TY - JOUR
T1 - Roles of TAO/TRITON and argo in tropical pacific observing systems
T2 - An OSSE study for multiple time scale variability
AU - Zhu, Jieshun
AU - Vernieres, Guillaume
AU - Sluka, Travis
AU - Flampouris, Stylianos
AU - Kumar, Arun
AU - Mehra, Avichal
AU - Cronin, Meghan F.
AU - Zhang, Dongxiao
AU - Wills, Samantha
AU - Wang, Jiande
AU - Wang, Wanqiu
N1 - Publisher Copyright:
Ó 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy
PY - 2021/8/15
Y1 - 2021/8/15
N2 - In this study, a series of ocean observing system simulation experiments (OSSEs) are conducted in support of the Tropical Pacific Observing System (TPOS) 2020 Project (TPOS 2020), which was established in 2014, with aims to develop a more sustainable and resilient observing system for the tropical Pacific. The experiments are based on an ocean data assimilation system that is under development at the Joint Center for Satellite Data Assimilation (JCSDA) and the Environmental Modeling Center (EMC)/National Centers for Environmental Prediction (NCEP). The atmospheric forcing and synthetic ocean observations are generated from a nature run, which is based on a modified CFSv2 with a vertical ocean resolution of 1 m near the ocean surface. To explore the efficacy of TAO/TRITON and Argo observations in TPOS, synthetic ocean temperature and salinity observations were constructed by sampling the nature run following their present distributions. Our experiments include a free run with no ‘‘observations’’ assimilated, and assimilation runs with the TAO/TRITON and Argo synthetic observations assimilated separately or jointly. These experiments were analyzed by comparing their long-term mean states and variabilities at different time scales [i.e., low-frequency (.90 days), intraseasonal (20–90 days), and high-frequency (,20 days)]. It was found that 1) both TAO/TRITON and especially Argo effectively improve the estimation of mean states and low-frequency variations; 2) on the intraseasonal time scale, Argo has more significant improvements than TAO/TRITON (except for regions close to TAO/TRITON sites); and 3) on the high-frequency time scale, both TAO/TRITON and Argo have evident deficits (although for TAO/TRITON, limited improvements were present close to TAO/TRITON sites).
AB - In this study, a series of ocean observing system simulation experiments (OSSEs) are conducted in support of the Tropical Pacific Observing System (TPOS) 2020 Project (TPOS 2020), which was established in 2014, with aims to develop a more sustainable and resilient observing system for the tropical Pacific. The experiments are based on an ocean data assimilation system that is under development at the Joint Center for Satellite Data Assimilation (JCSDA) and the Environmental Modeling Center (EMC)/National Centers for Environmental Prediction (NCEP). The atmospheric forcing and synthetic ocean observations are generated from a nature run, which is based on a modified CFSv2 with a vertical ocean resolution of 1 m near the ocean surface. To explore the efficacy of TAO/TRITON and Argo observations in TPOS, synthetic ocean temperature and salinity observations were constructed by sampling the nature run following their present distributions. Our experiments include a free run with no ‘‘observations’’ assimilated, and assimilation runs with the TAO/TRITON and Argo synthetic observations assimilated separately or jointly. These experiments were analyzed by comparing their long-term mean states and variabilities at different time scales [i.e., low-frequency (.90 days), intraseasonal (20–90 days), and high-frequency (,20 days)]. It was found that 1) both TAO/TRITON and especially Argo effectively improve the estimation of mean states and low-frequency variations; 2) on the intraseasonal time scale, Argo has more significant improvements than TAO/TRITON (except for regions close to TAO/TRITON sites); and 3) on the high-frequency time scale, both TAO/TRITON and Argo have evident deficits (although for TAO/TRITON, limited improvements were present close to TAO/TRITON sites).
KW - Data assimilation
KW - In situ oceanic observations
KW - Oceanic variability
KW - Pacific Ocean
UR - https://www.scopus.com/pages/publications/85111143452
U2 - 10.1175/JCLI-D-20-0951.1
DO - 10.1175/JCLI-D-20-0951.1
M3 - Article
AN - SCOPUS:85111143452
SN - 0894-8755
VL - 34
SP - 6797
EP - 6817
JO - Journal of Climate
JF - Journal of Climate
IS - 16
ER -