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Abstract The Yenisei River is the largest contributor of freshwater and energy fluxes among all rivers draining to the Arctic Ocean. Modeling long-term variability of Eurasian runoff to the Arctic Ocean is complicated by the considerable variability of river discharge in time and space, and the monitoring constraints imposed by a sparse gauged-flow network and paucity of satellite data. We quantify tree growth response to river discharge at the upper reaches of the Yenisei River in Tuva, South Siberia. Two regression models built from eight tree-ring width chronologies of Larix sibirica are applied to reconstruct winter (Nov–Apr) discharge for the period 1784–1997 (214 years), and annual (Oct–Sept) discharge for the period 1701–2000 (300 years). The Nov–Apr model explains 52% of the discharge variance whereas Oct–Sept explains 26% for the calibration intervals 1927–1997 and 1927–2000, respectively. This new hydrological archive doubles the length of the instrumental discharge record at the Kyzyl gauge and resets the temporal background of discharge variability back to 1784. The reconstruction finds a remarkable 80% upsurge in winter flow over the last 25 years, which is unprecedented in the last 214 years. In contrast, annual discharge fluctuated normally for this system, with only a 7% increase over the last 25 years. Water balance modeling with CRU data manifests a significant discrepancy between decadal variability of the gauged flow and climate data after 1960. We discuss the impact on the baseflow rate change of both the accelerating permafrost warming in the discontinuous zone of South Siberia and widespread forest fires. The winter discharge accounts for only one third of the annual flow, yet the persistent 25 year upsurge is alarming. This trend is likely caused by Arctic Amplification, which can be further magnified by increased winter flow delivering significantly more fresh water to the Kara Sea during the cold season. 

Abstract Tree-ring chronologies underpin the majority of annually-resolved reconstructions of Common Era climate. However, they are derived using different datasets and techniques, the ramifications of which have hitherto been little explored. Here, we report the results of a double-blind experiment that yielded 15 Northern Hemisphere summer temperature reconstructions from a common network of regional tree-ring width datasets. Taken together as an ensemble, the Common Era reconstruction mean correlates with instrumental temperatures from 1794–2016 CE at 0.79 ( p  < 0.001), reveals summer cooling in the years following large volcanic eruptions, and exhibits strong warming since the 1980s. Differing in their mean, variance, amplitude, sensitivity, and persistence, the ensemble members demonstrate the influence of subjectivity in the reconstruction process. We therefore recommend the routine use of ensemble reconstruction approaches to provide a more consensual picture of past climate variability.