Examining the capability of reanalyses in capturing the temporal clustering of heavy precipitation across Europe

Yang, Zhiqi; Villarini, Gabriele

doi:10.1007/s00382-019-04742-z

Cited by 27 publications

(33 citation statements)

References 61 publications

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“…It has been demonstrated that more aggregation indeed leads to a general predictability in upper air fields at longer lead times (Roads, 1986;Jung and Leutbecher, 2008;Buizza and Leutbecher, 2015) and in surface variables such as precipitation (Wheeler et al, 2017). Studies have also tailored the aggregation to a single conditional source of predictability: rainfall events in Europe that are clustered in time due to large-scale dynamics (Economou et al, 2015;Pasquier et al, 2019;Yang and Villarini, 2019), or extreme temperatures occurring simultaneously within a spatial region due to large-scale flow or sea-surface temperatures (Stefanon et al, 2012;McKinnon et al, 2016;Vijverberg et al, 2020). The forecast skill of such derived predictands can be high, but it is conditional on the occurrence of the source mechanism, and might also lose validity for less or more extreme events (Wulff and Domeisen, 2019).…”

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confidence: 99%

The influence of aggregation and statistical post‐processing on the subseasonal predictability of European temperatures

Straaten

Whan

Coumou

et al. 2020

Quart J Royal Meteoro Soc

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The succession of European surface weather patterns has limited predictability because disturbances quickly transfer to the large‐scale flow. Some aggregated statistics, however, such as the average temperature exceeding a threshold, can have extended predictability when adequate spatial scales, temporal scales and thresholds are chosen. This study benchmarks how the forecast skill horizon of probabilistic 2‐m temperature forecasts from the subseasonal forecast system of the European Centre for Medium‐Range Weather Forecasts (ECMWF) evolves with varying scales and thresholds. We apply temporal aggregation by rolling‐window averaging and spatial aggregation by hierarchical clustering. We verify 20 years of re‐forecasts against the E‐OBS dataset and find that European predictability extends at maximum into the fourth week. Simple aggregation and standard statistical post‐processing extend the forecast skill horizon with two and three skilful days on average, respectively. The intuitive notion that higher levels of aggregation capture large‐scale and low‐frequency variability and can therefore tap into extended predictability holds in many cases. However, we show that the effect can be saturated and that there exist regional optimums beyond which extra aggregation reduces the forecast skill horizon. We expect such windows of predictability to result from specific physical mechanisms that only modulate and extend predictability locally. To optimize subseasonal forecasts for Europe, aggregation should thus be limited in certain cases.

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mentioning

confidence: 99%

The influence of aggregation and statistical post‐processing on the subseasonal predictability of European temperatures

Straaten

Whan

Coumou

et al. 2020

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

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“…Portis et al (2001) provided the position of the NAO's activity centre for each of the 12 months, and we linearly interpolate them to a daily scale. This mobile NAO index is highly correlated with the daily NAO index in the Climate Prediction Center (CPC), especially during winter time (Yang and Villarini, 2019). We calculated EA and SCAND using the EOF method applied to daily SLP anomalies over the Atlantic (100 W-40 E, 10 N-80 N) as in Comas-Bru and McDermott (2014), where the time series of the second/third EOF pattern represent the EA/SCAND index.…”

Section: Methodsmentioning

confidence: 99%

“…As a reference, we compute the daily values of the AO, NAO, EA, and SCAND from the National Aeronautics and Space Administration's (NASA) Modern Era Retrospective‐analysis for Research and Applications version2 (MERRA2; Gelaro et al ., 2017), which provides SLP fields from January 1980 to January 2020. We use MERRA2 as the reference because of the good performance it exhibited in reproducing the relationship between heavy precipitation events and AO/NAO across Europe (Yang and Villarini, 2019). To identify and recognize these four climate modes clearly, we show the circulation patterns of the climate modes and the time series of climate indices based on observed data in the Supporting Information (Figure S1), which shows a similar EA/SCAND circulation pattern to Comas‐Bru and McDermott (2014).…”

Section: Methodsmentioning

confidence: 99%

“…Because of the nature of Cox processes, we move away from memoryless processes as the Poisson one, to clustered processes (Smith and Karr, 1983), which exhibit an alternating of active and quiet periods. Cox regression has been used in different hydrometeorological studies, including the Potomac River (Smith and Karr, 1983, 1986) and heavy rainfall and flooding (Villarini et al ., 2013; Mallakpour et al ., 2017; Yang and Villarini, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…In the hydrometeorological sense, there are many approaches to evaluate the capability of GCMs in reproducing precipitation statistics. In this study, we use Cox regression because it allows us to capture the role of the climate system in driving the occurrence of heavy precipitation (e.g., Smith and Karr, 1983, 1985, 1986; Villarini et al ., 2013; Mallakpour et al ., 2017; Yang and Villarini, 2019). Different from events that occur independently of each other (as is the case of memoryless Poisson processes), events that exhibit temporal clustering generally present the alternating of quiet and active periods (in terms of number of events).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the capability of global climate models in reproducing the temporal clustering in heavy precipitation over Europe

Yang

Villarini

2020

Intl Journal of Climatology

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Evaluating the capability of global climate models (GCMs) in reproducing the historical records represents a way of building confidence in their capability for future projections. Among the different ways of evaluating them, here we focus on their capability in reproducing the temporal clustering of heavy precipitation events across Europe in light of four climate modes [the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA) pattern, and the Scandinavia pattern (SCAND)], and GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the large ensemble runs using the Community Earth System Model (CESM). We use a peak over threshold (POT) approach to identify heavy precipitation events, and Cox regression to relate the occurrence of these events to the climate modes. We find that the GCMs can capture the temporal clustering in heavy precipitation across Europe as a function of these four climate modes; moreover, our findings indicate the GCMs can better reproduce the relationship between heavy precipitation and AO/SCAND, than NAO/EA. Comparing the results based on CMIP5 models and CESM, we find that the inter-model uncertainties are larger than the intra-model ones in most of the cases, even though CESM tends to have a poorer performance for EA; this shortcoming for CESM is likely due to the pattern of the Z500 anomalies which is different from the reference data when EA is in the positive phase, affecting the transport of moisture across Europe.

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Evaluation and projections of extreme precipitation using a spatial extremes framework

Yang

Franzke

Duan

2023

Intl Journal of Climatology

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Extreme precipitation events are a major natural hazard and cause significant socio-economic damages. Precipitation events are spatially extended and, thus, can cause large water accumulations, which can lead to flooding events. In order to help design flood protection infrastructure, a detailed investigation of the temporal and spatial dependencies of extreme precipitation is essential. Here, we use a statistical spatial extremes framework to systematically study the historical and projected spatial-temporal characteristics of extreme precipitation in Germany. For this purpose, we use data from 10 high-resolution global climate model-regional climate model (GCM-RCM) combinations from the EURO-CORDEX initiative and derive a statistical spatial extremes precipitation model.Our results show that there are large spreads in reproducing the temporalspatial characters of extreme precipitation. Few climate simulations can well present the temporal clustering of observed extreme precipitation in both summer and winter. In reproducing the spatial dependencies of the observations, most GCM-RCM combinations behave well in summer, while in winter most RCMs produce too many spatially localized extreme precipitation events. The derived statistical model, which accounts for both the spatial and temporal variability, performs well in representing the spatial dependency and intensity characteristics in summer. Furthermore, global warming will have a significant impact on the temporal and spatial dependencies of extreme precipitation in Germany. There will be more temporal-dependent and homogeneous extreme precipitation in summer; and more temporalindependent and localized extreme precipitation in winter. The intensity quantified by the 25-year return level of the 10 GCM-RCM combinations is increasing; with relative changes ranging from 5.33% to 53.24% in summer and from −15.38% to 32.33% in winter under RCP8.5. A future projection by

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Examining the capability of reanalyses in capturing the temporal clustering of heavy precipitation across Europe

Cited by 27 publications

References 61 publications

The influence of aggregation and statistical post‐processing on the subseasonal predictability of European temperatures

The influence of aggregation and statistical post‐processing on the subseasonal predictability of European temperatures

Evaluation of the capability of global climate models in reproducing the temporal clustering in heavy precipitation over Europe

Evaluation and projections of extreme precipitation using a spatial extremes framework

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