On Constraining Uncertainty in Cloud Feedback with Climate Change Retrieval Stability and Radiometric Calibration Requirements

Highly accurate and stable satellite measurements and retrieval algorithms are essential for detecting global, decadal trends in climate variables. To address the measurement requirements for climate change detection and monitoring, the 2007 Decadal Survey recommended a radiance inter-calibration and climate benchmarking mission called the Climate Absolute Radiance and Refractivity Observatory (CLARREO) as a high priority. Similarly, in the 2017 Decadal Survey, radiance inter-calibration remains a high priority, illustrated by its classification as a prioritized Target Observable. Radiance Inter-calibration was recommended as such to contribute to reducing climate sensitivity uncertainty, which was classified as a Most Important science priority.

In our studies, we have employed a framework that allows for determining the length of measurement record needed to achieve some level of trend uncertainty using instruments with various calibration uncertainties. Additionally, using the forcing-feedback equation and observation-based radiative kernels, we determined a relationship between cloud property trend accuracies, SW and LW cloud feedbacks, and Equilibrium Climate Sensitivity (ECS). This relationship allows for determining the level of calibration uncertainty needed to reduce climate model projection uncertainty, including than in ECS and SW and LW cloud feedback.

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, we have also conducted these analyses for different cloud types to provide a clearer understanding of the instrument accuracy requirements needed to detect changes in their cloud properties. This information combined with existing knowledge of the radiative impact of different cloud types has been applied to prioritize among requirements when designing future satellite sensors and understanding the detection capabilities of existing sensors.

Our studies demonstrate how significantly reduced cloud imager calibration uncertainty can reduce trend uncertainty among retrieved cloud properties and in turn reduce uncertainty in cloud feedback and climate sensitivity. Such improved accuracy levels can be achieved through inter-calibrating existing cloud imagers, such as MODIS and VIIRS, with in-orbit reference calibration systems. With improved accuracy by reference inter-calibration, SW and LW cloud feedback and therefore climate sensitivity uncertainty can be reduced much sooner than using instruments at current uncertainty levels by reducing trend uncertainty in cloud amount, optical depth, and altitude. We have evaluated how long it may take to reduce climate sensitivity uncertainty by a factor of two for different cloud properties (amount, optical depth, and altitude), cloud types (Total Cloud, Low Cloud, and Non-low clouds), and regions (Global, Southern and Northern Hemisphere Midlatitudes, and the Tropics). These studies showed that the time saved by reducing uncertainties in ECS by constraining SW cloud feedback uncertainty is larger than the time saved by constraining LW cloud feedback uncertainty. This presentation will give an overview our work demonstrating how high accuracy measurements can contribute to narrowing ECS uncertainty.