Search Results

You are looking at 11 - 15 of 15 items for

  • Author or Editor: Min Chen x
  • All content x
Clear All Modify Search
Paul A. Levine, James T. Randerson, Yang Chen, Michael S. Pritchard, Min Xu, and Forrest M. Hoffman

Abstract

El Niño–Southern Oscillation (ENSO) is an important driver of climate and carbon cycle variability in the Amazon. Sea surface temperature (SST) anomalies in the equatorial Pacific drive teleconnections with temperature directly through changes in atmospheric circulation. These circulation changes also impact precipitation and, consequently, soil moisture, enabling additional indirect effects on temperature through land–atmosphere coupling. To separate the direct influence of ENSO SST anomalies from the indirect effects of soil moisture, a mechanism-denial experiment was performed to decouple their variability in the Energy Exascale Earth System Model (E3SM) forced with observed SSTs from 1982 to 2016. Soil moisture variability was found to amplify and extend the effects of SST forcing on eastern Amazon temperature and carbon fluxes in E3SM. During the wet season, the direct, circulation-driven effect of ENSO SST anomalies dominated temperature and carbon cycle variability throughout the Amazon. During the following dry season, after ENSO SST anomalies had dissipated, soil moisture variability became the dominant driver in the east, explaining 67%–82% of the temperature difference between El Niño and La Niña years, and 85%–91% of the difference in carbon fluxes. These results highlight the need to consider the interdependence between temperature and hydrology when attributing the relative contributions of these factors to interannual variability in the terrestrial carbon cycle. Specifically, when offline models are forced with observations or reanalysis, the contribution of temperature may be overestimated when its own variability is modulated by hydrology via land–atmosphere coupling.

Open access
Xin-Min Zeng, B. Wang, Y. Zhang, Y. Zheng, N. Wang, M. Wang, X. Yi, C. Chen, Z. Zhou, and H. Liu

Abstract

To quantify and explain effects of different land surface schemes (LSSs) on simulated geopotential height (GPH) fields, we performed simulations over China for the summer of 2003 using 12-member ensembles with the Weather Research and Forecasting (WRF) Model, version 3. The results show that while the model can generally simulate the seasonal and monthly mean GPH patterns, the effects of the LSS choice on simulated GPH fields are substantial, with the LSS-induced differences exceeding 10 gpm over a large area (especially the northwest) of China, which is very large compared with climate anomalies and forecast errors. In terms of the assessment measures for the four LSS ensembles [namely, the five-layer thermal diffusion scheme (SLAB), the Noah LSS (NOAH), the Rapid Update Cycle LSS (RUC), and the Pleim–Xiu LSS (PLEX)] in the WRF, the PLEX ensemble is the best, followed by the NOAH, RUC, and SLAB ensembles. The sensitivity of the simulated 850-hPa GPH is more significant than that of the 500-hPa GPH, with the 500-hPa GPH difference fields generally characterized by two large areas with opposite signs due to the smoothly varying nature of GPHs. LSS-induced GPH sensitivity is found to be higher than the GPH sensitivity induced by atmospheric boundary layer schemes. Moreover, theoretical analyses show that the LSS-induced GPH sensitivity is mainly caused by changes in surface fluxes (in particular, sensible heat flux), which further modify atmospheric temperature and pressure fields. The temperature and pressure fields generally have opposite contributions to changes in the GPH. This study emphasizes the importance of choosing and improving LSSs for simulating seasonal and monthly GPHs using regional climate models.

Full access
Chu-Chun Chen, Min-Hui Lo, Eun-Soon Im, Jin-Yi Yu, Yu-Chiao Liang, Wei-Ting Chen, Iping Tang, Chia-Wei Lan, Ren-Jie Wu, and Rong-You Chien

Abstract

Tropical deforestation can result in substantial changes in local surface energy and water budgets, and thus in atmospheric stability. These effects may in turn yield changes in precipitation. The Maritime Continent (MC) has undergone severe deforestation during the past few decades but it has received less attention than the deforestation in the Amazon and Congo rain forests. In this study, numerical deforestation experiments are conducted with global (i.e., Community Earth System Model) and regional climate models (i.e., Regional Climate Model version 4.6) to investigate precipitation responses to MC deforestation. The results show that the deforestation in the MC region leads to increases in both surface temperature and local precipitation. Atmospheric moisture budget analysis reveals that the enhanced precipitation is associated more with the dynamic component than with the thermodynamic component of the vertical moisture advection term. Further analyses on the vertical profile of moist static energy indicate that the atmospheric instability over the deforested areas is increased as a result of anomalous moistening at approximately 800–850 hPa and anomalous warming extending from the surface to 750 hPa. This instability favors ascending air motions, which enhance low-level moisture convergence. Moreover, the vertical motion increases associated with the MC deforestation are comparable to those generated by La Niña events. These findings offer not only mechanisms to explain the local climatic responses to MC deforestation but also insights into the possible reasons for disagreements among climate models in simulating the precipitation responses.

Open access
Sara Lance, Jie Zhang, James J. Schwab, Paul Casson, Richard E. Brandt, David R. Fitzjarrald, Margaret J. Schwab, John Sicker, Cheng-Hsuan Lu, Sheng-Po Chen, Jeongran Yun, Jeffrey M. Freedman, Bhupal Shrestha, Qilong Min, Mark Beauharnois, Brian Crandall, Everette Joseph, Matthew J. Brewer, Justin R. Minder, Daniel Orlowski, Amy Christiansen, Annmarie G. Carlton, and Mary C. Barth

Abstract

Aqueous chemical processing within cloud and fog water is thought to be a key process in the production and transformation of secondary organic aerosol mass, found abundantly and ubiquitously throughout the troposphere. Yet, significant uncertainty remains regarding the organic chemical reactions taking place within clouds and the conditions under which those reactions occur, owing to the wide variety of organic compounds and their evolution under highly variable conditions when cycled through clouds. Continuous observations from a fixed remote site like Whiteface Mountain (WFM) in New York State and other mountaintop sites have been used to unravel complex multiphase interactions in the past, particularly the conversion of gas-phase emissions of SO2 to sulfuric acid within cloud droplets in the presence of sunlight. These scientific insights led to successful control strategies that reduced aerosol sulfate and cloud water acidity substantially over the following decades. This paper provides an overview of observations obtained during a pilot study that took place at WFM in August 2017 aimed at obtaining a better understanding of Chemical Processing of Organic Compounds within Clouds (CPOC). During the CPOC pilot study, aerosol cloud activation efficiency, particle size distribution, and chemical composition measurements were obtained below-cloud for comparison to routine observations at WFM, including cloud water composition and reactive trace gases. Additional instruments deployed for the CPOC pilot study included a Doppler lidar, sun photometer, and radiosondes to assist in evaluating the meteorological context for the below-cloud and summit observations.

Full access
Xin-Zhong Liang, Min Xu, Xing Yuan, Tiejun Ling, Hyun I. Choi, Feng Zhang, Ligang Chen, Shuyan Liu, Shenjian Su, Fengxue Qiao, Yuxiang He, Julian X. L. Wang, Kenneth E. Kunkel, Wei Gao, Everette Joseph, Vernon Morris, Tsann-Wang Yu, Jimy Dudhia, and John Michalakes

The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model.

This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.

Full access