Land Atmosphere Interactions

BACKGROUND

The main objectives of the land-atmosphere interaction group are to understand, through theoretical and observational studies, the complex interactions (including biophysical, hydrological, and biogeochemical) between the land surface and the atmosphere across a wide range of temporal and spatial scales. The ultimate goal is to improve the community Terrestrial System Model (CTSM), Noah, and Noah-MP land-surface models and to integrate such knowledge into numerical mesoscale weather prediction and regional climate models to improve prediction of the impacts of land-surface processes on regional weather, climate, and water systems.

Modeling agriculture management in earth system models

Fig. 1. Flowchart of the Noah-MP-Crop model. (“LAI” and “leaf mass” marked red for emphasizing the calculation process and the role of LAI in the model simulations.
Fig. 1. Flowchart of the Noah-MP-Crop model. (“LAI” and “leaf mass” marked red for emphasizing the calculation process and the role of LAI in the model simulations.

This project, sponsored by NSF/USDA EaSM and NSF INFEWS programs, aims to improve the representation of agriculture management and cropland-atmosphere interactions in earth system models, and to improve seasonal weather forecast and regional climate simulations for the NCAR Water System Program. Croplands cover 12.6% of the global land and 19.5% of the continental United States. Through seasonal change in phenology and transpiration, crops can efficiently transfer water vapor from the crop root zones to the atmosphere. Crops have a detectable influence on regional distributions of atmospheric water vapor and temperature, and can affect convective triggering by modifying mesoscale boundaries. We have introduced dynamic corn (Zea mays) and soybean (Glycine max) growth simulations and field management (e.g., planting date) into Noah-MP (Fig. 3) and evaluated the enhanced model (Noah-MP-Crop) at field scales using crop biomass datasets, surface heat fluxes, and soil moisture observations. This model is able to capture the seasonal and annual variability of LAI, and to differentiate corn and soybean in peak values of Leaf Area Index (LAI) as well as the length of growing seasons. Improved simulations of crop phenology in Noah-MP-Crop led to better surface heat flux simulations, especially in the early period of growing season where current Noah-MP significantly overestimated LAI (Liu et al. 2016). The new crop modeling capabilities, together with regional agriculture management variables such as planting and harvest dates, were implemented in the community WRF V3.9 in 2017. Further development of agriculture management variables, such as irrigation and evaluation, have been undertaken in the context of coupled WRF regional climate simulations.

REFERENCES

Liu, X., F. Chen, M. Barlage, G. Zhou, D. Niyogi, 2016: Noah-MP-Crop: Introducing Dynamic Crop Growth in the Noah-MP Land-Surface Model. J. Geophys. Res., DOI: 10.1002/2016JD025597

Enhancing the WRF-urban modeling system and its applications to urbanization studies

The global population has become increasingly urbanized; to date 52% of the world’s population live in cities, and this proportion is projected to increase to 67% by 2050. Urbanization modifies surface energy and water budgets, and has significant impacts on local and regional hydroclimates. In recent decades, a number of urban canopy models (UCM) have been developed and implemented into the WRF model to capture urban land-surface processes, but those UCMs were coupled to the simple Noah LSM. We recently coupled the more advanced Noah-MP LSM to WRF-Urban, as well as to the urbanized high-resolution land data assimilation system (u-HRLDAS) and evaluated the model output against the observations obtained in Phoenix and Beijing. This new modeling capability was released in WRF 3.9 in 2017.

Fig. 2. Model comparison of 2-m temperatures overlaid by 10-m wind vectors for nighttime SLUCMAH urban parameterization (a) with HRLDAS; (b) with no HRLDAS; (c) difference plot (a)−(b). Middle panel is same as top panel except that the plots are for daytime. Bottom panel shows the influence of SLUCM without AH (SLUCMnoAH) for nighttime and daytime in (g) and (h), respectively, and (i) nighttime difference between (a) and (g). The white color in difference plots for panels (c), (f), and (i) refers to a difference <0.2 ∘C. The reference wind vectors in each panel figure has units of m s−1.
Fig. 2. Model comparison of 2-m temperatures overlaid by 10-m wind vectors for nighttime SLUCMAH urban parameterization (a) with HRLDAS; (b) with no HRLDAS; (c) difference plot (a)−(b). Middle panel is same as top panel except that the plots are for daytime. Bottom panel shows the influence of SLUCM without AH (SLUCMnoAH) for nighttime and daytime in (g) and (h), respectively, and (i) nighttime difference between (a) and (g). The white color in difference plots for panels (c), (f), and (i) refers to a difference <0.2 ∘C. The reference wind vectors in each panel figure has units of m s−1.

This study explores the sensitivity of high-resolution mesoscale simulations of urban heat island (UHI) in the Chicago metropolitan area (CMA) and its environs to urban physical parameterizations, with emphasis on the role of lake breezes. A series of climate downscaling experiments was conducted using the urban-Weather Research and Forecasting (uWRF) model at 1-km horizontal resolution for a relatively warm period with a strong lake breeze. The study employed best available morphological data sets, selection of appropriate urban parameters, and estimates of anthropogenic heating sources for the CMA. Several urban parameterization schemes were then evaluated using these parameter values. The study also examined (1) the impacts of land data assimilation for initialization of the mesoscale model, (2) the role of urbanization on UHI and lake breezes, and (3) the effects of sub-grid scale land-cover variability on urban meteorological predictions. Comparisons of temperature and wind simulations with station observations and MODIS data in the CMA showed that uWRF, with appropriate selection of urban parameter values, was able to reproduce the measured near-surface temperature and wind speeds reasonably well (Fig. 4, Sharma et al. 2017). In particular, the model was able to capture the observed spatial variation of 2-m near-surface temperatures at night, when the UHI effect was pronounced. Results showed that inclusion of sub-grid scale variability of land-use and initializing models with more accurate land surface data from the high-resolution land data assimilation (HRLDAS) can yield improved simulations of near-surface temperatures and wind speeds, particularly in the context of simulating the extent and spatial heterogeneity of UHI effects.

REFERENCES

Li, Y., S. Miao, F. Chen, Y. Liu, 2016: Introducing and evaluating a new building-height categorization based on the fractal dimension into the Coupled WRF/urban Model. Int. J. Climatol., doi:10.1002/joc.4903

Sharma, A., H.J.S. Fernando, J.J. Hellmann, M. Barlage, and F. Chen, 2017: Regional climate modeling of urban meteorology: A sensitivity study. International Journal of Climatology. doi : 10.1002/joc.4819.

Enhancing the representation of land-atmospheric interactions in WRF convection-permitting simulations

Fig. 3.  Climatology of water-table depth (m) for the U.S. domain (left) and the percentage of sand soil composition.
Fig. 3.  Climatology of water-table depth (m) for the U.S. domain (left) and the percentage of sand soil composition.

Soil properties in WRF land surface models rely on look-up tables based on soil texture class. Since the number of texture classes is limited, this results in artificial boundaries in surface model states and diagnostics, including two-meter temperature. To improve model performance, new global continuous datasets of soil composition (shown in Fig. 3) have been included in the Noah-MP system. All WRF land models have a fixed shallow soil column, which prohibits the feedback of deep groundwater to the atmosphere. Global datasets (shown in Fig. 3 over the U.S.) that allow the general use of an existing groundwater capability provide continental scale water-table dynamics to the Noah-MP model in WRF v3.9. The use of these datasets will also improve model calibration for hydrologic studies. More importantly, incorporating the interactions between groundwater and shallow aquifers in WRF/Noah-MP helps reduce the summer warm temperature bias over the central U.S., which is a common problem in numerical weather prediction and regional climate simulations.

2019 Plans

Conduct expandED research for GEWEX Water for Foodbaskets