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.

NASA global satellite data provide observations of Earth's albedo, i.e., the fraction of incident solar radiation that is reflected back to space. The satellite data show that the last four years are within natural variability and fail to confirm the 6% relative increase in albedo inferred from observations of earthshine from the moon. Longer global satellite records will be required to discern climate trends in Earth's albedo.

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.

The NASA CERES Project has developed a combined radiation and cloud property dataset using the CERES scanners and matched spectral data from high-resolution imagers, the Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. The diurnal cycle can be well-characterized over most of the globe using the combinations of TRMM, Aqua, and Terra data. The cloud properties are derived from the imagers using state-of-the-art methods and include cloud fraction, height, optical depth, phase, effective particle size, emissivity, and ice or liquid water path. These cloud products are convolved into the matching CERES fields of view to provide simultaneous cloud and radiation data at an unprecedented accuracy. Results are available for at least 3 years of VIRS data and 1 year of Terra MODIS data. The various cloud products are compared with similar quantities from climatological sources and instantaneous active remote sensors. The cloud amounts are very similar to those from surface observer climatologies and are 6-7% less than those from a satellite-based climatology. Optical depths are 2-3 times smaller than those from the satellite climatology, but are within 5% of those from the surface remote sensing. Cloud droplet sizes and liquid water paths are within 10% of the surface results on average for stratus clouds. The VIRS and MODIS retrievals are very consistent with differences that usually can be explained by sampling, calibration, or resolution differences. The results should be extremely valuable for model validation and improvement and for improving our understanding of the relationship between clouds and the radiation budget.

[1] The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel‐derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.