Climate Dynamics

, Volume 43, Issue 5, pp 1439–1448

Uncertainty of AMSU-A derived temperature trends in relationship with clouds and precipitation over ocean


  • F. Weng
    • NOAA/NESDIS/Center for Satellite Applications and Research
    • Department of Earth, Ocean and Atmospheric ScienceFlorida State University
  • Z. Qin
    • NUIST/Center of Data Assimilation for Research and Application

DOI: 10.1007/s00382-013-1958-7

Cite this article as:
Weng, F., Zou, X. & Qin, Z. Clim Dyn (2014) 43: 1439. doi:10.1007/s00382-013-1958-7


Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit-A (AMSU-A) observations from a series of National Oceanic and Atmospheric Administration satellites have been extensively utilized for estimating the atmospheric temperature trend. For a given atmospheric temperature condition, the emission and scattering of clouds and precipitation modulate MSU and AMSU-A brightness temperatures. In this study, the effects of the radiation from clouds and precipitation on AMSU-A derived atmospheric temperature trend are assessed using the information from AMSU-A window channels. It is shown that the global mean temperature in the low and middle troposphere has a larger warming rate (about 20–30 % higher) when the cloud-affected radiances are removed from AMSU-A data. It is also shown that the inclusion of cloud-affected radiances in the trend analysis can significantly offset the stratospheric cooling represented by AMSU-A channel 9 over the middle and high latitudes of Northern Hemisphere.


Climate trendSatelliteCloud

1 Introduction

The Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) on board the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites measure the atmospheric radiation from 23 to 89 GHz. Under a clear-sky atmospheric condition, the radiative energy primarily comes from the oxygen emission. Since the oxygen concentration is nearly uniformly distributed through the Earth’s atmosphere and is stable with time, MSU and AMSU-A are unique satellite instruments for remotely sounding the atmospheric temperature. The MSU instruments on board Tiros-N, NOAA-6 to NOAA-14 have four channels and provided data from 1979 to 2006. Each of the four channels provides measurements of a weighted average of radiation emitted from a particular layer of the atmosphere at a specified frequency. The relative contributions to the total measured radiance from different levels of the atmosphere are quantified by the so-called weighting function (WF), which is channel-dependent. The measured radiation is most sensitive to the atmospheric temperature at the altitude where WF reaches the maximum value. The AMSU-A instruments on board NOAA-15 to NOAA-19 have 15 channels, among which four channels (i.e., AMSU-A channels 3, 5, 7, and 9) are similar, but not identical, to the four MSU channels in frequency. The other 11 AMSU-A channels sample more atmospheric layers than MSU. By putting together MSU and the MSU-like AMSU-A channels, a long-term series of global satellite microwave sounding data of more than 30 years is available for climate study related to atmospheric temperature changes (Zou et al. 2009; Wang et al. 2012).

The atmospheric temperature trends derived from the MSU and AMSU-A on board the NOAA polar-orbiting satellites have been a subject of debate. Pioneer investigations by Spencer and Christy (1992a, b) and their follow-on work at the University of Alabama at Huntsville (UAH) (Christy et al. 1998, 2000, 2003) showed nearly no warming trends for the mid-tropospheric temperature time series derived from the MSU channel 2 (53.74 GHz) and AMSU-A channel 5 (53.71 GHz) observations (called T2). However, the Remote Sensing Systems (RSS) (Mears et al. 2003; Mears and Wentz 2009) and University of Maryland (Vinnikov et al. 2006) and NOAA/NESDIS/Center for Satellite Applications and Research (STAR) (Zou et al. 2009) groups obtained a small warming trend from the same satellite observations. The most recent analysis of different datasets shows a global ocean mean T2 trend of 0.080 ± 0.103 K decade−1 for UAH, 0.135 ± 0.113 K decade−1 for RSS, 0.22 ± 0.07 K decade−1 for UMD and 0.200 ± 0.067 K decade−1 for STAR for the time period from 1987 to 2006 (Zou et al. 2009). In the past, the analysis of the MSU and AMSU-A decadal trend focused on the following areas: (1) diurnal adjustment of various instruments; (2) correction of earth incidence angle; and (3) inter-calibration of co-orbiting satellite (Mears and Wentz 2009). Qin et al. (2012) pointed out a nonlinear behavior of the decadal climate trends of most AMSU-A channels using data from NOAA-15 satellite over the time period from October 26, 1998 to August 7, 2010.

A concern earlier was also raised on how much of the MSU signal arises from non-oxygen emission. For example, for MSU channel 2, Spencer et al. (1990) predicted a small influence of contamination from precipitation-sized ice in deep convection, cloud water, water vapor and surface emissivity. It was concluded that the largest effects come from precipitation-sized ice in deep convection, causing a brightness temperature depression of up to several degrees Celsius. Therefore, the MSU data have been filtered to remove this particular contamination. Spencer (1993) also developed a technique for calculating the anomalous temperature increase in MSU channel 1 (50.3 GHz) and then calibrating it to a rainfall rate. Prabhakara et al. (1995, 1996) suggested that a substantial hydrometeor effect exists in the MSU records, which was criticized by Spencer et al. (1996) who argued that the residual hydrometeor contamination effects were greatly overestimated by Prabhakara et al. (1995, 1996).

This study further evaluates the impacts of both cloud and precipitation on AMSU-A sounding channels and investigates the uncertainty of tropospheric and low stratospheric temperature trends with and without their contributions. A radiative transfer model is used for illustrating the responses of AMSU-A brightness temperatures to several cloud parameters in Sect. 2. Numerical results of cloud impact on trend are provided in Sect. 3 using NOAA-15 AMSU-A observations. Summary and conclusions are provided in Sect. 4.

2 AMSU-A brightness temperature data

The brightness temperature observations used in this study are from the first AMSU-A instrument on board the NOAA Advanced TOVS (ATOVS) system, NOAA-15, which was launched on May 13, 1998. The NOAA-15 satellite has an inclination angle of 98.7°, a local equator crossing time (LECT) of about 7:30 pm in its ascending node at its launch time, and an altitude of 833 km above the Earth.

The AMSU-A instrument on board NOAA-15 is a direct descendant of a series of Microwave Sounding Unit (MSU) instruments on board the early NOAA satellites from Tiros-N, NOAA-6, -7, …, -12 and NOAA-14 from 1979 to 2006. The MSU was a four-channel cross-track scanning radiometer. The AMSU-A channels 3, 5, 7 and 9 are similar to the four MSU channels, and their frequencies are located within an oxygen absorption band at 50.3, 53.6, 54.9, and 57.3 GHz, respectively. The band width, polarization state, beam width, NEDT and peak weighting function height are listed in Table 1. The extreme scan position of the Earth view to the beam center is 48.3°. It takes 8 s for the NOAA-15 AMSU-A to complete one scan cycle with earth view and calibration samples. There are 30 Field of Views (FOV) along each scan line. Therefore, beam positions 15 and 16 are near the nadir direction of AMSU-A. The resolution of the AMSU-A FOV at nadir is about 48 km.
Table 1

Channel characteristics of AMSU-A channels 3, 5, 7 and 9


Frequency (GHz)

Band width (MHz)

Polarization (V/H)

Beam width (°)


Weighting function peak (hPa)









53.596 ± 0.115




















The emission and scattering of clouds and precipitation modulate brightness temperatures from both MSU and AMSU-A. In order to assess the effects of the radiation from clouds and precipitation on atmospheric temperature trend derived from microwave temperature sounding instruments (e.g., MSU and AMSU-A), cloud-affected brightness temperature measurements must be identified. Fortunately, the information from AMSU-A window channels 1 and 2 can be used for retrieving atmospheric cloud liquid water path (LWP) using the algorithm developed by Weng et al. (2003). For this reason, temperature trends for AMSU-A channels 3, 5, 7 and 9 from NOAA-15 are investigated in this study. Uncertainty of MSU derived temperature trends in relationship with clouds and precipitation requires a new algorithm to be developed for detecting cloud-affected MSU brightness temperature observations, which is planned for future research. Until then, cloud impacts on atmospheric temperature trends derived from a much longer climate data record of microwave temperature sounding data could be assessed.

Figure 1 displays the weighting functions (WFs) calculated from a standard US atmospheric profile for the four channels on NOAA-15 AMSU-A. Channel 3 is a window channel; channels 5 and 7 are two tropospheric channels, and channel 9 is a stratospheric channel. Since the altitude of WF peak varies with scan position, only the NOAA-15 AMSU-A brightness temperatures at nadir (e.g., FOVs 15 and 16) are used in this study. A more detailed description can be found in the NOAA KLM User Guide.1
Fig. 1

Weighting function of AMSU-A channel 3 (red curve), channel 5 (green curve), channel 7 (blue curve) and channel 9 (black curve) from NOAA-15 calculated by the CRTM using the US standard atmosphere profile, which is overlapped to a schematic illustration of a stratiform cloud with rainfall consisting of a 0.8-km depth non-precipitating cloud layer located below the freezing level with liquid water path of 0.5 kg m−2, and the raindrops below the non-precipitating cloud layer with the rainfall rates unchanged vertically. Emissivity is set to 0.5

3 An idealized sensitivity study

Here, the Community Radiative Transfer Model (CRTM) is first used for simulating the responses of AMSU-A brightness temperatures to the clouds characterized by rain rate. The CRTM was developed by the US Joint Center for Satellite Data Assimilation (JCSDA) for rapid calculations of satellite radiances and their derivatives under various atmospheric and surface conditions (Weng 2007; Han et al. 2007). It supports a large number of sensors, including the historical and near future sensors from the Joint Polar Satellite System (JPSS) and the Geostationary Operational Environmental Satellite-R Series (GOES-R), covering the microwave, infrared and visible wavelengths for a variety of research and applications. For calculations of cloud and aerosol absorption and scattering, lookup tables of the optical properties of six cloud and eight aerosol types are included in the cloud/aerosol optical property module. The fast doubling-adding method is used to solve the multi-stream radiative transfer equation.

An idealized cloud distribution similar to that in Liu and Curry (1993) is assumed for the sensitivity study. Namely, a 0.8-km non-precipitating cloud layer with a liquid water path (LWP) of 0.5 kg m−2 is added to the atmosphere below the freezing level and a precipitation layer is set below the cloud. Each specific rain rate is assumed for the entire precipitation layer. A schematic diagram of the cloud and precipitation system is illustrated in Fig. 1. Note that an atmospheric profile including temperature and water vapor is used as inputs to CRTM and the surface emissivity is set to 0.5, which is a value close to an ocean surface.

For a given atmospheric temperature condition, the emission and scattering of clouds and precipitation could increase or decrease the brightness temperatures measured by AMSU-A. As an example, Fig. 2 presents variations of model-simulated AMSU-A brightness temperatures at satellite nadir position versus rain rate with a mean raindrop size as a parameter. The droplet radius varies from 0.05 to 1.0 mm for the clouds as shown in Fig. 1. Since the assumed cloud is distributed below 4 km, the higher the channels, the smaller impact the clouds have on the brightness temperature. It is seen that the brightness temperature at AMSU-A channel 3 increases as rain rate increases due to the cloud emission when the mean radius of raindrops is <0.1 mm (Fig. 2a). When the rain droplets are less than 0.1 mm, the size parameter (i.e., \(2\pi /\lambda\), where \(\lambda\) is the wavelength) of clouds and raindrops all falls into the Rayleigh scattering regime where the absorption dominates over scattering (Bohren and Huffman 1998). Also, the absorption coefficient is proportional to liquid water content or rain rate (Weng et al. 2003). Note that the brightness temperature at AMSU-A channel 3 in clear-sky atmosphere is much lower due to the lower surface emissivity over ocean and thus an increasing of cloud and raindrop absorption will result in an increase in brightness temperature. When the raindrop size increases to 0.3 mm, the brightness temperature firstly increases rapidly with rain rate and then remains nearly constant (e.g., saturated) as the rain rate further increases. For the larger raindrops whose radius exceeds 0.5 mm, the brightness temperature increases more rapidly with rain rate than that for smaller raindrops when the rain rate is small; and the brightness temperature decreases slightly as the rain rate further increases (Fig. 2a). Except for very small rain rate, the brightness temperature at channel 5 decreases as rain rate increases, which is due to an increasing precipitation scattering (Fig. 2b). A decrease in brightness temperature occurs when the radiation from other atmospheric layers is scattered out of the instrument field of view. At AMSU channel 7 (Fig. 2c), cloud and precipitation have very small effects on brightness temperature since their weighting functions peak mostly above the clouds and therefore the brightness temperature decreases with rain rate for all raindrop sizes. For AMSU-A channel 9 (Figure omitted), clouds and precipitation have negligible effects on brightness temperature since this channel has its weighting function completely above the top of cloud and precipitation layer (see Fig. 1) and the radiation all arises from the oxygen absorption and emission in the atmosphere.
Fig. 2

Variations of brightness temperature of AMSU-A channels 3, 5 and 7 with respect to rainfall rate with an effect diameter of cloud droplets of 0.05 mm (solid black), 0.1 mm (dotted black), 0.3 mm (blue solid), 0.5 mm (dotted blue), and 0.7 mm (solid red) and 1.0 mm (dotted red). The emissivity is set to 0.5

Based on the results in Figs. 1, 2, it is concluded that the larger the raindrop size is, the larger the scattering and emission effects of clouds on the brightness temperatures are. The cloud emission dominates when the rain rate is low and the raindrop size is small. In reality, clouds are of different types and are located at different altitudes. Only the integrated effect of clouds on the temperature trend can be estimated from real data.

4 Trend analysis

Since the launch of NOAA-15 satellite on May 13, 1998, the AMSU data has been used in NOAA operational applications for over 13 years. The cloud algorithm developed by Weng et al. (2003) for NOAA-15 AMSU-A data over ocean makes an estimate of the cloud impact on trend possible using AMSU-A data. The two AMSU-A window channels 1 (23.8 GHz) and 2 (31.4 GHz) can be utilized for a physical retrieval of cloud liquid water path (LWP) over ocean (Weng et al. 2003). Figure 3a provides a distribution of an annual data count with LWP being less than 0.5 kg m−2 within each of 5° × 5° grid boxes over global ocean for NOAA-15 AMSU-A data in 2008. The data in Fig. 3a are further separated into two groups: One for clear-sky (Fig. 3b) and the other for cloudy (Fig. 3c) conditions, where the clear-sky and cloudy data are identified by LWP being <0.01 kg m−2 and between 0.01 and 0.5 kg m−2, respectively. More cloudy data with LWP values between 0.01 and 0.5 kg m−2 are found in the east Pacific, east Atlantic and east Indian oceans in the Southern Hemisphere than those in the middle latitudes (Fig. 3b). In the Northern Hemisphere, more cloudy data are found in the tropical warm pool and middle-latitude storm track regions than other oceanic regions (Fig. 3b). The tropical area has more clear-sky areas than that in the middle latitudes (Fig. 3c). The LWP threshold of 0.5 kg m−2 is used for identifying the precipitation.
Fig. 3

Global distributions of data counts within 5° × 5° grid boxes for NOAA-15 AMSU-A FOVs 15 and 16 in 2008 with a LWP < 0.5 kg m−2, b 0.01 ≤ LWP < 0.5 kg m−2 and c LWP < 0.01 kg m−2

In the following trend analysis, a threshold of LWP being >0.01 kg m−2 is used as an indicator of cloudy AMSU data. Figure 4 shows a latitudinal variation of the averaged daily counts of the total and clear-sky AMSU-A nadir FOVs from NOAA-15 satellite over global oceans. In general, about 30–50 % and 20–30 % of oceanic observations are cloud-affected in the middle to high latitudes and the low latitudes respectively.
Fig. 4

Averaged daily count of AMSU-A nadir data in each of 5° latitudinal bands averaged from August 1, 1999 to June 30, 2012 in clear-sky (blue) and all-weather condition (red) over ocean

The temperature trends derived from AMSU-A channels 3, 5, 7 and 9 with and without removing cloudy microwave measurements are shown in Fig. 5. The global temperature trends calculated from AMSU-A data between 60S and 60N for the window channel 3, the middle-troposphere channel 5, the upper-troposphere channel 7 and the upper-stratosphere and low-stratosphere channel 9 are 0.04, 0.0, −0.01 and −0.13 K decade−1 in all-weather condition (Fig. 5a). When the cloud effect is eliminated, the global temperature trend for both channels 3 and 5 is increased to 0.07 K decade−1 and 0.03 K decade−1 respectively. The cloud effect can give a more than 43 % decrease of the warming trend. The brightness temperatures at AMSUA channels 3 and 5 are sensitive to the emission and scattering from clouds and precipitation. A decrease in brightness temperature can be associated with cloud and precipitation scattering, rather than physical temperature in the lower and middle troposphere and therefore the trends from microwave sounding data could be misleading if the brightness temperatures from all weather conditions are averaged as a representation of atmospheric physical temperature.
Fig. 5

Decadal temperature trends between a 60S–60N, b 0–60S and c 0–60N in clear-sky (blue) and all-weather (red) conditions

The global impact of clouds on temperature trends in the upper troposphere and low stratosphere are negligible. If we separate Northern Hemisphere from Southern Hemisphere, impacts of clouds on temperature trends become more significant than the global average. In the Southern Hemisphere, the warming trends for both the window channel 3 and the middle-tropospheric channel 5 are 0.06 K decade−1 and 0.03 K decade−1, respectively, which decrease to nearly zero when clouds are present (Fig. 5b). The temperature cooling trends for channels 7 and 8 are slightly increased by clouds (Fig. 5b) in the Southern Hemisphere. Cloud impact on temperature trend for channel 5 in the Northern Hemisphere is similar to that in the Southern Hemisphere, i.e., cloud reduces the warming trend. Impact of clouds on channel 7 is also negligible in the Northern Hemisphere. However, an opposite effect of cloud on temperature trend is found in the Northern Hemisphere near the surface and in the upper troposphere and low stratosphere: clouds causes an overestimate of temperature warm trend of the middle-tropospheric channel 5 and an underestimate of temperature cooling trend of the upper troposphere and low stratosphere channel 9. The global ocean mean trends for channel 5 within the time period from October 26, 1998 to August 7, 2010 with or without removing cloud-affected data obtained in this study are much smaller than those obtained by UAH, UMD and STAR for the time period from 1987 to 2006 (Zou et al. 2009). Further investigation is needed to clarify if the different trends can be caused by the data record length.

The standard deviations of decadal temperature trends over the globe (60S–60N), Northern Hemisphere (0–60N) and Southern Hemisphere (0–60S) in clear-sky and all-weather conditions are provided in Fig. 6. Compared with Fig. 5, it is seen that the standard deviations are more than an order of magnitude smaller than the global or hemispheric trends. Differences of standard deviations between clear-sky and all-weather conditions are negligible except for channel 3. The standard deviations for the decadal temperature trends in clear-sky conditions are significantly smaller than those in all-weather conditions.
Fig. 6

Standard deviations of decadal temperature trends between a 60S–60N, b 0–60S and c 0–60N in clear-sky (blue) and all-weather (red) conditions

Figure 7 shows two time series of brightness temperatures from which the global temperature trends are derived for AMSU-A channel 3 data within 60S–60N with and removing the cloud effect. The annual mean is subtracted. It is seen that the global mean brightness temperatures without the cloud effect removal (Fig. 7a) have a larger spread than those with only clear-sky data (Fig. 7a). The trend calculated from the clear-sky data is thus not only larger, but also more reliable.
Fig. 7

Times series of brightness temperatures (yellow circles) in a all-weather and b clear-sky conditions for AMSU-A channel 3 data between 60S and 60N. The decade trends deduced from these data are shown in red line. The annual cycle has been removed

A latitudinal dependence of the cloud impact on temperature trend in the middle troposphere is provided in Fig. 8. Strong temperature warming trends are found in microwave sounding data in middle latitudes in both hemispheres. The clouds significantly reduce the derived warming trends. In the tropics, the middle troposphere experiences a weak cooling. The clouds slightly increase the derived cooling trends.
Fig. 8

Latitudinal dependence of decadal temperature trends of AMSU-A channel 5 in clear-sky (blue) and all-weather (red) conditions

Figure 9 presents the latitudinal dependence of decadal temperature trends of AMSU-A channel 3 in clear-sky and all-weather conditions. The temperature trends of the window channel 3 are similar to channel 5 in the middle latitudes. However, cloud emissions introduce a warming trend near the equator between 5N and 5S. It should be mentioned that microwave data associated with heavy precipitation are not included in any results presented in this study. The subtropical area in the Southern Hemisphere has a cooling trend and that in the Northern Hemisphere has a warming trend. Impact of cloud on the temperature trend in the subtropical areas is not significant.
Fig. 9

Same as Fig. 8 except for channel 3

The latitudinal dependences of decadal temperature trends of AMSU-A channel 9 in clear-sky and all-weather conditions are compared in Fig. 10. The clouds has a significant impact on the temperature cooling trends in the middle and high latitudes in the Northern Hemisphere. The cloud emission in high altitudes and upper troposphere in Northern Hemisphere reduces the cooling trends of channel 9 by more than half of the values without removing cloudy data. Cloud impacts are much smaller in low latitudes and Southern Hemisphere.
Fig. 10

Same as Fig. 8 except for channel 9

Regional contributions to the global trends could be inferred from the regressed trend \(R(\lambda ,\varphi )\), which is calculated as follows (see Appendix 1):
$$R(\lambda ,\varphi ) = \frac{{\frac{1}{N}\sum_{i = 1}^{N} {\left( {T_{b}^{LR,global} (t_{i} ) - \overline{{T_{b}^{LR,global} (t_{i} )}} } \right)\left( {T_{b} (\lambda ,\varphi ,t_{i} ) - \overline{{T_{b} (\lambda ,\varphi ,t_{i} )}} } \right)} }}{{\sigma_{{T_{b}^{LR,global} }} }}$$
where \(\lambda\) is longitude, \(\varphi\) is latitude, \(T_{b}^{LR,global} (t_{i} ) = \overline{{T_{b} }}^{global} (t_{1} ) + at_{i}\) is the linear evolution of global mean brightness temperature with the global trend “a”, the over bar in \(\overline{{T_{b}^{LR,global} (t_{i} )}}\) and \(\overline{{T_{b} (\lambda ,\varphi ,t_{i} )}}\) represents a time average from t1 to tN (N = 155), \(T_{b} (\lambda ,\varphi ,t_{i} )\) is the time series (ti, i = 1, 2, …, 155) of monthly mean brightness temperature within each 5° × 5° grid box defined by (\(\lambda\), \(\varphi\)), \(Std_{{T_{b}^{LR,global} }}\) is the standard deviation of \(T_{b}^{LR,global} (t_{i} )\), and \(\overline{{T_{b} }}^{global} (t_{1} )\) is the global mean of brightness temperatures at the beginning of the 13-year time series of NOAA-15 AMSU-A data.
The spatial distributions of the regressed trend \(R(\lambda ,\varphi )\) for AMSU-A channels 3 and 5 in both clear-sky and all-weather conditions are compared in Fig. 11. By comparing with the cloudy data-count distribution shown in Fig. 3b, it is seen that larger, positive regional contributions to the global warming trends were found over areas having a higher population of clouds in middle latitudes. Inclusions of cloudy radiances reduce the regional contributions to the global temperature trends. In other words, the temperatures over the areas where clouds are populated in the low and middle troposphere affect the global temperature trends.
Fig. 11

Regional contributions to the global trend [i.e., \(R(\lambda ,\varphi )\) Eq. (1)] in clear-sky (left panels) and all-weather (right panels) conditions for AMSU-A a, b channel 3 and c, d channel 5

5 Summary and conclusions

In this study, the cloudy and cloud-free atmospheric temperature trends are derived from satellite microwave temperature sounding observations. The global daily mean brightness temperatures observed by the NOAA polar-orbiting satellite NOAA-15 AMSU-A instrument over the 13-year time period from August 1, 1999 to June 30, 2012 over ocean are used in our analysis. The traditional linear regression method is applied to the 13-years global brightness temperatures of AMSU-A channels 3, 5, 7 and 9 to obtain global and hemispheric averaged warming and cooling trends. By binning all AMSU-A channels brightness temperature measurements within five-degree latitudinal bands, the latitudinal dependence of the cloud impact on global warming trend is calculated and its relationship to global average warming/cooling is determined. It is shown that the atmospheric warming trends in middle latitudes are significantly larger when cloud effects are removed from the 13-year microwave sounding data of AMSU-A channels 3 and 5 in both hemispheres. The scattering and emission effect of clouds and precipitation significantly reduces the values of the warm trends in the low and middle troposphere derived from the microwave data. The cooling trends are found in the high latitudes in the Southern Hemisphere and all latitudes in Northern Hemisphere. The high altitude clouds tend to reduce the cooling trends of AMSU-A channel 9 in Northern Hemisphere, especially in middle and high latitudes in Northern Hemisphere. The cloud impacts on the cooling trends in Southern Hemisphere are negligible. However, their impacts on the temperature trends could be much larger in latitudinal zones than that on the global warm trend.

In the present study, only a single satellite data (NOAA-15 AMSU-A) is used for the investigation of the cloud impact on temperature trends. It is recommended that a similar study be carried out on a longer time series (e.g., more than 30 years) by combining MSU data with AMSU-A data from multi-satellite data. However, the cloud algorithm for AMSU-A cannot be applied to MSU data since the two AMSU-A window channels used for the LWP retrieval are not available from MSU data. Further investigation is required for developing an approximate cloud algorithm for MSU data before the cloud impact on temperature trends can be assessed from MSU data.


This work was supported by Chinese Ministry of Science and Technology under 973 project 2010CB951600.

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