Improving the land-surface components of Climate Forecast System Reanalysis (CFSR)


NOAA Award: NA13OAR4310107

University of Washington

PI: Bart Nijssen

Reporting Period: 07/01/2014 - 06/30/2015

Report date: 06/30/2015

This is a joint project between the NOAA National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC), NOAA’s Climate Prediction Center (CPC), Princeton University, and the University of Washington. The overall intent of the project is to utilize past land data assimilation system activities (notably NLDAS and GLDAS) to improve upcoming reanalyses. Specific objectives are to improve (1) land characterization data sets (e.g. vegetation type and soil texture class, and the characterization of urban areas, etc.), (2) atmospheric forcing data sets (e.g. precipitation, downward solar and longwave radiation), (3) assimilation of near-real time land states (e.g. surface skin temperature, albedo, soil moisture, snow extent, vegetation greenness and density), (4) land-model spin-up procedures, and (5) downscaling techniques for forcing data and land states. The University of Washington has a modest advisory role in the project. As defined in the proposal, the University of Washington is to provide guidance and support … on the implementation of the river routing model into CFSR as well as calibration and validation of the model at a global scale. This work will directly build on current efforts by the UW to implement the same routing model as an option in CESM as part of their collaborative DOE proposal: “Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic System Model (RASM)”. CFSR routing results will be compared with offline global simulations from the VIC land model, which uses the same routing model, but different land surface forcings. In addition, snow and soil moisture data fields from the UW’s multi-model surface water monitor will be used to evaluate terrestrial water cycle changes in the CFSR simulations.

During Year 2 we have focused on further development of the routing model, hereafter referred to as RVIC, and integration of RVIC in a coupled regional climate model (RASM) to provide freshwater inputs to the Arctic Ocean. In addition, we have created an off-line version of the model in python, which is also used to provide some of the inputs that are used in the coupled version of RVIC. The offline version of the model is shared on a public code repository (github) and we have improved the model documentation. For details, see below.


The RVIC streamflow routing model is an expanded version of the routing model originally introduced by Lohmann et al. (1996). RVIC is a semi-distributed, source-to-sink, routing model that solves a linearized version of the Saint-Venant equations. The model develops impulse response functions (IRFs) to define the time evolution of flow between pairs of source and sink points. Following the development of the IRFs, a simple convolution is performed combining the IRF for each source to sink pair and the runoff flux from each source point (Fig 1. bottom). RVIC has been configured to run as a stand-alone routing model (e.g. as a post processor of semi-distributed hydrologic models) or coupled within earth system models (e.g. NCAR’s Community Earth System Model).

Compared to the original Lohmann et al. (1996) model, RVIC offers the following improvements:

  1. Development of the IRFs (Fig. 1 top) is done as a separate preprocess. In the original model, the development of the IRFs was done each time the model was executed. For short simulations, or simulations that required frequent restarts or checkpointing, the computational cost of the developing the IRFs limited the model’s applicability in coupled models. In the case of RVIC, the IRFs are developed prior to the beginning of the coupled