Abstract. Global climate models are important tools for understanding the climate system and how it is projected to evolve under scenario-driven emissions pathways. Their output is widely used in climate impacts research for modeling the current and future effects of climate change. However, climate model output remains coarse in relation to the high-resolution climate data needed for climate impacts studies, and it also exhibits biases relative to observational data. Treatment of the distribution tails is a key challenge in existing downscaled climate datasets available at a global scale; many of these datasets used quantile mapping techniques that were known to dampen or amplify trends in the tails. In this study, we apply the trend-preserving Quantile Delta Mapping (QDM) bias-adjustment method (Cannon et al., 2015) and develop a new downscaling method called the Quantile-Preserving Localized-Analog Downscaling (QPLAD) method that also preserves trends in the distribution tails. Both methods are integrated into a transparent and reproducible software pipeline, which we apply to global, daily model output for surface variables (maximum and minimum temperature and total precipitation) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments (O’Neill et al., 2016) for the historical experiment and four future emissions scenarios ranging from aggressive mitigation to no mitigation: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (Riahi et al., 2017). We use European Centre for Medium-RangeWeather Forecasts (ECMWF) ERA5 (Hersbach et al., 2018) temperature and precipitation reanalysis data as the reference dataset over the Sixth Intergovernmental Panel on Climate Change (IPCC) Assessment Report (AR6) reference period, 1995–2014. We produce bias-adjusted and downscaled data over the historical period (1950–2014) and for four emissions pathways (2015–2100) for 25 models in total. The output dataset of this study is the Global Downscaled Projections for Climate Impacts Research (GDPCIR), a global, daily, 0.25° horizontal-resolution product which is publicly hosted on Microsoft AI for Earth’s Planetary Computer (https://planetarycomputer.microsoft.com/dataset/group/cil-gdpcir/).
Abstract The Mg/Ca ratio of planktic foraminifera is a widely used proxy for sea‐surface temperature but is also sensitive to other environmental factors. Previous work has relied on correcting Mg/Ca for nonthermal influences. Here, we develop a set of Bayesian models for Mg/Ca in four major planktic groups— Globigerinoides ruber (including both pink and white chromotypes), Trilobatus sacculifer , Globigerina bulloides , and Neogloboquadrina pachyderma (including N. incompta )—that account for the multivariate influences on this proxy in an integrated framework. We use a hierarchical model design that leverages information from both laboratory culture studies and globally distributed core top data, allowing us to include environmental sensitivities that are poorly constrained by core top observations alone. For applications over longer geological timescales, we develop a version of the model that incorporates changes in the Mg/Ca ratio of seawater. We test our models—collectively referred to as BAYMAG—on sediment trap data and on representative paleoclimate time series and demonstrate good agreement with observations and independent sea‐surface temperature proxies. BAYMAG provides probabilistic estimates of past temperatures that can accommodate uncertainties in other environmental influences, enhancing our ability to interpret signals encoded in Mg/Ca.
This paper develops the first globally comprehensive and empirically grounded estimates of worker disutility due to future temperature increases caused by climate change. Harmonizing daily worker-level data from seven countries representing nearly a third of the world's population, we first evaluate the causal effect of daily temperature on labor supply, recovering an inverted U-shaped relationship where extreme cold and hot temperatures lead to labor supply losses for workers in weather-exposed industries. We then develop the first micro-founded, global estimates for how future climate change will impact workers, accounting for expected shifts in the global workforce towards less weather-exposed industries. Interpreting labor supply impacts of climate change through a simple theoretical framework, we monetize the implied disutility to workers of a warmer climate, a welfare cost not captured in any existing estimates. Under a high emissions scenario, we estimate the increase in labor disutility is valued at roughly 1.8% of global GDP in 2099, with damages being especially large in today's poor and/or hot locations while cold locations benefit. Finally, we estimate that the release of an additional ton of CO2 today will cause expected labor disutility damages of $17.0 under a high emissions scenario and $10.8 under a moderate scenario, using a 2% discount rate that is justified by US Treasury rates over the last two decades. Accounting for uncertainty in these marginal damages when individuals are risk averse increases their value by 31% (high emissions scenario) and 62% (moderate scenario) under a standard parameterization of the utility function.
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