The response of future projections of the North American monsoon when combining dynamical downscaling and bias correction of CCSM4 output

2016 ◽  
Vol 49 (1-2) ◽  
pp. 433-447 ◽  
Author(s):  
Jonathan D. D. Meyer ◽  
Jiming Jin
Keyword(s):  
North American ◽  
Bias Correction ◽  
Dynamical Downscaling ◽  
North American Monsoon ◽  
Future Projections ◽  
The North

Can a Regional Climate Model Improve the Ability to Forecast the North American Monsoon?

2012 ◽  
Vol 25 (23) ◽  
pp. 8212-8237 ◽  
Author(s):  
Christopher L. Castro ◽  
Hsin-I Chang ◽  
Francina Dominguez ◽  
Carlos Carrillo ◽  
Jae-Kyung Schemm ◽  
...  
Keyword(s):  
North American ◽  
Large Scale ◽  
Dynamical Downscaling ◽  
Warm Season ◽  
Regional Model ◽  
Precipitation Anomaly ◽  
Early Summer ◽  
North American Monsoon ◽  
The North ◽  
Temperature And Precipitation

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.

scholarly journals Dynamic Downscaling of the North American Monsoon with the NCEP–Scripps Regional Spectral Model from the NCEP CFS Global Model

2011 ◽  
Vol 24 (3) ◽  
pp. 653-673 ◽  
Author(s):  
Steven C. Chan ◽  
Vasubandhu Misra
Keyword(s):  
North American ◽  
Bias Correction ◽  
Seasonal Variability ◽  
Spectral Model ◽  
North American Monsoon ◽  
Southern Mexico ◽  
Low Level ◽  
Regional Spectral Model ◽  
The North ◽  
Low Level Jets

Abstract The June–September (JJAS) 2000–07 NCEP coupled Climate Forecasting System (CFS) global hindcasts are downscaled over the North and South American continents with the NCEP–Scripps Regional Spectral Model (RSM) with anomaly nesting (AN) and without bias correction (control). A diagnosis of the North American monsoon (NAM) in CFS and RSM hindcasts is presented here. RSM reduces errors caused by coarse resolution but is unable to address larger-scale CFS errors even with bias correction. CFS has relatively weak Great Plains and Gulf of California low-level jets. Low-level jets are strengthened from downscaling, especially after AN bias correction. The RSM NAM hydroclimate shares similar flaws with CFS, with problematic diurnal and seasonal variability. Flaws in both diurnal and monthly variability are forced by erroneous convection-forced divergence outside the monsoon core region in eastern and southern Mexico. NCEP reanalysis shows significant seasonal variability errors, and AN shows little improvement in regional-scale flow errors. The results suggest that extreme caution must be taken when the correction is applied relative to reanalyses. Analysis also shows that North American Regional Reanalysis (NARR) NAM seasonal variability has benefited from precipitation data assimilation, but many questions remain concerning NARR’s representation of NAM.

Vegetation controls on soil moisture distribution in the Valles Caldera, New Mexico, during the North American monsoon

Ecohydrology ◽  
2008 ◽  
Vol 1 (3) ◽  
pp. 225-238 ◽  
Author(s):  
Enrique R. Vivoni ◽  
Alex J. Rinehart ◽  
Luis A. Méndez-Barroso ◽  
Carlos A. Aragón ◽  
Gautam Bisht ◽  
...  
Keyword(s):  
Soil Moisture ◽  
New Mexico ◽  
North American ◽  
Moisture Distribution ◽  
North American Monsoon ◽  
Valles Caldera ◽  
The North ◽  
Soil Moisture Distribution

scholarly journals On the competition among aerosol number, size and composition in predicting CCN variability: a multi-annual field study in an urbanized desert

2015 ◽  
Vol 15 (12) ◽  
pp. 6943-6958 ◽  
Author(s):  
E. Crosbie ◽  
J.-S. Youn ◽  
B. Balch ◽  
A. Wonaschütz ◽  
T. Shingler ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Size Distribution ◽  
North American ◽  
Monsoon Season ◽  
Aerosol Size Distribution ◽  
North American Monsoon ◽  
Aerosol Composition ◽  
Data Set ◽  
The North ◽  
Concentration Changes

Abstract. A 2-year data set of measured CCN (cloud condensation nuclei) concentrations at 0.2 % supersaturation is combined with aerosol size distribution and aerosol composition data to probe the effects of aerosol number concentrations, size distribution and composition on CCN patterns. Data were collected over a period of 2 years (2012–2014) in central Tucson, Arizona: a significant urban area surrounded by a sparsely populated desert. Average CCN concentrations are typically lowest in spring (233 cm−3), highest in winter (430 cm−3) and have a secondary peak during the North American monsoon season (July to September; 372 cm−3). There is significant variability outside of seasonal patterns, with extreme concentrations (1 and 99 % levels) ranging from 56 to 1945 cm−3 as measured during the winter, the season with highest variability. Modeled CCN concentrations based on fixed chemical composition achieve better closure in winter, with size and number alone able to predict 82 % of the variance in CCN concentration. Changes in aerosol chemical composition are typically aligned with changes in size and aerosol number, such that hygroscopicity can be parameterized even though it is still variable. In summer, models based on fixed chemical composition explain at best only 41 % (pre-monsoon) and 36 % (monsoon) of the variance. This is attributed to the effects of secondary organic aerosol (SOA) production, the competition between new particle formation and condensational growth, the complex interaction of meteorology, regional and local emissions and multi-phase chemistry during the North American monsoon. Chemical composition is found to be an important factor for improving predictability in spring and on longer timescales in winter. Parameterized models typically exhibit improved predictive skill when there are strong relationships between CCN concentrations and the prevailing meteorology and dominant aerosol physicochemical processes, suggesting that similar findings could be possible in other locations with comparable climates and geography.

Observed relation between evapotranspiration and soil moisture in the North American monsoon region

2008 ◽  
Vol 35 (22) ◽  
Author(s):  
Enrique R. Vivoni ◽  
Hernan A. Moreno ◽  
Giuseppe Mascaro ◽  
Julio C. Rodriguez ◽  
Christopher J. Watts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North American ◽  
North American Monsoon ◽  
Monsoon Region ◽  
The North
Sign in / Sign up

Export Citation Format

Share Document