We present an 800-year δ¹⁸O chronology from the eastern part of the Tibetan Plateau (TP). The chronology dates back to AD 1193 and was sampled in AD 1996 from living Juniperus tibetica trees. This first long-term tree-ring-based δ¹⁸O chronology for eastern Tibet provides a reliable archive for hydroclimatic reconstructions. Highly significant correlations were obtained with hydroclimatic variables (relative humidity, vapour pressure, and precipitation) during the summer season. We applied a linear transfer model to reconstruct summer season relative humidity variations over the past 800 years. More moist conditions prevailed during the termination of the Medieval Warm Period while a systematic shift during the Little Ice Age is not detectable. A distinct trend towards more dry conditions since the 1870s is apparent. The moisture decline weakened around the 1950s but still shows a negative trend. The mid-19th century humidity decrease is in good accordance with several multiproxy hydroclimate reconstructions for south Tibet. However, the pronounced summer relative humidity decline is stronger on the central and eastern TP. Furthermore, the relative humidity at our study site is significantly linked to the relative humidity at large parts of the TP. Therefore, we deduce that the reconstructed relative humidity is mostly controlled by local and mesoscale climatic drivers, although significant connections to the higher troposphere of west-central Asia were observed.

The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km² area at the 79 °N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m². Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km² and a maximum individual lake size of 30 km². With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks.

The Nioghalvfjerdsfjorden glacier (also known as the 79^∘ North Glacier) drains approximately 8 % of the Greenland Ice Sheet. Supraglacial lakes (SGLs), or surface melt ponds, are a persistent summertime feature and are thought to drain rapidly to the base of the glacier and influence seasonal ice velocity. However, seasonal development and spatial distribution of SGLs in the north-east of Greenland are poorly understood, leaving a substantial error in the estimate of meltwater and its impacts on ice velocity. Using results from an automated detection of melt ponds, atmospheric and surface mass balance modelling, and reanalysis products, we investigate the role of specific climatic conditions in melt onset, extent, and duration from 2016 to 2019. The summers of 2016 and 2019 were characterised by above-average air temperatures, particularly in June, as well as a number of rainfall events, which led to extensive melt ponds to elevations up to 1600 m. Conversely, 2018 was particularly cold, with a large accumulated snowpack, which limited the development of lakes to altitudes less than 800 m. There is evidence of inland expansion and increases in the total area of lakes compared to the early 2000s, as projected by future global warming scenarios.