Model Evaluation and Intercomparison of Marine Warm Low Cloud Fractions With Neural Network Ensembles

Abstract Low cloud fractions (LCFs) and meteorological factors (MFs) over an oceanic region containing multiple cloud regimes are examined for three data sets: one Energy Exascale Earth System Model (E3SM) simulation with the default 72‐layer vertical grid (E3SM72), another one with 8‐times vertical...

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Autores principales:	Yao‐Sheng Chen, Takanobu Yamaguchi, Peter A. Bogenschutz, Graham Feingold
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	cloud controlling factors E3SM high resolution modeling machine learning shallow clouds Physical geography GB3-5030 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/abfe21993682470981f5f851cc7c2ed6
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spelling	oai:doaj.org-article:abfe21993682470981f5f851cc7c2ed62021-11-30T08:40:32ZModel Evaluation and Intercomparison of Marine Warm Low Cloud Fractions With Neural Network Ensembles1942-246610.1029/2021MS002625https://doaj.org/article/abfe21993682470981f5f851cc7c2ed62021-11-01T00:00:00Zhttps://doi.org/10.1029/2021MS002625https://doaj.org/toc/1942-2466Abstract Low cloud fractions (LCFs) and meteorological factors (MFs) over an oceanic region containing multiple cloud regimes are examined for three data sets: one Energy Exascale Earth System Model (E3SM) simulation with the default 72‐layer vertical grid (E3SM72), another one with 8‐times vertical resolution via the Framework for Improvement by Vertical Enhancement (E3SM×8), and one with MFs from ERA5 reanalysis and LCFs from the CERES SSF product (ERA5‐SSF). Neural networks (NNs) are trained to capture the relationship between MFs and LCF and to select the best‐performing MF subsets for predicting LCF. NN ensembles are used to (a) confirm the performance of selected MF subsets, (b) to serve as proxy models for each data set to predict LCFs for MFs from all data sets, and (c) to classify MFs into those in shared and uniquely occupied MF subspaces. Overall, E3SM72 and E3SM×8 have large fractions of MFs in shared MF subspace, but less so near the Californian and Peruvian stratocumulus decks. E3SM×8 and ERA5 have small fractions of MFs in shared MF subspace but greater than E3SM72 and ERA5, especially in the Southeast Pacific. The differences in LCFs between three pairs of data sets are decomposed into those associated with the differences in the LCF‐MF relationship and those involving different MFs. Given the same MFs, LCFs produced by E3SM×8 are greater than those produced by E3SM72 but are still different from those in ERA5‐SSF. In general, the shift in MFs dominates the difference in the LCFs.Yao‐Sheng ChenTakanobu YamaguchiPeter A. BogenschutzGraham FeingoldAmerican Geophysical Union (AGU)articlecloud controlling factorsE3SMhigh resolution modelingmachine learningshallow cloudsPhysical geographyGB3-5030OceanographyGC1-1581ENJournal of Advances in Modeling Earth Systems, Vol 13, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cloud controlling factors E3SM high resolution modeling machine learning shallow clouds Physical geography GB3-5030 Oceanography GC1-1581
spellingShingle	cloud controlling factors E3SM high resolution modeling machine learning shallow clouds Physical geography GB3-5030 Oceanography GC1-1581 Yao‐Sheng Chen Takanobu Yamaguchi Peter A. Bogenschutz Graham Feingold Model Evaluation and Intercomparison of Marine Warm Low Cloud Fractions With Neural Network Ensembles
description	Abstract Low cloud fractions (LCFs) and meteorological factors (MFs) over an oceanic region containing multiple cloud regimes are examined for three data sets: one Energy Exascale Earth System Model (E3SM) simulation with the default 72‐layer vertical grid (E3SM72), another one with 8‐times vertical resolution via the Framework for Improvement by Vertical Enhancement (E3SM×8), and one with MFs from ERA5 reanalysis and LCFs from the CERES SSF product (ERA5‐SSF). Neural networks (NNs) are trained to capture the relationship between MFs and LCF and to select the best‐performing MF subsets for predicting LCF. NN ensembles are used to (a) confirm the performance of selected MF subsets, (b) to serve as proxy models for each data set to predict LCFs for MFs from all data sets, and (c) to classify MFs into those in shared and uniquely occupied MF subspaces. Overall, E3SM72 and E3SM×8 have large fractions of MFs in shared MF subspace, but less so near the Californian and Peruvian stratocumulus decks. E3SM×8 and ERA5 have small fractions of MFs in shared MF subspace but greater than E3SM72 and ERA5, especially in the Southeast Pacific. The differences in LCFs between three pairs of data sets are decomposed into those associated with the differences in the LCF‐MF relationship and those involving different MFs. Given the same MFs, LCFs produced by E3SM×8 are greater than those produced by E3SM72 but are still different from those in ERA5‐SSF. In general, the shift in MFs dominates the difference in the LCFs.
format	article
author	Yao‐Sheng Chen Takanobu Yamaguchi Peter A. Bogenschutz Graham Feingold
author_facet	Yao‐Sheng Chen Takanobu Yamaguchi Peter A. Bogenschutz Graham Feingold
author_sort	Yao‐Sheng Chen
title	Model Evaluation and Intercomparison of Marine Warm Low Cloud Fractions With Neural Network Ensembles
title_short	Model Evaluation and Intercomparison of Marine Warm Low Cloud Fractions With Neural Network Ensembles
title_full	Model Evaluation and Intercomparison of Marine Warm Low Cloud Fractions With Neural Network Ensembles
title_fullStr	Model Evaluation and Intercomparison of Marine Warm Low Cloud Fractions With Neural Network Ensembles
title_full_unstemmed	Model Evaluation and Intercomparison of Marine Warm Low Cloud Fractions With Neural Network Ensembles
title_sort	model evaluation and intercomparison of marine warm low cloud fractions with neural network ensembles
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/abfe21993682470981f5f851cc7c2ed6
work_keys_str_mv	AT yaoshengchen modelevaluationandintercomparisonofmarinewarmlowcloudfractionswithneuralnetworkensembles AT takanobuyamaguchi modelevaluationandintercomparisonofmarinewarmlowcloudfractionswithneuralnetworkensembles AT peterabogenschutz modelevaluationandintercomparisonofmarinewarmlowcloudfractionswithneuralnetworkensembles AT grahamfeingold modelevaluationandintercomparisonofmarinewarmlowcloudfractionswithneuralnetworkensembles
_version_	1718406703490269184

Model Evaluation and Intercomparison of Marine Warm Low Cloud Fractions With Neural Network Ensembles

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