An Online Ensemble Coupled Data Assimilation Capability for the Community Earth System Model: System Design and Evaluation

The Community Earth System Model (CESM) developed at the National Center of Atmospheric Research (NCAR) has been used worldwide for climate studies. This study extends the efforts of CESM development to include an online (i.e., in-core) ensemble coupled data assimilation system (CESM-ECDA) to enhance CESM’s capability for climate predictability studies and prediction applications. The CESM-ECDA system consists of an online atmospheric data assimilation (ADA) component implemented to both the finite-volume and spectral-element dynamical cores, and an online oceanic data assimilation (ODA) component. In ADA, surface pressures (Ps) are assimilated, while in ODA, gridded sea surface temperature (SST) and ocean temperature and salinity profiles at real Argo locations are assimilated. The system has been evaluated within a perfect twin experiment framework, showing significantly reduced errors of the model atmosphere and ocean states through “observation”-constraints by ADA and ODA. The weakly CDA in which both the online ADA and ODA are conducted during the coupled model integration shows smaller errors of air-sea fluxes than the single ADA and ODA, facilitating the future utilization of cross-covariance between the atmosphere and ocean at the air-sea interface. A three-year CDA reanalysis experiment is also implemented by assimilating Ps, SST and ocean temperature and salinity profiles from the real world spanning the period 1978 to 1980 using 12 ensemble members. Results show that Ps RMSE is smaller than 20CR and SST RMSE is better than ERA-20C and close to CFSR. The success of the online CESM-ECDA system is the first step to implement a high-resolution long-term climate reanalysis once the algorithm efficiency is much improved.

Contributions of weakly coupled data assimilation-based land initialization to interannual predictability of summer climate over Europe

Land surface is a potential source of climate predictability over the Northern Hemisphere mid-latitudes but has received less attention than sea surface temperature in this regard. This study quantified the degree to which realistic land initialization contributes to interannual climate predictability over Europe based on a coupled climate system model named FGOALS-g2. The potential predictability provided by the initialization, which incorporates the soil moisture and soil temperature of a land surface reanalysis product into the coupled model with a DRP-4DVar-based weakly coupled data assimilation (WCDA) system, was analyzed first. The effective predictability (i.e., prediction skill) of the hindcasts by FGOALS-g2 with realistic and well-balanced initial conditions from the initialization were then evaluated. Results show an enhanced interannual prediction skill for summer surface air temperature and precipitation in the hindcast over Europe, demonstrating the potential benefit from realistic land initialization. This study highlights the significant contributions of land surface to interannual predictability of summer climate over Europe.

Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz 96 system

Localization is widely used in data assimilation schemes to mitigate the impact of sampling errors on ensemble-derived background error covariance matrices. Strongly coupled data assimilation allows observations in one component of a coupled model to directly impact another component through the inclusion of cross-domain terms in the background error covariance matrix. When different components have disparate dominant spatial scales, localization between model domains must properly account for the multiple length scales at play. In this work, we develop two new multivariate localization functions, one of which is a multivariate extension of the fifth-order piecewise rational Gaspari–Cohn localization function; the within-component localization functions are standard Gaspari–Cohn with different localization radii, while the cross-localization function is newly constructed. The functions produce positive semidefinite localization matrices which are suitable for use in both Kalman filters and variational data assimilation schemes. We compare the performance of our two new multivariate localization functions to two other multivariate localization functions and to the univariate and weakly coupled analogs of all four functions in a simple experiment with the bivariate Lorenz 96 system. In our experiments, the multivariate Gaspari–Cohn function leads to better performance than any of the other multivariate localization functions.

The impact of coupled data assimilation on Madden-Julian Oscillation predictability initialized from coupled satellite-era reanalysis

This study investigates the impact of coupled initialization on the extended-range prediction of the Madden-Julian Oscillation (MJO). A set of reforecasts using combinations of the oceanic and atmospheric initial conditions produced with coupled and uncoupled data assimilation (DA) are conducted to evaluate the impact of coupling in the different domains, from the perspective of MJO forecasts. The coupled initial conditions are provided by CERA-SAT pilot coupled reanalysis for the satellite era recently produced by ECMWF. We focus on the prediction skill of the MJO using the Real-time Outgoing Long-wave Radiation (OLR) MJO index in a series of re-forecasts. The impact of atmospheric initial conditions produced by coupled DA shows slight benefit for the MJO prediction. However, compared with the operational ocean reanalysis, the ocean initial conditions created by CERA-SAT degrade the MJO prediction skill during the first 2-3 weeks of the re-forecast by 1.5% to 5.8%. A moist static energy budget analysis revealed that the underestimation of 0.2 K sea surface temperature, 1.4 W m-2 top of atmosphere downward longwave radiation, and 3.8 W m-2 latent heat flux over the Maritime Continent lead to small but statistically significant degradation of the MJO forecast skill. The results demonstrate that the MJO is sensitive to ocean initial conditions, and illustrate the value of the extended range MJO prediction for evaluating the quality of coupled data assimilation, and suggest that future efforts on coupled data assimilation pay special attention to the balance of air-sea interaction processes over the warm pool area, in terms of modeling, observational needs and system.

CAFE60v1: A 60-year large ensemble climate reanalysis. Part I: System design, model configuration and data assimilation.

We detail the system design, model configuration and data assimilation evaluation for the CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1. CAFE60v1 has been designed with the intention of simultaneously generating both initial conditions for multi-year climate forecasts and a large ensemble retrospective analysis of the global climate system from 1960 to present. Strongly coupled data assimilation (SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in-situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are sub-sampled from the JRA-55 atmospheric reanalysis. Strong coupling is implemented via explicit cross domain covariances between ocean, atmosphere, sea ice and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean and sea ice. The system produces 96 climate trajectories (state estimates) over the most recent six decades as well as a complete data archive of initial conditions potentially enabling individual forecasts for all members each month over the 60 year period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses.

Strongly coupled data assimilation with the coupled ocean-atmosphere model AWI-CM: comparison with the weakly coupled data assimilation

<p>We compare the results of strongly coupled data assimilation and weakly coupled data assimilation by analyzing the assimilation effect on the prediction of the ocean as well as the atmosphere variables. The AWI climate model (AWI-CM), which couples the ocean model FESOM and the atmospheric model ECHAM, is coupled with the parallel data assimilation framework (PDAF, http://pdaf.awi.de). The satellite sea surface temperature is assimilated. For the weakly coupled data assimilation, only the ocean variables are directly updated by the assimilation while the atmospheric variables are influenced through the model. For the strongly coupled data assimilation, both the ocean and the atmospheric variables are directly updated by the assimilation algorithm. The results are evaluated by comparing the estimated ocean variables with the dependent/independent observational data, and the estimated atmospheric variables with the ERA-interim data. In the ocean, both the WCDA and the SCDA improve the prediction of the temperature and SCDA and WCDA give the same RMS error of SST. In the atmosphere, WCDA gives slightly better results for the 2m temperature and 10m wind velocity than the SCDA. In the free atmosphere, SCDA yields smaller errors for the temperature, wind velocity and specific humidity than the WCDA in the Arctic region, while in the tropical region, the error are larger in general.</p>