Influence of Thermodynamic State Changes on Surface Cloud Radiative Forcing in the Arctic: A Comparison of Two Approaches Using Data From AFLUX and SHEBA
The cloud radiative forcing (CRF) quantifies the warming or cooling effects of clouds. To derive the CRF, reference values of net (downward minus upward) irradiances in cloud‐free conditions are required. There are two groups of techniques to estimate these reference values; one is based on radiativ...
Main Authors: | , , |
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Format: | Text |
Language: | English |
Published: |
FID GEO
2021
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Subjects: | |
Online Access: | https://dx.doi.org/10.23689/fidgeo-4363 https://e-docs.geo-leo.de/handle/11858/8709 |
Summary: | The cloud radiative forcing (CRF) quantifies the warming or cooling effects of clouds. To derive the CRF, reference values of net (downward minus upward) irradiances in cloud‐free conditions are required. There are two groups of techniques to estimate these reference values; one is based on radiative transfer modeling, and a second group uses measurements in cloud‐free situations. To compare both approaches, we first look at a case study from the airborne measurements of radiative and turbulent FLUXes of energy and momentum in the Arctic boundary layer (AFLUX) campaign, where a moving cloud field with a sharp edge separating a cloudy boundary layer from an adjacent evolving cloud‐free area was probed. These data enabled the quantification of the impact of changing atmospheric and surface properties relevant for the reference net irradiances in cloud‐free conditions. The systematically higher surface albedo below clouds compared to cloud‐free conditions, results in a 11 W·m−2 smaller shortwave cooling effect by clouds estimated from the radiative transfer approach compared to the measurement‐based one. Due to the transition of thermodynamic parameters between the cloudy and cloud‐free atmospheric states, a 20 W·m−2 stronger warming effect is estimated by the radiative transfer approach. In a second step, radiative transfer simulations based on radiosoundings from the Surface Heat Budget of the Arctic Ocean campaign are used to quantify the impact of the vertical profiles of thermodynamic properties on the CRF. The largest difference between the longwave CRF estimated by the two methods is found in autumn with up to 25 W·m−2. : Key Points: Different approaches to derive the surface cloud radiative forcing (CRF) are compared using data of a case study of the AFLUX campaign. Radiative transfer‐based approaches provide a systematically stronger warming effect of clouds than observed. For Surface Heat Budget of the Arctic Ocean, atmospheric thermodynamic state changes and profile properties are identified as decisive for the respective annual cycle of longwave CRF. : Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659 |
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