The Impact of Land Surface Processes on Dust Storm. Simulations in Northern China

Transcription

1 The Impact of Land Surface Processes on Dust Storm Simulations in Northern China Zhaohui Lin 1, Hang Lei 1,2, Jason K. Levy 3, Jianhua Sun 1 and Michelle L. Bell Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China 2 Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA 3 Department of Environmental Studies, Huxley College of the Environment, 10 Western Washington University, Bellingham, Washington, USA 4 School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut, USA

2 ABSTRACT 15 Dust storms in northern China result in elevated particulate matter levels, detrimentally affecting human health, as well as socio-economic and ecological systems. To investigate the impact of land surface processes on dust storm simulations in northern China, a dust storm prediction system was developed at the Institute for Atmospheric Physics (IAP). One version of the IAP System (IAPS 1.0) is based on 20 the Oregon State University (OSU) land surface model, while a second version (IAPS 2.0) integrates the OSU and NOAH land surface models. For spring 2002 dust storm events in northern China, results show that IAPS 2.0 significantly improved soil moisture simulations, leading to better threshold frictional velocity estimates, a key parameter for estimating surface dust emissions. This study also discusses specific 25 mechanisms by which land surface processes impact modeling results and makes recommendations to improve numerical dust storm simulations

3 1. Introduction Spring dust storms occur frequently in northern China, producing high concentrations of airborne dust particles. These particles lead to adverse impacts including increased risk of mortality and other health consequences, as well as 40 socio-economic losses [Chen et al., 2004; Cheng and Ma, 1996]. In recent years, dust storm monitoring [Kim et al., 2004; Wu et al., 2004; Wehner et al., 2004; Ding et al., 2005] and background analysis of dust activities [Qian et al., 2002; Lin et al., 2004; Fan and Wang, 2004] have received more attention, as has the use of numerical dust storm modeling for the simulation and prediction of dust emissions and transportation 45 processes [Shao, 2004; Shen et al., 2005; Song 2004; Zhang et al., 2003a and 2003b; Gong et al., 2003]. Dust emission modeling involves simulating synoptic processes, land surface conditions (e.g., soil moisture, soil temperature), dust emissions, and transportation processes as well as parameterizing the wind erosion friction velocity (u * ) and the wind erosion threshold friction velocity (u * t). In general, u * is dependent 50 on the structure of the atmospheric boundary layer while u * t depends on land surface properties, including the soil moisture over land surfaces, which can be predicted by a land surface model (LSM). To better understand the impact of land surface processes on dust storm simulations, north China dust storm events occurring in the spring of 2002 are 55 simulated with two versions of the dust storm numerical modeling and prediction system (hereafter referred to as IAPS 1.0 and IAPS 2.0). Specifically, IAPS 1.0 [Sun et al., 2004], developed at the Institute for Atmospheric Physics (IAP), incorporates

4 the Oregon State University (OSU) LSM while IAPS 2.0 (also developed at IAP) uses the NOAH LSM. Results derived from these two land surface models are compared, 60 and the specific mechanisms by which land surface processes impact the simulated dust storms are investigated. Finally, recommendations are provided in order to enhance the aforementioned dust storm modeling systems. 2. IAP Dust Storm Simulation and Prediction System (IAPS) 65 IAPS 1.0 consists of the Pennsylvania State University (PSU) / National Center for Atmospheric Research (NCAR) Meso-scale Meteorological Model (MM5) [Dudhia et al. 2005], the OSU/Eta LSM, a wind erosion model, a dust transportation-deposition scheme, a pre-processor system, and a geographic information system (GIS) database (Figure 1a). The pre-processor components 70 involve creating a GIS database with vegetation, soil, and landuse information. Data generated through the pre-processor components are input into the tightly coupled LSM, MM5, wind erosion and dust transportation models. At each time step u *, the friction velocity from the planetary boundary layer (PBL) scheme, and the surface-layer soil moisture (from the land surface scheme) are used by the wind 75 erosion model to calculate the dust emission rate for six particle-size groups. The dust transport and deposition scheme considers advection, diffusion, and dry deposition based on the domain and grid specifications of the MM5 model. The wind erosion scheme comprises three key parameterizations including u * t [Shao and Lu, 2000], the streamwise sand flux, Q [Owen, 1964] and the dust emission

5 80 rate, F [Shao, 2001] (Figure 1b). Six dust particle diameter (d) sizes are considered: d 2 µm, 2<d 11 µm,11<d 22 µm, 22<d 52 µm, 52<d 90 µm, and 90<d 125 µm. For each of the n groups, dust emissions are estimated when u * exceeds u * t (for a specific dust particle size in a given region). The u * is determined by the MM5 boundary layer scheme while u * t depends on land surface properties as calculated by 85 u * t = RHM α1 σ p gd + α 2 ρd (1) where R (surface roughness), H (soil moisture), and M (soil aggregation) are estimated from soil and vegetation data, g is gravitational acceleration, d is particle diameter, p is the ratio of particle density to air density, is the density of air, and 1 and 2 are coefficients [Shao and Lu 2000; Sun et al., 2006]. The sand flux (Q) 90 and dust emissions rate (F) are also calculated separately for each particle size group. The main input data for the wind erosion scheme are soil texture, vegetation type, vegetation cover and dust emissions, calculated for erodible lands. Calculating u * t requires soil moisture data obtained from the LSM and frontal area surface roughness which is assumed to be constant for a given location over a period of about two weeks. 95 Moreover, frontal area surface roughness is primarily a function of vegetation and varies slowly with time. IAPS 1.0 uses the OSU LSM, which has a single canopy layer, and involves the following prognostic variables: soil moisture and temperature; water stored in or on the canopy; snowpack depth; and water equivalent. IAPS 1.0 has been recently applied to the simulation of northern China dust storm events [Sun et al., ].

6 IAPS 2.0 couples the mesoscale atmospheric model MM5 (version 3.6) and the Unified NOAH LSM with the wind erosion model, the dust transportation model, the GIS database for land surface characteristics, and a pre-processor for the wind erosion model. As well, the NOAH LSM integrates the diurnally dependent Penman potential 105 evaporation equation, the multilayer soil model, and the primitive canopy layer. This modeling system predicts soil moisture and temperature in four layers (at 10, 30, 60 and 100 cm depth), as well as canopy moisture and water-equivalent snow depth. It also outputs surface and underground run-off accumulations. Compared with the original OSU LSM, many improvements have been achieved for the enhanced 110 representation of physical processes in order to better predict variables such as snow depth, snow cover, and frozen soil effects. This research investigates the degree to which IAPS 2.0 improves model simulations, compared to IAPS 1.0 results In order to evaluate the impact of land surface processes on dust storm modeling, two typical northern China dust storms, occurring on March 2002 and April 2002, were simulated with the IAPS 1.0 and IAPS 2.0 modeling systems, and the results were compared. The simulated domain area contains Mongolia, China, the Korean Peninsula and Japan, centered at 40 N and 115 E, with 150 grid cells in longitude and 120 grid cells in latitude, a horizontal resolution of 45 km, and 16 vertical levels. Model initialization and boundary conditions were based on National 120 Centers for Environmental Prediction (NCEP) reanalysis atmospheric data with a horizontal resolution of 2.5 and a one-day spin-up period. The NCEP data were interpolated horizontally onto the model grid points and then interpolated from

7 pressure levels onto model s σ-levels. The initial values of dust concentration of each particle size group were set to zero. Numerical simulation results were compared and 125 verified with station observations. The impact of land surface processes on the dust storm simulations was investigated. 3. Impact of land surface processes on dust storm simulations Active dust storm activity occurred throughout northern China in the spring of , with strong dust storm events occurring every 2 to 5 days. The two analyzed dust storm episodes (24-25 March 2002 and April 2002) involve markedly different weather conditions, dust sources and dust distribution areas, although these episodes are typical for northern China Dust Episode of March 2002 For the March 2002 dust storm event, 850 hpa NCEP reanalysis geopotential height variations at 8:00 Beijing Standard Time (BST) were used, controlled by a southeastward moving cyclone. Prior to this event, a cyclone from the Lake Baikal area began moving in a southeastward direction, accompanied by strong 140 northwesterly wind behind a cold front. By 8:00 BST on March 25, the center of the cyclone was located in the vicinity of 122 E, 52 N. Behind this cold front were very strong NW and WNW winds, reaching speeds as high as 16 m/s. This dust episode was mainly limited to northeastern China including the eastern regions of Liaoning, Jilin, Hebei and Heilongjiang. Figure 2 shows dust deposition for this storm for

8 145 observational data (Figure 2a) and under the IAPS 1.0 and 2.0 modeling systems (Figures 2b and 2c). Elevated dust levels were also observed in Xinjiang (Figure 2a), and severe dust storms occurred in eastern Inner Mongolia. IAPS 1.0 dust deposition results capture the affected regions noted in observational data reasonably well, with high deposition estimated for regions 150 observed to have severe dust storms (Figures 2a and 2b). However, large areas not affected by the dust storm are predicted to have high dust deposition, primarily in the central northern regions (Figure 2b). Dust deposition estimates are improved with IAPS 2.0, particularly in eastern China (Figure 2c). IAPS 2.0 simulations are, in general, more accurate than the IAPS 1.0 results for northern China (Figure 2d). 155 Specifically, the wetter regions in Xinjiang and Gansu (Figure 2d) plausibly explain discrepancies between the IAPS 1.0 and IAPS 2.0 results. The distribution of u * - u * t, which directly represents erodibility, is shown for the IAPS 1.0 and IAPS 2.0 simulation results (Figures 3a and b) Dust Episode of April 2002 A complicated dust storm event occurred on April 2002 in northern China, which was affected by a southeastward moving cyclone, a cold front and an anticyclone system. A cyclone, centered at approximately 122 E, 50 N, moved eastward from northeastern China, and was accompanied by strong northwesterly 165 wind behind the cold front. By 8:00 BST on April 22, the center of the cyclone was located in the vicinity of 130 E, 52 N and a high pressure system was present in

9 western Mongolia, with the ridge of high pressure reaching Liaoning. This ridge of high pressure was preceded by very strong SE wind, reaching m/s in some areas. Next, an anticyclone system centered at approximately 102 E, 45 N formed 170 across the Mongolia-China border. By 8:00 BST on April 23 this eastward moving anticyclone system had affected most of northern China. By 8:00 BST on April 24, the anticyclone system weakened significantly as it moved into central China. Accordingly, dust storms were observed throughout most of northwestern China and in some parts of northeastern China, covering Xinjiang, Qinghai, Gansu, Inner 175 Mongolia, Liaoning, Beijing, Hebei and Shanxi, as shown in the observational data (Figure 4a). Specifically, synoptic records show that from April 2002, the following regions were affected by severe and extensive dust storms: Xingjiang, Qinghai, Gansu, Inner Mongolia, Ningxia, Shaanxi, Shanxi, Hebei, Beijing, Tianjin, Liaoning, and Shandong (Figure 4a). Severe dust storms also occurred in Xinjiang, 180 Inner Mongolia and Ningxia. The spatial distribution of this dust covers a large area and is asymmetric. The severe dust deposition is mainly concentrated in northwestern China, while lighter dust deposition occurs in northeastern China. The IAPS April 2002 simulation (Figure 4a) shows the same basic patterns as the observational data (Figure 4a). However, the model over-predicts dust 185 storm activity in some regions, particularly in the northeast. This may be due to heavy pollution that affects large areas of northeastern China, Visual inspection suggests that IAPS 2.0 results better match observational data than IAPS 1.0 results (Figure 4a and 4c). Dust deposition simulated by IAPS 2.0 exceeds observed values for some regions,

10 such as western Inner Mongolia (around 102 E, 40 N). In the East Liaoning 190 Peninsula (located at approximately 122 E, 40 N), a significant dust concentration was simulated, while no dust activities were reported by observational data. 4. Conclusions and Discussion Two dust storm numerical modeling and prediction systems (IAPS 1.0 and IAPS ) were applied to the prediction of dust storm events over northern China for two typical dust storm episodes representing different weather conditions, dust sources and affected areas. Both modeling systems provided reasonable estimates as gauged by comparison to observational data (Figures 2 and 4), but in both cases the IAPS 2.0 system (which uses the NOAH LSM) was better able to predict dust storm sources 200 and capture dust storm patterns than IAPS 1.0 (which uses the OSM LSM). Specifically, IAPS 2.0 improves the modeling of physical processes (e.g., the physics of frozen soil, fractional snow cover, time-varying snow density, and the roughness length calculations over snow covered areas). In particular, IAPS 2.0 more accurately simulates soil moisture, which likely improves simulated values for u * t, a key 205 parameter for the emission of surface dust. While IAPS 2.0 results were often superior to IAPS 1.0, the IAPS 2.0 simulations differed from observational data in certain regions of China including western Inner Mongolia (around 102 E, 40 N) and the eastern Liaoning Peninsula (around 122 E, 40 N). Several possible reasons exist for these discrepancies. First, the geographic 210 information data sets are limited in several ways. For example, the historical data does

11 not capture land use and land cover changes due to anthropogenic or natural factors over the past decade. The data also contains uncertainties in critical areas such as vegetation type, soil particle size distribution, and the soil type. Second, the NCEP reanalysis data, which was used for the initialization and boundary conditions of the 215 atmospheric model, have a coarser resolution (2.5 o ) than that of the atmospheric model itself (45 km) and therefore may miss sub-scale heterogeneity. Third, initial dust concentration estimates are currently unavailable, and wet deposition is not thoroughly considered in the present IAP 2.0 system. Finally, the wind erosion scheme and deposition scheme may be overly-simplistic and fail to capture important 220 factors (e.g. frozen soil effects) involved in the dust emission process. In summary, this work indicates that the application of the IAPS 2.0 model provides significant improvements over the IAPS 1.0 for the simulation of dust storm events in northern China. Future work should address the aforementioned modeling challenges in order to improve IAPS 2.0 dust estimates. 225

12 References Chen, Y.-S., P.-C. Sheen, E.-R. Chen, Y.-K. Liu, T.-N. Wu, and C.-Y. Yang (2004), Effects of Asian dust storm events on daily mortality in Taipei, Taiwan, Environ. Res., 95(2), Cheng, L. S., and Y. Ma (1996), The developing structure of a black storm and its numerical experiment of different model resolution, Q. J. Appl. Meteorol. 7(4), (in Chinese, with English Abstr.) Ding, R., J. Li, S. Wang, and F. Ren (2005), Decadal change of the spring dust storm in northwest China and the associated atmospheric circulation, Geophys. Res. Lett (2), 1-4. Dudhia, J., D. Gill, K. Manning, W. Wang, and C. Bruyere (2005), PSU/NCAR Mesoscale Modeling System Tutorial Class Notes and Users Guide (MM5 Modeling System Version 3). National Center for Atmospheric Research (NCAR), Boulder, CO. 240 Fan, K. and H. J. Wang (2004), Antarctic oscillation and the dust weather frequency in North China, Geophys. Res. Lett. 31(10), doi: /2004gl Gong, S. L., X. Y. Zhang, T. L. Zhao, I. G. McKendry, D. A. Jaffe, and N. M. Lu (2003), Characterization of soil dust aerosol in China and its transport and distribution during 2001 ACE-Asia: Model simulation and validation. J. Geophys. 245 Res. D: Atmospheres 108(9), ACH4-1, ACH4-19. Kim, Y. S., Y. Iwasaka, G.-Y. Shi, T. Nagatani, T. Shibata, D. Trochkin, A. Matsuki, M. Yamada, B. Chen, D. Zhang, M. Nagatani, and H. Nakata, H. (2004), Dust particles

13 in the free atmosphere over desert areas on the Asian continent: Measurements from summer 2001 to summer 2002 with balloon-borne optical particle counter and 250 lidar, Dunhuang, China, J. Geophys. Res. D: Atmospheres 109(19), D19S26, doi: /2002jd0032. Lin Z., H. Chen, S. Zhang, and X. Xu (2004), Climatic and environmental background for the anomalous spring sandstorms over the northern China during 2003, Clim. Environ. Res. 9, Niu, R., Sand-dust Weather Almanac 2002, Qixiang Press, Owen, R. P. (1964), Saltation of uniform grains in air, J. Fluid Mech. 20, Qian, W. H., L. S. Quan, and S. Y. Shi (2002), Variations of the dust storm in China and its climatic control, J. Clim. 15(10), Shao, Y. (2004), Simplification of a dust emission scheme and comparison with data, 260 J. Geophys. Res. D: Atmospheres, 109(10), doi: /2003jd Shao, Y. (2001), A model for mineral dust emission, J Geophys. Res. D: Atmospheres 106(D17), Shao, Y., and H. Lu (2000), A simple expression for wind erosion threshold friction velocity, J. Geophys. Res. D: Atmospheres, 105(D17), Shen, Y., Z. Shen, M. Du, and W. Wang (2005), Dust emission over different land surface in the arid region of Northwest China, J. Meteorol. Soc. Jpn. 83(6), Song, Z. (2004), A numerical simulation of dust storms in China, Environm. Model. Softw. 19(2),

14 270 Sun J., L. Zhao, S. Zhao, and R. Zhang (2006), An integrated dust storm prediction system suitable for east Asia and its simulation results, Glob. Planet. Change 52(1-4), Sun, J., L. Zhao and S. Zhao (2004), A numerical simulation on severe dust storm events in North China and their dust sources, Clim. Environ. Res. 9, Wehner, B., A. Wiedensohler,, T. M. Tuch, Z. J. Wu, M. Hu, J. Slanina, and C. S. Kiang (2004). Variability of the aerosol number size distribution in Beijing, China: New particle formation, dust storms, and high continental background, Geophys. Res. Lett. 31(22), 1-4. Wu X., X. Zheng, X. Li, J. Liu, L. Kang, X. Jiang (2004), Analyses on the 280 characteristics and weather pattern classifications of East-Asia spring dust storms by using meteorological satellite images. Clim. Environ. Res. 9, Zhang, X. L., L. Cheng, and Y. S. Chung (2003a). Development of a severe sand-dust storm model and its application to Northwest China, Water Air Soil Pollut. 3(2), Zhang, X.Y., S. L. Gong, T. L. Zhao, R. Arimoto, Y. O. Wang, and Z. J. Zhou (2003b). Sources of Asian dust and role of climate change versus desertification in Asian dust emission. Geophys. Res. Lett. 30(24), ASC 8-1 ASC 8-4.

15 Figure 1. The structure of the integrated dust storm numerical modeling systems, with 290 the dashed lines representing the IAPS modeling structure (a); structure of the IAPS wind-erosion scheme (b). Figure 2. Observed dust deposition from the March 2002 sand-dust storm [Niu et al. 2004] (a); IAPS 1.0 simulations of average dust deposition (mg m -2 s -1 ) (b); IAPS 2.0 simulations of average dust deposition (mg m -2 s -1 ) (c); difference in soil 295 moisture from IAPS 2.0 and IAPS 1.0 simulations (mg m -2 s -1 ) (d). Figure 3. Overall distribution of the positive value of (u * -u * t) in the IAPS 1.0 simulation (a); overall distribution of the positive value of (u * -u * t) in the IAPS 2.0 simulation (b). Figure 4. Observed dust deposition from the April 2002 sand-dust storm [Niu 300 et al. 2004] (a); IAPS 1.0 simulations of average dust deposition (mg m -2 s -1 ) (b); IAPS 2.0 simulations of average dust deposition (mg m -2 s -1 ) (c). 305

16 305 (a) (b) Figure 1

17 (a) (b) (c) (d) 310 Figure 2 315

18 (a) (b) Figure 3 320

19 (a)

20 (c) (b) Figure 4