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The El Nino-Southern Oscillation (ENSO) is the main driver of the interannual variability in eastern African rainfall, with a significant impact on vegetation and agriculture and dire consequences for food and social security. In this study, we identify and quantify the ENSO contribution to the eastern African rainfall variability to forecast future eastern African vegetation response to rainfall variability related to a predicted intensified ENSO. To differentiate the vegetation variability due to ENSO, we removed the ENSO signal from the climate data using empirical orthogonal teleconnection (EOT) analysis. Then, we simulated the ecosystem carbon and water fluxes under the historical climate without components related to ENSO teleconnections. We found ENSO-driven patterns in vegetation response and confirmed that EOT analysis can successfully produce coupled tropical Pacific sea surface temperature-eastern African rainfall teleconnection from observed datasets. We further simulated eastern African vegetation response under future climate change as it is projected by climate models and under future climate change combined with a predicted increased ENSO intensity. Our EOT analysis highlights that climate simulations are still not good at capturing rainfall variability due to ENSO, and as we show here the future vegetation would be different from what is simulated under these climate model outputs lacking accurate ENSO contribution. We simulated considerable differences in eastern African vegetation growth under the influence of an intensified ENSO regime which will bring further environmental stress to a region with a reduced capacity to adapt effects of global climate change and food security.
East Africa is a natural laboratory: Studying its unique geological and biological history can help us better inform our theories and models. Studying its present and future can help us protect its globally important biodiversity and ecosystem services. East African vegetation plays a central role in all these aspects, and this dissertation aims to quantify its dynamics through computer simulations.
Computer models help us recreate past settings, forecast into the future or conduct simulation experiments that we cannot otherwise perform in the field. But before all that, one needs to test their performance. The outputs that the model produced using the present day-inputs, agreed well with present-day observations of East African vegetation. Next, I simulated past vegetation for which we have fossil pollen data to compare. With computer models, we can fill the gaps of knowledge between sites where we have fossil pollen data from, and create a more complete picture of the past. Good level of agreement between model and pollen data where they overlapped in space further validated our model performance.
Once the model was tested and validated for the region, it became possible to probe one of the long standing questions regarding East African vegetation: How did East Africa lose its tropical forests? The present-day vegetation in the tropics is mainly characterized by continuous forests worldwide except in tropical East Africa, where forests only occur as patches. In a series of simulation experiments, I was able to show under which conditions these forest patches could have been connected and fragmented in the past. This study showed the sensitivity of East African vegetation to climate change and variability such as those expected under future climate change.
El Niño Southern Oscillation (ENSO) events that result from the fluctuations in temperature between the ocean and atmosphere, bring further variability to East African climate and are predicted to increase in intensity in the future. But climate models are still not good at capturing the pattens of these events. In a study where I quantified the influence of ENSO events on East African vegetation, I showed how different the future vegetation could be from what we currently predict with these climate models that lack accurate ENSO contribution. Consideration of these discrepancies is important for our future global carbon budget calculations and management decisions.