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

YEAR 2 PROGRESS REPORT

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.

RVIC

The RVIC streamflow r