The GloSea4 Ensemble Prediction System for Seasonal Forecasting

Arribas, Alberto; Glover, M.; Maidens, Anna; Peterson, K. Andrew; Gordon, Margaret; MacLachlan, Craig; Graham, Richard; Fereday, David; Camp, Joanne; Scaife, Adam A.; Xavier, Prince; McLean, P.; Colman, Andrew; Cusack, Stephen

doi:10.1175/2010mwr3615.1

Cited by 187 publications

(164 citation statements)

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“…Repairs to damaged homes, businesses and flood defences were required, and procedures for forecasting and mitigating the floods are understandably being examined. Until relatively recently, a lack of skill in seasonal weather forecasts in extratropical regions beyond a lead time of 1 month (Lavers et al, 2009;Arribas et al, 2011) discouraged the development of routine seasonal hydrological forecasts using a climate model output in Britain. However, the potential for seasonal hydrological forecasting in the UK is now being explored.…”

Section: Introductionmentioning

confidence: 99%

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

Bell

Davies

Kay

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead.The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts ("hindcasts") from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ∼ 70 % of the forecast skill (mostly in areas of high rainfall to the north and west) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, potentially benefitting practitioners interested in predicting hydrological extremes, not only in the UK but also across Europe.

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Section: Introductionmentioning

confidence: 99%

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

Bell

Davies

Kay

et al. 2017

Hydrol. Earth Syst. Sci.

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“…The UM is used for weather forecasting as well as seasonal forecasting (with GloSea systems [38,39]) and climate modelling (HadGEM1, HadGEM2-ES and HadGEM3 as discussed in §3). The various modelling systems are listed in appendix A.…”

Section: Using Seamless Prediction To Unlock Drivers Of High Latitudementioning

confidence: 99%

“…Climate models also show that summer ice loss depends strongly on winter ice thickness [30,44] which suggests that if seasonal forecasts can be initialized with accurate sea ice conditions, there is a good opportunity to predict summer sea ice extent several months ahead. Peterson et al [45] have shown that the GloSea4 system [38], which is also based on the UM, has predictive skill for the September sea ice extent with a six month lead time. Figure 4 shows the predictions of September sea ice from GloSea5 (initialized in April), which shows the skill in the hindcast as well as the skill in the 2013 ensemble forecast.…”

Section: Using Seamless Prediction To Unlock Drivers Of High Latitudementioning

confidence: 99%

“…GloSea4 [38] is the operational seasonal forecasting system based on HadGEM3 which is at N96-ORCA1 resolution. GloSea5 [39] is an upgrade to GloSea4 including an increase to N216-ORCA025 resolution, with initial sea ice and ocean conditions now being provided by the NEMOVAR assimilation scheme [51] as configured for the FOAM ocean forecasting system [52].…”

Section: Appendix a Met Office Modelling Systems (A) Hadgem1mentioning

confidence: 99%

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A seamless approach to understanding and predicting Arctic sea ice in Met Office modelling systems

Hewitt

Ridley

Keen

et al. 2015

Phil. Trans. R. Soc. A.

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One contribution of 9 to a discussion meeting issue 'Arctic sea ice reduction: the evidence, models and impacts (part 1)' . Recent CMIP5 models predict large losses of summer Arctic sea ice, with only mitigation scenarios showing sustainable summer ice. Sea ice is inherently part of the climate system, and heat fluxes affecting sea ice can be small residuals of much larger air-sea fluxes. We discuss analysis of energy budgets in the Met Office climate models which point to the importance of early summer processes (such as clouds and meltponds) in determining both the seasonal cycle and the trend in ice decline. We give examples from Met Office modelling systems to illustrate how the seamless use of models for forecasting on time scales from short range to decadal might help to unlock the drivers of high latitude biases in climate models. Subject Areas: meteorology Keywords

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“…A variety of methods have been proposed to convert ensemble outputs to calibrated probabilistic forecasts of future meteorologic variables such as temperature and precipitation [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The workflow for applying, assessing and comparing the performance of different methods involves many steps: downloading historic observations and ensemble outputs; applying the selected methods to produce probabilistic forecasts; comparing the performance of the selected methods over a common past period using any of several possible metrics; and producing summary graphics.…”

Section: Introductionmentioning

confidence: 99%

SeFo: A Package for Generating Probabilistic Forecasts from NMME Predictive Ensembles

Krakauer

2016

JORS

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The GloSea4 Ensemble Prediction System for Seasonal Forecasting

Cited by 187 publications

References 50 publications

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

A seamless approach to understanding and predicting Arctic sea ice in Met Office modelling systems

SeFo: A Package for Generating Probabilistic Forecasts from NMME Predictive Ensembles

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