The MODIS cloud mask uses several cloud detection tests to indicate a level of confidence that the MODIS is observing clear skies. It will be produced globally at single pixel resolution; the algorithm uses as many as fourteen of the MODIS 36 spectral bands to maximize reliable cloud detection and to mitigate past difficulties experienced by sensors with coarser spatial resolution or fewer spectral bands. The MODIS cloud mask is ancillary input to MODIS land, ocean and atmosphere science algorithms to suggest processing options. The MODIS cloud mask algorithm will operate in near-real time in a limited computer processing and storage facility with simple easy to follow algorithm paths. The MODIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination including land, water, snow/ice, desert, and coast for both day and night. Once a pixel has been assigned to a particular domain (defining an algorithm path), a series of threshold tests ...

Previously published work using satellite observations of the clear sky infrared emitted radiation by the Earth in 1970, 1997 and in 2003 showed the appearance of changes in the outgoing spectrum, which agreed with those expected from known changes in the concentrations of well-mixed greenhouse gases over this period. Thus, the greenhouse forcing of the Earth has been observed to change in response to these concentration changes. In the present work, this analysis is being extended to 2006 using the TES instrument on the AURA spacecraft. Additionally, simulated spectra have been calculated using LBLRTM with inputs from the HadGEM1 coupled model and compared to the observed satellite spectra.

[1] In the context of climate feedback associated with temperature change, there exist two potential mechanisms that affect the outgoing longwave radiation (OLR) and the downward longwave radiation (DLR). One is the ‘‘Planck’ ’ effect that determines the blackbody thermal emission at a considered temperature. The other is the ‘‘absorptivity’’ effect, in which a temperature change causes a change in gas absorptivities and thus influences the longwave radiative transfer. By using the line-by-line computed radiative Jacobians, which quantify the sensitivity of the radiative fluxes to a perturbation in the atmospheric temperature, the absorptivity effect is separated from the Planck effect. The absorptivity effect is further partitioned into components, with each one having a distinct physical meaning. It is demonstrated that the absorptivity-induced changes in the longwave radiation are individually significant even though the net effect is largely one of cancellation. As a consequence, the Planck effect dominates the overall OLR and DLR sensitivities to temperature change. The absorptivity effect tends to counteract the Planck effect. This tendency is particularly significant for the DLR and is more prominent for a warmer climate, with the result being a reduction in the surface warming. Citation: Huang, Y., and V. Ramaswamy (2007), Effect of the temperature dependence of gas absorption in climate feedback, J. Geophys. Res., 112, D07101, doi:10.1029/2006JD007398. 1.

A set of simulated high-resolution infrared (IR) emission spectra of synthetic cirrus clouds is used to perform a sensitivity analysis of top-of-atmosphere (TOA) radiance to cloud parameters. Principal Component Analysis (PCA) is applied to assess the variability of radiance across the spectrum with respect to microphysical and bulk cloud quantities. These quantities include particle shape, effective radius (reff), ice water path (IWP), cloud height Zcld and thickness ΔZcld, and vertical profiles of temperature T(z) and water vapor mixing ratio w(z). It is shown that IWP variations in simulated cloud cover dominate TOA radiance variability. Cloud height and thickness, as well as T(z) variations, also contribute to considerable TOA radiance variability. The empirical orthogonal functions (EOFs) of radiance variability show both similarities and differences in spectral shape and magnitude of variability when one physical quantity or another is being modified. In certain cases, it is possible to identify the EOF that represents variability with respect to one or more physical quantities. In other instances, similar EOFs result from different sets of physical quantities, emphasizing

[1] The global and tropical means of clear-sky outgoing longwave radiation (hereinafter OLRc) simulated by the new GFDL atmospheric general circulation model, AM2, tend to be systematically lower than ERBE observations by about 4 W m 2, even though the AM2 total-sky radiation budget is tuned to be consistent with these observations. Here we quantify the source of errors in AM2-simulated OLRc over the tropical oceans by comparing the synthetic outgoing IR spectra at the top of the atmosphere on the basis of AM2 simulations to observed IRIS spectra. After the sampling disparity between IRIS and AM2 is reduced, AM2 still shows considerable negative bias in the simulated monthly mean OLRc over the tropical oceans. Together with other evidence, this suggests that the influence of spatial sampling disparity, although present, does not account for the majority of the bias. Decomposition of OLRc shows that the negative bias comes mainly from the H2O bands and can be explained by a too humid layer around 6–9 km in the model. Meanwhile, a positive bias exists in channels sensitive to near-surface humidity and temperature, which implies that the boundary layer in the model might be too dry. These facts suggest that the negative bias in the simulated OLRc can be attributed to model deficiencies, especially the large-scale water vapor transport. We also find that AM2-simulated OLRc has 1Wm 2 positive bias originating from the stratosphere; this positive bias should exist in simulated total-sky OLR as well.