Human Disturbance Amplifies Amazonian El Niño–Southern Oscillation Signal
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Published on 2017 by


Abstract: The long‐term interaction between human activity and climate is subject to increasing scrutiny. Humans homogenize landscapes through deforestation, agriculture, and burning and thereby might reduce the capacity of landscapes to provide archives of climate change. Alternatively, land‐use change might overwhelm natural buffering and amplify latent climate signals, rendering them detectable. Here we examine a sub‐annually resolved sedimentary record from Lake Sauce in the western Amazonian lowlands that spans 6900 years. Finely‐laminated sediments were deposited from ca. 5000 years ago until the present, and human activity in the watershed was revealed through the presence of charcoal and maize agriculture. The laminations, analyzed for color content and bandwidth, showed distinctive changes that were coupled to more frequent occurrence of fossil maize pollen. As agricultural activity intensified ca. 2200 cal. BP, the 2‐ to 8‐year periodicity characteristic of El Niño–Southern Oscillation became evident in the record. These agricultural activities appeared to have amplified an existing, but subtle climatic signal that was previously absorbed by natural vegetation. When agricultural activity slowed, or land use around Lake Sauce changed at ca. 800 cal. BP, the signal of El Niño–Southern Oscillation (ENSO) activity became erratic. Abstract : The highest‐resolution paloeclimatic history yet available from Amazonia reveals a long history of maize agriculture around Lake Sauce, Peru. When agriculture intensified about 3000 years ago, it increased erosion into the lake. The erosion reflected local climatic variability and sediment color analysis revealed a characteristic 2‐ to 8‐year periodicity, a signal associated with El Nino–Southern Oscillation. The ENSO signal had been there prior to human‐induced erosion, but human activity amplified the pattern to make it detectable. Humans can induce climate change, and their activity can also make latent patterns evident.
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