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Xiangde Xu, Lian Xie, Xinghong Cheng, Jianming Xu, Xiuji Zhou, and Guoan Ding

Abstract

A major challenge for air quality forecasters is to reduce the uncertainty of air pollution emission inventory. Error in the emission data is a primary source of error in air quality forecasts, much like the effect of error in the initial conditions on the accuracy of weather forecasting. Data assimilation has been widely used to improve weather forecasting by correcting the initial conditions with weather observations. In a similar way, observed concentrations of air pollutants can be used to correct the errors in the emission data. In this study, a new method is developed for estimating air pollution emissions based on a Newtonian relaxation and nudging technique. Case studies for the period of 1–25 August 2006 in 47 cities in China indicate that the nudging technique resulted in improved estimations of sulfur dioxide (SO2) and nitrogen dioxide (NO2) emissions in the majority of these cities. Predictions of SO2 and NO2 concentrations in January, April, August, and October using the emission estimations derived from the nudging technique showed remarkable improvements over those based on the original emission data.

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Yongwen Liu, Shilong Piao, Xu Lian, Philippe Ciais, and W. Kolby Smith

Abstract

Seventeen Earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were evaluated, focusing on the seasonal sensitivities of net biome production (NBP), net primary production (NPP), and heterotrophic respiration (Rh) to interannual variations in temperature and precipitation during 1982–2005 and their changes over the twenty-first century. Temperature sensitivity of NPP in ESMs was generally consistent across northern high-latitude biomes but significantly more negative for tropical and subtropical biomes relative to satellite-derived estimates. The temperature sensitivity of NBP in both inversion-based and ESM estimates was generally consistent in March–May (MAM) and September–November (SON) for tropical forests, semiarid ecosystems, and boreal forests. By contrast, for inversion-based NBP estimates, temperature sensitivity of NBP was nonsignificant for June–August (JJA) for all biomes except boreal forest; whereas, for ESM NBP estimates, the temperature sensitivity for JJA was significantly negative for all biomes except shrublands and subarctic ecosystems. Both satellite-derived NPP and inversion-based NBP are often decoupled from precipitation, whereas ESM NPP and NBP estimates are generally positively correlated with precipitation, suggesting that ESMs are oversensitive to precipitation. Over the twenty-first century, changes in temperature sensitivities of NPP, Rh, and NBP are consistent across all RCPs but stronger under more intensive scenarios. The temperature sensitivity of NBP was found to decrease in tropics and subtropics and increase in northern high latitudes in MAM due to an increased temperature sensitivity of NPP. Across all biomes, projected temperature sensitivity of NPP decreased in JJA and SON. Projected precipitation sensitivity of NBP did not change across biomes, except over grasslands in MAM.

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Yu-Kun Hou, Hua Chen, Chong-Yu Xu, Jie Chen, and Sheng-Lian Guo

Abstract

Statistical downscaling is useful for managing scale and resolution problems in outputs from global climate models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a support vector classifier (SVC) and first-order two-state Markov chain to generate the occurrence and a support vector regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Downscaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using 10 meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd years for calibration and even years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd years for calibration and even years for validation can reduce the influence of possible climate change–induced nonstationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an at-site statistical downscaling method, and therefore this method will need to be modified for extension into a multisite domain.

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Zhenzhong Zeng, Shilong Piao, Laurent Z. X. Li, Tao Wang, Philippe Ciais, Xu Lian, Yuting Yang, Jiafu Mao, Xiaoying Shi, and Ranga B. Myneni

Abstract

Leaf area index (LAI) is increasing throughout the globe, implying Earth greening. Global modeling studies support this contention, yet satellite observations and model simulations have never been directly compared. Here, for the first time, a coupled land–climate model was used to quantify the potential impact of the satellite-observed Earth greening over the past 30 years on the terrestrial water cycle. The global LAI enhancement of 8% between the early 1980s and the early 2010s is modeled to have caused increases of 12.0 ± 2.4 mm yr−1 in evapotranspiration and 12.1 ± 2.7 mm yr−1 in precipitation—about 55% ± 25% and 28% ± 6% of the observed increases in land evapotranspiration and precipitation, respectively. In wet regions, the greening did not significantly decrease runoff and soil moisture because it intensified moisture recycling through a coincident increase of evapotranspiration and precipitation. But in dry regions, including the Sahel, west Asia, northern India, the western United States, and the Mediterranean coast, the greening was modeled to significantly decrease soil moisture through its coupling with the atmospheric water cycle. This modeled soil moisture response, however, might have biases resulting from the precipitation biases in the model. For example, the model dry bias might have underestimated the soil moisture response in the observed dry area (e.g., the Sahel and northern India) given that the modeled soil moisture is near the wilting point. Thus, an accurate representation of precipitation and its feedbacks in Earth system models is essential for simulations and predictions of how soil moisture responds to LAI changes, and therefore how the terrestrial water cycle responds to climate change.

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Qing Wang, Denny P. Alappattu, Stephanie Billingsley, Byron Blomquist, Robert J. Burkholder, Adam J. Christman, Edward D. Creegan, Tony de Paolo, Daniel P. Eleuterio, Harindra Joseph S. Fernando, Kyle B. Franklin, Andrey A. Grachev, Tracy Haack, Thomas R. Hanley, Christopher M. Hocut, Teddy R. Holt, Kate Horgan, Haflidi H. Jonsson, Robert A. Hale, John A. Kalogiros, Djamal Khelif, Laura S. Leo, Richard J. Lind, Iossif Lozovatsky, Jesus Planella-Morato, Swagato Mukherjee, Wendell A. Nuss, Jonathan Pozderac, L. Ted Rogers, Ivan Savelyev, Dana K. Savidge, R. Kipp Shearman, Lian Shen, Eric Terrill, A. Marcela Ulate, Qi Wang, R. Travis Wendt, Russell Wiss, Roy K. Woods, Luyao Xu, Ryan T. Yamaguchi, and Caglar Yardim

Abstract

The Coupled Air–Sea Processes and Electromagnetic Ducting Research (CASPER) project aims to better quantify atmospheric effects on the propagation of radar and communication signals in the marine environment. Such effects are associated with vertical gradients of temperature and water vapor in the marine atmospheric surface layer (MASL) and in the capping inversion of the marine atmospheric boundary layer (MABL), as well as the horizontal variations of these vertical gradients. CASPER field measurements emphasized simultaneous characterization of electromagnetic (EM) wave propagation, the propagation environment, and the physical processes that gave rise to the measured refractivity conditions. CASPER modeling efforts utilized state-of-the-art large-eddy simulations (LESs) with a dynamically coupled MASL and phase-resolved ocean surface waves. CASPER-East was the first of two planned field campaigns, conducted in October and November 2015 offshore of Duck, North Carolina. This article highlights the scientific motivations and objectives of CASPER and provides an overview of the CASPER-East field campaign. The CASPER-East sampling strategy enabled us to obtain EM wave propagation loss as well as concurrent environmental refractive conditions along the propagation path. This article highlights the initial results from this sampling strategy showing the range-dependent propagation loss, the atmospheric and upper-oceanic variability along the propagation range, and the MASL thermodynamic profiles measured during CASPER-East.

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