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Terence J. Pagano, Duane E. Waliser, Bin Guan, Hengchun Ye, F. Martin Ralph, and Jinwon Kim

Abstract

Atmospheric rivers (ARs) are long and narrow regions of strong horizontal water vapor transport. Upon landfall, ARs are typically associated with heavy precipitation and strong surface winds. A quantitative understanding of the atmospheric conditions that favor extreme surface winds during ARs has implications for anticipating and managing various impacts associated with these potentially hazardous events. Here, a global AR database (1999–2014) with relevant information from MERRA-2 reanalysis, QuikSCAT and AIRS satellite observations are used to better understand and quantify the role of near-surface static stability in modulating surface winds during landfalling ARs. The temperature difference between the surface and 1 km MSL (ΔT; used here as a proxy for near-surface static stability), and integrated water vapor transport (IVT) are analyzed to quantify their relationships to surface winds using bivariate linear regression. In four regions where AR landfalls are common, the MERRA-2-based results indicate that IVT accounts for 22-38% of the variance in surface wind speed. Combining ΔT with IVT increases the explained variance to 36-52%. Substitution of QuikSCAT surface winds and AIRS ΔT in place of the MERRA-2 data largely preserves this relationship (e.g., 44% compared to 52% explained variance for USA West Coast). Use of an alternate static stability measure–the bulk Richardson number–yields a similar explained variance (47%). Lastly, AR cases within the top and bottom 25% of near-surface static stability indicate that extreme surface winds (gale or higher) are more likely to occur in unstable conditions (5.3%/14.7% during weak/strong IVT) than in stable conditions (0.58%/6.15%).

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Ju-Mee Ryoo, Yohai Kaspi, Darryn W. Waugh, George N. Kiladis, Duane E. Waliser, Eric J. Fetzer, and Jinwon Kim

Abstract

This study demonstrates that water vapor transport and precipitation are largely modulated by the intensity of the subtropical jet, transient eddies, and the location of wave breaking events during the different phases of ENSO. Clear differences are found in the potential vorticity (PV), meteorological fields, and trajectory pathways between the two different phases. Rossby wave breaking events have cyclonic and anticyclonic regimes, with associated differences in the frequency of occurrence and the dynamic response. During La Niña, there is a relatively weak subtropical jet allowing PV to intrude into lower latitudes over the western United States. This induces a large amount of moisture transport inland ahead of the PV intrusions, as well as northward transport to the west of a surface anticyclone. During El Niño, the subtropical jet is relatively strong and is associated with an enhanced cyclonic wave breaking. This is accompanied by a time-mean surface cyclone, which brings zonal moisture transport to the western United States. In both (El Niño and La Niña) phases, there is a high correlation (>0.3–0.7) between upper-level PV at 250 hPa and precipitation over the west coast of the United States with a time lag of 0–1 days. Vertically integrated water vapor fluxes during El Niño are up to 70 kg m−1 s−1 larger than those during La Niña along the west coast of the United States. The zonal and meridional moist static energy flux resembles wave vapor transport patterns, suggesting that they are closely controlled by the large-scale flows and location of wave breaking events during the different phase of ENSO.

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Hye-Ryun Oh, Chang-Hoi Ho, Doo-Sun R. Park, Jinwon Kim, Chang-Keun Song, and Sun-Kyong Hur

Abstract

Cold-season air quality in Seoul, South Korea, has been improved noticeably between 2001 and 2015 with a near-50% decrease in the mean concentration of particulate matter with aerodynamic diameters ≤10 μm (PM10). Like the change in mean concentration, the occurrence frequency and intensity of the extreme-high-PM10 episodes exceeding 100 μg m−3 has significantly decreased as well. In addition to the multilateral efforts of the South Korean government to reduce air pollutant emissions, this study proposes that large-scale circulation changes also could have contributed to the air quality improvements. Specifically, the recent weakening of the Aleutian low may have intensified the tropospheric westerlies around the Korean Peninsula, resulting in a shorter residence time of particulate matter over South Korea. Thus, despite constant governmental effort to reduce pollutant emissions, the improvement in air quality over South Korea may be delayed if the Aleutian low recovers its past strength in the future. This study emphasizes the importance of the meteorological field in determining the air quality over South Korea.

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Baijun Tian, Huikyo Lee, Duane E. Waliser, Robert Ferraro, Jinwon Kim, Jonathan Case, Takamichi Iguchi, Eric Kemp, Di Wu, William Putman, and Weile Wang

Abstract

Several dynamically downscaled climate simulations with various spatial resolutions (24, 12, and 4 km) and spectral nudging strengths (0, 600, and 2000 km) have been run over the contiguous United States from 2000 to 2009 using the high-resolution NASA Unified Weather and Research Forecasting (NU-WRF) regional model initialized and constrained by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). This paper summarizes the authors’ efforts on the development of a model performance metric and its application to assess summer precipitation over the U.S. Great Plains (USGP) in these downscaled climate simulations. A new model performance metric T was first developed that uses both the linear correlation coefficient and mean square error and is consistent with other commonly used metrics, but gives a bigger separation between good and bad simulations. This metric T was then applied to the summer mean precipitation spatial pattern, diurnal Hovmöller diagram, and diurnal spatial pattern over the USGP from the simulations focusing on the summer precipitation diurnal cycle related to mesoscale convective systems (MCSs). The metric T skill scores increase significantly from the control simulation to the nudged simulations and from the nudged simulations with shorter wavelengths to the nudged simulations with longer wavelengths, but do not change much from MERRA-2 to the downscaled simulations or between the various downscaled simulations with different spatial resolutions. Thus, there is some credibility, but no significant value added compared to MERRA-2, of the downscaled climate simulations of the summer precipitation over the USGP.

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Paul C. Loikith, Duane E. Waliser, Huikyo Lee, Jinwon Kim, J. David Neelin, Benjamin R. Lintner, Seth McGinnis, Chris A. Mattmann, and Linda O. Mearns

Abstract

Methodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the North American Regional Climate Change Assessment Program (NARCCAP). A surface temperature dataset combining gridded station observations and reanalysis is employed as the primary reference. Temperature biases are documented across the distribution, focusing on the median and tails. Temperature variance is generally higher in the RCMs than reference, while skewness is reasonably simulated in winter over the entire domain and over the western United States and Canada in summer. Substantial differences in skewness exist over the southern and eastern portions of the domain in summer. Four examples with observed long-tailed probability distribution functions (PDFs) are selected for model comparison. Long cold tails in the winter are simulated with high fidelity for Seattle, Washington, and Chicago, Illinois. In summer, the RCMs are unable to capture the distribution width and long warm tails for the coastal location of Los Angeles, California, while long cold tails are poorly realized for Houston, Texas. The evaluation results are repeated using two additional reanalysis products adjusted by station observations and two standard reanalysis products to assess the impact of observational uncertainty. Results are robust when compared with those obtained using the adjusted reanalysis products as reference, while larger uncertainties are introduced when standard reanalysis is employed as reference. Model biases identified in this work will allow for further investigation into associated mechanisms and implications for future simulations of temperature extremes.

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Takamichi Iguchi, Wei-Kuo Tao, Di Wu, Christa Peters-Lidard, Joseph A. Santanello, Eric Kemp, Yudong Tian, Jonathan Case, Weile Wang, Robert Ferraro, Duane Waliser, Jinwon Kim, Huikyo Lee, Bin Guan, Baijun Tian, and Paul Loikith

Abstract

This study investigates the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall fields were simulated at 24-km resolution using the NASA-Unified Weather Research and Forecasting (NU-WRF) Model for June–August 2000. Four cumulus parameterization schemes and two options for shallow cumulus components in a specific scheme were tested. The spread in the domain-mean rainfall rates across the parameterization schemes was generally consistent between the entire CONUS and most subregions. The selection of the shallow cumulus component in a specific scheme had more impact than that of the four cumulus parameterization schemes. Regional variability in the performance of each scheme was assessed by calculating optimally weighted ensembles that minimize full root-mean-square errors against reference datasets. The spatial pattern of the seasonally averaged rainfall was insensitive to the selection of cumulus parameterization over mountainous regions because of the topographical pattern constraint, so that the simulation errors were mostly attributed to the overall bias there. In contrast, the spatial patterns over the Great Plains regions as well as the temporal variation over most parts of the CONUS were relatively sensitive to cumulus parameterization selection. Overall, adopting a single simulation result was preferable to generating a better ensemble for the seasonally averaged daily rainfall simulation, as long as their overall biases had the same positive or negative sign. However, an ensemble of multiple simulation results was more effective in reducing errors in the case of also considering temporal variation.

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Jinwon Kim, Duane E. Waliser, Chris A. Mattmann, Linda O. Mearns, Cameron E. Goodale, Andrew F. Hart, Dan J. Crichton, Seth McGinnis, Huikyo Lee, Paul C. Loikith, and Maziyar Boustani

Abstract

Surface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona–New Mexico region. RCMs generally overestimate surface insolation, especially in the eastern United States. Negative correlation between the biases in insolation and precipitation suggest that these two fields are related, likely via clouds. Systematic variations in biases for regions, seasons, variables, and metrics suggest that the bias correction in applying climate model data to assess the climate impact on various sectors must be performed accordingly. Precipitation evaluation with multiple observations reveals that observational data can be an important source of uncertainties in model evaluation; thus, cross examination of observational data is important for model evaluation.

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Lifeng Luo, Alan Robock, Konstantin Y. Vinnikov, C. Adam Schlosser, Andrew G. Slater, Aaron Boone, Pierre Etchevers, Florence Habets, Joel Noilhan, Harald Braden, Peter Cox, Patricia de Rosnay, Robert E. Dickinson, Yongjiu Dai, Qing-Cun Zeng, Qingyun Duan, John Schaake, Ann Henderson-Sellers, Nicola Gedney, Yevgeniy M. Gusev, Olga N. Nasonova, Jinwon Kim, Eva Kowalczyk, Kenneth Mitchell, Andrew J. Pitman, Andrey B. Shmakin, Tatiana G. Smirnova, Peter Wetzel, Yongkang Xue, and Zong-Liang Yang

Abstract

The Project for Intercomparison of Land-Surface Parameterization Schemes phase 2(d) experiment at Valdai, Russia, offers a unique opportunity to evaluate land surface schemes, especially snow and frozen soil parameterizations. Here, the ability of the 21 schemes that participated in the experiment to correctly simulate the thermal and hydrological properties of the soil on several different timescales was examined. Using observed vertical profiles of soil temperature and soil moisture, the impact of frozen soil schemes in the land surface models on the soil temperature and soil moisture simulations was evaluated.

It was found that when soil-water freezing is explicitly included in a model, it improves the simulation of soil temperature and its variability at seasonal and interannual scales. Although change of thermal conductivity of the soil also affects soil temperature simulation, this effect is rather weak. The impact of frozen soil on soil moisture is inconclusive in this experiment due to the particular climate at Valdai, where the top 1 m of soil is very close to saturation during winter and the range for soil moisture changes at the time of snowmelt is very limited. The results also imply that inclusion of explicit snow processes in the models would contribute to substantially improved simulations. More sophisticated snow models based on snow physics tend to produce better snow simulations, especially of snow ablation. Hysteresis of snow-cover fraction as a function of snow depth is observed at the catchment but not in any of the models.

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