ALBERT

Description: 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.

Description: 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 σret is within 20% of the standard deviation of true anomalies σn. If σret/σn − 1 is within ±0.2, 91% of the true trends fall within the 95% confidence interval of the corresponding retrieved trend.

Description: 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.

Description: The Clouds and the Earth’s Radiant Energy System (CERES) instruments on board the Terra and Aqua spacecraft continue to provide an unprecedented global climate record of the earth’s top-of-atmosphere (TOA) energy budget since March 2000. A critical step in determining accurate daily averaged flux involves estimating the flux between CERES Terra or Aqua overpass times. CERES employs the CERES-only (CO) and the CERES geostationary (CG) temporal interpolation methods. The CO method assumes that the cloud properties at the time of the CERES observation remain constant and that it only accounts for changes in albedo with solar zenith angle and diurnal land heating, by assuming a shape for unresolved changes in the diurnal cycle. The CG method enhances the CERES data by explicitly accounting for changes in cloud and radiation between CERES observation times using 3-hourly imager data from five geostationary (GEO) satellites. To maintain calibration traceability, GEO radiances are calibrated against Moderate Resolution Imaging Spectroradiometer (MODIS) and the derived GEO fluxes are normalized to the CERES measurements. While the regional (1° latitude × 1° longitude) monthly-mean difference between the CG and CO methods can exceed 25 W m−2 over marine stratus and land convection, these regional biases nearly cancel in the global mean. The regional monthly CG shortwave (SW) and longwave (LW) flux uncertainty is reduced by 20%, whereas the daily uncertainty is reduced by 50% and 20%, respectively, over the CO method, based on comparisons with 15-min Geostationary Earth Radiation Budget (GERB) data.

Description: The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a “NIST [National Institute of Standards and Technology] in orbit.” CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.