Land Atmosphere Interactions

BACKGROUND

The main objectives of the land-atmosphere interaction and modeling 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, and landscapes. 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. These R&D efforts are results of collaborations with domestic and international partners, and sponsored by the NCAR Water System, and research grants from NSF, USDA, NOAA, and Institute of Urban Meteorology.

UNDERSTANDING AND MITIGATING UNCERTAINTIES IN LAND MODELS, AND IMPROVING THE COMMUNITY NOAH-MP LAND-SURFACE MODEL

Modeling land-surface processes over the complex-terrain Tibetan Plateau (TP) is a challenging problem. We contributed to the scientific design of the Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III) field campaign (Zhao et al. 2018). We used its surface and boundary layer observations to investigate the effects of surface heterogeneity on the surface energy budgets (Xin et al. 2018) and to assess the uncertainties in the Noah-MP land surface model simulations over the Central Tibetan regions (Li et al. 2018). We quantified the effects of grain shape and multiple black carbon (BC)‐snow internal mixing on snow albedo by explicitly resolving shape and mixing structures (He et al. 2018a) and applied the updated SNICAR snow model with observed BC concentrations in the Tibetan Plateau snowpack to quantify the present-day (2000–2015) BC-induced snow albedo effects from a regional and seasonal perspective (He et al. 2018b).

Fig.1: Spatial distribution of seasonal mean FSC over the QTP region from the 0.04° FY-3B (2012–17), MODIS (2002–17), and 4-km IMS (2006–17) snow-cover data. The FSC of IMS is the ratio between the snow days and the total days in a specific season. Jiang et al. (2019).
Figure 1. Spatial distribution of seasonal mean FSC over the QTP region from the 0.04° FY-3B (2012–17), MODIS (2002–17), and 4-km IMS (2006–17) snow-cover data. The FSC of IMS is the ratio between the snow days and the total days in a specific season. Jiang et al. (2019).

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 using data obtained from TIPEX-III and global FluxNet, and to improve the Noah-MP LSM (Li et al. 2018, 2019; Gan et al. 2019; Yimam et al. 2019; Zhang et al. 2018, 2019; Chen et al. 2019; Brunsell et al. 2019). The results show that three subprocesses—surface exchange coefficient, runoff and groundwater, and surface resistance to evaporation—have the most significant impacts on the performance of the simulated sensible heat flux, latent heat flux, and net absorbed radiation in the Noah‐MP LSM. The interaction between two subprocesses could contribute up to 50% of the variation in model performance for some sites, which highlights the need for considering he interactions of subprocesses to improve LSMs.

Snow cover in the Qinghai–Tibet Plateau (QTP) is a critical component in the water cycle and regional climate of East Asia. We evaluated fractional snow cover (FSC) derived from five satellite sources (the three satellites comprising the multisensor synergy of FengYun-3 (FY-3A/B/C), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Interactive Multisensor Snow and Ice Mapping System (IMS)) over the QTP to examine uncertainties in mean snow cover and interannual variability over the last decade. A four-step cloud removal procedure was developed for MODIS and FY-3 data, which effectively reduced the cloud percentage from about 40% to 2%–3% with an error of about 2% estimated by a random sampling method. The cloud-removed FY-3B data have an annual classification accuracy of about 94% for both 0.04° and 0.01° resolutions, which is higher than other datasets and is recommended for use in QTP studies. Among the five datasets analyzed, IMS has the largest snow extent (22% higher than MODIS) and the highest FSC (4.7% higher than MODIS), while the morning-overpass MODIS and FY-3A/C FSC are similar and are around 5% higher than the afternoon-overpass FY-3B FSC (see Figure 1, Jiang et al. 2019).

Publications

Zhao, P., X. Xu, F. Chen, X. Guo, X. Zheng, L. Liu, Y. Hong, Y. Li, Z. La, H. Peng, L. Zhong, Y. Ma, S. Tang, Y. Liu, H. Liu, Y. Li, Q. Zhang, Z. Hu, J. Sun, S. Zhang, L. Dong, H. Zhang, X. Yan, A. Xiao, X. Zhou, 2018: The Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III): An Integrated Land-Troposphere-Stratosphere Observation Network. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-16-0050.1

Gao, Y., F. Chen, D. Lettenmaier, L. Xiao, X. Li, 2018: Does the elevation-dependent warming still hold true above 5000m altitude? npj Atmospheric Science and Climate, DOI: 10.1038/s41612-018-0030-z.  

Xin, Y., F. Chen; P. Zhao; M. Barlage; Y-L Chen; B. Chen; Y-J Wang, 2018: Surface Energy Balance Closure at ten Sites over the Tibetan Plateau and Implication to Land Modeling. Agricultural and Forest Meteorology, 259, 317-328. 

He, C., K. N. Liou, Y. Takano, P. Yang, L. Qi, and F. Chen, 2018a: Impact of grain shape and multiple black carbon internal mixing on snow albedo: parameterization and radiative effect analysis, J. Geophys. Res.-Atmos, 123. https://doi.org/10.1002/2017JD027752

He, C., M. Flanner, F. Chen, M. Barlage, K.-N. Liou, S. Kang, J. Ming, and Y. Qian, 2018b: Black carbon-induced snow albedo reduction over the Tibetan Plateau: Uncertainties from snow grain shape and aerosol-snow mixing state based on an updated SNICAR model. Atmospheric Chemistry and Physics,18, 11507-11527, https://doi.org/10.5194/acp-18-11507-2018

Li, J., F. Chen, G. Zhang, M. Marlage, Y. Gan, Y. Xin, W. Chen, 2018: Impacts of land cover and soil texture uncertainty on land model simulations over the central Tibetan Plateau. J. Adv. Model. Earth Syst., https://doi.org/10.1029/2018MS001377. 

Nayak, H., K. K. Osuri, P. Sinha, U. Mohanty, F. Chen, M. Rajeevan, and D. Niyogi, 2018: High resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region. Scientific Data, 5, 180264. 10.1038/sdata.2018.264

Li, J., G. Zhang, F. Chen, X. Peng, and Y. Gan, 2019: Evaluation of land surface sub-processes and their impacts on model performance with global flux data. J. Adv. Model. Earth Syst., https://doi.org/10.1029/2018MS001606.

Gan, Y., X. Liang, Q. Duan, F. Chen, J. Li, 2019: Assessment and Reduction of the Physical Parameterization Uncertainty for Noah-MP Land Surface Model. Water Resources Research, https://doi.org/10.1029/2019WR024814.

Jiang, Y., F. Chen, Y. Gao, M. Barlage; J. Li, 2019: Using multi-source satellite data to assess recent snow-cover change in the Qinghai-Tibet Plateau and its uncertainty. J. Hydromet, https://doi-org.cuucar.idm.oclc.org/10.1175/JHM-D-18-0220.1

Brunsell, N., G. de Oliveira, M. Barlage, Y. Shimabukuro, E. Moraes, and L. Aragao, 2019: Examination of seasonal water and carbon dynamics in eastern Amazonia. Submitted to Theor. Appl. Clim.

Yimam, Y., C. Morgan, M. Barlage, B. Dornblaser, J. Gross, D. Gochis, H. Neely and A. Kishne, 2019: Evaluation of Noah-MP performance with improved soil information. Submitted to J. Hydrometeor.

Zhang, Z., Y. Li, M. Barlage, F. Chen, G. Miguez-Macho, A. Ireson and Z. Li, 2019: Simulating groundwater responses to climate change in North America’s Prarie Pothole Region, Clim. Dyn., in review.

Chen, L., Y. Li, F. Chen, M. Barlage, Z. Zhang, and Z. Li, 2019: Using 4-km WRF CONUS Simulations to diagnose surface coupling strength, Clim. Dyn., https://doi.org/10.1007/s00382-019-04932-9.

Zhang, Z., Y. Li, F. Chen, M. Barlage, and Z. Li, 2018: Evaluation of convection-permitting WRF CONUS simulation on the relationship between soil moisture and heatwaves. Climate Dynamics, pp.1-18, Clim. Dyn., http://doi.org/10.1007/s00382-018-4508-5.

DEVELOPING THE WRF-URBAN MODELING SYSTEM AND APPLYING IT TO ADDRESS URBAN ENVIRONMENTAL ISSUES

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. The primary goal of developing WRF-Urban model is to provide the research community a useful modeling tool to address urban environmental issues. We have augmented the existing WRF-Urban capabilities by coupling its three urban canopy models (UCMs) with the new community Noah with the Noah‐MP LSM. The WRF‐Urban modeling system's performance were evaluated for two major cities of Arizona: Phoenix and Tucson metropolitan area in a semiarid urban environment. The results show that Noah‐MP reproduces somewhat better than Noah the daily evolution of surface skin temperature and near‐surface air temperature (especially nighttime temperature) and wind speed. Regarding near‐surface wind speed, only the multilayer UCM was able to reproduce realistically the daily evolution of wind speed, although maximum winds were slightly overestimated, while both the single‐layer and bulk urban parameterizations overestimated wind speed considerably. This paper demonstrates that the new community Noah‐MP LSM coupled to an UCM is a promising physics‐based predictive modeling tool for urban applications (Salamanca et al. 2018).

We collaborated with scientists at the Institute of Urban Meteorology to design the Study of Urban Impacts on Rainfall and Fog/Haze (SURF) field campaign in Beijing to improve understanding of urban, terrain, convection, and aerosol interactions for improved forecast accuracy (Liang et al. 2018). The data collected from SURF were used to evaluate WRF-Urban using three urban canopy models (the single-layer UCM, and the multi-layer BEP and BEM models) and four planetary boundary layer (PBL) schemes (the non-local first-order YSU, SH and ACM2 schemes, as well as the local TKE-based BouLac scheme for selected cloudy and clear-sky cases days. Results show that the WRF-Urban simulated 2-m temperature and 10-m wind speed are more sensitive to UCMs than to PBL schemes. The convective boundary layer (CBL) from the single-layer UCM experiment develops at the slowest pace when compared with other two multi-layer UCMs (Huang et al. 2018).

The WRF-Urban was applied to investigate the influence of sea breeze (SB) propagation on the development of the urban boundary layer (UBL) in the Metropolitan Region of Sao Paulo (MRSP), Brazil (Ribeiro et al. 2018).  Results show that the propagation of the SB front disrupts the convective growth of the UBL and establishes a thermal internal boundary layer, thereby reducing the UBL height. A capability was developed to run the urban model decoupled from a full atmosphere model to efficiently perform climate change mitigations strategies (Gao et al. 2019). Xu et al. (2019) examined the impact of air conditioning (AC) electric loads on local weather over Beijing during a 5-day heat wave event in 2010 by using WRF-Urban with the multilayer Building Effect Parameterization and Building Energy Model (BEP+BEM). The simulated AC electric loads in suburban and rural districts are significantly improved by introducing the urban class‐dependent building cooled fraction (Figure 2). Analysis reveals that the observed AC electric loads in each district are characterized by a common double peak at 3 p.m. and at 9 p.m. local standard time, and the incorporation of more realistic AC working schedules helps reproduce the evening peak. Influences of AC systems can only reach up to ~400 m above the ground for the evening air temperature and humidity due to a shallower urban boundary layer than daytime. Spatially varying maps of AC working schedules and the ratio of sensible to latent waste heat release are critical for correctly simulating the cooling electric loads and capturing the thermal stratification of urban boundary layer.

Fig. 2: Time series of observed and modeled with the introduction of the building cooled fraction (MP-COOLED-FRC) and with a new AC-usage schedule (MP-AC-SCHEDULE) air-conditioning electric loads (unit: 10 MW) for (a) Chaoyang, and (b) Huairou.
Figure 2. Time series of observed and modeled with the introduction of the building cooled fraction (MP-COOLED-FRC) and with a new AC-usage schedule (MP-AC-SCHEDULE) air-conditioning electric loads (unit: 10 MW) for (a) Chaoyang, and (b) Huairou.

Wu et al. (2019) examined, for the first time, the extreme hourly precipitation changes in the coastal South China using rain-gauge observations during 1971–2016, because sub-daily extreme rainfall is important for engineering practices and urban infrastructure design for mitigating urban flood. This study found a statistically significant increase of hourly precipitation intensity (leading to higher annual amounts of both total and extreme precipitation over the Pearl River Delta (PRD) urban cluster in the rapid urbanization period (~1994–2016) than during the pre-urbanization era (1971 to 1993). More importantly, the 120 hourly-extreme-rainfall events in the last 15 years are clearly related to strong urban heat islands (UHI) effect for a wide range of synoptic backgrounds and seasons, as shown in Figure 3. These new findings provide further evidence that UHI-induced dynamical and thermal perturbations play an important role in the convection initiation and intensification of the locally developed extreme-rain-producing storms. 

Fig. 3: Seasonality of the 2011-16 extreme rainfall events, classified by six weather types: (a) local/SW wind type, (b) local/shear line type, (c) migratory-NW type, (d) migratory-SW type, (e) migratory-NE type, and (f) migratory-S type. Pink and blue bars, respectively, represent the strong- and weak-UHI events. The number of events within each subtype is shown in parentheses. From Wu et al. (2019).
Figure 3. Seasonality of the 2011-16 extreme rainfall events, classified by six weather types: (a) local/SW wind type, (b) local/shear line type, (c) migratory-NW type, (d) migratory-SW type, (e) migratory-NE type, and (f) migratory-S type. Pink and blue bars, respectively, represent the strong- and weak-UHI events. The number of events within each subtype is shown in parentheses. From Wu et al. (2019).

Publications

Salamanca, F., Zhang Y., M. Barlage, F. Chen, A.Mahalov, and S. Miao, 2018: Evaluation of the Noah-MP land surface model coupled to WRF in a semiarid urban environment. J. Geophys. Res., DOI: 10.1002/2018JD028377

Liang, X., S. Miao, et al., 2018: SURF: understanding and predicting urban convection and haze. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-16-0178.1

Xu, X., F. Chen, S. Shen, S. Miao, M. Barlage, W. Guo, and A. Mahalov, 2018: Using WRF-Urban to assess summertime air conditioning electric loads and their impacts on urban weather in Beijing. J. Geophys. Res., 123, https://doi.org/10.1002/2017JD028168

Ribeiro, F., A. de Oliveira, J. Soares, R. de Miranda, M. Barlage and F. Chen, 2018: Characterization of sea breeze circulation effects on the urban boundary layer of the metropolitan region of Sao Paulo, Brazil, Atmospheric Research, 214, 174-188.

Sharma, A., S. Woodruff, M. Budhathoki, H.J.S. Fernando, A. Hamlet, and F. Chen, 2018: Role of green roofs in reducing heat stress in vulnerable urban communities - A multidisciplinary approach. Environ. Res. Lett., 13(9), 094011.

Huang, M., Z. Gao, S. Miao, F. Chen, 2018:Sensitivity of Urban Boundary Layer Simulation to Urban Canopy Models and PBL Schemes over Beijing. Meteorology and Atmospheric Physics, https://doi.org/10.1007/s00703-018-0634-1.

Ching, J., G. Mills, et al., 2018: World Urban Database and Access Portal Tools (WUDAPT), an urban weather, climate and environmental modeling infrastructure for the Anthropocene. Bull. Amer. Meteor. Soc., 99(9), 1907-1924

Ching, J., et al., 2019: Pathway using WUDAPT's Digital Synthetic City tool towards generating urban canopy parameters for multi-scale urban atmospheric modeling. Urban Climate, https://doi.org/10.1016/j.uclim.2019.100459 

Wu, M., Y. Luo, F. Chen, W.K. Wong, 2019: Observed Link of Extreme Hourly Precipitation Changes to Urbanization over Coastal South China. J. Appl. Meteorol. Climatol., 58(8), 1799-1819.

Xu, X. F. Chen, S. Shen, S. Miao, M. Barlage, W. Guo and A. Mahalov, 2018: Using WRF-Urban to assess summertime air conditioning electric loads and their impacts on urban weather in Beijing, J. Geophys. Res., 123, 2475-2490, doi:10.1002/2017JD028168.

Gao, M., F. Chen, H. Shen, M. Barlage, H. Li, Z. Tan, and L. Zhang, 2019: Trade-offs of possible strategies to mitigate the urban heat island based on u-HRLDAS, J. Meteor. Soc. Jap., 97,  https://doi.org/10.2151/jmsj.2019-060. 

DEVELOPING THE WRF-CROP MODELING SYSTEM  

The overarching goal for developing the WRF-Crop model is to capture fine-scale hydrology, agriculture, weather, and climate interactions, based on the coupling of crop-growth models (Figure 4) with the Noah-MP LSM (Noah-MP-Crop, Liu et al. 2016, JGR-Atmosphere), because of the significant coverage of croplands (12.6% of the global land and 19.5% of the continental United States) and its role in influencing regional weather.

Fig. 4. 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.
Figure 4. 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. 

Agriculture irrigation modifies land-surface water and energy budgets. Chen et al. (2018) used long-term data collected from two contrasting (irrigated and rainfed) nearby maize-soybean rotation fields, to study the effects of irrigation memory on local hydroclimate. For a 12-year average, irrigation decreases summer surface-air temperature by less than 1 °C and increases surface humidity by 0.52 g kg−1. The irrigation cooling effect is more pronounced and longer lasting for maize than for soybean. Irrigation reduces maximum, minimum, and averaged temperature over maize by more than 0.5 °C for the first six days after irrigation, but its temperature effect over soybean is mixed and negligible two or three days after irrigation (Figure 5). Irrigation increases near-surface humidity over maize by about 1 g kg−1 up to ten days and increases surface humidity over soybean (~ 0.8 g kg−1) with a similar memory. These differing effects of irrigation memory on temperature and humidity are associated with respective changes in the surface sensible and latent heat fluxes for maize and soybean. These findings highlight great need and challenges for earth-system models to realistically simulate how irrigation effects vary with crop species and with crop growth stages, and to capture complex interactions between agricultural management and water-system components (crop transpiration, precipitation, river, reservoirs, lakes, groundwater, etc.) at various spatial and temporal scales.

Fig 5.  The x-axis represents days from an irrigation application with amount > 7.5 mm day−1. The y-axis represents the differences in daily Tmin (°C, top), Tmax (°C, middle) and Tave (°C, bottom) between USNe2 and USNe3. Samples were taken from all irrigation events from 2001−2012 and the red stars represent their averaged values for a given day after irrigation. From Chen et al. (2018).
Figure 5.  The x-axis represents days from an irrigation application with amount > 7.5 mm day−1. The y-axis represents the differences in daily Tmin (°C, top), Tmax (°C, middle) and Tave (°C, bottom) between USNe2 and USNe3. Samples were taken from all irrigation events from 2001−2012 and the red stars represent their averaged values for a given day after irrigation. From Chen et al. (2018).

Xu et al. (2019) incorporated a dynamic irrigation scheme into Noah-MP and investigated three methods of determining crop growing season length by agriculture management data. The irrigation scheme was assessed at field scales. Results show that crop‐specific growing‐season length helped capture the first application timing and total irrigation amount, especially for soybeans. When transitioning from field to regional scales, the county‐level calibrated IRR_CRI helped mitigate overestimated (underestimated) total irrigation amount in southeastern Nebraska (lower Mississippi River Basin). In these two heavily irrigated regions, irrigation produced a cooling effect of 0.8–1.4 K, a moistening effect of 1.2–2.4 g/kg, a reduction in sensible heat flux by 60–105 W/m2, and an increase in latent heat flux by 75–120 W/m2. Most of irrigation water was used to increase soil moisture and evaporation, rather than runoff. Lacking regional‐scale irrigation timing and crop‐specific parameters makes transferring the evaluation and parameter‐constraint methods from field to regional scales difficult.

Publications

Chen, F., X. Xu, M. Barlage, R. Rasmussen, S. Shen, S. Miao, G. Zhou, 2018:  Memory of irrigation effects on hydroclimate and its modeling challenge. Environ. Res. Lett., https://doi.org/10.1088/1748-9326/aab9df

Xu, X., F. Chen, M. Barlage, D. Gochis, S. Miao, and S. Shen, 2019: Lessons learned from modeling irrigation from field to regional scales. J. Adv. Model. Earth Syst., DOI:10.1029/2018MS001595 

2019 PLANS

Improve the agriculture modeling in the CONUS convection-permitting regional climate model for the GEWEX Water for Foodbaskets.

Continue to improve the Noah-MP land model and agriculture management modeling in the National Water Model.