Forecasting the number of extreme daily events on seasonal timescales

Hamilton, Emily; Eade, Rosie; Graham, Richard; Scaife, Adam A.; Smith, Doug; Maidens, Anna; MacLachlan, Craig

doi:10.1029/2011jd016541

Cited by 44 publications

(47 citation statements)

References 40 publications

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“…Since the number of forecast-observation pairs was 28 for each point, the HSS estimation had considerable uncertainty given this relatively small sampling size. To quantify this uncertainty, a bootstrapping technique (Wilks, 2011;Hamilton et al, 2012) was applied to resample 28 samples (3000 times with replacement) from the 28-year reforecasts averaged over the CONUS. Then a number of 3000 HSS was calculated for constructing a distribution, with the confidence interval and significance level of the HSS estimated from this distribution.…”

Section: Methodsmentioning

confidence: 99%

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

Tian

Wood

Yuan

2017

Hydrol. Earth Syst. Sci.

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Abstract. This paper explored the potential of a global climate model for sub-seasonal forecasting of precipitation and 2 m air temperature. The categorical forecast skill of 10 precipitation and temperature indices was investigated using the 28-year sub-seasonal hindcasts from the Climate Forecast System version 2 (CFSv2) over the contiguous United States (CONUS). The forecast skill for mean precipitation and temperature as well as for the frequency and duration of extremes was highly dependent on the forecasting indices, regions, seasons, and leads. Forecasts for 7-and 14-day temperature indices showed skill even at weeks 3 and 4, and generally were more skillful than precipitation indices. Overall, temperature indices showed higher skill than precipitation indices over the entire CONUS region at sub-seasonal scale. While the forecast skill related to mean precipitations was low in summer over the CONUS, the number of rainy days, number of consecutive rainy days, and number of consecutive dry days showed considerably high skill for the western coastal region. The presence of active Madden-Julian Oscillation (MJO) events improved CFSv2 weekly mean precipitation forecast skill over most parts of the CONUS, but it did not necessarily improve the weekly mean temperature forecasts. The 30-day forecasts of precipitation and temperature indices calculated from the downscaled monthly CFSv2 forecasts were less skillful than those calculated directly from CFSv2 daily forecasts, suggesting the usefulness of CFSv2 for sub-seasonal hydrological forecasting.

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Section: Methodsmentioning

confidence: 99%

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

Tian

Wood

Yuan

2017

Hydrol. Earth Syst. Sci.

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“…Of course, skillful prediction of mean changes would be expected to imply skillful predictions of extremes, since a shift in the mean is likely to be accompanied by changes in the probabilities of extremes. Although this is the case for seasonal predictions of moderately extreme temperatures [ Hamilton et al , 2012], previous studies have made it clear that it cannot simply be assumed that changes in extremes can be inferred from changes in the mean. For example, Hegerl et al [2004] show that model projections of changes in mean temperature are significantly different to changes in extremes (annual hottest and coldest day) over about half of the globe.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the number of extreme daily events out to a decade ahead

et al. 2012

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[1] The predictability of daily temperature and precipitation extremes is assessed out to a decade ahead using the Met Office Decadal Prediction System. Extremes are defined using a simple percentile based counting method applied to daily gridded observation data sets and corresponding model forecasts. We investigate moderate extremes, with a 10% probability of occurrence, ensuring they are frequent enough for robust skill analysis while having sizable impacts. We quantify the predictability of extremes, assess the impact of initialization, and compare with the predictability of the mean climate. We find modest but significant skill for seasonal predictions of temperature extremes in most regions (global area-average correlation of 0.3) and for precipitation extremes over the USA (area-average correlation of 0.2). The skill of both temperature and European rainfall extremes improves for multiyear forecast periods, as longer averaging periods reduce the impact of unpredictable short-term variations, capitalizing on predictable trends from external forcings. For 5 year periods out to a decade ahead, root-mean square temperature errors are reduced by 20% compared to use of climatology in most regions, apart from the southeastern USA. Initialization improves forecast skill for temperature and precipitation extremes on seasonal timescales in most regions. However, there is little improvement beyond the first year suggesting that skill then arises largely from external forcings. The skill for extremes is generally similar to, but slightly lower than, that for the mean. However, extremes can be more skillful than the mean, for example, USA cold nights, where trends in extremes are greater than for the mean.

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“…Hamilton et al (2012) found seasonal forecasts of the number of daily extreme temperatures (outside the 10-90 % range) had significantly better skill than persistence, though, lower than the skill in predicting the seasonal mean especially in the extratropics. The summer season was the most skillful in the northern hemisphere.…”

Section: Introductionmentioning

confidence: 99%

Near-term prediction of impact-relevant extreme temperature indices

et al. 2014

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A previous study of predictability of European temperature indices revealed significant skill in predictions of 5/10-year average indices of summer mean and maximum 5-day average temperatures based on daily maximum and minimum temperatures for a large area of Europe, particularly in the Mediterranean. Here, this work is extended to study indices relevant to high heat-related impacts on energy use, human health and maize yields in Europe. The skill of predictions of these indices is assessed using decadal predictions of the number of days above critical thresholds of daily maximum, mean and minimum Summer temperatures. Following comparison of these predictions with observed conditions, there is skill found in parts of Europe where the decadal predictions exceed that of using observed climatology and persisting present conditions. Areas in the Mediterranean show the most skill in near-term predictions, while skill is small in Northern/Central Europe. There is even some evidence of skill on small scales. This system is determined to be not appropriate for predicting indices in the UK as the model significantly overestimates the trend in these indices. A further test studies the effect of initialising the decadal forecasts with observations. Simulations that include external forcing, such as greenhouse gas increases, show better skill in predicting changes in the frequency of hot events than those that do not, and the initialisation of forecasts with the model used here does not improve this skill.

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Forecasting the number of extreme daily events on seasonal timescales

Cited by 44 publications

References 40 publications

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

Forecasting the number of extreme daily events out to a decade ahead

Near-term prediction of impact-relevant extreme temperature indices

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