Abstract: We present a self-consistent, large ensemble, high-resolution global dataset of long‐term future climate developed by integrating a spatial disaggregation (SD) pattern-scaling technique and a bias-correction (BC) delta method. The delta method adds the anomalies or deltas (future climate trends) onto a historical, detrended climate that is based on the third phase of the Global Soil Wetness Project (GSWP3). The anomalies or deltas are derived by spatially disaggregating zonal climate projections from the MIT Integrated Global System Modeling (IGSM) framework based on regional hydroclimate change patterns from the 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models.

Four emission scenarios are considered to represent the existing energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2°C and Paris 1.5°C. For each emission scenario, a distribution of plausible trajectories is provided by a 50-member ensemble to represent the uncertainty in the Earth system (e.g., the climate sensitivity, rate of heat uptake by the ocean, uncertainty in carbon cycle), allowing for constructing a 900-member ensemble of regional climate outcomes. This global dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity.

Quantitative assessments clearly indicate the ability of the dataset to represent the expected large-scale climate features across various regions of the globe. This large‐ensemble, high-resolution dataset can be used for assessing impacts of climate change from a risk-based perspective across different applications, including hydropower, water resources, wind power resources to name a few.

Abstract: Studies exploring energy transitions typically focus on a single or small set of scenarios, often with idealized policy assumptions (e.g. with global carbon pricing and significant negative emissions). However, there are countless possible ways the future could unfold, with different implications for energy transitions. In this work, we develop a probabilistic multi-sector coupled human-natural system model and explore both deep uncertainty about climate policy design and parametric uncertainty about socioeconomic assumptions, and the implications of those uncertainties for energy transitions and sectoral responses. To reflect policy design uncertainty, we utilize a set of increasingly stringent global emissions pathways comprised of increasingly stringent regional GHG constraints, and consider both “Optimistic” and “Pessimistic” design conditions that represent deep uncertainties for climate strategy, including whether or not there is international emissions trading, coverage of land use emissions and availability of carbon dioxide removal technologies. For each of these scenarios, we then run large ensembles of our model, sampling from probability distributions for uncertain socioeconomic parameters (e.g. productivity growth, population, technology costs, fossil resources). Using this approach, we can quantify uncertainty in the future energy mix and sectoral responses (e.g. emissions, output and energy use) and how that uncertainty shifts for different policy design assumptions.

Results suggest many possible energy mixes are consistent with a given global emissions pathway, and the policy design has significant implications for future energy mixes. In particular, whether or not international emissions trading is allowed results in vastly different amounts of BECCS and afforestation pursued globally, which in turn affects how much fossil energy can continue to be used and decarbonization strategies employed in different regions and sectors. This approach demonstrates the importance of considering uncertainty when planning for energy transitions and that planning for a single future is risky.