Ensembles and probabilities: a new era in the prediction of climate change

Collins, Matthew

doi:10.1098/rsta.2007.2068

Cited by 173 publications

(114 citation statements)

References 13 publications

(26 reference statements)

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“…[2] In recent years there has been a concerted effort in the climate modeling community to understand and quantify the uncertainty in future climate projections [Collins, 2007], leading to probabilistic projections of temperature and precipitation that are more informative for decision making than any single model realization [Collins et al, 2006;Murphy et al, 2004]. Probability density functions (PDFs) are often formed by carrying out a sensitivity analysis [Saltelli et al, 2000a].…”

Section: Parameter Screeningmentioning

confidence: 99%

Exploration of parametric uncertainty in a surface mass balance model applied to the Greenland ice sheet

et al. 2012

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[1] Computational modeling of Earth system processes often requires simplifying assumptions of the real system. These necessary assumptions result in the definition of internal model parameters that can take a number of different values but must be explicitly defined for any one model simulation. The main issue with such an uncertain multidimensional input space is that many simulations are needed to adequately explore it. This study presents a generalized parameter screening experiment for use in future earth system modeling. This approach identifies model parameters that dominate uncertainty, therefore reducing to a manageable number the simulations required to explore the input space. The approach we adopt is relatively inexpensive to implement and can be applied at both the aggregate and disaggregate (e.g., regional) level. To demonstrate the potential of such a method, it is applied to a surface mass balance model of intermediate complexity over the Greenland ice sheet. All identified parameters were related to the surface melt parameterization, with albedo parameters being identified as the most important. Spatial distributions of the parameter sensitivities show that, in recent years, most parameter sensitivities are concentrated around the southwest and northern ice sheet margins. Simulations for the 21st century indicate an increase in sensitivity in these high melt areas especially in the northeast. Melt contributions from temperature and radiative effects are shown to be important on the order of parameters identified, and as a consequence, sensitivities are dependent on the present climate used for modeling surface mass balance.Citation: Fitzgerald, P. W., J. L. Bamber, J. K. Ridley, and J. C. Rougier (2012), Exploration of parametric uncertainty in a surface mass balance model applied to the Greenland ice sheet,

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Section: Parameter Screeningmentioning

confidence: 99%

Exploration of parametric uncertainty in a surface mass balance model applied to the Greenland ice sheet

et al. 2012

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“…Hawkins and Sutton (2009) showed that towards the end of the twenty-first century, the RCPs are the dominant source of uncertainty in climate projections. In regard of the climate models, also the choice of GCM and RCM can have a large impact on the results and generally an ensemble of projectionsencompassing different GCMs, RCMs and emission scenarios -is recommended in hydrological climate change impact assessment (Hewitt and Griggs 2004;Collins 2007). A clear distinction on the relative effects of hydrological model (HM) uncertainty and climate model (CM) uncertainty to the projected discharge uncertainty has not been concluded; results vary between studies depending on catchment climate and hydrological variable studied (Hagemann et al 2013;Velázquez et al 2013;Vetter et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Analysis of hydrological extremes at different hydro-climatic regimes under present and future conditions

et al. 2016

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We investigate simulated hydrological extremes (i.e., high and low flows) under the present and future climatic conditions for five river basins worldwide: the Ganges, Lena, Niger, Rhine, and Tagus. Future projections are based on five GCMs and four emission scenarios. We analyse results from the HYPE, mHM, SWIM, VIC and WaterGAP3 hydrological models calibrated and validated to simulate each river. The use of different impact models and future projections allows for an assessment of the uncertainty of future impacts. The analysis of extremes is conducted for four different time horizons: reference (1981-2010), early-century (2006-2035), mid-century (2036-2065) and end-century (2070-2099). In addition, Sen's non-parametric estimator of slope is used to calculate the magnitude of trend in extremes, whose statistical significance is assessed by the Mann-Kendall test. Overall, the impact of climate change is more severe at the end of the century and particularly in dry regions. High flows are generally sensitive to changes in precipitation, however sensitivity varies between the basins. Finally, results show that conclusions in climate change impact Center for Global Change and Water Cycle, Hohai University, Nanjing, China studies can be highly influenced by uncertainty both in the climate and impact models, whilst the sensitivity to climate modelling uncertainty becoming greater than hydrological model uncertainty in the dry regions.

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“…In landslide susceptibility assessment this can be considered as an unresolved problem [46][47][48] in [41]. In other disciplines, for example meteorology, a long experience in combining multiple model forecasts already exist [49]. Exploiting ensemble analysis to obtain the most probable forecast is a common practice [41].…”

Section: Resultsmentioning

confidence: 99%

Multi-scale Robust Modelling of Landslide Susceptibility: Regional Rapid Assessment and Catchment Robust Fuzzy Ensemble

Bosco

Rigo

Dijkstra

et al. 2013

IFIP Advances in Information and Communication Technology

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Abstract. Landslide susceptibility assessment is a fundamental component of effective landslide prevention. One of the main challenges in landslides forecasting is the assessment of spatial distribution of landslide susceptibility. Despite the many different approaches, landslide susceptibility assessment still remains a challenge. A semi-quantitative method is proposed combining heuristic, deterministic and probabilistic approaches for a robust catchment scale assessment. A fuzzy ensemble model has been exploited for aggregating an array of different susceptibility zonation maps. Each susceptibility zonation has been obtained by applying heterogeneous statistical techniques as logistic regression (LR), relative distance similarity (RDS), artificial neural network (ANN) and two different landslide susceptibility techniques based on the infinite slope stability model. The sequence of data-transformation models has been enhanced following the semantic array programming paradigm. The ensemble has been applied to a study area in Italy. This catchment scale methodology may be exploited for analysing the potential impact of landscape disturbances. At regional scale, a qualitative approach is also proposed as a rapid assessment technique -suitable for application in real-time operations such as wildfire emergency management.

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Ensembles and probabilities: a new era in the prediction of climate change

Cited by 173 publications

References 13 publications

Exploration of parametric uncertainty in a surface mass balance model applied to the Greenland ice sheet

Exploration of parametric uncertainty in a surface mass balance model applied to the Greenland ice sheet

Analysis of hydrological extremes at different hydro-climatic regimes under present and future conditions

Multi-scale Robust Modelling of Landslide Susceptibility: Regional Rapid Assessment and Catchment Robust Fuzzy Ensemble

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