Quantifying the dependence of satellite cloud retrievals on instrument uncertainty.
Clouds significantly affect Earth's radiation budget, having a net cooling effect on the climate system. But cloud response to Earth's warming climate is one of the largest sources of uncertainty among global climate model projections. Our research focused on how more stringent instrument calibration requirements reduce the time needed to constrain cloud property uncertainties and, in turn, uncertainties in cloud feedback and anthropogenic radiative forcing. Climate models generally agree that the net cloud feedback is positive but disagree on its magnitude.
We estimated relationships among global, decadal trends in cloud properties (cloud fraction, optical thickness, and effective temperature), equilibrium climate sensitivity (ECS), and shortwave and longwave cloud feedback. In doing so our analysis provided the first direct link between satellite instrument calibration requirements and their impact on constraints on ECS and detection times of global cloud properties. We also related trends in water cloud effective radius to trends in radiative forcing (ERFaci) to demonstrate how more accurate instrument calibration could reduce the uncertainty in aerosol indirect effect several decades sooner than operational instruments.
We used cloud properties retrieved by the Clouds and the Earth's Radiant Energy System (CERES) Cloud Property Retrieval System, which uses measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS). Detecting trends in climate variables on decadal scales requires highly accurate, stable measurements and retrieval algorithms. Trend detection time depends on the trend magnitude, natural variability, and instrument and retrieval algorithm uncertainty, the relationship among which is represented by a climate uncertainty framework used in this study. This framework was used to demonstrate how more accurate reflected solar and infrared satellite measurements shorten the time it takes to constrain cloud property trend uncertainties by several decades, particularly for total cloud optical thickness, effective temperature, and water cloud effective radius.
Different cloud types have varied radiative impacts on the climate system depending on several attributes, such as their thermodynamic phase, altitude, and optical thickness. Therefore, conducting these studies by cloud types would be a valuable extension of this work. Combining the trend uncertainty analysis with the radiative impact of different cloud types would help to prioritize among requirements for future satellite sensors and understanding the climate detection capabilities of existing sensors. Additionally, time varying algorithm uncertainties and biases may also contribute to climate change-scale cloud property trend uncertainties and can extend trend detection times. Such uncertainties should also be estimated and, if possible, reduced. Such studies will become increasingly important within the current U.S. and global challenge to appropriate sufficient resources for climate change monitoring.-Yolanda L. Shea (NASA Langley Research Center), B. A. Wielicki, S. Sun-Mack, and P. Minnis, "Quantifying the dependence of satellite cloud retrievals on instrument uncertainty," in a forthcoming issue of the Journal of Climate.
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|Title Annotation:||NOW CAST: PAPERS OF NOTE|
|Publication:||Bulletin of the American Meteorological Society|
|Date:||Jul 1, 2017|
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