A Big Data approach to reconstruct Australia’s hydroclimate (Neogene - Quaternary)

今天，澳大利亚经历了不同的气候制度，北部是热带气候带，中部是亚热带气候带（包括沙漠和草原），南部是温带地中海式气候带。因此，澳大利亚的气候是由哈德利和费雷尔大气细胞支撑的。卫星观测和气候模式表明，热带辐合带（ITCZ）目前正在缩小和加强，预计在不久的将来这一趋势不会逆转（Byrne et al., 2018）。这些大气细胞的纬度范围和强度的更广泛变化可能在仪器记录之外的时间尺度上存在，但缺乏高分辨率的古气候重建（在空间和时间上）阻碍了对各种强迫的澳大利亚气候敏感性的评估。在这项提议中，目标是扩大通过海洋钻探获得的地质沉积序列的观测记录的范围。我的出发点是基于这样一个事实：在整个新近纪，澳大利亚的降雨模式在百万年的时间尺度上经历了重大的政权转变。然而，目前还缺乏对其时空概况的研究。我建议用大陆尺度的方法来填补这一需求，根据过去23myr的海洋钻探代理数据生成古气候时间片图。对这些地图进行排序可以产生古气候延时图，可视化水文气候和海表温度（SST）随空间和时间的变化。这种大数据方法利用了现有数据估值的潜力，这是我们以前从未有过的。

Today, Australia experiences different climate regimes, being under the tropical climate belt in the north, the subtropical climate belt (including deserts and grasslands) in the centre, and the temperate Mediterranean-style climate belt in the south. Therewith, Australian climate is underpinned by the Hadley and Ferrel atmospheric cells. Satellite observations and climate models suggest that the Intertropical Convergence Zone (ITCZ) is currently narrowing and strengthening, with no reversal of this trend projected in the near future (Byrne et al., 2018). More extensive changes in latitudinal extent and intensity of these atmospheric cells may have existed on timescales beyond the instrumental record, but the lack of high-resolution paleoclimate reconstructions (in space and time) hampers the assessment of Australian climate sensitivity to a variety of forcings. In this proposal, the ambition is to expand the range of the observational record with geological sedimentary sequences obtained through ocean drilling. My starting point is based on the fact that rainfall patterns in Australia experienced major regime shifts on million-year timescales, throughout the Neogene. However, a spatiotemporal overview is still lacking. I propose to fill this need by a continent-scale approach, generating paleoclimate time-slice maps based on ocean-drilling proxy data of the last 23 Myr. Sequencing those maps results in a paleoclimate time-lapse, visualizing hydroclimate and sea surface temperature (SST) change through space and time. This Big Data approach exploits the potential to valorise existing data in ways we have not before.