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.

Global monthly sea surface temperature and sea ice reconstruction for historical simulations

2020 ◽  
Author(s):  
Eric Samakinwa ◽  
Stefan Brönnimann
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
General Circulation Models ◽  
Sea Surface ◽  
Annual Variability ◽  
Instrumental Records

<p>Variability in Sea Surface Temperature (SST) is one of the prime sources of intra-annual variability, and also an important boundary condition for Atmospheric General Circulation Models (AGCMs). In many AGCM simulations, SST and Sea Ice Concentration (SIC) are prescribed. While SSTs are specified according to observations available in recent period of instrumental records (1850 – present), SIC depends on climatological averages with less variability prior to the inception of satellite measurements. This limits our understanding of large-scale climate variations in the past.</p><p>In this study, we augment multi-proxy reconstructed annual mean temperature of Neukom et al. (2019) with intra-annual variability from HadISST (v2.0), for 850 years (1000 – 1849). Intra-seasonal variability, such as the phase-locking of El-Nino Southern Oscillation, Indian Ocean Dipole and Tropical Atlantic SST indices to annual-cycle, are utilized. The intra-annual component of HadISST and SST indices estimated from the multi-proxy reconstructed annual mean, are used to develop grid-based multivariate linear regression models using the Frisch-Waugh-Lovell theorem, in a monthly stratified approach. Furthermore, we introduce a scaling technique to ensure homogeneous mean and variance, similar to that of the target. SST observations obtained from ship measurements by ICOADS before 1850, will be integrated in an off-line data assimilation approach.</p><p>Similarly, we reconstruct SIC via analogue resampling of HadISST SIC (1941 – 2000), for both hemispheres. We pool our analogues in four seasons, comprising of 3 months each, such that for each month within a season, there are 180 possible analogues. The best analogues are selected based on correlation coefficients between reconstructed SST and its target.</p>

scholarly journals Assessing bias corrections of oceanic surface conditions for atmospheric models

2019 ◽  
Vol 12 (1) ◽  
pp. 321-342 ◽  
Author(s):  
Julien Beaumet ◽  
Gerhard Krinner ◽  
Michel Déqué ◽  
Rein Haarsma ◽  
Laurent Li
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
General Circulation Models ◽  
Substantial Improvement ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Analogue Method

Abstract. Future sea surface temperature and sea-ice concentration from coupled ocean–atmosphere general circulation models such as those from the CMIP5 experiment are often used as boundary forcings for the downscaling of future climate experiments. Yet, these models show some considerable biases when compared to the observations over present climate. In this paper, existing methods such as an absolute anomaly method and a quantile–quantile method for sea surface temperature (SST) as well as a look-up table and a relative anomaly method for sea-ice concentration (SIC) are presented. For SIC, we also propose a new analogue method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiments and some real-case applications using observations. We find that with respect to other previously existing methods, the analogue method is a substantial improvement for the bias correction of future SIC. Consistency between the constructed SST and SIC fields is an important constraint to consider, as is consistency between the prescribed sea-ice concentration and thickness; we show that the latter can be ensured by using a simple parameterisation of sea-ice thickness as a function of instantaneous and annual minimum SIC.

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

Optimal Tropical Sea Surface Temperature Forcing of North American Drought

2010 ◽  
Vol 23 (14) ◽  
pp. 3907-3917 ◽  
Author(s):  
Sang-Ik Shin ◽  
Prashant D. Sardeshmukh ◽  
Robert S. Webb
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North American ◽  
General Circulation ◽  
Southern Oscillation ◽  
Circulation Model ◽  
Sea Surface ◽  
Drought Severity ◽  
Sst Anomaly

Abstract The optimal anomalous sea surface temperature (SST) pattern for forcing North American drought is identified through atmospheric general circulation model integrations in which the response of the Palmer drought severity index (PDSI) is determined for each of 43 prescribed localized SST anomaly “patches” in a regular array over the tropical oceans. The robustness and relevance of the optimal pattern are established through the consistency of results obtained using two different models, and also by the good correspondence of the projection time series of historical tropical SST anomaly fields on the optimal pattern with the time series of the simulated PDSI in separate model integrations with prescribed time-varying observed global SST fields for 1920–2005. It is noteworthy that this optimal drought forcing pattern differs markedly in the Pacific Ocean from the dominant SST pattern associated with El Niño–Southern Oscillation (ENSO), and also shows a large sensitivity of North American drought to Indian and Atlantic Ocean SSTs.

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