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Joon-Hee Jung and Akio Arakawa
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Joon-Hee Jung and Akio Arakawa

Abstract

A three-dimensional anelastic model has been developed using the vorticity equation, in which the pressure gradient force is eliminated. The prognostic variables of the model dynamics are the horizontal components of vorticity at all heights and the vertical component of vorticity and the horizontally uniform part of the horizontal velocity at a selected height. To implement the anelastic approximation, vertical velocity is diagnostically determined from the predicted horizontal components of vorticity by solving an elliptic equation. This procedure replaces solving the elliptic equation for pressure in anelastic models based on the momentum equation. Discretization of the advection terms uses an upstream-weighted partially third-order scheme. When time is continuous, the solution of this scheme is quadratically bounded. As an application of the model, interactions between convection and its environment with vertical shear are studied without and with model physics from the viewpoint of vorticity dynamics, that is, the deceleration/acceleration process of the basic flow in particular. The authors point out that the process is purely three-dimensional, especially when the convection is relatively localized, involving the twisting terms and the horizontal as well as vertical transports of vorticity. Finally, it is emphasized that parameterization of cumulus friction is a resolution-dependent problem of vorticity dynamics associated with cumulus convection.

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Joon-Hee Jung and Akio Arakawa

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The goal of this paper is to gain insight into the resolution dependence of model physics, the parameterization of moist convection in particular, which is required for accurately predicting large-scale features of the atmosphere. To achieve this goal, experiments using a two-dimensional nonhydrostatic model with different resolutions are conducted under various idealized tropical conditions. For control experiments (CONTROL), the model is run as a cloud-system-resolving model (CSRM). Next, a “large-scale dynamics model” (LSDM) is introduced as a diagnostic tool, which is a coarser-resolution version of the same model but with only partial or no physics. Then, the LSDM is applied to an ensemble of realizations selected from CONTROL and a “required parameterized source” (RPS) is identified for the results of the LSDM to become consistent with CONTROL as far as the resolvable scales are concerned.

The analysis of RPS diagnosed in this way confirms that RPS is highly resolution dependent in the range of typical resolutions of mesoscale models even in ensemble/space averages, while “real source” (RS) is not. The time interval of implementing model physics also matters for RPS. It is emphasized that model physics in future prediction models should automatically produce these resolution dependencies so that the need for retuning parameterizations as resolution changes can be minimized.

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Joon-Hee Jung and Akio Arakawa

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Preliminary tests of the multiscale modeling approach, also known as the cloud-resolving convective parameterization, or superparameterization, are performed using an idealized framework. In this approach, a two-dimensional cloud-system resolving model (CSRM) is embedded within each vertical column of a general circulation model (GCM) replacing conventional cloud parameterization. The purpose of this study is to investigate the coupling between the GCM and CSRMs and suggest a revised method of coupling that abandons the cyclic lateral boundary condition for each CSRM used in the original cloud-resolving convective parameterization. In this way, the CSRM extends into neighboring GCM grid boxes while sharing approximately the same mass fluxes with the GCM at the borders of the grid boxes.

With the original and revised methods of coupling, numerical simulations of the evolution of cloud systems are conducted using a two-dimensional model that couples CSRMs with a lower-resolution version of the CSRM with no physics [large-scale dynamics model (LSDM)]. The results with the revised method show that cloud systems can propagate from one LSDM grid column to the next as expected. Comparisons with a straightforward application of a single CSRM to the entire domain (CONTROL) show that the biases of the large-scale thermodynamic fields simulated by the coupled model are significantly smaller with the revised method. The results also show that the biases are near the smallest when the velocity fields of the LSDM and CSRM are nudged to each other with the time scale of a few hours and the thermodynamic field of the LSDM is instantaneously updated at each time step with the domain-averaged CSRM field.

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Akio Arakawa, Joon-Hee Jung, and Chien-Ming Wu

Abstract

One of the most important contributions of Michio Yanai to tropical meteorology is the introduction of the concepts of apparent heat source Q 1 and apparent moisture sink Q 2 in the large-scale heat and moisture budgets of the atmosphere. Through the inclusion of unresolved eddy effects, the vertical profiles of apparent sources (and sinks) are generally quite different from those of true sources taking place locally. In low-resolution models, such as the conventional general circulation models (GCMs), cumulus parameterization is supposed to determine the apparent sources for each grid cell from the explicitly predicted grid-scale processes. Because of the recent advancement of computer technology, however, increasingly higher horizontal resolutions are being used even for studying the global climate, and, therefore, the concept of apparent sources must be expanded rather drastically. Specifically, the simulated apparent sources should approach and eventually converge to the true sources as the horizontal resolution is refined. For this transition to take place, the conventional cumulus parameterization must be either generalized so that it is applicable to any horizontal resolutions or replaced with the mean effects of cloud-scale processes explicitly simulated by a cloud-resolving model (CRM). These two approaches are called ROUTE I and ROUTE II for unifying low- and high-resolution models, respectively. This chapter discusses the conceptual and technical problems in exploring these routes and reviews the authors’ recent work on these subjects.

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Joon-Hee Jung, Celal S. Konor, Carlos R. Mechoso, and Akio Arakawa

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The principal goal of this paper is to gain further insight into the dynamical processes during the stratospheric major warming of February and early March 1979, with a special emphasis on the recovery stage. To achieve this goal, first the entire evolution of the warming event is numerically simulated using an isentropic vertical coordinate model. Then the results from the following complementary points of view are quantitatively analyzed: wave effects on the mean flow, potential enstrophy conversion and transport, and potential vorticity redistribution on a synoptic chart.

There is an indication that wavenumber 1 during the recovery stage amplifies through a mechanism within the stratosphere and propagates downward. The simulated Eliassen–Palm flux field shows that the amplified wave 1 is responsible for the mean flow acceleration in the recovery stage. It is therefore concluded that the in situ amplification mechanism for wave 1 plays a crucial role in the dynamics of the flow recovery. In order to examine the mechanism, detailed analyses of the simulated eddy potential enstrophy budget are performed. It is found that there is an “anticascade” of potential enstrophy in the recovery stage through which wave 1 amplifies in the midstratosphere. This result is consistent with a synoptic description of the event in terms of potential vorticity distribution on isentropic surfaces.

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Jhoon Kim, Ukkyo Jeong, Myoung-Hwan Ahn, Jae H. Kim, Rokjin J. Park, Hanlim Lee, Chul Han Song, Yong-Sang Choi, Kwon-Ho Lee, Jung-Moon Yoo, Myeong-Jae Jeong, Seon Ki Park, Kwang-Mog Lee, Chang-Keun Song, Sang-Woo Kim, Young Joon Kim, Si-Wan Kim, Mijin Kim, Sujung Go, Xiong Liu, Kelly Chance, Christopher Chan Miller, Jay Al-Saadi, Ben Veihelmann, Pawan K. Bhartia, Omar Torres, Gonzalo González Abad, David P. Haffner, Dai Ho Ko, Seung Hoon Lee, Jung-Hun Woo, Heesung Chong, Sang Seo Park, Dennis Nicks, Won Jun Choi, Kyung-Jung Moon, Ara Cho, Jongmin Yoon, Sang-kyun Kim, Hyunkee Hong, Kyunghwa Lee, Hana Lee, Seoyoung Lee, Myungje Choi, Pepijn Veefkind, Pieternel F. Levelt, David P. Edwards, Mina Kang, Mijin Eo, Juseon Bak, Kanghyun Baek, Hyeong-Ahn Kwon, Jiwon Yang, Junsung Park, Kyung Man Han, Bo-Ram Kim, Hee-Woo Shin, Haklim Choi, Ebony Lee, Jihyo Chong, Yesol Cha, Ja-Ho Koo, Hitoshi Irie, Sachiko Hayashida, Yasko Kasai, Yugo Kanaya, Cheng Liu, Jintai Lin, James H. Crawford, Gregory R. Carmichael, Michael J. Newchurch, Barry L. Lefer, Jay R. Herman, Robert J. Swap, Alexis K. H. Lau, Thomas P. Kurosu, Glen Jaross, Berit Ahlers, Marcel Dobber, C. Thomas McElroy, and Yunsoo Choi

Abstract

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

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