Sherman, Peter

In Press
Meng Gao, Peter Sherman, Shaojie Song, Yueyue Yu, Zhiwei Wu, and Michael B. McElroy. In Press. “Seasonal Prediction of Indian Wintertime Aerosol Pollution using the Ocean Memory Effect.” Science Advances.
Submitted
Meng Gao, Zirui Liu, Bo Zheng, Dongsheng Ji, Peter Sherman, Shaojie Song, Jinyuan Xin, Cheng Liu, Yuesi Wang, Qiang Zhang, Zifa Wang, Gregory R. Carmichael, and Michael B. McElroy. Submitted. “China's Clean Air Action has suppressed unfavorable influences of climate on wintertime PM2.5 concentrations in Beijing since 2002.” Atmospheric Chemistry and Physics.
Peter Sherman, Meng Gao, Shaojie Song, Patrick Ohiomoba, Alex Archibald, and Michael B. McElroy. Submitted. “The influence of dynamics and emissions changes on China’s wintertime haze.” Journal of Applied Meteorology and Climatology.
Peter Sherman, Xinyu Chen, and Michael B. McElroy. Submitted. “Offshore wind: an opportunity for cost-competitive decarbonization of China’s energy economy.” Joule.
2017
Peter Sherman, Xinyu Chen, and Michael B. McElroy. 2017. “Wind-generated electricity in China: Decreasing potential, inter-annual variability, and association with climate change.” Scientific Reports, 7. Publisher's VersionAbstract
China hosts the world’s largest market for wind-generated electricity. The financial return and carbon reduction benefits from wind power are sensitive to changing wind resources. Wind data derived from an assimilated meteorological database are used here to estimate what the wind generated electricity in China would have been on an hourly basis over the period 1979 to 2015 at a geographical resolution of approximately 50 km × 50 km. The analysis indicates a secular decrease in generating potential over this interval, with the largest declines observed for western Inner Mongolia (15 ± 7%) and the northern part of Gansu (17 ± 8%), two leading wind investment areas. The decrease is associated with long-term warming in the vicinity of the Siberian High (SH), correlated also with the observed secular increase in global average surface temperatures. The long-term trend is modulated by variability relating to the Pacific Decadal Oscillation (PDO) and the Arctic Oscillation (AO). A linear regression model incorporating indices for the PDO and AO, as well as the declining trend, can account for the interannual variability of wind power, suggesting that advances in long-term forecasting could be exploited to markedly improve management of future energy systems.