Recent studies have started to explore coupled data assimilation (CDA) in coupled ocean–atmosphere models because of the great potential of CDA to improve climate analysis and seamless weather–climate prediction on weekly-to-decadal time scales in advanced high-resolution coupled models. In this review article, we briefly introduce the concept of CDA before outlining its potential for producing balanced and coherent weather–climate reanalysis and minimizing initial coupling shocks. We then describe approaches to the implementation of CDA and review progress in the development of various CDA methods, notably weakly and strongly coupled data assimilation. We introduce the method of coupled model parameter estimation (PE) within the CDA framework and summarize recent progress. After summarizing the current status of the research and applications of CDA-PE, we discuss the challenges and opportunities in high-resolution CDA-PE and nonlinear CDA-PE methods. Finally, potential solutions are laid out.

Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review

It has been a great challenge for global weather and climate models to simulate realistic Madden–Julian Oscillation (MJO) while keeping global energy and water balance unaffected. This work demonstrates that, in the Nanjing University of Information Science and Technology Earth System Model, enhanced boundary layer (BL) convergence feedback to the lower tropospheric heating in both the modified Tidtke (TDK) and relaxed Arakawa–Schubert (RAS) convective schemes have significantly improved the quality of MJO simulation in terms of both the eastward propagation and three-dimensional dynamic and thermodynamic structures. The modifications to the TDK and RAS schemes include (a) a BL depth-dependent convective inhibition, and (b) a bottom-heavy diffusivity in the shallow convection scheme. To understand how these modifications improved the MJO simulation, we applied dynamics-oriented diagnostics to reveal the critical role of the interaction between the lower-tropospheric heating and the BL convergence. The modified schemes enhance the lower-tropospheric diabatic heating to the east of the MJO convective center, which leads to increased Kelvin wave easterly winds. The strengthened MJO easterly winds reinforce the BL moisture convergence to the east of the MJO center and therefore result in increased upward transports of moisture and heat from the BL to the free atmosphere, which further moisten and destabilize the lower troposphere and thereby increase the lower-tropospheric heating. The positive feedback between the BL convergence and lower tropospheric heating improves MJO eddy available potential energy generation to the east of major convection and promotes MJO eastward propagation. The results indicate that correct simulation of the heating induced by shallow and/or congestus clouds and its interaction with BL dynamics is critical for realistic simulation of the MJO as suggested by the trio-interaction theory.

/pdf/improving-mjo-simulation-by-enhancing-the-interaction-i54oliy56x.pdf

Improving MJO simulation by enhancing the interaction between boundary layer convergence and lower tropospheric heating

We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of se...

/pdf/parameter-optimization-for-real-world-enso-forecast-in-an-4q8zjfp9f9.pdf

Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

Abstract. Convection influences climate and weather events over a
wide range of spatial and temporal scales. Therefore, accurate predictions
of the time and location of convection and its development into severe
weather are of great importance. Convection has to be parameterized in
global climate models and Earth system models as the key physical processes
occur at scales much lower than the model grid size. This parameterization
is also used in some numerical weather prediction (NWP) models when
convection is not explicitly resolved. The convection schemes described in
the literature represent the physics by simplified models that require
assumptions about the processes and the use of a number of parameters based
on empirical values. These empirical values and assumptions are rarely
discussed in the literature. The present paper examines these choices and
their impacts on model outputs and emphasizes the importance of observations
to improve our current understanding of the physics of convection. The focus
is mainly on the empirical values and assumptions used in the activation of
convection (trigger), the transport and microphysics (commonly referred to
as the cloud model), and the intensity of convection (closure). Such
information can assist satellite missions focused on elucidating convective
processes (e.g., the INCUS mission) and the evaluation of model output
uncertainties due to spatial and temporal variability of the empirical
values embedded into the parameterizations.


Empirical values and assumptions in the convection schemes of numerical models

In real applications, one common issue of parameter estimation using ensemble-based data assimilation methods is the accumulation of sampling errors when a large number of observations are used to update single-value parameters. In this article, a new parameter estimation method which assimilates a large number of observations to estimate the states while assimilates adaptive observations to update the parameters is introduced. The observations resulting in maximum total variance reduction to the parameter ensembles are identified to perform parameter estimation. To validate this new method, the two-scale Lorenz-96 model is used to generate true states, while a parameterized one-scale Lorenz-96 model is used to perform state and parameter estimation experiments. The comparison between state estimation and parameter estimation with fixed or adaptive observations shows the new method can be more effective in estimating the model parameters and providing more accurate analyses. This method also shows its potential to be used in the data assimilation with large general circulation models to better produce reanalyzes.

Parameter Estimation Using Adaptive Observations Toward Maximum Total Variance Reduction With Ensemble Adjustment Kalman Filter

Parametric uncertainty in convection parameterization is one major source of model errors that cause model climate drift. Convection parameter tuning has been widely studied in atmospheric models to help mitigate the problem. However, in a fully coupled general circulation model (CGCM), convection parameters which impact the ocean as well as the climate simulation may have different optimal values. This study explores the possibility of estimating convection parameters with an ensemble coupled data assimilation method in a CGCM. Impacts of the convection parameter estimation on climate analysis and forecast are analyzed. In a twin experiment framework, five convection parameters in the GFDL coupled model CM2.1 are estimated individually and simultaneously under both perfect and imperfect model regimes. Results show that the ensemble data assimilation method can help reduce the bias in convection parameters. With estimated convection parameters, the analyses and forecasts for both the atmosphere and the ocean are generally improved. It is also found that information in low latitudes is relatively more important for estimating convection parameters. This study further suggests that when important parameters in appropriate physical parameterizations are identified, incorporating their estimation into traditional ensemble data assimilation procedure could improve the final analysis and climate prediction.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017MS001222

Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation

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Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation