80 research outputs found
Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling
Nonlinear regression is a useful statistical tool, relating observed data and
a nonlinear function of unknown parameters. When the parameter-dependent
nonlinear function is computationally intensive, a straightforward regression
analysis by maximum likelihood is not feasible. The method presented in this
paper proposes to construct a faster running surrogate for such a
computationally intensive nonlinear function, and to use it in a related
nonlinear statistical model that accounts for the uncertainty associated with
this surrogate. A pivotal quantity in the Earth's climate system is the climate
sensitivity: the change in global temperature due to doubling of atmospheric
concentrations. This, along with other climate parameters, are
estimated by applying the statistical method developed in this paper, where the
computationally intensive nonlinear function is the MIT 2D climate model.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS210 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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
Optimization of multiple storm surge risk mitigation strategies for an Island City On a Wedge
Managing coastal flood risks involves choosing among portfolios of different
options. Analyzing these choices typically requires a model. State-of-the-art
coastal risk models provide detailed regional information, but can be difficult
to implement, computationally challenging, and potentially inaccessible to
smaller communities. Simple economic damage models are more accessible, but may
not incorporate important features and thus fail to model risks and trade-offs
with enough fidelity to effectively support decision making. Here we develop a
new framework to analyze coastal flood control. The framework is
computationally inexpensive, yet incorporates common features of many coastal
cities. We apply this framework to an idealized coastal city and assess and
optimize two objectives using combinations of risk mitigation strategies
against a wide range of future states of the world. We find that optimization
using combinations of strategies allows for identification of Pareto optimal
strategy combinations that outperform individual strategy options.Comment: 22 pages, 5 figures, supplemental discussio
Trade-offs and synergies in managing coastal flood risk: A case study for New York City
Decisions on how to manage future flood risks are frequently informed by both sophisticated and computationally expensive models. This complexity often limits the representation of uncertainties and the consideration of strategies. Here we use an intermediate complexity model framework that enables us to analyze a richer set of strategies, a wider range of objectives, and greater levels of uncertainty than are typically considered by more sophisticated and computationally expensive models. We find that allowing for more combinations of risk mitigation strategies can help expand the solution set, help explain synergies and trade-offs, and point to strategies that can improve outcomes
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