Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe

Prodhomme, Chloé; Doblas‐Reyes, F. J.; Bellprat, Omar; Dutra, Emanuel

doi:10.1007/s00382-015-2879-4

Cited by 73 publications

(70 citation statements)

References 79 publications

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“…For instance, realistic initialization of snow enhances the forecast skill on S2S time scales (Schlosser and Mocko, 2003;Peings et al, 2011;Jeong et al, 2012;Orsolini et al, 2013;Lin et al, 2016). Similar results have been obtained by initializing soil moisture (Dirmeyer, 2000;Douville, 2003;Conil et al, 2009;Koster et al, 2010;Koster et al, 2011;van den Hurk et al, 2012;Kumar et al, 2014), as well as both snow and soil moisture (Douville, 2010;Prodhomme et al, 2016;Thomas et al, 2016).…”

Section: Introductionsupporting

confidence: 71%

Causal Pathways for Temperature Predictability from Snow Depth

Kolstad¹

2017

Preprint

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Subseasonal-to-seasonal (S2S) weather forecasting has improved in recent years, thanks partly to better representation of physical variables in models. For instance, realistic initializations of snow and soil moisture in models yield enhanced predictability on S2S time scales. Snow depth and soil moisture also mediate month-to-month persistence of near-surface air temperature. Here the role of snow depth as predictor of temperature one month ahead in the Northern Hemisphere is probed via two causal pathways. Through the first pathway, snow depth anomalies in month 1 cause snow depth anomalies in month 2, which then cause temperature anomalies in month 2. This pathway represents the snow-albedo feedback, as well as cooling due to insulation, emissivity and heat loss. It is active from fall to summer, and its effect peaks in March/April in the midlatitudes and in May/June at high latitudes. A complementary second pathway, where snow depth anomalies in month 1 cause soil moisture anomalies in month 2, which then cause temperature anomalies in month 2 through soil moisture-temperature feedbacks, is only active in spring and summer. Its effect peaks later in the warm season than the effect of the first pathway. Geographically, snow depth mediates north of, and soil moisture south of, the areas with the highest temperature predictability from snow depth. These results indicate that the two pathways describe complementary physical mechanisms. The first pathway embodies month-to-month persistence of snow depth, and the second pathway represents melting of snow from one month to the next.

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Section: Introductionsupporting

confidence: 71%

Causal Pathways for Temperature Predictability from Snow Depth

Kolstad¹

2017

Preprint

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“…Although it is beyond the scope of this work to analyse in depth the factors explaining this relative success, previous seasonal studies showed the influence of anomalous initial conditions in subsequent summer heat waves. For example, sea surface temperature warm anomalies in June 2003 likely contributed to enhance the severity of the following summer heat wave (Feudale and Shukla, 2011a, b) and low soil moisture in spring was revealed as a key ingredient of the 2010 heat wave over Russia (Prodhomme et al, 2016). In 2015, the sea surface temperature of the Mediterranean and Near-Atlantic was not particularly warm in early June, whereas soils were anomalously dry over Western Europe (not shown).…”

Section: Discussionmentioning

confidence: 99%

“…A larger re-forecast period would allow a better sampling. Additionally, it would allow us to perform a conditional skill study by selecting enough start dates with strong external forcing in the initial conditions, since these may lead to enhanced predictability of a particular extreme event such as in July 2015 (Prodhomme et al, 2016). This point is further discussed in Sect.…”

Section: Re-forecast Forecastmentioning

confidence: 99%

Subseasonal-to-seasonal (S2S) forecasts with CNRM-CM: a case study on the July 2015 West-European heat wave

Ardilouze¹,

Batté²,

Déqué³

2017

Adv. Sci. Res.

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Abstract. An intense heat wave struck West Europe in early July 2015. The degree of anticipation of that event is assessed through the new CNRM near-real time subseasonal to seasonal forecast system. A warm anomaly over France was detected for the first week of July in all the successive forecasts issued in June, even up to one month ahead. On the other hand, the positive 500 hPa geopotential anomaly observed during that period was little anticipated. Despite the limited skill of the forecast system beyond twelve days, the relatively successful anticipation of that event pleads for a predictability study based on a multi-system assessment.

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“…Prodhomme et al, 2016a), the improvement of initialization procedures (e.g. Prodhomme et al, 2016b), and the more accurate representation of some physical processes (e.g. Hourdin et al, 2013) has considerably advanced the ac-curacy of W&C services, with current state-of-the-art products showing good forecast skills even over seasonal and longer timescales (Doblas-Reyes et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A coupled human–natural system to assess the operational value of weather and climate services for agriculture

Liu

Giuliani

Castelletti

2017

Hydrol. Earth Syst. Sci.

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Abstract. Recent advances in weather and climate (W&C) services are showing increasing forecast skills over seasonal and longer timescales, potentially providing valuable support in informing decisions in a variety of economic sectors. Quantifying this value, however, might not be straightforward as better forecast quality does not necessarily imply better decisions by the end users, especially when forecasts do not reach their final users, when providers are not trusted, or when forecasts are not appropriately understood. In this study, we contribute an assessment framework to evaluate the operational value of W&C services for informing agricultural practices by complementing traditional forecast quality assessments with a coupled human-natural system behavioural model which reproduces farmers' decisions. This allows a more critical assessment of the forecast value mediated by the end users' perspective, including farmers' risk attitudes and behavioural factors. The application to an agricultural area in northern Italy shows that the quality of state-ofthe-art W&C services is still limited in predicting the weather and the crop yield of the incoming agricultural season, with ECMWF annual products simulated by the IFS/HOPE model resulting in the most skillful product in the study area. However, we also show that the accuracy of estimating crop yield and the probability of making optimal decisions are not necessarily linearly correlated, with the overall assessment procedure being strongly impacted by the behavioural attitudes of farmers, which can produce rank reversals in the quantification of the W&C services operational value depending on the different perceptions of risk and uncertainty.

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Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe

Abstract: Abstract

Cited by 73 publications

References 79 publications

Causal Pathways for Temperature Predictability from Snow Depth

Causal Pathways for Temperature Predictability from Snow Depth

Subseasonal-to-seasonal (S2S) forecasts with CNRM-CM: a case study on the July 2015 West-European heat wave

A coupled human–natural system to assess the operational value of weather and climate services for agriculture

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