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

Abstract

[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.

Sharing Skills and Needs between Providers and Users of Climate Information to Create Climate Services: Lessons from the Northern Adriatic Case Study

“…These results highlights that the potential for using seasonal forecasts as a tool to predict intense precipitation events with a few months in advance is fairly limited for this region. The results presented are broadly consistent with a similar analysis performed by Eade et al (Eade et al, 2012) using the UK Met Office system. These analogies further corroborate the indication that reliable forecasting of intense rainfall on seasonal timescales are still a challenge for state-of-the-art seasonal prediction systems (for additional details see ).…”

Near-term prediction of impact-relevant extreme temperature indices

“…It is still possible to obtain skillful predictions for more moderate extremes for the UK region, as demonstrated by H13 and Eade et al (2012).…”

“…The summer season was the most skillful in the northern hemisphere. A recent study by Eade et al (2012) demonstrated significantly skillful predictions of moderate (1 in 10) temperature extremes on decadal timescales, especially for multi-year periods. These assessments of skill were performed using the Spearman rank correlation coefficient and standardised root mean square error.…”

Recent Progress in Understanding and Predicting Atlantic Decadal Climate Variability

“…Modest multi-year-lead skill improvement for surface temperature over the continents surrounding the Atlantic is present in some systems, particularly over Western Europe and North/Central America, but the improvement varies with season and seems most evident as a reduction of error rather than enhancement of correlation [123, 125, 127, 130•]. At least one study reports modest but significant skill in predicting temperature and precipitation climate extremes over North America and Europe at decadal lead times, although most of the skill appears to be due to external forcing rather than initialization [93]. There is also emerging evidence that rainfall over Africa can be predicted.…”

“…Not only does AMOC appear to correlate with seasonal mean climate signals over land but also with (possibly) predictable shifts in surface climate extremes that have the greatest societal impact [92,93]. Furthermore, AMOC-related heat transport through the Nordic Seas is a key predictor of simulated Arctic sea ice extent, and it appears to control low-frequency atmospheric heat transport variability in that region, in line with Bjerknes' compensation hypothesis [4, 37•, 94, 95, 96•, 97•].…”