If future net-zero emissions energy systems rely heavily on solar and wind resources, spatial and temporal mismatches between resource availability and electricity demand may challenge system reliability. Using 39 years of hourly reanalysis data (1980–2018), we analyze the ability of solar and wind resources to meet electricity demand in 42 countries, varying the hypothetical scale and mix of renewable generation as well as energy storage capacity. Assuming perfect transmission and annual generation equal to annual demand, but no energy storage, we find the most reliable renewable electricity systems are wind-heavy and satisfy countries’ electricity demand in 72–91% of hours (83–94% by adding 12 h of storage). Yet even in systems which meet >90% of demand, hundreds of hours of unmet demand may occur annually. Our analysis helps quantify the power, energy, and utilization rates of additional energy storage, demand management, or curtailment, as well as the benefits of regional aggregation.

Winter storm Uri brought severe cold to the southern United States in February 2021, causing a cascading failure of interdependent systems in Texas where infrastructure was not adequately prepared for such cold. In particular, the failure of interconnected energy systems restricted electricity supply just as demand for heating spiked, leaving millions of Texans without heat or electricity, many for several days. This motivates the question: did historical storms suggest that such temperatures were known to occur, and if so with what frequency? We compute a temperature-based proxy for heating demand and use this metric to answer the question ‘what would the aggregate demand for heating have been had historic cold snaps occurred with today’s population?’. We find that local temperatures and the inferred demand for heating per capita across the region served by the Texas Interconnection were more severe during a storm in December 1989 than during February 2021, and that cold snaps in 1951 and 1983 were nearly as severe. Given anticipated population growth, future storms may lead to even greater infrastructure failures if adaptive investments are not made. Further, electricity system managers should prepare for trends in electrification of heating to drive peak annual loads on the Texas Interconnection during severe winter storms.

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: https://doi.org/10.5281/zenodo.3690240.

The assessment and implementation of structural or financial instruments for climate risk mitigation requires projections of future climate risk over the operational life of each proposed instrument. A point often neglected in the climate adaptation literature is that the physical sources of predictability differ between projects with long and short planning periods: While historical and paleo climate records emphasize low‐frequency modes of variability, anthropogenic climate change is expected to alter their occurrence at longer time scales. In this paper we present a set of stylized experiments to assess the uncertainties and biases involved in estimating future climate risk over a finite future period, given a limited observational record. These experiments consider both quasi‐periodic and secular change for the underlying risk, as well as statistical models for estimating this risk from an N‐year historical record. The uncertainty of IPCC‐like future scenarios is considered through an equivalent sample size N. The relative importance of estimating short‐ or long‐term risk depends on the investment life M. Shorter design lives are preferred for situations where interannual to decadal variability can be successfully identified and predicted, highlighting the importance of sequential investment strategies for adaptation.

General circulation models (GCMs) have been demonstrated to produce estimates of precipitation, including the frequency of extreme precipitation, with substantial bias and uncertainty relative to their representation of other fields. Thus, while theory predicts changes in the hydrologic cycle under anthropogenic warming, there is generally low confidence in future projections of extreme precipitation frequency for specific river basins. In this paper, we explore whether a GCM simulates large‐scale atmospheric circulation indices that are associated with regional extreme precipitation (REP) days more accurately than it simulates REP days themselves, and thus whether conditional simulation of the precipitation events based on the circulation indices may improve the simulation of REP events. We show that a coupled Geophysical Fluid Dynamics Laboratory GCM simulates too many springtime REP days in the Ohio River Basin in historical (1950–2005) simulations. The GCM, however, does credibly simulate the distributional and persistence properties of several indices (which represent the large‐scale atmospheric pressure features, local atmospheric moisture content, and local vertical velocity) that are shown to modulate the likelihood of REP occurrence in the reanalysis/observational record. We show that simulation of REP events based on the GCM‐based atmospheric indices greatly reduces the bias of GCM REP frequency relative to the observed record. The simulation is conducted via a Bayesian regression model by imposing the empirical relationship between observed REP occurrence and the reanalysis‐based atmospheric indices. Application of this model to future (2006–2100) representative concentration pathway 8.5 scenario suggests an increasing trend in springtime REP incidence in the study region. The proposed approach of simulating precipitation events of interest, particularly those poorly represented in GCMs, with a statistical model based on climate indices that are reasonably simulated by GCMs could be applied to subseasonal to seasonal forecasts as well as future projections.