Compound climate-pollution extremes in Santiago de Chile

Cities in the global south face dire climate impacts. It is in socioeconomically marginalized urban communities of the global south that the effects of climate change are felt most deeply. Santiago de Chile, a major mid-latitude Andean city of 7.7 million inhabitants, is already undergoing the so-called “climate penalty” as rising temperatures worsen the effects of endemic ground-level ozone pollution. As many cities in the global south, Santiago is highly segregated along socioeconomic lines, which offers an opportunity for studying the effects of concurrent heatwaves and ozone episodes on distinct zones of affluence and deprivation. Here, we combine existing datasets of social indicators and climate-sensitive health risks with weather and air quality observations to study the response to compound heat-ozone extremes of different socioeconomic strata. Attributable to spatial variations in the ground-level ozone burden (heavier for wealthy communities), we found that the mortality response to extreme heat (and the associated further ozone pollution) is stronger in affluent dwellers, regardless of comorbidities and lack of access to health care affecting disadvantaged population. These unexpected findings underline the need of a site-specific hazard assessment and a community-based risk management.</p

Surface Solar Extremes in the Most Irradiated Region on Earth, Altiplano

Satellites have consistently pointed to the Altiplano of the Atacama Desert as the place on Earth where the world’s highest surface irradiance occurs. This region, near the Tropic of Capricorn, is characterized by its high elevation, prevalent cloudless conditions, and relatively low concentrations of ozone, aerosols, and precipitable water. Aimed at studying the variability of the surface solar irradiance and detecting atmospheric composition changes in the Altiplano, an atmospheric observatory was set up in 2016 at the northwestern border of the Chajnantor Plateau (5,148 m MSL, 22.95°S, 67.78°W, Chile). Here, we report on the first 5 years of measurements at this observatory that establish the Altiplano as the region that receives the highest-known irradiation on Earth and illuminate the unique features of surface solar extremes at high-altitude locations. We found that the global horizontal shortwave (SW) irradiance on the plateau is on average 308 W m−2 (equivalent to an annual irradiation of 2.7 MWh m−2 yr−1, the highest worldwide). We also found that forward scattering by broken clouds often leads to intense bursts of SW irradiance; a record of 2,177 W m−2 was measured, equivalent to the extraterrestrial SW irradiance expected at approximately 0.79 astronomical units (AU) from the Sun. These cloud-driven surface solar extremes occur on the Chajnantor Plateau at a frequency, intensity, and duration not previously seen anywhere in the world, making the site an ideal location for studying the response of photovoltaic (PV) power plants to periods of enhanced SW variability.</p

Impacts of climate change, population growth, and power sector decarbonization on urban building energy use

Climate, technologies, and socio-economic changes will influence future building energy use in cities. However, current low-resolution regional and state-level analyses are insufficient to reliably assist city-level decision-making. Here we estimate mid-century hourly building energy consumption in 277 U.S. urban areas using a bottom-up approach. The projected future climate change results in heterogeneous changes in energy use intensity (EUI) among urban areas, particularly under higher warming scenarios, with on average 10.1–37.7% increases in the frequency of peak building electricity EUI but over 110% increases in some cities. For each 1 °C of warming, the mean city-scale space-conditioning EUI experiences an average increase/decrease of ~14%/ ~ 10% for space cooling/heating. Heterogeneous city-scale building source energy use changes are primarily driven by population and power sector changes, on average ranging from –9% to 40% with consistent south–north gradients under different scenarios. Across the scenarios considered here, the changes in city-scale building source energy use, when averaged over all urban areas, are as follows: –2.5% to –2.0% due to climate change, 7.3% to 52.2% due to population growth, and –17.1% to –8.9% due to power sector decarbonization. Our findings underscore the necessity of considering intercity heterogeneity when developing sustainable and resilient urban energy systems.<br/

Urbanization and sustainability under transitional economies:a synthesis for Asian Russia

Spanning a vast territory of approximately 13 million km ^2 , Asian Russia was home to 38 million people in 2016. In an effort to synthesize data and knowledge regarding urbanization and sustainable development in Asian Russia in the context of socioeconomic transformation following the breakup of the Soviet Union in 1990, we quantified the spatiotemporal changes of urban dynamics using satellite imagery and explored the interrelationships between urbanization and sustainability. We then developed a sustainability index, complemented with structural equation modeling, for a comprehensive analysis of their dynamics. We chose six case cities, i.e., Yekaterinburg, Novosibirsk, Krasnoyarsk, Omsk, Irkutsk, and Khabarovsk, as representatives of large cities to investigate whether large cities are in sync with the region in terms of population dynamics, urbanization, and sustainability. Our major findings include the following. First, Asian Russia experienced enhanced economic growth despite the declining population. Furthermore, our case cities showed a general positive trend for population dynamics and urbanization as all except Irkutsk experienced population increases and all expanded their urban built-up areas, ranging from 13% to 16% from 1990 to 2014. Second, Asian Russia and its three federal districts have improved their sustainability and levels of economic development, environmental conditions, and social development. Although both regional sustainability and economic development experienced a serious dip in the 1990s, environmental conditions and social development continuously improved from 1990 to 2014, with social development particularly improving after 1995. Third, in terms of the relationships between urbanization and sustainability, economic development appeared as an important driver of urbanization, social development, and environmental degradation in Asian Russia, with economic development having a stronger influence on urbanization than on social development or environmental degradation

Albedo changes caused by future urbanization contribute to global warming

The replacement of natural lands with urban structures has multiple environmental consequences, yet little is known about the magnitude and extent of albedo-induced warming contributions from urbanization at the global scale in the past and future. Here, we apply an empirical approach to quantify the climate effects of past urbanization and future urbanization projected under different shared socioeconomic pathways (SSPs). We find an albedo-induced warming effect of urbanization for both the past and the projected futures under three illustrative scenarios. The albedo decease from urbanization in 2018 relative to 2001 has yielded a 100-year average annual global warming of 0.00014 [0.00008, 0.00021] °C. Without proper mitigation, future urbanization in 2050 relative to 2018 and that in 2100 relative to 2018 under the intermediate emission scenario (SSP2-4.5) would yield a 100-year average warming effect of 0.00107 [0.00057,0.00179] °C and 0.00152 [0.00078,0.00259] °C, respectively, through altering the Earth’s albedo

Belowground plant allocation regulates rice methane emissions from degraded peat soils

Carbon-rich peat soils have been drained and used extensively for agriculture throughout human history, leading to significant losses of their soil carbon. One solution for rewetting degraded peat is wet crop cultivation. Crops such as rice, which can grow in water-saturated conditions, could enable agricultural production to be maintained whilst reducing CO$_{2}$ and N$_{2}$O emissions from peat. However, wet rice cultivation can release considerable methane (CH$_{4}$). Water table and soil management strategies may enhance rice yield and minimize CH$_{4}$ emissions, but they also influence plant biomass allocation strategies. It remains unclear how water and soil management influences rice allocation strategies and how changing plant allocation and associated traits, particularly belowground, influence CH$_{4}$-related processes. We examined belowground biomass (BGB), aboveground biomass (AGB), belowground:aboveground ratio (BGB:ABG), and a range of root traits (root length, root diameter, root volume, root area, and specific root length) under different soil and water treatments; and evaluated plant trait linkages to CH$_{4}$. Rice (Oryza sativa L.) was grown for six months in field mesocosms under high (saturated) or low water table treatments, and in either degraded peat soil or degraded peat covered with mineral soil. We found that BGB and BGB:AGB were lowest in water saturated conditions where mineral soil had been added to the peat, and highest in low-water table peat soils. Furthermore, CH$_{4}$ and BGB were positively related, with BGB explaining 60% of the variation in CH$_{4}$ but only under low water table conditions. Our results suggest that a mix of low water table and mineral soil addition could minimize belowground plant allocation in rice, which could further lower CH$_{4}$ likely because root-derived carbon is a key substrate for methanogenesis. Minimizing root allocation, in conjunction with water and soil management, could be explored as a strategy for lowering CH$_{4}$ emissions from wet rice cultivation in degraded peatlands

Evaluation of MODIS-derived estimates of the albedo over the Atacama Desert using ground-based spectral measurements

Surface albedo is an important forcing parameter that drives the radiative energy budget as it determines the fraction of the downwelling solar irradiance that the surface reflects. Here we report on ground-based measurements of the spectral albedo (350–2200 nm) carried out at 20 sites across a North–South transect of approximately 1300 km in the Atacama Desert, from latitude 18° S to latitude 30° S. These spectral measurements were used to evaluate remote sensing estimates of the albedo derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). We found that the relative mean bias error (RMBE) of MODIS-derived estimates was within ± 5% of ground-based measurements in most of the Atacama Desert (18–27° S). Although the correlation between MODIS-derived estimates and ground-based measurements remained relatively high (R= 0.94), RMBE values were slightly larger in the southernmost part of the desert (27–30° S). Both MODIS-derived data and ground-based measurements show that the albedo at some bright spots in the Atacama Desert may be high enough (up to 0.25 in visible range) for considerably boosting the performance of bifacial photovoltaic technologies (6–12%)

Prospects for the sustainability of social-ecological systems (SES) on the Mongolian plateau: five critical issues

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Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%-31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency similar to 0.52-0.63 and 0.53). UpCH(4) estimated annual global wetland CH4 emissions of 146 +/- 43 TgCH4 y(-1) for 2001-2018 which agrees closely with current bottom-up land surface models (102-181 TgCH4 y(-1)) and overlaps with top-down atmospheric inversion models (155-200 TgCH4 y -1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25 degrees from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ ORNLDAAC/2253).Plain Language Summary Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (similar to 30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001-2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state-of-the-art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground-based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties