GC34A-01
Application of Infrared Hyperspectral Sounder Data to Climate Research: Interannual Variability and climate trend evaluation.
Satellite measurements of the spectrally resolved upwelling infrared radiances have a unique role in the observation of climate and climate change: They give direct insight into the way the Earth Climate System responds to periodic and long term changes in forcing with changes in surface and atmospheric temperatures and changes in large scale atmospheric circulation patterns. The Atmospheric Infrared Sounder (AIRS), the first in a series of hyper-spectral polar orbiting sounders, was launch on the EOS Aqua into a 1:30 pm polar orbit at 705 km altitude in May 2002, with an anticipated lifetime of 12 years. The Infrared Atmospheric Sounding Interferometer (IASI) was launched in October 2006 into a 9:30 AM orbit, to be followed by the Crosstrack InfraRed Sounder (CRIS) in a 2 PM orbit in 2010. The AIRS radiometric stability since 2002 has been verified at the better than 0.01 K/year level. We report on observations of the oceans between 30S and 30N. The 0.05 K/year trend in co2 sensitive channels due to the 2 ppmv/year increase in the co2 column abundance is readily detectable and statistically reliable. The AIRS data show very consistent seasonal modulations of key surface, cloud, water vapor and atmospheric temperatures. After removing the seasonal variation, the anomaly shows interannual rms variability in the monthly means larger than 0.1 K. The rms variability in the monthly means in the mid- tropospheric temperature with peak excursions as large as 0.6 K are observed by the AIRS 2388 cm-1 channel and AMSU channel 5 at 57 GHz. The interannual variability is not obviously correlated with the Multivariate Enso Index (MEI). This variability places limits on the length of time required to measure global warming trends at the 0.1 K/decade level. These limits exceed the expected 12 year lifetime of AIRS and need to be taken into account in the design of space missions and instruments to measure climate change.
GC34A-02
Determination of atmospheric temperature, water vapor, and heating rates from mid- and far- infrared hyperspectral measurements
Comprehensive satellite-borne far-infrared (15-100 μm) hyperspectral measurements of the earth have not been implemented since the short-lived Infrared Interferometer Sounder-D (IRIS-D) instrument on the Nimbus-4 satellite ceased operation in 1971 due primarily to instrumentation limitations and mission cost considerations. Recently, the development of the Far Infrared Spectroscopy of the Troposphere (FIRST) instrument [Mlynczak et al, 2006], a balloon-borne FTS which records spectra from 5 to 200 μm, provides a test-bed for the development of space-based far-infrared measurements for climate change monitoring. A comparison of the retrieval capabilities of a notional space-based instrument of comparable performance to FIRST and the currently-operational mid-infrared instrument AIRS is presented. Temperature and water vapor retrievals are compared (in an orbital simulation framework) along with the relative ability of the retrievals from these two instruments to constrain the heating rate profile. Also, the skill with which the AIRS measurements can be used to extrapolate the cloud radiative effect into the far-infrared is explored. Finally, FIRST test flight spectra are presented in the framework of other A-Train measurements such as MODIS and CALIPSO, followed by a discussion of climate applications. http://www.gps.caltech.edu/~drf/misc/agu2007
GC34A-03
Spectrally resolved infrared radiances from AIRS observation and GCM simulation
Global multi-year spectrally resolved infrared radiances observed by the Atmospheric Infrared Sound (AIRS) satellite instrument and simulated from the General Circulation Models (GCMs) of the Geophysical Fluid Dynamics Lab (GFDL) are processed to obtain long-term global and regional means as well as the associated spatial and temporal variability. The accumulated radiance data comprise a host of phenomena that are still largely unrecognized but reveal important physical processes. For instance, the correlation between the radiances and the Sea Surface Temperatures (SSTs) discloses the roles of water vapor in both upper (via its v2 band) and lower (via the continuum in the window region) troposphere, and that of clouds regarding the so called "super greenhouse effect" in Tropics. A comparison between observed and simulated radiances demonstrates that radiance affords a stricter and more insightful metric than the broadband flux. A seemingly good agreement of OLR flux may arise from cancellation of errors of opposite signs in different spectral regions; radiance biases are indicative of physical causes because the radiances at each frequency are sensitive to factor(s) at different levels. Model validation at the radiance level thus provides a complementary and integrative perspective to that obtained using meteorological variables. It is demonstrated that the radiance discrepancies between the GFDL model and the observation are consistent with the model biases in temperature, water vapor and clouds.
GC34A-04
Deriving Climate Level Trends with Infrared Hyperspectral Data
Detection of climate trends with infrared hyperspectral radiances requires careful attention to instrument stability, and calibration differences between hyperspectral instruments (AIRS and IASI), since trend signals are quite small. The infrared spectrum also mixes the forcings (increased CO2 for example) with the response (temperature, water vapor, clouds). We examine the issues involved in attempting to separate long-term (and seasonal) mid-tropospheric temperature trends from CO2 variability. We will present results for various trends in AIRS radiances, over its first our years of operation, as well as the ability to detect similar trends with IASI radiances.
GC34A-05
AIRS-Based Atmospheric Parameter Climatologies: A High Quality Tool for Monitoring Short-, and Longer-Term Climate Variabilities to Improve Modeling of Climate Processes
Satellites provide an ideal platform to study the Earth-atmosphere system on practically all spatial and temporal scales. Thus, one may expect that their rapidly growing datasets could provide crucial insights not only for short- term weather processes/predictions but into ongoing and future climate change processes as well. For example, outgoing longwave radiation (OLR) which is probably the most important parameter to assess global climate change since the Earth-atmosphere system has to adjust to the new energy balance, is well suited for satellite monitoring. In addition to its primary dependence of the atmospheric temperature profile and cloud distribution, the OLR is dependent on natural and man-induced changes of various radiatively important atmospheric constituents like water vapor, carbon-dioxide, and other trace gases. The AIRS instrument is the best space- based tool so far to simultaneously monitor all of the above-mentioned parameters, and has provided high quality data for more than 5 years. AIRS analysis results produced at the GODDARD/DAAC, based on Versions 4 & 5 of the AIRS retrieval algorithm, are currently available for public use. Here, first we present an assessment of interrelationships of anomalies (proxies of climate variability based on 5 full years, since Sept. 2002) of various climate parameters at different spatial scales with the aim that these "reality checks" will be very useful for the evaluation and refinement of climate models and climate related atmospheric processes. We also present AIRS-retrievals-based global, regional and 1x1 degree grid-scale "trend"-analyses of important atmospheric parameters for this 5-year period. Note that here "trend" simply means the linear fit to the anomaly (relative the mean seasonal cycle) time series of various parameters at the above- mentioned spatial scales, and we present these to illustrate the usefulness of continuing AIRS-based climate observations. Preliminary validation efforts, in terms of intercomparisons of interannual variabilities with other available satellite data analysis results, will also be addressed. For example, we show that the OLR interannual spatial variabilities from the available state-of-the-art CERES measurements and from the AIRS computations are in remarkably good agreement. Version 6 of the AIRS retrieval scheme (currently under development) promises to further improve bias agreements for the absolute values by implementing a more accurate radiative transfer model for the OLR computations.
GC34A-06
Identifying Modes of Temperature Variability Using AIRS Data.
We use the Atmospheric Infrared Sounder (AIRS) and Advance Microwave Sounding Unit (AMSU) data obtained on Aqua spacecraft to study mid-tropospheric temperature variability between 2002-2007. The analysis is focused on daily zonal means of the AIRS channel at 2388 1/cm in the CO2 R-branch and the AMSU channel #5 in the 57 GHz Oxygen band, both with weighting function peaking in the mid-troposphere (400 mb) and the matching sea surface temperature from NCEP (Aumann et al., 2007). Taking into account the nonlinear and non- stationary behavior of the temperature we apply the Empirical Mode Decomposition (Huang et al., 1998) to better separate modes of variability. All-sky (cloudy) and clear sky, day and night data are analyzed. In addition to the dominant annual variation, which is nonlinear and latitude dependent, we identified the modes with higher frequency and inter-annual modes. Some trends are visible and we apply stringent criteria to test their statistical significance. References: Aumann, H. H., D. T. Gregorich, S. E. Broberg, and D. A. Elliott, Geophys. Res. Lett., 34, L15813, doi:10.1029/2006GL029191, 2007. Huang, N. E. Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, Proc. R. Soc. Lond., A 454, 903-995, 1998.
GC34A-07
Applying AIRS Hyperspectral Infra-Red Data to Cloud and Water Vapor Studies of Climate
Advantages of using spectrally resolved radiance in climate studies were initially pointed out by Iacono and Clough (1996) and Haskins et al. (1997). In a recent paper by Huang and Yung (2005), an overview of the spatial variability of (time-space averaged) spectra in different climate zones derived from a limited amount of AIRS data was discussed. In their studies, the EOFs were performed on spatial-temporal averages of AIRS spectra. Since atmospheric processes, specifically with regards to the hydrological cycle ( e.g., cloudiness), are non-linear, it is important to consider the impact of this averaging on the results. Understanding the impact of this averaging has implications for interpreting satellite data and developing the spatial and temporal sampling requirements of new satellite missions intended to characterize the role of atmospheric radiation and clouds on climate. As a test of these ideas, a subset of the AIRS data is subjected to the EOF analysis before time-space averaging and compared to an analysis of the same data that have first undergone time-space averaging. While the sum of the variances percentages of the first two modes in both cases is similar, their partitioning is quite different. In the EOFs without time-space averaging, the first mode is dominated by variability in the window region while the second the variability is located in the water vapor bands. However, in the EOFs with time-space averaging, the first and second modes do not clearly distinguish between these two impacts/processes, since the averaging process tends to make all locations cloudy. In addition, the spectral variations associated with the third EOF mode - which exhibits an influence from ozone and CO2 - is virtually averaged away in the case with time- space averaging. This inconsistency between the two results is important for understanding the variability of the atmospheric hydrological cycle, as well as considering measurement and model-diagnostic strategies, particularly those associated with clouds and their impact on climate.