Concept: Dust Bowl
Climate models project rising drought risks over the southwestern and central U.S. in the twenty-first century due to increasing greenhouse gases. The projected drier regions largely overlay the major dust sources in the United States. However, whether dust activity in U.S. will increase in the future is not clear, due to the large uncertainty in dust modeling. This study found that changes of dust activity in the U.S. in the recent decade are largely associated with the variations of precipitation, soil bareness, and surface winds speed. Using multi-model output under the Representative Concentration Pathways 8.5 scenario, we project that climate change will increase dust activity in the southern Great Plains from spring to fall in the late half of the twenty-first century - largely due to reduced precipitation, enhanced land surface bareness, and increased surface wind speed. Over the northern Great Plains, less dusty days are expected in spring due to increased precipitation and reduced bareness. Given the large negative economic and societal consequences of severe dust storms, this study complements the multi-model projection on future dust variations and may help improve risk management and resource planning.
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 5 years ago
The global terrestrial carbon sink offsets one-third of the world’s fossil fuel emissions, but the strength of this sink is highly sensitive to large-scale extreme events. In 2012, the contiguous United States experienced exceptionally warm temperatures and the most severe drought since the Dust Bowl era of the 1930s, resulting in substantial economic damage. It is crucial to understand the dynamics of such events because warmer temperatures and a higher prevalence of drought are projected in a changing climate. Here, we combine an extensive network of direct ecosystem flux measurements with satellite remote sensing and atmospheric inverse modeling to quantify the impact of the warmer spring and summer drought on biosphere-atmosphere carbon and water exchange in 2012. We consistently find that earlier vegetation activity increased spring carbon uptake and compensated for the reduced uptake during the summer drought, which mitigated the impact on net annual carbon uptake. The early phenological development in the Eastern Temperate Forests played a major role for the continental-scale carbon balance in 2012. The warm spring also depleted soil water resources earlier, and thus exacerbated water limitations during summer. Our results show that the detrimental effects of severe summer drought on ecosystem carbon storage can be mitigated by warming-induced increases in spring carbon uptake. However, the results also suggest that the positive carbon cycle effect of warm spring enhances water limitations and can increase summer heating through biosphere-atmosphere feedbacks.
Personalized (N-of-1) trials are single-patient, crossover-design trials that may be useful for personalizing the selection of depression treatments. We conducted a systematic review of published N-of-1 trials for depression to determine the feasibility and suitability of this methodology for personalizing depression care.
Health systems face resource and time barriers to developing and implementing cancer survivorship care plans (SCPs) when active cancer treatment is completed. To address this problem, the South Dakota (SD) Department of Health partnered with two of SD’s largest health systems to create the SD Survivorship Program. The purpose of this program evaluation study was to describe and compare SCP development and implementation at the two health systems.
Asian dust storms occur often and have a great impact on East Asia and the western Pacific in spring. Early warnings based on reliable forecasts of dust storms thus are crucial for protecting human health and industry. Here we explore the efficacy of 4-D variational method-based data assimilation in a chemical transport model for dust storm forecasts in East Asia. We use a 3-D global chemical transport model (GEOS-Chem) and its adjoint model with surface PM10 mass concentration observations. We evaluate the model for several severe dust storm events, which occurred in May 2007 and March 2011 in East Asia. First of all, simulated the PM10 mass concentrations with the forward model showed large discrepancies compared with PM10 mass concentrations observed in China, Korea, and Japan, implying large uncertainties of simulated dust emission fluxes in the source regions. Based on our adjoint model constrained by observations for the whole period of each event, the reproduction of the spatial and temporal distributions of observations over East Asia was substantially improved (regression slopes from 0.15 to 2.81 to 0.85-1.02 and normalized mean biases from -74%-151% to -34%-1%). We then examine the efficacy of the data assimilation system for daily dust storm forecasts based on the adjoint model including previous day observations to update the initial condition of the forward model simulation for the next day. The forecast results successfully captured the spatial and temporal variations of ground-based observations in downwind regions, indicating that the data assimilation system with ground-based observations effectively forecasts dust storms, especially in downwind regions. However, the efficacy is limited in nearby the dust source regions, including Mongolia and North China, due to the lack of observations for constraining the model.
Our objective was to identify the factors that impact mental health service use among American Indian (AI) older adults living in South Dakota compared to their White counterparts.
Vegetation phenology changes have been widely applied in the disaster risk assessments of the spring dust storms, and vegetation green-up date shifts have a strong influence on dust storms. However, the effect of earlier vegetation green-up dates due to climate warming on the evaluation of dust storms return periods remains an important, but poorly understood issue. In this study, we evaluate the spring dust storm return period (February to June) in Inner Mongolia, Northern China, using 165 observations of severe spring dust storm events from 16 weather stations, and regional vegetation green-up dates as an integrated factor from NDVI (Normalized Difference Vegetation Index), covering a period from 1982 to 2007, by building the bivariate Copula model. We found that the joint return period showed better fitting results than without considering the integrated factor when the actual dust storm return period is longer than 2years. Also, for extremely severe dust storm events, the gap between simulation result and actual return period can be narrowed up to 0.4888years by using integrated factor. Furthermore, the risk map based on the return period results shows that the Mandula, Zhurihe, Sunitezuoqi, Narenbaolige stations are identified as high risk areas. In this study area, land surface is extensively covered by grasses and shrubs, vegetation green-up date can play a significant role in restraining spring dust storm outbreaks. Therefore, we suggest that Copula method can become a useful tool for joint return period evaluation and risk analysis of severe dust storms.
Asian dust storms originating from arid or semi-arid regions of China or her adjacent regions have important impact on the atmosphere and water composition, and ecological environment of the Eastern China Seas. This research used data collected in the middle of the South Yellow Sea, China, during a dust storm event from 23 April to 24 April 2006 to analyze the instantaneous influence of dust storms on optical scattering properties, which are closely related to particle characteristics. The analysis results showed that the dust storm had a remarkable influence on the optical scattering property in the upper mixed layer of water, and dust particles drily deposited from the dust storm with an aerosol optical depth of nearly 2.5 into the water could induce a 0.14 m-1 change in the water optical scattering coefficient at 532 nm at the depth of 4 m. The duration of the instantaneous influence of the dust storm on the water optical scattering properties was short, and this influence disappeared rapidly within approximately 3 hours after the end of the dust storm.
Dust storms are devastating natural disasters that cost billions of dollars and many human lives every year. Using the Non-Hydrostatic Mesoscale Dust Model (NMM-dust), this research studies how different spatiotemporal resolutions of two input parameters (soil moisture and greenness vegetation fraction) impact the sensitivity and accuracy of a dust model. Experiments are conducted by simulating dust concentration during July 1-7, 2014, for the target area covering part of Arizona and California (31, 37, -118, -112), with a resolution of ~ 3 km. Using ground-based and satellite observations, this research validates the temporal evolution and spatial distribution of dust storm output from the NMM-dust, and quantifies model error using measurements of four evaluation metrics (mean bias error, root mean square error, correlation coefficient and fractional gross error). Results showed that the default configuration of NMM-dust (with a low spatiotemporal resolution of both input parameters) generates an overestimation of Aerosol Optical Depth (AOD). Although it is able to qualitatively reproduce the temporal trend of the dust event, the default configuration of NMM-dust cannot fully capture its actual spatial distribution. Adjusting the spatiotemporal resolution of soil moisture and vegetation cover datasets showed that the model is sensitive to both parameters. Increasing the spatiotemporal resolution of soil moisture effectively reduces model’s overestimation of AOD, while increasing the spatiotemporal resolution of vegetation cover changes the spatial distribution of reproduced dust storm. The adjustment of both parameters enables NMM-dust to capture the spatial distribution of dust storms, as well as reproducing more accurate dust concentration.
To test the applicability of lichens in the biomonitoring of atmospheric elemental deposition in a typical steppe zone of Inner Mongolia, China, six foliose lichens (Physcia aipolia, PA; P. tribacia, PT; Xanthoria elegans, XE; X. mandschurica, XM; Xanthoparmelia camtschadalis, XPC; and Xp. tinctina, XPT) were sampled from the Xilin River Basin, Xilinhot, Inner Mongolia, China. Twenty-five elements (Al, Ba, Cd, Ce, Cr, Cs, Cu, Fe, K, La, Mn, Mo, Na, Ni, P, Pb, Sb, Sc, Sm, Tb, Th, Ti, Tl, V and Zn) in the lichens were analysed using inductively coupled plasma mass spectrometry (ICP-MS). The results show that Cd, Pb and Zn were mainly atmospheric in origin, whereas the other elements were predominantly of crustal origin. Compared with other studies, our data were higher in crustal element concentrations and lower in atmospheric element concentrations, matching with the frequent, severe dust storms and road traffic in the area. The elemental concentrations in lichens are both species- and element-specific, highlighting the importance of species selection for biomonitoring air pollution using lichens. We recommend PT, XE, XM and XPT for monitoring atmospheric deposition of crustal elements; XPC and XPT for Cd and Pb; PA for Cd and Zn; and PT for Cd.