Sensitivity of future climate change and uncertainty over India to performance-based model weighting

Singh, Ram; AchutaRao, Krishna

doi:10.1007/s10584-019-02643-y

Cited by 13 publications

(8 citation statements)

References 60 publications

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“…But in T04 the two types of evidence (model output and observations) are less "complete" than they appear, as a consequence of how they are combined to inform the statements. In the quantification of uncertainty in model-based projections the choice of observational dataset can itself be a source of bias (Singh and AchutaRao 2020). These methodological choices affect T04's results because their model performance measures (see Table 1) are based on model performance against past observations.…”

Section: Towards a Quality Assessment Frameworkmentioning

confidence: 99%

Assessing the Quality of Regional Climate Information

Pacchetti

Dessai

Bradley

et al. 2021

Bulletin of the American Meteorological Society

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Section: Towards a Quality Assessment Frameworkmentioning

confidence: 99%

Assessing the Quality of Regional Climate Information

Pacchetti

Dessai

Bradley

et al. 2021

Bulletin of the American Meteorological Society

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“…Since the probabilistic projections aim to provide an estimate of uncertainty, there is one more way in which comprehensiveness should be assessed. Singh and AchutaRao (2020) show that observational uncertainty can affect estimates of future change, as the assessment of model performance varies depending on the observational dataset used. This uncertainty may be minimal for datasets of variables that have an extensive record in space and time and bias may be easily removed for variables that are well understoodsuch as temperature.…”

Section: Diversity and Completenessmentioning

confidence: 99%

Assessing the quality of state-of-the-art regional climate information: the case of the UK Climate Projections 2018

et al. 2021

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In this paper, we assess the quality of state-of-the-art regional climate information intended to support climate adaptation decision-making. We use the UK Climate Projections 2018 as an example of such information. Their probabilistic, global, and regional land projections exemplify some of the key methodologies that are at the forefront of constructing regional climate information for decision support in adapting to a changing climate. We assess the quality of the evidence and the methodology used to support their statements about future regional climate along six quality dimensions: transparency; theory; independence, number, and comprehensiveness of evidence; and historical empirical adequacy. The assessment produced two major insights. First, a major issue that taints the quality of UKCP18 is the lack of transparency, which is particularly problematic since the information is directed towards non-expert users who would need to develop technical skills to evaluate the quality and epistemic reliability of this information. Second, the probabilistic projections are of lower quality than the global projections because the former lack both transparency and a theory underpinning the method used to produce quantified uncertainty estimates about future climate. The assessment also shows how different dimensions are satisfied depending on the evidence used, the methodology chosen to analyze the evidence, and the type of statements that are constructed in the different strands of UKCP18. This research highlights the importance of knowledge quality assessment of regional climate information that intends to support climate change adaptation decisions.

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“…The Multi-Model Ensemble members (MMEs) and Multiple Initial Condition Ensemble members (MICE) are commonly employed to estimate the model uncertainty and ICV, respectively [3,49,58,59]. MMEs are single realizations from multiple models, and MICE are generated by applying minor perturbations to the model's initial state such that different climate projections behave as surrogates of climate variability [53,60,61,3,62].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Reservoir Operation With Demand Uncertainty and Internal Climate Variability

Upadhyay,

Bhatia

2024

Preprint

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Climate change projections, essential for water resource management, are dominated by multiple sources of uncertainty, including scenario uncertainty and Internal Climate Variability (ICV). The latter is often classified as irreducible and thereby overlooked in traditional decision-making processes on regional and local scales. Our study translates these traditionally irreducible uncertainties into actionable insights by leveraging multiple initial condition ensemble (MICE) outputs, a physically based hydrological model and a multiobjective stochastic optimisation approach. We focus on the Sardar Sarovar Dam in Gujarat, India, a multipurpose reservoir integral to flood control, hydropower generation and meeting diverse water demands under two future climate scenarios (SSP245 and SSP585). We find that for the multipurpose reservoir under consideration, despite considering a broad spectrum of outputs, which are equally plausible due to the inherent variability of the climate, the system is highly reliable for meeting the drinking water supply for the region for the next century, although at the cost of agricultural and industrial water supply. Incorporating ICV provides a robust assessment of system attributes, including reliability, vulnerability, and resilience, particularly when these objectives are in trade-off with each other. Our study emphasizes the critical role of accounting for ICV in water resource planning. This analytical approach allows stakeholders to pinpoint specific vulnerabilities, allowing targeted planning for more adaptable and resilient water resource management strategies, including sustainable water supply and flood control.

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Sensitivity of future climate change and uncertainty over India to performance-based model weighting

Cited by 13 publications

References 60 publications

Assessing the Quality of Regional Climate Information

Assessing the Quality of Regional Climate Information

Assessing the quality of state-of-the-art regional climate information: the case of the UK Climate Projections 2018

Optimizing Reservoir Operation With Demand Uncertainty and Internal Climate Variability

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