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Sheng Chen, Jonathan J. Gourley, Yang Hong, Qing Cao, Nicholas Carr, Pierre-Emmanuel Kirstetter, Jian Zhang, and Zac Flamig

Abstract

In meteorological investigations, the reference variable or “ground truth” typically comes from an instrument. This study uses human observations of surface precipitation types to evaluate the same variables that are estimated from an automated algorithm. The NOAA/National Severe Storms Laboratory’s Multi-Radar Multi-Sensor (MRMS) system relies primarily on observations from the Next Generation Radar (NEXRAD) network and model analyses from the Earth System Research Laboratory’s Rapid Refresh (RAP) system. Each hour, MRMS yields quantitative precipitation estimates and surface precipitation types as rain or snow. To date, the surface precipitation type product has received little attention beyond case studies. This study uses precipitation type reports collected by citizen scientists who have contributed observations to the meteorological Phenomena Identification Near the Ground (mPING) project. Citizen scientist reports of rain and snow during the winter season from 19 December 2012 to 30 April 2013 across the United States are compared to the MRMS precipitation type products. Results show that while the mPING reports have a limited spatial distribution (they are concentrated in urban areas), they yield similar critical success indexes of MRMS precipitation types in different cities. The remaining disagreement is attributed to an MRMS algorithmic deficiency of yielding too much rain, as opposed to biases in the mPING reports. The study also shows reduced detectability of snow compared to rain, which is attributed to lack of sensitivity at S band and the shallow nature of winter storms. Some suggestions are provided for improving the MRMS precipitation type algorithm based on these findings.

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Xuhui Lee, Shoudong Liu, Wei Xiao, Wei Wang, Zhiqiu Gao, Chang Cao, Cheng Hu, Zhenghua Hu, Shuanghe Shen, Yongwei Wang, Xuefa Wen, Qitao Xiao, Jiaping Xu, Jinbiao Yang, and Mi Zhang

Lakes are an important component of the climate system. They provide moisture for precipitation, buffer temperature variations, and contribute to regional atmospheric carbon budgets. This article describes an eddy covariance (EC) mesonet on Lake Taihu, a large (area 2400 km2) and shallow (depth 2 m) lake situated in the heavily populated Yangtze River Delta, China. The mesonet consists of five lake sites, representing different biological attributes and wind–wave patterns, and a land site near the lake shore. Common to all the sites are standard EC instruments for measurement of the momentum, sensible heat, water vapor, and CO2 flux. One site is also equipped with laser-based analyzers for precise measurement of the CO2, CH4, and H2O mixing ratios and their isotopic compositions. To the authors' best knowledge, this is the first lake eddy flux mesonet. Early results reveal evidence of biological and pollution controls on the surface–air fluxes of energy, momentum, and greenhouse gases across the lake. The data will be used to address five science questions: 1) Are lake–air parameterizations established for deep lakes applicable to shallow lakes? 2) Why are lake–land breeze circulations less prevalent in the Taihu lake basin than in lake basins in northern latitudes? 3) How do algal blooms alter the lake–atmosphere interactions? 4) Is this eutrophic lake a source or sink of atmospheric CO2? 5) Does the decay of algal and macrophyte biomass contribute significant amounts of CH4 to the atmosphere?

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Benzhi Zhou, Lianhong Gu, Yihui Ding, Lan Shao, Zhongmin Wu, Xiaosheng Yang, Changzhu Li, Zhengcai Li, Xiaoming Wang, Yonghui Cao, Bingshan Zeng, Mukui Yu, Mingyu Wang, Shengkun Wang, Honggang Sun, Aiguo Duan, Yanfei An, Xu Wang, and Weijian Kong

Abstract

Extreme events often expose vulnerabilities of socioeconomic infrastructures and point to directions of much-needed policy change. Integrated impact assessment of such events can lead to finding of sustainability principles. Southern and central China has for decades been undergoing a breakneck pace of socioeconomic development. In early 2008, a massive ice storm struck this region, immobilizing millions of people. The storm was a consequence of sustained convergence between tropical maritime and continental polar air masses, caused by an anomalously stable atmospheric general circulation pattern in both low and high latitudes. Successive waves of freezing rain occurred during a month period, coating southern and central China with a layer of ice 50–160 mm in thickness. We conducted an integrated impact assessment of this event to determine whether and how the context of socioeconomic and human-disturbed natural systems may affect the transition of natural events into human disasters. We found that 1) without contingency plans, advanced technologies dependent on interrelated energy supplies can create worse problems during extreme events, 2) the weakest link in disaster response lies between science and decision making, 3) biodiversity is a form of long-term insurance for sustainable forestry against extreme events, 4) sustainable extraction of nontimber goods and services is essential to risk planning for extreme events in forest resources use, 5) extreme events can cause food shortage directly by destroying crops and indirectly by disrupting food distribution channels, 6) concentrated economic development increases societal vulnerability to extreme events, and 7) formalized institutional mechanisms are needed to ensure that unexpected opportunities to learn lessons from weather disasters are not lost in distracting circumstances.

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Maria Rugenstein, Jonah Bloch-Johnson, Ayako Abe-Ouchi, Timothy Andrews, Urs Beyerle, Long Cao, Tarun Chadha, Gokhan Danabasoglu, Jean-Louis Dufresne, Lei Duan, Marie-Alice Foujols, Thomas Frölicher, Olivier Geoffroy, Jonathan Gregory, Reto Knutti, Chao Li, Alice Marzocchi, Thorsten Mauritsen, Matthew Menary, Elisabeth Moyer, Larissa Nazarenko, David Paynter, David Saint-Martin, Gavin A. Schmidt, Akitomo Yamamoto, and Shuting Yang

Abstract

We present a model intercomparison project, LongRunMIP, the first collection of millennial-length (1,000+ years) simulations of complex coupled climate models with a representation of ocean, atmosphere, sea ice, and land surface, and their interactions. Standard model simulations are generally only a few hundred years long. However, modeling the long-term equilibration in response to radiative forcing perturbation is important for understanding many climate phenomena, such as the evolution of ocean circulation, time- and temperature-dependent feedbacks, and the differentiation of forced signal and internal variability. The aim of LongRunMIP is to facilitate research into these questions by serving as an archive for simulations that capture as much of this equilibration as possible. The only requirement to participate in LongRunMIP is to contribute a simulation with elevated, constant CO2 forcing that lasts at least 1,000 years. LongRunMIP is an MIP of opportunity in that the simulations were mostly performed prior to the conception of the archive without an agreed-upon set of experiments. For most models, the archive contains a preindustrial control simulation and simulations with an idealized (typically abrupt) CO2 forcing. We collect 2D surface and top-of-atmosphere fields and 3D ocean temperature and salinity fields. Here, we document the collection of simulations and discuss initial results, including the evolution of surface and deep ocean temperature and cloud radiative effects. As of October 2019, the collection includes 50 simulations of 15 models by 10 modeling centers. The data of LongRunMIP are publicly available. We encourage submissions of more simulations in the future.

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