Sources of Uncertainty in Greenland Surface Mass Balance in the 21st century.

<p>The surface mass balance (SMB) of the Greenland Ice Sheet is subject to considerable uncertainties that complicate predictions of sea-level rise caused by climate change.<br>We examine the SMB of the Greenland Ice Sheet and its uncertainty in the 21st century using a wide ensemble of simulations with the surface energy and mass balance model "BEr<em>ge</em>n Snow SImulator" (BESSI). We conduct simulations for four greenhouse gas emission scenarios using the output of 26 climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to force BESSI. In addition, the uncertainty of the SMB simulation is estimated by using 16 different parameter sets in our SMB model. The median SMB across climate models, integrated over the ice sheet, decreases for every emission scenario and every parameter set. As expected, the decrease in SMB is stronger for higher greenhouse gas emissions. The uncertainty range in SMB is considerably greater in our ensemble than in other studies that used fewer climate models as forcing. An analysis of the different sources of uncertainty shows that the differences between climate models are the main reason for SMB uncertainty, exceeding even the uncertainty due to the choice of climate scenario. In comparison, the uncertainty caused by the snow model parameters is negligible. The differences between the climate models are most pronounced in the north of Greenland and in the area around the equilibrium line, whereas the ensemble of simulations agrees that the SMB decrease is greatest in the west of Greenland. </p>

A test of an alternative approach for uncertainty representation in weather forecasting

<p>Quantification of evolving uncertainties is required for both probabilistic forecasting and data assimilation in weather prediction. In current practice, the ensemble of model simulations is often used as primary tool to describe the required uncertainties. In this work, we explore an alternative approach, so called stochastic Galerkin method which integrates uncertainties forward in time using a spectral approximation in the stochastic space. </p><p>In an idealized two-dimensional model that couples compressible non-hydrostatic Navier-Stokes equations to cloud dynamics, we investigate the propagation of initial uncertainty. The propagation of initial perturbations is followed through time for all model variables during two types of forecasts: the ensemble forecast and stochastic Galerkin forecast. Since model simulations are very expensive in weather forecasting, our hypothesis is that the stochastic Galerkin would provide more accurate and cheaper forecast statistics than the ensemble simulations. Results indicate that uncertainty as represented with mean, standard deviation and evolution of trace through time provides almost identical results if a 10000-member ensemble is used and truncation of stochastic Galerkin is made at ten spectral modes.  However, for coarser approximations,  for example if 50 ensemble members are used or the stochastic Galerkin is truncated at two modes, differences in standard deviations become significant in both approaches.  A series of experiments indicates that differences in performance of the two methods depend on the system state. For example, for stable flows, the stochastic Galerkin outperforms the ensemble of simulations for every truncation and every variable. In very unstable,  turbulent flows the estimate of the mean between the two methods still remains similar. However,  the ensemble of simulations needs more than 100 members (depending on the model variable) and the stochastic Galerkin a truncation with more than five spectral modes, to produce accurate results.</p>

Identifying the the key factors of uncertainty in Helheim Glacier’s response to climate change

<p>Helheim Glacier is one of the largest glaciers in Greenland and, despite its importance, remains poorly understood. While this glacier has been relatively stable in the 1980s and 1990s, its terminus retreated dramatically by 6 km between 2001 and 2005. By 2006, the glacier stopped thinning, slowed down, and re-advanced 4 km and has been stable since 2007. Helheim is today the third fastest glacier of Greenland, reaching speeds >7 km/a, and drains a surface area of 50,000 km<sup>2</sup>. It is not clear how this glacier will change over the coming century and if another episode of exceptional retreat will occur in the very near future. We construct here a large ensemble of simulations of Helheim glacier over the next century, using a numerical model that includes a dynamic ice front forced by oceanic and atmospheric scenarios. This large ensemble allows to quantify the uncertainty in future retreat and mass loss, and also to attribute the fraction of mass loss uncertainty due to poorly constrained model parameters using main-effect Sobol indices for each input variable. This work helps determine the processes that affect projections the most and provide error bars on model projections.</p>

South Asian summer monsoon projections constrained by the interdecadal Pacific oscillation

A reliable projection of future South Asian summer monsoon (SASM) benefits a large population in Asia. Using a 100-member ensemble of simulations by the Max Planck Institute Earth System Model (MPI-ESM) and a 50-member ensemble of simulations by the Canadian Earth System Model (CanESM2), we find that internal variability can overshadow the forced SASM rainfall trend, leading to large projection uncertainties for the next 15 to 30 years. We further identify that the Interdecadal Pacific Oscillation (IPO) is, in part, responsible for the uncertainties. Removing the IPO-related rainfall variations reduces the uncertainties in the near-term projection of the SASM rainfall by 13 to 15% and 26 to 30% in the MPI-ESM and CanESM2 ensembles, respectively. Our results demonstrate that the uncertainties in near-term projections of the SASM rainfall can be reduced by improving prediction of near-future IPO and other internal modes of climate variability.

Atmospherically Forced Regional Ocean Simulations of the South China Sea: Scale Dependency of the Signal-to-Noise Ratio

When subjecting ocean models to atmospheric forcing, the models exhibits two types of variability—a response to the external forcing (hereafter referred to as signal) and inherently generated (internal, intrinsic, unprovoked, chaotic) variations (hereafter referred to as noise). Based on an ensemble of simulations with an identical atmospherically forced oceanic model that differ only in the initial conditions at different times, the signal-to-noise ratio of the atmospherically forced oceanic model is determined. In the large scales, the variability of the model output is mainly induced by the external forcing and the proportion of the internal variability is small, so the signal-to-noise ratio is large. For smaller scales, the influence of the external forcing weakens and the influence of the internal variability strengthens, so the signal-to-noise ratio becomes less and less. Thus, the external forcing is dominant for large scales, while most of the variability is internally generated for small scales.

Weather Simulation Uncertainty Estimation Using Bayesian Hierarchical Models

Estimates of the uncertainty of model output fields (e.g., 2-m temperature, surface radiation fluxes, or wind speed) are of great value to the weather and climate communities. The traditional approach for the uncertainty estimation is to conduct an ensemble of simulations where the model configuration is perturbed and/or different models are considered. This procedure is very computationally expensive and may not be feasible, in particular for higher-resolution experiments. In this paper, a new method based on Bayesian hierarchical models (BHMs) that requires just one model run is proposed. It is applied to the Weather Research and Forecasting (WRF) Model’s 2-m temperature in the Botnia–Atlantica region in Scandinavia for a 10-day period in the winter and summer seasons. For both seasons, the estimated uncertainty using the BHM is found to be comparable to that obtained from an ensemble of experiments in which different planetary boundary layer (PBL) schemes are employed. While WRF-BHM is not capable of generating the full set of products obtained from an ensemble of simulations, it can be used to extract commonly used diagnostics including the uncertainty estimation that is the focus of this work. The methodology proposed here is fully general and can easily be extended to any other output variable and numerical model.

Bayesian Exploration of Multivariate Orographic Precipitation Sensitivity for Moist Stable and Neutral Flows

Recent idealized studies examined the sensitivity of topographically forced rain and snowfall to changes in mountain geometry and upwind sounding in moist stable and neutral environments. These studies were restricted by necessity to small ensembles of carefully chosen simulations. Research presented here extends earlier studies by utilizing a Bayesian Markov chain Monte Carlo (MCMC) algorithm to create a large ensemble of simulations, all of which produce precipitation concentrated on the upwind slope of an idealized Gaussian bell-shaped mountain. MCMC-based probabilistic analysis yields information about the combinations of sounding and mountain geometry favorable for upslope rain, as well as the sensitivity of orographic precipitation to changes in mountain geometry and upwind sounding. Exploration of the multivariate sensitivity of rainfall to changes in parameters also reveals a nonunique solution: multiple combinations of flow, topography, and environment produce similar surface rainfall amount and distribution. Finally, the results also divulge that the nonunique solutions have different sensitivity profiles, and that changes in observation uncertainty also alter model sensitivity to input parameters.