Noah‐MP With the Generic Crop Growth Model Gecros in the WRF Model: Effects of Dynamic Crop Growth on Land‐Atmosphere Interaction

Abstract

In this paper we coupled a crop growth model to the Weather Research and Forecasting model with its land surface model Noah‐MP and demonstrated the influence of the weather driven crop growth on land‐atmosphere (L‐A) feedback. An impact study was performed at the convection permitting scale of 3 km over Germany. While the leaf area index (LAI) in the control simulation was the same for all cropland grid cells, the inclusion of the crop growth model resulted in heterogeneous crop development with higher LAI and stronger seasonality. For the analyses of L‐A coupling, a two‐legged metric was applied based on soil moisture, latent heat flux and convective available potential energy. Weak atmospheric coupling is enhanced by the crop model, the terrestrial coupling determines the regions with the L‐A feedback. The inclusion of the crop model turns regions with no L‐A feedback on this path into regions with strong positive coupling. The number of non‐atmospherically controlled days between April and August is increased by 10–15 days in more than 50% of Germany. Our work shows that this impact results in a reduction of both cold bias and warm biases and thus improves the metrics of distributed added value of the monthly mean temperatures. The study confirms that the simulation of the weather driven annual phenological development of croplands for the regional climate simulations in mid‐latitudes is crucial due to the L‐A feedback processes and the currently observed and expected future change in phenological phases.

Effects of Elevated Ozone Exposure on Regional Meteorology and Air Quality in China Through Ozone‐Vegetation Coupling

“…The prognostic treatment of carbon cycle and LAI calculation in dynamic vegetation model is known to be problematic, with large uncertainties in the model parameterization, photosynthetic parameters and dynamic treatment of nitrogen (Bonan et al, 2011). Comparisons in several major regions of China (Figure S1 in Supporting Information S1) further show that the model deviates from satellite measurements within ±30% in most regions, except in North China Plain with substantial regional bias probably due to the absence of crop growth model in this study (Warrach-Sagi et al, 2022).…”

Evolution of the Convective Boundary Layer in a WRF Simulation Nested Down to 100 m Resolution During a Cloud‐Free Case of LAFE, 2017 and Comparison to Observations

“…We applied WRF with a previously tested set of physical schemes (e.g., Balzarini et al., 2014; Bauer et al., 2020; Branch et al., 2021; Cohen et al., 2015; Milovac et al., 2016; Schwitalla et al., 2019; Schwitalla & Wulfmeyer, 2014; Warrach‐Sagi et al., 2022). Cloud microphysics was parameterized with the aerosol‐aware Thompson scheme (Thompson & Eidhammer, 2014).…”

“…A complete list of the NOAHMP namelist options, their meaning and the values selected for this study are included in Table 1. The selection of the options was based on experience with the scheme in earlier studies (Branch et al., 2021; Milovac et al., 2016; Warrach‐Sagi et al., 2022).…”

Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability

“…Note that the carbon processes for crop growth are treated similarly to those for natural vegetation, except for the fact that the wood component of plants is removed, and the grain component of crops is added with additional carbon conversion from the leaf, stem, and root to grain, depending on the crop-growing stages. (Valayamkunnath et al, 2022); (4) dynamic irrigation schemes (sprinkler, micro-, and flooding irrigation) (Valayamkunnath et al, 2021); (5) a dynamic crop growth model for corn and soybeans (Liu et al, 2016) with enhanced C3 and C4 crop parameters (Zhang et al, 2020); (6) coupling with urban canopy models (Xu et al, 2018;Salamanca et al, 2018) with local-climate-zone modeling capabilities (Zonato et al, 2021); (7) enhanced snow cover, snow compaction, and wind canopy absorption parameters (He et al, 2021); and (8) a wet-bulb temperature-based snow-rain partitioning scheme (Wang et al, 2019).…”

“…3) and new data types (Sect. 4) in the Noah-MP v5.0, we have further refined (Niu et al, 2011) 4 Use WRF microphysics output (Barlage et al, 2015) 5 Use wet-bulb temperature (Wang et al, 2019) OptSoilWaterTranspiration Options for soil moisture factor for stomatal resistance & ET 1 * Noah (soil moisture) (Ek et al, 2003) 2 CLM (matric potential) (Oleson et al, 2004) 3 SSiB (matric potential) (Xue et al, 1991) OptGroundResistanceEvap Options for ground resistance to evaporation and/or sublimation (Brutsaert, 1982) 2 Original Noah (Chen et al, 1997) OptStomataResistance Options for canopy stomatal resistance 1 * Ball-Berry scheme (Ball et al, 1987;Bonan, 1996) 2 Jarvis scheme (Jarvis, 1976) OptSnowAlbedo Options for ground snow surface albedo 1 * BATS snow albedo (Dickinson et al, 1993) 2 CLASS snow albedo (Verseghy, 1991) OptCanopyRadiationTransfer Options for canopy radiation transfer 1 Modified two-stream (gap = f (solar angle, 3D structure, etc.) < 1-VegFrac) (Niu and Yang, 2004) 2 Two-stream applied to grid cell (gap = 0) (Niu et al, 2011) 3 * Two-stream applied to vegetated fraction (gap = 1-VegFrac) (Dickinson, 1983;Sellers, 1985) Table 1.…”