Radiative Influence of Saharan Dust on North Atlantic Hurricane Genesis

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Doctoral Thesis

Radiative influence of Saharan dust on North Atlantic hurricane genesis

Author(s): Bretl, Sebastian E.

Publication Date: 2015

Permanent Link: https://doi.org/10.3929/ethz-a-010451957

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ETH Library DISS. ETH NO. 22289

Radiative inﬂuence of Saharan dust on North Atlantic hurricane genesis

A thesis submitted to attain the degree of

DOCTOR OF SCIENCES of ETH ZURICH

(Dr. sc. ETH Zurich)

presented by

SEBASTIAN EMANUEL BRETL

Dipl.-Met., Ludwig-Maximilians-Universität München, Germany born on March 25th, 1982 citizen of Germany

accepted on the recommendation of

Prof. Dr. Ulrike Lohmann, examiner Dr. Philipp Reutter, co-examiner Dr. Christoph C. Raible, co-examiner Prof. Dr. Daniel Rosenfeld, co-examiner

2015 ii Contents

Abstract iii

Zusammenfassung v

1 Introduction 1 1.1 Tropical cyclones ...... 1 1.1.1 Structure of tropical cyclones ...... 2 1.1.2 The Safﬁr-Simpson hurricane scale ...... 5 1.1.3 Tropical cyclogenesis ...... 6 1.2 Aerosols ...... 12 1.2.1 General aspects about aerosols ...... 12 1.2.2 Dust and the Saharan air layer ...... 15 1.3 Interactions of aerosol particles with tropical cyclones ...... 19 1.3.1 Microphysical effect of aerosols ...... 19 1.3.2 Radiative/dynamic effects of dust and the Saharan air layer ...... 21 1.4 Inﬂuence of model resolution on tropical cyclone activity ...... 23 1.5 Outline of thesis ...... 24

2 Steady-state simulations of tropical cyclones 25 2.1 The steady-state model of Rotunno and Emanuel(1987)...... 25 2.2 Experiments ...... 28

3 Model description, development and simulations 33 3.1 The aerosol-climate model ECHAM6-HAM ...... 33 3.2 Model simulations ...... 35 3.3 Simpliﬁcation of ECHAM6-HAM ...... 36

4 Tracks of hurricanes in ECHAM6 and the simpliﬁed ECHAM6-HAM 41 4.1 Tracking method ...... 41 4.1.1 Standard tracking criteria ...... 42 4.1.2 Adaption of tracking criteria to model simulations ...... 42 4.2 Categorization of hurricanes ...... 43 4.3 Nudged simulations (ECHAM6) ...... 43 4.3.1 Variation of thresholds ...... 44 4.3.2 Case studies ...... 49 4.4 Free simulations (simpliﬁed ECHAM6-HAM) ...... 52

5 Impacts of radiative heating of dust on temperature and vertical velocity 57 5.1 Technical set-up ...... 57 5.2 Results ...... 58 5.2.1 Absorbed solar radiation in clear sky conditions ...... 59 5.2.2 Implications of absorbed solar radiation on temperature ...... 60 5.2.3 Vertical velocity ...... 64

6 The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis 69 6.1 Abstract ...... 69

i 6.2 Introduction ...... 70 6.3 Method ...... 73 6.3.1 Model and simulations ...... 73 6.3.2 Seasonal differences between active and passive-dust simulations ...... 74 6.3.3 Tracking ...... 75 6.3.4 Composites and box difference indices ...... 76 6.4 Results ...... 77 6.4.1 Limitations due to spatial resolution ...... 77 6.4.2 Simulated and observed dust ...... 78 6.4.3 Mean background climate during DDs and NDDs ...... 79 6.4.4 Frequency of disturbances ...... 85 6.4.5 Composites ...... 85 6.4.6 BDIs ...... 88 6.5 Discussion and conclusions ...... 89 6.6 Acknowledgments ...... 91

7 Summary and outlook 93 7.1 Summary ...... 93 7.2 Outlook ...... 95

List of Symbols and Abbreviations 97

List of Figures 99

List of Tables 103

Bibliography 105

Acknowledgements 119

Curriculum Vitae 121

ii Abstract

The radiative inﬂuence of mineral dust and the Saharan air layer (SAL) on North Atlantic tropical cyclones (hurricanes) has been discussed thoroughly since more than a decade. However, to date a dominating supporting or inhibiting effect could not be determined. On the one hand, dust warms and dries the environment, thus suppressing convection and stabilizing the atmosphere. Furthermore, the SAL midlevel easterly jet enhances the vertical shear of the horizontal winds, making the atmosphere less conducive for hurricane genesis. On the other hand, as convection is more likely to be suppressed when dust is present, convection is shifted southward to regions with lower wind shear and higher sea surface temperatures (SSTs). Hence, a vertical circulation around the SAL is maintained, enhancing upward vertical motions on the southern edge of the SAL. In this thesis, we investigate the radiative effect of dust on North Atlantic hurricane genesis on a statistical basis. For this, we perform simulations with a steady-state tropical cyclone model and an aerosol-climate model.

Idealized simulations are performed with a steady-state tropical cyclone (TC) model. In these simulations, the role of moisture on the development of steady-state TCs is investigated. We use three different initial moisture profiles to determine the intensification of a TC when varying initial wind speed, TC diameter and SST. We find that when initial wind speed is low no TC will develop, regardless of initial moisture. For an initial dry SAL profile the inhibiting influence of dry air decreases when the SST and storm size increase.

Hurricane tracks in simulations with the general circulation model (GCM) ECHAM6 are detected with a TC tracking- and detection method. To adapt threshold values of criteria for the relative vorticity and wind shear for our horizontal resolution we run simulations nudged towards ERA-Interim reanalyses at

T255 (around 0.5°×0.5°). While the threshold value for vorticity can remain unchanged from previous studies we need a less strict threshold value for wind shear to earlier detect tropical storms.

For an efﬁcient use of the aerosol-climate model ECHAM6-HAM, we simplify ECHAM6-HAM, with dust remaining the only interactive aerosol species. For this, we run low-resolution simulations

(T63, around 1.9°×1.9°) with varying scavenging parameters to optimize wet scavenging of dust also

iii without sulphate-dependent coagulation- and coating processes. To determine the radiative effect of dust on hurricanes, we perform ensemble simulations with the simplified ECHAM6-HAM (T255). 10 simulations of the year 2005 are performed with dust being radiatively active and 10 with dust not interacting with radiation (passive dust). We find that disturbances which develop into a hurricane (DDs) are located 1° more to the south when dust is radiatively active. This is due to a more stable SAL when dust is active, which shifts deep convection southward. However, we do not find a significant change in hurricane frequency between passive- and active-dust simulations.

Our simulations show that the dynamic processes in and around the SAL are reproduced well with the simpliﬁed ECHAM6-HAM. This aids in understanding large-scale processes induced by dust radiative effects. However, no signiﬁcant impact of dust on hurricane genesis could be determined. Therefore, our results emphasize the complexity of this subject and question whether dust has a dominant positive or negative effect on hurricane activity.

iv Zusammenfassung

Der Strahlungseinﬂuss von Mineralstaub und der Saharan air layer (SAL) auf tropische Wirbelstürme im Nordatlantik wird seit mehr als einem Jahrzehnt intensiv diskutiert. Bis heute konnte jedoch kein do- minierender Effekt nachgewiesen werden. Einerseits erwärmt und trocknet Staub die Atmosphäre, was

Konvektion unterdrückt und die Stabilität erhöht. Des Weiteren verstärkt der SAL midlevel easterly jet die vertikale Windscherung und verringert somit die Wahrscheinlichkeit von tropischer Zyklogenese.

Andererseits verschiebt Staub durch die erhöhte Stabilität Konvektion südwärts und verstärkt so die vertikale Zirkulation im Bereich der SAL, was an deren Südseite mehr Konvektion zur Folge hat und so die

Bildung von Wirbelstürmen begünstigt. In dieser Doktorarbeit untersuchen wir den Strahlungseinﬂuss von Staub auf tropische Zyklogenese im Nordatlantik auf einer statistischen Basis. Hierfür führen wir eine Reihe von Simulationen mit verschiedenen Modellen durch.

Mit einem stationären Modell werden idealisierte Simulationen von Wirbelstürmen durchgeführt. In diesen Simulationen wird die Rolle der Feuchtigkeit auf die Entwicklung von stationären Wirbelstürmen untersucht. Wir benützen drei verschiedene vertikale Anfangsprofile der Feuchtigkeit, um die Intensivie- rung von Wirbelstürmen bei variierender Anfangswindgeschwindigkeit, Durchmesser des Wirbelsturms und Meeresoberflächentemperatur zu untersuchen. Mit einem typischen Anfangsprofil der SAL verringert sich der hemmende Einfluss von trockener Luft, wenn die Grösse des Wirbelsturms wie auch die

Meeresoberﬂächentemperatur ansteigt.

Die Zugbahnen der Wirbelstürme werden durch einen “Trackingalgorithmus” anhand verschiedener

Kriterien erkannt. Um Schwellwerte für die relative Vorticity und die vertikale Windscherung für unsere Zwecke anzupassen, führen wir Simulationen mit dem allgemeinen Zirkulationsmodell ECHAM6 in einer horizontalen Auﬂösung von T255 (ca. 0.5°×0.5°) durch, das an ERA-Interim Reanalysedaten genudgt wird. Während der Schwellwert für die Vorticity von vorherigen Studien auch für unsere Arbeit optimal erscheint für die Erkennung von Wirbelstürmen, ist für uns ein weniger striktes Kriterium für die Windscherung nötig um die Stürme früher zu erkennen.

Um das Aerosol-Klimamodell ECHAM6-HAM efﬁzient für unsere Zwecke zu nutzen, vereinfachen

v wir zuerst ECHAM6-HAM. In dieser Version verbleibt Mineralstaub als die einzige interaktive Aero- solspezies. Hierfür führen wir Simulationen (T63, ca. 1.9°×1.9°) mit variierenden Auswasch-Parametern durch, um das Auswaschen von Staub auch ohne Koagulations- und Beschichtungsprozesse zu ermög- lichen. Durch Ensemble-Sensitivitätstudien mit dem vereinfachten ECHAM6-HAM (T255) bestimmen wir den Strahlungseinﬂuss von Staub auf die tropische Zyklogenese im Nordatlantik. 10 Simulationen werden mit strahlungsaktivem Staub durchgeführt (aktiver Staub) und 10 ohne Wechselwirkung von

Staub und solarer Strahlung (passiver Staub). In den Simulationen mit aktivem Staub beﬁnden sich die auftretenden Wirbelstürme um ca. 1° weiter südlich als in Simulationen mit passivem Staub. Dies wird durch eine stabilere SAL bei aktivem Staub hervorgerufen, welche die konvektiven Regionen etwas weiter nach Süden verschiebt. Die Anzahl der Wirbelstürme bei aktivem Staub ist hingegen nur geringfügig kleiner als bei passivem Staub und ohne statistische Signiﬁkanz.

Unsere Ergebnisse zeigen, dass die dynamischen Prozesse in und in der Nähe der SAL mit dem vereinfachten ECHAM6-HAM adäquat simuliert werden können. Ebenso unterstreichen die Resultate die

Komplexität der Thematik des Strahlungseinﬂusses von Mineralstaub auf Wirbelstürme. Wenngleich sich die für tropische Zyklogenese günstigen Regionen bei aktivem Staub etwas nach Süden verschie- ben, können wir trotzdem keinen eindeutig unterstützenden oder hemmenden langfristigen Einﬂuss von

Staub auf die Wirbelstürme feststellen. Um einen möglichen Einﬂuss von Mineralstaub auf die Wirbel- stürme zu untersuchen, sollten daher Wirbelstürme als auch atmosphärische Störungen, die sich nicht zu

Wirbelstürmen entwickelten, von Fall zu Fall untersucht werden.

vi Chapter 1

Introduction

Tropical cyclones1 (TCs) and mineral dust are two natural components of the earth’s climate system which could hardly be more different. On the one hand there are TCs- enormous atmospheric systems with extreme weather conditions. They are a huge threat to humans and nature as they can devastate wide areas with their intense winds and severe storm surges. On the other hand there are dust particles- microscale objects, which are an essential part of deserts and other arid regions. As the discrepancy between their size is tremendous, one might not intuitively consider a possible interaction of TCs and dust particles. In fact only since about 15 years ago a possible impact of mineral dust on tropical cyclones is discussed considerably. There are studies claiming a positive inﬂuence of dust on hurricane activity

(e.g. Braun(2010) and those which suggest the opposite, e.g. Dunion and Velden(2004); Lau and Kim

(2007a)). To date, it is not yet clear whether dust inhibits or supports hurricane activity.

1.1 Tropical cyclones

A TC is a rotating system of cumulonimbus clouds characterized by a number of attributes such as strong winds and a low-pressure center (Section 1.1.1). TCs can form in or close to tropical regions over warm waters. They are called differently depending on the region where they form (Table 1.1). In the northern hemisphere, peak TC season lasts from June to November, with few storms possible in the western North

Paciﬁc also throughout the rest of the year. In the South Indian ocean and Australian region, the season lasts from November to April.

TCs form due to the coincidence of certain dynamical and thermodynamical factors. Once these conditions are met, an initial atmospheric disturbance becomes more structured and can potentially intensify up to a tropical cyclone (Section 1.1.3).

1In this PhD thesis, the naming convention of hurricanes will obey the global nomenclature: “Tropical Cyclone” is the umbrella term and will be used hereafter for describing a tropical cyclone in general. The term “hurricane” is utilized for TCs in the Atlantic and eastern Paciﬁc. This thesis focusses only on hurricanes in the North Atlantic.

1 2 Chapter 1. Introduction

Region Name North Atlantic, eastern North Paciﬁc Hurricane Western North Paciﬁc Typhoon Indian ocean Cyclone Australian region Tropical cyclone

Table 1.1: Naming of tropical cyclones depending on their region of formation.

On average 40 to 50 TCs with maximum winds of more than 33 m s−1 form each year worldwide

(Frank and Young, 2007). Recorded TCs range in sizes between diameters of 100 km (tropical cyclone

Tracy in 1974) and 2200 km (typhoon Tip in 1979, Merrill(1984)). Especially their enormous maximum wind gusts of up to 80 m s−1 and their hazardous storm surges put them on the list of the most dangerous natural catastrophes.

This thesis speciﬁcally focusses on the North Atlantic, hence in the following an emphasis is set on hurricanes.

1.1.1 Structure of tropical cyclones

A detailed illustration of the structure of a TC requires the description of several complex mechanisms.

The key attributes of a TC are listed below, for further details see e.g. Emanuel(2005) or Kepert(2010).

Figure 1.1: Schematic development of surface pressure (orange), maximum surface wind (blue) and midlevel temperature (red) from a TC’s center towards its periphery during a TC’s mature stage. c denotes the center, rmw the radius of maximum wind. 1.1. Tropical cyclones 3

Surface low: Similar to extratropical cyclones (ECs), TCs rotate counterclockwise (clockwise) around a center in the northern (southern) hemisphere. While ECs usually do not reach central sea level pressures

(SLPs) lower than 40 hPa below the standard pressure of 1013 hPa (Zishka and Smith, 1980; Nielsen and

Dole, 1992), TCs can reach signiﬁcantly lower pressures. The lowest SLP of 870 hPa was recorded for typhoon Tip in 1979 (Dunnavan and Diercks, 1980), the all-time low in the Atlantic was set by hurricane

Wilma (2005) with 882 hPa (Pasch et al., 2006). The development from the surface pressure from a TC’s center towards its outer regions is shown in Fig. 1.1.

Eye: The eye is the central region of the TC (Fig. 1.2) with subsiding air and usually calm weather where SLP reaches its minimum value, which develops during TC formation. TCs evolve from wide areas of disturbed weather. An initial disturbance causes an accumulation of cumulonimbus clouds.

Rainbands are developed by the disturbance that rotate around a collective location, the storm’s center.

When disturbance intensity increases due to fueling of the TC’s circulation by the ongoing release of latent heat, a circular area of deep convection forms around the TC’s center, the eyewall. In this region, precipitation, cloud top heights and winds reach their maximum within the TC. However, lightning activity rather maximizes in the outer rainbands instead of in the eyewall (Cecil and Zipser, 1999). While deep convection occurs in the eyewall, in the center air subsidizes over a deep layer and convection is suppressed, thereby creating a cloud-free eye.

Figure 1.2: Cross section of a TC with exaggerated vertical dimension. Source: Geophysical ﬂuid dynamics laboratory, http://www.wired.com/2012/11/what-is-the- true-measure-of-a-storm/ 4 Chapter 1. Introduction

Warm core: The warm core is the TC’s central region with enhanced temperatures in mid- to upper levels compared to environmental temperature (Fig. 1.1). The warming of the eye’s mid- to upper levels results from latent heat release through condensation and freezing in deep eyewall convection and sub- sidence near and within the eye itself (Halverson et al., 2006). Using Advanced Microwave Sounding

Unit satellite data, Kidder et al.(2000) speciﬁed the positive temperature anomaly of hurricane Bonnie

(1998) compared to the environment outside of the TC to be more than 14 K at a height of 250 hPa.

Performing hundreds of observational radial ﬂights, Shea and Gray(1973) denoted inward temperature increases of 2 K (900 hPa) - 7 K (525 hPa) within the innermost 45 nautical miles (ca. 83 km). The largest horizontal temperature gradients around a TC’s warm core in mid- and upper levels exist close to the radius of maximum winds.

Central dense overcast (CDO): The CDO is a shield of enhanced cumulonimbus activity around the

TC’s center of circulation. It encompasses the eye and the eyewall and is usually present in pre-mature stages of TCs, i.e. tropical depressions and storms (Velden et al., 1998). Determining the storm center can be difﬁcult as cloud patterns may be obscured when a CDO is present (Dvorak, 1984). However, microwave (Velden, 1989) and infrared satellite imagery (Velden et al., 1998) help to resolve this problem.

Primary and secondary circulation: The air ﬂow within a TC consists of two major circulations. The primary circulation is rotating horizontally around the storm’s center, which is approximately in gradient wind balance

v2 1 ∂p + f v = , (1.1) r ρ ∂r with f being the Coriolis parameter, r the radius, v the azimuthal velocity, ρ the air density and p the pressure. The gradient wind represents the balance between the centrifugal force (term 1), the Coriolis force (term 2) and the pressure gradient force (term 3). In the secondary circulation, air is moving inward close to the surface, rising mainly in the eyewall to upper levels, where the air is heading outward. Due to the thermal wind balance, upper-level outwards directed winds rotate anticyclonically (Smith, 1980;

Emanuel, 1997). Albeit the primary circulation causes the major part of a TC’s wind-caused damage, it is the secondary circulation that fuels the storm and drives the primary circulation.

The secondary circulation can be thought of as a Carnot heat engine (Fig. 1.3): Air close to the surface ﬂows towards the center and undergoes isothermal expansion, equivalent-potential temperature

(θe) increases (A→B). In the eyewall, air rises to upper levels and expands adiabatically, θe remains constant (B→C). Subsequently, through isothermal compression latent heat is lost by infrared radiation 1.1. Tropical cyclones 5

Figure 1.3: Energy ﬂux of an idealized Carnot heat engine within a TC. Source: Emanuel(2005).

to space and the air cools while θe decreases (C→D). Finally, the amount of total heat is conserved while the air subsides in an adiabatic compression with constant θe (D→A).

If no external torque acts on the primary circulation, the angular momentum

1 M = f r2 + vr (1.2) 2 has to be conserved. The air motion from A to B leads to a reduction of r. Because f can be regarded as constant the velocity has to increase. As long as inward airﬂow is supported, the primary circulation is maintained and the TC does not decay.

1.1.2 The Safﬁr-Simpson hurricane scale

Tropical storms in different ocean basins are classiﬁed in different scales. Tropical storms in the eastern

North Pacific and the North Atlantic are categorized in the Saffir-Simpson hurricane-wind scale (Simpson and Saffir, 1974). Accurate wind speeds did not become available before 1990 (Sheets, 1990), thus central pressures were used as a proxy for wind speeds during the 1970s and 1980s. In early versions of the scale, also storm surge was included. However, since other factors than topography, e.g. depth of near-shore waters, hurricane size and forward speed also influence the resulting storm surge, this parameter is not in use any more. Central pressure is not the best measure for the caused damages, 6 Chapter 1. Introduction

Classiﬁcation Wind speed (km h−1) Tropical depression ≤ 62 Tropical storm 63 - 118 Category 1 119 - 153 Category 2 154 - 177 Category 3 178 - 208 Category 4 209 - 251 Category 5 ≥ 252

Table 1.2: Saffir-Simpson hurricane-wind scale and additional classifications for tropical depressions and storms. therefore the Saffir-Simpson-scale is now a pure wind scale (Table 1.2).

The pre-stage of a tropical depression is a tropical disturbance, characterized by organized convection of usually 200-600 km in diameter. As perturbations in the wind ﬁeld might not yet be detected in tropical disturbances, it cannot be included in the Safﬁr/Simpson-wind scale. Tropical depressions can be distinguished from disturbances as they show a closed circulation (Fig. 1.4).

Figure 1.4: Characteristic stages of a tropical cyclone during its formation process. Source: Na- tional Oceanic and Atmospheric Administration (NOAA), National Hurricane Center (NHC); http://www.hurricanescience.org/science/science/hurricanelifecycle/ .

1.1.3 Tropical cyclogenesis

One of the most recent summaries of the current knowledge on TC formation is given by Tory and Frank

(2010), providing a basis for the remaining chapter.

In a recap of Frank(1987)’s definition, Tory and Frank(2010) characterized “genesis” to be the transition of a tropical disturbance to a tropical depression, e.g. the formation of a circulation on a scale of a few 100 km. Transitions between a tropical depression to a tropical storm and from a tropical storm to a hurricane are described as “development” and “intensification” respectively. According to this definition, this PhD-thesis uses the term “genesis” to depict only the first stage of TC development, while “forma- 1.1. Tropical cyclones 7 tion” describes the whole process including the development of the primary and secondary circulations.

Required conditions and parameters for TC genesis

Tropical cyclogenesis requires the atmosphere to be in a conducive state for the initialization of the process. This comprises a couple of dynamic and thermodynamic conditions which need to be fulﬁlled.

Thermodynamic conditions

TCs are one of the most impressive revelations of air-sea interactions. They obtain energy from latent heat release in the eyewall and partially return heat to the sea by drag on the sea surface (Drennan et al.,

2007). Hence, air and water temperature and humidity are the key parameters for the thermodynamic processes:

T1) Ocean temperatures in the topmost 50-60 m have to exceed 26.0-26.5°C

TCs obtain their energy primarily through condensation of water vapour in the boundary layer (Ooyama,

1969). To maintain the moisture ﬂux to the atmosphere, large amounts of humidity as available in the ocean are needed (Palmen, 1948). Hence, for TCs to form, sea surface temperatures (SSTs) need to be sufﬁciently high. Suggested threshold SSTs vary slightly in the literature and range from 26°C (Emanuel,

1986; Gray, 1998; Rodgers et al., 2000), 26.5°C (Gray, 1968; Dengler, 1997) to intervals of 26.0-27.0°C

(Palmen, 1948; Ramage, 1959) or 26.5-27.0°C (Tory and Frank, 2010). Temperatures slightly below the proposed thresholds are unusual but possible. Still lower SSTs as e.g. 23-24°C for hurricane Vince in

2005 (Vaquero et al., 2008) are an exception. Globally, 93% of TCs occur with an SST >26.5°C and

98% with SSTs >25.5°C (Dare and McBride, 2011). It has to be emphasized that the high SSTs need to extend down to a depth of around 50 m (Tory and Frank, 2010) to 60 m (Gray, 1998) to provide sufﬁcient amounts of energy. A thinner layer of warm water could lead to stirring up of colder water, cutting off the TC’s energy supply if the TC moves slowly (Lohmann and Lüönd, 2014).

T2) The atmosphere must be potentially unstable

Vertical instability is an important factor for the evolution of cumulonimbi (Byers and Braham Jr, 1948).

To allow for deep moist convection, the atmospheric lapse rate needs to have a large negative value which enables sufﬁciently large amounts of convective available potential energy (Johns and Doswell

III, 1992). Palmen(1948) pointed to the connection of TC formation with vertical instability in the tropical atmosphere; too little instability leads to a suppression of convection. Especially within the 8 Chapter 1. Introduction eye-wall cloud, large vertical moist instability is present (Gray and Shea, 1973).

T3) The mid-troposphere has to be moist

If the mid-troposphere contains only less moisture, the condensed water in the convective clouds can evaporate more easily than with a moist troposphere. This leads to a loss of latent heat, decrease in temperature and possibly cool downdrafts. Rotunno and Emanuel(1987) found that convective activity slows if cool, dry, midlevel air descends to the surface. Hence, for tropical cyclogenesis to occur, the atmosphere initially needs to contain high values of moisture in the lower to middle troposphere to prevent negative effects of downdrafts (Bister and Emanuel, 1997). The loss of initial moisture was also found to hinder the production of concentric hurricane eyewalls (Nong and Emanuel, 2003).

Dynamic conditions

Tropical waves are known to be a key factor for TC genesis (Frank and Clark, 1980; Landsea, 1993;

Frank and Roundy, 2006). In the North Atlantic, tropical African easterly waves (AEWs, Fig. 1.5) are the dominant synoptic weather system of the summertime West African monsoon. A monsoon trough is characterized by rather low vertical wind shear and large relative vorticity, supplying very favorable conditions for tropical cyclogenesis (Gray, 1968; Ramage, 1974). Several studies suggest monsoon troughs to occasionally break down spontaneously and, if not perturbed, spawn TCs (e.g. Ferreira and

Schubert, 1997; Briegel and Frank, 1997; Ritchie and Holland, 1999).

Figure 1.5: Satellite image of African easterly waves and a tropical cyclone. Source: University Corpo- ration for Atmospheric Research; http://www.meted.ucar.edu/tropical/synoptic/Afr_E_Waves/ print.htm .

D1) An initial disturbance must be present

An initial dynamic disturbance with sufﬁcient convergence and relative vorticity needs to be present to initiate the rotating motion. In the North Atlantic, initial disturbances are often supplied by AEWs. 1.1. Tropical cyclones 9

AEWs cause around 60% of all North Atlantic tropical storms and minor hurricanes and nearly 85% of major (category 3 or higher) hurricanes (Landsea, 1993). In the eastern North Paciﬁc hurricane season in

1993 almost all hurricanes originated from African easterly waves (Avila and Pasch, 1995). Convectively active AEWs in the vicinity of the Guinea Highlands are able to intensify and evolve into intense low- level circulations near the trough, making them perfect seedlings for TC genesis (Berry and Thorncroft,

2005; Hopsch et al., 2010). In their review article, Tory and Frank(2010) described zonally propagating tropical waves, which raise the potential for genesis in various ways. Enhanced upward vertical motion and low-level vorticity cause increasing convection (which also favors an increased deep-layer moisture).

Additionally, tropical waves determine local vertical wind shear. Tory and Frank(2010) concluded that as long as environments protected from outside negative inﬂuences of vertical and horizontal shear and dry air contain e.g. initial enhanced vorticity and/or relative humidity, they are more likely to form a tropical cyclone. These conducive initial conditions can be provided by tropical waves, which can also allow closed circulations relative to the mean ﬂow, possibly persisting for a couple of days (Dunkerton et al.,

2009). As Tory and Frank(2010) explained, it may take a couple of days until a vortex is established that can resist the destructive inﬂuences of environmental wind shear and dry air.

D2) TCs will only form at latitudes greater than 5-6° away from the equator

The Coriolis force is important for TC formation (Palmen, 1948; Ramage, 1959). In this connection a sufﬁcient amount of absolute vorticity needs to be present to reinforce an initial rotating motion. Based on numerical simulations, DeMaria and Pickle(1988) suggested that TC size reduces if it is located too close to the equator. The proposed value of 5-6° (Ramage, 1959; Anthes, 1982; Lighthill et al., 1994) is no strict threshold but an empirical value. Albeit no historical typhoons have been recorded within

3° of the equator before (Chambers and Li, 2007), typhoon Vamei formed close to Indonesia in 2001, being only 1.5° north of the equator (Chang et al., 2003). Chambers and Li(2007) mentioned two further notable hurricane observations at 3-5° distance from the equator.

D3) The vertical shear of the horizontal winds needs to be low

Vertical shear has a considerable inﬂuence on cumulonimbus clouds, supporting multicell and supercell storm structure with larger shear, e.g. Marwitz(1972); Fovell and Ogura(1989). However, for the formation of tropical cyclones the wind shear has to be low (Gray, 1968, 1975). If vertical shear is too large, the evolving warm mid-level core will be displaced from the surface circulation, disturbing TC formation. In a numerical study, a signiﬁcant response of TC intensity to strong shear of 15 m s−1 was reported, “literally tearing an intense storm apart within about one day” (Frank and Ritchie, 2001). On 10 Chapter 1. Introduction the basis of their calculations a threshold shear value between 10 and 15 m s−1 between 200 and 850 hPa was suggested. Furthermore, wind shear close to the evolving storm is often large but weak over the storm’s core (McBride and Zehr, 1981). A majority of developing disturbances forms in an environment with minimum tropospheric vertical shear (Gray, 1968). In contrast to that, a case study suggested the development process of hurricane Danny to have even been accelerated by moderate vertical wind shear due to the shear-induced creation of additional cyclonic vorticity (Molinari et al., 2004). Emanuel et al.

(2004) could simulate some storms almost unaffected by environmental shear, albeit they found the majority of storms to suffer to some degree from shear effects. Examining key parameters for their quality as TC predictors, Peng et al.(2012) found the 600-1000 hPa-shear to be a better parameter to identify a

TC genesis-favoring environment in the North Atlantic than 200-850 hPa. However, since tropical data sources tend to be at upper and lower levels, hence in connection with TCs the vertical shear is often expressed as the 200-850 hPa-wind difference (Elsberry and Jeffries, 1996).

Discussion

The formulation of the six thermodynamic and dynamic conditions often varies depending on the authors, e.g. Ramage(1959); Lighthill et al.(1994); Gray(1998); Tory and Frank(2010). Although there seems to be a broad consensus regarding the climatological conditions associated with tropical cyclogenesis

(Tory and Frank, 2010), a dominant factor is still being debated. Trends in hurricane activity are not necessarily correlated to SST (Goldenberg et al., 2001). Neither is the SST, once exceeding a minimum threshold, the dominant factor in determining the maximum storm intensity (Evans, 1993). However, environmental humidity, which is inﬂuenced by SST to some extent, is closely connected to TC size

(Hill and Lackmann, 2009).

A number of studies investigated the effect of individual thermodynamic or dynamic conditions on whether an existing disturbance will develop into a hurricane or not. The major differences between developing and non-developing disturbances found by McBride and Zehr(1981) are all related to dynamic parameters. They further show that the three thermodynamic parameters connected to SST, temperature and humidity (Gray, 1975) are generally present throughout the whole season, while dynamic variables have a large day-to-day variation. These ﬁndings were supported by Gray(1998), stating the dynamical and not the thermodynamic factors to be dominant in determining whether an individual tropical disturbance will evolve into a hurricane or not gain in strength. In contrast, the air-sea interaction theory of TCs

(Emanuel, 1986; Rotunno and Emanuel, 1987) claims that development of TCs “depends exclusively on 1.1. Tropical cyclones 11 self-induced heat transfer from the ocean”. Emanuel(1999) furthermore stated thermodynamic factors to control intensity as soon as the disturbance reaches tropical storm strength, but agreed that vertical wind shear and dynamical interactions with the environment appear to be strongly inﬂuential mostly during the formative stages. In recent studies, thermodynamic variables were found to be more important for TC genesis in the North Atlantic (Peng et al., 2012), while dynamic parameters are the determining factors in the Western North Paciﬁc (Fu et al., 2012).

Historical overview

TC formation has been a continuous ﬁeld of research, numerous studies have analyzed tropical cyclogenesis since several decades. This section summarizes the historical evolution of research concerning TC formation with no claim of completeness.

Among the ﬁrst ones to describe the genesis process was Riehl(1948a,b). Gray(1968) described

Riehl’s view of the formation process to be a “progressive intensiﬁcation of a westerly moving disturbance or wave embedded in the trade winds which moves under a favorable upper tropospheric divergent environment”. Palmen(1948) summarized the early knowledge on this topic and pointed out the necessity of vertical instability for TC formation. Riehl(1950) discussed previous genesis theories and presented a model of hurricane formation, stating the pressure reduction at the surface to be a result of mass divergence at high levels. In another milestone of TC formation research, Yanai(1964) considered mechanisms in three postulated stages of formation: 1) the pre-existing large-scale vertical motion and relative vorticity associated with easterly waves, 2) organization of cumulus convection by the large-scale vertical transport, and 3) formation of a warm-core temperature ﬁeld and evolution of a baroclinic vortex into a mature cyclone. Fundamental advances of the mean seasonal climatology of tropical cyclogenesis were introduced by Gray(1968).

Starting in the 1960’s, meteorologists could use satellite images to gather more information on TC genesis, structure and intensity. Although images were only available once a day in early years (Fett,

1964; Fritz et al., 1966), hurricane Hilda in 1964 was among the ﬁrst storms whose formation process was analyzed in detail by means of satellite imagery (Hawkins and Rubsam, 1968). Shapiro(1977) indicated a mechanism involving vorticity advection for tropical storm formation from easterly waves.

Montgomery and Farrell(1993) considered the physics of TC formation not to be well understood due to insufﬁcient upper-level atmospheric data. Gray(1998) supported this argument by stating that many advanced ideas on TC formation and numerical simulations seem to be unsupportable by observations.

While TC track forecast has improved signiﬁcantly in the past decades (Kurihara et al., 1995), according 12 Chapter 1. Introduction to Li et al.(2003) the prediction of TC formation is still in an infant stage. Tory and Frank(2010) summarized the recent advancements in TC formation research as described above.

1.2 Aerosols

Atmospheric aerosols are microscale, solid or liquid particles which are suspended in the atmosphere.

Due to their diminutive size and mass their fall velocity is very small. The lifetime of aerosol particles ranges from a couple of minutes to weeks in the troposphere and to years in the stratosphere before they are removed from the atmosphere. Common aerosol species are black and organic carbon, sea salt, sulphate, biological particles, nitrate and mineral dust.

1.2.1 General aspects about aerosols

If not stated otherwise, information of the current section is based on Wallace and Hobbs(2006). As mineral dust is the most important aerosol species for this thesis, it is discussed in detail in section 1.2.2.

Sources

Natural: Biological particles stem from all kinds of plants and animals. They include e.g. seeds, spores and pollen, which range between 1-250 µm in diameter. Biological particles of smaller size range are e.g. bacteria, fungi and viruses. Sea salt (2-20 µm) originates from oceans, the main release mechanism is surface bubble bursting (Fig. 1.6): When air bubbles in the sea burst at the surface, they release sea salt dissolved in the water via ﬁlm droplets and larger jet drops into the air. Once the water droplets

Figure 1.6: Schematic of production of ﬁlm droplets and jet drops by air bubble bursting. Source: Wallace and Hobbs(2006). 1.2. Aerosols 13 evaporate, sea salt remains in the atmosphere until it is removed. Additionally a large fraction of global dimethyl sulﬁde (DMS), a gas-phase precursor for sulphate aerosols, originates to a large extent from the oceans. It is mainly emitted by phytoplankton and is the most frequent biological compound of sulfur emitted to the atmosphere (Simpson et al., 1999). Besides other sulfuric gases, DMS can be oxidized to SO2, which is subsequently oxidized by the hydroxyl radical (OH) in a three-body-reaction. The 2− third body remains chemically unaltered by the reaction, the reaction product is the sulphate ion (SO4 ). Mineral dust is usually emitted by natural sources as e.g. deserts, but can also stem from anthropogenic sources (Section 1.2.2).

Anthropogenic: According to the summary of Boucher et al.(2013), the fraction of anthropogenic aerosols and its precursors on total global emission ranges approximately from 2-13%, depending on different inventories. Anthropogenic aerosols stem from biomass burning, fuel combustion or industrial air pollution. Biomass burning and industrial processes result mainly in black and organic carbon.

Furthermore, sulphates and nitrates are caused by industrial emissions as well.

Sulphate, black and organic carbon are the main contributors for anthropogenic emissions, with an estimated doubling of emissions due to fossil fuel combustion and biomass burning until 2040 compared to 2006. Tropospheric sulphate burden is supposed to decrease between 2000 to 2050 by 15-35%, depending on the global warming scenario (RCP2.6, RCP4.6, RCP6.0 and RCP8.5), while nitrate burden is projected to increase by 10-35% (Hauglustaine et al., 2014). Desertiﬁcation and wind erosion of tilled land results in increased emissions of dust as well.

Size distribution

A ﬁrst classiﬁcation was proposed by Junge(1955) using three different size classes: Aitken particles

(r < 0.1 µm), large particles (0.1 − 1 µm) and giant particles (r > 1 µm). Whitby(1978) described size distributions similar as Junge(1955)’s as nuclei mode, accumulation mode, and coarse mode. He furthermore differed especially between coarse (r > 1 µm) and fine (r < 1 µm) particles. This discrimination was based on the finding that condensation produces fine particles while mechanical processes rather cause coarse particles (Whitby, 1978). Wallace and Hobbs(2006) explained the segregation into three modes with a maximum in number concentration by particles with diameters < 0.2 µm, while particles with a diameter of > 1 µm dominate surface and volume distributions. Another particle peak occurs between 0.2 and 2 µm due to weak sinks in this size range, hence particles accumulate in the atmosphere.

Fig. 1.7 shows common size distributions for different aerosol modes. 14 Chapter 1. Introduction

Figure 1.7: Common volume and number distributions for different aerosol modes with examples of various aerosol types, based on Brasseur et al.(2003).

Concentrations

Typical aerosol number concentrations are in the range of 10.000 (summer) - 100.000 cm−3 (winter) for polluted cities such as Rome and Barcelona and 5.000 (summer) - 20.000 cm−3 (winter) for smaller cities with rather clean urban areas such as Stockholm, Helsinki or Augsburg, Germany (Aalto et al.,

2005). Virtanen et al.(2006) attributed differences between summer and winter to the smaller height of the mixing layer in winter time, which could result in higher particle concentrations. Marine areas typically contain around 300-600 particles cm−3 (O’Dowd et al., 1997; Yoon et al., 2007), with sea salt dominating the boundary layer mass concentration in the open ocean (Houghton et al., 2001).

Transport, lifetime and sinks

Aerosol transport is governed by air masses, convection and small-scale processes such as turbulence and diffusion. Particles are occasionally transported over long ranges on intercontinental and even global scales. Besides dust, which is known to be transported over long distances, long-range transport for gas- to-particle-produced aerosol particles as sulphates is likely, e.g. sulphates stemming from SO2 released into the stratosphere in large volcanic eruptions. Stier et al.(2005) estimated mean aerosol lifetimes with the aerosol-climate model ECHAM5-HAM as 3.9 days (sulphate), 5.4 days (black and organic 1.2. Aerosols 15 carbon), 4.6 days (dust) and 0.8 days (sea salt). The lower lifetime of sea salt is due to its larger size and solubility. With a different global model, Heald and Spracklen(2009) estimated lifetimes of primary biological aerosol particles, which are not represented in ECHAM5-HAM, to 2.2 days for coarse and 2.8 days for ﬁne particles.

On average, emission and removal rates are the same. Aerosols can be removed from the atmosphere by scavenging through precipitation (wet deposition), wind-driven motion to the ground (dry deposition) and gravitation (sedimentation). Another sink for aerosol number is coagulation of particles. Since

Brownian motion becomes less important for larger particles, this sink is conﬁned to aerosol particles with diameters less than ca. 0.2 µm. By this mechanism mass is not removed from the atmosphere, but particle mass and size is increased this way and thus the particles can be moved to size ranges in which other removal processes become relevant.

1.2.2 Dust and the Saharan air layer

A part of the atmospheric aerosols consists of minerals as illite, quartz, kaolinite and montmorillonite

(Glaccum and Prospero, 1980). This part of the aerosol burden is called mineral dust and is dispersed in mainly arid regions by wind erosion. Surface threshold velocities, above which dust particles can be lifted off the ground, depend on dust composition, soil moisture, and surface crusts which form after rain (Gillette et al., 1980). Threshold velocities close to the ground (determined after Gillette

(1978)) for loose, erodible soils in dry conditions increase from about 20-30 cm s−1 in sand textures to a maximum of 60-90 cm s−1 for loamy soils. When grounds are crusted, except for sand, threshold velocities are often above 100 cm s−1 (Gillette, 1988). The threshold velocity also depends on particle diameter, with minimum friction velocities of about 20 cm s−1 for particles around 60 µm in diameter

(Marticorena and Bergametti, 1995). Besides the mass-induced increase in threshold velocity for larger particles, velocity also increases for smaller particles (Bagnold, 1941). Iversen et al.(1976) considered interparticle cohesian forces to be responsible for this effect.

Mineral dust can be transported over long distances within the earth’s atmosphere. Dust aerosols are observed in polar and remote oceanic regions, which are far away from the main dust emission regions

(Schütz and Sebert, 1987). There are numerous different dust sources around the world. Especially in the Northern hemispheric summer, dust activity in the “global dust belt”, ranging from the west coast of North Africa through the Middle East into Central Asia (Prospero et al., 2002), is at a maximum.

Dust sources are also found in parts of North and South America, Australia and South Africa (Pros- 16 Chapter 1. Introduction pero et al., 2002). The world’s major dust source is the Sahara (Schütz et al., 1981; d’Almeida, 1989), emitting annual amounts of 130-760×106 tons per year (Goudie and Middleton, 2001). Estimated annual global emissions range from 500 to almost 5000×106 tons (Middleton and Goudie, 2006). Desert dust represents more than 40% of the annual mass of aerosols emitted into the troposphere, with half of this amount originating from the Sahara (Andreae, 1995). Despite satellite observations suggest that anthropogenic dust contributes around 25% (Ginoux et al., 2012), this estimation seems to be not quan- tiﬁed well (Boucher et al., 2013). Washington et al.(2009) and Reheis and Urban(2011) predicted dust emissions to increase further due to future climate change, which are suggested to be attributable to an intensifying pressure gradient over Northern African regions which will increase low-level wind speeds.

Future dust emissions may increase or decrease depending on several climate and land-use scenarios, but will be either way dominated by climate change effects (Tegen et al., 2004). Total Saharan dust emissions are the highest between February and July with peak average months in early spring (Marticorena and Bergametti, 1996; Laurent et al., 2008). Taking into account only the western Sahara, dust emissions are highest in June and July, while eastern Saharan emissions show a minimum in these months (Laurent et al., 2008).

Dust transport over long distances was mentioned in literature already long time ago (Stebbing, 1937;

Sutton, 1950). Saharan dust can be transported to various distant areas, e.g. the Mediterranean (Ganor and Mamane, 1982; Moulin et al., 1998; Kubilay et al., 2000), Southern and Central Europe (Prodi and

Fea, 1979; Reiff et al., 1986; Barkan et al., 2005; Klein et al., 2010), the Black Sea (Kubilay et al., 1995;

Hongisto and Soﬁev, 2004), towards the Amazon Basin (Swap et al., 1992; Koren et al., 2006) and even to

Figure 1.8: Schematic illustration of North African dust transport towards the East Atlantic during the Northern Hemisphere winter and summer. Source: Schepanski et al.(2009). 1.2. Aerosols 17

Northern Regions as Scandinavia (Franzen et al., 1995; Hongisto and Soﬁev, 2004), Canada (Zdanowicz et al., 1998) and Greenland (Steffensen, 1997). About 25% of the Saharan dust is transported westward to the Atlantic (Shao et al., 2011). Schütz(1980) described the transport of dust across the Atlantic Ocean to be a more or less continuous ﬂow, upon which perturbations caused by heavy sandstorms are occasionally superimposed. Nevertheless, there are distinct differences in vertical and horizontal dust distributions between Northern Hemisphere summer and winter. During the Northern Hemisphere summer, insolation and subsequent surface heating is higher. Furthermore, the inner-tropical discontinuity, depicting the meeting location of tropical moist and dry, dusty desert air, is located further to the north during summer

(Schepanski et al., 2009). This causes a deeper boundary layer and higher upward mixing of dust, leading to rather elevated dust layers in summer in contrast to near-surface layers during winter months.

During summertime, westward moving dust characteristically crosses the West African coastline between ca. 10° and 20°N (Fig. 1.8), while wintertime trajectories are rather located around 0° - 10°N (Ben-Ami et al., 2009; Schepanski et al., 2009).

The summertime elevated dust is embedded in the Saharan air layer (SAL, Fig. 1.9). Carlson and

Prospero(1972) and Prospero and Carlson(1981) summarized the features of the SAL as the following:

Air passing over the Sahara during summer and early fall months experiences prolonged and intense heating (up to 65°C). A deep isentropic mixing layer develops and strong, gusty winds elevate dust particles up to a height of 500 hPa. The dust-laden air crosses the West African coastline in meso- to synoptic scale outbreaks, which are closely connected to the passage of an easterly wave trough. The outbreaks are undercut by the cool, moist, southward coastal winds, generating a sharp inversion at about

850 hPa. The Saharan air mass typically crosses the Atlantic in 5-6 days, having a velocity of about 8 m s−1 with peak values of 20-25 m s−1 between 600 and 700 hPa. The layer thickness decreases towards the West: Over the eastern Atlantic the SAL is usually located between 500-850 hPa, while SALs in

Caribbean regions are observed rather between 550 and 750 hPa. This is due to convective erosion of the lowermost parts of the SAL and gravitational sinking of larger particles from the top layers. Fig. 1.10 shows a typical dust layer associated with the SAL.

In addition, cloud microphysics of the tropical North Atlantic is possibly affected by the SAL. Clouds forming with dust contain rather small droplets, producing less precipitation by collision-coalescence

(Rosenfeld et al., 2001). The contribution of dust aerosols acting as cloud condensation nuclei (CCN) to total cloud droplet number concentrations in simulations with present-day emissions are up to 5% in the open North Atlantic and even 20% with pre-industrial emissions (Karydis et al., 2011). Mineral dust 18 Chapter 1. Introduction

Figure 1.9: A typical Saharan dust plume model. Source: Karyampudi(1979). aerosols are also observed to act as ice nuclei (IN). In case studies, concentrations of African dust acting as IN over Florida were determined to be over 1 cm−3, exceeding typical IN concentrations by at least

20 to 100 times at temperatures warmer than -38°C (DeMott et al., 2003). In Saharan dust events at

Jungfraujoch in Switzerland, IN concentrations of up to 14 l−1 were detected, one order of magnitude greater than typically observed (Chou et al., 2011). High IN concentrations might play an important role in cloud processes altering e.g. cloud forcing and relative humidity (Gierens, 2003) or depleting cloud water and preventing homogeneous freezing in maritime clouds (Rogers et al., 1994). 1.3. Interactions of aerosol particles with tropical cyclones 19

Figure 1.10: SAL northeast of Barbados, dust layer is visible as milky white haze. Source: Jason Dunion, NOAA, Hurricane Research Division. http://www.aoml.noaa.gov/hrd/project2007/sal.html .

1.3 Interactions of aerosol particles with tropical cyclones

The impact of aerosol particles on tropical cyclones has received growing interest within the last 15 years. A recent climatological study (Dunstone et al., 2013) depicted a close connection between North

Atlantic tropical storm frequency and aerosol-induced north-south shifts of the Hadley circulation. For the Arabian Sea, Evan et al.(2011) found an anomalous circulation radiatively forced by anthropogenic aerosols reducing vertical wind shear and thus creating an environment even more conducive for TC intensiﬁcation. Up to date two main effects of aerosols on TCs are studied: The microphysical approach, discussing the effect of aerosol particles acting as CCN (Section 1.3.1); and the radiative/dynamic approach, taking into account only the effect of absorption and scattering of solar radiation by aerosol particles with possible subsequent implications on atmospheric dynamics (Section 1.3.2).

1.3.1 Microphysical effect of aerosols

Ramanathan et al.(2001) proposed a subsequent weaker hydrological cycle caused by anthropogenic increase of aerosol particles: additional particles acting as CCN in clouds cause them to be brighter and to decrease rain formation. In this way, solar irradiance to the Earth’s surface is decreased while solar heating of the atmosphere increases as long as absorbing aerosol particles are present. Hence according to Ramanathan et al.(2001), changes in temperature structure occur such that precipitation rates are decreased and hence removal rates of pollution aerosols. On the other hand, a number of studies 20 Chapter 1. Introduction demonstrated that small, hygroscopic aerosols intensify tropical convection (e.g. Koren et al., 2005; van den Heever et al., 2006; Lee et al., 2008). This applies to cloud clusters and tropical cyclones as well.

The concept of seeding TCs with aerosols to reduce hurricane intensity was already brought up during the STORMFURY project (Gentry and Hawkins, 1970; Willoughby et al., 1985). Cotton et al.(2007) and Zhang et al.(2007) gave an overview of simulations of hurricane response to African dust, proposing that seeding hurricanes with dust aerosols could reduce hurricane intensity. Besides CCN, giant CCN

(GCCN) and IN potentially contribute to this mechanism (DeMott et al., 2003; van den Heever et al.,

2006). A case study showed that continental aerosols invigorate convection largely at the TC periphery, leading to TC weakening (Khain et al., 2010). With varying CCN concentrations and seeding times in a series of simulations with mimicking flights of a particle-seeding aircraft, an inhibiting effect of ingested CCN on TC intensification was found (Carrio and Cotton, 2011). Another case study presented an increase in low-level wind speeds in the first 10 hours after ingestion of CCN, followed by a period of substantial weakening of wind speeds being generally below the corresponding winds of a control run

(Krall and Cottom, 2012). Rosenfeld et al.(2011) separated aerosols from meteorological factors in two independent TC forecast models, supporting the hypothesis that additional CCN aerosols weaken TC intensity.

The mechanism of how dust aerosols/CCN reduce TC intensit was summarized by Rosenfeld et al.

(2012) as follows (Fig. 1.11): Entering outer TC cloud bands, dust and/or aerosol particles slow the formation of rain in these regions. Thus, early wash out of the aerosols is decreased, and more water is lifted to freezing levels. Enhanced freezing releases further latent heat, invigorating convection. The increased cloud formation in peripheral areas diminishes the low-level airﬂow towards the center, weakening the

TC-maintaining inward energy ﬂux. This effect was documented in two case studies, pointing out that several of these previous studies support the robustness of the concept of TC weakening by ingestion of CCN (Hazra et al., 2013). They stated that “there is a high probability that ingestion of high CCN concentrations in a TC will lead to a weakening of the storm (...)”.

However, note that the major part of the studies ﬁnding an inhibiting microphysical effect of dust on hurricane activity are idealized studies. Zhang et al.(2007) emphasized that the interaction of CCN,

GCCN, IN and the SAL in TCs are more complex in nature than presented in their idealized simulations because other particles than sea salt can act as substantial CCN- and GCCN-sources as well. 1.3. Interactions of aerosol particles with tropical cyclones 21

Figure 1.11: Schematic of microphysical effect of aerosols on TCs. See text for explanations. Source: Rosenfeld et al.(2012).

1.3.2 Radiative/dynamic effects of dust and the Saharan air layer

The radiative effect of dust on TC genesis is not yet completely understood neither. A number of studies in the last decade claimed an inhibiting effect of dust on TCs, in particular concerning North Atlantic hurricanes. According to observations, Dunion and Velden(2004) suggested a weakening effect of the dust-laden SAL on hurricane intensity (Fig. 1.12) due to three mechanisms: 1.) An enhanced low- level temperature inversion within the SAL stabilizes the lower troposphere. 2.) Dry air is intruded into the TC, suppressing convection by increasing evaporatively triggered downdrafts. 3.) Increased vertical wind shear induced by the SAL midlevel easterly jet. An inverse relationship of dust cover and

North Atlantic TC activity by using Advanced Very High Resolution Radiometer-data was detected for the years 1982-2005 (Evan et al., 2006). However, it was emphasized that there was no evidence of dust directly controlling TC activity. Comparing hurricane activity of the years 2005 and 2006, Lau and Kim(2007a) attributed the lower activity in 2005 to signiﬁcantly lower sea surface temperatures,

30% of which were attributed to the radiative cooling effect of dust aerosols (Lau and Kim, 2007b).

Lower TC activity in 2007 compared to 2005 was shown to be due to a further westward transport of the SAL (Sun et al., 2008). Using assimilated aerosol optical depth from the Moderate Resolution

Imaging Spectroradiometer combined with interactive aerosol modeling, Reale et al.(2014) found the environment to be less conducive to TC development if the air is heated due to the presence of dust particles. 22 Chapter 1. Introduction

Figure 1.12: Time series of NHC best-track intensity for three exemplary Atlantic TCs in 2000 and 2001. x-axis shows time in days, y-axis represents intensity in knots. Red shading indicates the TC being under the suppressing inﬂuence of the SAL (edge of the SAL less than 2° in latitude/longitude away from the center of the TC). Green shading indicates periods when the SAL was not impacting the TC. Source: Dunion and Velden(2004)

On the contrary, a number of studies show a positive impact of the SAL on hurricane formation. SAL outbreaks in the Global Atmospheric Research Program Atlantic Tropical Experiment (Kuettner, 1974) are considered to be possibly necessary, but at least important for the initialization of easterly wave disturbances (Karyampudi and Carlson, 1988). Three case studies reveal an inhibiting effect of the SAL on hurricane Andrew (1992) and supporting effects on Tropical Storm Ernesto (1994) and Hurricane

Luis (1995) (Karyampudi and Pierce, 2002). Braun(2010) stressed that the African Easterly Jet (AEJ) acts as a source of energy for AEWs, and that the vertical circulation associated with the AEJ induces a positive inﬂuence of the SAL on hurricanes. Each storm must thus be carefully examined within the context of the thermodynamic ﬁelds and large-scale wind, especially concerning other possible sources of vertical wind shear and dry air.

Concluding, one cannot assume that a storm in the vicinity of the SAL will necessarily weaken or intensify. Hence, it is necessary to examine storms case by case and assess how the SAL may be pen- 1.4. Influence of model resolution on tropical cyclone activity 23 etrating or not penetrating close to the storm’s peripheral convection. Furthermore, it is difficult to attribute a general positive or negative effect of the SAL on hurricane genesis. The strength of the SAL- induced vertical shear (horizontal and vertical extent) may be influential as well to determine a possible supporting or inhibiting effect.

1.4 Inﬂuence of model resolution on tropical cyclone activity

In recent studies, the inﬂuence of horizontal resolution in general circulation models on TC activity has been discussed thoroughly. Depending on the research goals, different model resolutions are necessary.

As long-term simulations at high resolutions are computationally expensive, it is important to choose model resolution depending on the research question. Bengtsson et al.(2007b) compared spectral resolutions of T63 (~200 km), T213 (~60 km) and T319 (~40 km) in past, present-day and future warming scenarios: they found simulations at T213 and T319 to have about three times as many TCs as at T63 as well as a marked increase in intensity with increasing resolution. The highest resolution of T319 also shows the largest number of the most intense storms.

In 25-year present-day and future global warming projections, Murakami and Sugi(2010) detected signiﬁcant differences in TC activity in meshes of 20, 60, 120 and 180 km. The best representation of TC intensity and interannual/seasonal variations in TC genesis number was obtained with the ﬁnest resolution (20 km). However, besides the 20 km-mesh also a grid spacing of 60 km still revealed good skills in tracking TC frequency. Future increases in TC intensity due to climate warming were only detected with resolutions of 60 km or higher.

Strachan et al.(2013) used grid sizes of 60, 90, 135 and 270 km to compare representation of TCs with a hierarchy of GCMs. They achieved an adequate simulation of TC frequency globally and in individual basins with a resolution of 135 km or higher. The aforementioned studies showed that the global distribution of TCs is well simulated in models with resolutions of 100 km or higher. However, none of the studies above could reproduce TC intensity adequately. Gentry and Lackmann(2010) estimated a required resolution of a few km to reach a proper simulation of TC intensities in GCMs. However, for the ﬁrst time ever Murakami et al.(2012a) were able to simulate high-intense TCs adequately on a climatological basis with a GCM. In contrast to Gentry and Lackmann(2010)’s estimate, their model, which is an advancement of Murakami and Sugi(2010)’s model with an updated cumulus-convection scheme, uses a mesh of 20 km. However, due to the computationally too expensive demand of high-resolution 24 Chapter 1. Introduction global models, Murakami et al.(2012a) suggested to furthermore explore the uncertainties in their projections of future changes in TC activity with coarse resolution models. Another possibility to obtain a realistic TC climatology is by using a nested approach, e.g. Lavender and Walsh(2011); Zarzycki and

Jablonowski(2014). For this, a coarse global resolution is applied with a ﬁner grid either over a deﬁned area or on the locations where TCs occur.

In our study we use a resolution of roughly 50 km within the tropics. This resolution prevents us from investigating realistic TC intensities but enables studies about TC genesis and frequency.

1.5 Outline of thesis

In this PhD thesis the radiative influence of Saharan dust on hurricane genesis in the North Atlantic is investigated using a simplified version of the aerosol-climate model ECHAM6-HAM. In chapter2, results of simulations with a simple hurricane model (Rotunno and Emanuel, 1987) are shown to evaluate the role of moisture on the evolution of a steady-state vortex. Chapter3 describes ECHAM6-HAM and the modifications towards the simplified ECHAM6-HAM. Results of sensitivity simulations are shown, in which a reasonable dust transport over the North Atlantic is achieved in ECHAM6-HAM even without dust-sulphate interactions. The tracking algorithm which is used to detect and track hurricanes is introduced in chapter4. Adjustments to threshold values are shown as well as case studies to depict differences between the model and observations. Chapter5 presents the evaluation of changes in vertical velocity due to the radiative effects of dust by comparing simulations with active and passive dust. Impli- cations for hurricane genesis are presented in chapter6. For this purpose, a box difference index (BDI) is applied to compare the impact of dust to other parameters. The last chapter summarizes the relevant

ﬁndings of this thesis and gives an outlook. Chapter 2

Steady-state simulations of tropical cyclones

A TC during its mature stage can be described as a steady-state tropical cyclone. The deﬁnition of this term is generally based on the time period, during which the maximum tangential wind speed is approximately constant (Smith et al., 2014). In the past, several steady-state models have been used to understand steady-state TCs with a focus on e.g. air-sea interaction (Emanuel, 1986; Rotunno and

Emanuel, 1987), dynamics (Wirth and Dunkerton, 2006) and statistical equilibrium (Hakim, 2011) of the TC. Rotunno and Emanuel(1987) developed a nonhydrostatic, axisymmetric model with humidity and convection explicitly accounted for. The found a hurricane-like vortex could also evolve in an atmospheric state which is neutral to cumulus convection. In this chapter, we present simulations with

Rotunno and Emanuel(1987)’s model to understand the inﬂuence of moisture on a TC during the evolution of a vortex towards a steady-state TC. For this, we use initial moisture proﬁles of a typical moist tropical atmosphere, SAL-conditions and hypothetical dry conditions.

2.1 The steady-state model of Rotunno and Emanuel(1987)

Rotunno and Emanuel(1987)’s steady-state model is based on the work of Emanuel(1986), which is regarded as an important milestone in TC research (Smith et al., 2008, 2014). Emanuel(1986)’s model assumes an axisymmetric circulation, hydrostatic and gradient wind balance and neutrality of the vortex to slantwise moist convection. Combined with the assumption of reversible thermodynamics, the

∗ latter implies that the saturated equivalent potential temperature (θe) is uniform along surfaces of constant angular momentum M. Furthermore conditional neutrality of boundary layer air to displacements along surfaces of constant angular momentum is assumed. This is comparable to a simple Carnot engine

(Chapter 1.1.1) acquiring heat from high-temperature sea surfaces and losing heat in the upper tropo-

25 26 Chapter 2. Steady-state simulations of tropical cyclones

Figure 2.1: The Carnot heat engine within a TC. See text for abbreviations. Source: Emanuel(1986). sphere (Fig. 2.1). The net heat exchange is given by

θe θe ∆Q = ∆Qin + ∆Qout = cpTBln − cpT¯out ln , (2.1) θea θea

where cp is the speciﬁc heat at constant pressure of dry air, TB the absolute temperature at the top of the boundary layer in a height h, T¯out the averaged outﬂow temperature, θe the equivalent potential temperature and θea the corresponding ambient θe.

An approximation of the maximum wind speed can be obtained when using a TC’s thermodynamic efﬁciency

∆Q T + T η = = B out (2.2) QAB TB and the rate of generation of available energy

? G = ηCkρvs(ks − kB) (2.3)

with Ck being the enthalpy transfer coefﬁcient, vs the tangential surface wind speed, kB the enthalpy at

? the ocean surface and ks the saturated enthalpy at the atmosphere near the surface. Using the rate of mechanical dissipation

3 D = CDρvs (2.4)

with the drag coefﬁcient CD, in equilibrium (G = D) the maximum wind speed at the radius of maximum 2.1. The steady-state model of Rotunno and Emanuel(1987) 27 winds is given by

2 Ck ? vm = η(ks − kB) . (2.5) CD

Assuming a Rankine vortex for cyclostrophic balance

r v(r) = vm , r ≤ rm (2.6) rm

with rm being the radius of maximum winds and vm the corresponding wind speed, using the hydrostatic equation and the ideal gas law leads to an estimation of the central pressure deviation

2 vm pc = pm exp − . (2.7) 2RTB

The schematic development of p(r) and v(r) is shown in Fig. 1.1. However, Emanuel(1986)’s model is incapable of describing the development of the vortex with time and is not sensitive to initial environmental humidity. As back then all existing numerical simulations of TCs started with very unstable soundings, the question arose after Emanuel(1986)’s work whether a vortex can evolve in a conditionally neutral environment and how sensitive the development is on the geometric size of the storm. To answer these questions, an axisymmetric model based on the primitive equations was developed by Rotunno and

Emanuel(1987). In contrast to Emanuel(1986)’s model this model is nonhydrostatic, with convection and humidity now explicitly accounted for. In their framework, Rotunno and Emanuel(1987) conducted numerical experiments with a ﬁnite-amplitude vortex and apply an initial sounding, which has been also used by Hakim(2011). In a case-study, Bister and Emanuel(1997) applied the model to explore the evolution of the initially cold-core, midlevel vortex into 1991-hurricane Guillermo.

The mechanism of the development of a steady-state-TC is explained by Rotunno and Emanuel(1987) as follows: Wind-induced latent heat ﬂuxes from warm sea surfaces lead to locally increased values of equivalent potential temperature (θe) in the boundary layer which lead to perturbations in temperature aloft after being transported upward along angular momentum surfaces. The perturbations enhance the

TC’s circulation, which subsequently enhances the wind-surface ﬂuxes. Intensiﬁcation of the TC will continue until the boundary layer becomes saturated or as long as boundary-layer processes allow con- tinuously increasing values of θe near the core. 28 Chapter 2. Steady-state simulations of tropical cyclones

2.2 Experiments

The SAL is not only characterized by large amounts of dust, but also by dry air as it emerges over the

Saharan desert. Rotunno and Emanuel(1987) performed sensitivity simulations with relative humidity reduced to 30%. They found that as the inner region of the circulation humidiﬁes towards saturation, the

ﬁnal vortex is identical to the control vortex, albeit 2-3 days later. In our study, the model by Rotunno and Emanuel(1987) is used to understand the inﬂuence of dry air within SAL conditions on a developing

TC before reaching its steady-state. The following experiments are performed with three different initial moisture proﬁles (Fig. 2.2): The dark blue proﬁle is an extrapolated Jordan mean tropical sounding

(Jordan, 1958), showing a typical moist tropical troposphere, while the orange proﬁle is created after an

SAL dropwindsonde (Dunion et al., 2006). The red profile represents a fictive dry profile with relative humidity constantly set to 2% throughout the troposphere. To isolate the effect of varying moisture, the same temperature profile is used for all three moisture profiles.

Figure 2.2: Initial profiles of the steady-state model. Blue: Moist profile (RH), Orange: SAL profile (RH), Red: Dry profile (RH), Green: Temperature profile for all three moisture profiles.

Four experiments are performed with varying initial conditions (Table 2.1) which are similar to parts of the experiments performed by Rotunno and Emanuel(1987). Experiment A serves as our control run, while in experiments B, C and D we evaluate the inﬂuence of initial maximum wind, size of the storm and sea surface temperature on the evolution of the vortex with our three different initial moisture 2.2. Experiments 29

Experiment A B C D Radius of maximum winds (km) 80 80 160 80 Radius of zero winds (km) 400 400 800 400 Sea surface temperature (°C) 27 27 27 29 Maximum winds (m/s) 12 2 12 12

Table 2.1: Initial conditions of steady-state simulations. profiles. Fig. 2.3 shows the evolution of the minimum central pressure with the initial conditions of the reference case A for the three initial moisture profiles. With the initial moist sounding, it takes 2-3 days until central pressure starts to decrease noticeably. Despite minimum pressure is attained not until day 7, starting with day 4 the vortex reaches minimum SLPs between approximately 920 and 940 hPa. Vortex intensification with the initial SAL profile starts after 5 days, reaching the minimum pressure around the same time as with the moist profile. With the dry profile, rapid intensification occurs in the course of the

6th day with a minimum central pressure obtained on day 8. For the SAL and dry proﬁle, intensiﬁcation occurs later. This experiment shows the role of moisture for a developing TC: The dryer the atmosphere, the longer it takes until the vortex is established, and the more unlikely it is in reality that a TC will develop.

Figure 2.3: Experiment A, evolution of minimum central pressure. Blue: Initial moist profile. Orange: SAL profile. Red: Dry profile.

In experiment B (Fig. 2.4), the initial windspeed is lowered from 12 to 2 m s−1. With the moist sounding, the minimum central pressure does not show any signiﬁcant decrease over the whole time.

Little convection occurs starting on the 7th day with subsequent minor development in the maximum windspeed from 2 to 5 m s−1 (not shown). However, the temporary increase in windspeed is not followed by a distinct decrease in minimum SLP. For both the SAL and the dry sounding, no convection takes place throughout the whole simulation and thus no evolution of the vortex. This experiment denotes the necessity of an initial disturbance for a TC to form, which is independent of the relative humidity in the atmosphere. If the windspeed in the beginning is too small, a steady-state TC will not develop. 30 Chapter 2. Steady-state simulations of tropical cyclones

Figure 2.4: Experiment B, evolution of minimum central pressure.

From the radii of maximum and zero winds, the horizontal extent of the hurricane can be estimated.

Experiment C is performed with the regular initial windspeed, but with a doubling of the radius of maximum winds and the radius where the winds decrease to zero (Fig. 2.5). In this simulation, the central pressure of the vortex with the initial moist sounding is decreasing only slowly within the ﬁrst

five days, between day 5 and 6 the intensity increases substantially. Convection close to the radius of maximum winds thoroughly increases on day 6 (not shown). As expected, with the initial SAL sounding the onset of the steady-state circulation is delayed by a few days compared to the moist sounding. For the dry sounding it is not yet fully established after 10 days. The delay in vortex intensification for the moist profile between a regular (exp. A) and a large vortex (exp. C) is around two days. When the vortex is larger, it takes considerably longer until enough water is evaporated to intensify the storm in drier conditions. As a doubling of the radius leads to a quadrupling of the area of the TC, accordingly it takes considerably longer until the vortex contains enough moisture to increase its intensity. However, the time delay of intensification with the SAL profile compared to the moist profile is around three days in experiment A but only two days in experiment C. Hence dry air seems to play a more crucial role when storm size is smaller.

Figure 2.5: Experiment C, evolution of minimum central pressure. 2.2. Experiments 31

For experiment D the initial settings as in A are used, but the sea surface temperature is increased by

2°C (Fig. 2.6). In these simulations, three features in the evolution of the pressure can be distinguished:

First, the TC gains intensity very quickly, after 3 (moist profile) respectively 4 days (SAL profile) the steady-state-like condition is achieved. Minimum pressures are attained around day 5. Second, as the increased SST enables more water to evaporate, the maximum intensity for simulations with all three profiles is higher than with a lower SST with central pressures being more than 20 hPa lower than for experiment A. Third, while intensification for the moist and SAL-profile take place with a time lag of not more than one day, the initial dry profile hinders evolution of the vortex for around two more days.

This is different from case A, where the delay between simulations with the SAL and dry proﬁle is only one day while the gap between moist and SAL proﬁle is around three days. This implicates that dry

SAL conditions seem to have a larger inhibiting inﬂuence on vortex generation when SSTs are rather low. When SSTs are higher, the inhibiting effect of dry air can be overcome faster by larger evaporation compared to lower SSTs.

Figure 2.6: Experiment D, evolution of minimum central pressure.

Although these experiments do not specifically investigate a possible aerosol effect on TCs, they ex- hibit the clear influence of moisture/dryness on the development or non-development of a TC. We find the evolution of steady-state vortices in moist and dry conditions to depend crucially on SST and storm size. Compared to a moist profile, in simulations with the SAL profile intensification is delayed longer when storm size is small, while higher SSTs diminish the inhibiting influence of dry SAL air. 32 Chapter 3

Model description, development and simulations

In this work, we use the general circulation model (GCM) ECHAM6-HAM at horizontal resolutions of

T63 and T255 (Chapter 3.1) in free mode and nudged towards reanalysis data (Chapter 3.2). To save computational time, ECHAM6-HAM is simpliﬁed. In the simpliﬁed model, dust remains as the only radiatively active aerosol species (Chapter 3.3).

3.1 The aerosol-climate model ECHAM6-HAM

ECHAM6 is the sixth generation of the GCM ECHAM (Roeckner et al., 1989), the atmospheric component of the Earth System Model of the Max-Planck-Institute of Meteorology in Hamburg, Germany.

It is based on the primitive equations and focuses on the coupling of the large-scale circulation and diabatic processes, driven by radiative forcing. Essential parts of ECHAM6 are a dry spectral-transform dynamical core, physical parameterizations to represent diabatic processes, a transport model for scalar quantities different from surface pressure and temperature, and boundary data sets for externalized parameters (Stevens et al., 2013). For a dynamic representation of dust aerosols in this study, the Hamburg

Aerosol Module (HAM) is coupled to ECHAM6 (Stier et al., 2005; Zhang et al., 2012). HAM contains

ﬁve different aerosol species: Sea salt, black carbon, particulate organic matter, sulphate and mineral dust. These species are separated into the soluble/mixed and the insoluble modes as well as into four different size classes, resulting into 25 transported tracers (Table 3.1).

The dust emission scheme in HAM is taken over from Tegen et al.(2002). In this scheme the emission of dust is computed interactively for the accumulation and coarse mode. The dust ﬂuxes depend on wind speed and soil properties (Zhang et al., 2012). Surface conditions in the model were modiﬁed by Cheng et al.(2008), leading to an update of East Asia soil properties in the recent version of HAM (Zhang et al.,

2012). The aerosol sink processes in HAM, sedimentation, dry and wet deposition, are parameterized as functions of mixing state, composition, particle size and the meteorological conditions (Zhang et al.,

33 34 Chapter 3. Model description, development and simulations

Modes Soluble/Mixed Insoluble r[¯ µm] Nucleation SU r¯≤0.005 N1,M1 Aitken SU BC POM BC POM 0.005

Table 3.1: The structure of HAM with the aerosol species sulphate (SU), black carbon (BC), particulate organic matter (POM), sea salt (SS) and mineral dust (DU). Aerosol number and mass of the j modes i and compounds j are depicted by Ni (number) and Mi (mass) respectively (Stier et al., 2005).

2012). Stier et al.(2005) quantified the fraction of global annual dust wet deposition to 55.3%, dry deposition to 5.5% and sedimentation to 39.2%. The removal of aerosols by wet deposition in the model is governed by the scavenging coefficient R. R is defined as the fraction of the tracer in the cloudy part of the grid box that is embedded in cloud liquid/ice water (Stier et al., 2005). The modification of the scavenging parameters for the insoluble accumulation and coarse modes, the only modes dust is emitted into, is central to simplifying ECHAM6-HAM (Chapter 3.3).

The two horizontal resolutions applied in this work are T63 (~1.88°×1.88°) and T255 (~0.47°×0.47°); all simulations use 31 vertical sigma-pressure levels. T63L31 is a standard resolution which is often used in studies about the effect of aerosols on climate and aerosol-cloud interactions, e.g. Stier et al.(2005);

Fischer-Bruns et al.(2010); Roelofs et al.(2010); Grandey et al.(2013). In this thesis, simulations in T63 resolution are performed for testing the performance of the simpliﬁed ECHAM6-HAM with modiﬁed scavenging parameters (Chapter 3.3). To simulate TC activity, the resolution has to be increased. As explained in Chapter 1.4, for realistically simulating global TC distribution and frequencies, resolutions of 100 and 135 km respectively are necessary. However, realistic simulations of TC intensities require resolutions of 20 km, with spatial resolution being not the only crucial factor though. Murakami et al.

(2012a) reported improvements in TC intensity in their updated global Meteorological Research Institute atmospheric GCM with a resolution of 20 km to be due to the use of a new cumulus convection scheme.

A horizontal resolution of T255 is selected to simulate TC activity, which represents a tradeoff between a horizontal resolution that reasonably reproduces TC actvity and computational cost, which becomes too large at greater spatial resolution. As the potential effect of Saharan dust on hurricane intensities could not be investigated, we focus solely on hurricane genesis and frequency. The vertical resolution of L31 extending up to 10 hPa (∼30 km), is unusual in combination with a ﬁne resolution as T255. It is not 3.2. Model simulations 35 tested whether vertical resolutions with higher topmost levels would improve our results, but as TCs do not reach into the stratosphere (Biondi et al., 2013) this vertical resolution seems to be sufﬁcient for our purpose.

3.2 Model simulations

To simulate a speciﬁc time period and compare it with observations, nudging is often applied. Nudging or Newtonian relaxation is a kind of data assimilation that adjusts dynamical variables of a free running

GCM using meteorological analysis data to relax the model results towards these analysis data such that the atmosphere is realistically simulated (Jeuken et al., 1996). A variable X is nudged with

Xn = Xm + CN(Xo − Xm), (3.1)

where Xn, the value of X after the nudging time step, results from the sum of the value of X produced by the free model between the two nudging steps Xm and the nudging term CN(Xo − Xm). Xo designates the observation value at the nudging time step with CN being the nudging factor with a value between 0 (no nudging, free model) and 1 (absolute nudging) (Genthon et al., 2002). Only variables are nudged which govern the atmospheric dynamics and thermodynamics: Vorticity, divergence, temperature, and the logarithm of surface pressure. The characteristical time scales for nudging are 6 hours for vorticity, 24 hours for temperature and the logarithm of the surface pressure, and 48 hours for divergence (Lohmann and

Ferrachat, 2010). Nudged simulations require a data set towards which the simulations can be relaxed.

Typically reanalyses are used, e.g. the European Centre for Medium-Range Weather Forecasts (ECMWF)

Reanalysis-40 (ERA-40) (Uppala et al., 2005) and Interim Reanalysis (ERA-Interim) (Simmons et al.,

2007; Dee et al., 2011).

We perform our simulations with the reanalysis dataset ERA-Interim, which is available at T255 horizontal resolution. We use nudging for the following tasks (Table 3.2): 1.) Sensitivity simulations at T63 resolution for the simpliﬁcation of ECHAM6-HAM (Chapter 3.3). 2.) Simulating the hurricane seasons

2005 and 2006 at T255 resolution (Chapter 4.3). 3.) Simulations with active and inactive dust (Chapter

5).

Additionally, free running simulations are performed. In our work the atmosperic GCM is not coupled to an ocean model, therefore we use or impose SSTs obtained from the Atmospheric Model Intercom- parison Project (Gates, 1992; Kanamitsu et al., 2002) as lower boundary. For our work we perform 20 36 Chapter 3. Model description, development and simulations free running simulations of the year 2005 with slightly different initial conditions obtained by minimal perturbations of stratospheric horizontal diffusion (Vamborg et al., 2014) (Chapter 4.4).

Description Model Resolution Mode Sim. name Year Control 2005 a) Sensitivity ECHAM6-HAM - 2000 simulations for the - 2006 simplification of T63 nudged Test O 2005 ECHAM6-HAM simplified Test S 2005 (Chapter 3.3) ECHAM6-HAM Test M 2005 Test Q 2005 b) Simulations of 2005 2005 observed hurricane ECHAM6 T255 nudged seasons 2006 2006 (Chapter 4.3) DustAN 2005 nudged c) Simulations with DustIN 2005 simplified active and inactive dust T255 ECHAM6-HAM (Chapter5) DustAF 10 × 2005 free DustIF 10 × 2005

Table 3.2: Overview over all performed simulations. For further explanations see the corresponding chapters.

3.3 Simpliﬁcation of ECHAM6-HAM

In comparison to ECHAM6, ECHAM6-HAM requires signiﬁcantly more computational time. A comparison between the standard ECHAM6 and ECHAM6-HAM, both with a one-moment cloud microphysical scheme, reveals an increase of computational time by a factor of 5 for T63L31 and approximately 8 for T255L31. The output of ECHAM6-HAM contains various properties of its ﬁve different aerosol species. However, for this project, only information about mineral dust is of interest. By using

ECHAM6-HAM for this study, a lot of mathematical operations concerning the four additional aerosol species would have to be performed with no use. Hence, to omit unnecessary calculations to save CPU- time, dust is kept as the only radiatively active species in HAM.

A problem arises when it comes to aerosol processes where dust is involved. In HAM, dust particles are emitted only into the insoluble accumulation and coarse modes. Subsequently, they are transferred to the corresponding soluble/mixed modes by condensation of sulfate on their surface or by coagula- 3.3. Simpliﬁcation of ECHAM6-HAM 37

Mode Stratiform Stratiform Stratiform Convective Liquid Clouds Mixed Clouds Ice Clouds Mixed Clouds Nucleation Soluble 0.10 0.10 0.10 0.20 Aitken Soluble 0.25 0.40 0.10 0.60 Accumulation Soluble 0.85 0.75 0.10 0.99 Coarse Soluble 0.99 0.75 0.10 0.99 Aitken Insoluble 0.20 0.10 0.10 0.20 Accumulation Insoluble 0.40 0.40 0.10 0.40 Coarse Insoluble 0.40 0.40 0.10 0.40

Table 3.3: Original scavenging parameters R for the modes of HAM. Values in cells with red background denote values, which were modiﬁed in the test simulations. tion with particles of soluble modes (Stier et al., 2005). An important difference in the attributes of soluble/mixed and insoluble dust particles exists in their lifetime. Due to their water solubility, soluble/mixed particles are washed out faster than insoluble particles. This process is governed in HAM by the scavenging parameter R (Table 3.3). The higher R is, the faster an aerosol particle, located in the corresponding clouds and modes, is removed from the atmosphere. Global annual mean aerosol lifetime can be estimated as the ratio of the column burden to the total sources (Stier et al., 2005). Using this approach, in T63-simulations of the years 2000, 2005 and 2006, the average lifetime of dust increases from 4.3 to 4.8 days when aging processes are eliminated. This shows that dust particles stay in the atmosphere for a longer time when they are in the insoluble mode.

For the above-discussed reasons, ECHAM6-HAM was simplified. This simplification solely signifies

Mode Stratiform Stratiform Stratiform Convective Liquid Clouds Mixed Clouds Ice Clouds Mixed Clouds Control Accumulation Soluble 0.85 0.75 0.10 0.99 Coarse Soluble 0.99 0.75 0.10 0.99 Accumulation Insoluble 0.40 0.40 0.10 0.40 Coarse Insoluble 0.40 0.40 0.10 0.40 Test O Accumulation Insoluble 0.40 0.40 0.10 0.40 Coarse Insoluble 0.40 0.40 0.10 0.40 Test S Accumulation Insoluble 0.85 0.75 0.10 0.99 Coarse Insoluble 0.99 0.75 0.10 0.99 Test M Accumulation Insoluble 0.62 0.58 0.10 0.70 Coarse Insoluble 0.70 0.58 0.10 0.70 Test Q Accumulation Insoluble 0.51 0.49 0.10 0.55 Coarse Insoluble 0.55 0.49 0.10 0.55

Table 3.4: Scavenging parameters in ECHAM6-HAM (Control) and modiﬁed parameters in the testing simulations with the simpliﬁed ECHAM6-HAM. 38 Chapter 3. Model description, development and simulations that dust remains the only interactive aerosol species. All remaining aerosol species now use climatological values. This implies that sulphate cannot coagulate with or condense on dust particles. As a result, dust is not transferred to the soluble modes with the higher scavenging parameters and the emitted dust particles reside too long in the atmosphere. Hence Saharan dust, which is emitted over the northern part of Africa and mostly moving westward over the North Atlantic, travels farther to the west than in HAM.

To mimic the coagulation/condensation-mechanism of sulphate and dust, the scavenging parameters of the insoluble modes that dust is emitted into, accumulation and coarse, are modiﬁed. To gain a realistic dust concentration, the scavenging parameters R are varied in a series of test simulations: Five T63- simulations of the year 2005 are performed with different sets of scavenging parameters (Table 3.4). To ensure that dust concentrations are not subject to different meteorology, these sensitivity simulations are performed in nudged mode.

Figs. 3.1 and 3.2 show atmospheric dust burden, wet deposition, and emission in the control simulation with ECHAM6-HAM. All four test simulations are performed with the simpliﬁed ECHAM6-HAM, with dust aging processes eliminated.

Figure 3.1: Dust burden average (June-September), control simulation.

Figure 3.2: Average (June-September) of control simulation, a) dust wet deposition and b) dust emission. 3.3. Simpliﬁcation of ECHAM6-HAM 39

The simulations in test O are performed with original scavenging parameters. As explained above, dust remains in the atmosphere for too long (Fig. 3.3a). Over a wide part of the North Atlantic between approximately 5°N and 25°N, signiﬁcantly more dust is present than in the control simulation. This effect is also displayed in dust wet deposition (Fig. 3.3b): In test O, the wet deposition south of the main emission region in western North Africa is smaller. In contrast to that, wet deposition over the Caribbean

Sea is substantially larger.

Figure 3.3: Difference of a) dust burden, and b) dust wet deposition, between control and test O simulations (Test O-Control) averaged over June-September.

For test S, the scavenging parameters of the insoluble modes are set to the same values as the parameters for the soluble modes. As consequence, dust is washed out too quickly from the atmosphere. Due to the largely increased values of R for the insoluble modes, dust is virtually emitted right away into the soluble modes. As dust lifetime is decreased, less dust is located in the region of the SAL. Compared to the control run the increased dust burden over Northwest Africa (Fig. 3.4a) in test S is due to larger dust emissions in this region (Fig. 3.4b). This is caused by an increase in 10m-windspeed of up to 0.3 m s−1 over the main emission region, this feature occurs in all test simulations. Hence, in addition to the effect caused by the modiﬁed scavenging parameters, the dust burden is always larger over the main emission region in the test simulations.

Figure 3.4: Difference of a) dust burden, and b) dust emission, between control and test S simulations (Test S-Control) averaged over June-September.

In test M, the scavenging parameters of the insoluble modes are set in between the original values and the corresponding values of the soluble modes. Besides the artefact over the main emission region, over 40 Chapter 3. Model description, development and simulations a wide region of the North Atlantic the dust burden of test M is very similar to the control simulation

(Fig. 3.5a). In test Q the scavenging ratios of the insoluble modes are set halfway in between the original values and the values in test M. The dust burden in test Q (Fig. 3.5b) is more similar to the control simulation than in test O, but a fraction of the dust is still transported too far towards South and North

America.

Figure 3.5: Difference of dust burden between a) control and test M, and b) control and test Q, simulations (Test-Control) averaged over June-September.

Comparing the images for the difference in dust burden between the control run and each of the four sensitivity simulations, it appears that test simulation M mimics the dust load over the North Atlantic best. To verify this assumption, regional averages of dust burden are calculated for the North Atlantic between 30°W - 90°W and 5°N - 30°N for all ﬁve simulations. In addition to 2005, the years 2000 and

2006 are examined. Table 3.5 reveals that average dust burden values of test M indeed come closest to the corrsponding control simulations of each year. Furthermore, mean dust lifetime of test M (4.32 days) is closest to the respective value of the control run (4.26 days). Thus, in the simpliﬁed ECHAM6-HAM the scavenging parameters of sensitivity simulation M are used.

2000 %-diff 2005 %-diff 2006 %-diff Lifetime Control 79.3 - 121.7 - 111.1 - 4.26 Sens O 106.7 +34.6 157.8 +29.7 143.6 +29.2 4.82 Sens Q 91.0 +14.6 133.9 +10.0 122.0 +9.8 4.51 Sens M 82.7 +4.3 123.3 +1.3 110.1 -0.9 4.32 Sens V 74.3 -6.3 109.8 -9.8 98.7 -11.2 4.11

Table 3.5: Dust burden averages (mg m−2) in the box 90° W - 30° W, 5° N - 30° N and their deviations from the corresponding control simulation. Last column denotes global mean annual lifetime of dust in days, averaged over the simulations of 2000, 2005 and 2006. Chapter 4

Tracks of hurricanes in ECHAM6 and the simpliﬁed ECHAM6-HAM

In this chapter, tracks of hurricanes in nudged and free simulations with ECHAM6 and the simpliﬁed

ECHAM6-HAM are evaluated. For this, a TC tracking and detection method (Kleppek et al., 2008;

Raible et al., 2012) is used to track the storms. Different threshold values in the tracking algorithm for vertical wind shear and relative vorticity are tested for the hurricane seasons of 2005 and 2006 (Chapter

4.3.1). In two case studies, the performance of ECHAM6 during the hurricanes Wilma and Ophelia is investigated in more details (Chapter 4.3.2). Afterwards, hurricane activity in free simulations is evaluated and compared with the nudged simulations.

4.1 Tracking method

For tracking of TCs, a tracking algorithm is applied to the output of the simulations. A TC tracking algorithm usually contains several parameters that help to detect TCs, e.g. sea level pressure (SLP), relative vorticity, wind speed, and temperature. A TC always shows a minimum in central SLP (Fig. 1.1), thus a local minimum in SLP is a common criterion. In addition, TCs are characterized by a broad area of a positive relative vorticity anomaly, thus a vorticity criterion with a certain threshold value that needs to be exceeded is often applied (Bengtsson et al., 2007b; Strachan et al., 2013). Another signiﬁcant feature of TCs are their strong winds. For this reason, wind speed can be used as a criterion (e.g. Kleppek et al., 2008; Murakami et al., 2012a). Some studies take advantage of the warm core in TCs by searching for temperature anomalies (Camargo and Zebiak, 2002; Murakami et al., 2012b). As mentioned in

Chapter 1.1.1, a TC’s warm core results from thermal wind balance, which is conserved between the low-level/midlevel cyclonic rotation in a TC and an anti-cyclonic movement in the upper-level outﬂow region. Thus, the difference in relative vorticity between low and upper tropospheric levels can be used as another detection criterion (e.g. Bengtsson et al., 2007a; Strachan et al., 2013). As a low vertical wind shear is known to be beneﬁcial for TC genesis (Gray, 1998), Kleppek et al.(2008) used a threshold

41 42 Chapter 4. Tracks of hurricanes in ECHAM6 and the simpliﬁed ECHAM6-HAM value for the difference in zonal wind speed between 850 and 200 hPa to track hurricanes. Occasionally, a location criterion is used as well to exclude storm tracks which start too far to the north (south on the southern hemisphere) (Bengtsson et al., 2007a). All TC algorithms found in the literature have in common that they additionally use a minimum lifetime criterion to exclude short-lived storms, e.g. 24

(Strachan et al., 2013), 36 (Murakami et al., 2012a) or 48 hours (Bengtsson et al., 2007a). As different basins and models may require different criteria, Camargo et al.(2007) used basin- and model-dependent threshold criteria, based on the climatology of each of their used models (Camargo and Zebiak, 2002).

4.1.1 Standard tracking criteria

For the tracking of tropical cyclones in our simulations, the tracking and detection method from Kleppek et al.(2008) is applied to the ECHAM output. This algorithm is successfully utilized for hurricanes in the North Atlantic in previous studies (Kleppek et al., 2008; Raible et al., 2012). The standard criteria

#1-5 for the detection of a tropical cyclone are the following:

1. A local minimum of SLP is observed within a neighborhood of eight grid points.

2. The magnitude of the maximum relative vorticity at 850 hPa exceeds 5 × 10−5 s−1.

3. The vertical shear of the horizontal winds (difference of zonal wind speed between 200 hPa and

850 hPa) remains below the threshold of 10 m s−1.

4. The TC lifetime is at least 36 hours.

5. Over land: Either the relative vorticity condition is fulﬁlled or the wind speed at 850 hPa has a

maximum in the ambient 24 grid points (approximately 250 km in all directions).

The threshold values for vertical wind shear and vorticity are varied and tested (Chapter 4.3.1), keeping the vorticity-criterion at 5 × 10−5 s−1 while increasing the wind shear threshold to 15 m s−1 for all relevant simulations.

4.1.2 Adaption of tracking criteria to model simulations

Two extensions are added to this set of criteria. To eliminate tracks which started over water with lower temperatures, an additional sea surface criterion is introduced:

6. The SST at a starting point of a track has to be at least 25°C.

Occasionally, erroneous short-lived and stationary tracks are produced over land. Therefore, a land-sea- 4.2. Categorization of hurricanes 43 criterion is added as well:

7. Storm tracks must not start over land.

4.2 Categorization of hurricanes

As mentioned previously, North Atlantic and eastern North Paciﬁc hurricanes are classiﬁed into the

Saffir-Simpson hurricane wind scale (Chapter 1.2). Minimum SLPs are removed from the official Saffir-

Simpson hurricane scale in 2010, making it a pure wind scale (Table 1.2). However, detected and tracked storms in our work are classiﬁed into the pressure scale (Table 4.1) introduced by Simpson and Safﬁr

(1974). The maximum winds of TCs are obtained in the eyewall, which denotes the radius of maximum winds (Fig. 1.1). With our grid spacing of approximately 50 km, the winds in the eyewall can not be simulated adequately. Testing the 10 m-wind speed as classification criterion for our simulated hurricanes, no single storm is classified even as a category 1-hurricane. The characteristical low central pressures of observed TCs are also not simulated well with our spatial resolution. However, with the pressure scale, in our free simulations at least 0.6 and 1.2 storms per year are classified as a category 1-hurricane or higher (Table 4.3). Hence, categorization of the storms into a pressure scale is more appropriate than a wind scale in our study.

Category Minimum central SLP (hPa) 1 p ≥ 980 2 980 > p ≥ 965 3 965 > p ≥ 945 4 945 > p ≥ 920 5 p < 920

Table 4.1: Categorization of hurricanes according to the Safﬁr/Simpson scale for sea level pressure (Simp- son and Safﬁr, 1974).

4.3 Nudged simulations (ECHAM6)

Kleppek et al.(2008) applied their tracking algorithm on the reanalysis dataset ERA-40 (Uppala et al.,

2005) from the European Centre for Medium-range Weather Forecast (ECMWF), which is available in T159 horizontal resolution. They downscaled their grid for their study to a coarser resolution of

1.125° × 1.125° in longitude and latitude. Applying the tracking algorithm on our finer horizontal resolution of T255, detection criteria need to be tested again as realistic threshold values for TC detection 44 Chapter 4. Tracks of hurricanes in ECHAM6 and the simplified ECHAM6-HAM depend on model resolution. To find threshold values that detect hurricanes as good as possible whilst not tracking too many weak storms, various threshold criteria of the relative vorticity in 850 hPa and vertical wind shear are tested in simulations of the years 2005 and 2006. These simulations are nudged towards ERA-Interim (Simmons et al.(2007); Dee et al.(2011)). ERA-Interim is provided in T255, which is the same resolution that we use. The years 2005 and 2006 are chosen because of the different hurricane activity of these two seasons. 2005 was the most active North Atlantic hurricane season since records started (Beven et al., 2008), 2006 was a decent season with a slightly below average activity. The tracking algorithm is applied on the whole North Atlantic hurricane season of June-November.

4.3.1 Variation of thresholds

In the hurricane season of 2005 (Fig. 4.1), a record high of 15 hurricanes and 13 additional tropical storms are observed. For the statistics in our test, two observed hurricanes are excluded: Hurricane

Vince (#21 in observations of the National Hurricane Center (NHC)) because of the abnormally low SST

Figure 4.1: Atlantic hurricane season 2005. Source: National Hurricane Center, http://www.nhc.noaa.gov/ 2005atlan.shtml . 4.3. Nudged simulations (ECHAM6) 45 during his formation, and hurricane Epsilon (Fig. 4.1 #27) because of its very late occurrence (November

29th-December 8th). For the same reasons, subtropical storm #19 (too low SST) and tropical storm Zeta

(#28, formed December 30th) are not included. Kleppek et al.(2008) denote that a track is occasionally split in two if one or more of the detection criteria are not met at one or more timesteps between the ﬁrst and the last detection timestep of this storm. This is e.g. the case for tracks #377 and 380 close to Latin

America in Fig. 4.2. However, if a track is split into two or more tracks during this test, the storm is only counted once for the statistics in table 6.2.

For the ﬁrst test (Fig. 4.2), the original criteria for wind shear (≤ 10 m s−1) and vorticity (≥ 5 ×

10−5 s−1) by Kleppek et al.(2008) are used. Except for hurricane Nate (#14, Fig. 4.1), all storms with hurricane strength are detected successfully. The ﬁrst and last detected timesteps of each storm do generally not exactly match the observations. Despite the simulations are nudged towards reanalysis data, inconsistencies between simulations and observations are possible.

5 Saffir/Simpson scale 4

-100 -60 -20 3

1 0

Figure 4.2: 2005, June-November. Storm tracks with thresholds for wind shear: 10 m s−1, vorticity: 5 × 10−5 s−1.

Although detecting all storms of hurricane strength, only 2 out of 11 tropical storms are tracked.

While for tropical storms signiﬁcant vorticity anomalies are found as well, tropical storms are weaker than hurricanes and not resolved well enough in the reanalysis to be tracked regularly by our tracking method. Lowering the thresholds to increase the probability for tracking of tropical storms does not increase the number of tracks either. This is predominantly due to the fact that in some weaker tropical storms no continuous distinct SLP minima are found. 46 Chapter 4. Tracks of hurricanes in ECHAM6 and the simpliﬁed ECHAM6-HAM

5 Saffir/Simpson scale 4

-100 -60 -20 3

1 0

Figure 4.3: 2005, June-November. Storm tracks with thresholds for wind shear: 15 m s−1, vorticity: 5 × 10−5 s−1.

Modifying the 200 to 850 hPa wind shear criterion to ≤ 15 m s−1 results in one further hurricane track not being observed (# 500 in Fig. 4.3), but improves the overall tracking: Hurricane Nate is now detected

(#283) while Hurricanes Dennis and Emily (#76 and 96 in Fig. 4.3) are tracked a few timesteps earlier and Hurricane Maria (#262/285) a few days earlier. As the time of ﬁrst detection is a crucial detail for the Box Difference Index (Chapter6), this advantage favors the threshold of 15 m s −1. Increasing the wind shear threshold to 20 m s−1 (not shown) does not cause signiﬁcant changes except for one more track which is not categorized as a hurricane or tropical storm by the NHC.

Keeping the threshold value for the wind shear constant at ≤ 15 m s−1, the vorticity threshold is varied as well. In a first test, it is lowered to ≥ 3 × 10−5 s−1. In addition to two hurricanes, which are tracked at earlier timesteps, a couple of non-classified storms are also tracked. Especially around Latin America, lowering the vorticity criterion led to a number of storms, whose locations on different timesteps are not tracked properly, connecting detected systems in consecutive time steps to unrealistic tracks (not shown). This affirms that the vorticity threshold should not be decreased below 5×10−5 s−1. Increasing the vorticity threshold to ≥ 10 × 10−5 s−1 causes two tracks not to be tracked any more, but only one not observed storm is still detected compared to four with the thresholds of 15 m s−1 and 5 × 10−5 s−1. However, a couple of the remaining tracks are shorter than with using the ≤ 5 × 10−5 s−1 vorticity threshold.

In 2006, only 10 storms are observed, of which ﬁve reached hurricane strength (Fig. 4.4). Examination 4.3. Nudged simulations (ECHAM6) 47

Figure 4.4: Atlantic hurricane season 2006. Only 10 storms with 5 reaching hurricane strength were observed. None of the hurricanes reached coastal areas. Source: National Hurricane Center, http://www.nhc.noaa.gov/2006atlan.shtml . of the tracks with ECHAM6 reveals similar performances of the tracking algorithm as for 2005. With all tested wind shear- and vorticity thresholds, four out of ﬁve hurricanes are tracked. The missed hurricane

(Isaac, #10 in Fig. 4.4) is after Ernesto (#6) the weakest of the season. Similar as in 2005, the performance of the tracking algorithm improves for 2006 by increasing the wind shear criterion from ≤ 10 m s−1 to ≤

15 m s−1, by which three instead of only one system of tropical storm strength can be tracked. However, three identiﬁed storms are also tracked which can not be regarded as tropical storms (#93, 229 and 327 in Fig. 4.5). One of these tracks begins very far to the north and just narrowly meets the SST-criterion of

≥ 25°C.

As in 2005, a further increase of the wind shear criterion to ≤ 20 m s−1 shows no improvement in tracking. Varying the vorticity thresholds reveals the same tendency in tracking as in 2005: There are less proper connections of the storms at different timesteps for a threshold of ≥ 3 × 10−5 s−1 and too sparse tracking for ≥ 10 × 10−5 s−1. For both years, a wind shear criterion of ≤ 50 m s−1 is tested as well, practically eliminating this criterion as wind shears above 50 m s−1 are rare and make TC genesis 48 Chapter 4. Tracks of hurricanes in ECHAM6 and the simpliﬁed ECHAM6-HAM

5 Saffir/Simpson scale 4

-100 -60 -20 3

1 0

Figure 4.5: 2006, June-November. Stormtracks with thresholds for wind shear: 15 m s−1, vorticity: 5 × 10−5 s−1 . basically impossible. No signiﬁcant improvements are observed as compared to the ≤ 15 m s−1-criterion.

Table 6.2 shows the numbers of tracks for the simulations of 2005 and 2006 with the tested threshold values. The most obvious change occurs by raising the wind shear threshold from ≤ 10 m s−1 to ≤ 15 m s−1, while the variations in vorticity do not improve the results. Therefore the thresholds of ≤ 15 m s−1 and ≥ 5 × 10−5 s−1 are used for all simulations in this thesis.

V 5 W 15 2005 Obs. W 10 W 15 W 20 W 50 V 3 V 10 Hurricanes 13 12 13 13 13 13 12 Tropical storms 11 2 2 2 2 2 1 Additional tracks - 3 4 5 6 9 1 2006 Hurricanes 5 4 4 4 4 4 4 Tropical storms 5 1 3 3 3 3 1 Additional tracks - 1 3 4 6 6 -

Table 4.2: Number of storm tracks in nudged simulations of 2005 and 2006, June-November, compared to observations. “Tropical storms” shows only storms which did not reach hurricane intensity. Additional tracks were detected systems that were not categorised as a hurricane or tropical storm by the National Hurricane Center (NHC). “W” denotes the wind shear threshold in m s−1, “V” the vorticity threshold in 10−5 s−1. 4.3. Nudged simulations (ECHAM6) 49

4.3.2 Case studies

ECHAM6 has problems reproducing TC intensities at T255 and comparable resolutions and even more on a regional scale in the North Atlantic (Chapter 3.1). For 2005 and 2006, only once an intensity of less than 980 hPa (during landfall of Katrina, #242 in Fig. 4.3), corresponding to the threshold between category one and two of the Saffir/Simpson pressure scale, is simulated in the North Atlantic. However, in 2005 three storms with very low central SLPs were observed (Katrina, Rita and Wilma). Katrina reached 902 hPa and Rita 895 hPa. Besides these two, even the record-low minimum SLP of 882 hPa of hurricane Wilma (# 411 in Fig. 4.3) is far off from being simulated adequately. In our simulation, minimum pressure does not fall below 980 hPa. According to the observations of Bell(1975), the mean diameter of the eye of a tropical cyclone is 51.4 km with a standard deviation of 34.2 km. Intense TCs tend to have an even smaller eye. This implicates that the resolution of T255 is not sufficient to simulate the structure of the eye adequately. In our free simulations though two hurricanes reach a minimum SLP of 962 and 948 hPa (Chapter 4.4). It is remarkable that the very intense storms in 2005 do not reach a lower pressure as well in our nudged simulations. Hence the overestimation of minimum pressure in our nudged simulations is only partly due to the simulations’ horizontal resolution of T255, which roughly corresponds to a grid spacing of 50 km on the equator. To understand the influence of nudging on central sea level pressure, two case studies are performed. In these hurricane tracks from observations, simulations and ERA-Interim reanalysis data are compared. As mentioned in Chapter 4.3, ERA-Interim data is available at the same resolution we use for our simulations, T255.

Hurricane Wilma

With a minimum central pressure of 882 hPa, maximum 1-minute sustained winds of 295 km h−1 and a minimum eye diameter of 3.7 km (Pasch et al., 2006), Wilma was the most powerful hurricane ever recorded in the North Atlantic (Fig. 4.6a). With ECHAM6, Wilma is tracked fairly well except for the last stage during Wilma’s transition to an extratropical cyclone (Fig. 4.6b) which leads to a loss of its

TC character and not meeting the tracking algorithms’ conditions any more. The evolution of Wilma’s central SLP is plotted from tracks of the nudged simulation and ERA-Interim data (Fig. 4.7) compared to observations. As indicated above, the central SLP of Wilma in the nudged simulation does not fall below 980 hPa, while NHC-observations depict an extreme decrease to 882 hPa on October 19th. Note that the Wilma-track in ERA-Interim, which is very closely related to the SLP of the model output, does not reproduce the NHC observations. As the logarithm of the surface pressure is one of the four nudging parameters (Lohmann and Ferrachat, 2010), this is not a surprising result. 50 Chapter 4. Tracks of hurricanes in ECHAM6 and the simpliﬁed ECHAM6-HAM

Figure 4.6: Track of Hurricane Wilma, a) NHC-observations, b) nudged simulation with ECHAM6 in T255. Red numbers indicate the location of Ophelia on the corresponding day in October 2005.

Figure 4.7: Hurricane Wilma, evolution of central SLP (hPa) in October 2005. The dark blue graph shows the storm tracked by the TC tracking algorithm in the nudged simulation, the red graph represents NHC observations. The orange graph depicts the storm tracked by the TC algorithm in ERA-Interim reanalysis input ﬁelds.

Hurricane Ophelia

A comparison of observed and modeled SLPs is also conducted in a second case study. Hurricane Ophe- lia was observed in 2005 and reached category 1 on the Safﬁr/Simpson wind scale with a minimum SLP of 976 hPa (Fig. 4.8a). The path of Ophelia is tracked adequately by ECHAM6 (Fig. 4.8b). As for Hur- ricane Wilma, when the storm weakens and looses its tropical characteristics the extratropical transition and the last timesteps of Ophelia being a tropical storm are not tracked any more. Ophelia’s central SLP from ECHAM6 output remains between 1010 and 1000 hPa throughout her life cycle (Fig. 4.9), which is up to about 25 hPa above the observations. As for hurricane Wilma, Ophelia’s simulated central SLPs are very close to the SLPs in the track obtained from ERA-Interim. 4.3. Nudged simulations (ECHAM6) 51

Figure 4.8: As in Fig. 4.6, but for Hurricane Ophelia in September 2005.

Figure 4.9: As in Fig. 4.7, but for Hurricane Ophelia.

In the case studies of Wilma and Ophelia, the close connection between simulated central SLP and central SLP in ERA-Interim becomes evident. The horizontal resolution of the available reanalysis dataset governing the intensities of the storms clearly indicates that a higher horizontal resolution is necessary to adequately reproduce storm intensities in both model simulations and reanalyses. Comparing the low intensities in our nudged simulation of 2005 with free simulations (Chapter 4.4) supports this ﬁnding:

The hurricane season of 2005 had ﬁve hurricanes reaching central SLPs of 930 hPa and lower (Dennis,

Emily, Katrina, Rita and Wilma), in the nudged simulation none of them falls below 978 hPa. 52 Chapter 4. Tracks of hurricanes in ECHAM6 and the simpliﬁed ECHAM6-HAM

4.4 Free simulations (simpliﬁed ECHAM6-HAM)

Ensemble simulations of the hurricane season of 2005 are performed with the simpliﬁed ECHAM6-

HAM (Chapter 3.3) in T255L31 in free running mode. 10 simulations are conducted with radiatively active dust, 10 with dust not interacting with radiation (inactive dust). Different initial conditions for the ensemble simulations are obtained by varying stratospheric horizontal diffusion as mentioned in Chap- ter 3.2. As this work focuses on the possible inﬂuence of dust on hurricanes, in contrast to the nudged simulations (Chapter 4.3) only the peak dust months of the hurricane season, June to September, are simulated. North African dust emission and transport to the hurricane main development region over the

North Atlantic in October and November is low compared to the previous months. Table 4.3 shows the number and classiﬁcation of detected tracks during these 20 seasons. Storms that are split into two or more tracks are manually counted as only one in the statistics. Additional information in Table 4.3 on track numbers are given from the nudged simulations of 2005 and 2006 with ECHAM6 and observations, hereby all named systems are considered.

Storms Hurr. Maj. Years τ (d) Observations (2005) 17 10 5 1 6.3 ± 3.7 E6 (2005, nudged) 13 4 0 1 5.0 ± 2.7 simpl. E6HAM (2005, free, active dust) 7.4 ± 1.9 1.2 0.2 10 2.9 ± 1.7 simpl. E6HAM (2005, free, inactive dust) 7.9 ± 2.6 0.6 0 10 3.1 ± 1.8 Observations (2006) 10 5 2 1 5.2 ± 3.3 E6 (2006, nudged) 10 2 0 1 3.7 ± 3.1 Observations (1966-2009) 8.5 4.8 2.0 44 - Observations (1851-2012) 7.1 4.3 - 162 -

Table 4.3: Annual numbers (June-September) of North Atlantic storms and hurricanes in observations and simulations. “Storms” shows the number of all named tropical systems, “Hurr.” considers all tropical systems with hurricane strength, and “Maj.” denotes the number of major hurricanes (category 3 or higher). τ depicts the average lifetime of the storms in days including the standard deviation. Historical observations were taken from two different climatologies. Standard deviations are given where reasonable and available. Source of observations: National Hurricane Center

The annual track numbers of the ensemble simulations show a clear underestimation of storms in comparison with the observations of 2005. This is in agreement with the too low numbers of North

Atlantic TCs of Bengtsson et al.(2007a) with the ECHAM6-predecessor ECHAM5 (Chapter 3.1). As

Bengtsson et al.(2007a) reported a too high number of TCs for the North Pacific, this seems to be a systematic feature of ECHAM rather than a deficiency of the tracking algorithm. By considering also storms below hurricane strength, they obtained a similar number of systems for the North Atlantic as in 4.4. Free simulations (simplified ECHAM6-HAM) 53 the observations. Compared with the observations of the satellite era (since around 1966) this is also the case for our simulations. However, our simulations are performed with the above-average SSTs of the year 2005, comparable TC numbers of simulations with other years are not available.

Using pressure as intensity scale we ﬁnd an overwhelming majority of detected storms in the simpliﬁed

ECHAM6-HAM to not reach category 1 (≤ 994 hPa). Only two major hurricanes occur in the simulations with active dust and none in the simulations with inactive dust. This is mainly due to the insufﬁcient resolution (Chapter 3.1), as a grid spacing of ca. 50 km cannot resolve the inner structure of the hurricane including the eye. The number of storms classiﬁed as hurricanes in the nudged simulation of 2005 is

Figure 4.10: Storm tracks in ensemble simulations of 2005 (June-September) from a) 10 simulations with radiatively active dust, b) 10 simulations with radiatively passive dust. Category 1 includes hurricanes of category 1- and tropical storm strength. 54 Chapter 4. Tracks of hurricanes in ECHAM6 and the simpliﬁed ECHAM6-HAM larger than in the free simulations (4 vs. 1.2/0.6) despite the intensity-limiting inﬂuence of ERA-Interim

(Chapter 4.3.2). This is basically due to the extraordinary strong hurricane season of 2005, as several very intense hurricanes (e.g. Rita, Katrina, Fig. 4.1) are still resolved in the simulation with a minimum central SLP in the category 1 range (980-994 hPa).

No statistically signiﬁcant differences in lifetime are found between the simulations with active dust and with passive dust. The average storm lifetime in the nudged simulations is around 2 days longer than in the free simulations. Storms categorized as hurricanes have a longer lifetime than storms which do not exceed tropical storm strength. As the fraction of hurricanes to total storm number is smaller for the two simulated years of 2005 and 2006 compared to observations, the lifetime of tracks is 1.3 days (2005) respectively 1.5 days (2006) longer in observations than in nudged simulations.

Fig. 4.10 shows all detected tracks during the 20 ensemble simulations of 2005 with the simpliﬁed

ECHAM6-HAM. It is noteworthy that compared to the regions north of 20°N, rather few storms are tracked in the tropical North Atlantic and the Caribbean Sea, which are the main development region

(MDR) for North Atlantic hurricanes. The lack of tracks in the Caribbean Sea can be explained by the temporal and spatial distribution of tropical storm/hurricane genesis. According to the HURDAT database of the NHC, storm genesis in the Caribbean Sea generally peaks in October (Fig. 4.11). This month is not considered in our study. Although several storms form in the tropical eastern North Atlantics in August and September in our model (south of ~15°N, east of ~50°W), the majority of these storms does not yet reach their full strength as long as they are in this region, but intensify when leaving the area, gaining full strength elsewhere, often northwest of that region. Because of this track density south of 20°N seems a bit sparse in our simulations.

Figure 4.11: Genesis locations of hurricanes, North Atlantic: 1851-2009, Eastern Paciﬁc: 1949-2009. a) October 1st-10th, b) October 11th-20th. Source: NHC, http://www.nhc.noaa.gov/climo/

With the tracking and detection method from Kleppek et al.(2008) and Raible et al.(2012), we can successfully track hurricanes in nudged and free running simulations. We slightly adapted the criteria, adding a SST- and a land-sea criterion and modifying the threshold for vertical wind shear from 10 to 4.4. Free simulations (simpliﬁed ECHAM6-HAM) 55

15 m s−1. With two case studies we demonstrate the horizontal resolution of our simulations to be still too coarse to reproduce realistic TC intensities. Additionally, we evaluated the track number of free ensemble simulations, which are the basis for the chapters5 and6. 56 Chapter 5

Impacts of radiative heating of dust on temperature and vertical velocity

Using satellite datasets and global analyses, Braun(2010) found that the SAL shifts deep convection to the south where vertical shear of the horizontal wind associated with the African easterly jet is weak.

From this, Braun(2010) concluded that the SAL has a supporting effect on hurricane genesis. In this chapter, results of nudged and free simulations are analyzed to examine Braun(2010)’s postulated inﬂu- ence of dust on solar radiation and its implications for temperature and subsequently convection in and around the SAL.

5.1 Technical set-up

To test the radiative effect of dust on the convection of large-scale dynamics in the simpliﬁed ECHAM6-

HAM, four different sets of simulations are performed at a resolution of T255L31 (Table 5.1). All simulations are conducted for the year 2005, taking only the summertime peak dust months (June to

September) into account. One has to keep in mind that we use ﬁxed monthly climatological SSTs, hence a decrease of the SST due to absorption and scattering of solar radiation by dust (Lau and Kim, 2007a,b) and a subsequent inhibiting inﬂuence on tropical cyclogenesis is excluded.

The ﬁrst two simulations of 2005 are nudged towards ERA-Interim reanalysis data. One is performed with dust being radiatively active (“DustAN”), in the second one dust does not interact with radiation

(inactive dust, “DustIN”). Using this set-up, we investigate whether differences in absorbed solar radiation occur between simulations with active and inactive dust and thus temperature and vertical velocity.

Nudging does not allow large changes in temperature, pressure, vorticity and divergence between different simulations, but it aids in comparing signals in radiation that are not caused by differences in meteorology.

The second part of the comparison consists of the free simulations described in Chapter 4.4: one

57 58 Chapter 5. Impacts of radiative heating of dust on temperature and vertical velocity ensemble of 10 simulations with radiatively active dust (“DustAF”) and another one with radiatively inactive dust (“DustIF”). In free simulations, the meteorology of each simulation is different. Hence, averages over 10 years are used.

Dataset Description DustAN Nudged simulation with radiatively active dust DustIN Nudged simulation with radiatively inactive dust DustAF Ensemble average over 10 free simulations with radiatively active dust DustIF Ensemble average over 10 free simulations with radiatively inactive dust

Table 5.1: Overview over free and nudged simulations.

5.2 Results

In this chapter, absolute values of parameters or differences between either the nudged or the free simulations are shown. For differences, the dataset with radiatively inactive dust is always subtracted from the dataset with active dust.

Figure 5.1: Dust burden averaged over June-September from simulations DustAF.

The SAL was defined previously, e.g. by potential temperature, dust mixing ratio and horizontal wind between 600-700 hPa (Carlson and Prospero, 1972). Here, the horizontal extent of the SAL is defined as the region over the ocean with a dust burden (Fig. 5.1) larger than 0.2 g m−2. As dust in the simulations with inactive dust does not have any radiative effects by definition, the minor difference in the dust burden between DustAF and DustIF (respectively DustAN and DustIN) is not relevant. Larger dust burden in nudged compared to free simulations are due to a larger surface wind speed of up to 0.3 m s−1 in the main dust emission region. This feature is also observed in low-resolution simulations in T63 5.2. Results 59

(not shown). However, the difference in dust concentrations between nudged and free simulations is not crucial for our study, as we only evaluate the differences between radiatively active- and inactive-dust simulations, but not between nudged and free simulations. In most of the following images dust burden of the simulations with active dust is added in terms of 0.2, 0.5 and 1 g m−2 isolines.

5.2.1 Absorbed solar radiation in clear sky conditions

The absorbed solar radiation (ASR) is obtained as difference of the solar radiation at the top of the atmosphere (TOA) and the surface (TOA-surface). To avoid biases due to clouds, ASR is investigated in clear sky conditions only. In the simpliﬁed ECHAM6-HAM, dust is the only aerosol species that is radiatively active. Hence, the pattern of clear sky ASR goes along with the dust burden, e.g. the 2 W m−2 isoline coincides largely with the 1 g m−2 isoline of the dust burden (Fig. 5.2). This shows the direct inﬂuence of radiatively active dust on ASR.

Figure 5.2: Differences in absorbed solar radiation (clear sky) averaged over June-September between simulations DustAN and DustIN. Black isolines depict the 0.2, 0.5 and 1 g m−2 isolines of the dust burden.

Differences between free simulations (Fig. 5.3) do not show such a clear pattern as the nudged simulations. The maximum in the clear sky ASR in the free simulations is displaced eastward from the maximum dust burden. As water vapor also absorbes solar radiation this can be explained by examinig the vertically integrated water vapour mass (WVM). In the two nudged simulations, signals in WVM hardly differ over North Africa (not shown). By comparing clear sky ASR and WVM in the free simulations though (Fig. 5.4), the pattern of ASR better matches that of WVM. This is visible not only at the location of the maximum ASR, but also partly in the region of enhanced clear sky ASR in Algeria. 60 Chapter 5. Impacts of radiative heating of dust on temperature and vertical velocity

Figure 5.3: Differences in absorbed solar radiation (clear sky) averaged over June-September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2.

Figure 5.4: Differences in vertically integrated water vapour mass averaged over June-September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2.

5.2.2 Implications of absorbed solar radiation on temperature

The regions where dust occurs and/or where water vapour changes, correspond to regions of increased temperature in the (nudged) simulations with active dust compared to the inactive-dust simulations. In the free simulations, these locations can be displaced due to changes in the dynamics. Hence, temperatures in the SAL and close to the surface are examined. Fig. 5.5 shows the 2-m temperature difference between the nudged simulations. Over land, the pattern of the increased temperature corresponds very well to the dust burden and differences in clear sky ASR (Fig. 5.2). Although the temperature is nudged to reanalysis data with a 24-hour nudging timescale, heating over the Sahara is so intense due to the near-surface dust- layer that the temperature signal close to the ground is very pronounced. Over the ocean, heating is 5.2. Results 61

Figure 5.5: Differences in 2-m temperature averaged over June-September between simulations DustAN and DustIN. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2. smaller as the 2-m temperature is governed by the SST, which is prescribed in all simulations. In the free simulations, the temperature over land also mainly follows the clear sky ASR (Fig. 5.6).

Figure 5.6: Differences in 2-m temperature averaged over June-September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2.

According to Prospero and Carlson(1981), the SAL is located between 500 and 850 hPa over the eastern Atlantic, while SAL top and base are at around 550 and 750 hPa over the Caribbean sea. Looking at average dust mixing ratio in 500, 700 and 850 hPa conﬁrms the presence of a distinct dust layer in

700 and 850 hPa (Fig. 5.7), which is, in contrast to Prospero and Carlson(1981)’s ﬁndings, also present over the Caribbean sea at 850 hPa. To examine whether dust lifted in the SAL substantially increases the temperature of the surrounding air, temperatures at 850 and 700 hPa are evaluated as well.

In the nudged simulations, hardly any temperature differences can be seen in 850 hPa (Fig. 5.8). In contrast to the surface, dust-induced heating is not as pronounced in this layer. As the dust mixing ratio 62 Chapter 5. Impacts of radiative heating of dust on temperature and vertical velocity

Figure 5.7: Mean dust mixing ratio at a) 500 hPa, b) 700 hPa, and c) 850 hPa, averaged over June- September, DustAF. in the SAL close to the sea surface is substantially lower than at 850 and 700 hPa this is probably due to nudging, which erases almost all potential changes in temperature. In the free simulations at 700 hPa, temperature in the DustAF simulations is higher over parts of North Africa (Fig. 5.9a), but not over the tropical North Atlantic. In the southern SAL, the temperature at 850 hPa is increased up to 0.3 K between about 10°-18°N, extending up to 50°W (Fig. 5.9b). The discrepancy between 700 and 850 hPa suggests that the dust-induced warming of the SAL over the Atlantic is not large enough in upper SAL-levels. In addition to less dust being present at 700 hPa than at 850 hPa, the size of the dust particles may play a 5.2. Results 63

Figure 5.8: Differences at 850 hPa-temperature averaged over June-September between simulations Dus- tAN and DustIN. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2.

Figure 5.9: Differences in temperature at a) 700 hPa, and b) 850 hPa, averaged over June-September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2. role. Large dust aerosols settle more quickly than small ones, hence the fraction of small dust particles at 700 hPa is higher than at 850 hPa. As large dust particles absorb more solar radiation than smaller ones (Tegen and Lacis, 1996; Helmert et al., 2007), this could explain the lack of warming at 700 hPa in addition to the slightly smaller dust mixing ratio in this layer. 64 Chapter 5. Impacts of radiative heating of dust on temperature and vertical velocity

The warming at 850 hPa occurs only in the southern part of the SAL. The northern part is inﬂuenced by winds from Europe, which are slightly stronger when dust is active. As these winds advect cooler air, temperatures along the northwest African coast between 35°-25°N are lower in active-dust simulations.

The dust-induced warming of dust on the North Atlantic SAL becomes thus pronounced only south of

~18°N.

5.2.3 Vertical velocity

Given that the SAL is more stable when the free troposphere is warmer due to dust-induced radiative heating, reduced vertical motions are expected to occur. Over the North Atlantic, a broad area of upward vertical velocity values is visible between approximately 5°N-10°N, which is independent of dust

(Fig. 5.10). This area characterizes the region of enhanced convection where southeast and northeast trade winds meet, the intertropical convergence zone (ITCZ). Over the African continent, this area is located further to the North during the Northern Hemisphere summer months and is less structured as over the ocean.

Figure 5.10: Mean 700-hPa vertical velocity averaged over June-September from simulations DustAF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2.

The differences in vertical velocity in the nudged simulations between radiatively active and inactive dust do not reveal a clear structure in 700 hPa (Fig. 5.11). North Africa and the southern North Atlantic show several rather small, deﬁned local areas with either an increase or decrease in vertical velocity. In the region between ~20°W-40°W, there are several small areas of decreased convection in the simulations with active dust north of the 0.2 g m−2-dust burden isoline. South of this isoline, the opposite is found implying that the ITCZ is located further to the south in DustAN.

The vertical velocity differences in the free simulations show clearer differences in ω (Fig. 5.12). 5.2. Results 65

Figure 5.11: Differences in 700-hPa vertical velocity averaged over June-September between simulations DustAN and DustIN. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2.

First, the vertical velocity close to the main emission regions in West Africa at around 20°N (Fig. 5.13) shows a broader T-shaped region with enhanced upward motions in simulations DustAF. This is caused by the larger dust-induced surface temperatures (Fig. 5.6) with subsequently enhanced convection in this region. Second, a well-deﬁned area of reduced vertical velocity of up to 25 hPa d−1 in simulations

DustAF is located along the 0.2 g m−2 dust burden isoline. Furthermore, two smaller, less deﬁned regions between 30°-40°W and 45°-50°W (around 5°-8°N) indicate larger upward vertical velocities in simulations DustAF. Differences in convective precipitation (Fig. 5.14) go along well with the vertical velocity.

As mentioned above the high stability in the SAL suppresses convection where it is less important for hurricane development and shifts the convection to the more southerly cyclonic vorticity-rich area south of the African easterly jet (Braun, 2010). In a more stable SAL with radiatively active dust, convection

Figure 5.12: Differences in 700-hPa vertical velocity averaged over June and September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2. 66 Chapter 5. Impacts of radiative heating of dust on temperature and vertical velocity

Figure 5.13: Mean dust emissions averaged over June-September from simulations DustAF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2.

Figure 5.14: Differences in convective precipitation averaged over June and September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2. should occur more southward than with inactive dust. In fact, the two above mentioned regions of enhanced convection in the inactive-dust (8°-12°N) and active-dust (5°-8°N) simulations depict convection to occur further south when dust is active. Hence, Braun(2010)’s result of SAL-induced southwards- shifted convection can be supported by our model simulations.

However, when examining vertical velocity, it is important to be aware of the spatial scales in the simulations of our comparison. In the free simulations, averages of the summer months from June to

September are taken over ten seasons each, with the absolute ω-values on the order of up to 100 hPa d−1 (Fig. 5.10). This corresponds to roughly 1 cm s−1, respresenting large-scale vertical motions (Liljequist,

2007). A vertical velocity of this magnitude is much smaller than the values obtained in thunderstorms or deep convection, which are essential for tropical cyclones. Upward vertical motions in hurricanes 5.2. Results 67 observed in the Global Atmospheric Research Program Atlantic Tropical Experiment (Kuettner, 1974) are reported to reach 10 m s−1 on average with peak values of up to 20 m s−1 (Jorgensen et al., 1985).

Vertical up- and downdrafts in thunderstorms and hurricanes do not occur on a large scale, but only in small distinct domains. Jorgensen et al.(1985) deﬁne up- and downdraft-domains to have at least a horizontal expansion of 500 m with 99% of the updraft (downdraft) domains to be <7 km (5.6 km) in diameter. The horizontal resolution in our simulations of ~50 km is by far too coarse to capture localized updraft events, even in hurricanes. Thus, detected maximum updraft velocities in our simulations do not exceed ~0.5-1 m s−1. Additionally, taking averages over one or ten seasons in the nudged or free simulations, eliminates individual convective events, and exclusively represents large-scale dynamics.

Concluding, examining the effects of radiatively active dust on absorbed solar radiation shows a dust- induced low-level warming in the southern part of the SAL and a subsequent southern shift of convection within the ITCZ. This conﬁrms the mechanism suggested by Braun(2010). A possible impact of the results of the free simulations on hurricane genesis is evaluated in the following chapter. 68 Chapter 6

The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis

Authors:

Sebastian Bretl1, Philipp Reutter2, Christoph C. Raible3, Sylvaine Ferrachat1, Christina Schnadt-Poberaj1,

Laura E. Revell1 and Ulrike Lohmann1.

Article accepted on Feb. 11th, 2015, Journal of Geophysical Research, 119, doi: 10.1002/2014JD022441

6.1 Abstract

To date, the radiative impact of dust and the Saharan air layer (SAL) on North Atlantic hurricane activity is not yet known. According to previous studies, dust stabilizes the atmosphere due to absorption of solar radiation but thus shifts convection to regions more conducive for hurricane genesis. Here, we analyze differences in hurricane genesis and frequency from ensemble sensitivity simulations with radiatively active and inactive dust in the aerosol-climate model ECHAM6-HAM. We investigate dust burden and other hurricane-related variables and determine their inﬂuence on disturbances which develop into hurricanes (developing disturbances, DDs) and those which do not (non-developing disturbances, NDDs).

Dust and the SAL are found to potentially have both inhibiting and supporting inﬂuences on background conditions for hurricane genesis. A slight southward shift of DDs is determined when dust is active as well as a signiﬁcant warming of the SAL, which leads to a strengthening of the vertical circulation associated with the SAL. The dust burden of DDs is smaller in active dust simulations compared to

DDs in simulations with inactive dust, while NDDs contain more dust in active dust simulations. How- ever, no signiﬁcant inﬂuence of radiatively active dust on other variables in DDs and NDDs is found.

1Institute of Atmospheric and Climate Sciences, ETH Zürich, Zürich, Switzerland 2Institute for Atmospheric Physics, Johannes Gutenberg-University, Mainz, Germany 3Climate and Environmental Physics and Oeschger Centre for Climate Change Research, University of Bern, Switzerland

69 70 Chapter 6. The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis

Furthermore, no substantial change in the DD- and NDD-frequency due to the radiative effects of dust can be detected.

6.2 Introduction

Tropical cyclones (TCs) and the processes leading to their genesis have since long been a topic of interest in the scientiﬁc community. Since the 1950s, six prerequisites were identiﬁed as important for TC- generation, e.g., Ramage(1959); Lighthill et al.(1994); Gray(1998); Tory and Frank(2010)): 1.) Ocean temperatures in the topmost 50 m exceeding 26.0-26.5 °C; 2.) A potentially unstable atmosphere; 3.)

A moist mid-troposphere; 4.) Latitudes more than 5° away from the equator; 5.) An initial dynamic disturbance with sufﬁcient convergence and vorticity, e.g., a tropical easterly wave; 6.) A low vertical shear of the horizontal winds.

Dust can potentially inﬂuence four of these criteria (numbers 1, 2, 3 and 6, as discussed below). In this regard the Sahara as the world’s largest dust source (Washington et al., 2003) is a key factor. In distinct outbreaks, the dry, warm and dust-laden Saharan air layer (SAL) is elevated over West Africa and transports dust across the North Atlantic during the Northern Hemisphere summer months (Carlson and Prospero, 1972; Prospero and Carlson, 1981). In total, about 25% of Saharan dust emissions are transported westward to the Atlantic (d’Almeida, 1986; Shao et al., 2011).

To date, two main effects of aerosols on TCs have been studied: The microphysical effect, which examines how the aerosol particles act as cloud condensation nuclei (CCN) and ice nuclei, and the radiative/dynamic effect, which takes into account the effect of absorption and scattering of solar radiation by aerosol particles with possible subsequent implications on atmospheric dynamics. The concept of seeding TCs with aerosols to reduce their intensity was analyzed as part of the STORMFURY project (Gentry and Hawkins, 1970; Willoughby et al., 1985). Cotton et al.(2007), Zhang et al.(2007) and Zhang et al.

(2009) gave an overview of the simulated hurricane response to African dust. They proposed that seeding hurricanes with dust acting as CCN could reduce hurricane intensity. Besides CCN, giant CCN and ice nuclei potentially contribute to this mechanism (DeMott et al., 2003; van den Heever et al., 2006). In a case study, Khain et al.(2010) showed that continental aerosols invigorate convection largely at the TC periphery, leading to TC weakening. Similar results were found by Carrio and Cotton(2011) in seeding during virtual aircraft ﬂights. Although another case study found an increase in low-level wind speed in the ﬁrst 10 hours after ingestion of CCN prior to a substantial weakening of wind speeds below the winds 6.2. Introduction 71 of a control run (Krall and Cottom, 2012), most studies found that additional CCN generally weaken TC intensity, e.g. Rosenfeld et al.(2011) and Rosenfeld et al.(2012).

While there seems to be a consensus on the microphysical impact of dust on TCs, it is controversial whether the radiative effects of dust and the SAL reduce or strengthen TC activity. Using Geostationary

Operational Environmental Satellite split-window satellite imagery, Dunion and Velden(2004) identiﬁed

SAL outbreaks and assigned reduced intensiﬁcation of TCs to these SAL outbreaks. They attributed this to three mechanisms: An enhanced low-level temperature inversion triggered by the SAL, possibly inhibiting convection in weak African easterly waves (AEWs); dry air intrusion into the TC, which is associated with a reduction of convective available potential energy; and increased vertical wind shear induced by the SAL midlevel easterly jet. Evan et al.(2006) detected an inverse relationship of dust cover and North Atlantic TC activity by evaluating Advanced Very High Resolution Radiometer data for the years 1982-2005, but admitted that this relationship is not proof for a causal effect of dust controlling

TC activity. Lau and Kim(2007a) assigned the reduced hurricane activity of 2006 compared to 2005 to the signiﬁcantly lower sea surface temperatures (SSTs); 30-40% of the SST reduction was attributed to the radiative cooling effect of dust aerosols (Lau and Kim, 2007b). In contrast to Lau and Kim(2007a),

Foltz and McPhaden(2008) suggested that dust-induced changes in surface shortwave radiation played only a minor role in the cooling of the North Atlantic between 2005 and 2006. The reduced TC activity in

2007 with respect to 2005 was associated with further westward transport of the SAL (Sun et al., 2008).

Therefore, except for conditions 4 and 5, dust could indeed inﬂuence the remaining four prerequisites for TC genesis.

However, recent studies afﬁrming a negative impact of the SAL on hurricane activity (e.g., Dunion and

Velden(2004); Lau and Kim(2007a)) barely considered alternative causes of storm weakening or lack of intensiﬁcation such as changes in vertical wind shear, ocean cooling induced by hurricanes or weak convective activity not associated with the SAL. Hence, Braun(2010) claimed that these studies were largely built on limited evidence and some false assumptions and emphasized the African Easterly Jet

(AEJ) as a source of energy for AEWs. AEWs are known to be conducive for hurricane formation (Frank and Clark, 1980; Landsea, 1993; Frank and Roundy, 2006) as well as for the transport of desert dust over the Atlantic (Jones et al., 2003). In fact, there are also a number of studies showing a supporting effect of the SAL on hurricane formation: Using the Global Atmospheric Research Program Atlantic Tropical

Experiment (Kuettner, 1974), Karyampudi and Carlson(1988) claimed that SAL outbreaks are possibly necessary, but at least important for the initialization of easterly wave disturbances. In case studies, an 72 Chapter 6. The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis inhibiting effect of the SAL was found on Hurricane Andrew (1992) whereas enhancing effects were observed for Tropical Storm Ernesto (1994) and Hurricane Luis (1995) (Karyampudi and Pierce, 2002).

Furthermore, Braun(2010) noted that the frontogenetic properties of the warm SAL seem to facilitate an indirect vertical circulation enhancing convection and thus promoting storm development. He further mentioned that deep saturated ascent is conﬁned to the region to the south where vertical shear associated with the AEJ is weak. However, this does not imply that the Intertropical Convergence Zone (ITCZ) is shifted to the south due to dust aerosols, it seems to be rather the opposite. Reale et al.(2011) emphasized that recent experiments with general circulation models show a northward shift of the Atlantic ITCZ caused by radiative effects of Saharan dust. In their quarter-degree simulations with 72 vertical levels, they found the AEJ to be displaced slightly northward during strong Saharan dust outbreaks. This is in agreement with Wilcox et al.(2010), who found a northward shift of the Intertropical Convergence Zone

(ITCZ) during SAL dust outbreaks using satellite observations and atmospheric reanalysis data products, and a recent model study by Woodage and Woodward(2014). Combining microphysical and radiative effects of anthropogenic aerosols, Wang et al.(2014) hypothesized that anthropogenic aerosols have an opposite effect to that of greenhouse gases and noted the importance of using microphysical-radiative modules in atmospheric models for TC research.

Recently, Reale et al.(2014) used the assimilated aerosol optical depth from the Moderate Resolution

Imaging Spectroradiometer for interactive aerosol modeling. They performed two sets of simulations with aerosol direct radiative effects (termed active dust hereafter) and without radiative effects (termed inactive dust hereafter) for one month in 2006 to determine the role of dust on the development of a TC, and found that dust radiative effects cause the environment to be less conducive to TC development.

Here, we use a similar approach with radiatively active and inactive dust, but evaluate the influence of dust on hurricane genesis in the North Atlantic on a statistical basis. For this purpose, sensitivity simulations are performed with the general circulation model ECHAM6 (Stevens et al., 2013) coupled to the aerosol module HAM (Stier et al., 2005; Zhang et al., 2012) in a simplified version. Two sets of ensemble sensitivity simulations are performed: One with radiatively active dust and one with inactive dust. Using seasonal ensemble means, we evaluate dust-induced mechanisms associated with the SAL as proposed by Dunion and Velden(2004) and Braun(2010). A “box difference index” (BDI, Peng et al.(2012)) is used to detect a possible influence of dust on hurricanes and to compare the importance of dust in hurricane genesis to other variables such as wind shear and vorticity. With this set-up, we investigate which role Saharan dust plays during hurricane genesis when interacting with incoming solar 6.3. Method 73 radiation and how large its impact is. The mechanisms suggested by Dunion and Velden(2004) and

Braun(2010) on the relationship between dust and hurricane intensity and genesis are tested in our sensitivity simulations.

6.3 Method

6.3.1 Model and simulations

We use a simpliﬁed version of the aerosol-climate model ECHAM6-HAM (Zhang et al., 2012; Stevens et al., 2013) in a spectral resolution of T255 on a quadratic Gaussian grid (approximately 0.5°×0.5°) with

31 σ-pressure levels reaching up to 10 hPa and a one-moment microphysical cloud convection scheme. The horizontal resolution enables us to obtain a realistic number of storms, but intense hurricanes exceeding category 1 on the Saffir/Simpson-hurricane scale (Simpson and Saffir, 1974), using minimum sea level pressure as categorization criterion, are hardly simulated (Murakami and Sugi, 2010; Raible et al., 2012; Murakami et al., 2012a; Strachan et al., 2013). In the model, which is simplified for this study due to computational constraints, dust is the only interactive aerosol. All remaining aerosol species use climatological values.

The particle size distribution of interactive aerosols in HAM is represented by a superposition of log- normal modes, where dust is conﬁned to the accumulation and coarse modes. Dust emissions in HAM are governed by wind speed and hydrological parameters (Tegen et al., 2002), while removal processes include sedimentation and dry and wet deposition (Stier et al., 2005). In the standard ECHAM6-HAM model, wet deposition of dust particles includes coating and coagulation processes with sulfates. In our simpliﬁed ECHAM6-HAM sulfate is not calculated interactively and thus does not interact with dust.

Therefore the scavenging parameters, which govern removal processes of dust particles, are adjusted to adequately simulate North Atlantic dust concentrations. The complex refractive index of dust in HAM is

1.52+1.1×10−3 i at 550 nm, following Kinne et al.(2003). Shallow convection in ECHAM6 is treated following the Tiedtke scheme (Tiedtke, 1989), while deep convection includes changes introduced by

Nordeng(1994). The simulated atmospheric state of ECHAM6 was thoroughly evaluated with ERA-

Interim reanalyses (Stevens et al., 2013).

SSTs are prescribed as climatological monthly averages. This implies that the direct radiative effects of dust are simulated with a non-interactive ocean surface. Previous studies determined some dust-induced decrease of North Atlantic SST (Lau and Kim, 2007a,b; Martínez Avellaneda et al., 2010), but it may be 74 Chapter 6. The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis small (Foltz and McPhaden, 2008). With our approach of ﬁxed SSTs, we try to isolate the atmospheric responses of the radiative effects of dust as proposed by Dunion and Velden(2004).

Two sets of simulations are performed: The ﬁrst set consists of 10 free ensemble simulations of the year 2005 with dust being radiatively active (ECHAM6-Dust). Differences in initial conditions are obtained by minimal perturbations of stratospheric horizontal diffusion (Vamborg et al., 2014). The second set of simulations uses the same set-up, but with the interaction between dust and radiation switched off (ECHAM6-no-dust). Table 6.1 shows the types of ECHAM6 used in this study, if the simpliﬁed

HAM is used and how radiative effects of dust are treated in the different simulations. The year 2005 is selected because its North Atlantic hurricane season was the most active ever recorded (Beven et al.,

2008), with SSTs more than 1°C above the long-term mean (1950-2000) in the main development region

(Sun et al., 2008). With the above-average SSTs of 2005 we expect a larger number of hurricanes than we would obtain for other years, generating a larger database to detect possible changes in hurricane activity between ECHAM6-Dust and ECHAM6-no-dust simulations. Because we use prescribed SSTs only the direct influence of dust leading to a warming, drying and a subsequent stabilization of the atmosphere are considered in this study. AEWs peak during the summer months, therefore only the first four months of the North Atlantic hurricane season, June-September, are taken into account in our study. For the analysis we use 6-hourly model output data. The microphysical influence of dust aerosols acting as cloud condensation nuclei or ice nuclei and thus modulating hurricane activity (e.g. Zhang et al.(2007);

Rosenfeld et al.(2011, 2012)) is not included in our model set-up.

Radiative effects of dust Version of HAM ECHAM6-Dust interactive (active dust) simpliﬁed ECHAM6-no-dust none (inactive dust) simpliﬁed ECHAM6 climatological not included

Table 6.1: Radiative effects of dust and usage of HAM in different versions of ECHAM6 used in this study.

6.3.2 Seasonal differences between active and passive-dust simulations

Dunion and Velden(2004) named three mechanisms which explain the inhibiting effect of dust and the

SAL on hurricane intensity (Section 6.2). We examine these three mechanisms also for tropical cyclogenesis in our model by looking at possible changes in seasonal ensemble averages (June-September) in temperature, relative humidity, and wind shear between ECHAM6-Dust and ECHAM6-no-dust simulations. Braun(2010) described a vertical circulation associated with the AEJ, which conﬁnes convection to the south where vertical wind shear is weak. Hence, we also investigate dust-induced changes in 6.3. Method 75 seasonal ensemble averages of zonal and meridional wind and 700-hPa vertical velocity.

6.3.3 Tracking

We track disturbances which developed into a TC (developing disturbances, DDs) and those which did not develop (non-developing disturbances, NDDs). The DDs are tracked using the detection and tracking criteria from Kleppek et al.(2008) and Raible et al.(2012): A local minimum in sea level pressure (SLP), relative vorticity at 850 hPa exceeding 5×10−5 s−1, vertical wind shear between 200 and 850 hPa below

15 m s−1 and a minimum lifetime criterion. The threshold value of wind shear is set to ≤15 m s−1 instead of the original ≤10 m s−1 to detect storms at an earlier stage. The vorticity threshold is tested in simulations of the years 2005 and 2006, nudged to ERA-Interim reanalyses (Table 6.2). Although other studies used also higher vorticity threshold values (Bengtsson et al., 2007a,b), 5×10−5 s−1 is the optimal threshold tested to detect and track hurricanes in our simulations. An additional SST-criterion is added, tracking only storms over areas with SSTs ≥25°C on their initial time step (Dare and McBride, 2011).

We furthermore reduced the minimum lifetime criterion to ≥18 hours. This threshold is a bit lower than in comparable studies (Bengtsson et al., 2007b; Kleppek et al., 2008; Murakami et al., 2013), but is required to counteract the too low number of DDs in the North Atlantic in ECHAM6 with minimum lifetimes, e.g. 24 or 36 hours.

2005 Observed ζ : 3×10−5 ζ : 5×10−5 ζ : 10×10−5 Hurricanes 13 13 13 12 Tropical storms 11 2 2 1 Additional tracks - 9 4 1 2006 Hurricanes 5 4 4 4 Tropical storms 5 3 3 1 Additional tracks - 6 3 0

Table 6.2: Number of storm tracks in observations and simulations of 2005 and 2006, nudged towards ERA-Interim reanalysis data (June-November). “ζ” denotes the vorticity threshold of the simulations (s−1). “Tropical storms” shows only storms which did not reach hurricane intensity. “Additional tracks” refer to detected systems that were not categorized as a hurricane or tropical storm by the National Hurricane Center (NHC).

To detect NDDs, the tracking algorithm of Kleppek et al.(2008) is modiﬁed. NDDs are tracked as local maxima of 850-hPa relative vorticity. Three criteria, similar to the NDD-criteria of Murakami et al.

(2013), need to be fulﬁlled with this new vorticity tracking method: 1) The magnitude of the maximum

850-hPa vorticity needs to exceed 4 × 10−5 s−1; 2) To obtain NDDs with a certain size, the averaged

850-hPa vorticity in a 5°×5°-box around its center needs to exceed 2.5 × 10−5 s−1; 3) The minimum 76 Chapter 6. The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis lifetime of a detected system is 66 hours. The latter is just slightly below the NDD-lifetime-criterion of

72 hours used by Peng et al.(2012). With these settings we tried to achieve a ratio of roughly 1 to 2 between DDs and NDDs, which is similar to the ratios found in Peng et al.(2012) and Murakami et al.

(2013).

6.3.4 Composites and box difference indices

The BDI is an index designed to determine the ability of a given variable to control TC genesis (Peng et al., 2012). For a given box size the BDI is deﬁned as

M − M BDI = DD NDD σDD + σNDD where the indices DD and NDD depict composites of DDs and NDDs, respectively. M and σ denote the mean and standard deviation of the respective composites of a variable of interest. The sign of the BDI indicates a correlation (+) or anti-correlation (-).

Using this technique, various variables can be compared with each other in terms of their importance as controlling variables for TCs. In observational studies, Peng et al.(2012) used the BDI to identify controlling parameters for hurricane genesis in the North Atlantic while Fu et al.(2012) examined TCs in the western North Paciﬁc. Murakami et al.(2013) applied the BDI on TC genesis in a global warming scenario compared to a present-day climate. In this study, the BDI is also applied on dust. As the average dust loading over the North Atlantic signiﬁcantly decreases towards the west, we only consider disturbances between 20°W and 50°W. This excludes disturbances in regions with dust burdens too low to have an impact on hurricane genesis.

In this study composites are made for DDs and NDDs in a 5°×5°-box in longitude and latitude. The box moves with the disturbances so that the storm center is always in the middle. The NDD-composites consist of 6-hourly output of a NDD once it is located inside our deﬁned region between 20°W and

50°W. With this, we use characteristic conditions of NDDs which inhibit development during their whole lifetime. Detected DDs are traced back according to their 850-hPa relative vorticity centers. For the DD- composites, including time steps at the stage of a TC would lead to an ordinary comparison between hurricanes and weaker tropical storms, losing the crucial development stage of hurricanes. However, the aim is to examine DDs at a stage where they do not yet show hurricane-like features but are in a transition phase when environmental conditions become more conducive for hurricane development. For this reason, we present only results for the time step 24 hours prior to their detection as TCs. 6.4. Results 77

In our ensemble sensitivity simulations the following variables are investigated: Dust burden, SST, relative humidity in typical SAL-heights of 850 and 700 hPa, maximum relative vorticity in 700 hPa and vertical wind shear between 200 and 850 hPa. As the 850 hPa level is close to the bottom of the

SAL, low-to midlevel winds may be underestimated (Dunion and Velden, 2004). Hence, our lower tropospheric winds in 850 hPa denote the mean of the 700-925 hPa-layer. As mentioned above, SST, vorticity and wind shear are known to be decisive factors for TC formation.

6.4 Results

6.4.1 Limitations due to spatial resolution

The simulation of realistic tropical cyclones requires an adequate spatial resolution. While to date the inﬂuence of vertical resolution on TCs is hardly discussed in literature, the effect of horizontal resolution has been subject to several studies, e.g. Bengtsson et al.(2007b); Murakami and Sugi(2010); Strachan et al.(2013). Our horizontal resolution of around 52 km enables us to realistically simulate the frequency of tropical storms (Strachan et al., 2013), but not with the observed intensity (Table 6.3).

Tropical Storms Hurricanes Major Hurricanes Observations (1851-2010) 6.5 4.0 1.5 Observations (1981-2010) 9.0 4.7 2.2 Observations (2005) 17 10 5 Simulations (20 × 2005) 7.6 0.9 0.1

Table 6.3: Numbers of the on average detected North Atlantic storms in NHC-observations and ensemble simulations (ECHAM6-Dust and ECHAM6-no-dust) between June and September. “Tropical storms” includes all named tropical systems (simulations: all tracks), “Hurricanes” all storms reaching hurricane intensity, “Major Hurricanes” all hurricanes of categories 3, 4 and 5.

Within 20 ensemble simulations (June-September), only two intense hurricanes are generated with minimum SLPs of 948 and 962 hPa, located in the Gulf of Mexico. All remaining storms in the North

Atlantic did not undershoot 980 hPa. The most intense storm forming in our deﬁned region between

20°W and 50°W only reached 1000 hPa (Fig. 6.1). Furthermore, the maximum wind is found in a height of 550 hPa, which is in much higher altitudes than in reality (Nolan et al., 2009). Two timesteps after the minimum SLP was obtained the storm was not detected any more due to a slight increase in wind shear.

In a recent study, Lim et al.(2014) suggested that a resolution of 0.5° is not suitable to investigate TCs in a global framework, but a grid of 0.25° or smaller is needed. Zarzycki and Jablonowski(2014) used a 1°-resolution for simulations with the Community Atmosphere Model and embedded a 0.25°-grid for 78 Chapter 6. The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis the region around a TC once it is detected during the Atlantic hurricane season (June-November). They found TC count, spatial distribution and tracks to resemble observations, with intensities of up to 80 m s−1. The low intensity of our hurricanes is a limitation of our results that needs to be kept in mind.

Figure 6.1: Depiction of the most intense storm within our 20 ensemble simulations originating in our deﬁned region between 20°W-50°W. a) Wind speed at 850 hPa and sea level pressure during its most intense phase, b) Zonal cross-section of the storm with horizontal wind speed (shading), temperature in °C (black lines) and vorticity in 0.5, 2 and 4×10−4 s−1 (red lines) during the same timestep as in a), c) Evolution of minimum SLP and 850 hPa wind speed of the storm during its whole lifetime. The black box shows the time of the storm in a) and b), 12 hours after reaching its maximum intensity it was not detected any more.

6.4.2 Simulated and observed dust

For validation of atmospheric dust loadings, the mean aerosol optical depth (AOD) is compared to observations. For the simulations, we took mean values of the ten free ensemble simulations of 2005 with ECHAM6-Dust. AOD observations were obtained using the MODerate resolution Imaging Spec- troradiometer (MODIS) on the Terra spacecraft (Savtchenko et al., 2004). For both simulations and observational data, average values of June-September 2005 were taken. The model was not sampled along the Terra track. Hence, the simulation mean consists of all model time steps, while MODIS observations take into account solely data points of Terra overﬂights. In ECHAM6-Dust simulations the

AOD is slightly lower than in observations (Fig. 6.2). As simulated dust emissions are highly sensitive to wind speed and aerosol removal processes, differences are likely due to them. The simulated decrease 6.4. Results 79

Figure 6.2: June to September (2005) mean aerosol optical depth at 550 nm with a) MODIS Terra observations and b) Average of 10 ensemble simulations with ECHAM6-Dust. in AOD towards the west is similar to what MODIS observed. Hence, the adjustment of our scavenging parameters for wet deposition in our simpliﬁed ECHAM6-HAM seems justiﬁed. Further comparisons of simulated AOD and dust concentrations in ECHAM6-HAM to observations were given by Stanelle et al.

(2014).

6.4.3 Mean background climate during DDs and NDDs

NDDs in ECHAM6-Dust and ECHAM6-no-dust simulations are located mainly between 12.5°N-17.5°N

(hereafter termed the NDD-region), while DDs are located primarily between 7.5°N-12.5°N (hereafter termed the DD-region, see Fig. 6.3). For the discussion of Figs. 6.4 to 6.7, we deﬁne the SAL as regions where the dust mixing ratio exceeds 20 ng kg−1, averaged over 5° in latitude. The results do not depend crucially on this threshold. The main NDD-region coincides with the main part of the SAL over the tropical North Atlantic. The SAL extends up to around 500 hPa at 20°W (Fig. 6.4a) with peak dust con- 80 Chapter 6. The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis

Figure 6.3: June to September mean of a) dust burden and b) SST in the simulations with ECHAM6-Dust. Black crosses show the DDs in (a) ECHAM6-Dust and (b) ECHAM6-no-dust 24 hours prior to detection. Grey crosses denote NDDs every 6 hours in (a) ECHAM6-Dust and (b) ECHAM6- no-dust. Dark red lines denote our deﬁned region between 20°W-50°W. centrations between 800-900 hPa. The SAL in ECHAM6 becomes thinner to the west with the top height decreasing and base slightly increasing resembling the observations of Prospero and Carlson(1981). Due to absorption of solar radiation by dust, the atmosphere shows a statistically signiﬁcant warming in the lower part of the SAL (roughly 950-850 hPa, Fig. 6.5a) between 20°W and 50°W, and averaged over

12.5°N-17.5°N when comparing ECHAM6-Dust with ECHAM6-no-dust simulations. This warming increases the stability in this region and subsequently reduces the probability of storms forming during dust outbreaks. The same warming pattern is found by Reale et al.(2014) and is probably caused by the de- 6.4. Results 81

Figure 6.4: Ensemble mean dust mixing ratio of the ECHAM6-Dust simulations (June-September) meridionally averaged between a) 12.5°N-17.5°N and b) 7.5°N-12.5°N. crease in dust concentration with altitude and the dependence of absorption on particle size as larger dust particles absorb more solar radiation than smaller ones (Tegen and Lacis, 1996; Helmert et al., 2007).

The settling of larger particles leaves mainly smaller particles within the upper parts of the SAL, which are too small to cause a warming. However, the warming depends on the dust particle size distribution, optical properties and removal parameterization in the model. On the contrary, the upper part of the SAL shows a slight cooling. We hypothesize the conservation of radiative equilibrium to be responsible for this cooling. However, as in comparable studies (Wong et al., 2009; Wilcox et al., 2010; Reale et al.,

2014) the exact mechanism of the cooling above the dust-induced warming could not be determined.

Concomitant with the increase in temperature, the relative humidity shows a signiﬁcant decrease within

Figure 6.5: Ensemble-mean temperature differences between ECHAM6-Dust and ECHAM6-no-dust simulations (June-September) meridionally averaged between a) 12.5°N-17.5°N and b) 7.5°N- 12.5°N. Lined shading denotes statistical signiﬁcant changes at the 5%-level (two side t-test). 82 Chapter 6. The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis

Figure 6.6: As Fig. 6.5, but for relative humidity. the lower SAL (Fig. 6.6a).

In the main DD-region (Fig. 6.4b) in lower latitudes, dust concentrations are not as high as in the

NDD-region between 12.5°N-17.5°N. Hence, the warming is smaller (Fig. 6.5b) but also reaches slightly higher altitudes than in the higher latitudes of the NDD-region with only a few areas showing a statistically signiﬁcant warming. Nevertheless, this small warming could still inhibit convection when dust is radiatively active because the atmosphere is also slightly drier than in ECHAM6-Dust simulations

(Fig. 6.6b). However, dust concentrations in the DD-region are lower than in the NDD-region. Thus, one cannot attribute dust the same impact in the DD-region compared to the NDD-region. Neglecting other factors as SST and vertical wind shear which are known to have a large inﬂuence on hurricane genesis

(Dare and McBride, 2011; Molinari et al., 2004), the potential inﬂuence of dust seems to be larger in the

NDD-region than in the DD-region.

As seen before, the variations in temperature between ECHAM6-Dust and ECHAM6-no-dust simulations cause a more stable low-level SAL at 12.5°N-17.5°N in ECHAM6-Dust simulations which subsequently cause differences in air motions. To illustrate this the meridional wind averaged over the

NDD-region shows a low-level southward flow between approximately 20°W-40°W, while mid-levels reveal northward flow (Fig. 6.7a). This is in agreement with a vertical circulation associated with the SAL previously mentioned by Braun(2010). This vertical circulation indicates southward flow below the AEJ and northward flow above with upward vertical motions on its southern edge and sinking on its northern edge. The dust-induced warming in the ECHAM6-Dust simulations causes both statistically significant increases in low-level southward and mid-level northward flow between 15°W and 30°W (Fig. 6.7b) intensifying the meridional wind compared with ECHAM6-no-dust simulations. This is accompanied by 6.4. Results 83

Figure 6.7: Ensemble mean meridional wind between 12.5°N-17.5°N (June-September): a) simulations with ECHAM6-no-dust and b) difference between ECHAM6-Dust and ECHAM6-no-dust simulations. Thick contours in b) denote dust mixing ratio in ECHAM6-Dust, intervals of 25 ng kg−1. The black arrow depicts the schematic position of the African easterly jet, lined shading denotes statistical signiﬁcant changes at the 5%-level (two side t-test).

Figure 6.8: As for Fig. 6.7, but for zonal wind between 20°W-40°W (June-September). The black circle depicts the schematic position of the African easterly jet in our simulation. a statistically significant increase of the westward flow of the AEJ south of the main dust region within the North Atlantic SAL and a slight decrease of this flow to the north (Fig. 6.8).

Due to the increase in low-level southward meridional wind, convective regions are partly deﬂected.

The vertical velocity ω in the ECHAM6-no-dust simulations shows a broad region with upward motions around 10°N (Fig. 6.9a), depicting the intertropical convergence zone (ITCZ). Although there are statistically signiﬁcant changes in upward vertical velocity which result in a southward shift of convection 84 Chapter 6. The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis

Figure 6.9: Ensemble mean vertical velocity in 700 hPa (June-September), a) ECHAM6-no-dust and b) difference between ECHAM6-Dust and ECHAM6-no-dust simulations. The red arrow depicts the schematic position of the African easterly jet in our simulations, lined shading denotes statistical signiﬁcant changes at the 5%-level (two side t-test).

(Fig. 6.9b), these changes are rather small. This shift is opposite to both observational (Wilcox et al.,

2010) and modeling studies (Reale et al., 2011; Woodage and Woodward, 2014), hence it may be rather an artifact of our model resolution than an effect of dust aerosols. Vertical shear of zonal winds decreases towards the south between approximately 10°N-18°N (Fig. 6.10a), supporting hurricane genesis in lower latitudes. However, the vertical wind shear is higher in the tropical North Atlantic in ECHAM6-Dust simulations (Fig. 6.10b). A moderate wind shear of up to 10 m s−1 is suggested to support TC genesis

(Bracken and Bosart, 2000; Molinari et al., 2004). Thus it depends on the magnitude of the background wind shear whether the dust-induced increase in wind shear lowers or raises the potential of hurricane genesis.

Figure 6.10: As in Fig. 6.9, but for vertical wind shear between 200 and 850 hPa. a) The dark blue dot depicts the average location of DDs in ECHAM6-no-dust, the dark green dot for NDDs, both with corresponding standard deviations. b) Dark blue and green dots as in a), light blue dot shows the average location of DDs in ECHAM6-Dust, light green dot for NDDs. 6.4. Results 85

6.4.4 Frequency of disturbances

Between 20°W and 50°W during June to September 2005, three tropical depressions were observed which developed into a hurricane. Fostered by the above-average SSTs in the North Atlantic main development region (10°N-20°N, 20°W-80°W, Sun et al.(2008)), this is higher than the long-term average of the National Hurricane Center HURDAT database (1851-2009) of 1.3 hurricanes per season

(June-September) in this region. A non-signiﬁcant change in the frequency of DDs is found between

ECHAM6-no-dust and ECHAM6-Dust simulations (1.9 vs. 1.5 per season, Table 6.4). Compared to the observations of 2005, we underestimate the number of hurricanes/developing disturbances per season in ECHAM6-Dust and ECHAM6-no-dust simulations. The underestimation of hurricanes in the North

Atlantic was also previously found for ECHAM5 (Bengtsson et al., 2007a).

# Developing # Non-developing ECHAM6-Dust 1.5 ± 1.4 3.3 ± 1.6 ECHAM6-no-dust 1.9 ± 1.4 4.2 ± 2.0

Table 6.4: Numbers of the on average detected disturbances in both sets of simulations and corresponding standard deviations between June and September. Only disturbances within the region between 20°W-50°W were taken into account.

However, the frequency of DDs strongly depends on the lifetime criterion of the tracking algorithm.

With 24 instead of 18 hours as the minimum lifetime, an increase in frequency is found between ECHAM6- no-dust and ECHAM6-Dust simulations (1.0 vs. 1.4 per season), while with 6 and 36 hours we simulate a similar frequency of DDs. Hence we cannot determine a robust dependence of DD-frequency on the radiative properties of dust.

The number of NDDs between 20°W and 50°W per season is 4.2 for ECHAM6-no-dust and 3.3 for

ECHAM6-Dust. This represents a decrease of around 25%, which is largely independent of the lifetime of the NDDs, but this decrease is not statistically signiﬁcant.

6.4.5 Composites

DDs and NDDs in ECHAM6-Dust (Fig. 6.3a) and ECHAM6-no-dust (Fig. 6.3b) are characterized with a composite analysis of key variables for hurricane development. For this purpose, changes of the mean values between ECHAM6-no-dust and ECHAM6-Dust simulations are determined. Note that the DDs are located 4°-5° southward of the NDDs (Table 6.5). For most variables, differences between DDs and

NDDs are therefore primarily due to different locations. This includes, e.g., SST, which increases considerably between 10°N and 20°N when going from North to South, while wind shear shows a decrease. 86 Chapter 6. The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis

In addition, dust concentrations decrease substantially between main NDD- and DD-regions.

In ECHAM6-no-dust, the mean dust burden for the DDs is around 170 mg m−2, this amount triples for the NDDs (Fig. 6.11a). The average dust burden close to the West African coastline at around 15°N to 18°N decreases rapidly west- and southward (Fig. 6.3a). Therefore, a lower dust burden in DDs compared with NDDs is accompanied by warmer SSTs (Fig. 6.3b). The SSTs are 1.5°C higher for DDs than for NDDs (Fig. 6.11b). Background relative humidity between 12.5°N-17.5°N (Fig. 6.6a) shows an opposite development between ECHAM6-no-dust and ECHAM6-Dust in low- and mid-levels, hence composites are evaluated for two levels (Fig. 6.11c,d). At 850 (700) hPa, the mean RH is 86% (82%) for the DDs and 59% (61%) for the NDDs. This is consistent with the background mean RH, which decreases between 850 and 700 hPa in the main DD-region between 7.5°N-12.5°N but partly increases

Figure 6.11: Composites of variables for DDs and NDDs for a 5°×5°-box. Red dots show values for the simulations with ECHAM6-no-dust, blue dots for ECHAM6-Dust. Error bars indicate one standard deviation in each case. Straight diagonal shading denotes statistical signiﬁcant changes between ECHAM6-no-dust and ECHAM6-Dust at the 5%-level and dashed diagonal shading at the 10%-level (two-sided t-test). 6.4. Results 87 between 12.5°N-17.5°N (not shown). Vertical shear in the eastern tropical North Atlantic decreases towards the south, hence mean vertical shear between 200-850 hPa is 7 m s−1 for DDs and around 10 m s−1 for NDDs (Fig. 6.11e). The maximum 700 hPa-relative vorticity is slightly lower for NDDs than for

DDs (7.6 vs. 8.7 s−1, Fig. 6.11f).

ECHAM6-Dust ECHAM6-no-dust Longitudes (DDs) -30.9° ± 6.8° -32.5° ± 8.0° Longitudes (NDDs) -28.5° ± 8.1° -27.2° ± 6.9° Latitudes (DDs) 9.8° ± 1.8° 10.8° ± 2.4° Latitudes (NDDs) 14.7° ± 2.2° 15.0° ± 1.9°

Table 6.5: Average locations of DDs and NDDs in both sets of simulations and corresponding standard deviations.

In ECHAM6-Dust, developing disturbances are located 1° southward of the DDs in ECHAM6-no- dust (Table 6.5). This southward shift matches the southward shift in convection mentioned in Section

6.4.3, but is not statistically significant. While NDDs are only deflected 0.3° to the south, the southward shift of DDs has implications on composite averages. Between ECHAM6-no-dust and ECHAM6-Dust the mean dust burden decreases in DDs with changes being significant at the 10% level (two-side t-test).

For NDDs, dust burden is significantly increased at the 5% level. As the location of the boxes around the disturbances varies from case to case, different composite values for SSTs are possible despite the fact that we use fixed SSTs. The mean SST of the composites is 0.2°C higher at the 10% significance level due to the southward shift of the DDs in ECHAM6-Dust. For NDDs, mean SSTs are larger by

0.5°C with signiﬁcance at the 5% level. The reason for this is the temporal displacement of the NDDs.

Figure 6.12: Number of NDD-time steps per month and simulation within 20°W-50°W. Standard deviations are omitted below 0. 88 Chapter 6. The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis

In our deﬁned region, SSTs rise between June and September, but the majority of NDD-time steps which contribute to the BDI occur earlier in ECHAM6-no-dust than in the ECHAM6-Dust simulations

(Fig. 6.12). Hence, NDDs in ECHAM6-no-dust have lower SSTs than those in ECHAM6-Dust. As monthly mean locations between ECHAM6-no-dust and ECHAM6-Dust simulations are about the same, on average NDDs in ECHAM6-Dust need warmer waters to compensate for the increased stability in these simulations. Similar results are found for the mean relative humidity at 700 and 850 hPa. While there is an increase in mean relative humidity in DDs due to the southward shift of DDs in ECHAM6-

Dust, the increase in humidity of NDDs is caused by the temporal displacement of the NDDs as described above. Changes in humidity at 700 hPa are significant at the 5% (NDDs) and 10% (DDs) levels of confidence, but no statistical significant changes can be identified for 850 hPa. Mean vertical shear and maximum relative vorticity show only minor variations between ECHAM6-no-dust and ECHAM6-Dust simulations with no statistical significance.

6.4.6 BDIs

For determining the inﬂuence of dust on hurricane genesis we investigate differences in BDIs between

ECHAM6-no-dust and ECHAM6-Dust. To quantify the differences in the DD- and NDD-composite means, the BDI is calculated for ﬁve variables for the 5°×5°-box (Fig. 6.13). For the dust burden, the

BDI varies only slightly between ECHAM6-no-dust and ECHAM6-Dust (-0.51 to -0.57). This indicates that the presence of dust in and around disturbances does not vary substantially when dust is radiatively active or inactive. For SST, the BDI decreases slightly from 1.09 to 1.02.

Figure 6.13: BDIs of evaluated variables for the 5°×5°-box for simulations with ECHAM6-no-dust and ECHAM6-Dust. (+) and (-) denote the sign of the variable’s BDI. 6.5. Discussion and conclusions 89

Relative humidity shows an increase in BDI between ECHAM6-no-dust and ECHAM6-Dust simulations from 0.68 to 0.88 at 700 hPa and from 0.87 to 1.03 at 850 hPa. These increases are both caused by the further southward located DDs in ECHAM6-Dust. The standard deviations of these DDs are smaller than in ECHAM6-no-dust. Due to the only minor and non-signiﬁcant changes in wind shear in the composite means, vertical shear between 200 and 850 hPa reveals only small differences in BDIs of -0.36 to

-0.41. The BDI of the maximum vorticity in 700 hPa remains almost constant between ECHAM6-no- dust (0.16) and ECHAM6-Dust (0.17).

Generally, the BDIs of all variables do not differ substantially between ECHAM6-no-dust and ECHAM6-

Dust simulations. However, one has to keep in mind that only direct aerosol effects are considered.

According to our results, controlling parameters for hurricane genesis do not depend crucially on dust.

As the BDIs hardly differ between ECHAM6-no-dust and ECHAM6-Dust, the presence of dust in and around the disturbances is not a sufﬁcient criterion to determine an inﬂuence of dust on hurricane genesis.

With our model we can reproduce the results obtained by Peng et al.(2012) who found that thermodynamic variables are the more important controlling parameters than dynamic variables.

6.5 Discussion and conclusions

Previous studies showed inhibiting impacts of dust and the SAL on hurricane activity (Dunion and

Velden, 2004; Lau and Kim, 2007a; Reale et al., 2014), while others found opposite effects (Karyampudi and Carlson, 1988; Braun, 2010). Our study conﬁrms the complexity of this subject. Radiatively active dust causes several effects in background conditions: (1) A low-level warming with a cooling above in the central SAL-region, in agreement with Wong et al.(2009); Wilcox et al.(2010) and Reale et al.

(2014), (2) a drying of the atmosphere in this region, (3) an increase in vertical shear in large parts of the tropical North Atlantic and (4) a strengthening of the vertical circulation associated with the AEJ. Results

(1)-(3) confirm the findings of Dunion and Velden(2004) and (4) those of Braun(2010). There is also a southward shift of convection within the ITCZ, but this shift is only small. Developing disturbances are shifted to the south by 1°, which is not statistically significant. For both types of disturbances no significant change in frequency can be determined. Changes in environmental conditions also depend on the optical properties of dust. An increase in dust absorption in the ECHAM6-Dust simulations would lead to a further warming of the SAL and stabilization of the atmosphere. Hence, subsequent increases in meridional and vertical motions presumably lead to an enhanced vertical circulation around the SAL. 90 Chapter 6. The influence of absorbed solar radiation by Saharan dust on hurricane genesis

On average, our DDs are located 4°-5° in latitude southward of the NDDs, due to the southward increasing SST west off the North African coastline. In addition the northward increasing shear of 0 to

15 m s−1 between 10°N and 20°N is a crucial inﬂuencing factor for hurricane formation depending on its location. The large difference in dust burden between DDs and NDDs thus coincides with different average locations as the DDs are located mainly at the southern edge of the SAL.

We detect statistically signiﬁcant changes in dust burden-composites between ECHAM6-no-dust and

ECHAM6-Dust simulations such that dust burden decreases for DDs in ECHAM6-Dust but increases for NDDs. However, neither shear nor vorticity reveal significant changes between ECHAM6-no-dust and ECHAM6-Dust. Accounting for the limitations in spatial resolution in our setup, this indicates that radiatively active dust does not have a significant impact on dynamical hurricane-influential parameters during hurricane genesis. Changes in relative humidity also coincide with the variation in DD- and

NDD-locations.

Variations in BDIs between ECHAM6-no-dust and ECHAM6-Dust do not show a pattern which could assign dust a crucial role. However, dust has a modulating effect due to changes in atmospheric stability as discussed above. In the study of Peng et al.(2012), thermodynamic variables (water vapor content, rain rate and SST) have larger BDIs than dynamic variables (vorticity, vertical wind shear and divergence) and are thus more important as hurricane genesis controlling variables. Even though we do not use all of these variables, we can reproduce this result with our model. A quantitative comparison of the BDIs with the variables from Peng et al.(2012) and ours is not feasible because of the different approaches of defining DDs and NDDs in the two studies. Although we also calculated BDIs for the 20°×20°-boxes as presented by Peng et al.(2012), uncertainties arise from our model resolution of 0.5°×0.5°, which lead to a simulation of only low-intensity hurricanes that do not exceed category 1 on the Saffir/Simpson- hurricane scale (Simpson and Saffir, 1974). Additionally, our area of interest is different from Peng et al.

(2012) (20°W-50°W vs. 10°E-100°W in Peng et al.(2012)), which causes further inconsistencies.

One has to keep in mind that our findings are statistical results and do not exclude a potentially larger effect of dust and the SAL on individual storms. To assign (non-)genesis and intensification of certain storms to the presence of dust and the SAL, these storms need to be examined carefully and individually as done by Karyampudi and Pierce(2002). Furthermore, the too low spatial resolution in our simulations is a considerable constraint of our study. The results presented here only refer to systems of tropical storm intensity. However, the numbers in storms obtained by our TC-tracking algorithm are similar to long- term observations. Hence we believe our results to be valid for a discussion about hurricane genesis and 6.6. Acknowledgments 91 frequency. To evaluate intense storms as well, our method needs to be applied with significantly higher horizontal resolutions in a global or regional model. Besides resolution the second limitation of our study is the lack of atmosphere-ocean interactions in our model setup. We focused on the mechanisms suggested by Dunion and Velden(2004), but did not find any significant influence of dust on hurricane genesis in our simulations. Using prescribed SSTs instead of interactive SSTs may lead to variations in regional circulation (Miller et al., 2004). Hence for future studies we strongly recommend a coupled atmosphere-ocean model.

6.6 Acknowledgments

This work was supported by ETH Research Grant ETH-05 10-3 and by the Karin and Oskar Müller

Fund of the ETH Zurich Foundation. The authors would like to thank Dr. Bing Fu and Dr. Hiroyuki

Murakami from the International Paciﬁc Research Center in the School of Ocean and Earth Science and Technology, University of Hawaii, for their assistance in realizing the method of the box difference index in this study. Furthermore, we thank the editor Steve Ghan and three anonymous reviewers for their valuable suggestions. The output of the ECHAM6-Dust and ECHAM6-no-dust simulations can be obtained by sending a written request to [email protected]. 92 Chapter 7

Summary and outlook

7.1 Summary

This thesis investigates the radiative inﬂuence of Saharan dust on North Atlantic tropical cyclogenesis

(TCs). To accomplish this task, a number of simulations with different models were performed. This comprises a pre-study with steady-state tropical cyclones, sensitivity simulations during the simplification of ECHAM6-HAM, nudged simulations for the calibration of a TC-tracking algorithm and ensemble simulations with the simplified ECHAM6-HAM model to analyze the influence of dust on hurricane genesis and frequency over the North Atlantic.

1. Using the axisymmetric hurricane-model of Rotunno and Emanuel(1987) the role of the initial moisture content on the development of idealized steady-state TCs is investigated. To mimic the dry conditions in the dust-laden Saharan Air Layer (SAL) coming from the desert regions of North Africa, the evolution of the steady-state vortices is analyzed in four different settings, varying initial wind speed, radius of maximum and zero winds, and sea surface temperature. For each setting, simulations are conducted with initial profiles of a typical moist tropical sounding, a sounding in SAL conditions and a hypothetical dry sounding. It takes the fewest time for the vortex to form with the moist profile in every case. Additionally, while intensification with the SAL-profile occurs around three days later than with the moist profile, it occurs only 2 days later if the diameter of a storm is doubled. If the diameter is kept constant and SST is increased by two °C, intensification occurs only one day later. Hence increasing storm size and SSTs signify decreasing influence of dry SAL-air.

2. To analyze the inﬂuence of dust on hurricane activity efﬁciently, the aerosol-climate model ECHAM6-

HAM is simplified. With this simplification, the required computational time is reduced to almost half of the amount needed when running the full ECHAM6-HAM. In this new model, dust remains the only interactive aerosol species, all other aerosol species are prescribed. To mimic coating and coagulation processes between dust and sulphate in the simplified ECHAM6-HAM adequately, low-resolution (T63)

93 94 Chapter 7. Summary and outlook sensitivity simulations with the simpliﬁed ECHAM6-HAM were performed. For this, the scavenging ratios, which govern the wet deposition of dust, are varied. The scavenging ratios of the insoluble modes dust is emitted into yield dust distributions closest to ECHAM6-HAM when they are in the middle between the original values and the values of the soluble modes. Hence, despite aerosol processes being not present in the simpliﬁed ECHAM6-HAM its dust burden over the North Atlantic is simulated reasonably well.

3. The TC-tracking algorithm developed by Kleppek et al.(2008) and Raible et al.(2012) contains a couple of criteria with variable thresholds. To optimize these thresholds for our simulations, various threshold values of vertical wind shear and relative vorticity are tested with ECHAM6 in nudged simulations for the years 2005 and 2006 for the North Atlantic. For vertical wind shear between 200 and

850 hPa, an optimal threshold of ≤ 15 m s−1 is found, which is 5 m s−1 larger than the threshold used by Kleppek et al.(2008) and Raible et al.(2012). With 15 m s −1 the length of the tracks is captured better than with 10 m s−1. For relative vorticity, 5×10−5 s−1 seems to be the most adequate, this is the same threshold previously used. Smaller vorticity thresholds occasionally allow unrealistic tracks due to varying centers of weak storms while with larger thresholds too few hurricanes were tracked.

4. The simulations analyzing the radiative effect of dust on hurricane genesis are conducted in a resolution of T255 (~0.5°×0.5°) with the simplified ECHAM6-HAM. Ensemble simulations with radiatively active dust and dust not interacting with radiation (passive dust) are performed. Different mechanisms in which dust and the SAL affect North Atlantic hurricane activity are suggested in observational studies. In our study, the inhibiting effects suggested by Dunion and Velden(2004) as well as the supporting impacts of dust suggested by Braun(2010) are found in our simulations. These effects yield a non-significant southward shift of developing disturbances (DDs) of 1° in latitude. This is due to a southward shift of the convective regions caused by a more stable SAL when dust is active. As the average dust burden in this region decreases to the south, a significant decrease in dust burden in composites of DDs between passive- and active-dust simulations can be seen, but with no changes in the hurricane-related variables vertical wind shear, vorticity and relative humidity. Additionally the box difference index (BDI), which uses composite means of DDs and non-developing disturbances (NDDs) and is capable of determining hurricane controlling variables, does not assign dust a crucial role in influencing hurricane-related variables. However, with the BDI the observational finding that thermodynamic variables are more important for TC genesis in the North Atlantic than dynamic variables (Peng et al., 2012) is also true in the simplified ECHAM6-HAM. Furthermore, with active dust a rather low number of 15 developing distur- 7.2. Outlook 95 bances is found in the tropical northeast Atlantic. For passive dust it increases to 19, but this increase is statistically not significant.

7.2 Outlook

To this date, a single dominating mechanism regarding the radiative effect of dust on North Atlantic hurricane activity is not determined. For future studies, we suggest three approaches.

1. Karyampudi and Pierce(2002) showed two case studies where the SAL had a positive influence on hurricane/tropical storm genesis, and one with a negative influence. This indicates that one cannot generalize the effect of dust/SAL on hurricane genesis. To be able to judge whether dust and the SAL have a positive or negative influence, each storm needs to be examined individually. The basic approach of performing simulations with radiatively active and passive dust is very valuable as recently also shown in a single case study (Reale et al., 2014). Considering Karyampudi and Pierce(2002)’s opposite SAL- effects on TC genesis, it would be beneficial to test several case studies for the dust radiative effects on hurricane genesis with the approach of using simulations with active and passive dust.

2. Despite dust/SAL can cause positive and negative effects on individual hurricanes, a long-term trend favoring dominantly positive or negative effects is possible. A thorough evaluation of dust radiative effects causing a statistical signiﬁcant in- or decrease in hurricane genesis requires simulations with a considerably larger horizontal resolution. With our resolution, we only detect TCs of category 1 regularly. Thus, for a global model we suggest a horizontal resolution of at least 20 km as used by Murakami et al.(2012a). However, coupling a general circulation model to an aerosol model with a resolution of

20 km potentially requires huge amounts of computational time. As dust concentrations in TC regions are highest over the North Atlantic, a regional model with an even ﬁner resolution could be used instead.

3. In our simulations, we used ﬁxed climatological SSTs. This eliminates the effect of a dust-induced reduction of the SST, which made up to 30-40% of total SST reduction between 2005 and 2006 (Lau and

Kim, 2007b). In order to investigate the effect of dust on the SST and the subsequent implications on hurricane activity (Lau and Kim, 2007a), simulations need to be performed with a coupled atmospheric- ocean general circulation model. 96 List of Symbols and Abbreviations

Symbol Unit Description

∂p hPa Vertical increment, pressure coordinate ∂r m Horizontal increment, radial coordinate Cd Drag coefficient Ck Enthalpy transfer coefficient CN Nudging factor −1 −1 cp 1004 J kg K Specific heat of dry air at constant pressure D W m−2 Rate of mechanical dissipation f s−1 Coriolis parameter G W m−2 Rate of generation of available energy ? −2 ks J kg Saturated enthalpy at ocean surface −2 kB J kg Enthalpy at atmosphere near the surface M kg m2 s−1 Angular momentum M g Aerosol mass in HAM N Aerosol number in HAM p hPa Pressure pc hPa Pressure deviation Q J kg−1 Net heat exchange r m, km Radius rm km Radius of maximum winds R Scavenging parameter in HAM t s, min, h Time T °C, K Temperature TB °C, K Absolute temperature at the top of the boundary layer T¯out °C, K Averaged outflow temperature v m s−1 Azimuthal velocity −1 vm m s Wind speed at radius of maximum winds −1 vs m s Tangential surface wind speed x Horizontal coordinate z Vertical coordinate Symbol Unit Description

η % Thermodynamic efﬁcienty ρ kg m−3 Density σr Geometric standard deviation θ K Potential temperature θe K Equivalend-potential temperature ∗ θe K Saturated equivalend-potential temperature ω hPa d−1 Vertical velocity τ d Lifetime of hurricanes/tropical storms

97 98 List of Symbols and Abbreviations

Abbreviation Description

AEJ African easterly jet AEW African easterly wave ASR Absorbed solar radiation BC Black carbon (HAM aerosol species) BDI Box difference index CCN Cloud condensation nuclei CDO Central dense overcast CPU Central processing unit DD Developing disturbance DMS Dimethyl sulﬁde DU Mineral dust (HAM aerosol species) DustAN Nudged simulation with active dust DustPN Nudged simulation with passive dust DustAF Free simulations with active dust DustPF Free simulations with passive dust EC Extratropical cyclone ECHAM6 General circulation model of MPI Hamburg, version 6 ECMWF European Centre for Medium-Range Weather Forecasts ERA-40 ECMWF reanalysis dataset (1957-2002) ERA-Interim ECMWF reanalysis dataset (1979-current) GCCN giant CCN GCM General circulation model HAM Hamburg aerosol model HAM-Dust HAM reduced to dust being the only interactive aerosol species HURDAT Hurricane database of the NHC IN Ice nuclei ITCZ Innertropical convergence zone MDR Main development region MSW Maximum sustained winds NA North Atlantic NHC National Hurricane Center NOAA National Oceanic and Atmospheric Administration NDD Non-developing disturbance POM Particulate organic matter (HAM aerosol species) RCP Representative concentration pathway SAL Saharan air layer SLP Sea level pressure SS Sea salt (HAM aerosol species) SST Sea surface temperature SU Sulphate (HAM aerosol species) TC Tropical cyclone TOA Top of atmosphere WVM Water vapor mass List of Figures

1.1 Schematic development of surface pressure (orange), maximum surface wind (blue) and mid-level temperature (red) from a TC’s center towards its periphery during a TC’s mature stage. c denotes the center, rmw the radius of maximum wind...... 2 1.2 Cross section of a TC with exaggerated vertical dimension. Source: Geophysical fluid dynamics laboratory, http://www.wired.com/2012/11/what-is-the-true-measure-of-a-storm/3 1.3 Energy flux of an idealized Carnot heat engine within a TC. Source: Emanuel(2005). . .5 1.4 Characteristic stages of a tropical cyclone during its formation process. Source: National Oceanic and Atmospheric Administration (NOAA), National Hurricane Center (NHC); http://www.hurricanescience.org/science/science/hurricanelifecycle/ ...... 6 1.5 Satellite image of African easterly waves and a tropical cyclone. Source: University Cor- poration for Atmospheric Research; http://www.meted.ucar.edu/tropical/synoptic/Afr_E_Waves/ print.htm ...... 8 1.6 Schematic of production of film droplets and jet drops by air bubble bursting. Source: Wallace and Hobbs(2006)...... 12 1.7 Common volume and number distributions for different aerosol modes with examples of various aerosol types, based on Brasseur et al.(2003)...... 14 1.8 Schematic illustration of North African dust transport towards the East Atlantic during the Northern Hemisphere winter and summer. Source: Schepanski et al.(2009)...... 16 1.9 A typical Saharan dust plume model. Source: Karyampudi(1979)...... 18 1.10 SAL northeast of Barbados, dust layer is visible as milky white haze. Source: Jason Dunion, NOAA, Hurricane Research Division. http://www.aoml.noaa.gov/hrd/project2007/sal.html ...... 19 1.11 Schematic of microphysical effect of aerosols on TCs. See text for explanations. Source: Rosenfeld et al.(2012)...... 21 1.12 Time series of NHC best-track intensity for three exemplary Atlantic TCs in 2000 and 2001. x-axis shows time in days, y-axis represents intensity in knots. Red shading indicates the TC being under the suppressing influence of the SAL (edge of the SAL less than 2° in latitude/longitude away from the center of the TC). Green shading indicates periods when the SAL was not impacting the TC. Source: Dunion and Velden(2004).. 22

2.1 The Carnot heat engine within a TC. See text for abbreviations. Source: Emanuel(1986). 26 2.2 Initial profiles of the steady-state model. Blue: Moist profile (RH), Orange: SAL profile (RH), Red: Dry profile (RH), Green: Temperature profile for all three moisture profiles. . 28 2.3 Experiment A, evolution of minimum central pressure. Blue: Initial moist profile. Or- ange: SAL profile. Red: Dry profile...... 29 2.4 Experiment B, evolution of minimum central pressure...... 30 2.5 Experiment C, evolution of minimum central pressure...... 30 2.6 Experiment D, evolution of minimum central pressure...... 31

3.1 Dust burden average (June-September), control simulation...... 38 3.2 Average (June-September) of control simulation, a) dust wet deposition and b) dust emission...... 38 3.3 Difference of a) dust burden, and b) dust wet deposition, between control and test O simulations (Test O-Control) averaged over June-September...... 39

99 100 List of Figures

3.4 Difference of a) dust burden, and b) dust emission, between control and test S simulations (Test S-Control) averaged over June-September...... 39 3.5 Difference of dust burden between a) control and test M, and b) control and test Q, simulations (Test-Control) averaged over June-September...... 40

4.1 Atlantic hurricane season 2005. Source: National Hurricane Center, http://www.nhc.noaa.gov/ 2005atlan.shtml ...... 44 4.2 2005, June-November. Storm tracks with thresholds for wind shear: 10 m s−1, vorticity: 5 × 10−5 s−1...... 45 4.3 2005, June-November. Storm tracks with thresholds for wind shear: 15 m s−1, vorticity: 5 × 10−5 s−1...... 46 4.4 Atlantic hurricane season 2006. Only 10 storms with 5 reaching hurricane strength were observed. None of the hurricanes reached coastal areas. Source: National Hurricane Center, http://www.nhc.noaa.gov/2006atlan.shtml ...... 47 4.5 2006, June-November. Stormtracks with thresholds for wind shear: 15 m s−1, vorticity: 5 × 10−5 s−1 ...... 48 4.6 Track of Hurricane Wilma, a) NHC-observations, b) nudged simulation with ECHAM6 in T255. Red numbers indicate the location of Ophelia on the corresponding day in October 2005...... 50 4.7 Hurricane Wilma, evolution of central SLP (hPa) in October 2005. The dark blue graph shows the storm tracked by the TC tracking algorithm in the nudged simulation, the red graph represents NHC observations. The orange graph depicts the storm tracked by the TC algorithm in ERA-Interim reanalysis input ﬁelds...... 50 4.8 As in Fig. 4.6, but for Hurricane Ophelia in September 2005...... 51 4.9 As in Fig. 4.7, but for Hurricane Ophelia...... 51 4.10 Storm tracks in ensemble simulations of 2005 (June-September) from a) 10 simulations with radiatively active dust, b) 10 simulations with radiatively passive dust. Category 1 includes hurricanes of category 1- and tropical storm strength...... 53 4.11 Genesis locations of hurricanes, North Atlantic: 1851-2009, Eastern Paciﬁc: 1949-2009. a) October 1st-10th, b) October 11th-20th. Source: NHC, http://www.nhc.noaa.gov/climo/ 54

5.1 Dust burden averaged over June-September from simulations DustAF...... 58 5.2 Differences in absorbed solar radiation (clear sky) averaged over June-September between simulations DustAN and DustIN. Black isolines depict the 0.2, 0.5 and 1 g m−2 isolines of the dust burden...... 59 5.3 Differences in absorbed solar radiation (clear sky) averaged over June-September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2...... 60 5.4 Differences in vertically integrated water vapour mass averaged over June-September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2...... 60 5.5 Differences in 2-m temperature averaged over June-September between simulations Dus- tAN and DustIN. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2. 61 5.6 Differences in 2-m temperature averaged over June-September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2.... 61 5.7 Mean dust mixing ratio at a) 500 hPa, b) 700 hPa, and c) 850 hPa, averaged over June- September, DustAF...... 62 5.8 Differences at 850 hPa-temperature averaged over June-September between simulations DustAN and DustIN. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2...... 63 List of Figures 101

5.9 Differences in temperature at a) 700 hPa, and b) 850 hPa, averaged over June-September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2...... 63 5.10 Mean 700-hPa vertical velocity averaged over June-September from simulations DustAF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2...... 64 5.11 Differences in 700-hPa vertical velocity averaged over June-September between simulations DustAN and DustIN. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2...... 65 5.12 Differences in 700-hPa vertical velocity averaged over June and September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2...... 65 5.13 Mean dust emissions averaged over June-September from simulations DustAF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2...... 66 5.14 Differences in convective precipitation averaged over June and September between simulations DustAF and DustIF. Black isolines depict thresholds in dust burden of 0.2, 0.5 and 1 g m−2...... 66

6.1 Depiction of the most intense storm within our 20 ensemble simulations originating in our defined region between 20°W-50°W. a) Wind speed at 850 hPa and sea level pressure during its most intense phase, b) Zonal cross-section of the storm with horizontal wind speed (shading), temperature in °C (black lines) and vorticity in 0.5, 2 and 4×10−4 s−1 (red lines) during the same timestep as in a), c) Evolution of minimum SLP and 850 hPa wind speed of the storm during its whole lifetime. The black box shows the time of the storm in a) and b), 12 hours after reaching its maximum intensity it was not detected any more...... 78 6.2 June to September (2005) mean aerosol optical depth at 550 nm with a) MODIS Terra observations and b) Average of 10 ensemble simulations with ECHAM6-Dust...... 79 6.3 June to September mean of a) dust burden and b) SST in the simulations with ECHAM6- Dust. Black crosses show the DDs in (a) ECHAM6-Dust and (b) ECHAM6-no-dust 24 hours prior to detection. Grey crosses denote NDDs every 6 hours in (a) ECHAM6-Dust and (b) ECHAM6-no-dust. Dark red lines denote our defined region between 20°W-50°W. 80 6.4 Ensemble mean dust mixing ratio of the ECHAM6-Dust simulations (June-September) meridionally averaged between a) 12.5°N-17.5°N and b) 7.5°N-12.5°N...... 81 6.5 Ensemble-mean temperature differences between ECHAM6-Dust and ECHAM6-no-dust simulations (June-September) meridionally averaged between a) 12.5°N-17.5°N and b) 7.5°N-12.5°N. Lined shading denotes statistical significant changes at the 5%-level (two side t-test)...... 81 6.6 As Fig. 6.5, but for relative humidity...... 82 6.7 Ensemble mean meridional wind between 12.5°N-17.5°N (June-September): a) simulations with ECHAM6-no-dust and b) difference between ECHAM6-Dust and ECHAM6- no-dust simulations. Thick contours in b) denote dust mixing ratio in ECHAM6-Dust, intervals of 25 ng kg−1. The black arrow depicts the schematic position of the African easterly jet, lined shading denotes statistical significant changes at the 5%-level (two side t-test)...... 83 6.8 As for Fig. 6.7, but for zonal wind between 20°W-40°W (June-September). The black circle depicts the schematic position of the African easterly jet in our simulation. . . . . 83 6.9 Ensemble mean vertical velocity in 700 hPa (June-September), a) ECHAM6-no-dust and b) difference between ECHAM6-Dust and ECHAM6-no-dust simulations. The red arrow depicts the schematic position of the African easterly jet in our simulations, lined shading denotes statistical significant changes at the 5%-level (two side t-test)...... 84 102 List of Figures

6.10 As in Fig. 6.9, but for vertical wind shear between 200 and 850 hPa. a) The dark blue dot depicts the average location of DDs in ECHAM6-no-dust, the dark green dot for NDDs, both with corresponding standard deviations. b) Dark blue and green dots as in a), light blue dot shows the average location of DDs in ECHAM6-Dust, light green dot for NDDs. 84 6.11 Composites of variables for DDs and NDDs for a 5°×5°-box. Red dots show values for the simulations with ECHAM6-no-dust, blue dots for ECHAM6-Dust. Error bars indicate one standard deviation in each case. Straight diagonal shading denotes statistical signiﬁcant changes between ECHAM6-no-dust and ECHAM6-Dust at the 5%-level and dashed diagonal shading at the 10%-level (two-sided t-test)...... 86 6.12 Number of NDD-time steps per month and simulation within 20°W-50°W. Standard deviations are omitted below 0...... 87 6.13 BDIs of evaluated variables for the 5°×5°-box for simulations with ECHAM6-no-dust and ECHAM6-Dust. (+) and (-) denote the sign of the variable’s BDI...... 88 List of Tables

1.1 Naming of tropical cyclones depending on their region of formation...... 2 1.2 Safﬁr-Simpson hurricane-wind scale and additional classiﬁcations for tropical depressions and storms...... 6

2.1 Initial conditions of steady-state simulations...... 29

3.1 The structure of HAM with the aerosol species sulphate (SU), black carbon (BC), particulate organic matter (POM), sea salt (SS) and mineral dust (DU). Aerosol number j and mass of the modes i and compounds j are depicted by Ni (number) and Mi (mass) respectively (Stier et al., 2005)...... 34 3.2 Overview over all performed simulations. For further explanations see the corresponding chapters...... 36 3.3 Original scavenging parameters R for the modes of HAM. Values in cells with red background denote values, which were modified in the test simulations...... 37 3.4 Scavenging parameters in ECHAM6-HAM (Control) and modified parameters in the testing simulations with the simplified ECHAM6-HAM...... 37 3.5 Dust burden averages (mg m−2) in the box 90° W - 30° W, 5° N - 30° N and their deviations from the corresponding control simulation. Last column denotes global mean annual lifetime of dust in days, averaged over the simulations of 2000, 2005 and 2006. . 40

4.1 Categorization of hurricanes according to the Safﬁr/Simpson scale for sea level pressure (Simpson and Safﬁr, 1974)...... 43 4.2 Number of storm tracks in nudged simulations of 2005 and 2006, June-November, compared to observations. “Tropical storms” shows only storms which did not reach hurricane intensity. Additional tracks were detected systems that were not categorised as a hurricane or tropical storm by the National Hurricane Center (NHC). “W” denotes the wind shear threshold in m s−1, “V” the vorticity threshold in 10−5 s−1...... 48 4.3 Annual numbers (June-September) of North Atlantic storms and hurricanes in observations and simulations. “Storms” shows the number of all named tropical systems, “Hurr.” considers all tropical systems with hurricane strength, and “Maj.” denotes the number of major hurricanes (category 3 or higher). τ depicts the average lifetime of the storms in days including the standard deviation. Historical observations were taken from two different climatologies. Standard deviations are given where reasonable and available. Source of observations: National Hurricane Center ...... 52

5.1 Overview over free and nudged simulations...... 58

6.1 Radiative effects of dust and usage of HAM in different versions of ECHAM6 used in this study...... 74 6.2 Number of storm tracks in observations and simulations of 2005 and 2006, nudged towards ERA-Interim reanalysis data (June-November). “ζ” denotes the vorticity threshold of the simulations (s−1). “Tropical storms” shows only storms which did not reach hurricane intensity. “Additional tracks” refer to detected systems that were not categorized as a hurricane or tropical storm by the National Hurricane Center (NHC)...... 75

103 104 List of Tables

6.3 Numbers of the on average detected North Atlantic storms in NHC-observations and ensemble simulations (ECHAM6-Dust and ECHAM6-no-dust) between June and Septem- ber. “Tropical storms” includes all named tropical systems (simulations: all tracks), “Hurricanes” all storms reaching hurricane intensity, “Major Hurricanes” all hurricanes of categories 3, 4 and 5...... 77 6.4 Numbers of the on average detected disturbances in both sets of simulations and corresponding standard deviations between June and September. Only disturbances within the region between 20°W-50°W were taken into account...... 85 6.5 Average locations of DDs and NDDs in both sets of simulations and corresponding standard deviations...... 87 Bibliography

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At last, I would like to thank to a number of people who contributed to the successful finish of this PhD thesis. First, I owe my deepest gratitude to Prof. Ulrike Lohmann, who gave me the chance to work on this interesting topic of hurricanes and dust. Also, I want to thank her for her support throughout the last four years and especially for her patience which enabled me to successfully finish my PhD. I am grateful to Dr. Philipp Reutter for his assistance on scientific issues as well as for motivating me during the hard times. I would like to show my gratitude to Dr. Christoph C. Raible for permission to use the tropical cyclone tracking and detection algorithm and for his aid that was needed several times concerning the use of it. Thanks a lot to Dr. Sylvaine Ferrachat, who helped me numerous times in understanding ECHAM and HAM, and especially for her ideas which made ECHAM6-HAM-Dust possible. It is an honor for me to thank Prof. Daniel Rosenfeld for his time on reading my thesis and taking the long trip from Israel to Switzerland for my defense. Special thanks go to Dr. Ana Sesartic on the one hand for her help on HAM, but, even more important, for cheering me up and encouraging me to continue when I was close to quitting. I would like to thank as well Dr. Christina Schnadt-Poberaj for taking time to read my thesis before submission and her valuable suggestions for its improvement. Furthermore, my thanks go to Eva Choffat and Petra Forney for their aid in bureaucratic questions, and to the IT-crew of the IAC, Peter Isler, Dr. Urs Beyerle, Dr. Daniel Lüthi and Hans-Heini Vogel. Through the course of the past years, each of them helped me a couple of times on different technical issues. Thanks as well to my bachelor student Stephanie Westerhuis on her valuable work on the influence of model resolution on tropical cyclones. A big thank you is going to my current and former officemates, Dr. Laura Revell, Monika Kohn, Dr. Andrew Huisman, Dr. Valeria Pinti and Dr. Colombe Siegenthaler-Le Drian. It was always a good atmosphere in our office, all of you contributed significantly to my well-being at this institute! I would like to thank all my co-workers as well for the many many scientific and non-scientific discussions that we had on various occasions. Another special and warm thank goes to my family and especially to Sandrine, who was always on my side during my PhD thesis and cheered me up when it was necessary! Last but not least I want to express the most special thanks to everyone who hosted me many nights in the last months of my work when I already moved away from Zurich: Monika Kohn, Dr. Laura Revell, Dr. David Neubauer, Pavle Arsenovic, Dr. Jan Henneberger, Luisa Ickes, Yvonne Boose and Manuel Abegglen.

119 120 Curriculum Vitae

PERSONAL DETAILS Name Bretl First Name Sebastian Date of Birth March 25th, 1982 Place of Birth Munich, Germany Citizenship German

EDUCATION Doctorate in Atmospheric Science 2010 - present Institute for Atmospheric and Climate Science (IAC), ETH Zurich, Switzerland Thesis: Radiative inﬂuence of Saharan dust on North Atlantic hurricane genesis Diploma in Meteorology (Master Degree) 2003 - 2010 Meteorological Institute Munich, University of Munich, Germany Thesis: Untersuchung des Lebenszyklus von Gewittern in Mitteleuropa mit Hilfe von Fernerkundungs- und Modelldaten

Abitur (Matura Degree) 1992 - 2002 St. Anna-Gymnasium, Munich, Germany

CONFERENCESAND WORKSHOPS ECMWF Training Course April 2011 Parameterization of diabatic and subgrid physical processes Reading (UK)

3rd International Conference on Earth System Modelling September 2012 Hamburg (Germany)

DKRZ User workshop February 2013 Hamburg (Germany)

4th International Summit on Hurricanes and Climate Change June 2013 Kos (Greece)

EGU General Assembly May 2014 Vienna (Austria)

121 PUBLICATIONS

Gierens, K., S. Bretl, 2009: Analytical treatment of ice sublimation and test of sublimation parameteri- sations in two-moment ice microphysics models. Atmospheric Chemistry and Physics 9(19), 7481-7490, doi: 10.5194/acp-9-7481-2009

Bretl, S., P. Reutter, C. C. Raible, S. Ferrachat, U. Lohmann, 2015: The inﬂuence of absorbed solar radiation by Saharan dust on hurricane genesis. Journal of Geophysical Research, 119, doi: 10.1002/2014JD022441