Can a Decadal Forecasting System Predict Temperature Extreme Indices?*

Hanlon, Helen M.; Hegerl, Gabriele C.; Tett, Simon F. B.; Smith, Doug

doi:10.1175/jcli-d-12-00512.1

Cited by 33 publications

(38 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The procedure applied follows the methodology described in Hanlon et al (2013a); now termed H13. That paper presented a method to calculate decadal predictions for climate indices from climate model simulations, and evaluates them based on a comparison to alternative prediction methods.…”

Section: Methodology For Predicting Extreme Temperature Indicesmentioning

confidence: 99%

“…A bias exists between the modelled and observed extremes which is influenced by small scale parametrised processes and local feedbacks, which are not always well captured by the model and are different for extreme temperatures than for mean temperatures (Hanlon et al 2013a).…”

Section: Bias Correctionmentioning

confidence: 99%

“…In this study we build upon the work of Hanlon et al (2013a) who found significant skill, beyond observed climatology using the mean square skill score (Murphy 1988), in predicting the summer average and hottest 5-day average daily maximum (Tmax) and daily minimum (Tmin) temperatures in Europe with the Met Office Hadley Centre decadal prediction system (DePreSys). Hanlon et al (2013a) determined that this skill is due almost entirely to the forecast recreating the climate change signal rather than from its initialisation.…”

Section: Introductionmentioning

confidence: 99%

“…Hanlon et al (2013a) determined that this skill is due almost entirely to the forecast recreating the climate change signal rather than from its initialisation. Subsequently, work shown in Hanlon et al (2013b) found similar results using four CMIP5 models (CanCM4, HadCM3, MIROC5 and MPI-ESM-LR).…”

Section: Introductionmentioning

confidence: 99%

“…Following these previous studies, we apply the same methodology as Hanlon et al (2013a) comparing decadal hindcasts to observations and assessing the accuracy of them compared to observed climatology and persistence. However, here we use indices which are based on exceedance of temperature thresholds which are more directly relevant to impacts and this paper investigates the usefulness of such decadal predictions for informing adaptation decisions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Near-term prediction of impact-relevant extreme temperature indices

et al. 2014

View full text Add to dashboard Cite

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.

show abstract

Section: Methodology For Predicting Extreme Temperature Indicesmentioning

confidence: 99%

Section: Bias Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Near-term prediction of impact-relevant extreme temperature indices

et al. 2014

View full text Add to dashboard Cite

show abstract

Estimating daily climatologies for climate indices derived from climate model data and observations

et al. 2015

View full text Add to dashboard Cite

Climate indices help to describe the past, present, and the future climate. They are usually closer related to possible impacts and are therefore more illustrative to users than simple climate means. Indices are often based on daily data series and thresholds. It is shown that the percentile-based thresholds are sensitive to the method of computation, and so are the climatological daily mean and the daily standard deviation, which are used for bias corrections of daily climate model data. Sample size issues of either the observed reference period or the model data lead to uncertainties in these estimations. A large number of past ensemble seasonal forecasts, called hindcasts, is used to explore these sampling uncertainties and to compare two different approaches. Based on a perfect model approach it is shown that a fitting approach can improve substantially the estimates of daily climatologies of percentile-based thresholds over land areas, as well as the mean and the variability. These improvements are relevant for bias removal in long-range forecasts or predictions of climate indices based on percentile thresholds. But also for climate change studies, the method shows potential for use.Key PointsMore robust estimates of daily climate characteristicsStatistical fitting approachBased on a perfect model approach

show abstract

Projections of climate change indices of temperature and precipitation from an ensemble of bias‐adjusted high‐resolution EURO‐CORDEX regional climate models

2016

View full text Add to dashboard Cite

Statistical bias-adjustment of climate models' outputs is being increasingly used for assessing the impact of climate change on several sectors. It is known that these techniques may alter the mean climate signal of the adjusted variable; however, the effect on the projected occurrence of climate extremes is less commonly investigated. Here the outputs of an ensemble of high-resolution (0.11 ∘ ) regional climate models (RCM) from the Coordinated Regional-climate Downscaling Experiment for Europe (EURO-CORDEX) have been bias adjusted, and a number of climate indices from the Expert Team on Climate Change Detection and Indices have been calculated for the present and future (2071-2100) climate. Indices include absolute-thresholds indices, percentile-based indices, and indices based on the duration of an event. Results show that absolute-threshold indices are largely affected by bias adjustment, as they depend strongly on both the present mean climate value (usually largely biased in the original RCMs) and its shift under climate change. The change of percentile-based indices is less affected by bias adjustment, as that of indices based on the duration of an event (e.g., consecutive dry days or heat waves) although the present climate value can differ between original and bias-adjusted results. Indices like R95ptot (the total amount of precipitation larger than the 95th reference percentile) are largely affected by bias adjustment, although, when analyzing an ensemble of RCMs, the differences are usually smaller than, or comparable to, the intermodel variability.

show abstract

Can a Decadal Forecasting System Predict Temperature Extreme Indices?*

Cited by 33 publications

References 39 publications

Near-term prediction of impact-relevant extreme temperature indices

Near-term prediction of impact-relevant extreme temperature indices

Estimating daily climatologies for climate indices derived from climate model data and observations

Projections of climate change indices of temperature and precipitation from an ensemble of bias‐adjusted high‐resolution EURO‐CORDEX regional climate models

Contact Info

Product

Resources

About