Correction to: How will southern hemisphere subtropical anticyclones respond to global warming? Mechanisms and seasonality in CMIP5 and CMIP6 model projections

Fahad, Abdullah A.; Burls, Natalie J.; Strasberg, Zachary

doi:10.1007/s00382-020-05329-9

Cited by 3 publications

(2 citation statements)

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“…and S6) and to reach 80% (25%) by the end of this century. For the SSP5-8.5 (SSP2-4.5) scenario, the likelihood of an event 1517 would thus increase by a factor of 120 (40) relative to earlier in the 20th century (Fig. 2H).…”

Section: Drought Risk Projectionsmentioning

confidence: 99%

“…S10), but with anomaly patterns that are more consistent across models in AM and less consistent in June-September. Indeed, the Subtropical Anticyclone response in the Southern Hemisphere features larger intermodel uncertainty in the austral winter (40). A more prolonged dry season into the late austral fall (AM) over SSA is, therefore, a robust indication in terms of future precipitation reduction and droughts risk.…”

Section: Comparison With Other Large Ensemblesmentioning

confidence: 99%

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Increasing risk of another Cape Town “Day Zero” drought in the 21st century

Pascale

Kapnick

Delworth

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Three consecutive dry winters (2015–2017) in southwestern South Africa (SSA) resulted in the Cape Town “Day Zero” drought in early 2018. The contribution of anthropogenic global warming to this prolonged rainfall deficit has previously been evaluated through observations and climate models. However, model adequacy and insufficient horizontal resolution make it difficult to precisely quantify the changing likelihood of extreme droughts, given the small regional scale. Here, we use a high-resolution large ensemble to estimate the contribution of anthropogenic climate change to the probability of occurrence of multiyear SSA rainfall deficits in past and future decades. We find that anthropogenic climate change increased the likelihood of the 2015–2017 rainfall deficit by a factor of five to six. The probability of such an event will increase from 0.7 to 25% by the year 2100 under an intermediate-emission scenario (Shared Socioeconomic Pathway 2-4.5 [SSP2-4.5]) and to 80% under a high-emission scenario (SSP5-8.5). These results highlight the strong sensitivity of the drought risk in SSA to future anthropogenic emissions.

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Section: Drought Risk Projectionsmentioning

confidence: 99%

Section: Comparison With Other Large Ensemblesmentioning

confidence: 99%

Increasing risk of another Cape Town “Day Zero” drought in the 21st century

Pascale

Kapnick

Delworth

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

The Influence of South Pacific Convergence Zone Heating on the South Pacific Subtropical Anticyclone and Southern Hemisphere Storm Tracks

Fahad

Burls

Swenson

et al. 2020

Preprint

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Deep learning for stochastic precipitation generation – deep SPG v1.0

Bird¹,

Walker²,

Bodeker³

et al. 2023

Geosci. Model Dev.

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Abstract. We present a deep-neural-network-based single-site stochastic precipitation generator (SPG), capable of producing realistic time series of daily and hourly precipitation. The neural network outputs a wet-day probability and precipitation distributions in the form of a mixture model. The SPG was tested in four different locations in New Zealand, and we found it accurately reproduced the precipitation depth, the autocorrelations seen in the original data, the observed dry-spell lengths, and the seasonality in precipitation. We present two versions of the hourly and daily SPGs: (i) a stationary version of the SPG that assumes that the statistics of the precipitation are time independent and (ii) a non-stationary version that captures the secular drift in precipitation statistics resulting from climate change. The latter was developed to be applicable to climate change impact studies, especially studies reliant on SPG projections of future precipitation. We highlight many of the pitfalls associated with the training of a non-stationary SPG on observations alone and offer an alternative method that replicates the secular drift in precipitation seen in a large-ensemble regional climate model. The SPG runs several orders of magnitude faster than a typical regional climate model and permits the generation of very large ensembles of realistic precipitation time series under many climate change scenarios. These ensembles will also contain many extreme events not seen in the historical record.

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Correction to: How will southern hemisphere subtropical anticyclones respond to global warming? Mechanisms and seasonality in CMIP5 and CMIP6 model projections

Cited by 3 publications

References 0 publications

Increasing risk of another Cape Town “Day Zero” drought in the 21st century

Increasing risk of another Cape Town “Day Zero” drought in the 21st century

The Influence of South Pacific Convergence Zone Heating on the South Pacific Subtropical Anticyclone and Southern Hemisphere Storm Tracks

Deep learning for stochastic precipitation generation – deep SPG v1.0

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