Observed Relationships between Arctic Longwave Cloud Forcing and Cloud Parameters Using a Neural Network

Yonghua Chen Institute of Marine and Coastal Sciences, Rutgers—The State University of New Jersey, New Brunswick, New Jersey

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Filipe Aires Laboratoire de Météorologie Dynamique du CNRS/Institut Pierre Simon Laplace, Université Pierre et Marie Curie, Paris, France

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Jennifer A. Francis Institute of Marine and Coastal Sciences, Rutgers—The State University of New Jersey, New Brunswick, New Jersey

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James R. Miller Institute of Marine and Coastal Sciences, Rutgers—The State University of New Jersey, New Brunswick, New Jersey

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Abstract

A neural network technique is used to quantify relationships involved in cloud–radiation feedbacks based on observations from the Surface Heat Budget of the Arctic (SHEBA) project. Sensitivities of longwave cloud forcing (CFL) to cloud parameters indicate that a bimodal distribution pattern dominates the histogram of each sensitivity. Although the mean states of the relationships agree well with those derived in a previous study, they do not often exist in reality. The sensitivity of CFL to cloud cover increases as the cloudiness increases with a range of 0.1–0.9 W m−2 %−1. There is a saturation effect of liquid water path (LWP) on CFL. The highest sensitivity of CFL to LWP corresponds to clouds with low LWP, and sensitivity decreases as LWP increases. The sensitivity of CFL to cloud-base height (CBH) depends on whether the clouds are below or above an inversion layer. The relationship is negative for clouds higher than 0.8 km at the SHEBA site. The strongest positive relationship corresponds to clouds with low CBH. The dominant mode of the sensitivity of CFL to cloud-base temperature (CBT) is near zero and corresponds to warm clouds with base temperatures higher than −9°C. The low and high sensitivity regimes correspond to the summer and winter seasons, respectively, especially for LWP and CBT. Overall, the neural network technique is able to separate two distinct regimes of clouds that correspond to different sensitivities; that is, it captures the nonlinear behavior in the relationships. This study demonstrates a new method for evaluating nonlinear relationships between climate variables. It could also be used as an effective tool for evaluating feedback processes in climate models.

* Current affiliation: Department of Applied Physics and Mathematics, Columbia University, New York, New York

Corresponding author address: Yonghua Chen, NASA GISS, 2880 Broadway, New York, NY 10025. Email: ychen@giss.nasa.gov

Abstract

A neural network technique is used to quantify relationships involved in cloud–radiation feedbacks based on observations from the Surface Heat Budget of the Arctic (SHEBA) project. Sensitivities of longwave cloud forcing (CFL) to cloud parameters indicate that a bimodal distribution pattern dominates the histogram of each sensitivity. Although the mean states of the relationships agree well with those derived in a previous study, they do not often exist in reality. The sensitivity of CFL to cloud cover increases as the cloudiness increases with a range of 0.1–0.9 W m−2 %−1. There is a saturation effect of liquid water path (LWP) on CFL. The highest sensitivity of CFL to LWP corresponds to clouds with low LWP, and sensitivity decreases as LWP increases. The sensitivity of CFL to cloud-base height (CBH) depends on whether the clouds are below or above an inversion layer. The relationship is negative for clouds higher than 0.8 km at the SHEBA site. The strongest positive relationship corresponds to clouds with low CBH. The dominant mode of the sensitivity of CFL to cloud-base temperature (CBT) is near zero and corresponds to warm clouds with base temperatures higher than −9°C. The low and high sensitivity regimes correspond to the summer and winter seasons, respectively, especially for LWP and CBT. Overall, the neural network technique is able to separate two distinct regimes of clouds that correspond to different sensitivities; that is, it captures the nonlinear behavior in the relationships. This study demonstrates a new method for evaluating nonlinear relationships between climate variables. It could also be used as an effective tool for evaluating feedback processes in climate models.

* Current affiliation: Department of Applied Physics and Mathematics, Columbia University, New York, New York

Corresponding author address: Yonghua Chen, NASA GISS, 2880 Broadway, New York, NY 10025. Email: ychen@giss.nasa.gov

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