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The paper focuses on analysis of macro‐ and micromorphological characteristics of relict slope deposits in Serra da Estrela (Portugal) to understand the significance of different slope processes and paleoenvironmental settings. Micromorphology is a useful sedimentology technique allowing significant advances compared to macroscopic techniques. Results show that different processes are involved in
Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness
The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth,
We provide the first dedicated laboratory study of collisions of supercooled water drops with ice particles as a secondary ice production mechanism. We experimentally investigated collisions of supercooled water drops (∼5 mm in diameter) with ice particles of a similar size (∼6 mm in diameter) placed on a glass slide at temperatures >-12 °C. Our results showed that secondary drops were generated d
The matched phase reassignment, developed to estimate phase synchrony of transient oscillatory signals, is extended into a multitaper phase reassignment (MTPR) method. The method gives perfect time-frequency localization for two transients with zero phase difference and estimates of time locations and oscillatory frequencies in low signal-to-noise ratios. For different signal-to-noise ratios betwe
Southeast Asia is a region known for active land-use changes (LUC) over the past 60 years; yet, how trends in net CO2 uptake and release resulting from LUC activities (net LUC flux) have changed through past decades remains uncertain. The level of uncertainty in net LUC flux from process-based models is so high that it cannot be concluded that newer estimates are necessarily more reliable than old
Basis pursuit (BP), basis pursuit deNoising (BPDN), and least absolute shrinkage and selection operator (LASSO) are popular methods for identifying important predictors in the high-dimensional linear regression model (Formula presented.). By definition, when (Formula presented.), BP uniquely recovers (Formula presented.) when (Formula presented.) and (Formula presented.) implies (Formula presented
Climate warming has substantially advanced the timing of spring leaf-out of woody species at middle and high latitudes, albeit with large differences. Insights in the spatial variation of this climate warming response may therefore help to constrain future trends in leaf-out and its impact on energy, water and carbon balances at global scales. In this study, we used in situ phenology observations
Methane emissions from natural wetlands tend to increase with temperature and therefore may lead to a positive feedback under future climate change. However, their temperature response includes confounding factors and appears to differ on different time scales. Observed methane emissions depend strongly on temperature on a seasonal basis, but if the annual mean emissions are compared between sites
The role of the Tibetan Plateau (TP) in El Niño-Southern Oscillation (ENSO) variability is investigated using coupled model experiments with different topography setups. Removing the TP results in weakened trade winds in the tropical Pacific, an eastward shift of atmospheric convection center, a shallower mixed layer in the equatorial Pacific, and a flattened equatorial thermocline, which leads to
The Quaternary East Asian summer rainfall evolution reconstructed with the Chinese speleothem δ18O records shows the consistency throughout the region of East Asia, and has long been considered equivalent to the monsoon intensity. Its variation and teleconnection with global climate were usually interpreted by the meridional shift of the intertropical convergence zone (ITCZ). However, many other p
The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivity (GPP) at finer spatial resolution by better capturing the spatial variation in a heterogeneous landscapes. This study investigates the potential of 10 m resolution reflectance from the Sentinel-2 Multispectral Instrument to improve the accuracy of GPP estimation across Nordic vegetation types, compa
The Earth is experiencing unprecedented climate change. Vegetation phenology has already showed strong response to the global warming, which alters mass and energy fluxes on terrestrial ecosystems. With technology and method developments in remote sensing, computer science and citizen science, many recent phenology-related studies have been focused on macrophenology. In this perspective, we 1) rev
The magnitude, direction and cause of precipitation changes across the Indo-Pacific Warm Pool (IPWP) during the Last Glacial Maximum (LGM) remain elusive. In particular, it is still inconclusive whether tropical or extra-tropical factors controlled such precipitation changes. Determining the spatio-temporal distribution of precipitation in the IPWP during the LGM is a valid strategy to address thi
Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high-spatial-resolution temperature dataset are needed to improve our understanding of temperature variations and for
Dissolved Organic Carbon (DOC) in inland waters plays an essential role in the global carbon cycle and has significant public health effects. Machine learning (ML) together with remote sensing has emerged as a powerful and promising combination to quantify water quality parameters from space. However, inland water sample data for DOC is limited. Hence, little is known about the potential to quanti
