Observation-Based Decomposition of Radiative Perturbations and Radiative Kernels

Area of Science:

• Earth Science
• Atmospheric Science
• Climate Science

Background:

• Understanding Earth's radiation budget variability is crucial for climate modeling.
• Existing methods often struggle to isolate individual contributions from cloud, atmospheric, and surface properties.
• Accurate radiative kernels are essential for improving climate model predictions.

Purpose of the Study:

• To introduce and validate the CERES-PRP methodology for isolating radiative budget components.
• To construct observation-based radiative kernels, particularly for water vapor.
• To assess the accuracy of climate model-derived radiative kernels against observational data.

Main Methods:

• Applied the CERES-PRP methodology to observational datasets.
• Utilized CERES SYN data and CloudSat/CALIPSO data for cloud properties.
• Incorporated a clustering method for CloudSat/CALIPSO cloud profiles in radiative transfer calculations.
• Validated computed fluxes against CERES TOA fluxes.

Main Results:

• Successfully isolated individual contributions of cloud, atmospheric, and surface properties to radiation budget variability.
• Demonstrated an approximately linear decomposition of total flux anomalies.
• Derived observation-based water vapor radiative kernels.
• Found marginally larger differences between observation-based and NCAR CAM3.0 water vapor kernels compared to previous GCM kernel intercomparisons.

Conclusions:

• The CERES-PRP methodology provides a robust way to analyze radiation budget variability using observational data.
• Observation-based radiative kernels offer valuable benchmarks for evaluating climate models.
• Further refinement of climate model radiative kernels is warranted based on observational findings.