Historical and future learning about climate sensitivity

Urban, Nathan M.; Edwards, Neil R.; Sriver, R. L.; Keller, Klaus

doi:10.1002/2014gl059484

Cited by 37 publications

(37 citation statements)

References 27 publications

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“…The BRICK model considers this as an uncertain model parameter denoted as the aerosol forcing scaling factor (α DOECLIM ). This aerosol scaling factor has been used elsewhere in the literature (Urban et al, 2014;Urban and Keller, 2010) and accounts for some uncertainty in the radiative forcing of aerosols (Meinshausen et al, 2011b). The interested reader is directed to Kriegler (2005) and Tanaka and Kriegler (2007) for more information about the DOECLIM model.…”

Section: Global Mean Climatementioning

confidence: 99%

“…The AR1 error model has the effect of "whitening" the residuals to satisfy this assumption. This type of calibration approach for DOECLIM has been implemented previously in the literature (Urban et al, 2014;Urban and Keller, 2010), and we direct the interested reader to these studies for further details.…”

Section: Global Mean Climatementioning

confidence: 99%

“…In this example application, we use the calibrated ensemble in the BRICK control configuration to obtain local sea level projections for New Orleans, Louisiana (29 • 57 N, 90 • 4 W). We use a common didactic model for coastal flood protection (Van Dantzig, 1956;Jonkman et al, 2009). In this flood risk model, the policy lever available to decision-makers is the amount by which to heighten the dikes protecting the coastal community.…”

Section: Experimental Descriptionmentioning

confidence: 99%

“…We use this illustrative cost-benefit approach to calculate an economically efficient dike-heightening by weighing the decrease in probable losses due to flooding achieved by building taller dikes against the increase in costs due to investments in construction. The flood risk model implemented here follows a commonly used simple approach (Van Dantzig, 1956). The present implementation considers the current year as 2015 and a time horizon of 85 years (to 2100).…”

Section: Experimental Descriptionmentioning

confidence: 99%

“…For example, they are used for uncertainty quantification (Bakker et al, 2017;Grinsted et al, 2010;Urban et al, 2014;Urban and Keller, 2010) and complex model emulation (Applegate et al, 2012;Bakker et al, 2016;Hartin et al, 2015;Meinshausen et al, 2011a), and are incorporated into integrated assessment models (Hartin et al, 2015;Meinshausen et al, 2011a).…”

Section: Introductionmentioning

confidence: 99%

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BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections

et al. 2017

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Abstract. Simple models can play pivotal roles in the quantification and framing of uncertainties surrounding climate change and sea-level rise. They are computationally efficient, transparent, and easy to reproduce. These qualities also make simple models useful for the characterization of risk. Simple model codes are increasingly distributed as open source, as well as actively shared and guided. Alas, computer codes used in the geosciences can often be hard to access, run, modify (e.g., with regards to assumptions and model components), and review. Here, we describe the simple model framework BRICK (Building blocks for Relevant Ice and Climate Knowledge) v0.2 and its underlying design principles. The paper adds detail to an earlier published model setup and discusses the inclusion of a land water storage component. The framework largely builds on existing models and allows for projections of global mean temperature as well as regional sea levels and coastal flood risk. BRICK is written in R and Fortran. BRICK gives special attention to the model values of transparency, accessibility, and flexibility in order to mitigate the above-mentioned issues while maintaining a high degree of computational efficiency. We demonstrate the flexibility of this framework through simple model intercomparison experiments. Furthermore, we demonstrate that BRICK is suitable for risk assessment applications by using a didactic example in local flood risk management.

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Section: Global Mean Climatementioning

confidence: 99%

Section: Global Mean Climatementioning

confidence: 99%

Section: Experimental Descriptionmentioning

confidence: 99%

Section: Experimental Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections

et al. 2017

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Multidecadal Scale Detection Time for Potentially Increasing Atlantic Storm Surges in a Warming Climate

Lee

Haran

Keller

2017

Geophysical Research Letters

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Storm surges are key drivers of coastal flooding, which generate considerable risks. Strategies to manage these risks can hinge on the ability to (i) project the return periods of extreme storm surges and (ii) detect potential changes in their statistical properties. There are several lines of evidence linking rising global average temperatures and increasingly frequent extreme storm surges. This conclusion is, however, subject to considerable structural uncertainty. This leads to two main questions: What are projections under various plausible statistical models? How long would it take to distinguish among these plausible statistical models? We address these questions by analyzing observed and simulated storm surge data. We find that (1) there is a positive correlation between global mean temperature rise and increasing frequencies of extreme storm surges; (2) there is considerable uncertainty underlying the strength of this relationship; and (3) if the frequency of storm surges is increasing, this increase can be detected within a multidecadal timescale (≈20 years from now).

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Bayesian estimation of climate sensitivity using observationally constrained simple climate models

Bodman

Jones

2016

WIREs Climate Change

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One‐dimensional simple climate models (SCMs) play an important role within a hierarchy of climate models. They have largely been used to investigate alternative emission scenarios and estimate global‐mean temperature change. This role has expanded through the incorporation of techniques that include Monte Carlo methods and Bayesian statistics, adding the ability to generate probabilistic temperature change projections and diagnose key uncertainties, including equilibrium climate sensitivity (ECS). The latter is the most influential parameter within this class of models where it is ordinarily prescribed, rather than being an emergent property. A series of recent papers based on SCMs and Bayesian statistical methods have endeavored to estimate ECS by using instrumental observations and results from other more complex models to constrain the parameter space. Distributions for ECS depend on a variety of parameters, such as ocean diffusivity and aerosol forcing, so that conclusions cannot be drawn without reference to the joint parameter distribution. Results are affected by the treatment of natural variability, observational uncertainty, and the parameter space being explored. In addition, the highly simplified nature of SCMs means that they contain a number of implicit assumptions that do not necessarily reflect adequately the true nature of Earth's nonlinear quasi‐chaotic climate system. Differences in the best estimate and range for ECS may be partly due to variations in the structure of the SCMs reviewed in this study, along with the selection of data and the calibration details, including the choice of priors. Further investigations and model intercomparisons are needed to clarify these issues. WIREs Clim Change 2016, 7:461–473. doi: 10.1002/wcc.397 This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models

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Historical and future learning about climate sensitivity

Cited by 37 publications

References 27 publications

BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections

BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections

Multidecadal Scale Detection Time for Potentially Increasing Atlantic Storm Surges in a Warming Climate

Bayesian estimation of climate sensitivity using observationally constrained simple climate models

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