Alexandra Jonko

Large differences in regional precipitation change between a first and second 2 K of global warming

For adaptation and mitigation planning, stakeholders need reliable information about regional precipitation changes under different emissions scenarios and for different time periods. A significant amount of current planning effort assumes that each K of global warming produces roughly the same regional climate change. Here using 25 climate models, we compare precipitation responses with three 2 K intervals of global ensemble mean warming: a fast and a slower route to a first 2 K above pre-industrial levels, and the end-of-century difference between high-emission and mitigation scenarios. We show that, although the two routes to a first 2 K give very similar precipitation changes, a second 2 K produces quite a different response. In particular, the balance of physical mechanisms responsible for climate model uncertainty is different for a first and a second 2 K of warming. The results are consistent with a significant influence from nonlinear physical mechanisms, but aerosol and land-use effects may be important regionally.

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

The Colorado River basin is a fundamentally important river for society, ecology and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model. We combine global sensitivity analysis with a space-filling Latin Hypercube sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach. We find that snow-dominated regions are much more sensitive to uncertainties in VIC parameters. Although baseflow and runoff changes respond to parameters used in previous sensitivity studies, we discover new key parameter sensitivities. For instance, changes in runoff and evapotranspiration are sensitive to albedo, while changes in snow water equivalent are sensitive to canopy fraction and Leaf Area Index (LAI) in the VIC model. It is critical for improved modeling to narrow uncertainty in these parameters through improved observations and field studies. This is important because LAI and albedo are anticipated to change under future climate and narrowing uncertainty is paramount to advance our application of models such as VIC for water resource management.

Quantifying climate feedbacks in polar regions

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.Estimating the magnitude of radiative and non-radiative feedbacks is key for understanding the climate dynamics of polar regions. Here the authors propose an inclusive methodology to quantify the influence of all those feedbacks, stimulating more systematic analyses in observational and model ensembles.

Influence of dimethyl sulfide on the carbon cycle and biological production

Dimethyl sulfide (DMS) is a significant source of marine sulfate aerosol and plays an important role in modifying cloud properties. Fully coupled climate simulations using dynamic marine ecosystem and DMS calculations are conducted to estimate DMS fluxes under various climate scenarios and to examine the sign and strength of phytoplankton-DMS-climate feedbacks for the first time. Simulation results show small differences in the DMS production and emissions between pre-industrial and present climate scenarios, except for some areas in the Southern Ocean. There are clear changes in surface ocean DMS concentrations moving into the future, and they are attributable to changes in phytoplankton production and competition driven by complex spatially varying mechanisms. Comparisons between parallel simulations with and without DMS fluxes into the atmosphere show significant differences in marine ecosystems and physical fields. Without DMS, the missing subsequent aerosol indirect effects on clouds and radiative forcing lead to fewer clouds, more solar radiation, and a much warmer climate. Phaeocystis, a uniquely efficient organosulfur producer with a growth advantage under cooler climate states, can benefit from producing the compound through cooling effects of DMS in the climate system. Our results show a tight coupling between the sulfur and carbon cycles. The ocean carbon uptake declines without DMS emissions to the atmosphere. The analysis indicates a weak positive phytoplankton-DMS-climate feedback at the global scale, with large spatial variations driven by individual autotrophic functional groups and complex mechanisms. The sign and strength of the feedback vary with climate states and phytoplankton groups. This highlights the importance of a dynamic marine ecosystem module and the sulfur cycle mechanism in climate projections.

Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data

Despite decades of research, large multi-model uncertainty remains about the Earth’s equilibrium climate sensitivity to carbon dioxide forcing as inferred from state-of-the-art Earth system models (ESMs). Statistical treatments of multi-model uncertainties are often limited to simple ESM averaging approaches. Sometimes models are weighted by how well they reproduce historical climate observations. Here, we propose a novel approach to multi-model combination and uncertainty quantification. Rather than averaging a discrete set of models, our approach samples from a continuous distribution over a reduced space of simple model parameters. We fit the free parameters of a reduced-order climate model to the output of each member of the multi-model ensemble. The reduced-order parameter estimates are then combined using a hierarchical Bayesian statistical model. The result is a multi-model distribution of reduced-model parameters, including climate sensitivity. In effect, the multi-model uncertainty problem within an ensemble of ESMs is converted to a parametric uncertainty problem within a reduced model. The multi-model distribution can then be updated with observational data, combining two independent lines of evidence. We apply this approach to 24 model simulations of global surface temperature and net top-of-atmosphere radiation response to abrupt quadrupling of carbon dioxide, and four historical temperature data sets. Our reduced order model is a 2-layer energy balance model. We present probability distributions of climate sensitivity based on (1) the multi-model ensemble alone and (2) the multi-model ensemble and observations.

Resolving Rapid Variation in Energy for Particle Transport

Resolving the rapid variation in energy in neutron and thermal radiation transport is needed for the predictive simulation capability in high-energy density physics applications. Energy variation is difficult to resolve due to rapid variations in cross sections and opacities caused by quantized energy levels in the nuclei and electron clouds. In recent work, we have developed a new technique to simultaneously capture slow and rapid variations in the opacities and the solution using homogenization theory, which is similar to multiband (MB) and to the finite-element with discontiguous support (FEDS) method, but does not require closure information. We demonstrated the accuracy and efficiency of the method for a variety of problems. We are researching how to extend the method to problems with multiple materials and the same material but with different temperatures and densities. In this highlight, we briefly describe homogenization theory and some results.