reasonable timestep range for WRF-CLM

Any issues with the actual running of the WRF.

reasonable timestep range for WRF-CLM

Postby twhilton » Tue Nov 20, 2018 7:14 pm


I'm having a hard time with segmentation faults in SFCLAY in WRF-CLM. I started with an integration timestep of 180 seconds (shorter than the 6*dx recommendation), and I've progressively lowered it all the way to 2. Each timestep reduction pushes the segmentation fault a few simulated seconds or minutes into the simulated future, but the seg fault happens eventually (and between 9 and 9.5 hours into my 90-day run).

I don't have a good sense of what a reasonable lower limit is for continuing down this path. 1? fractions of a second? Or should I start to look for other causes of segmentation faults?

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Joined: Mon Sep 11, 2017 11:48 am

Re: reasonable timestep range for WRF-CLM

Postby kwthomas » Mon Nov 26, 2018 6:02 pm


Is the a current WRF version? If not, try upgrading. It is always possible that you've tripped a bug that
has been fixed.

Check *all* rsl* files. Sometimes, there may be a complain where you least suspect.
Kevin W. Thomas
Center for Analysis and Prediction of Storms
University of Oklahoma
Posts: 279
Joined: Thu Aug 07, 2008 6:53 pm

Re: reasonable timestep range for WRF-CLM

Postby twhilton » Tue Dec 04, 2018 12:30 pm

Thanks, Kevin, for these helpful suggestions. I am indeed using the latest WRF (WRF Model Version 4.0 (June 8, 2018)). Downgrading to 3.9 didn't solve the problem.

For any poor soul of the future who runs into this and finds this thread, what solved it eventually was switching the driver data from NCEP FNL Operational Model Global Tropospheric Analyses (ds083.2; DOI: 10.5065/D6M043C6) to NCEP/DOE Reanalysis 2 (R2) (ds091.0; I got the idea because I noticed that two published WRF-CLM papers (Lu and Kueppers 2012, Subin et al. 2011) used ds091.0. I have not dug any farther into why driving WRF-CLM with ds83.2 results in segmentation faults and ds091.0 does not (at least so far, for me). WRF-NOAH has worked just fine for me for the same domain and time period driven by 83.2.



Lu, Y., and L. M. Kueppers (2012), Surface energy partitioning over four dominant vegetation types across the United States in a coupled regional climate model (Weather Research and Forecasting Model 3–Community Land Model 3.5), J. Geophys. Res., 117, D06111, doi: 10.1029/2011JD016991.

Subin, Z.M., W.J. Riley, J. Jin, D.S. Christianson, M.S. Torn, and L.M. Kueppers, 2011: Ecosystem Feedbacks to Climate Change in California: Development, Testing, and Analysis Using a Coupled Regional Atmosphere and Land Surface Model (WRF3–CLM3.5). Earth Interact., 15, 1–38,
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