the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convection Resolving E3SM Atmosphere Model (SCREAM) Version 0
Abstract. Using the Regionally Refined Mesh (RRM) configuration of the U.S. Department of Energy's Simple Cloud Resolving E3SM Atmosphere Model (SCREAM), we simulate and evaluate four meteorologically distinct atmospheric river events over California. We test five different RRM configurations, each differing in terms of the areal extent of the refined mesh and the resolution (ranging from 800 m to 3.25 km). We find that SCREAM-RRM generally has a good representation of the AR generated precipitation in CA, even for the control simulation which has a very small 3 km refined patch, and is able to capture the fine scale regional distributions that are controlled largely by the fine scale topography of the state. Although, it is found that SCREAM generally has a wet bias over topography, most prominently over the Sierra Nevada mountain range, with a corresponding dry bias on the lee side. We find that refining the resolution beyond 3 km (specifically 1.6 km and 800 m) has virtually no benefit towards reducing systematic precipitation biases, but that improvements can be found when increasing the areal extent of the upstream refined mesh. However, these improvements are relatively modest and only realized if the size of the refined mesh is expanded to the scale where employing RRM no longer achieves the substantial cost benefit it was intended for.
- Preprint
(11062 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 25 Jun 2024)
-
RC1: 'Comment on egusphere-2024-839', Anonymous Referee #1, 18 May 2024
reply
Summary:
Bogenshutz et al. present four detailed case studies chosen to highlight different types and aspects of ARs impacting the California coast using the ultra-high regionally refined E3SM atmosphere model, SCREAM. The purpose of the study is to show SCREAM’s potential utility simulating ARs for both climate and weather communities. Their main findings, aside from the realism of AR representation, include (1) refining resolution beyond 3km has little impact for improving precipitation biases, but (2) expanding the spatial extent of the refinement has modest benefits.
Overall Comments:
This is a very nice paper. It is clear, well-written and organized, and absolutely fits within the GMD scope. I have a few comments I would like to see addressed before full publication. Although these are relatively minor, they are important for clarity. Please see specific comments below, but the most important ones are regarding SCREAM model description and method/domain for computing max/mean IVT.
Specific Comments:
Line 18 and elsewhere: Typically the words “atmospheric rivers” are not capitalized.
Line 89: Out of curiosity, when you moved to using SHOC from a simplified PBL, was there anything that needed to be re-tuned?
Figure 2: Did you mean ne32 and not ne21?
SCREAM description and References: There are missing citations in the reference list, I definitely noticed both Caldwell et al 2021 and 2019 missing as I searched for these to get more information on SCREAM. Also, I recommend adding a bit more to the description part of the paper for ease rather than digging into the cited Caldwell papers, i.e. vertical resolution, features, biases, improvements not already mentioned, etc.
AR cases (these are more curiosity questions than comments on the paper itself): There are all great choices and span important and different types of ARs and dynamics. I am curious, however, on how SCREAM-RRR would perform for 1) a weak Cat 1 storm, or 2) different regions , or 3) wet vs windy “flavors”? Perhaps these are already planned activities, and I am not suggesting these be done for this paper. (I understand the intense computational and person-resources involved in these activities). Some questions to consider for future experiments (or answer if you have already done some of this): For a weak category 1 AR, or a “windy” AR, are there components of physics, dycore, and/or resolution that do better with an overwet environment vs not? Are biases different across the varied AR dynamics, i.e. Pineapple Express vs North Atlantic warm conveyor belt lift? (Has this been looked at)? Are there plans to look at moisture transports such as ARs within monsoonal flow which can be found in the E.Pacific?
Line 148 and elsewhere: The acronym “AR” only needs to be defined once.
Line 253: Why not add polygons to one of your maps with the locations of each of your evaluation points? This would be helpful visualizing where the skill is being evaluated. (Or maybe an inset with a zoomed map)?
Line 287 and Figure 5c: Pearson pattern correlation?
Line 327: Comparing SC2005 reanalysis and SCREAM, the character of the IVT plume and footprint are very different. One might argue that SCREAM really doesn’t capture the AR structure well here, especially in the ocean. Ideas as to why?
Lines 359-360: I understand the tension between the improvements via resolution and mesh area, but the other degree of freedom here is E3SM vs CESM when consulting with the results from the Rhoades paper. I think(?) that the vertical levels are different between these two modelling frameworks? How different is SCREAM vertical structure from the Rhoades study, and do you think this may matter regarding convection schemes?
Line 401: Did you mean Figure 10 (and not 3)?
Figure 10: I find it interesting that the EPAC 3km case seems to overdo the snow compared to the CA 3km in most cases, i.e. most notably at Virginia Lakes for NC2021 and everywhere SC2005. Thoughts?
Line 428, Figures 12/13, Lines 440-447: For the event maximum/mean IVT, did you use find, at each grid point in your RR domain, the maximum/mean values and plot this? Or just across the plotting extent in Fig 12/13? For SC2005 where SCREAM’s IVT core could arguably be over the ocean (based on Figure 4), this might be relevant. Are some of SCREAMs biases due to AR characterization and misplacement of the core, and not really just the fact that the plume hitting the coast is more narrow? For clarity, please add a sentence or two clarifying IVT max/mean methodology.
Citation: https://doi.org/10.5194/egusphere-2024-839-RC1
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
106 | 35 | 9 | 150 | 3 | 3 |
- HTML: 106
- PDF: 35
- XML: 9
- Total: 150
- BibTeX: 3
- EndNote: 3
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1