Die Vorhersage von extremen Wetterereignissen wie Hitzewellen ist aufgrund ihrer Auswirkungen auf die Gesellschaft von großer Bedeutung. Subsaisonale Wettervorhersage (von 2 Wochen bis 2 Monaten) kann Frühwarnungen liefern, die für Pläne zur Risikoeinschränkung unerlässlich sind. Das fachübergreifende Ziel dieser Arbeit ist daher die Verbesserung der Wettervorhersage von Hitzewellen über Europoa auf subsaisonalen Zeitskala.
Um dieses Ziel zu erreichen, werden zunächst die Quellen der Vorhersagbarkeit auf der subsaisonalen Skala herausarbeitet und analysiert und anschließend die Vorteile quantifiziert, die diese einzelne Prädiktoren bieten können.
Im ersten Teil werden die Haupttypen von Hitzewellen über Europa anhand ihrer atmosphärischen Zirkulation definiert. Die europäischen Hitzewellen werden dazu in fünf Hitzewellentypen mit spezifischen Grosswetterlagen eingeteilt. Diese ermöglichen es wiederum, die vorhersagbare Komponente der Hitzewellenereignisse zu bestimmen: allen gemein sind starke, anhaltende antizyklonale Anomalien über der Region der Höchsttemperaturen.
Anhand dieser Klassifizierung lässt sich zudem die relative Bedeutung anderer subsaisonaler Faktoren wie Bodenfeuchtigkeit und verstärkte tropische Konvektion bestimmen. Dabei hat es sich erwiesen, dass eine geringere Bodenfeuchtigkeit vor Hitzewellen nur für Hitzewellen über Südeuropa und für sehr extreme Hitzewellen von Bedeutung ist, da sie die Temperaturanomalien weiter verstärkt. Die Boreale Sommer Intrasaisonale Oszillation (BSISO) ist durch verschiedene Phasen schwacher und starker tropischer Konvektion gekennzeichnet. Die Beobachtung der Entwicklung der BSISO zeigt einen klaren Zusammenhang zwischen bestimmten aktiven Phasen der BSISO und dem Beginn russischer Hitzewellen, dass deren Überwachung zur einer besseren Vorhersagbarkeit beitragen könnte.
Im zweiten Teil dieser Arbeit wird der durch die Verwendung der identifizierten Prädiktoren entstehende Vorteil quantifiziert. Dazu wird eine musterbasierte Methode entwickelt, bei der die Großwetterlagen als Prädiktoren verwendet werden, mit dem Ziel die Wahrscheinlichkeit extrem warmer Temperaturen abzuleiten. Anhand von Hindcast-Daten des Europäischen Zentrums für mittelfristige Wettervorhersage (EZMW) wird diese Methode mit der direkten gitterpunktbasierten Wettervorhersage verglichen. Die musterbasierte Vorhersage erzielt für kurze bis mittelfristige Vorhersagen zwar keine gute Vorhersagequalität. Die Vorhersagequalität dieser Ergebnisse wird jedoch auch nach mehr als 10 Tagen beibehalten, was die Vorhersagequalität-Horizont über den Hitzewellenregionen erheblich verbessert. Die Verwendung von anhaltenden Großwetterlagen zur Vorhersage anhaltenden Hitzewellen führt zu ähnlichen Ergebnissen, allerdings mit lokal begrenzteren und bescheideneren Verbesserungen. Außerdem verbessern aktive BSISO-Phasen die Vorhersage zwar nicht systematisch, aber sie verbessern die Vorhersagbarkeit des Ausbruchs von russischen Hitzewellen, indem sie die Vorhersagespanne deutlich verringern und deren Genauigkeit, wenn auch in geringerem Maße erhöhen.
Die Einbeziehung dieser subsaisonalen Einflussfaktoren in einen Entscheidungsprozess zur Aktivierung von Risikominderungsplänen könnte wesentliche Informationen für Frühwarnungen liefern.:1. Introduction ................................................................................................................................. 13
1.1 Impact of extremes............................................................................................................... 13
1.2 Mitigation and early warnings .............................................................................................. 14
1.3 Content ................................................................................................................................. 14
2. Background knowledge ................................................................................................................ 16
2.1 Generalities on heatwaves ................................................................................................... 16
2.1.1 Heatwave definitions ................................................................................................ 16
2.1.2 Processes responsible for heatwaves ....................................................................... 17
2.1.3 Climatic trends for heatwaves .................................................................................. 18
2.2 Generalities on predictions................................................................................................... 20
2.2.1 Numerical Prediction ................................................................................................ 21
2.2.1.1 Governing equations............................................................................................. 21
2.2.1.2 Discretisation and parametrisation....................................................................... 23
2.2.1.3 Data assimilation .................................................................................................. 24
2.2.2 Predictability and forecast verification ..................................................................... 24
2.2.2.1 Predictability ......................................................................................................... 25
2.2.2.1.1 Predictability of the first kind ............................................................................. 25
2.2.2.1.2 Predictability of the second kind ........................................................................ 27
2.2.2.2 Forecast verification ............................................................................................. 30
2.2.2.2.1 Observations and reanalysis ............................................................................... 30
2.2.2.2.2 Forecast skill metrics .......................................................................................... 31
3. European heatwaves and their link to large-scale circulation patterns and subseasonal drivers 34
3.1 Data and methods ................................................................................................................ 35
3.1.1 Land surface feedback .............................................................................................. 35
3.1.2 Enhanced tropical convection ................................................................................... 36
3.1.3 Heatwave detection.................................................................................................. 37
3.1.4 Classification of heatwave patterns .......................................................................... 38
3.2 Heatwave types and their relation to circulation patterns ................................................... 42
3.2.1 Heatwave types description ..................................................................................... 42
3.2.2 Heatwave circulation patterns .................................................................................. 47
3.3 Potential sources of predictability at the subseasonal time scale ........................................ 49
3.3.1 Land surface feedback .............................................................................................. 50
3.3.2 The Boreal Summer IntraSeasonal Oscillation .......................................................... 55
3.3.2.1 BSISO phases facouring the occurrence of heatwaves ......................................... 57
3.4 Summary .............................................................................................................................. 64
12
4. Subseasonal prediction of heatwaves enhanced using a pattern-based forecasting system ....... 66
4.1 Data and methods ................................................................................................................ 67
4.1.1 Connection between extreme high temperatures and circulation patterns ............. 68
4.1.2 Skill evaluation of ECMWF forecasts ......................................................................... 70
4.2 Prediction of extreme temperatures at the subseasonal range ........................................... 72
4.2.1 Direct forecasting ..................................................................................................... 72
4.2.2 Pattern-based/conditional forecast .......................................................................... 77
4.2.3 Skill sensitivity to tropical convections ..................................................................... 83
4.3 Summary .............................................................................................................................. 88
5. Conclusion .................................................................................................................................... 91
5.1 Summary and key findings .................................................................................................... 91
5.2 Outlook ................................................................................................................................. 92
References ............................................................................................................................... / The prediction of extreme events such as heatwaves is of high importance due to their impact on society. Subseasonal prediction (from 2 weeks to 2 months) can provide early warnings which are essential for setting up mitigation plans. Therefore, the overarching goal of this work is to improve the forecast of heatwaves at the subseasonal time scale over Europe.
The approach used to tackle this goal, is to first identify and analyse the sources of predictability at the subseasonal scale, and to then quantify the benefits of each of these predictors.
In the first phase, the main heatwave types over Europe are defined based on their atmospheric circulation. European heatwaves are therefore classified into five heatwave types with specific circulation patterns, allowing to determine the predictable component of heatwave events. They all have strong persistent anti-cyclonic anomalies over the region of maximum temperatures.
The classification further allows to determine the relative importance of other subseasonal drivers such as soil moisture and tropical enhanced convection. Reduced soil moisture content prior to heatwaves is shown to be relevant only to heatwaves over southern Europe and for very extreme heatwaves, by further amplifying the temperature anomalies. The Boreal Summer Intraseasonal Oscillation (BSISO) is characterised by different phases of weak and strong tropical convection. Monitoring the evolution of the BSISO shows a clear link between certain active phases of the BSISO and the onset of Russian heatwaves in particular, suggesting that they could provide enhanced predictability for the onset of Russian heatwaves.
In the second phase, the advantage of using the identified predictors is quantified. A pattern-based method is constructed, using the circulation patterns as predictors to infer the probability of extreme warm temperatures. Using reforecast data from the European Centre for Medium-Range Weather Forecasts (ECMWF), this method is compared to the direct grid-point based prediction. The pattern-based prediction shows low skill at short to medium range, however it maintains this skill beyond 10 days and significantly improves the forecast range over the regions of heatwaves. Using persistent circulation patterns to forecast persistent heatwaves shows similar results, but with more localised and modest improvements. In addition, while active BSISO phases do not systematically improve the prediction, they do enhance the predictability of the onset of Russian heatwaves by reducing significantly the forecast spread and to a lesser extent increase accuracy.
Incorporating these subseasonal drivers into a decision-making process for mitigation plans could provide essential information for early warnings.:1. Introduction ................................................................................................................................. 13
1.1 Impact of extremes............................................................................................................... 13
1.2 Mitigation and early warnings .............................................................................................. 14
1.3 Content ................................................................................................................................. 14
2. Background knowledge ................................................................................................................ 16
2.1 Generalities on heatwaves ................................................................................................... 16
2.1.1 Heatwave definitions ................................................................................................ 16
2.1.2 Processes responsible for heatwaves ....................................................................... 17
2.1.3 Climatic trends for heatwaves .................................................................................. 18
2.2 Generalities on predictions................................................................................................... 20
2.2.1 Numerical Prediction ................................................................................................ 21
2.2.1.1 Governing equations............................................................................................. 21
2.2.1.2 Discretisation and parametrisation....................................................................... 23
2.2.1.3 Data assimilation .................................................................................................. 24
2.2.2 Predictability and forecast verification ..................................................................... 24
2.2.2.1 Predictability ......................................................................................................... 25
2.2.2.1.1 Predictability of the first kind ............................................................................. 25
2.2.2.1.2 Predictability of the second kind ........................................................................ 27
2.2.2.2 Forecast verification ............................................................................................. 30
2.2.2.2.1 Observations and reanalysis ............................................................................... 30
2.2.2.2.2 Forecast skill metrics .......................................................................................... 31
3. European heatwaves and their link to large-scale circulation patterns and subseasonal drivers 34
3.1 Data and methods ................................................................................................................ 35
3.1.1 Land surface feedback .............................................................................................. 35
3.1.2 Enhanced tropical convection ................................................................................... 36
3.1.3 Heatwave detection.................................................................................................. 37
3.1.4 Classification of heatwave patterns .......................................................................... 38
3.2 Heatwave types and their relation to circulation patterns ................................................... 42
3.2.1 Heatwave types description ..................................................................................... 42
3.2.2 Heatwave circulation patterns .................................................................................. 47
3.3 Potential sources of predictability at the subseasonal time scale ........................................ 49
3.3.1 Land surface feedback .............................................................................................. 50
3.3.2 The Boreal Summer IntraSeasonal Oscillation .......................................................... 55
3.3.2.1 BSISO phases facouring the occurrence of heatwaves ......................................... 57
3.4 Summary .............................................................................................................................. 64
12
4. Subseasonal prediction of heatwaves enhanced using a pattern-based forecasting system ....... 66
4.1 Data and methods ................................................................................................................ 67
4.1.1 Connection between extreme high temperatures and circulation patterns ............. 68
4.1.2 Skill evaluation of ECMWF forecasts ......................................................................... 70
4.2 Prediction of extreme temperatures at the subseasonal range ........................................... 72
4.2.1 Direct forecasting ..................................................................................................... 72
4.2.2 Pattern-based/conditional forecast .......................................................................... 77
4.2.3 Skill sensitivity to tropical convections ..................................................................... 83
4.3 Summary .............................................................................................................................. 88
5. Conclusion .................................................................................................................................... 91
5.1 Summary and key findings .................................................................................................... 91
5.2 Outlook ................................................................................................................................. 92
References ...............................................................................................................................
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89568 |
| Date | 07 February 2024 |
| Creators | Rouges, Emmanuel |
| Contributors | Ferranti, Laura, Kantz, Holger, Matschullat, Jörg, Technische Universität Dresden, Max-Planck-Institut für die Physik komplexer Systeme, European Centre for Medium-Range Weather Forecasts |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | German |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.1002/joc.8024, info:eu-repo/grantAgreement/Europäische Komission/EU International Training Network part of Horizon 2020 research and innovation programme Marie Skłodowska-Curie/813844//Climate Advanced Forecasting of sub-seasonal Extremes/CAFE |
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