Double catastrophe: intermittent stratospheric geoengineering induced by societal collapse

Perceived failure to reduce greenhouse gas emissions has prompted interest in avoiding the harms of climate change via geoengineering, that is, the intentional manipulation of Earth system processes. Perhaps the most promising geoengineering technique is stratospheric aerosol injection (SAI), which reflects incoming solar radiation, thereby lowering surface temperatures. This paper analyzes a scenario in which SAI brings great harm on its own. The scenario is based on the issue of SAI intermittency, in which aerosol injection is halted, sending temperatures rapidly back toward where they would have been without SAI. The rapid temperature increase could be quite damaging, which in turn creates a strong incentive to avoid intermittency. In the scenario, a catastrophic societal collapse eliminates society’s ability to continue SAI, despite the incentive. The collapse could be caused by a pandemic, nuclear war, or other global catastrophe. The ensuing intermittency hits a population that is already vulnerable from the initial collapse, making for a double catastrophe. While the outcomes of the double catastrophe are difficult to predict, plausible worst-case scenarios include human extinction. The decision to implement SAI is found to depend on whether global catastrophe is more likely from double catastrophe or from climate change alone. The SAI double catastrophe scenario also strengthens arguments for greenhouse gas emissions reductions and against SAI, as well as for building communities that could be self-sufficient during global catastrophes. Finally, the paper demonstrates the value of integrative, systems-based global catastrophic risk analysis.     

Uncertainty analysis of the external gamma-dose rate due to the variability of the vertical distribution of 137Cs in the soil

The external gamma-dose rate at 1 m height above a flat area due to the presence of fallout radiocesium in the soil is frequently calculated from the observed depth profile of the 137Cs activity as well as the soil mass per unit area. At a given site, these depth profiles may, however, vary considerably, thus introducing an uncertainty to the external gamma-dose calculated in this way. To assess this source of uncertainty for a typical grassland site, the activity of Chernobyl-derived 137Cs and the wet bulk density in the three upper soil layers at 100 plots in a 100 m x 100 m pasture were determined. Analysis of these data shows that the frequency distribution of the dose rates calculated from the corresponding depth profiles of all plots is similar to a log-normal distribution (mean 25 nGy h-1, median 22 nGy h-1, standard deviation 11 nGy h-1; range 1.6-56 nGy h-1). The various sources which contribute to the uncertainty of the dose rate are quantified. The semi-variogram indicates that any spatial dependence of the dose rates occurs on this pasture only over distances that are smaller than the shortest sampling interval (here about 10 m). It is estimated which errors have to be expected for the median dose rate when the depth profiles of 137Cs and of the wet bulk density are determined only for a small number of plots. It is preferable to calculate the mean dose rate as a mean from the n individual dose rates rather than from an averaged 137Cs depth profile of the n plots.     

Non-thermal effects of EMF upon the mammalian brain: the Lund experience

The environment in which biology exists has dramatically changed during the last decades. Life was formed during billions of years, exposed to, and shaped by the original physical forces such as gravitation, cosmic irradiation and the terrestrial magnetism. The existing organisms are created to function in harmony with these forces. However, in the late 19th century mankind introduced the use of electricity and during the very last decades, microwaves of the modern communication society spread around the world. Today one third of the world’s population is owner of the microwave-producing mobile phones. The question is: to what extent are living organisms affected by these ubiquitous radio frequency fields? Since 1988 our group has studied the effects upon the mammalian blood-brain barrier (BBB) by non-thermal radio frequency electromagnetic fields (RF-EMF). These have been revealed to cause significantly increased leakage of albumin through the BBB of exposed rats as compared to non-exposed animals—in a total series of about two thousand animals. One remarkable observation is the fact that the lowest energy levels give rise to the most pronounced albumin leakage. If mobile communication, even at extremely low energy levels, causes the users’ own albumin to leak out through the BBB, also other unwanted and toxic molecules in the blood, may leak into the brain tissue and concentrate in and damage the neurons and glial cells of the brain. In later studies we have shown that a 2-h exposure to GSM 915 MHz at non-thermal levels, gives rise to significant neuronal damage, seen 28 and 50 days after the exposure. In our continued research, the non-thermal effects (histology, memory functions) of long-term exposure for 13 months are studied as well as the effects of short term GSM 1,800 MHz upon gene expression. Most of our findings support that living organisms are affected by the non-thermal radio frequency fields. Studies from other laboratories in some cases find effects, while in other cases effects are not seen. Our conclusion is that all researchers involved in this field have the obligation to intensify this research in order to reduce, or avoid, the possible negative effects of the man made microwaves!     

Regime shift in a coastal marine ecosystem

We demonstrate changes in ecosystem stable states in a coastal lagoon that are consistent with what a regime shift would hypothesize. In the nutrient-stressed Ringk?bing Fjord, Denmark, a small change in one variable (salinity) facilitated by a change in sluice management, caused a sudden regime shift from a bottom-up controlled turbid state, into a top-down controlled clear-water state. The change in dominating pathway of organic matter production from pelagic turnover to benthic-pelagic coupling was facilitated by new recruitment and growth of existing suspension-feeding clams, Mya arenaria. With the invasion of clams, benthic grazing became the key feature of the biological structure. Phytoplankton composition and zooplankton abundance were also affected by the change in biological structure. The small, but sudden, increase in salinity caused by the change in sluice management led to a dramatic reduction in biomass and coverage of benthic vegetation and thus affected herbivorous waterbird populations. In recent years, plant coverage has been increasing, as can be expected with increased water transparency. The regime shift has some major implications for coastal water management and revealed some conflicts between different types of nature and environmental protection legislation.     

Air quality modeling in the South Coast Air Basin of California: what do the numbers really mean?

This study evaluates air quality model sensitivity to input and to model components. Simulations are performed using the California Institute of Technology (CIT) airshed model. Results show the impacts on ozone (O3) concentration in the South Coast Air Basin (SCAB) of California because of changes in: (1) input data, including meteorological conditions (temperature, UV radiation, mixing height, and wind speed), boundary conditions, and initial conditions (ICs); and (2) model components, including advection solver and chemical mechanism. O3 concentrations are strongly affected by meteorological conditions and, in particular, by temperature. ICs also affect O3 concentrations, especially in the first 2 days of simulation. On the other hand, boundary conditions do not significantly affect the absolute peak O3 concentration, although they do affect concentrations near the inflow boundaries. Moreover, predicted O3 concentrations are impacted considerably by the chemical mechanism. In addition, dispersion of pollutants is affected by the advection routine used to calculate its transport. Comparison among CIT, California Photochemical Grid Model (CALGRID), and Urban Airshed Model air quality models suggests that differences in O3 predictions are mainly caused by the different chemical mechanisms used. Additionally, advection solvers contribute to the differences observed among model predictions. Uncertainty in predicted peak O3 concentration suggests that air quality evaluation should not be based solely on this single value but also on trends predicted by air quality models using a number of chemical mechanisms and with an advection solver that is mass conservative.     

Supporting habitat conservation with automated change detection in Google Earth Engine

A significant limitation in biodiversity conservation has been the effective implementation of laws and regulations that protect species’ habitats from degradation. Flexible, efficient, and effective monitoring and enforcement methods are needed to help conservation policies realize their full benefit. As remote sensing data become more numerous and accessible, they can be used to identify and quantify land-cover changes and habitat loss. However, these data remain underused for systematic conservation monitoring in part because of a lack of simple tools. We adapted 2 algorithms that automatically identify differences between pairs of images. We used free, publicly available satellite data to evaluate their ability to rapidly detect land-cover changes in a variety of land-cover types. We compared algorithm predictions with ground-truthed results at 100 sites of known change in the United States. We also compared algorithm predictions to manually created polygons delineating anthropogenic change in 4 case studies involving imperiled species’ habitat: oil and gas development in the range of the Greater Sage Grouse (Centrocercus urophasianus); sand mining operations in the range of the dunes sagebrush lizard (Sceloporus arenicolus); loss of Piping Plover (Charadrius melodus) coastal habitat after Hurricane Michael (2018); and residential development in St. Andrew beach mouse (Peromyscus polionotus peninsularis) habitat. Both algorithms effectively discriminated between pixels corresponding to land-cover change and unchanged pixels as indicated by area under a receiver operating characteristic curve >0.90. The algorithm that was most effective differed among the case-study habitat types, and both effectively delineated habitat loss as indicated by low omission (min. = 0.0) and commission (min. = 0.0) rates, and moderate polygon overlap (max. = 47%). Our results showed how these algorithms can be used to help close the implementation gap of monitoring and enforcement in biodiversity conservation. We provide a free online tool that can be used to run these analyses ( https://conservationist.io/habitatpatrol ).     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.