OCTOBER 2018 K O M O R I E T A L . 3311 Ensemble-Based Atmospheric Reanalysis Using a Global Coupled Atmosphere–Ocean GCM a b c a NOBUMASA KOMORI, TAKESHI ENOMOTO, TAKEMASA MIYOSHI, AKIRA YAMAZAKI, d e AKIRA KUWANO-YOSHIDA, AND BUNMEI TAGUCHI a Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan b Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto, and Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan c RIKEN Center for Computational Science, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, and RIKEN Cluster for Pioneering Research, Kobe, and Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan, and Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland d Disaster Prevention Research Institute, Kyoto University, Shirahama, Wakayama, and Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan e Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, and Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan (Manuscript received 28 November 2017, in final form 29 July 2018) ABSTRACT Ensemble-based atmospheric data assimilation (DA) systems are sometimes afflicted with an underesti- mation of the ensemble spread near the surface caused by the use of identical boundary conditions for all ensemble members and the lack of atmosphere–ocean interaction. To overcome these problems, a new DA system has been developed by replacing an atmospheric GCM with a coupled atmosphere–ocean GCM, in which atmospheric observational data are assimilated every 6 h to update the atmospheric variables, whereas the oceanic variables are subject to no direct DA. Although SST suffers from the common biases among many coupled GCMs, two months of a retrospective analysis–forecast cycle reveals that the ensemble spreads of air temperature and specific humidity in the surface boundary layer are slightly increased and the forecast skill in the midtroposphere is rather improved by using the coupled DA system in comparison with the atmospheric DA system. In addition, surface atmospheric variables over the tropical Pacific have the basinwide horizontal correlation in ensemble space in the coupled DA system but not in the atmospheric DA system. This suggests the potential benefit of using a coupled GCM rather than an atmospheric GCM even for atmospheric reanalysis with an ensemble-based DA system. 1. Introduction Atmospheric General Circulation Model for the Earth Simulator (AFES; Ohfuchi et al. 2004; Enomoto et al. Ensemble-based data assimilation (DA) techniques 2008) to construct the AFES–LETKF ensemble DA sys- such as the ensemble Kalman filter (Evensen 1994, 2003) tem. Miyoshi et al. (2007a) performed one and a half years have been rapidly growing because of their advantages of the AFES–LETKF experimental ensemble reanalysis of the flow-dependent estimation of analysis and fore- using the observational dataset of the Japan Meteorolog- cast errors, relative ease of implementation, and effi- ical Agency operational system. Enomoto et al. (2013) ciency with parallel computers. constructed the second generation of the DA system Miyoshi and Yamane (2007) applied the local ensemble (ALEDAS2) using the latest version of AFES with an transform Kalman filter (LETKF; Hunt et al. 2007)tothe improved cloud scheme for better representation of low-level clouds (Kuwano-Yoshida et al. 2010)and Denotes content that is immediately available upon publica- LETKF employing physical distances for localization tion as open access. instead of using local patches (Miyoshi et al. 2007b), and performed five years of an experimental ensemble re- Corresponding author: Nobumasa Komori, komori@jamstec. analysis (ALERA2) assimilating observational data of go.jp the NCEP global DA system (PREPBUFR) archived at DOI: 10.1175/MWR-D-17-0361.1 Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/28/21 06:08 AM UTC 3312 MONTHLY WEATHER REVIEW VOLUME 146 UCAR. The ALERA2 has been used as the reference strongly coupled DA in a realistic situation and dem- dataset for a series of observing system experiments onstrated its usefulness based on their regional opera- using the ALEDAS2 (e.g., Yamazaki et al. 2015; Hattori tional system. Additionally, modern techniques such et al. 2016, 2017; Kawai et al. 2017). as a particle filter are also applied to CGCMs to deal In many ensemble DA systems based on AGCMs with the intrinsic nonlinearity of the coupled DA (e.g., including the ALEDAS2, surface boundary conditions Browne and van Leeuwen 2015). such as SST and sea ice distribution are identical among In this study, to enhance the capability of the all ensemble members. It leads to an underestimated ALEDAS2, a new system has been developed by ensemble spread near the surface, or equivalently an replacing AFES with the Coupled Atmosphere–Ocean overestimate of the accuracy of the first-guess fields, and General Circulation Model for the Earth Simulator may eventually lead to a degradation of the resulting (CFES; Komori et al. 2008). As a first step toward a fully analyses. Kunii and Miyoshi (2012) showed that intro- coupled version of the CFES–LETKF ensemble DA ducing SST perturbations in the LETKF for a regional system (CLEDAS), the new system assimilates only at- atmospheric DA system improves the analyses and sub- mospheric observational data to update the atmospheric sequent forecasts. In addition, air–sea coupled phenom- variables and is referred to as CLEDAS-A. Using this ena, such as the lead–lag relationship between SST and system, two months of experimental ensemble analysis precipitation over the tropics, are not well reproduced in has been conducted, and two-month-averaged fields are AGCM-based systems (Arakawa and Kitoh 2004; Saha compared with those of ALERA2. This approach is et al. 2010). By using a coupled atmosphere–ocean GCM categorized as quasi-weakly coupled DA (Penny et al. (CGCM) instead of an AGCM in an ensemble DA sys- 2017) and could be considered as the atmospheric coun- tem, it is expected that the effects of perturbed surface terpart of the attempt by Fujii et al. (2009),inwhichocean boundary conditions and atmosphere–ocean interaction DA constrains the ocean component of their CGCM to are naturally introduced into the system. construct a coupled reanalysis dataset. Data assimilation into CGCMs has progressed in the The rest of this article is organized as follows. Section last decade. Zhang et al. (2007) conducted a series of perfect 2 describes the ensemble DA system using a CGCM. model experiments, assimilating pseudo-observations Section 3 describes the setting of experimental ensemble made from a CGCM simulation with an ensemble fil- retrospective analysis. The results are presented in sec- ter to reconstruct climate variability and trends. Sugiura tion 4. Finally, a summary and conclusions are provided et al. (2008) assimilated 10-day-averaged atmospheric in section 5. and oceanic observational data using a four-dimensional variational method and controlled surface fluxes by in- troducing adjustment factors for the coupled state esti- 2. Ensemble data assimilation system mation from 1996 to 1998. Several operational centers a. Forecast model have constructed their global coupled DA systems mainly for seasonal to interannual prediction or climate CFES is used as the forecast model in CLEDAS-A reanalysis based on their existing operational atmo- (Fig. 1), and its configuration is the same as used in the spheric and oceanic DA systems. In these systems, previous studies (Richter et al. 2010; Taguchi et al. 2012; CGCMs are used in the forecast step to construct the Bajish et al. 2013; Sasaki et al. 2013; Kuwano-Yoshida first-guess fields but atmospheric and oceanic DA are et al. 2013; Miyasaka et al. 2014; Taguchi and Schneider conducted separately in the analysis step (e.g., Saha 2014). CFES consists of AFES as an atmospheric com- et al. 2010; Lea et al. 2015), or atmospheric and oceanic ponent including a land surface process and OFES systems are integrated only in a limited portion of the (Masumoto et al. 2004)asanoceaniccomponent DA process (e.g., Laloyaux et al. 2016). The former including a sea ice process. Surface variables such as SST, methodology is called weakly coupled DA and the latter sea surface and sea ice velocities, sea ice concentration, sea quasi-strongly coupled DA by the definition of Penny ice thickness, and snow depth over sea ice are transferred et al. (2017). Recently, some studies show the effec- from OFES to AFES, while the surface fluxes and sea level tiveness of strongly coupled DA, in which atmospheric pressure are passed from AFES to OFES. AFES is cou- and oceanic DA are conducted integrally in the whole pled with OFES every hour in CLEDAS-A (Fig. 1b), DA process and observational data in one component whereas it is forced with prescribed surface boundary are directly used to update the state of the other, in an conditions (BC) of the NOAA 1/48 daily OISST (Reynolds idealized or simplified framework (e.g., Smith et al. 2015; et al. 2007; Banzon et al. 2016)inALEDAS2(Fig. 1a). Lu et al. 2015a,b; Sluka et al. 2016). On the other hand, AFES is an AGCM and solves the primitive equa- Frolov et al. (2016) proposed an ‘‘interface solver’’ for tions using the spectral transform method and Eulerian Unauthenticated | Downloaded 09/28/21 06:08 AM UTC OCTOBER 2018 K O M O R I E T A L . 3313 Vertical mixing is parameterized by the Noh model (Noh and Kim 1999; Noh et al. 2005), in which mixing depends on both the Richardson and Prandtl numbers. In addition, the shortwave penetration scheme is im- proved especially for the use with the free surface (Komori et al.