Forecasts of South China Sea surface temperature anomalies using the Nio indices and dipole mode index as predictors

Based on an empirical orthogonal function （EOF） analysis of the monthly NCEP Optimum Interpolation Sea Surface Temperature （OISST） data in the South China Sea （SCS） after removing the climatological mean and trends of SST, over the period of January 1982 to October 2003, the corresponding TCF correlates best with the Dipole Mode Index （DMI）, Nino1＋2, Nino3.4, Nino3, and Niflo4 indices with time lags of 10, 3, 6, 5, and 6 months, respectively. Thus, a statistical hindcasts in the prediction model are based on a canonical correlation analysis （CCA） model using the above indices as predictors spanning from 1993/1994 to 2003/2004 with a 1-12 month lead time after the canonical variants are calculated, using data from the training periods from January 1982 to December1992. The forecast model is successful and steady when the lead times are 1-12 months. The SCS warm event in 1998 was successfully predicted with lead times from 1-12 months irrespective of the strength or time extent. The prediction ability for SSTA is lower during weak ENSO years, in which other local factors should be also considered as local effects play a relatively important role in these years. We designed the two forecast models： one using both DMI and Nino indices and the other using only Nino indices without DMI, and compared the forecast accuracies of the two cases. The spatial distributions of forecast accuracies show different confidence areas. By turning off the DMI, the forecast accuracy is lower in the coastal areas off the Philippines in the SCS, suggesting some teleconnection may occur with the Indian Ocean in this area. The highest forecast accuracies occur when the forecast interval is five months long without using the DMI, while using both of Nino indices and DMI, the highest accuracies occur when the forecast interval time is eight months, suggesting that the Nino indices dominate the interannual variability of SST anomalies in the SCS. Meanwhile the forecast accuracy is evaluated over an independent test period of more than 11 years （1993/94 to October 2004） by comparing the model performance with a simple prediction strategy involving the persistence of sea surface temperature anomalies over a 1-12 month lead time （the persisted prediction）. Predictions based on the CCA model show a significant improvement over the persisted prediction, especially with an increased lead time （longer than 3 months）. The forecast model performs steadily and the forecast accuracy, i.e., the correlation coefficients between the observed and predicted SSTA in the SCS are about 0.5 in most middle and southern SCS areas, when the thresholds are greater than the 95% confidence level. For all 1 to 12 month lead time forecasts, the root mean square errors have a standard deviation of about 0.2. The seasonal differences in the prediction performance for the 1-12 month lead time are also examined.

RELATIONSHIP BETWEEN DIPOLE OSCILLATION OF SSTA OF INDIAN OCEAN REGION AND PRECIPITATION AND TEMPERATURE IN CHINA

The work is a general survey using SSTA data of the Indian Ocean and of precipitation at 160Chinese weather stations over 1951～1997 (47 years). It reveals that the dipole oscillation of SST, especially the dipole index of March～May, in the eastern and western parts of the ocean correlates well with the precipitation during the June～August raining season in China. As shown in analysis of 500-hPa Northern Hemisphere geopotential height height by NCEP for 1958～1995, the Indian Ocean dipole index (IODI) is closely related with geopotential height anomalies in the middle- and higher- latitudes in the Eurasian region. As a negative phase year of IODI corresponds to significant Pacific-Japan (P J) wavetrain, it is highly likely that the SST for the dipole may affect the precipitation in China through the wavetrain. Additionally, correlation analysis of links between SST dipole index of the Indian Ocean region and air temperature in China also shows good correlation between the former and wintertime temperature in southern China.

Assessment of the Off-season Rainfall of January to February 2020 and Its Socio Economic Implications in Tanzania: A Case Study of the Northern Coast of Tanzania

This article examines the off season rainfall in northern coast Tanzania(NCT)including Zanzibar which occurred in January and February 2020(JF).Like the JF rainfalls of 2001,2004,2010,2016 and 2018,the JF(2020)rainfall was more unique in damages including loss of lives,properties and infrastructures.The study used the NCEP/NCAR reanalysis data to examine the cause of uniqueness of JF rainfall in 2001,2004,2010,2016,2018 and 2020 over NCT and Zanzibar.These datasets include monthly mean u,v wind at 850,700,500,and 200 mb;SSTs,mean sea level pressure(MSLP)anomalies,Dipole Mode Index(DMI),and monthly rainfall from NCT and Zanzibar stations.Datasets were processed and calculated into long term,seasonal,and monthly averages,indeed,Precipitation Index(PI)was calculated.Correlation analysis between the rainfall(December to January),SST,DMI and 850 mb wind vectors;and long-term percentage contribution of investigated parameters was calculated.Results revealed significant positive and negative correlations between JF rainfall,SSTs and DMI.Moreover,JFs of 2004 and 2016 had higher rainfalls of 443 mm with percentage contribution of up to 406%,while January and February,2020 had the highest of 269.1 and 101.1mm in Zanzibar and 295 and 146.1 mm over and NCT areas,with highest January long-term rainfall contribution of 356%in Zanzibar and 526%over NCT.The DJF(2019/20)had the highest rainfall record of 649.5 mm in Zanzibar contributing up to 286%,while JF 2000 rainfall had a good spatial and temporal distribution over most NCT areas.JF,2020 rainfall had impacts of more than 20 people died in Lindi and several infrastructures including Kiyegeya Bridge in Morogoro were damaged.Conclusively,more research works on understanding the dynamics of wet and dry JF seasons should be conducted.