Selecting climate simulations for impact studies based on multivariate patterns of climate change

Mendlik, Thomas; Gobiet, Andreas

doi:10.1007/s10584-015-1582-0

Cited by 112 publications

(90 citation statements)

References 22 publications

(21 reference statements)

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“…In order to recommend a practical subset of climate simulations for end-users who handle the assessment of climate change impacts on hydrology, various selection methods have be proposed based on different criteria (Mendlik and Gobiet, 2016;Cannon, 2015;Gleckler et al, 2008;Lutz et al, 2016;McSweeney et al, 2012;Warszawski et al, 2014;Perkins et al, 2007). 25…”

Section: Discussionmentioning

confidence: 99%

“…There are two strengths of using MMEs: (1) the MME mean typically performs better in representing historical climate observations than any individual model (Gleckler et al, 2008;Pierce 5 et al, 2009;Pincus et al, 2008;Mehran et al, 2014); and (2) the spread of a MME can be used to estimate climate change uncertainties, for example those related to GCM structure, future greenhouse gas concentrations and internal climate variability (Mendlik and Gobiet, 2016;Knutti et al, 2010;Chen et al, 2011b;Tebaldi and Knutti, 2007). While climate projection uncertainty and spread or coverage of a MME are not equivalent, the latter does provide an imperfect estimate of uncertainty and, for sake of simplicity, we use the terms interchangeably in the remainder of this study.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies have proposed selection methods that combine both near-past performance and climate change envelope coverage criteria (Mendlik and Gobiet, 2016;McSweeney et al, 2012;Lutz et al, 2016;Giorgi and Mearns, 2002). For 5 example, Lutz et al (2016) took both model historical skill and the range of projection uncertainty in means and extremes into consideration through a three-step sequential selection process.…”

Section: Introductionmentioning

confidence: 99%

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Transferability of climate simulation uncertainty to hydrological climate change impacts

Wang¹,

Chen²,

Cannon³

et al. 2018

Preprint

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Increasing number of climate models are being produced to cover the uncertainty, which makes it infeasible to use all of them in climate change impact studies. In order to thoughtfully select subsets of climate simulations from a large 10 ensemble, several envelope-based methods have been proposed. The subsets are expected to cover a similar uncertainty envelope as the full ensemble in terms of climate variables. However, it is not a given that the uncertainty in hydrological impacts will be similarly well represented. Therefore, this study investigates the transferability of climate uncertainty related to the choice of climate simulations to hydrological impacts. Two envelope-based selection methods, K-means clustering and Katsavounidis-Kuo-Zhang (KKZ) method, are used to select subsets from an ensemble of 50 climate simulations over two 15 watersheds with very different climates using 31 precipitation and temperature variables. Transferability is evaluated by comparing uncertainty coverage between climate variables and 17 hydrological variables simulated by a hydrological model. The importance of properly choosing climate variables in selecting subsets is investigated by including and excluding temperature variables. Results show that KKZ performs better than K-means at selecting subsets of climate simulations for hydrological impacts, and the uncertainty coverage of climate variables is similar to that of hydrological variables. The subset 20 of first 10 simulations covers over 85% of total uncertainty. As expected, temperature variables are important for the snowrelated watershed, but less important for the rainfall-driven watershed. Overall, envelope-based selection of around 10 climate simulations, based on climate variables that characterize the physical processes controlling hydrology of the watershed, is recommended for hydrological impact studies. 25Hydrol. Earth Syst. Sci. Discuss., https://doi

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Transferability of climate simulation uncertainty to hydrological climate change impacts

Wang¹,

Chen²,

Cannon³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Common approaches to reduce the data footprint are data compression (see, e.g., Iverson et al 2012;Storer 1988;Xia et al 2016b), sampling (see, e.g., Herrmann 2010Lohr 2009;Mendlik and Gobiet 2016) as well as statistical methods such as cluster analysis and principal component analysis (see, e.g., Rogerson 2015).…”

Section: Related Workmentioning

confidence: 99%

GPU-Accelerated Rendering Methods to Visually Analyze Large-Scale Disaster Simulation Data

et al. 2017

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Emerging methodologies for natural hazard risk assessments involve the execution of a multitude of different interacting simulation models that produce vast amounts of spatio-temporal datasets. This data pool is further enlarged when such simulation results are post-processed using GIS operations, for example to derive information for decisionmaking. The novel approach presented in this paper makes use of the GPU-accelerated rendering pipeline to perform such operations on-the-fly without storing any results on secondary memory and thus saving large amounts of storage space. Particularly, algorithms for three frequently used geospatial analysis methods are provided, namely for the computation of difference maps using map algebra and overlay operations, distance maps and buffers as examples for proximity analyses as well as kernel density estimation and inverse distance weighting as examples for statistical surfaces. In addition, a visualization tool is presented that integrates these methods using a node-based data flow architecture. The application of this visualization tool to the results of a real-world risk assessment methodology used in civil engineering shows that the memory footprint of post-processing datasets can be reduced at the order of terabytes. Although the technique has several limitations, most notably the reduced interoperability with conventional analysis tools, it can be beneficial for other use cases. When integrated into desktop GIS applications, for example, it can be used to quickly generate a preview of the results of complex analysis chains or it can reduce the amount of data to be transferred to web or mobile GIS applications.

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“…We used MRI-AGCM3.2S, a super high resolution 20 km grid model developed by Mizuta et al (2012), with the Yoshimura cumulus convection scheme and four Sea Surface Temperature (SST) cases only in the future climate experiment (three SST patterns, C1, C2 and C3, from the cluster analysis of Coupled Model Intercomparison Project Phase 5 (CMIP5) (Mizuta et al, 2014) and the multi-model ensemble (MME) (Mendlik and Gobiet 2015;Kitoh and Endo, 2016) as the 20 km model is too heavy to prepare many cases (Kitoh and Endo, 2016). The duration of the climate experiment of MRI-AGCM3.2S was 25 years for both present and future (2075-2099 with Representative Concentration Pathways (RCP) 8.5 (Kitoh and Endo, 2016)) periods.…”

Section: Bias Correction and Downscaling Of Mri-agcm32smentioning

confidence: 99%

Comparative study on climate change impact on precipitation and floods in Asian river basins

Iwami

Hasegawa

Miyamoto

et al. 2017

Hydrological Research Letters

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Abstract:As many water related disasters occur frequently around the world, proper assessment of future climate change impact on floods and droughts is essential. In this study, we focused on basin-scale climate change impact assessment as necessary information for studying adaptation measures on the basis of integrated water resources management. We used Meteorological Research Institute-Atmospheric General Circulation Model (MRI-AGCM) 3.2S (20 km grid super high resolution model) and a series of simulation methods for climate change analysis. We conducted a comparative study on changes in precipitation, flood discharge and inundation in the future during the wet and dry seasons for five target river basins in the Asian monsoon area. We found that regional precipitation outputs from the high resolution model in this study were in good agreement in the point of tendency of their changes in wet and dry monsoon seasons with the regional precipitation analysis in the Fifth Assessment Report (AR5) of Working Group 1 (WG1) of the International Panel on Climate Change (IPCC, 2013). This study illustrated that the proposed methodology can make more detailed descriptions of climate change possible. The study also found the importance of basin-scale runoff and inundation analysis with downscaling especially for basins where floods occur for a short period, suggesting potential differences between flood change and precipitation change from General Circulation Model (GCM) outputs such as maximum 5-day precipitation index in a basin. As a result, this paper confirms the importance of basin-scale discharge and inundation analysis for climate change considering basin characteristics from the viewpoint of river management.

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Selecting climate simulations for impact studies based on multivariate patterns of climate change

Cited by 112 publications

References 22 publications

Transferability of climate simulation uncertainty to hydrological climate change impacts

Transferability of climate simulation uncertainty to hydrological climate change impacts

GPU-Accelerated Rendering Methods to Visually Analyze Large-Scale Disaster Simulation Data

Comparative study on climate change impact on precipitation and floods in Asian river basins

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