Simulation of future climate under changing temporal covariance structures

Leeds, William B.; Moyer, E. J.; Stein, Michael L.

doi:10.5194/ascmo-1-1-2015

Cited by 15 publications

(31 citation statements)

References 30 publications

(36 reference statements)

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“…Over the past 2 decades, numerous studies have sought to identify changes in temperature extremes both in observations (Easterling et al, 2000;Shaby and Reich, 2012;Parey et al, 2013;Westra et al, 2013;Lee et al, 2014;Naveau et al, 2014) and in general circulation model (GCM) simulations of future climate (Kharin and Zwiers, 2000;Tebaldi et al, 2006;Kharin et al, 2007;Sterl et al, 2008;Frías et al, 2012;Kharin et al, 2013). A number of studies find that extreme changes follow closely with changes in means and standard deviations (e.g., de Vries et al, 2012;Parey et al, 2013).…”

Section: W K Huang Et Al: Temperature Extremes In Ccsm3mentioning

confidence: 99%

“…The GCM output used is part of an ensemble of climate simulations completed by the Center for Robust Decision Making on Climate and Energy Policy (RDCEP) (e.g., Castruccio et al, 2014;Leeds et al, 2015), using the Community Climate System Model version 3 (CCSM3) , a fully coupled model with full representation of the atmosphere (CAM3), land (CLM3), sea ice (CSIM5) and ocean (POP 1.4.3) components. The model was run at the relatively coarse T31 spatial resolution (3.75 • × 3.75 • grid) (Yeager et al, 2006), which made the lengthy runs used here possible.…”

Section: Gcm Outputmentioning

confidence: 99%

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Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Huang

Stein

McInerney

et al. 2016

Adv. Stat. Clim. Meteorol. Oceanogr.

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Abstract. Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the Community Climate System Model version 3 (CCSM3) in equilibrated pre-industrial and possible future (700 and 1400 ppm CO 2 ) conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that warm extremes largely change in accordance with mean shifts in the distribution of summertime temperatures. Cold extremes warm more than mean shifts in the distribution of wintertime temperatures, but changes in GEV location parameters are generally well explained by the combination of mean shifts and reduced wintertime temperature variability. For cold extremes at inland locations, return levels at long recurrence intervals show additional effects related to changes in the spread and shape of GEV distributions. We then examine uncertainties that result from using shorter model runs. In theory, the GEV distribution can allow prediction of infrequent events using time series shorter than the recurrence interval of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes.

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Section: W K Huang Et Al: Temperature Extremes In Ccsm3mentioning

confidence: 99%

Section: Gcm Outputmentioning

confidence: 99%

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Huang

Stein

McInerney

et al. 2016

Adv. Stat. Clim. Meteorol. Oceanogr.

View full text Add to dashboard Cite

show abstract

“…Leeds et al (2013) have done work on simulating future climate under changing covariance structures. They discuss forcing by spatial stochastic processes with thicker tails than the spatial white noise processes used by, for example Kim and North (1992) in their spherical energy balance model.…”

Section: Bifurcations and Tipping Pointsmentioning

confidence: 99%

“…Later versions of spherical spatial energy balance models used by have polar ampli…cation e¤ects. We believe that a useful direction for future research would be to include the bifurcation possibility of Abbot et al (2011) and the resulting impact on damages across space and to compare the changing spatial covariance structure that results with the …ndings of Leeds et al (2013). In the same context designing an optimal "Tech Fix" path to a sustainable low carbon economy is an area of promising future research (David and van Zon, 2014) 8 Appendix 8.1 Appendix I: The Case of Additive Uncertainty While we believe that the case of multiplicative uncertainty that we treated in the main text does a better job of re ‡ecting the model uncertainty, i.e.…”

Section: Bifurcations and Tipping Pointsmentioning

confidence: 99%

Modeling Coupled Climate, Ecosystems, and Economic Systems

Brock¹,

Xepapadeas²

2015

SSRN Journal

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Human economies and ecosystems form a coupled system coevolving in time and space, since human economies use ecosystems services and at the same time a¤ect ecosystems through their production and consumption activities. The study of the interactions between human economies and ecosystems is fundamental for the e¢ cient use of natural resources and the protection of the environment. This necessitates the development and use of models capable of tracing the main interactions, links and feedbacks. In developing this chapter, our objective was to focus on a segment of rapidly developing literature on coupled ecological/economic models with an emphasis on climate change. The advantage of this approach is that it introduces the reader to a very important current research topic, but it also allows, by using climate as the reference ecosystem, the exploration of new modeling approaches which are relevant and useful for the modeling of other types of coupled ecological/economic systems. These include modeling of deep structural uncertainty by using robust control methods, exploring modeling through cumulative carbon budgeting, studying spatial transport phenomena and spatial aspects in economic/ecological modelling.

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“…In Figure 1, bottom row, our proposed method, unlike simple bias correction or the Delta method, both accounts for the relevant changes projected by the cartoon model and retains other distributional properties of the observations. Our method reduces to the Delta method in the case that the model predicts no changes in variability, and reduces to the method in Leeds et al (2015) if the past and future climates are both in equilibrium. Since such a simulation uses projected changes in covariances from a GCM, our methodology must provide a way of modeling and estimating these changes in transient GCM runs.…”

Section: Introductionmentioning

confidence: 99%

Temperatures in transient climates: Improved methods for simulations with evolving temporal covariances

Poppick¹,

McInerney²,

Moyer³

et al. 2016

Ann. Appl. Stat.

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Future climate change impacts depend on temperatures not only through changes in their means but also through changes in their variability. General circulation models (GCMs) predict changes in both means and variability; however, GCM output should not be used directly as simulations for impacts assessments because GCMs do not fully reproduce present-day temperature distributions. This paper addresses an ensuing need for simulations of future temperatures that combine both the observational record and GCM projections of changes in means and temporal covariances. Our perspective is that such simulations should be based on transforming observations to account for GCM projected changes, in contrast to methods that transform GCM output to account for discrepancies with observations. Our methodology is designed for simulating transient (non-stationary) climates, which are evolving in response to changes in CO 2 concentrations (as is the Earth at present). This work builds on previously described methods for simulating equilibrium (stationary) climates. Since the proposed simulation relies on GCM projected changes in covariance, we describe a statistical model for the evolution of temporal covariances in a GCM under future forcing scenarios, and apply this model to an ensemble of runs from one GCM, CCSM3. We find that, at least in CCSM3, changes in the local covariance structure can be explained as a function of the regional mean change in temperature and the rate of change of warming. This feature means that the statistical model can be used to emulate the evolving covariance structure of GCM temperatures under scenarios for which the GCM has not been run. When combined with an emulator for mean temperature, our methodology can simulate evolving temperatures under such scenarios, in a way that accounts for projections of changes while still retaining fidelity with the observational record. The emulator for variability changes is also of interest on its own as a summary of GCM projections of variability changes.

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Simulation of future climate under changing temporal covariance structures

Cited by 15 publications

References 30 publications

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Modeling Coupled Climate, Ecosystems, and Economic Systems

Temperatures in transient climates: Improved methods for simulations with evolving temporal covariances

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