Quantifying the Climate-Scale Accuracy of Satellite Cloud Retrievals

Wednesday, 17 December 2014
Yolanda Roberts, NASA, Hampton, VA, United States, Bruce A Wielicki, NASA Langley Research Center, Hampton, VA, United States, Sunny Sun-Mack, Science Systems and Applications, Inc. Hampton, Hampton, VA, United States, Patrick Minnis, Nasa Larc, Hampton, VA, United States, Lusheng Liang, SSAI, Hampton, VA, United States and Larry Di Girolamo, University of Illinois, Urbana, IL, United States
Instrument calibration and cloud retrieval algorithms have been developed to minimize retrieval errors on small scales. However, measurement uncertainties and assumptions within retrieval algorithms at the pixel level may alias into decadal-scale trends of cloud properties.

We first, therefore, quantify how instrument calibration changes could alias into cloud property trends. For a perfect observing system the climate trend accuracy is limited only by the natural variability of the climate variable. Alternatively, for an actual observing system, the climate trend accuracy is additionally limited by the measurement uncertainty. Drifts in calibration over time may therefore be disguised as a true climate trend. We impose absolute calibration changes to MODIS spectral reflectance used as input to the CERES Cloud Property Retrieval System (CPRS) and run the modified MODIS reflectance through the CPRS to determine the sensitivity of cloud properties to calibration changes. We then use these changes to determine the impact of instrument calibration changes on trend uncertainty in reflected solar cloud properties.

Secondly, we quantify how much cloud retrieval algorithm assumptions alias into cloud optical retrieval trends by starting with the largest of these biases: the plane-parallel assumption in cloud optical thickness (τC) retrievals. First, we collect liquid water cloud fields obtained from Multi-angle Imaging Spectroradiometer (MISR) measurements to construct realistic probability distribution functions (PDFs) of 3D cloud anisotropy (a measure of the degree to which clouds depart from plane-parallel) for different ISCCP cloud types. Next, we will conduct a theoretical study with dynamically simulated cloud fields and a 3D radiative transfer model to determine the relationship between 3D cloud anisotropy and 3D τC bias for each cloud type. Combining these results provides distributions of 3D τC bias by cloud type. Finally, we will estimate the change in frequency of occurrence of cloud types between two decades and will have the information needed to calculate the total change in 3D optical thickness bias between two decades. If we uncover aliases in this study, the results will motivate the development and rigorous testing of climate specific cloud retrieval algorithms.