Land Atmosphere Interactions

Land-Atmosphere Interactions

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.

1.  Understanding the uncertainties in land model simulations over the complex terrain of the Tibetan Plateau and their impacts on downstream weather forecasts

Despite the widespread use of the new community Noah with multi-parameterization (Noah-MP) land-surface model (LSM), it has not been rigorously evaluated over the complex Tibetan Plateau (TP). To help fill this gap, we conducted a number of studies using the physics ensemble simulations with Noah-MP and developed a method of assessing and mitigating the uncertainty range of Noah-MP simulations (Zhang et al., 2016). We extended this analysis to more observation sites for the Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III) (Zhao et al. 2017), including a study of impact of uncertainties in the specification of land-cover types and soil textures on land model simulations.

Fig. 1. Time series of mean precipitation and 2-m air temperature for the 10 PEPEs selected in this study. (a) Temporal and spatial average precipitation over the 126 southern China stations in the red rectangle in Fig. 1. The x axis represents days from onset. Onset is the starting day of PEPE. (b) Temporal and spatial average of 2-m air temperature of the 87 stations over TP. Gray lines represent the second maximum and second minimum of the 10 chosen cases. The second maximum and second minimum represent the 10th and 90th percentile cases, respectively. Red lines represent the 10 chosen cases and blue lines indicate the 1981–2010 daily long-term means.
Fig. 1. Time series of mean precipitation and 2-m air temperature for the 10 PEPEs selected in this study. (a) Temporal and spatial average precipitation over the 126 southern China stations in the red rectangle in Fig. 1. The x axis represents days from onset. Onset is the starting day of PEPE. (b) Temporal and spatial average of 2-m air temperature of the 87 stations over TP. Gray lines represent the second maximum and second minimum of the 10 chosen cases. The second maximum and second minimum represent the 10th and 90th percentile cases, respectively. Red lines represent the 10 chosen cases and blue lines indicate the 1981–2010 daily long-term means.
Fig. 2. (a) Differences of geopotential height, wind speed, and direction at 700 mb between the DRY and CNTL cases during the precipitation period. Shading color for geopotential height difference and vectors for wind speed difference; (b) Water vapor transport difference (kg m-1 s-1) between the DRY and CNTL cases.
Fig. 2. (a) Differences of geopotential height, wind speed, and direction at 700 mb between the DRY and CNTL cases during the precipitation period. Shading color for geopotential height difference and vectors for wind speed difference; (b) Water vapor transport difference (kg m-1 s-1) between the DRY and CNTL cases.

We also combined observations, climatic analysis, and WRF modeling to investigate the TP surface heating conditions’ influence on extreme persistent precipitation events (PEPEs) in southeastern China. Observations indicated an increase of TP surface air temperature 3–4 days prior to extreme persistent precipitation events in southeastern China (Fig.1). NCEP reanalysis data revealed a significant low-pressure anomaly in southern China and a high-pressure anomaly in northern China during extreme persistent precipitation event periods. Using correlation analysis and random resampling nonparametric statistics, a typical PEPE event from 17 to 25 June 2010 was selected for numerical simulation. Three contrasting WRF experiments were conducted with different surface heating strengths by changing initial soil moisture over the TP. Different soil conditions generate different intensities of surface sensible heat fluxes and boundary layer structures over the TP resulting in two main effects on downstream convective rainfall: modulating large-scale atmospheric circulations and modifying the water-vapor transport at southern China. Increased surface heating in the TP strengthens a high- pressure system over the Yangtze Plain (Fig. 2a), thereby blocking the northward movement of precipitation. It also enhances the water vapor transport from the South China Sea to southern China (Fig .2b). The combined effects substantially increase precipitation over most of the southeastern China region.

References

Zhang, G., F. Chen, and Y. Gan, 2016: Assessing uncertainties in the Noah-MP ensemble simulations of a cropland site during the Tibet Joint International Cooperation program (JICA) field campaign. J. Geophys. Res., 121, doi:10.1002/2016JD024928.

Zhao, P., X. Xu, F. Chen, X. Guo, et al., 2017: The Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III): An Integrated Land-Troposphere-Stratosphere Observation Network. Bull. Amer. Meteor. Soc., in press.

Wan, B.; Z. Gao; F. Chen; C. Lu, 2017: Impact of Tibetan-Plateau Surface Heating over on Persistent Extreme Precipitation Events in Southeastern China. Mon. Wea. Rev., doi.org/10.1175/MWR-D-17-0061.1

2. Modeling agriculture management in earth system models

Fig. 3. 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. 3. 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

3.  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. 4. 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. 4. 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.

4. Study of boundary-layer depth over Beijing using Doppler lidar data during SURF-2015

Fig. 5. a) the Doppler lidar collocated with the IAP 325-m tower, b) terrain map of Beijing showing the locations of the IAP 325-m tower (yellow×), the Nanjiao sounding site (blue+). The three black rings represent the second, fourth, and sixth Ring Roads in Beijing, respectively; and c) IAP tower environment and information on surrounding buildings. Very high buildings in Guanchengyuan Court located south of the IAP tower are indicated by a solid-line yellow rectangle. Relatively low buildings in Mudanyuan Court, north of the IAP tower are indicated by a dashed-line rectangle.
Fig. 5. a) the Doppler lidar collocated with the IAP 325-m tower, b) terrain map of Beijing showing the locations of the IAP 325-m tower (yellow×), the Nanjiao sounding site (blue+). The three black rings represent the second, fourth, and sixth Ring Roads in Beijing, respectively; and c) IAP tower environment and information on surrounding buildings. Very high buildings in Guanchengyuan Court located south of the IAP tower are indicated by a solid-line yellow rectangle. Relatively low buildings in Mudanyuan Court, north of the IAP tower are indicated by a dashed-line rectangle.
Fig. 6. For 6 July, 30-min averaged vertical velocity variance, σ2w (m2 s−2), calculated from the DBS data. PBL depths based on the threshold method are indicated by black dots. Also displayed is the potential temperature (θ) profile, based on the 1315 LST Nanjiao sounding. The vertical lines labeled 297 K and 307 K define the scale for the sounding profile. Sunrise and sunset times are marked by triangles.  Plus (+) is NBL estimate using the fractional method.
Fig. 6. For 6 July, 30-min averaged vertical velocity variance, σ2w (m2 s−2), calculated from the DBS data. PBL depths based on the threshold method are indicated by black dots. Also displayed is the potential temperature (θ) profile, based on the 1315 LST Nanjiao sounding. The vertical lines labeled 297 K and 307 K define the scale for the sounding profile. Sunrise and sunset times are marked by triangles.  Plus (+) is NBL estimate using the fractional method.

Planetary boundary-layer (PBL) structure was investigated using observations from a Doppler lidar and the 325-m Institute of Atmospheric Physics (IAP) meteorological tower in the center of Beijing during the summer 2015 Study of Urban-impacts on Rainfall and Fog/haze (SURF-2015) field campaign (Fig. 5). Using six fair-weather days of lidar and tower data under clear to cloudy skies, we evaluated the ability of the Doppler lidar to probe the urban boundary-layer structure, and then proposed a composite method for estimating the diurnal cycle of the PBL depth using the Doppler lidar. For the convective boundary layer (CBL), a threshold method using vertical velocity variance (σ2w > 0.1m2 s−2) was used, since it provides more reliable CBL depths than a conventional maximum wind-shear method. The nocturnal boundary-layer (NBL) depth is defined as the height at which σ2w decreases to 10% of its near-surface maximum minus a background variance. The PBL depths determined by combining these methods have average values ranging from 270 to 1500 m for the six days, with the greatest maximum depths associated with clear skies (Fig. 6). Release of stored and anthropogenic heat contributes to the maintenance of turbulence until late evening, keeping the NBL near-neutral and deeper at night than would be expected over a natural surface. The NBL typically becomes more shallow with time, but grows in the presence of low-level nocturnal jets (Huang et al. 2017).

References:

Huang, M., Z. Gao, S. Miao, F. Chen, M.A. LeMone, J. Li, F. Hu, 2017: Estimate of boundary-layer depths over Beijing, China, using Doppler Lidar data during SURF-2015. Boundary Layer Meteorol., doi:10.1007/s10546-016-0205-2.

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

Fig. 7.  Climatology of water-table depth (m) for the U.S. domain (left) and the percentage of sand soil composition.
Fig. 7.  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. 7) 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. 7 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.