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Seiji Kato, Fred G. Rose, Xu Liu, Bruce A. Wielicki, and Martin G. Mlynczak

Abstract

A surface, atmospheric, and cloud (fraction, height, optical thickness, and particle size) property anomaly retrieval from highly averaged longwave spectral radiances is simulated using 28 years of reanalysis. Instantaneous nadir-view spectral radiances observed from an instrument on a 90° inclination polar orbit are computed. Spectral radiance changes caused by surface, atmospheric, and cloud property perturbations are also computed and used for the retrieval. This study’s objectives are 1) to investigate whether or not separating clear sky from cloudy sky reduces the retrieval error and 2) to estimate the error in a trend of retrieved properties. This simulation differs from earlier studies in that annual 10° latitude zonal cloud and atmospheric property anomalies defined as the deviation from 28-yr climatological means are retrieved instead of the difference of these properties from two time periods. The root-mean-square (RMS) difference of temperature and humidity anomalies retrieved from all-sky radiance anomalies is similar to the RMS difference derived from clear-sky radiance anomalies computed by removing clouds. This indicates that the cloud property anomaly retrieval error does not affect the retrieved temperature and humidity anomalies. When retrieval errors are nearly random, the error in the trend of retrieved properties is small. Approximately 30% of 10° latitude zones meet conditions that the true temperature and water vapor amount trends are within a 95% confidence interval of retrieved trends, and that the standard deviation of retrieved anomalies σ ret is within 20% of the standard deviation of true anomalies σ n. If σ ret/σ n − 1 is within ±0.2, 91% of the true trends fall within the 95% confidence interval of the corresponding retrieved trend.

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Rolando R. Garcia, Ruth Lieberman, James M. Russell III, and Martin G. Mlynczak

Abstract

Observations made by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on board NASA’s Thermosphere–Ionosphere–Mesosphere Energetics and Dynamics (TIMED) satellite have been processed using Salby’s fast Fourier synoptic mapping (FFSM) algorithm. The mapped data provide a first synoptic look at the mean structure and traveling waves of the mesosphere and lower thermosphere (MLT) since the launch of the TIMED satellite in December 2001. The results show the presence of various wave modes in the MLT, which reach largest amplitude above the mesopause and include Kelvin and Rossby–gravity waves, eastward-propagating diurnal oscillations (“non-sun-synchronous tides”), and a set of quasi-normal modes associated with the so-called 2-day wave. The latter exhibits marked seasonal variability, attaining large amplitudes during the solstices and all but disappearing at the equinoxes. SABER data also show a strong quasi-stationary Rossby wave signal throughout the middle atmosphere of the winter hemisphere; the signal extends into the Tropics and even into the summer hemisphere in the MLT, suggesting ducting by westerly background zonal winds. At certain times of the year, the 5-day Rossby normal mode and the 4-day wave associated with instability of the polar night jet are also prominent in SABER data.

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Nipa Phojanamongkolkij, Seiji Kato, Bruce A. Wielicki, Patrick C. Taylor, and Martin G. Mlynczak

Abstract

Two climate signal trend analysis methods are the focus of this paper. The uncertainty of trend estimate from these two methods is investigated using Monte Carlo simulation. Several theoretically and randomly generated series of white noise, first-order autoregressive and second-order autoregressive, are explored. The choice of method that is most appropriate for the time series of interest depends upon the autocorrelation structure of the series. If the structure has its autocorrelation coefficients decreased with increasing lags (i.e., an exponential decay pattern), then the method of Weatherhead et al. is adequate. If the structure exhibits a decreasing sinusoid pattern of coefficient with lags (or a damped sinusoid pattern) or a mixture of both exponential decay and damped sinusoid patterns, then the method of Leroy et al. is recommended. The two methods are then applied to the time series of monthly and globally averaged top-of-the-atmosphere (TOA) irradiances for the reflected solar shortwave and emitted longwave regions, using radiance observations made by Clouds and the Earth’s Radiant Energy System (CERES) instruments during March 2000 through June 2011. Examination of the autocorrelation structures indicates that the reflected shortwave region has an exponential decay pattern, while the longwave region has a mixture of exponential decay and damped sinusoid patterns. Therefore, it is recommended that the method of Weatherhead et al. is used for the series of reflected shortwave irradiances and that the method of Leroy et al. is used for the series of emitted longwave irradiances.

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Tao Li, Natalia Calvo, Jia Yue, James M. Russell III, Anne K. Smith, Martin G. Mlynczak, Amal Chandran, Xiankang Dou, and Alan Z. Liu

Abstract

In the Southern Hemisphere (SH) polar region, satellite observations reveal a significant upper-mesosphere cooling and a lower-thermosphere warming during warm ENSO events in December. An opposite pattern is observed in the tropical mesopause region. The observed upper-mesosphere cooling agrees with a climate model simulation. Analysis of the simulation suggests that enhanced planetary wave (PW) dissipation in the Northern Hemisphere (NH) high-latitude stratosphere during El Niño strengthens the Brewer–Dobson circulation and cools the equatorial stratosphere. This increases the magnitude of the SH stratosphere meridional temperature gradient and thus causes the anomalous stratospheric easterly zonal wind and early breakdown of the SH stratospheric polar vortex. The resulting perturbation to gravity wave (GW) filtering causes anomalous SH mesospheric eastward GW forcing and polar upwelling and cooling. In addition, constructive inference of ENSO and quasi-biennial oscillation (QBO) could lead to stronger stratospheric easterly zonal wind anomalies at the SH high latitudes in November and December and early breakdown of the SH stratospheric polar vortex during warm ENSO events in the easterly QBO phase (defined by the equatorial zonal wind at ~25 hPa). This would in turn cause much more SH mesospheric eastward GW forcing and much colder polar temperatures, and hence it would induce an early onset time of SH summer polar mesospheric clouds (PMCs). The opposite mechanism occurs during cold ENSO events in the westerly QBO phase. This implies that ENSO together with QBO could significantly modulate the breakdown time of SH stratospheric polar vortex and the onset time of SH PMC.

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Xu Liu, Wan Wu, Bruce A. Wielicki, Qiguang Yang, Susan H. Kizer, Xianglei Huang, Xiuhong Chen, Seiji Kato, Yolanda L. Shea, and Martin G. Mlynczak

Abstract

Detecting climate trends of atmospheric temperature, moisture, cloud, and surface temperature requires accurately calibrated satellite instruments such as the Climate Absolute Radiance and Refractivity Observatory (CLARREO). Previous studies have evaluated the CLARREO measurement requirements for achieving climate change accuracy goals in orbit. The present study further quantifies the spectrally dependent IR instrument calibration requirement for detecting trends of atmospheric temperature and moisture profiles. The temperature, water vapor, and surface skin temperature variability and the associated correlation time are derived using the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The results are further validated using climate model simulation results. With the derived natural variability as the reference, the calibration requirement is established by carrying out a simulation study for CLARREO observations of various atmospheric states under all-sky conditions. A 0.04-K (k = 2; 95% confidence) radiometric calibration requirement baseline is derived using a spectral fingerprinting method. It is also demonstrated that the requirement is spectrally dependent and that some spectral regions can be relaxed as a result of the hyperspectral nature of the CLARREO instrument. Relaxing the requirement to 0.06 K (k = 2) is discussed further based on the uncertainties associated with the temperature and water vapor natural variability and relatively small delay in the time to detect for trends relative to the baseline case. The methodology used in this study can be extended to other parameters (such as clouds and CO2) and other instrument configurations.

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Seiji Kato, Bruce A. Wielicki, Fred G. Rose, Xu Liu, Patrick C. Taylor, David P. Kratz, Martin G. Mlynczak, David F. Young, Nipa Phojanamongkolkij, Sunny Sun-Mack, Walter F. Miller, and Yan Chen

Abstract

Variability present at a satellite instrument sampling scale (small-scale variability) has been neglected in earlier simulations of atmospheric and cloud property change retrievals using spatially and temporally averaged spectral radiances. The effects of small-scale variability in the atmospheric change detection process are evaluated in this study. To simulate realistic atmospheric variability, top-of-the-atmosphere nadir-view longwave spectral radiances are computed at a high temporal (instantaneous) resolution with a 20-km field-of-view using cloud properties retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, along with temperature humidity profiles obtained from reanalysis. Specifically, the effects of the variability on the necessary conditions for retrieving atmospheric changes by a linear regression are tested. The percentage error in the annual 10° zonal mean spectral radiance difference obtained by assuming linear combinations of individual perturbations expressed as a root-mean-square (RMS) difference computed over wavenumbers between 200 and 2000 cm−1 is 10%–15% for most of the 10° zones. However, if cloud fraction perturbation is excluded, the RMS difference decreases to less than 2%. Monthly and annual 10° zonal mean spectral radiances change linearly with atmospheric property perturbations, which occur when atmospheric properties are perturbed by an amount approximately equal to the variability of the10° zonal monthly deseasonalized anomalies or by a climate-model-predicted decadal change. Nonlinear changes in the spectral radiances of magnitudes similar to those obtained through linear estimation can arise when cloud heights and droplet radii in water cloud change. The spectral shapes computed by perturbing different atmospheric and cloud properties are different so that linear regression can separate individual spectral radiance changes from the sum of the spectral radiance change. When the effects of small-scale variability are treated as noise, however, the error in retrieved cloud properties is large. The results suggest the importance of considering small-scale variability in inferring atmospheric and cloud property changes from the satellite-observed zonally and annually averaged spectral radiance difference.

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