Collaborative Project: Developing Coupled Data Assimilation Strategy

Funding Program: 

In progress. 

The initialization of coupled climate simulations, particularly at high resolution, remains a grand challenge for earth system models (ESMs). Further, biases that emerge in coupled climate system are notoriously difficult to diagnose and remedy. To address these issues, we propose to develop and implement a flexible, high-performance computing capability and ensemble coupled data assimilation (ECDA) capability within the Energy Exascale Earth System Model (E3SM) to understand model climate biases, as well as to improve coupled model initialization.

Our ECDA system will be a computationally efficient, on-line implementation that bypasses frequent read and write to the file system. Our ECDA also has the flexibility to accommodate both a computationally efficient Ensemble Optimal Interpolation (EnOI) scheme and a flow-dependent Ensemble Kalman Filter (EnKF). Our ECDA system will be applicable to both low and high-resolution configurations of E3SM for improved initialization and climate predictions. The ECDA will also enable us to diagnose and understand rapidly emerging coupled model biases, such as the tropical biases in rainfall and sea surface temperatures, by performing ensemble coupled experiments initialized around observations. Our ECDA study will be carried out in E3SM v1 and the experience gained here will help in shaping a future, long-term implementation of ECDA in future versions of E3SM.

This study will explore the implementation of a computationally efficient ECDA system in a complex ESM. The improved initialization and climate prediction will have significant benefit for the society. The efficient DA schemes that we will develop will also help in improving the DA application in general across different disciplines. 

 

Project Term: 
2018 to 2021
Project Type: 
University Funded Research

Publications:

None Available

Research Highlights:

None Available