Stochastic climate theory and modeling

Franzke, Christian; O’Kane, Terence J.; Berner, Judith; Williams, Paul D.; Lucarini, Valerio

doi:10.1002/wcc.318

Cited by 149 publications

(129 citation statements)

References 180 publications

(365 reference statements)

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“…The basic results are that one can approximately treat the effect of fast modes on the slow dynamics by adding suitably defined deterministic and stochastic (with white spectrum) forcing terms in the evolution equations of the slow variables [327,328]. While the use of deterministic, mean field parametrizations for processes like convection, which cannot yet be captured by the relatively coarse grids of most weather and climate models is a standard practise in geophysical fluid dynamical modelling [329,330,24], currently weather and climate modelling centres are moving in the direction of introducing stochastic parametrizations [331,332], as mounting evidences suggest that they are more effective than usual deterministic methods [333,334]. It is indeed not clear to what extent such methods, which aim at being able to describe the typical behaviour of the slow variables, perform in terms of providing a good representation of extreme events.…”

Section: Extremes Coarse Graining and Parametrizationsmentioning

confidence: 99%

Extremes and Recurrence in Dynamical Systems

Lucarini¹,

Faranda²,

Freitas³

et al. 2016

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175

250

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ii 4.1.2 The New Conditions 44 4.1.3 The Existence of EVL for General Stationary Stochastic Processes under Weaker Hypotheses 46 4.1.4 Proofs of Theorem 4.1.4 and Corollary 4.1.5 48 4.2 Extreme Values for Dynamically Defined Stochastic Processes 53 4.2.1 Observables and Corresponding Extreme Value Laws 55 4.2.2 Extreme Value Laws for Uniformly Expanding Systems 59 4.2.3 Example 4.2.1 revisited 61 4.2.4 Proof of the Dichotomy for Uniformly Expanding Maps 63 4.3 Point Processes of Rare Events 64 4.3.1 Absence of Clustering 64 4.3.2 Presence of Clustering 65 4.3.3 Dichotomy for Uniformly Expanding Systems for Point Processes 67 4.4 Conditions Д q (u n ), D 3 (u n ), D p (u n ) * and Decay of Correlations 68 4.5 Specific Dynamical Systems where the Dichotomy Applies 71 4.5.1 Rychlik Systems 72 4.5.2 Piecewise Expanding Maps in Higher Dimensions 73 4.6 Extreme Value Laws for Physical Observables 74

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Section: Extremes Coarse Graining and Parametrizationsmentioning

confidence: 99%

Extremes and Recurrence in Dynamical Systems

Lucarini¹,

Faranda²,

Freitas³

et al. 2016

Self Cite

175

250

View full text Add to dashboard Cite

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“…It has broad applications in farming systems (Collinson 2000;Darnhofer et al 2012), socio-ecological systems (Folke 2006;Young et al 2006;Lambin and Meyfroidt 2010) and climate systems (Franzke et al 2015). Real-world systems are inextricably physical, natural, social, economic, cultural and political in nature, resulting in system behaviours that individual disciplines lack the tools to understand and predict.…”

Section: Background 21 Epistemologiesmentioning

confidence: 99%

Crop modelling: towards locally relevant and climate-informed adaptation

Beveridge

Challinor

2018

Climatic Change

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A gap between the potential and practical realisation of adaptation exists: adaptation strategies need to be both climate-informed and locally relevant to be viable. Place-based approaches study local and contemporary dynamics of the agricultural system, whereas climate impact modelling simulates climate-crop interactions across temporal and spatial scales. Cropclimate modelling and place-based research on adaptation were strategically reviewed and analysed to identify areas of commonality, differences, and potential learning opportunities to enhance the relevance of both disciplines through interdisciplinary approaches. Cropmodelling studies have projected a 7-15% mean yield change with adaptation compared to a non-adaptation baseline (Nature Climate Change 4:1-5, 2014). Of the 17 types of adaptation strategy identified in this study as place-based adaptations occurring within Central America, only five were represented in crop-climate modelling literature, and these were as follows: fertiliser, irrigation, change in planting date, change in cultivar and area cultivated. The breath and agency of real-life adaptation compared to its representation in modelling studies is a source of error in climate impact simulations. Conversely, adaptation research that omits assessment of future climate variability and impact does not enable to provide sustainable adaptation strategies to local communities so risk maladaptation. Integrated and participatory methods can identify and reduce these sources of uncertainty, for example, stakeholder's engagement can identify locally relevant adaptation pathways. We propose a research agenda that uses methodological approaches from both the modelling and place-based approaches to work towards climate-informed locally relevant adaptation.

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“…The suggestion that the climate system may be modeled using stochastic techniques was first made by Hasselmann (1976) and has been the subject of several recent review articles (Palmer 2001;Palmer and Williams 2008;Franzke et al 2015;Berner et al 2017). Stochastic parameterizations have demonstrated considerable success in modeling atmospheric convection (Lin and Neelin 2002), enhancing sea surface temperature predictability (Scott 2003), modeling El Niño-Southern Oscillation (ZavalaGaray et al 2003), capturing regime transitions in rotating annulus laboratory experiments (Williams et al 2003(Williams et al , 2004, improving the simulated atmospheric blocking frequency (Jung et al 2005), and modeling sudden stratospheric warmings (Birner and Williams 2008).…”

Section: Introductionmentioning

confidence: 99%

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

et al. 2016

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In climate simulations, the impacts of the subgrid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the subgrid variability in a computationally inexpensive manner. This study shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a highresolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a nonzero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the highresolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations.

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Stochastic climate theory and modeling

Cited by 149 publications

References 180 publications

Extremes and Recurrence in Dynamical Systems

Extremes and Recurrence in Dynamical Systems

Crop modelling: towards locally relevant and climate-informed adaptation

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

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