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.

Decarbonizing the energy system is a major challenge facing the richest countries, whereas provision of energy services is a major challenge facing the poorest countries. What would be the climate consequences if only richer countries focus on decarbonization, and only poorer countries focus on provision of energy services? To address this question, we create future scenarios in which carbon dioxide (CO2) emissions increase according to a historical trend and then start to decline only when countries reach specified income levels. In our central case, we assume that when countries start to decarbonize, they reduce emissions at 2% yr(-1). With this assumption and if all countries begin to decarbonize in 2020, the world would be expected to warm by 2.0 degrees C relative to pre-industrial times. If countries begin to decarbonize only when their per capita gross domestic product (GDP) exceeds $10 000, there would be less than 0.3 degrees C of additional warming. Yet over half the world's population currently lives in countries below such an income threshold, and continued direct CO2 emissions by people who live in these countries, while they remain underdeveloped, would increase global average temperature rise by 14% relative to the case, in which all people begin to decarbonize in 2020. The primary concern of developments driven by fossil fuels in lower income countries might relate to issues such as the technological lock-in to high-emission technologies.

A number of radiation modification approaches have been proposed to counteract anthropogenic warming by intentionally altering Earth's shortwave or longwave fluxes. While several previous studies have examined the climate effect of different radiation modification approaches, only a few have investigated the carbon cycle response. Our study examines the response of plant carbon uptake to four radiation modification approaches that are used to offset the global mean warming caused by a doubling of atmospheric CO2. Using the National Center for Atmospheric Research Community Earth System Model, we performed simulations that represent four idealized radiation modification options: solar constant reduction, sulfate aerosol increase (SAI), marine cloud brightening, and cirrus cloud thinning (CCT). Relative to the high CO2 state, all these approaches reduce gross primary production (GPP) and net primary production (NPP). In high latitudes, decrease in GPP is mainly due to the reduced plant growing season length, and in low latitudes, decrease in GPP is mainly caused by the enhanced nitrogen limitation due to surface cooling. The simulated GPP for sunlit leaves decreases for all approaches. Decrease in sunlit GPP is the largest for SAI which substantially decreases direct sunlight, and the smallest for CCT, which increases direct sunlight that reaches the land surface. GPP for shaded leaves increases in SAI associated with a substantial increase in surface diffuse sunlight, and decreases in all other cases. The combined effects of CO2 increase and radiation modification result in increases in primary production, indicating the dominant role of the CO2 fertilization effect on plant carbon uptake.

Spectral analyses of past relative sea-level oscillations as represented by the ages of 57 Phanerozoic (the last 545 Myr) stratigraphic sequence boundaries from the Canadian Arctic show a strong spectral peak at 32 Myr (>99.9% confidence). These findings concur with previous reports of significant cycles with periods of around 30 Myr in various records of fluctuations of sea level, and in potentially related episodes of tectonism, volcanism, climate, and biotic extinctions. Sequence boundaries commonly coincide with stage boundaries based on biostratigraphy, and are correlated with episodes of extinction and times of flood-basalt volcanism. The connection between tectonics and sea-level variations may come from changes in rates of ocean-floor spreading and subduction, intraplate stresses from plate-reorganizations, and pulsations of hotspot volcanism. These coordinated periodic fluctuations in tectonics, sea level and climate may be modulated by cyclical activity in the Earth's mantle, although some pacing by astronomical cycles is suspected.

To reduce atmospheric carbon dioxide emissions and mitigate impacts of climate change, countries across the world have mandated quotas for renewable electricity. But a question has remained largely unexplored: would low-cost, firm, zero-carbon electricity generation technologies enhance-or would they displace-deployment of variable renewable electricity generation technologies, i.e., wind and solar photovoltaics, in a least-cost, fully reliable, and deeply decarbonized electricity system? To address this question, we modeled idealized electricity systems based on historical weather data and considered only technoeconomic factors. We did not apply a predetermined use model. We found that cost reductions in firm generation technologies (starting at current costs, ramping down to nearly zero) uniformly resulted in increased penetration of the firm technologies and decreased penetration of variable renewable electricity generation, in electricity systems where technology deployment is primarily driven by relative costs, and across a wide array of future technology cost assumptions. Similarly, reduced costs of variable renewable electricity (starting at current costs, ramping down to nearly zero) drove out firm generation technologies. Yet relative to deployment of "must-run" firm generation technologies, and when the studied firm technologies have high fixed costs relative to variable costs, the addition of flexibility to firm generation technologies had only limited impacts on the system cost, less than a 9% system cost reduction in our idealized model. These results reveal that policies and funding that support particular technologies for lowor zero-carbon electricity generation can inhibit the development of other lowor zero-carbon alternatives.