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Evaluating Arctic cloud radiative effects simulated by NICAM with A‐train
Author(s) -
Hashino Tempei,
Satoh Masaki,
Hagihara Yuichiro,
Kato Seiji,
Kubota Takuji,
Matsui Toshihisa,
Nasuno Tomoe,
Okamoto Hajime,
Sekiguchi Miho
Publication year - 2016
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2016jd024775
Subject(s) - environmental science , cloud fraction , lidar , shortwave , meteorology , radiative transfer , albedo (alchemy) , snow , atmospheric model , satellite , cloud top , remote sensing , climate model , radar , cirrus , cloud feedback , ice cloud , cloud forcing , atmosphere (unit) , aeronet , cloud cover , cloud computing , climate sensitivity , aerosol , climate change , computer science , geology , geography , radiative forcing , physics , art , oceanography , operating system , telecommunications , quantum mechanics , art history , astronomy , performance art
Evaluation of cloud radiative effects (CREs) in global atmospheric models is of vital importance to reduce uncertainties in weather forecasting and future climate projection. In this paper, we describe an effective way to evaluate CREs from a 3.5 km mesh global nonhydrostatic model by comparing it against A‐train satellite data. The model is the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), and its output is run through a satellite‐sensor simulator (Joint Simulator for satellite sensors) to produce the equivalent CloudSat radar, CALIPSO lidar, and Aqua Clouds and the Earth's Radiant Energy System (CERES) data. These simulated observations are then compared to real observations from the satellites. We focus on the Arctic, which is a region experiencing rapid climate change over various surface types. The NICAM simulation significantly overestimates the shortwave CREs at top of atmosphere and surface as large as 24 W m −2 for the month of June. The CREs were decomposed into cloud fractions and footprint CREs of cloud types that are defined based on the CloudSat‐CALIPSO cloud top temperature and maximum radar reflectivity. It turned out that the simulation underestimates the cloud fraction and optical thickness of mixed‐phase clouds due to predicting too little supercooled liquid and predicting overly large snow particles with too little mass content. This bias was partially offset by predicting too many optically thin high clouds. Offline sensitivity experiments, where cloud microphysical parameters, surface albedo, and single scattering parameters are varied, support the diagnosis. Aerosol radiative effects and nonspherical single scattering of ice particles should be introduced into the NICAM broadband calculation for further improvement.