Palynology of the Middle to Late Miocene from ODP Hole 175-1085A off the west coast of South Africa

Aim To test whether the radiation of the extremely rich Cape flora is correlated with marine-driven climate change. Location Middle to Late Miocene in the south-east Atlantic and the Benguela Upwelling System (BUS) off the west coast of South Africa. Methods We studied the palynology of the thoroughly dated Middle to Late Miocene sediments of Ocean Drilling Program (ODP) Site 1085 retrieved from the Atlantic off the mouth of the Orange River. Both marine upwelling and terrestrial input are recorded at this site, which allows a direct correlation between changes in the terrestrial flora and the marine BUS in the south-east Atlantic. Results Pollen types from plants of tropical affinity disappeared, and those from the Cape flora gradually increased, between 10 and 6 Ma. Our data corroborate the inferred dating of the diversification in Aizoaceae c. 8 Ma. Main conclusions Inferred vegetation changes for the Late Miocene south-western African coast are the disappearance of Podocarpus-dominated Afromontane forests, and a change in the vegetation of the coastal plain from tropical grassland and thicket to semi-arid succulent vegetation. These changes are indicative of an increased summer drought, and are in step with the development of the southern BUS. They pre-date the Pliocene uplift of the East African escarpment, suggesting that this did not play a role in stimulating vegetation change. Some Fynbos elements were present throughout the recorded period (from 11 Ma), suggesting that at least some elements of this vegetation were already in place during the onset of the BUS. This is consistent with a marine-driven climate change in south-western Africa triggering substantial radiation in the terrestrial flora, especially in the Aizoaceae.

High-resolution dynamical downscaling products for the North Slope of Alaska and surrounding areas, links to model result files

Climatic changes are most pronounced in northern high latitude regions. Yet, there is a paucity of observational data, both spatially and temporally, such that regional-scale dynamics are not fully captured, limiting our ability to make reliable projections. In this study, a group of dynamical downscaling products were created for the period 1950 to 2100 to better understand climate change and its impacts on hydrology, permafrost, and ecosystems at a resolution suitable for northern Alaska. An ERA-interim reanalysis dataset and the Community Earth System Model (CESM) served as the forcing mechanisms in this dynamical downscaling framework, and the Weather Research & Forecast (WRF) model, embedded with an optimization for the Arctic (Polar WRF), served as the Regional Climate Model (RCM). This downscaled output consists of multiple climatic variables (precipitation, temperature, wind speed, dew point temperature, and surface air pressure) for a 10 km grid spacing at three-hour intervals. The modeling products were evaluated and calibrated using a bias-correction approach. The ERA-interim forced WRF (ERA-WRF) produced reasonable climatic variables as a result, yielding a more closely correlated temperature field than precipitation field when long-term monthly climatology was compared with its forcing and observational data. A linear scaling method then further corrected the bias, based on ERA-interim monthly climatology, and bias-corrected ERA-WRF fields were applied as a reference for calibration of both the historical and the projected CESM forced WRF (CESM-WRF) products. Biases, such as, a cold temperature bias during summer and a warm temperature bias during winter as well as a wet bias for annual precipitation that CESM holds over northern Alaska persisted in CESM-WRF runs. The linear scaling of CESM-WRF eventually produced high-resolution downscaling products for the Alaskan North Slope for hydrological and ecological research, together with the calibrated ERA-WRF run, and its capability extends far beyond that. Other climatic research has been proposed, including exploration of historical and projected climatic extreme events and their possible connections to low-frequency sea-atmospheric oscillations, as well as near-surface permafrost degradation and ice regime shifts of lakes. These dynamically downscaled, bias corrected climatic datasets provide improved spatial and temporal resolution data necessary for ongoing modeling efforts in northern Alaska focused on reconstructing and projecting hydrologic changes, ecosystem processes and responses, and permafrost thermal regimes. The dynamical downscaling methods presented in this study can also be used to create more suitable model input datasets for other sub-regions of the Arctic.