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Recent work has shown that (sub-)seasonal variability in tropical Pacific convection, closely linked to the El Niño–Southern Oscillation (ENSO), relates to summertime circulation over the Euro-Atlantic. The teleconnection is non-stationary, probably due to long-term changes in both the tropical Pacific and extra-tropical Atlantic. It also appears imperfectly captured by numerical models. A dipole in west Pacific sea surface temperatures (SSTs) was found to be the best predictor of errors in numerical sub-seasonal forecasts of European temperature. In this diagnostic study we use reanalysis data to further investigate the teleconnection pathway and the processes behind its non-stationarity. We show that SST gradients associated with the dipole represent a combination of ENSO variability and west Pacific warming, and have become stronger since 1980. Associated patterns of suppressed and enhanced tropical heating are followed by quasi-stationary waves that linger for multiple weeks. Situations with La Niña-like gradients are followed by high-pressure centres over eastern Europe and Russia, three to six weeks later. Inverted situations are followed by high pressure over western Europe, three to six weeks later. The latter situation is conditional on a strong meridional tripole in north Atlantic SSTs and a co-located jet stream. Overall, the sub-seasonal pathway diagnosed in this study connects to patterns detected on seasonal scales, and confirms earlier findings that the summertime connectivity between the Pacific and Europe has shifted in recent decades. [ABSTRACT FROM AUTHOR]

A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (US), starting around 10 August 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a 3-day maximum found at a station in Livingston, LA (east of Baton Rouge), from 12 to 14 August 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30 000 people had been rescued, nearly 10 600 people had slept in shelters on the night of 14 August and at least 60 600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least 13 people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the US since Super Storm Sandy made landfall in New Jersey on 24 October 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real time using the best readily available observational data and high-resolution global climate model simulations. The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data and analysis methods are readily available upon the start. It is the authors' aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here, we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the central US Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region of 29-31° N, 85-95°W, which we refer to as the central US Gulf Coast. Using observational data, we find that the observed local return time of the 12-14 August precipitation event in 2016 is about 550 years (95% confidence interval (CI): 450-1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (CI 11-110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A central US Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story; in the most accurate analyses, the regional probability of 3-day extreme precipitation increases by more than a factor of 1.4 due to anthropogenic climate change. The magnitude of the shift in probabilities is greater in the 25 km (higher-resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none. [ABSTRACT FROM AUTHOR]

A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (U.S.) starting around 10 August, 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a three-day maximum found at a station in Livingston, LA (east of Baton Rouge) from 12-14 August, 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30,000 people had been rescued, nearly 10,600 people had slept in shelters on the night of 14 August, and at least 60,600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least thirteen people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the U.S. since Super Storm Sandy made landfall in New Jersey on 24 October, 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real-time using the best readily available observational data and high-resolution global climate model simulations. The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data, and analysis methods are readily available upon the start. It is the authors aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the Central U.S. Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region: 29-31º N, 85-95º W, which we refer to as the Central U.S. Gulf Coast. Using observational data, we find that the observed local return time of the 12-14 August precipitation event in 2016 is about 550 years (95% confidence interval (C.I.): 450-1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (C.I. 11-110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A Central U.S. Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story, with the regional probability of 3-day extreme precipitation increasing due to anthropogenic climate change by a factor of more than a factor 1.4 in the most accurate analyses. The magnitude of the shift in probabilities is greater in the 25 km (higher resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none. [ABSTRACT FROM AUTHOR]

Climate indices have strong relationships with seasonal rainfall in Java and therefore they are natural candidates for use as potential predictors in a statistical post-processing application. In this study we explore the added value of a more advanced statistical post-processing method to improve precipitation forecasts from the ECMWF Seasonal Forecast System 5 (ECMWF-SEAS5), compared to the empirical quantile mapping (EQM) that has been previously applied by us. It is also expected that the skill of the ECMWF-SEAS5 precipitation forecasts in capturing extreme events, such as those occurring during El Nino and La Nina years, could be improved by statistical post-processing. In this study, a parametric statistical post processing method is applied. We use a variety of potential predictors, such as, ECMWF-SEAS5 precipitation, and indices of ENSO (such as Nino 3.4), the Madden Julian Oscillation (MJO), and the Indian Ocean Dipole (IOD). The ECMWF-SEAS5 forecast that we use is for the period of 1981-2010. This seasonal forecast system runs for a 7-month lead time, with 25 ensemble members on a 35 km horizontal grid. We conduct an objective predictor selection and use the best predictors to correct the precipitation of SEAS5. We show the improved forecast skill in a comparative verification of raw, bias-corrected (using EQM) and post-processed seasonal precipitation forecasts, based on several verification metrics such as the continuous ranked probability skill score (CRPSS) and the Brier skill score (BSS). [ABSTRACT FROM AUTHOR]