Air flow and stability indices in GCM future and control runs

Hanafin, J. A.; McGrath, Ray; Semmler, Tido; Wang, S.; Lynch, Peter; Steele‐Dunne, Susan; Nolan, P.

doi:10.1002/joc.2125

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Several studies established a relation between thunderstorm probability of occurrence and specific convective parameters and their thresholds [e.g., Haklander and van Delden , 2003; Kunz , 2007]. This connection provides the basis for statistical analyses of long‐term variability including trends in the convective parameters over past [e.g., DeRubertis , 2006; Cao , 2008] and future decades [ Leslie et al , 2007; Van Klooster and Roebber , 2009; Marsh et al , 2009; Hanafin et al , 2011]. In summary, most studies found evidence for a slight increase in the convective potential of the atmosphere both for past and future.…”

Section: Introductionmentioning

confidence: 99%

Long‐term trends of hail‐related weather types in an ensemble of regional climate models using a Bayesian approach

Kapsch¹,

Kunz²,

Vitolo³

et al. 2012

J. Geophys. Res.

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[1] This paper investigates the long-term variability of specific weather types that are associated with damaging hailstorms in Germany for past and future time periods. Forty large-scale weather types are determined by the objective weather type classification scheme of German Weather Service. This scheme is applied to both reanalyses (ERA-40) and eight different regional climate model (RCM) simulations. It is shown that the RCMs are able to approximately reproduce the distribution of weather type occurrences obtained from the reference of ERA-40. Using additional insurance loss data, the weather types are further identified as hail-related or hail-unrelated. Hailstorms are neither captured comprehensively by existing observation systems nor can they be modeled reliably and the large-scale weather types are here considered as proxies for hail occurrence. Four weather types that are most likely associated with damaging hailstorms show a slight increase both during the past and future period according to the RCM simulations. A novel statistical model is developed for the probabilistic prediction of the fraction of hail damage days conditional on the weather types. The model is Bayesian and uses a Markov Chain Monte Carlo approach. For the ERA-40 reanalysis the model prediction agrees well with fraction of hail damage days observed in the insurance data. For most of the RCM projections, the statistical model predicts a slight increase in the number of hail days in the future (2031)(2032)(2033)(2034)(2035)(2036)(2037)(2038)(2039)(2040)(2041)(2042)(2043)(2044)(2045), with relative changes between 7 and 15% compared to the period 1971-2000.Citation: Kapsch, M.-L., M. Kunz, R. Vitolo, and T. Economou (2012), Long-term trends of hail-related weather types in an ensemble of regional climate models using a Bayesian approach,

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Section: Introductionmentioning

confidence: 99%

Long‐term trends of hail‐related weather types in an ensemble of regional climate models using a Bayesian approach

Kapsch¹,

Kunz²,

Vitolo³

et al. 2012

J. Geophys. Res.

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“…Climate variables projected under A1B climate change scenario from a general circulation model (Hadley Centre coupled model (HadCM3)) for two time periods, i.e., 2021-2040 (denoted as 2030) and 2071-2090 (denoted as 2080), were obtained (IPCC, 2013). The A1B climate change scenario describes a future world with very sharp economic growth, with global population peaking in the mid-century and with rapid introduction of new and more efficient technologies (Hanafin et al, 2011). This scenario assumes balances across all sources of technologies (Prudhomme et al, 2010).…”

Section: Future Climate Change Scenariomentioning

confidence: 99%

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province, Central Iran

et al. 2017

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Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network (GRNN) model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998-2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data (including climate and topography data), and the performance of each network model was assessed using the mean estimation error (MEE), model efficiency factor (MEF), and correlation coefficient (r). The best neural network model was then selected and further applied to predict the forage production of rangelands in the future (in 2030 and 2080) under A1B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm 2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production (0-100 kg/hm 2) will increase while the areas with moderate, moderately high and high levels of forage production (≥100 kg/hm 2) will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.

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Assessment of GCM capabilities to simulate tropospheric stability on the Arabian Peninsula

Barfus¹,

Bernhofer²

2014

Int. J. Climatol.

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Air flow and stability indices in GCM future and control runs

Cited by 7 publications

References 27 publications

Long‐term trends of hail‐related weather types in an ensemble of regional climate models using a Bayesian approach

Long‐term trends of hail‐related weather types in an ensemble of regional climate models using a Bayesian approach

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province, Central Iran

Assessment of GCM capabilities to simulate tropospheric stability on the Arabian Peninsula

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