*B) to compare to those simulated using yearly mean profiles (ΔTB).

A plot of ΔTB against ΔT*B is shown in Fig. 6, and a different symbol is used for each set of 15 points corresponding to a particular magnitude of relative humidity change. A linear least squares fit was performed between the two sets of data. Figure 6 reveals that the main effect of using yearly mean profiles is that the magnitude of ΔTB compared to ΔT*B is typically reduced by a factor of 0.59, that is, the overall pattern of changes simulated from yearly mean atmospheres will be reduced in amplitude compared to the true pattern of changes. The pattern itself will be relatively unchanged;there is a good correlation of 0.98 between ΔT*B and ΔTB, but the linear fit reveals a slight bias of −0.04 K. The largest deviations from the straight line (i.e., from the pattern) occur for points corresponding to subtropical dry regions when large relative humidity changes are made, as might be expected.

The main effect on the patterns of brightness temperature change appears, therefore, to be a reduction in the overall pattern amplitude. This is not very important given the nature of the pattern correlation statistics and the experimental setup. If future studies compare the evolution of absolute model and data values, rather than looking for patterns of change, more accurate mean radiances will be needed. One way to make this easier would be to generate spectrally resolved top-of-the-atmosphere radiances as part of the climate model.

b. Relative humidity, temperature, and TB changes in the model

This section analyzes the humidity and temperature changes predicted by the model and their effect on the predicted change in brightness temperatures.

The model-predicted change of HIRS-12 TB between 1865 and 2045, ΔM(x) is shown in Fig. 7a, and the TB simulated for 1865 is shown in Fig. 8a. Figure 7a shows a complex pattern of changes, with some latitudinal structure. In the higher latitudes, the change is small and typically less than +0.5 K. Large areas of the subtropics and Tropics show increases in brightness temperatures of up to 2.5 K, which would be expected to come from decreasing upper-tropospheric humidities. Decreasing brightness temperatures are seen in the subtropics mainly over the South Atlantic and in a band over the South Pacific. The decrease in the South Atlantic is quite strong, peaking at approximately −3 K over a large region. These brightness temperature changes will be related to relative humidity and temperature changes in this subsection. Because of the logarithmic dependence of brightness temperature on UTH, one cannot associate a particular magnitude change in TB with a change in UTH unless the background level of UTH is specified.

The area-weighted mean change in TB over the region studied (70°N–70°S) is 0.35 K, which is small compared to the peak changes seen in Fig. 7a. It should not be taken to indicate a slight overall drying of the upper troposphere, as the combined effects of temperature and relative humidity must be evaluated.

In order to determine the effect of changes in relative humidity and temperature between 1865 and 2045 on the pattern of HIRS-12 TB, quantities must be extracted in the atmospheric layer that influences HIRS-12. Using the relevant zonal sensitivity functions (e.g., Fig. 1) layer weighted humidities (UTH) and temperatures (UTT) can be extracted from the 1865 and 2045 HADCM2 atmospheres. For clarity we have chosen to look at a fixed layer in the atmosphere using the 1865 sensitivity function to extract UTT and UTH for both 1865 and 2045. Figures 7b and 8b show respectively the difference UTH2045 − UTH1865 and the 1865 UTH distribution. The visible differences between UTH plots for 1865 and 2045 are small, so a plot for 2045 is not shown. Figures 7c and 8c show similar plots for UTT. We could have chosen instead to look at changes in UTH and UTT by following the contributing layer as it moves in the atmosphere rather than looking at changes in a fixed layer. Changes in the humidity profiles and increases in tropospheric temperature result in a small upward shift in the zonal mean sensitivity functions between 1865 and 2045. The choice of either of these methods has little effect on the pattern of change of UTH but the pattern of change of UTT is markedly affected. Figure 7c shows large temperature increases in the fixed layer of the atmosphere, as expected. However, if we extract UTT2045 using the 2045 sensitivity functions, there is a much-reduced change in UTT, since the sensitivity functions move up in the atmosphere with the increasing temperatures. Essentially, the effect of UTT on HIRS-12 brightness temperatures comes partly from its effect on the sensitivity functions. This points to a more subtle effect of UTT on the HIRS-12 brightness temperatures than that of UTH and means that the clearest way of looking at the changes in temperature is using a fixed layer in the atmosphere.

Figure 8b shows a distribution of UTH with strong zonal differences and regional features in the Tropics and subtropics. Subtropical dry regions can be seen at around 20°N and 20°S, and the equatorial regions of higher UTH are associated with the intertropical convergence zone (ITCZ). A feature of high UTH extending across the South Pacific is thought to be associated with the convection of the South Pacific convergence zone (SPCZ). The UTH increases to 60%–70% in the region of the midlatitude depressions.

In contrast, the distribution of UTT (Fig. 8c) shows a mainly zonal distribution with the greatest departures from the zonal pattern over a region at about 160°E. The changes in UTT (Fig. 7c) also show a mainly zonal pattern. The greatest tropospheric temperature increases in the model are found in the tropical upper troposphere, in exactly the layer sensed by HIRS-12. The midlatitudes show smaller UTT increases of 1 to 2.5 K with less warming in the Northern Hemisphere.

The changes in UTH (Fig. 7b) are the result of both changes in the patterns of the mean circulation and changes in the humidity of the regions associated with this. The dry region of the South Atlantic moistens by about 5% in relative humidity, causing a strong decrease in TB (Fig. 7a). In comparison, increases in UTH of similar magnitude in the SPCZ region have a smaller influence on the brightness temperatures, again due to the lower sensitivity of HIRS-12 TB to changes at high UTH. The Pacific shows a drying of the ITCZ but a slight moistening and shift to the northeast of the SPCZ. There is a drying of the subtropical dry regions of the Indian Ocean but some of the strongest drying is seen where the dry region over Australia enlarges to the northeast following the move in the SPCZ. A strong moistening is seen in the northeast Pacific. The origin of these changes is unfortunately obscured by the decadal mean nature of the data, and analysis using the output of the model on seasonal scales might in future give clearer and more understandable trends. The area weighted 70°N–70°S mean UTH change is only −0.15% in relative humidity, which shows that on a nearly global scale the model predicts relatively constant UTH. This should be contrasted with the strong changes on regional scales.

It is clear from Fig. 7 that the HIRS-12 TB changes between 1865 and 2045 in the Tropics and subtropics are dominated by the changes in UTH, despite the UTT increase of typically 4 K. Increases in UTH result in a decrease in the brightness temperature, and vice versa. However, at latitudes higher than 40°N and 40°S, despite small predicted UTH increases, some areas show an increase in brightness temperature. Increases in UTT would be expected to increase the TB and clearly this must be influencing the changes at high latitudes.

To assess the influence of the change in humidity (ΔUTH) on the change in TBM), scatterplots of these two quantities were produced. Two regions were considered: between 30°N and 30°S and 30°–70°N and 30°–70°S, which are plotted in Figs. 9a and 9b, respectively. In both cases the expected inverse relationship between ΔUTH and ΔM is seen. It is clear from Fig. 9b that most points in the extratropics show TB increases despite increases in UTH. To obtain an estimate of the influence of the temperature changes, a linear fit was performed for each region and the best fit lines plotted in Fig. 9. A linear dependence of ΔM on ΔUTH would not be expected for larger ΔUTH, as is clear from the scatter in the plots, but the displacement of this line from the origin will give an estimate of the effect of the mainly zonal temperature changes, if the effect of temperature is assumed to be independent of UTH and ΔUTH. The gradient will give an estimate of the influence of ΔUTH on ΔM. Table 1 gives the y intercepts and gradient of the linear fits.

Two main conclusions can be drawn from this table. First, the effect of the ∼4 K UTT increase in the Tropics is extremely minimal, compared to the strong influence of UTH changes. At high latitudes, where the UTT change is of order 2 K, the UTT influence on HIRS-12 brightness temperature is actually greater, while the effect of ΔUTH on ΔM is approximately halved. The halving of the response to ΔUTH is due to the higher-background UTH at midlatitudes than in the Tropics. The effect on TB of UTH changes is opposed by the UTT changes, and with the lower response to ΔUTH most points reveal slight TB increases, showing that temperature changes have dominated. Only stronger increases in UTH of the order of 3%–5% cause decreases in TB at these latitudes, as can be seen comparing Figs. 7 and 9b.

The above analysis shows that the Tropics and subtropics are the most promising regions for the detection of climate change as regards its effect on HIRS-12 TB and on upper-tropospheric humidities. At higher latitudes, the changes in UTH are smaller and the competing effects of temperature and relative humidity allow only a small signal in brightness temperature. In the Tropics and subtropics, changes in UTH dominate the signal despite the large UTT increases also seen in this region. This result gives confidence that in the real HIRS-12 data, a reasonable change in TB in the Tropics can be attributed to UTH change rather than temperature change. The changes in TB in this region are quite large, of up to ±2.5 K.

It is important to notice that in this climate simulation, the change in upper-tropospheric relative humidity is minimal on a global scale compared to the significant changes on regional scales. On a global scale it seems a fair assumption that very little extra cooling of the earth can occur in this part of the 6.3-μm water vapor band, though clearly one cannot draw inferences from this to the outgoing longwave radiation as a whole. The temperature increases of up to 4 K in the tropical upper troposphere show little effect on the brightness temperature compared to that of changes in UTH. Again the importance of looking for spatial patterns in the detection of climate change is emphasized.

5. Results of pattern similarity statistics

Figure 10 shows the statistics R(t) and C(t), which are the pattern similarities between the 17 yr of HIRS-12 brightness temperature anomalies and the model-predicted effects of climate change. An increasing trend in R(t) and C(t) indicates increasing appearance of the pattern of climate change in the data. Since the reference state D0(x) is the 17-yr mean, we would not expect to see positive correlations over the whole period. The trends in these statistics and their significances are listed in Table 2. The calculations of significance as described in the appendix take no account of autocorrelation in the data and we make no attempt to compare with natural variability.

Both R(t) and C(t) show a very small trend with low statistical significance. Since we have not accounted for any autocorrelation in the data, which would be expected to reduce the significance of any trend, this should be interpreted as showing essentially no significance. The high interannual variability in both R(t) and C(t) reduces the significance of these trends. Both R(t) and to a lesser extent C(t) show peaks in the ENSO years 1983, 1987, and 1992, which suggests that ENSO years have higher pattern similarity with the predictions than do non-ENSO years. However, some positive correlations such as peaks in R(t) in 1980 and in C(t) in 1979 are unrelated to ENSO events, and C(t) appears less sensitive to the brightness temperature patterns in the 1992–93 ENSO event than R(t). One of the effects of climate change on the modeled brightness temperatures is an increase over the Pacific subtropics and Indonesia, which is also seen in the data anomalies in El Niño years. However, one of the major effects of the El Niño on HIRS-12 TB (Bates et al. 1996; Soden and Bretherton 1996) is the increased moisture (lower TB) over the eastern equatorial Pacific where the model predictions show a drying (higher TB). Thus one cannot conclude that the model is predicting a pattern of UTH changes similar to the short-term effects of the El Niño, merely that some of the pattern is similar. This is problematic for the interpretation of trends in the HIRS-12 data. The positioning of a long positive ENSO period (1991–93) near the end of the time series may have helped to produce better correlations and possibly have influenced the trend.

The smaller trend in C(t) than in R(t) is partly explained by the small mean changes in TB observed (0.02 K) compared to those predicted (0.35 K), since C(t) is sensitive to mean changes. In 1979 where C(t) shows positive correlations, the mean brightness temperature anomaly of these years is noticeably higher (Fig. 3). More information can be obtained from comparing Fig. 7a, which shows the model predictions ΔM(x), to Fig. 5, which shows the linear trends in the observational data. The reduction seen in high-latitude brightness temperatures in the HIRS-12 data is contrary to the model predictions, which show a slight increase as a residual of competing trends in UTT and UTH. Predictions of increased TB (upper-tropospheric drying) over Arabia contrast with an observed slight moistening (TB decrease). A similar drying to that predicted by the model in the Austral subtropics is observed in the HIRS-12 data, and a moistening (increasing TB) over the Southern Atlantic is seen but is centered about 10° farther north than in the predictions. In the equatorial Pacific, trends in Fig. 5 bear some resemblance to the predicted pattern but also show marked differences. The advantage of the pattern similarity technique is that these qualitative comparisons can be backed up by a quantitative measure of the increasing similarity of data to simulation.

It appears that natural variability has a strong effect on the pattern correlations, a major part of this variability resulting from ENSO. The brightness temperature anomalies in ENSO years do not correspond exactly with the model-predicted changes, but it is a possibility that a climate change simulation could predict a climate shift similar to ENSO conditions. Current climate models are unable to provide a fully realistic simulation of ENSO events (Trenberth and Hoar 1996) but the effect of climate change on this phenomenon is an important issue. In an integration of a climate model with increasing CO2, Meehl and Washington (1996) saw mean shifts in circulation anomalies similar in some aspects to ENSO events. In contrast, Smith et al. (1997) suggest that there will not be significant effects on ENSO due to the temperature increases predicted under doubled CO2. Knutson and Manabe (1994) saw the intensity of ENSO-like fluctuations in a climate model decrease slightly under a 4 × CO2 scenario. The recent change to more frequent and longer ENSO events since 1976 is highly unusual (Trenberth and Hoar 1996), though whether this is a natural climate fluctuation or a result of greenhouse gas–induced climate change is open to question. A better understanding of the effect of climate change on ENSO is required here because of its effect on HIRS-12 brightness temperatures and on the pattern correlations.

6. Summary

This paper has demonstrated a new technique using a radiance dataset in spatial pattern similarity studies of the effect of greenhouse gas–induced climate change. Working in radiance (or brightness temperature) terms gives a direct link to the radiative balance of the earth and the accuracy of forward simulation of radiances is higher than that of deriving meteorological products from a radiance. Future studies could look for a fingerprint of climate change in the many existing satellite channels but this requires substantial processing and calibration of the raw datasets. The TOVS Radiance Pathfinder Project is working toward these goals.

The observational HIRS-12 dataset showed some significant trends over 17 yr especially at a latitude of 60°S and to some extent at 60°N where there was a small but significant trend of decreasing brightness temperatures. At this high latitude, the trend could be due to either decreases in upper-tropospheric temperature (UTT) or increases in the relative humidity (UTH). Some areas of the southern subtropics show significant brightness temperature increases, which can be attributed to decreasing UTH. A moistening trend in UTH is almost certainly responsible for the brightness temperature decrease at the equator off the coast of East Africa. The HIRS-12 observations reveal high interannual variability, especially in the Tropics. A major component of the natural variability is due to ENSO.

The use of decadal mean atmospheres for the simulation of changes in HIRS-12 brightness temperature was examined and it appeared that the effects were not that serious. Simulations of the effect of climate change on HIRS-12 brightness temperatures using data from the HADCM2 model revealed that the major effects were in the Tropics and the subtropics. In these regions changes in UTH were the dominant influence upon HIRS-12 brightness temperatures, despite the large increases of around 4 K predicted in upper-tropospheric temperatures. HIRS-12 in the Tropics and subtropics is primarily a meter of relative humidity changes. At higher latitudes, where UTH changes were shown to be less important, the effect on HIRS-12 of the smaller increases predicted in UTH was opposed by upper-tropospheric temperature rises, resulting in slight rises in TB in some areas.

In both observations and data it is clear that the assumption of constant relative humidity in climate change is applicable only on a global scale. Significant changes can and do occur zonally and regionally in upper-tropospheric humidity. These changes can be detected in HIRS-12 brightness temperatures, though it is more ambiguous at higher latitudes where the influence of temperature changes are greater.

The statistics revealed essentially no significant trend between 1979 and 1995 in the similarity of the data to the predicted effects of anthropogenic climate forcing. The high interannual variability in the observed brightness temperature data was in part responsible for the low significances. The anomalies in the HIRS-12 data seen in ENSO events were partly similar to the predicted climate change. In order to improve these comparisons we need a longer observational dataset and a better understanding of the influence of climate change on ENSO. Since it has an important effect on the HIRS-12 radiances in the Tropics where the predicted effect of climate change is largest, ENSO is fundamental to the problem of detection of climate change in this satellite channel. Improved modeling would allow the effect of ENSO to be in some way accounted for, if it is considered a source of background noise. The other possibility is that the magnitude and frequency of ENSO events could be affected by climate change, and HIRS-12 radiances would be one way of resolving this.

This study cannot claim to either validate or reject the climate change hypothesis. In order to use the techniques we have demonstrated in a full climate change study, the significance of trends must be obtained by comparison to natural variability. It would be more justifiable to use a transient climate change response to compare to the observed data, so avoiding the assumption that a simple pattern will emerge slowly in the observations. However, if the effects of climate change can be observed in both the spatial and spectral patterns of multiple satellite channels, the attribution of climate change will be one step closer.

Acknowledgments

We thank D. Jackson and J. Bates for supplying the HIRS-12 dataset and information on the number of observations; J. Mitchell, A. Slingo, and M. J. Webb of the Hadley Centre for supplying the HADCM2 data; the reviewers for their helpful comments; and the British Atmospheric Data Centre for the ECMWF analyses.

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APPENDIX

Statistical Significance of a Linear Trend

In order to determine if the linear trend b obtained from a regression fit to Y = a + bX is significantly greater than zero, we make use of the fact (Spiegel 1988) that the statistic
i1520-0442-12-7-1940-ea1
has a Student’s t-distribution with N − 2 degrees of freedom. Here N is the number of samples used in the regression. Defined below are sY,X and sX, respectively the standard error of the regression estimate and the standard deviation of the independent variable (time):
i1520-0442-12-7-1940-ea2
The overbar on the X denotes a mean. It is important to note that this statistic makes no allowance for any autocorrelation in the data.

Fig. 1.
Fig. 1.

HIRS-12 sensitivity functions derived from a zonal mean 1865 HADCM2 atmosphere. The sensitivity functions are normalized in log pressure.

Citation: Journal of Climate 12, 7; 10.1175/1520-0442(1999)012<1940:SPOCVI>2.0.CO;2

Fig. 2.
Fig. 2.

The dependence of HIRS-12 brightness temperature on upper-tropospheric humidity as predicted by the Soden and Bretherton relation.

Citation: Journal of Climate 12, 7; 10.1175/1520-0442(1999)012<1940:SPOCVI>2.0.CO;2

Fig. 3.
Fig. 3.

HIRS-12 yearly mean brightness temperature anomalies, ΔD(x, t), in K.

Citation: Journal of Climate 12, 7; 10.1175/1520-0442(1999)012<1940:SPOCVI>2.0.CO;2

Fig. 4.
Fig. 4.

Temporal standard deviations of HIRS-12 yearly mean brightness temperature data, in K.

Citation: Journal of Climate 12, 7; 10.1175/1520-0442(1999)012<1940:SPOCVI>2.0.CO;2

Fig. 5.
Fig. 5.

Linear trends in HIRS-12 data between 1979 and 1995. Shaded areas indicate trends of greater than 90% significance.

Citation: Journal of Climate 12, 7; 10.1175/1520-0442(1999)012<1940:SPOCVI>2.0.CO;2

Fig. 6.
Fig. 6.

Comparison of TB changes obtained from yearly mean profiles (ΔTB) and those correctly generated using individual profiles (ΔT*B). Superimposed is a least squares linear fit to the data (a = −0.04 K; b = 0.59).

Citation: Journal of Climate 12, 7; 10.1175/1520-0442(1999)012<1940:SPOCVI>2.0.CO;2

Fig. 7.
Fig. 7.

Changes between 1865 and 2045 in the model data: (a) HIRS-12 brightness temperature, (b) upper-tropospheric humidity (UTH), and (c) upper-tropospheric temperature (UTT).

Citation: Journal of Climate 12, 7; 10.1175/1520-0442(1999)012<1940:SPOCVI>2.0.CO;2

Fig. 8.
Fig. 8.

Model data for 1865: (a) HIRS-12 brightness temperatures, (b) UTH, and (c) UTT.

Citation: Journal of Climate 12, 7; 10.1175/1520-0442(1999)012<1940:SPOCVI>2.0.CO;2

Fig. 9.
Fig. 9.

The effect of a change in relative humidity (ΔUTH) on brightness temperatures (ΔM). Scatterplots for (a) 30°N–30°S, (b) 30°–70°N, and 30°–70°S. Superimposed are least squares linear fits to the data.

Citation: Journal of Climate 12, 7; 10.1175/1520-0442(1999)012<1940:SPOCVI>2.0.CO;2

Fig. 10.
Fig. 10.

Pattern similarity statistics R(t) and C(t). Also plotted are the linear least squares fits.

Citation: Journal of Climate 12, 7; 10.1175/1520-0442(1999)012<1940:SPOCVI>2.0.CO;2

Table 1.

Dependence of ΔM on ΔUTH—coefficients of linear fit.

Table 1.
Table 2.

Trends and significances in the pattern similarity statistics.

Table 2.
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