Development of a four-dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations

2008 ◽  
Vol 113 (C10) ◽  
Author(s):  
Nozomi Sugiura ◽  
Toshiyuki Awaji ◽  
Shuhei Masuda ◽  
Takashi Mochizuki ◽  
Takahiro Toyoda ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Climate Variations ◽  
Coupled Data Assimilation ◽  
Data Assimilation System ◽  
Assimilation System

The decadal climate variations simulated in a coupled data assimilation system using only surface pressure observations

2020 ◽  
Author(s):  
Xiaosong Yang ◽  
Thomas Delworth ◽  
Fanrong Zeng ◽  
William Cooke ◽  
Liping Zhang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Surface Pressure ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Fluxes ◽  
Decadal Prediction ◽  
Climate Variations ◽  
Decadal Climate ◽  
Data Assimilation System ◽  
Assimilation System

<p>Initializing climate models for decadal prediction is a major challenge, in part due to the lack of long-term subsurface ocean observations and the changing nature of observing systems. In order to overcome these limitations, we have developed a novel method for initializing a climate model for decadal prediction. Using GFDL’s next-generation prediction system, we developed a coupled ensemble data assimilation system, which assimilated only surface pressure observations, since the surface pressure measurements have been made since the late 1800s. Physically, by assimilating high-frequency surface pressure observations we constrain the model to experience a sequence of wind and storms, and thus surface fluxes, that is very similar to what is observed. The hypothesis is that by having the ocean component of the coupled model experience a very similar sequence of surface fluxes as observations, the ocean component of the coupled model will gradually reproduce the same variations as the observed system.</p><p>We assimilated the observed surface pressure station data used in the latest 20-century reanalysis. A coupled simulation during 1960 to 2016 has been completed. In this talk, we will review how well the observed decadal climate variations (e.g., PDO and AMO) can be reproduced solely from the surface pressure observations.  In addition, we will explore the multi-decadal variations of the Atlantic meridional overturning circulation (AMOC) and its connection with the North Atlantic sea surface temperature. The feasibility of using this method to initialize coupled climate models for realistic decadal predictions will be discussed in the talk.            </p>

scholarly journals Assessing a New Coupled Data Assimilation System Based on the Met Office Coupled Atmosphere–Land–Ocean–Sea Ice Model

2015 ◽  
Vol 143 (11) ◽  
pp. 4678-4694 ◽  
Author(s):  
D. J. Lea ◽  
I. Mirouze ◽  
M. J. Martin ◽  
R. R. King ◽  
A. Hines ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Coupled Model ◽  
Background Information ◽  
Ocean Data Assimilation ◽  
Weakly Coupled ◽  
Coupled Data Assimilation ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract A new coupled data assimilation (DA) system developed with the aim of improving the initialization of coupled forecasts for various time ranges from short range out to seasonal is introduced. The implementation here is based on a “weakly” coupled data assimilation approach whereby the coupled model is used to provide background information for separate ocean–sea ice and atmosphere–land analyses. The increments generated from these separate analyses are then added back into the coupled model. This is different from the existing Met Office system for initializing coupled forecasts, which uses ocean and atmosphere analyses that have been generated independently using the FOAM ocean data assimilation system and NWP atmosphere assimilation systems, respectively. A set of trials has been run to investigate the impact of the weakly coupled data assimilation on the analysis, and on the coupled forecast skill out to 5–10 days. The analyses and forecasts have been assessed by comparing them to observations and by examining differences in the model fields. Encouragingly for this new system, both ocean and atmospheric assessments show the analyses and coupled forecasts produced using coupled DA to be very similar to those produced using separate ocean–atmosphere data assimilation. This work has the benefit of highlighting some aspects on which to focus to improve the coupled DA results. In particular, improving the modeling and data assimilation of the diurnal SST variation and the river runoff should be examined.

scholarly journals Erratum to ‘A coupled data assimilation system for climate reanalysis’

2016 ◽  
Vol 142 (696) ◽  
pp. 1564-1564
Author(s):  
Patrick Laloyaux ◽  
Magdalena Balmaseda ◽  
Dick Dee ◽  
Kristian Mogensen ◽  
Peter Janssen
Keyword(s):  
Data Assimilation ◽  
Coupled Data Assimilation ◽  
Data Assimilation System ◽  
Assimilation System
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