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Meeting Notes 2020 Science

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will wieder edited this page Aug 13, 2020 · 36 revisions

Aug 13, 2020

Agenda

Welcome new participants

  • Yifan Cheng, postdoc in RAL
  • Meg Fowler, PS in TSS & AMP
  • Hannah Holland-Moritz, graduate student @ CU

CSL proposal & LMWG allocation, Dave

  • A number of simulations to consider, esp. full matrix of FATES and CESM-FATES simulations
  • Also discussion of 'high res' simulations (e.g. Hill slope model & vertically resolved canopy).

Interesting MIP results, Dave

  • snow density from LUMIP
  • coupled vs. offline model results in LS3MIP

CESM tutorial, Keith

  • CLM tutorial, how much lead time do we need?
  • In person seems preferred.
  • We need ~ 6 month lead time to plan.
  • Will can look into reserving the main seminar room in Summer - Fall 2021.
  • Danica noted how busy summers are, with CESM workshop and CESM tutorial in June & Aug.
  • Danica also suggested we could update current tutorial information (and upload lectures) in short term if needed?
  • Brief discussion on FATES contributions.

July 9, 2020

Agenda

Updates

  • CTSM 5.1 will have new surface datasets
    • Modifications to urban, gross unrepresented land use change, changes to crop/pft distributions
    • Suggest not maintaining backwards compatibility
  • FATES logging PR seems ready to go for beta-testing, hopefully an AMIP run this summer!
    • Will need new forest harvest datasets down road (directly from LUH2 through streams file).
  • LILAC tag made!
  • Leah's PR and LUNA bugs (nearly) finalized.

Presentation: Geng Xia, NREL

Reports on WRF runs with different land models, inc. CLM4.5

January 23, 2020 - CTSM-Agriculture

Bill Sacks: Overview of a few crop phenology datasets

Discussion of what we might want to do in CTSM moving forward

Let's consider having spatially-explicit planting date windows for each crop, based on Bill's dataset.

To get interannual variability, we could change the time within that window based on some rules, using shorter-term indicators than what we currently use - i.e., ditching the current metric of GDD since the start of the year, since that probably has low correlation with planting dates. These metrics could include soil moisture (soil moisture not too wet, not too dry) and/or antecedent precipitation (in some regions), absence of snow, and soil temperatures (10cm?) above freezing. A literature review may turn up additional or more refined possibilities.

Related to this, Beth Drewniak tested a few additional planting date rules in ELM based on a paper by Dobor et al. (2016). This analysis builds on a series of rules to see which provided the most reliability. The soil moisture rule is pretty broad (between 20-80% saturation). Beth added it to the ELM climate driven planting date, but didn’t see much of an impact. There is a trend toward earlier planting in the ELM planting date model, but it is very small.

We probably won't focus on trying to capture long-term trends for now, since those are probably based partly on technology / management rather than just climate.

Once we have ingested this data set, we could also have an option to use fixed, specified dates (e.g., the midpoint of the planting date range). And actually, it could be useful to do an initial sensitivity experiment: how sensitive are various model results to the planting date when it is varied from the start date to the end date of the planting window in each region?

Dobor, L., Barcza, Z., Hlásny, T., Árendás, T., Spitkó, T., & Fodor, N. (2016). Crop planting date matters: Estimation methods and effect on future yields. Agricultural and Forest Meteorology, 223, 103–115. http://doi.org/10.1016/j.agrformet.2016.03.023

January 22, 2020

Mike Barlage - CTSM-WRF coupling

This is a very preliminary analysis: no effort has gone into improving scientific performance of this coupling (we're still verifying some of the coupling fields).

CTSM took about 20% of the time of WRF. This was with hourly output, with a 90s time step. Expectation is that this could be improved, partly by reducing output, and possibly through other means.

Evaluations of scientific performance: Again, it's really important to note that no effort has gone into improving this.... That said, CTSM is currently showing a high bias in temperature across much of CONUS.

Tim Hoar: Based on his experience with WRF DA, WRF with Noah could be getting the right answers for the wrong reasons (e.g., bad soil moisture; improving soil moisture degrades the coupled simulation). This further emphasizes the point that not too much should be made of the degraded scientific performance with CTSM at this point.

Initial conditions for land for CTSM: Currently, use climatological initial conditions, not specific to the given time period. Tim Hoar points out that we could use the DART hindcast data assimilation product for the sake of initialization.

Bill Sacks - Software of CTSM-WRF coupling

(I didn't get many notes on this, since I was giving the presentation.)

Ethan G: It seems possible that the redistribution of data between the atmosphere and land decompositions could be a bottleneck, at least at high processor counts.

  • Bill: We definitely want to measure this. Based on experiences with CESM, this redistribution doesn't seem to generally be a bottleneck, but we do plan to check on this. If necessary, we could introduce a new decomposition method in CTSM that allows it to use the atmosphere's decomposition.

Dave Lawrence - upcoming CTSM scientific additions to the model

Ned: How to handle urban, specifically thinking about buildings that extend into the atmosphere?

  • Mike Barlage: There is some question about where the urban model should live: inside CTSM vs. inside WRF.
  • We'll have a future meeting focused on urban
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