scholarly journals Seasonal prediction of regional surface air temperature and first‐flowering date over South Korea

2015 ◽  
Vol 35 (15) ◽  
pp. 4791-4801 ◽  
Author(s):  
Jina Hur ◽  
Joong‐Bae Ahn
Keyword(s):  
South Korea ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Seasonal Prediction ◽  
Flowering Date ◽  
First Flowering Date

scholarly journals Seasonal Prediction of Surface Air Temperature across Vietnam Using the Regional Climate Model Version 4.2 (RegCM4.2)

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Tan Phan Van ◽  
Hiep Van Nguyen ◽  
Long Trinh Tuan ◽  
Trung Nguyen Quang ◽  
Thanh Ngo-Duc ◽  
...  
Keyword(s):  
Air Temperature ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Seasonal Prediction ◽  
Ensemble Prediction ◽  
Model Bias ◽  
Skill Scores ◽  
Model Predictions

To investigate the ability of dynamical seasonal climate predictions for Vietnam, the RegCM4.2 is employed to perform seasonal prediction of 2 m mean (T2m), maximum (Tx), and minimum (Tn) air temperature for the period from January 2012 to November 2013 by downscaling the NCEP Climate Forecast System (CFS) data. For model bias correction, the model and observed climatology is constructed using the CFS reanalysis and observed temperatures over Vietnam for the period 1980–2010, respectively. The RegCM4.2 forecast is run four times per month from the current month up to the next six months. A model ensemble prediction initialized from the current month is computed from the mean of the four runs within the month. The results showed that, without any bias correction (CTL), the RegCM4.2 forecast has very little or no skill in both tercile and value predictions. With bias correction (BAS), model predictions show improved skill. The experiment in which the results from the BAS experiment are further successively adjusted (SUC) with model bias at one-month lead time of the previous run showed further improvement compared to CTL and BAS. Skill scores of the tercile probability forecasts were found to exceed 0.3 for most of the target months.

scholarly journals Influence of air temperature on the first flowering date ofPrunus yedoensisMatsum

2014 ◽  
Vol 4 (3) ◽  
pp. 292-299 ◽  
Author(s):  
Peijian Shi ◽  
Zhenghong Chen ◽  
Qingpei Yang ◽  
Marvin K. Harris ◽  
Mei Xiao
Keyword(s):  
Air Temperature ◽  
Flowering Date ◽  
First Flowering Date

scholarly journals Impact of Snow Depth Initialization on Seasonal Prediction of Surface Air Temperature over East Asia for Winter Season

Atmosphere ◽  
2012 ◽  
Vol 22 (1) ◽  
pp. 117-128 ◽  
Author(s):  
Sung-Ho Woo ◽  
Jee-Hoon Jeong ◽  
Baek-Min Kim ◽  
Seong-Joong Kim
Keyword(s):  
East Asia ◽  
Air Temperature ◽  
Snow Depth ◽  
Winter Season ◽  
Surface Air Temperature ◽  
Seasonal Prediction

Interdecadal Changes in the Relationship between Wintertime Surface Air Temperature over the Indo-China Peninsula and ENSO

2021 ◽  
pp. 1-45
Author(s):  
Juncong Li ◽  
Zhiping Wen ◽  
Xiuzhen Li ◽  
Yuanyuan Guo
Keyword(s):  
Air Temperature ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
Sea Surface ◽  
Warm Temperature ◽  
Southwesterly Wind ◽  
The Relationship ◽  
Sst Anomalies

AbstractInterdecadal variations of the relationship between El Niño-Southern Oscillation (ENSO) and the Indo-China Peninsula (ICP) surface air temperature (SAT) in winter are investigated in the study. Generally, there exists a positive correlation between them during 1958–2015 because the ENSO-induced anomalous western North Pacific anticyclone (WNPAC) is conducive to pronounced temperature advection anomalies over the ICP. However, such correlation is unstable in time, having experienced a high-to-low transition around the mid-1970s and a recovery since the early-1990s. This oscillating relationship is owing to the anomalous WNPAC intensity in different decades. During the epoch of high correlation, the anomalous WNPAC and associated southwesterly winds over the ICP are stronger, which brings amounts of warm temperature advections and markedly heats the ICP. Differently, a weaker WNPAC anomaly and insignificant ICP SAT anomalies are the circumstances for the epoch of low correlation. It is also found that substantial southwesterly wind anomalies over the ICP related to the anomalous WNPAC occur only when large sea surface temperature (SST) anomalies over the northwest Indian Ocean (NWIO) coincide with ENSO (namely when the ENSO-NWIO SST connection is strong). The NWIO SST anomalies are capable of driving favorable atmospheric circulation that effectively alters ICP SAT and efficiently modulates the ENSO-ICP SAT correlation, which is further supported by numerical simulations utilizing the Community Atmospheric Model, version 4 (CAM4). This paper emphasizes the non-stationarity of the ENSO-ICP SAT relationship and also uncovers the underlying modulation factors, which has important implications for the seasonal prediction of the ICP temperature.

scholarly journals Probabilistic Seasonal Prediction of Meteorological Drought Using the Bootstrap and Multivariate Information

2015 ◽  
Vol 54 (7) ◽  
pp. 1510-1522 ◽  
Author(s):  
Ali Behrangi ◽  
Hai Nguyen ◽  
Stephanie Granger
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Meteorological Drought ◽  
Seasonal Prediction ◽  
Skill Score ◽  
Drought Severity ◽  
Initial Surface ◽  
Predictive Probability ◽  
Multivariate Prediction ◽  
The Impact

AbstractIn the present work, a probabilistic ensemble method using the bootstrap is developed to predict the future state of the standard precipitation index (SPI) commonly used for drought monitoring. The methodology is data driven and has the advantage of being easily extended to use more than one variable as predictors. Using 110 years of monthly observations of precipitaton, surface air temperature, and the Niño-3.4 index, the method was employed to assess the impact of the different variables in enhancing the prediction skill. A predictive probability density function (PDF) is produced for future 6-month SPI, and a log-likelihood skill score is used to cross compare various combination scenarios using the entire predictive PDF and with reference to the observed values set aside for validation. The results suggest that the multivariate prediction using complementary information from 3- and 6-month SPI and initial surface air temperature significantly improves seasonal prediction skills for capturing drought severity and delineation of drought areas based on observed 6-month SPI. The improvement is observed across all seasons and regions over the continental United States relative to other prediction scenarios that ignore the surface air temperature information.

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