the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
WRF-ELM v1.0: a Regional Climate Model to Study Atmosphere-Land Interactions Over Heterogeneous Land Use Regions
Abstract. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) is a state-of-the-art land surface model that simulates the intricate interactions between the terrestrial land surface and other components of the Earth system. Originating from the Community Land Model (CLM) version 4.5, ELM has been under active development, with added new features and functionality, including plant hydraulics, radiation-topography interaction, subsurface multiphase flow, and more explicit land use and management practices. This study integrates ELM v2.1 with the Weather Research and Forecasting (WRF) Model through a modified Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) framework, enabling affordable high-resolution regional modeling by leveraging ELM’s innovative features alongside WRF’s diverse atmospheric parameterization options. This framework includes a top-level driver for variable communication between WRF and ELM and Earth System Modeling Framework (ESMF) caps for WRF atmospheric component and ELM workflow control, encompassing initialization, execution, and finalization. Importantly, this LILAC-ESMF framework demonstrates a more modular approach compared to previous coupling efforts between WRF and land surface models. It maintains the integrity of the ELM’s source code structure and facilitates the transfer of future developments in ELM to WRF-ELM.
To test the ability of the coupled model in capturing land-atmosphere interactions over regions with a variety of land uses and land covers, we conducted high-resolution (4 km) WRF-ELM ensemble simulations over the Great Lakes Region (GLR) in the summer of 2018 and systematically compared the results against observations, reanalysis data, and WRF-CTSM (WRF-coupled with the Community Terrestrial Systems Model). In general, the coupled WRF-ELM model has reasonably captured the spatial distribution of surface state variables and fluxes across the GLR, particularly over the natural vegetation areas. The evaluation results provide a baseline reference for further improvements of ELM in the regional application of high-resolution weather and climate predictions. Our work serves as an example to the model development community for expanding an advanced land surface model’s capability to represent fully-coupled land-atmosphere interactions at fine spatial scales. The development and release of WRF-ELM marks a significant advancement for the ELM user community, providing opportunities for fine-scale regional representation, parameter calibration in coupled mode, and examination of new schemes with atmospheric feedback.
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RC1: 'Comment on egusphere-2024-1555', Anonymous Referee #1, 22 Sep 2024
Review of the manuscript titled “Evaluation of hydroclimatic variables over Nordic Fennoscandia using WRF-CTSM”.
This paper integrates ELM v2.1 with the Weather Research and Forecasting (WRF) Model through a modified Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) framework, enabling affordable high-resolution regional modeling by leveraging ELM’s innovative features alongside WRF’s diverse atmospheric parameterization options. High-resolution (4 km) WRF-ELM ensemble simulations over the Great Lakes Region (GLR) in the summer of 2018 are evaluated with observations, reanalysis data, and the WRF-CTSM. The manuscript is very well-written and has a very nice flow to it. I have some minor comments and suggestions to strengthen the manuscript:
- Figure 6: The numbers on the top right of (c)-(f) indicate the spatial correlation coefficient between each reanalysis product and the two simulation results. However, the numbers and figure 6f are missing.
- The partitioning of surface energy between latent and sensible heat fluxes plays an important role in regulating heat and water exchange between the land surface and the atmosphere. The spatial distributions of latent and sensible heat fluxes should be evaluated in the manuscript.
Citation: https://doi.org/10.5194/egusphere-2024-1555-RC1 - AC1: 'Reply on RC1', Huilin Huang, 24 Nov 2024
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RC2: 'Comment on egusphere-2024-1555', Anonymous Referee #2, 31 Oct 2024
This study introduces the development of the U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) Land Model (ELM) with Weather Research and Forecasting (WRF) Model coupled by a modified Lightweight Infrastructure for Land-Atmosphere Coupling (LILAC) framework (WRF-ELM v1.0). It is well-written and easy to understand. The method is publishable to improve the performance of land-atmosphere interaction in the land surface model regarding land heterogeneity. However, it will need major and minor revisions before it is considered for publication. This is because the authors made ESMF coupler (LILAC) but did not provide any improved performance of the model. Please see the following comments:
General/ Major Comments:
1) Land-atmosphere interactions (Atmosphere-land interaction in the title):
How much does WRF-ELM improve to represent land-atmosphere interaction? Figures and tables in the paper show that WRF-ELM and WRF-CTSM have similar performances (surface sensible heat flux looks improved). The authors show figures of energy fluxes/temperature (land) and precipitation (atmosphere) but do not include information about land-atmosphere interaction.
2) Heterogeneous Land Use:
Table 3 and figure 11 are made by forest regions from AmeriFlux. It is hard to generalize that the improvement in the model within only forest regions and the time series and biases of AmeriFlux are not matched with both models.
3) Urban heat island (UHI):
It is suspicious that figure 9 is related to UHI, urban physics. It looks like more depending on the location by lake physics. Urban locations are closer than crop ones from the lake. For the maximum temperature from the diurnal cycle, urban regions are cooler than crop regions . It would not be the proper representation of UHI. It would be better to write the representation of lake breezes on urbanization in models, as mentioned in Wang et al. (2023), authors’ reference. FYI, ASOS sites also show that urbans are cooler than crops.
Minor Comments:
1) Figure1: There are lots of abbreviations. It would be needed to make a supplementary table to explain them.
2) Figure 4 and MPI: If geographic domain is 7 x 4 gird cells, does MPI not work due to P0 is not equal to P1?
3) Table 2: NCEP Stabe IV dataset missed.
4) I may think that figure 7 and L346 – 347 are not necessary. I am not sure about the relationship between spatial smoothing and land heterogeneity.
5) Are soil properties updated in WRF-ELM?
Other comments:
1) Title: “Atmosphere-land” but “Land-atmosphere” in the context.
2) Figure 2 and L262: Plant functional type (PFT)
Citation: https://doi.org/10.5194/egusphere-2024-1555-RC2 - AC2: 'Reply on RC2', Huilin Huang, 24 Nov 2024
Model code and software
ELM code within WRF-ELM Huilin Huang https://doi.org/10.5281/zenodo.11289807
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