scholarly journals A Skillful Prediction Model for Winter NAO Based on Atlantic Sea Surface Temperature and Eurasian Snow Cover

2015 ◽  
Vol 30 (1) ◽  
pp. 197-205 ◽  
Author(s):  
Baoqiang Tian ◽  
Ke Fan
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Snow Cover ◽  
Correlation Coefficient ◽  
Cross Validation ◽  
Sea Surface ◽  
Interdecadal Variability ◽  
Eurasian Snow Cover ◽  
Eurasian Snow

Abstract A new statistical forecast scheme, referred to as scheme 1, is developed using observed autumn Atlantic sea surface temperature (SST) and Eurasian snow cover in the preceding autumn to predict the upcoming winter North Atlantic Oscillation (NAO) using the year-to-year increment prediction approach (i.e., DY approach). Two predictors for the year-to-year increment are identified that are available in the preceding autumn. Cross-validation tests for the period 1950–2011 and independent hindcasts for the period 1990–2011 are performed to validate the prediction ability of the proposed technique. The cross-validation test results for 1950–2011 reveal a high correlation coefficient of 0.52 (0.58) between the predicted and observed NAO indices (DY of the NAO). The model also successfully predicts the independent hindcasts for the period 1990–2011 with a correlation coefficient of 0.55 (0.74). In addition, scheme 0 (i.e., anomaly approach) is established using the SST and snow cover anomalies during the preceding autumn. Compared with scheme 0, this new prediction model has higher predictive skill in reproducing the interdecadal variability of NAO. Therefore, this study provides an effective climate prediction scheme for the interannual and interdecadal variability of NAO in boreal winter.

scholarly journals Seasonal Covariability of Dryness or Wetness in China and Global Sea Surface Temperature

2020 ◽  
Vol 33 (2) ◽  
pp. 727-747
Author(s):  
Chunxiang Li ◽  
Chunzai Wang ◽  
Tianbao Zhao
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Sea Surface ◽  
Interdecadal Variability ◽  
Coupled Modes ◽  
Indian Ocean Basin Mode ◽  
Global Sea Surface Temperature

AbstractSeasonal covariability of the dryness/wetness in China and global sea surface temperature (SST) is investigated by using the monthly self-calibrated Palmer drought severity index (PDSI) data and other data from 1950 to 2014. The singular value decomposition (SVD) analysis shows two recurring PDSI–SST coupled modes. The first SVD mode of PDSI is associated with the warm phases of the eastern Pacific–type El Niño–Southern Oscillation (ENSO), the interdecadal Pacific oscillation (IPO) or Pacific decadal oscillation (PDO), the Indian Ocean basin mode (IOBM) in the autumn and winter, and the cold phase of the IOBM in the spring. Meanwhile, the Atlantic multidecadal oscillation (AMO) pattern appears in every season except the autumn. The second SVD mode of PDSI is accompanied by a central Pacific–type El Niño developing from the winter to autumn over the tropical Pacific and a positive phase of IPO or PDO from the winter to summer. Moreover, an AMO pattern is observed in all seasons except the summer, whereas the SST over the tropical Indian Ocean shows negligible variations. The further analyses suggest that AMO remote forcing may be a primary factor influencing interdecadal variability of PDSI in China, and interannual to interdecadal variability of PDSI seems to be closely associated with the ENSO-related events. Meanwhile, the IOBM may be a crucial factor in interannual variability of PDSI during its mature phase in the spring. In general, the SST-related dryness/wetness anomalies can be explained by the associated atmospheric circulation changes.

scholarly journals Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Ocean Science ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 301-320 ◽  
Author(s):  
Mei Hong ◽  
Xi Chen ◽  
Ren Zhang ◽  
Dong Wang ◽  
Shuanghe Shen ◽  
...  
Keyword(s):  
Statistical Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficient ◽  
El Niño ◽  
Temperature Anomaly ◽  
El Nino ◽  
Sea Surface ◽  
Sea Surface Temperature Anomaly

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical–statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

scholarly journals Long-range prediction of monsoon onset over Kerala

MAUSAM ◽  
2022 ◽  
Vol 46 (3) ◽  
pp. 287-290
Author(s):  
M. RAJEEVAN ◽  
D. P. DUBEY
Keyword(s):  
Surface Temperature ◽  
Snow Cover ◽  
Long Range ◽  
Late Onset ◽  
Central India ◽  
Monsoon Onset ◽  
Onset Date ◽  
Eurasian Snow Cover ◽  
Eurasian Snow ◽  
Long Range Prediction

ABSTRACT. Using the data of 33 years ( 1961-1993) the effect of the intensity of heat low over central India during the Month of April and Winter (December to February) Eurasian snow cover on interannual variation of monsoon date over Kerala were examined. Composite mean surface temperature over central India during the month of April was higher during early onset years by 3.5° C. April mean surface temperature index (MST) and Winter (December to February) Eurasian snow cover (WSC) are significantly correlated with Monsoon onset dates al 1% and 5% significant levels respectively. Lower surface temperature and excessive snow cover indicate a late onset. A regression equation was developed for long range prediction of onset date over Kerala using MST and WSC as independent variables. The root mean square error (RMSE) of the relationship was found to be 4.6 days. The model was tested using independent data of five years and was found performing well. Contingency tables were developed between the pairs MOD and WSC and MOD and MST. The tables can be used for probability forecasts of early and late onset years.  

scholarly journals Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network

2020 ◽  
Vol 8 (4) ◽  
pp. 249 ◽  
Author(s):  
Zhen Zhang ◽  
Xinliang Pan ◽  
Tao Jiang ◽  
Baikai Sui ◽  
Chenxi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Short Term Memory ◽  
Absolute Error ◽  
Sea Surface ◽  
Neural Network Algorithm ◽  
Stability And Accuracy ◽  
Gated Recurrent Unit

The sea surface temperature (SST) is an important parameter of the energy balance on the Earth’s surface. SST prediction is crucial to marine production, marine protection, and climate prediction. However, the current SST prediction model still has low precision and poor stability. In this study, a medium and long-term SST prediction model is designed on the basis of the gated recurrent unit (GRU) neural network algorithm. This model captures the SST time regularity by using the GRU layer and outputs the predicted results through the fully connected layer. The Bohai Sea, which is characterized by a large annual temperature difference, is selected as the study area, and the SSTs on different time scales (monthly and quarterly) are used to verify the practicability and stability of the model. The results show that the designed SST prediction model can efficiently fit the results of the real sea surface temperature, and the correlation coefficient is above 0.98. Regardless of whether monthly or quarterly data are used, the proposed network model performs better than long short-term memory in terms of stability and accuracy when the length of the prediction increases. The root mean square error and mean absolute error of the predicted SST are mostly within 0–2.5 °C.

scholarly journals Forecasting experiments of a dynamical-statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

2017 ◽  
Author(s):  
Mei Hong ◽  
Xi Chen ◽  
Ren Zhang ◽  
Dong Wang ◽  
Shuanghe Shen ◽  
...  
Keyword(s):  
Statistical Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficient ◽  
El Niño ◽  
Temperature Anomaly ◽  
El Nino ◽  
Sea Surface ◽  
Sea Surface Temperature Anomaly

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical-statistical forecast model of sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamic reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical-statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a correlation coefficient of approximately 0.80 and a mean absolute percentage error of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field, but the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in summer and those in winter is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

Skillful prediction of the winter North Atlantic Oscillation

2020 ◽  
Author(s):  
Ke Fan
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Coupled Model ◽  
Sea Surface ◽  
Prediction Skill ◽  
Statistical Prediction ◽  
Coupled Models

<p>The winter North Atlantic oscillation (NAO), is a crucial part of our understanding of Eurasian and Atlantic climate variability and predictability. However, both the statistical forecast model and the coupled model showed the limited forecasting skill for the winter NAO. In this study, we developed effective prediction schemes based on the interannual increment prediction method and verified their performance based on the climate hindcasts of the coupled ocean–atmosphere climate models（DEMETER, ENSEMBLES，CFSV2）. This approach utilizes the year-to-year increment of a variable (i.e. a difference in a variable between the current year and the previous year, e.g. DY of a variable) as the predictand rather than the anomaly of the variable. The results demonstrate that the new schemes can generally improve prediction skill of the winter NAO compared to the raw coupled model’s output（DEMETER, ENSEMBLES，CFSV2）. Also, the new schemes show higher skill in prediction of abnormal NAO cases than the climatological prediction. Scheme-I uses just the NAO in the form of year-to-year increments as a predictor that is derived from the direct outputs of the models. Scheme-II is a hybrid prediction model that contains two predictors: the NAO derived from the coupled models, and the observed preceding autumn Atlantic sea surface temperature in the form of year-to-year increments. Scheme-II shows an even better prediction skill of the winter NAO than Scheme-I. Besides, a new statistical forecast scheme was also developed using observed North Atlantic sea surface temperature and Eurasian snow cover in the preceding autumn to predict the upcoming winter NAO. The statistical prediction model showed high predictive skill in reproducing the interannual and interdecadal variability of NAO in boreal winter.</p>

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