parts of Antarctica were amongst the most rapidly changing regions of the planet during the second half of the Twentieth Century. Even so, today, most of Antarctica remains in the grip of continental ice sheets, with only about 0.2% of its overall area being ice-free. The continent's terrestrial fauna consists only of invertebrates, with just two native species of insects, the chironomid midges Parochlus steinenii and Belgica antarctica. We integrate ecophysiological information with the development of new highresolution climatic layers for Antarctica, to better understand how the distribution of P. steinenii may respond to change over the next century under different IPCC climate change scenarios. We conclude that the species has the potential to expand its distribution to include parts of the west and east coasts of the Antarctic Peninsula and even coastal ice-free areas in parts of continental Antarctica. We propose P. steinenii as an effective native sentinel and indicator species of climate change in the Antarctic. Antarctica and the sub-Antarctic islands are some of the last wilderness areas remaining on the planet. These remote areas remain, to a great extent, free from direct anthropogenic impacts such as overpopulation and overexploitation of native ecosystems 1 , although they are not immune to wider global anthropogenic processes such as climate change and long-range pollution 2,3. The high latitude regions of the Antarctic Peninsula, Scotia Arc, and the Magellanic Sub-Antarctic have been amongst the most rapidly warming areas in the world in the second half of the Twentieth Century, showing significant glacier retreat and reduction of snow and ice cover in terrestrial and freshwater ecosystems 3. While these strong regional warming trends have currently paused, they are predicted to resume through the remainder of the Twenty-first Century 4. These regions are highly sensitive to environmental change and thus are considered natural laboratories in which to study its effects, at all scales, on their ecosystems and biota 3,5. Today, Antarctica remains in the grip of continental ice sheets, with only about 0.2% of its overall area being ice-free 6 , this proportion is somewhat higher in the Antarctic Peninsula region (~3%; British Antarctic Survey unpublished data, Lee et al.. 2017). Terrestrial and freshwater ecosystems are generally small and isolated, populated by small invertebrates, lower plants, and microbes 7. The terrestrial fauna consists only of invertebrates, with just two native species of insects, both chironomid midges (the winged Parochlus steinenii Gerke and the brachypterous Belgica antarctica Jacobs), and two established invasive species with currently restricted ranges, Eretmoptera murphyi (Diptera: Chironomidae) and Trichocera maculipennis (Diptera: Trichoceridae) 8. Climatic gradients have changed over geological time at different spatio-temporal scales in these high latitude southern regions, shaping the composition and distribution of modern landscapes and their biota 3,9. The Eocen...

Urban heat islands (UHIs) can present significant risks to human health. Santiago, Chile has around 7 million residents, concentrated in an average density of 480 people/km2. During the last few summer seasons, the highest extreme maximum temperatures in over 100 years have been recorded. Given the projections in temperature increase for this metropolitan region over the next 50 years, the Santiago UHI could have an important impact on the health and stress of the general population. We studied the presence and spatial variability of UHIs in Santiago during the summer seasons from 2005 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery and data from nine meteorological stations. Simple regression models, geographic weighted regression (GWR) models and geostatistical interpolations were used to find nocturnal thermal differences in UHIs of up to 9 °C, as well as increases in the magnitude and extension of the daytime heat island from summer 2014 to 2017. Understanding the behavior of the UHI of Santiago, Chile, is important for urban planners and local decision makers. Additionally, understanding the spatial pattern of the UHI could improve knowledge about how urban areas experience and could mitigate climate change.

Air temperature records are acquired by networks of weather stations which may be several kilometres apart. In complex topographies the representativeness of a meteorological station may be diminished in relation to a flatter valley, and the nearest station may have no relation to a place located near it. The present study shows a simple method to estimate the spatial distribution of minimum and maximum air temperatures from MODIS land surface temperature (LST) and normalized difference vegetation index (NDVI) images. Indeed, there is a strong correlation between MODIS day and night LST products and air temperature records from meteorological stations, which is obtained by using geographically weighted regression equations, and reliable results are found. Then, the results allow to spatially interpolate the coefficients of the local regressions using altitude and NDVI as descriptor variables, to obtain maps of the whole region for minimum and maximum air temperature. Most of the meteorological stations show air temperature estimates that do not have significant differences compared to the measured values. The results showed that the regression coefficients for the selected locations are strong for the correlations between minimum temperature with LSTnight (R2 = 0.69–0.82) and maximum temperature with LSTday (R2 = 0.70–0.87) at the 47 stations. The root mean square errors (RMSE) of the statistical models are 1.0 °C and 0.8 °C for night and daytime temperatures, respectively. Furthermore, the association between each pair of data is significant at the 95% level (p<0.01).

Lake temperature has proven to act as a good indicator of climate variability and change. Thus, a surface temperature analysis at different temporal scales is important, as this parameter influences the physical, chemical, and biological cycles of lakes. Here, we analyze monthly, seasonal, and annual surface temperature trends in south central Chilean lakes during the 2000–2016 period, using MODIS satellite imagery. To this end, 14 lakes with a surface area greater than 10 km2 were examined. Results show that 12 of the 14 lakes presented a statistically significant increase in surface temperature, with a rate of 0.10 °C/decade (0.01 °C/year) over the period. Furthermore, some of the lakes in the study present a significant upward trend in surface temperature, especially in spring, summer, and winter. In general, a significant increase in surface water temperature was found in lakes located at higher altitudes, such as Maule, Laja and Galletué lakes. These results contribute to the provision of useful data on Chilean lakes for managers and policymakers.

Atmospheric longwave downward radiation (Ld) is one of the significant components of net radiation (Rn), and it drives several essential ecosystem processes. Ld can be estimated with simple empirical methods using atmospheric emissivity (εa) submodels. In this study, eight εa global models were evaluated, and the one with the best performance was calibrated on a global scale using a parametric instability analysis approach. Climatic data were obtained from a dynamically consistent scale resolution of basic atmospheric quantities and computed parameters known as NCEP/NCAR reanalysis (NNR) data. The model's goodness of fit was evaluated with monthly average values of the NNR data. The εa Brutsaert model resulted in the best performance, and then it was calibrated. The seasonal global trend of Brutsaert’s εa equation calibrated coefficient ranged between 1.2 and 1.4, and five homogeneous zones with similar behavior (clusters) were found with the K-means analysis. Finally, the calibrated Brutsaert’s εa equation improved the Rn estimation, with an error reduction, at the worldwide scale, of 64%. Meanwhile, the error reduction for every cluster ranged from 18 to 77%. Hence, Brutsaert’s equation coefficient should not be considered a constant value for use in εa estimation, nor in time nor space.