Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-corrected climate information, making it important to understand the uncertainties and potential biases of this approach. Here, we perform a variance decomposition to partition uncertainty in global climate projections and quantify the relative importance of downscaling and bias-correction. We analyze simple climate metrics such as annual temperature and precipitation averages, as well as several indices of climate extremes. We find that downscaling and bias-correction often contribute substantial uncertainty to local decision-relevant climate outcomes, though our results are strongly heterogeneous across space, time, and climate metrics. Our results can provide guidance to impact modelers and decision-makers regarding the uncertainties associated with downscaling and bias-correction when performing local-scale analyses, as neglecting to account for these uncertainties may risk overconfidence relative to the full range of possible climate futures.

We explore the in-medium properties of heavy-quarkonium states at finite baryochemical potential and finite transverse momentum based on a modern complex-valued potential model. Our starting point is a novel, rigorous derivation of the generalized Gauss law for in-medium quarkonium, combining the nonperturbative physics of the vacuum bound state with a weak coupling description of the medium degrees of freedom. Its relation to previous models in the literature is discussed. We show that our approach is able to reproduce the complex lattice QCD heavy quark potential even in the nonperturbative regime, using a single temperature dependent parameter, the Debye mass $m_D$. After vetting the Gauss law potential with state-of-the-art lattice QCD data, we extend it to the regime of finite baryon density and finite velocity, currently inaccessible to first principles simulations. In-medium spectral functions computed from the Gauss law potential are subsequently used to estimate the ${\psi }^{\prime }/J/\psi$ ratio in heavy-ion collisions at different beam energies and transverse momenta. We find qualitative agreement with the predictions from the statistical model of hadronization for the $\sqrt{s_{NN}}$ dependence and a mild dependence on the transverse momentum.

Simulation models of multi‐sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi‐sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single‐ and multi‐sector systems. Here, we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi‐sector model and their interactions with policy design as they emerge at different stages of analysis: (a) inference and model calibration; (b) projecting future outcomes; and (c) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi‐sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.

Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.