Role of Antarctic Circumpolar Current in decadal climate variability over southern Africa

第6回極域科学シンポジウム分野横断セッション：[IG] 全球環境変動を駆動する南大洋・南極氷床11月17日（火）　国立極地研究所 2階 大会議

Role of Weddell Sea ice in South Atlantic atmospheric variability

We investigated the role of Weddell Sea ice in atmospheric variability over the South Atlantic by conducting observational data analysis and coupled general circulation model (CGCM) experiments. Weddell Sea ice exhibits a pronounced interannual variability in early austral summer (November-January). Low sea-ice concentration (SIC) anomalies in the Weddell Sea are strongly associated with anticyclonic atmospheric circulation anomalies in the South Atlantic. Composite analysis reveals that the low SIC anomalies in the Weddell Sea may be attributed to increased solar radiation and northwesterly wind anomalies. The low SIC anomalies, in turn, contribute to warmer skin temperature in the band of 60-70°S and enhance the near-surface atmospheric stability north of this band, implying favorable conditions for sustaining the anticyclonic circulation anomalies in the South Atlantic. This intriguing association between the SIC and atmospheric circulation anomalies is also simulated in CGCM experiments, e.g. when the interannual sea surface temperature variability in the tropics and mid-latitudes is suppressed. These results suggest that Weddell Sea ice, which may undergo interannual variation via air-sea-ice interactions in the high latitudes, influences atmospheric variability over the South Atlantic

Examining the impact of multiple climate forcings on simulated Southern Hemisphere climate variability

The study examines the influence of external climate forcings, and atmosphere–ocean–sea–ice coupled interaction on the Southern Hemisphere (SH) atmospheric circulation variability. We analysed observed and simulated changes in view of Antarctic sea–ice and Southern Ocean surface temperature trends over recent decades. The experiment embraces both idealised and comprehensive methods that involves an Earth System Model (ESM) prototype. The sensitivity experiment is conducted in a manner that decomposes the signatures of sea–ice, sea surface temperature and feedback mechanisms. The results reveal that the Southern Annular Mode (SAM) multidecadal variability is found to be modulated by coupled interactions whereas its sub-seasonal to interannual vacillation seems to follow a random trajectory. The latter may strengthen the notion that its predictability is limited even with the use of ESMs. Most of the atmospheric circulation variability and recent changes may be explained by the ocean thermal forcing and coupled interactions. However, the influence of sea–ice forcing alone is largely indistinguishable and predominantly localised in nature. The result also confirms that the Antarctic dipole-like sea–ice pattern, a leading climate mode in the SH, has intensified in the last three decades irrespective of season. The probable indication is that processes within the Southern Ocean may play a key role, which deserves further investigation.The National Research foundation through the Alliance for Collaboration on Climate & Earth Systems Science (ACCESS). The iDEWS project, which supported the study under the auspices of the Japan Science and Technology Agency/Japan Agency for Medical Research and Development through the Science and Technology Research Partnership for Sustainable Development (SATREPS), and the ACCESS in South Africa.http://link.springer.com/journal/3822021-04-27hj2020Geography, Geoinformatics and Meteorolog

Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model

Although there have been enormous demands and efforts to develop an early warning system for malaria, no sustainable system has remained. Well-organized malaria surveillance and high-quality climate forecasts are required to sustain a malaria early warning system in conjunction with an effective malaria prediction model. We aimed to develop a weather-based malaria prediction model using a weekly time-series data including temperature, precipitation, and malaria cases from 1998 to 2015 in Vhembe, Limpopo, South Africa and apply it to seasonal climate forecasts. The malaria prediction model performed well for short-term predictions (correlation coefficient, r?>?0.8 for 1- and 2-week ahead forecasts). The prediction accuracy decreased as the lead time increased but retained fairly good performance (r?>?0.7) up to the 16-week ahead prediction. The demonstration of the malaria prediction process based on the seasonal climate forecasts showed the short-term predictions coincided closely with the observed malaria cases. The weather-based malaria prediction model we developed could be applicable in practice together with skillful seasonal climate forecasts and existing malaria surveillance data. Establishing an automated operating system based on real-time data inputs will be beneficial for the malaria early warning system, and can be an instructive example for other malaria-endemic areas.Publisher Correction: A supplementary file containing Fig S1 was omitted from the original version of this Article. This has been corrected in the HTML version of the Article; the PDF version was correct at time of publication. https://doi.org/10.1038/s41598-020-58890-

Seasonally lagged effects of climatic factors on malaria incidence in South Africa

Globally, malaria cases have drastically dropped in recent years. However, a high incidence of malaria remains in some sub-Saharan African countries. South Africa is mostly malaria-free, but northeastern provinces continue to experience seasonal outbreaks. Here we investigate the association between malaria incidence and spatio-temporal climate variations in Limpopo. First, dominant spatial patterns in malaria incidence anomalies were identified using self-organizing maps. Composite analysis found significant associations among incidence anomalies and climate patterns. A high incidence of malaria during the pre-peak season (Sep-Nov) was associated with the climate phenomenon La Nina and cool air temperatures over southern Africa. There was also high precipitation over neighbouring countries two to six months prior to malaria incidence. During the peak season (Dec-Feb), high incidence was associated with positive phase of Indian Ocean Subtropical Dipole. Warm temperatures and high precipitation in neighbouring countries were also observed two months prior to increased malaria incidence. This lagged association between regional climate and malaria incidence suggests that in areas at high risk for malaria, such as Limpopo, management plans should consider not only local climate patterns but those of neighbouring countries as well. These findings highlight the need to strengthen cross-border control of malaria to minimize its spread