scholarly journals Large-scale anomalies in sea-surface temperature and air-sea fluxes during wind relaxation events off the United States West Coast in summer

2017 ◽  
Vol 122 (3) ◽  
pp. 2574-2594 ◽  
Author(s):  
Kayla R. Flynn ◽  
Melanie R. Fewings ◽  
Christopher Gotschalk ◽  
Kelly Lombardo
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
West Coast ◽  
Large Scale ◽  
The United States ◽  
Sea Surface

scholarly journals North American April tornado occurrences linked to global sea surface temperature anomalies

2019 ◽  
Vol 5 (8) ◽  
pp. eaaw9950 ◽  
Author(s):  
J.-E. Chu ◽  
A. Timmermann ◽  
J.-Y. Lee
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North American ◽  
General Circulation ◽  
Large Scale ◽  
Moisture Flux ◽  
General Circulation Models ◽  
The United States ◽  
Sea Surface

Annual tornado occurrences over North America display large interannual variability and a statistical linkage to sea surface temperature (SST) anomalies. However, the underlying physical mechanisms for this connection and its modulation in a rapidly varying seasonal environment still remain elusive. Using tornado data over the United States from 1954 to 2016 in combination with SST-forced atmospheric general circulation models, we show a robust dynamical linkage between global SST conditions in April, the emergence of the Pacific-North American teleconnection pattern (PNA), and the year-to-year tornado activity in the Southern Great Plains (SGP) region of the United States. Contrasting previous studies, we find that only in April SST-driven atmospheric circulation anomalies can effectively control the northward moisture-laden flow from the Gulf of Mexico, boosting low-level moisture flux convergence over the SGP. These strong large-scale connections are absent in other months because of the strong seasonality of the PNA and background moisture conditions.

Present and Past Sea Surface Temperatures: A Recipe for Better Seasonal Climate Forecasts

2020 ◽  
Vol 35 (4) ◽  
pp. 1221-1234
Author(s):  
Matthew B. Switanek ◽  
Joseph J. Barsugli ◽  
Michael Scheuerer ◽  
Thomas M. Hamill
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
The United States ◽  
Cold Season ◽  
Sea Surface ◽  
Precipitation Forecast ◽  
The North ◽  
Model Ensembles ◽  
Tropical Sea

AbstractMonthly tropical sea surface temperature (SST) data are used as predictors to make statistical forecasts of cold season (November–March) precipitation and temperature for the contiguous United States. Through the use of the combined-lead sea surface temperature (CLSST) model, predictive information is discovered not just in recent SSTs but also from SSTs up to 18 months prior. We find that CLSST cold season forecast anomaly correlation skill is higher than that of the North American Multimodel Ensemble (NMME) and the SEAS5 model from the European Centre for Medium-Range Weather Forecasts (ECMWF) when averaged over the United States for both precipitation and 2-m air temperature. The precipitation forecast skill obtained by CLSST in parts of the Intermountain West is of particular interest because of its implications for water resources. In those regions, CLSST dramatically improves the skill over that of the dynamical model ensembles, which can be attributed to a robust statistical response of precipitation in this region to SST anomalies from the previous year in the tropical Pacific.

scholarly journals Summer aridity in the United States: Response to mid-Holocene changes in insolation and sea surface temperature

2006 ◽  
Vol 33 (22) ◽  
Author(s):  
Noah S. Diffenbaugh ◽  
Moetasim Ashfaq ◽  
Bryan Shuman ◽  
John W. Williams ◽  
Patrick J. Bartlein
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
The United States ◽  
Sea Surface

scholarly journals Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables

2010 ◽  
Vol 23 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Liew Juneng ◽  
Fredolin T. Tangang ◽  
Hongwen Kang ◽  
Woo-Jin Lee ◽  
Yap Kok Seng
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Winter Season ◽  
Peninsular Malaysia ◽  
Sea Surface ◽  
Asia Pacific ◽  
Forecast Data ◽  
Multimodel Ensembles

Abstract This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

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