Publication Abstracts

Zhang et al. 2021, submitted

Zhang, Y., Z. Jin, and M.V. Sikand, 2021: The top-of-atmosphere, surface and atmospheric cloud radiative kernels based on ISCCP-H datasets: method and evaluation. J. Geophys.Res. Atmos., submitted, essoar:10.1002/essoar.10506767.1.

This study aims to create observation-based cloud radiative kernel (CRK) datasets and evaluate them by direct comparison of CRK and the CRK-derived cloud feedback datasets. Based on the International Satellite Cloud Climatology Project (ISCCP) H datasets, we calculate CRKs (called ISCCP-FH or FH CRKs) as 2D joint function/histogram of cloud optical depth and cloud top pressure for shortwave, longwave, and their sum, Net, at the top of atmosphere (TOA), as well as, for the first time, at the surface (SFC) and in the atmosphere (ATM). All the FH CRKs are physically plausible. The direct comparison shows that FH agrees reasonably well with three other TOA CRK datasets. With cloud fraction change (CFC) datasets of the same histogram for doubled-CO2 simulation from 10 CFMIP1 models, we derive all the TOA, SFC and ATM cloud feedback using the FH CRKs. Our TOA cloud feedback is highly similar to the previous counterparts. Based on the comparison for the 4 CRK datasets and the 10 CFC datasets, we estimate the uncertainty budget for the CRK-derived cloud feedback, which shows that the CFC-associated uncertainty contributes > 98.5% of the total cloud feedback uncertainty while CRK's is very small. Our preliminary evaluation also shows that some near-zero/small cloud feedback in the TOA-alone feedback indeed results from the compensation of sizable cloud feedback of the SFC and ATM feedback, demonstrating how the SFC- and ATM-CRK derived cloud feedback can be valuable in revealing some significant surface and atmospheric cloud feedback whose sum appears insignificant in TOA-alone feedback.

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BibTeX Citation

@unpublished{zh03400r,
  author={Zhang, Y. and Jin, Z. and Sikand, M. V.},
  title={The top-of-atmosphere, surface and atmospheric cloud radiative kernels based on ISCCP-H datasets: method and evaluation},
  year={2021},
  journal={J. Geophys.Res. Atmos.},
  doi={10.1002/essoar.10506767.1},
  note={Manuscript submitted for publication}
}

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RIS Citation

TY  - UNPB
ID  - zh03400r
AU  - Zhang, Y.
AU  - Jin, Z.
AU  - Sikand, M. V.
PY  - 2021
TI  - The top-of-atmosphere, surface and atmospheric cloud radiative kernels based on ISCCP-H datasets: method and evaluation
JA  - J. Geophys.Res. Atmos.
DO  - 10.1002/essoar.10506767.1
ER  -

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