Solar geoengineering by deliberate injection of sulfate aerosols in the stratosphere is one of the proposed options to counter anthropogenic climate warming. In this study, we focus on the effect of a specific microphysical property of sulfate aerosols in the stratosphere: hygroscopic growth-the tendency of particles to grow by accumulating water. We show that stratospheric sulfate aerosols, for a given mass of sulfates, cause more cooling when prescribed at the lower levels of the stratosphere because of hygroscopic growth. The larger relative humidity in the lower stratosphere causes an increase in the aerosol size through hygroscopic growth that leads to a larger scattering efficiency. In our study, hygroscopic growth provides an additional cooling of 23% (0.7 K) when 20 Mt-SO4 of sulfate aerosols, an amount that approximately offsets the warming due to a doubling of CO2, are prescribed at 100 hPa. The hygroscopic effect becomes weaker at higher levels as relative humidity decreases with height. Hygroscopic growth also leads to secondary effects such as an increase in near-infrared shortwave absorption by the aerosols that causes a decrease in high clouds and an increase in stratospheric water vapor. The altitude dependence of the effects of hygroscopic growth is opposite to that of sedimentation effects or the fast adjustment effects due to aerosol-induced warming identified in a recent study.

Hypoxia, a condition of low dissolved oxygen concentration, is a widespread problem in marine and freshwater ecosystems. To date, prevention and mitigation of hypoxia has centered on nutrient reduction to prevent eutrophication. However, nutrient reduction is often slow and sometimes insufficient to remedy hypoxia. We investigate the utility of a complementary strategy of pumping oxygenated surface water to depth, termed induced downwelling, as a technique to remedy hypoxia in the bottom water of marine and freshwater ecosystems. We introduce simple energy-based models and apply them to depth profiles in hypoxic estuaries, lakes, and freshwater reservoirs. Our models indicate that induced downwelling may be similar to 3 to 10(2) times more efficient than bubbling air, and 10(4) to 10(6) times more efficient than fountain aerators, at oxygenating hypoxic bottom waters. A proof-of-concept downwelling field experiment highlighted potential advantages and shortcomings. We estimate that regional-scale downwelling for continual hypoxia avoidance would require 0.4 to 4 megawatts per cubic kilometer of water (depending on local conditions), or 50 to 500 US dollars per hour per cubic kilometer of water (assuming 125 USD MWh(-1) of electricity). Many potential side effects of downwelling are discussed, each of which would need to be explored and assessed before implementation. Downwelling does not replace nutrient management strategies, but under some circumstances may provide an efficient means to augment these strategies. (C) 2020 The Authors. Published by Elsevier B.V.

Induced energy-saving efficiency improvements strongly influence energy use and climate change. This mechanism has previously been studied by bottomup methods in models, but the effect is debatable because of lack of empirical data needed to calibrate model parameters. We provide a top-down calibration of the relation between historical rates of various efficiency changes and energy's share of costs. To do this, we develop a modification of Solow's model of economic productivity. We find that a 1% rise in energy cost share increases energy-use efficiency by about 1.2% in the following 20 years, a higher gain compared to previous bottom-up estimates. When we incorporate this relationship into an integrated assessment model, we find that carbon prices save up to 30% more energy by 2120, relative to model configurations without the inducing mechanism. A carbon tax induces energy-saving efficiency improvements and could therefore be a more effective mitigation tool than previously recognized.

Understanding the extent to which laboratory findings of low pH on marine organisms can be extrapolated to the natural environment is key toward making better projections about the impacts of global change on marine ecosystems. We simultaneously exposed larvae of the sea urchin Arbacia lixula to ocean acidification in laboratory and natural CO2 vents and assessed the arm growth response as a proxy of net calcification. Populations of embryos were simultaneously placed at both control and volcanic CO2 vent sites in Ischia (Italy), with a parallel group maintained in the laboratory in control and low pH treatments corresponding to the mean pH levels of the field sites. As expected, larvae grown at constant low pH (pH(T) 7.8) in the laboratory exhibited reduced arm growth, but counter to expectations, the larvae that developed at the low pH vent site (pH(T) 7.33-7.99) had the longest arms. The larvae at the control field site (pH(T) 7.87-7.99) grew at a similar rate to laboratory controls. Salinity, temperature, oxygen and flow regimes were comparable between control and vent sites; however, chlorophyll a levels and particulate organic carbon were higher at the vent site than at the control field site. This increased food availability may have modulated the effects of low pH, creating an opposite calcification response in the laboratory from that in the field. Divergent responses of the same larval populations developing in laboratory and field environments show the importance of considering larval phenotypic plasticity and the complex interactions among decreased pH, food availability and larval responses. (C) 2020 The Authors. Published by Elsevier B.V.

Global mean surface air temperature (T-global) variability on subdecadal timescales can be of substantial magnitude relative to the long-term global warming signal, and such variability has been associated with considerable environmental and societal impacts. Therefore, probabilistic foreknowledge of short-term T-global evolution may be of value for anticipating and mitigating some course-resolution climate-related risks. Here we present a simple, empirically based methodology that utilizes only global spatial patterns of annual mean surface air temperature anomalies to predict subsequent annual T-global anomalies via partial least squares regression. The method's skill is primarily achieved via information on the state of long-term global warming as well as the state and recent evolution of the El Nino-Southern Oscillation and the Interdecadal Pacific Oscillation. We test the out-of-sample skill of the methodology using cross validation and in a forecast mode where statistical predictions are made precisely as they would have been if the procedure had been operationalized starting in the year 2000. The average forecast errors for lead times of 1 to 4 years are smaller than naive benchmarks on average, and they perform favorably relative to most dynamical Global Climate Models retrospectively initialized to the observed state of the climate system. Thus, this method can be used as a computationally efficient benchmark for dynamical model forecast systems.

Reliable and affordable electricity systems based on variable energy sources, such as wind and solar may depend on the ability to store large quantities of low-cost energy over long timescales. Here, we use 39 years of hourly U.S. weather data, and a macro-scale energy model to evaluate capacities and dispatch in least cost, 100% reliable electricity systems with wind and solar generation supported by long-duration storage (LDS; 10 h or greater) and battery storage. We find that the introduction ofLDS lowers total systemcosts relative towind-solar-battery systems, and that systemcosts are twice as sensitive to reductions in LDS costs as to reductions in battery costs. In least-cost systems, batteries are used primarily for intra-day storage and LDS is used primarily for inter-season andmulti-year storage. Moreover, dependence on LDS increases when the system is optimized over more years. LDS technologies could improve the affordability of renewable electricity.

Global climate change mitigation is often framed in public discussions as a tradeoff between environmental protection and harm to the economy. However, climate-economy models have consistently calculated that the immediate implementation of greenhouse gas emissions restriction (via e.g. a global carbon price) would be in humanity's best interest on purely economic grounds. Despite this, the implementation of global climate policy has been notoriously difficult to achieve. This evokes an apparent paradox: if the implementation of a global carbon price is not only beneficial to the environment, but is also 'economically optimal', why has it been so difficult to enact? One potential reason for this difficulty is that economically optimal greenhouse gas emissions restrictions arenoteconomically beneficial for the generation of people that launch them. The purpose of this article is to explore this issue by introducing the concept of the break-even year, which we define as the year when the economically optimal policy begins to produce global mean net economic benefits. We show that in a commonly used climate-economy model (DICE), the break-even year is relatively far into the future-around 2080 for mitigation policy beginning in the early 2020s. Notably, the break-even year is not sensitive to the uncertain magnitudes of the costs of climate change mitigation policy or the costs of economic damages from climate change. This result makes it explicit and understandable why an economically optimal policy can be difficult to implement in practice.

We use 36 years (1980-2015) of hourly weather data over the contiguous United States (CONUS) to assess the impact of low-cost energy storage on highly reliable electricity systems that use only variable renewable energy (VRE; wind and solar photovoltaics). Even assuming perfect transmission of wind and solar generation aggregated over CONUS, energy storage costs would need to decrease several hundred-fold from current costs (to similar to$1/kWh) in fully VRE electricity systems to yield highly reliable electricity without extensive curtailment of VRE generation. The role of energy storage changes from high-cost storage competing with curtailment to fill short-term gaps between VRE generation and hourly demand to near-free storage serving as seasonal storage for VRE resources. Energy storage faces "double penalties" in VRE/storage systems: with increasing capacity, (1) the additional storage is used less frequently and (2) hourly electricity costs would become less volatile, thus reducing price arbitrage opportunities for the additional storage.

Human migration is both motivated and constrained by a multitude of socioeconomic and environmental factors, including climate-related factors. Climatic factors exert an influence on local and regional population density. Here, we examine the implications of future motivation for humans to migrate by analyzing today's relationships between climatic factors and population density, with all other factors held constant. Such "all other factors held constant" analyses are unlikely to make quantitatively accurate predictions, but the order of magnitude and spatial pattern that come out of such an analysis can be useful when considering the influence of climate change on the possible scale and pattern of future incentives to migrate. Our results indicate that, within decades, climate change may provide hundreds of millions of people with additional incentive to migrate, largely from warm tropical and subtropical countries to cooler temperate countries, with India being the country with the greatest number of people with additional incentive to migrate. These climate-driven incentives would be among the broader constellation of incentives that influence migration decisions. Areas with the highest projected population growth rates tend to be areas that are likely to be most adversely affected by climate change.

Electricity usage (demand) data are used by utilities, governments, and academics to model electric grids for a variety of planning (e.g., capacity expansion and system operation) purposes. The U.S. Energy Information Administration collects hourly demand data from all balancing authorities (BAs) in the contiguous United States. As of September 2019, we find 2.2% of the demand data in their database are missing. Additionally, 0.5% of reported quantities are either negative values or are otherwise identified as outliers. With the goal of attaining non-missing, continuous, and physically plausible demand data to facilitate analysis, we developed a screening process to identify anomalous values. We then applied a Multiple Imputation by Chained Equations (MICE) technique to impute replacements for missing and anomalous values. We conduct cross-validation on the MICE technique by marking subsets of plausible data as missing, and using the remaining data to predict this "missing" data. The mean absolute percentage error of imputed values is 3.5% across all BAs. The cleaned data are published and available open access: 10.5281/zenodo.3690240.