Assessing the Impact of Retreat Mechanisms in a Simple Antarctic Ice Sheet Model Using Bayesian Calibration

The response of the Antarctic ice sheet (AIS) to changing climate forcings is an important driver of sea-level changes. Anthropogenic climate change may drive a sizeable AIS tipping point response with subsequent increases in coastal flooding risks. Many studies analyzing flood risks use simple models to project the future responses of AIS and its sea-level contributions. These analyses have provided important new insights, but they are often silent on the effects of potentially important processes such as Marine Ice Sheet Instability (MISI) or Marine Ice Cliff Instability (MICI). These approximations can be well justified and result in more parsimonious and transparent model structures. This raises the question of how this approximation impacts hindcasts and projections. Here, we calibrate a previously published and relatively simple AIS model, which neglects the effects of MICI and regional characteristics, using a combination of observational constraints and a Bayesian inversion method. Specifically, we approximate the effects of missing MICI by comparing our results to those from expert assessments with more realistic models and quantify the bias during the last interglacial when MICI may have been triggered. Our results suggest that the model can approximate the process of MISI and reproduce the projected median melt from some previous expert assessments in the year 2100. Yet, our mean hindcast is roughly 3/4 of the observed data during the last interglacial period and our mean projection is roughly 1/6 and 1/10 of the mean from a model accounting for MICI in the year 2100. These results suggest that missing MICI and/or regional characteristics can lead to a low-bias during warming period AIS melting and hence a potential low-bias in projected sea levels and flood risks.Comment: v1: 16 pages, 4 figures, 7 supplementary files; v2: 15 pages, 4 figures, 7 supplementary files, corrected typos, revised title, updated according to revisions made through publication proces

Sea-level projections representing the deeply uncertain contribution of the West Antarctic ice sheet.

There is a growing awareness that uncertainties surrounding future sea-level projections may be much larger than typically perceived. Recently published projections appear widely divergent and highly sensitive to non-trivial model choice

Impacts of representing sea-level rise uncertainty on future flood risks: An example from San Francisco Bay

<div><p>Rising sea levels increase the probability of future coastal flooding. Many decision-makers use risk analyses to inform the design of sea-level rise (SLR) adaptation strategies. These analyses are often silent on potentially relevant uncertainties. For example, some previous risk analyses use the expected, best, or large quantile (i.e., 90%) estimate of future SLR. Here, we use a case study to quantify and illustrate how neglecting SLR uncertainties can bias risk projections. Specifically, we focus on the future 100-yr (1% annual exceedance probability) coastal flood height (storm surge including SLR) in the year 2100 in the San Francisco Bay area. We find that accounting for uncertainty in future SLR increases the return level (the height associated with a probability of occurrence) by half a meter from roughly 2.2 to 2.7 m, compared to using the mean sea-level projection. Accounting for this uncertainty also changes the shape of the relationship between the return period (the inverse probability that an event of interest will occur) and the return level. For instance, incorporating uncertainties shortens the return period associated with the 2.2 m return level from a 100-yr to roughly a 7-yr return period (∼15% probability). Additionally, accounting for this uncertainty doubles the area at risk of flooding (the area to be flooded under a certain height; e.g., the 100-yr flood height) in San Francisco. These results indicate that the method of accounting for future SLR can have considerable impacts on the design of flood risk management strategies.</p></div

Hypsometric curve covering the elevations between -2 and 8 m in San Francisco County.

<p>The gray area within the inset plot displays the area analyzed in this curve. The area in blue is water, whereas the area in tan is land. The dashed lines represent the elevation associated with the mean sea level (black), the baseline 100-yr storm surge (yellow), and the future 100-yr flood height (orange and dark red). The black curve displays the cumulative density or percentage of the area analyzed at elevations between -2–8 m. For example, ∼42% of the area analyzed has an elevation of 2.7 m (100-yr flood height accounting for uncertain sea-level rise) or lower.</p

Estimated (panel a) probability density function of global mean sea-level rise in 2100 and (panel b) flood survival functions for San Francisco Bay.

<p>In panel a, the dark red line represents the sea-level distribution in the year 2100, whereas, the orange and red points display the mean and Heberger et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174666#pone.0174666.ref007" target="_blank">7</a>] (not accounting for land storage changes) sea-level estimate. In panel b, the baseline survival function for San Francisco Bay (yellow) is shifted relative to increases in global mean sea level by the mean sea-level projection (orange), the Heberger et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174666#pone.0174666.ref007" target="_blank">7</a>] estimate (0.8 m; red), and each individual sea-level projection from the distribution of Markov chain Monte Carlo samples (gray) for the year 2100. Accounting for sea-level uncertainty produces the survival function in dark red. The associated return period is displayed on the right axis. The distance between the points on the dashed line (100-yr return period) to the same color point on the dark red curve display the flood risk underestimation.</p

Comparison of historical sea-level anomalies in San Francisco Bay (SFB) to global mean sea-level anomalies and projections.

<p>The black dots represent the monthly mean sea level at the SFB tide gauge [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174666#pone.0174666.ref031" target="_blank">31</a>]. Note that the SFB tide gauge observations are not used in the global mean sea level modeling process. The green line represents synthesized global mean sea-level anomalies relative to the mean sea level in the year 2000 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174666#pone.0174666.ref032" target="_blank">32</a>], where the gray envelope is the 90% credible interval and the blue line is the projected mean fitted to those anomalies.</p

Sequential zoom in of the baseline and future 100-yr flood risk area in San Francisco.

<p>The maps display the baseline 100-yr flood risk area in yellow. In the year 2100, the potential 100-yr flood height includes accounting for the mean sea-level projection (orange), the Heberger et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174666#pone.0174666.ref007" target="_blank">7</a>] (not accounting for land storage changes) sea-level projection (red), and accounting for sea-level rise uncertainty (dark red). The star is the location of the tide gauge.</p

Complex Interactions Among Successional Trajectories and Climate Govern Spatial Resilience After Severe Windstorms in Central Wisconsin, USA

Context Resilience is a concept central to the field of ecology, but our understanding of resilience is not sufficient to predict when and where large changes in species composition might occur following disturbances, particularly under climate change. Objectives Our objective was to estimate how wind disturbance shapes landscape-level patterns of engineering resilience, defined as the recovery of total biomass and species composition after a windstorm, under climate change in central Wisconsin. Methods We used a spatially-explicit, forest simulation model (LANDIS-II) to simulate how windstorms and climate change affect forest succession and used boosted regression tree analysis to isolate the important drivers of resilience. Results At mid-century, biomass fully recovered to current conditions, but neither biomass nor species composition completely recovered at the end of the century. As expected, resilience was lower in the south, but by the end of the century, resilience was low throughout the landscape. Disturbance and species’ characteristics (e.g., the amount of area disturbed and the number of species) explained half of the variation in resilience, while temperature and soil moisture comprised only 17% collectively. Conclusions Our results illustrate substantial spatial patterns of resilience at landscape scales, while documenting the potential for overall declines in resilience through time. Species diversity and windstorm size were far more important than temperature and soil moisture in driving long term trends in resilience. Finally, our research highlights the utility of using machine learning (e.g., boosted regression trees) to discern the underlying mechanisms of landscape-scale processes when using complex spatially-interactive and non-deterministic simulation models

Comparison of the percentage of runs passing through individual constraints, all the constraints, or no constraints for the Pre-calibration (n = 1.3 × 10³) versus full Bayesian inversion method (subset n = 3.5 × 10³).

<p>Comparison of the percentage of runs passing through individual constraints, all the constraints, or no constraints for the Pre-calibration (n = 1.3 × 10³) versus full Bayesian inversion method (subset n = 3.5 × 10³).</p