On the reliability of seasonal climate forecasts

Weisheimer, Antje; Palmer, Tim

doi:10.1098/rsif.2013.1162

Cited by 292 publications

(287 citation statements)

References 34 publications

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“…connections (Barnston et al, 2010a;Weisheimer and Palmer, 2014). Given the low correlations we have found here between floodiness and either seasonal total rainfall or other rainfall indicators, forecasts of any of these proxies are unlikely to provide strong signals of increased risk.…”

Section: Discussionmentioning

confidence: 90%

“…While seasonal total rainfall has demonstrated some predictability in this part of the world (Barnston et al, 2010b;Weisheimer and Palmer, 2014), there are other variables that might be predicted at the seasonal level: frequency of extreme events within a season, sub-seasonal rainfall patterns, soil moisture, and rainfall intensity. Here, we investigate whether variables in each of those categories could serve as a better indicator of flood risk in sub-Saharan Africa.…”

Section: Predictor Variablesmentioning

confidence: 99%

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Should seasonal rainfall forecasts be used for flood preparedness?

Pérez

Stephens

Bischiniotis

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.

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Section: Discussionmentioning

confidence: 90%

Section: Predictor Variablesmentioning

confidence: 99%

Should seasonal rainfall forecasts be used for flood preparedness?

Pérez

Stephens

Bischiniotis

et al. 2017

Hydrol. Earth Syst. Sci.

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“…Higher summer ESP forecast skill could be capitalised upon operationally given seasonal climate predictability over Northern Europe is notoriously challenging for summer rainfall (e.g. Weisheimer and Palmer, 2014).…”

Section: When Is Esp Skilful?mentioning

confidence: 99%

Benchmarking Ensemble Streamflow Prediction skill in the UK

Harrigan¹,

Smith²,

Parry³

et al. 2017

Preprint

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Abstract. Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decisionmaking in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble Streamflow Prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in 10 the absence of skilful future meteorological predictions, instead using Initial Hydrological Conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1-day to 12-months initialised at the first of each month over a 50-year 15 hindcast period from 1965-2015. Results show ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialisation month, and individual catchment location and storage properties. UK-wide mean ESP skill decays exponentially as a function of lead time with mean squared error skill scores across the year of 0.839, 0.303, and 0.179 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialisation months. For lead times up to 1-20 month, ESP skill was higher than average when initialised in summer and lower in winter, whereas for longer seasonal and annual lead times skill was highest when initialised in autumn and winter months and lowest in April. ESP is most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment Baseflow Index (BFI) and ESP skill was very strong ( = 0.896 at 1-month lead time). This is in contrast to the more highly responsive catchments in the north and west which are generally not skilful 25 at seasonal lead times. Overall, this work provides a scientifically defensible justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK and creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.Hydrol. Earth Syst. Sci. Discuss., https://doi

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“…Indeed, by comparing the risk of some climatic event with the cost of taking precautionary action, one can demonstrate quantitatively the value of probabilistic forecasts for decision-making (Palmer 2002). However, realizing this value assumes that the forecast probabilities are reliable (Weisheimer and Palmer 2014).…”

Section: Introductionmentioning

confidence: 99%

Calibrating Climate Change Time-Slice Projections with Estimates of Seasonal Forecast Reliability

2016

Self Cite

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In earlier work, it was proposed that the reliability of climate change projections, particularly of regional rainfall, could be improved if such projections were calibrated using quantitative measures of reliability obtained by running the same model in seasonal forecast mode. This proposal is tested for fast atmospheric processes (such as clouds and convection) by considering output from versions of the same atmospheric general circulation model run at two different resolutions and forced with prescribed sea surface temperatures and sea ice. Here output from the high-resolution version of the model is treated as a proxy for truth. The reason for using this approach is simply that the twenty-first-century climate change signal is not yet known and, hence, no climate change projections can be verified using observations. Quantitative assessments of reliability of the low-resolution model, run in seasonal hindcast mode, are used to calibrate climate change time-slice projections made with the same low-resolution model. Results show that the calibrated climate change probabilities are closer to the proxy truth than the uncalibrated probabilities. Given that seasonal forecasts are performed operationally already at several centers around the world, in a seamless forecast system they provide a resource that can be used without cost to help calibrate climate change projections and make them more reliable for users.

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On the reliability of seasonal climate forecasts

Cited by 292 publications

References 34 publications

Should seasonal rainfall forecasts be used for flood preparedness?

Should seasonal rainfall forecasts be used for flood preparedness?

Benchmarking Ensemble Streamflow Prediction skill in the UK

Calibrating Climate Change Time-Slice Projections with Estimates of Seasonal Forecast Reliability

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