Coupled Ocean‐Atmosphere Modeling Over the Maritime Continent: A Review. Issue 6 (20th June 2020)

• Record Type: Journal Article
• Authors: Xue, Pengfei; Malanotte‐Rizzoli, Paola; Wei, Jun; Eltahir, Elfatih A. B.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-20
• Subjects: Maritime Continent -- ocean‐atmosphere feedback -- regional climate -- coupled model -- air‐sea interaction -- Indonesian Throughflow; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2019JC014978
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: The Maritime Continent (MC) plays a vitally important role in the Earth's climate system from both oceanic and atmospheric perspectives. While the critical role of ocean‐atmosphere coupled dynamics over the MC has long been recognized, development of two‐way coupled regional climate models for this region is still in its early stages. In this work, the authors review recent progress in two‐way coupled ocean‐atmosphere regional climate modeling. Development of coupled models and their applications in the MC are summarized. Model performances are discussed with a focus on regional oceanic and atmospheric characteristics. Through a critical review of modeling advances and limitations in simulating sea surface temperature, precipitation, and oceanic throughflows, the authors identify deficiencies of current models and discuss possible reasons. The review shows that model biases mainly stem from unresolved physical processes, inadequate model representations of the coupled system, and uncertainties in model configurations. The study reveals large‐scale coupled modes of variability, local air‐sea interactions, atmospheric dynamics, and oceanic processes play various roles in the observed modeling biases. Lastly, the authors offer suggestions on emerging opportunities for improving regional coupled modeling over the MC. Key Points: Development of two‐way coupled regional climate models over the Maritime Continent is still in its infancy; mixed results from coupled models are observed Unresolved physical processes, inadequate model representations of the coupled system, and uncertainties in model configurations are main factors responsible for modeling biases Opportunities emerge for improving regional coupled modeling through advancing the representation of physics and dynamics in coupled models