Climate change, in particular the increase in air temperature, has been shown to influence lake thermal dynamics, with climatic warming resulting in higher surface temperatures, stronger stratification, and altered mixing regimes. Less‐studied is the influence on lake thermal dynamics of atmospheric stilling, the decrease in near‐surface wind speed observed in recent decades. Here we use a lake model to assess the influence of atmospheric stilling, on lake thermal dynamics across the Northern Hemisphere. From 1980‐2016, lake thermal responses to warming have accelerated as a result of atmospheric stilling. Lake surface temperatures and thermal stability have changed at respective rates of 0.33 and 0.38°C decade^‐1, with atmospheric stilling contributing 15 and 27% of the calculated changes, respectively. Atmospheric stilling also resulted in a lengthening of stratification, contributing 23% of the calculated changes. Our results demonstrate that atmospheric stilling has influenced lake thermal responses to warming.

Very‐high resolution (VHR) satellite imagery is increasingly used to visualize the effects of armed conflicts in near‐real time. Yet these data, typically commercial, are generally released selectively or for a fee, impeding scientific and humanitarian applications. Such images also tend to focus on cities or infrastructure, obscuring the effects of war in rural or sparsely populated areas such as forests. To leverage VHR imagery for improved and more holistic conflict monitoring, we call for (a) archiving VHR images for scientific and humanitarian use; (b) producing analysis‐ready, conflict‐wide VHR mosaics to harmonize monitoring; and (c) forming a sustainably funded public VHR satellite program. International coordination on such a satellite could also support the transformation of VHR Earth observation (EO) data into a public good. Taking these three steps promises to revolutionize conflict monitoring, much in the way that opening access to lower‐resolution EO data has stimulated new discoveries about the planet's ecosystems and human impacts. Broadening and standardizing access to and analysis of archived VHR imagery may improve understandings of war, empower and refine responses, and potentially, mitigate future conflicts.

The Colombian Andes foothills have seen an expansion of forest disturbance since the 1950s. While understanding the drivers of disturbance is important for quantifying the implications of land use change on regional biodiversity, methods for attributing disturbance to specific drivers of change at a high temporal and spatial resolution are still lacking in the Andes region, in part due to persistent cloud cover. Using 20 years of Landsat images (1996–2015) covering Picachos National Park in the Colombian Andes, we detected sub-annual forest cover disturbances using the Breaks For Additive Season and Trend (BFAST) Monitor algorithm; characterized different types of disturbance using spectral, spatial, and topographic indicators; and attributed causes of forest disturbance such as conversion to pasture, conversion to agriculture, and non-stand replacing disturbance (i.e., thinning) using a Random Forest (RF) classifier. Conversion to pasture has been the main driver of forest disturbance in Picachos, responsible for 11,395 ± 72 ha (17%) of forest cover loss, followed by non-stand replacing disturbance and conversion to agriculture. Disturbance detection had 96% overall agreement with validation data, although we had a high omission error of 21% primarily associated with forest to agriculture conversion. Other change drivers had a much more reliable attribution with forest to pasture conversion or non-stand-replacing disturbance, showing only 1–5% commission and 2–14% omission errors. Our results provide spatially-explicit information on sub-annual disturbances and associated drivers of change that are necessary for evaluating and improving domestic conservation efforts and establishing systematic ecological observations, which is currently absent from Colombia. While effective at revealing forest change dynamics in a geographically remote and socio-politically complex region like Picachos, our approach is highly automated and it can be easily extended to the rest of Andes-Amazon transition belt where low availability of remote sensing data and high cloud cover impede efforts at consistent monitoring of forest cover change dynamics and drivers.