Atmosphere's emission fingerprint affected by how clouds are stacked

From AGU highlights:

Clouds, which can absorb or reflect incoming radiation and affect the amount of radiation escaping from Earth’s atmosphere, remain the greatest source of uncertainty in global climate modeling.

By combining space-based observations with climate models, researchers are able to derive baseline spectral signals, called spectral fingerprints, of how changes in the physical properties of the Earth’s atmosphere, such as the concentration of carbon dioxide or the relative humidity, affect the amount of radiation escaping from the top of the atmosphere.

Researchers can then use these spectral fingerprints to attribute changes in the observed top-of-atmosphere radiation to changes in individual atmospheric properties. However, recent research has shown that the way global climate models represent the interactions between clouds and radiation can complicate the process of making these spectral fingerprints. Researchers are finding that what matters is not only the presence or absence of clouds at each location represented in the model but also how the clouds are stacked vertically within each model grid.

Using a simulation experiment to mimic the future climate, Chen et al. tested how different approaches to parameterize cloud stacking affect the attributions of climate change signals in the longwave spectra recorded at the top of the atmosphere. The authors tested three approaches to parameterize cloud stacking and find that the differences in stacking assumptions affected the modeled global mean for outgoing longwave radiation by only a few watts per square meter. The global average for outgoing longwave radiation at the top of the atmosphere is around 240 watts per square meter. However, based on which parameterization is used, similar changes in the portion of the sky covered by clouds (especially the clouds in the middle and lower troposphere) can lead to spectral fingerprints that differ by up to a factor of two in the amplitude.

Source: Journal of Geophysical Research-Atmospheres, doi:10.1002/jgrd.50562, 2013 http://onlinelibrary.wiley.com/doi/10.1002/jgrd.50562/abstract

Title: Non-negligible effects of cloud vertical overlapping assumptions on longwave spectral fingerprinting studies

Authors: Xiuhong Chen and Xianglei Huang: Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann Arbor, Michigan, USA;

Xu Liu: NASA Langley Research Center, Hampton, Virginia, USA.

Abstract:

In order to monitor and attribute secular changes from outgoing spectral radiances, spectral fingerprints need to be constructed first. Large-scale model outputs are usually used to derive such spectral fingerprints. Different models make different assumptions on vertical overlapping of subgrid clouds. We explore the extent to which the spectral fingerprints constructed under different cloud vertical overlapping assumptions can affect such spectral fingerprinting studies. Utilizing a principal component-based radiative transfer model with high computational efficiency, we build an OSSE (Observing System Simulation Experiment) with full treatment of subgrid cloud variability to study this issue. We first show that the OLR (outgoing longwave radiation) computed from this OSSE is consistent with the OLR directly output from the parent large-scale models.

We then examine the differences in spectral fingerprints due to cloud overlapping assumptions alone. Different cloud overlapping assumptions have little effect on the spectral fingerprints of temperature and humidity. However, the amplitude of the spectral fingerprints due to the same amount of cloud fraction change can differ as much as a factor of two between maximum random versus random overlap assumptions, especially for middle and low clouds. We further examine the impact of cloud overlapping assumptions on the results of linear regression of spectral differences with respect to predefined spectral fingerprints.

Cloud-relevant regression coefficients are affected more by different cloud overlapping assumptions than regression coefficients of other geophysical variables. These findings highlight the challenges in constructing realistic longwave spectral fingerprints and in detecting climate change using all-sky observations.