Abstract
Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time.
Highlights
Over the past 30 years, the science of predicting seasonal-timescale fluctuations in weather has grown from a research activity undertaken in a few academic and research institutes [1], to a routine operational activity in a number of meteorological forecast services [2,3,4]
The results presented in this paper are based on System 4’s retrospective seasonal forecasts of 2 m temperature and precipitation over land
We focus on the reliability of the forecasts, as discussed in the Introduction and §4
Summary
Over the past 30 years, the science of predicting seasonal-timescale fluctuations in weather has grown from a research activity undertaken in a few academic and research institutes [1], to a routine operational activity in a number of meteorological forecast services [2,3,4]. Information about seasonal average rainfall and temperature for the growing season can potentially influence a farmer’s decision about which crops to plant ahead of time, or a humanitarian organization’s strategy for anticipating food shortages in drought-prone regions of the developing world. This information is only useful if it is skilful. If, in reality, above-average rain only occurs 50% of the time when the forecast probability exceeds 80%, the potential economic benefit of planting the particular crop may be completely lost by the unreliable probabilistic forecast. Depending on the region and variable being studied, we find examples of all five of our categories
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