Introduction¶
This guide describes how to use the replay functionality with CESM. It is assumed the user is already familiar with how to run CESM on their machine. Documentation from the original model version can be found in the CESM Quickstart Guide
What is the replay?¶
The replay is a version of the Incremental Analysis Update (IAU) used for performing data assimilation, described in Bloom et al., 1996 and relating to work on empirical bias correction such as in Leith 1978 and DelSole and Hou 1999. In standard data assimilation cases, the IAU is used to push the model towards observations; in the replay, the model is pushed towards a reanalysis. This can be useful for determining short-term model biases and evaluating model performance (see Schubert et al., 2019), among other use cases.
The replay is similar to nudging, but is less sensitive to nudging parameters and instabilities (Bloom et al., 1996). The difference is in the timing of the forcing towards the reanalysis. In the replay, the model runs forward 3 hours normally, with no forcing applied. Then, the difference between the model state and the reanalysis is measured (here any or all of U,V,T, and Q can be used) and saved in the model output. The model then backs up 3 hours to exactly where it was before, and runs forward 6 hours, this time with the forcing tendency applied based on the 3-hour difference. After 6 hours, the model moves forward another 3 hours without forcing, and the process repeats. This procedure allows the model to stay tightly constrained to the reanalysis throughout the run, assuming the 3-hour model drift is nearly linear. The outputs of the model difference from reanalysis at 3-hour increments are the short-term model biases from the reanalysis. The stability of the replay performance is assessed in Takacs et al., 2018.
The replay is currently implemented and used at NASA’s Global Modeling and Assimilation Office in the GEOS model (Chang et al., 2019). Here we implement the same tool in CESM2 using open source code, a simple startup, and adjustable replay parameters. Having the replay in multiple models allows for comparison of results across models, and the ability to easily assess short-term model drift in CESM.
Software requirements¶
Installing, building and running the replay requires:
For instructions on how to properly interpolate the reanalysis to the CAM grid, we suggest using the interpolation tools created for the CESM Nudging toolbox. Documentation for the interpolation tools can be found in the CAM Users Guide: Nudging.
References¶
Bloom, S. C., Takacs, L. L., Silva, A. M. da, & Ledvina, D. (1996). Data Assimilation Using Incremental Analysis Updates. Monthly Weather Review, 124(6), 1256–1271. https://doi.org/10.1175/1520-0493(1996)124%3C1256:DAUIAU%3E2.0.CO;2
Chang, Y., Schubert, S. D., Koster, R. D., Molod, A. M., & Wang, H. (2019). Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America. Journal of Climate, 32(2), 639–661. https://doi.org/10.1175/JCLI-D-18-0598.1
DelSole, T., & Hou, A. Y. (1999). Empirical Correction of a Dynamical Model. Part I: Fundamental Issues. Monthly Weather Review, 127(11), 2533–2545. https://doi.org/10.1175/1520-0493(1999)127%3C2533:ECOADM%3E2.0.CO;2
Leith, C. E. (1978). Objective Methods for Weather Prediction. Annual Review of Fluid Mechanics, 10(1), 107–128. https://doi.org/10.1146/annurev.fl.10.010178.000543
Schubert, S. D., Chang, Y., Wang, H., Koster, R. D., & Molod, A. M. (2019). A Systematic Approach to Assessing the Sources and Global Impacts of Errors in Climate Models. Journal of Climate, 32(23), 8301–8321. https://doi.org/10.1175/JCLI-D-19-0189.1
Takacs, L. L., Suárez, M. J., & Todling, R. (2018). The Stability of Incremental Analysis Update. Monthly Weather Review, 146(10), 3259–3275. https://doi.org/10.1175/MWR-D-18-0117.1