An assessment of CMIP5 multi-model decadal hindcasts over Australia from a hydrological viewpoint

Mehrotra, R.; Sharma, Ashish; Bari, M. A.; Tuteja, Narendra; Amirthanathan, Gnanathikkam E.

doi:10.1016/j.jhydrol.2014.07.053

Cited by 31 publications

(33 citation statements)

References 20 publications

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“…However, other emerging studies have reported no improvements of note in CMIP5 compared to CMIP3 [23]. Mehrotra et al [24] explored the potential skill of CMIP5 decadal hindcasts from 9 GCMs and their ensembles over the period 1960-2010 in Australia. Their results suggested that precipitation predictions show very limited skill when as-sessed at annual and multi-annual time-scales.…”

Section: Introductionmentioning

confidence: 99%

Assessment of CMIP3-CMIP5 Climate Models Precipitation Projection and Implication of Flood Vulnerability of Bangkok

Supharatid¹

2015

AJCC

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Reliable estimates of precipitation are essential for both research and practical applications. CMIP3 and CMIP5 climate simulations provide both historical simulations and future projections of extreme climate. The 2011 monsoon season was one of case studies with exceptionally heavy and led to extensive and long-lasting flooding in the Chao Phraya river basin, Thailand. Flooding was exacerbated by the rapid expansion of urban areas into flood plains and was the costliest natural disaster in the country's history, with direct damages estimated at US$45 billion. The present paper focuses on the precipitation downscaling of CMIP3 and CMIP5 models. The majority of CMIP3 and CMIP5 models overestimate the dry spell (in June and July) and underestimate the peak precipitation (in May and September). The interquartile model range for precipitation, which is spanned by the 25 th and 75 th quantiles, is closer to the observed data for CMIP5 than CMIP3 models. However, overall results suggest that the performance of CMIP5 models cannot be readily distinguished from of CMIP3 models

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Section: Introductionmentioning

confidence: 99%

Assessment of CMIP3-CMIP5 Climate Models Precipitation Projection and Implication of Flood Vulnerability of Bangkok

Supharatid¹

2015

AJCC

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“…Moreover, compared to the raw predictions ( Figure S2), the SD-BC precipitation predictions do not always have a higher skill. The limited skill in decadal predictions of precipitation was also shown, for instance, by Mehrotra et al (2014) for Australia. The skill of the temperature forecasts is better than that of precipitation ( Figure 2).…”

Section: Resultsmentioning

confidence: 84%

“…The limited skill in decadal predictions of precipitation was also shown, for instance, by Mehrotra et al . () for Australia.…”

Section: Resultsmentioning

confidence: 97%

On the decadal predictability of the frequency of flood events across the U.S. Midwest

Neri

Villarini

Salvi³

et al. 2018

Intl Journal of Climatology

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Skilful predictions of the frequency of flood events over long lead times (e.g., from 1 to 10 years ahead) are essential for governments and institutions making near‐term flood risk plans. However, little is known about current flood prediction capabilities over annual to decadal timescales. Here we address this knowledge gap at 286 U.S. Geological Survey gaging stations across the U.S. Midwest using precipitation and temperature decadal predictions from the Coupled Model Intercomparison Project (CMIP) phase 5 models. We use the 1–10‐year predictions of precipitation and temperature as inputs to statistical models that have significant skill in reproducing inter‐annual and decadal changes in the observed frequency of flood events. Our results indicate that the limited skill of basin‐averaged precipitation predictions suppresses the skill of flood event frequency predictions, even at the shortest lead time, but downscaling and bias correction improves predictions across all lead times and especially in spring.

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“…In full‐field initialization, the model's initial state is constrained to be as close to the observed state as possible. As climate models are imperfect replicas (approximations) of the real world, the model state tends to revert back to the free‐running model equilibrium state from the imposed observed state, over a period of time [ Mehrotra et al , ]. The resulting spurious linear or nonlinear transition is generally referred to as “climate drift” (Figure ).…”

Section: Introductionmentioning

confidence: 99%

Sampling biases in CMIP5 decadal forecasts

et al. 2016

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Recent studies examining the fidelity of decadal hindcast experiments from phase 5 of the Coupled Model Intercomparison Project have highlighted the need for larger ensembles of forecasts, compared to the initial five yearly spaced initializations, to help correct for model biases (drift). This study quantifies differences in the two drift estimates in sea surface temperature (SST) and SST anomaly (SSTA) predictions, between experiments initialized every 5 years and those initialized every year. The effect of the recommended mean drift correction, on the two sets of predictions, is also analyzed. Our results indicate that differences between the SST drift estimates are largest over the tropical Pacific. Moreover, this difference is large for Niño 3.4 and almost negligible for the global average SSTA. Drift correction as per the mean drift from the 5 year case leads to spurious peaks in the drift‐corrected Niño 3.4 (and the tropical Pacific) and sporadic improvements in skill. This problem with Niño 3.4 stems from an aliasing that occurs during the drift calculation that results from a combination of the timing of major El Niño events in relation to the initialization dates. The study recommends accounting for such sampling effects while considering any subset of the full data set.

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An assessment of CMIP5 multi-model decadal hindcasts over Australia from a hydrological viewpoint

Cited by 31 publications

References 20 publications

Assessment of CMIP3-CMIP5 Climate Models Precipitation Projection and Implication of Flood Vulnerability of Bangkok

Assessment of CMIP3-CMIP5 Climate Models Precipitation Projection and Implication of Flood Vulnerability of Bangkok

On the decadal predictability of the frequency of flood events across the U.S. Midwest

Sampling biases in CMIP5 decadal forecasts

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