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Christopher L. Castro
,
Hsin-I Chang
,
Francina Dominguez
,
Carlos Carrillo
,
Jae-Kyung Schemm
, and
Hann-Ming Henry Juang

Abstract

Global climate models are challenged to represent the North American monsoon, in terms of its climatology and interannual variability. To investigate whether a regional atmospheric model can improve warm season forecasts in North America, a retrospective Climate Forecast System (CFS) model reforecast (1982–2000) and the corresponding NCEP–NCAR reanalysis are dynamically downscaled with the Weather Research and Forecasting model (WRF), with similar parameterization options as used for high-resolution numerical weather prediction and a new spectral nudging capability. The regional model improves the climatological representation of monsoon precipitation because of its more realistic representation of the diurnal cycle of convection. However, it is challenged to capture organized, propagating convection at a distance from terrain, regardless of the boundary forcing data used. Dynamical downscaling of CFS generally yields modest improvement in surface temperature and precipitation anomaly correlations in those regions where it is already positive in the global model. For the North American monsoon region, WRF adds value to the seasonally forecast temperature only in early summer and does not add value to the seasonally forecast precipitation. CFS has a greater ability to represent the large-scale atmospheric circulation in early summer because of the influence of Pacific SST forcing. The temperature and precipitation anomaly correlations in both the global and regional model are thus relatively higher in early summer than late summer. As the dominant modes of early warm season precipitation are better represented in the regional model, given reasonable large-scale atmospheric forcing, dynamical downscaling will add value to warm season seasonal forecasts. CFS performance appears to be inconsistent in this regard.

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Jeremy J. Mazon
,
Christopher L. Castro
,
David K. Adams
,
Hsin-I Chang
,
Carlos M. Carrillo
, and
John J. Brost

Abstract

Almost one-half of the annual precipitation in the southwestern United States occurs during the North American monsoon (NAM). Given favorable synoptic-scale conditions, organized monsoon thunderstorms may affect relatively large geographic areas. Through an objective analysis of atmospheric reanalysis and observational data, the dominant synoptic patterns associated with NAM extreme events are determined for the period from 1993 to 2010. Thermodynamically favorable extreme-weather-event days are selected on the basis of atmospheric instability and precipitable water vapor from Tucson, Arizona, rawinsonde data. The atmospheric circulation patterns at 500 hPa associated with the extreme events are objectively characterized using principal component analysis. The first two dominant modes of 500-hPa geopotential-height anomalies of the severe-weather-event days correspond to type-I and type-II severe-weather-event patterns previously subjectively identified by Maddox et al. These patterns reflect a positioning of the monsoon ridge to the north and east or north and west, respectively, from its position in the “Four Corners” region during the period of the climatological maximum of monsoon precipitation from mid-July to mid-August. An hourly radar–gauge precipitation product shows evidence of organized, westward-propagating convection in Arizona during the type-I and type-II severe weather events. This new methodological approach for objectively identifying severe weather events may be easily adapted to inform operational forecasting or analysis of gridded climate data.

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Thang M. Luong
,
Christopher L. Castro
,
Hsin-I Chang
,
Timothy Lahmers
,
David K. Adams
, and
Carlos A. Ochoa-Moya

Abstract

Long-term changes in North American monsoon (NAM) precipitation intensity in the southwestern United States are evaluated through the use of convective-permitting model simulations of objectively identified severe weather events during “historical past” (1950–70) and “present day” (1991–2010) periods. Severe weather events are the days on which the highest atmospheric instability and moisture occur within a long-term regional climate simulation. Simulations of severe weather event days are performed with convective-permitting (2.5 km) grid spacing, and these simulations are compared with available observed precipitation data to evaluate the model performance and to verify any statistically significant model-simulated trends in precipitation. Statistical evaluation of precipitation extremes is performed using a peaks-over-threshold approach with a generalized Pareto distribution. A statistically significant long-term increase in atmospheric moisture and instability is associated with an increase in extreme monsoon precipitation in observations and simulations of severe weather events, corresponding to similar behavior in station-based precipitation observations in the Southwest. Precipitation is becoming more intense within the context of the diurnal cycle of convection. The largest modeled increases in extreme-event precipitation occur in central and southwestern Arizona, where mesoscale convective systems account for a majority of monsoon precipitation and where relatively large modeled increases in precipitable water occur. Therefore, it is concluded that a more favorable thermodynamic environment in the southwestern United States is facilitating stronger organized monsoon convection during at least the last 20 years.

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Rezaul Mahmood
,
Roger A. Pielke Sr.
,
Kenneth G. Hubbard
,
Dev Niyogi
,
Gordon Bonan
,
Peter Lawrence
,
Richard McNider
,
Clive McAlpine
,
Andres Etter
,
Samuel Gameda
,
Budong Qian
,
Andrew Carleton
,
Adriana Beltran-Przekurat
,
Thomas Chase
,
Arturo I. Quintanar
,
Jimmy O. Adegoke
,
Sajith Vezhapparambu
,
Glen Conner
,
Salvi Asefi
,
Elif Sertel
,
David R. Legates
,
Yuling Wu
,
Robert Hale
,
Oliver W. Frauenfeld
,
Anthony Watts
,
Marshall Shepherd
,
Chandana Mitra
,
Valentine G. Anantharaj
,
Souleymane Fall
,
Robert Lund
,
Anna Treviño
,
Peter Blanken
,
Jinyang Du
,
Hsin-I Chang
,
Ronnie Leeper
,
Udaysankar S. Nair
,
Scott Dobler
,
Ravinesh Deo
, and
Jozef Syktus
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