a cloud-resolving model study of aerosol-cloud correlation...

Confidential manuscript submitted to Geophysical Research Letters

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A Cloud-Resolving Model Study of Aerosol-Cloud Correlation in a Pristine 1 Maritime Environment. 2 3

Nidhi Nishant1 and Steven Sherwood1 4

5 6 7 1 Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, 8 University of New South Wales, Sydney, New South Wales, Australia 9 10

Corresponding author: Nidhi Nishant ([email protected]) 11

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Key Points: 24

• Satellite observations show an apparent invigoration of the convective clouds induced by 25 aerosol. 26

• Model with fixed aerosol loading, simulates vigorous clouds at times of high real-world 27 aerosol concentrations. 28

• Wind-cloud and wind-aerosol relationship explains the apparent convective invigoration. 29


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Abstract 30

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In convective clouds, satellite-observed deepening or increased amount of clouds with increasing 32

aerosol concentration has been reported and is sometimes interpreted as aerosol-induced 33

invigoration of the clouds. However, such correlations can be affected by meteorological factors 34

that affect both aerosol and clouds, as well as observational issues. In this study, we examine the 35

behaviour in a 660x660 km2 region of the South Pacific during June 2007, previously found by 36

Koren et al., [2014] to show strong correlation between cloud fraction, cloud top pressure and 37

aerosols, using a cloud-resolving model with meteorological boundary conditions specified from 38

a reanalysis. The model assumes constant aerosol loading, yet reproduces vigorous clouds at times 39

of high real-world aerosol concentrations. Days with high and low aerosol loading exhibit deep-40

convective and shallow clouds respectively, in both observations and the simulation. Synoptic 41

analysis shows that vigorous clouds occur at times of strong surface troughs, which are associated 42

with high winds and advection of boundary-layer air from the Southern Ocean where sea salt 43

aerosol is abundant, thus accounting for the high correlation. Our model results show that aerosol-44

cloud relationships can be explained by coexisting but independent wind-aerosol and wind-cloud 45

relationships and that no cloud condensation nuclei (CCN) effect is required. 46

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1 Introduction 48

49 A process of aerosol-cloud invigoration has been hypothesized whereby the development 50

of cloud is enhanced due to the strong coupling between the cloud-microphysics (induced by the 51

aerosol) and cloud system dynamics. For example, studies have suggested that under certain 52

meteorological conditions, aerosol can suppress liquid-phase precipitation which may lead to the 53


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subsequent invigoration of the deep convection due to the increased release of latent heat of fusion 54

[Andreae et al., 2004; Khain et al., 2005; Koren et al., 2005, Rosenfeld et al., 2008; Stevens and 55

Feingold, 2009]. Ascent of clouds to the higher altitudes can have a potential effect on the top of 56

atmosphere cloud radiative forcing. The expansive anvils with cold cloud tops can lead to warming 57

of the atmosphere due to their smaller thermal emittance [Koren et al., 2010; Rosenfeld et al., 58

2014]. The radiative perturbations due to this aerosol-cloud interaction is one of the complex and 59

least understood effect as per the 5th IPCC report [IPCC, 2013]. 60

The strong correlation seen between aerosol and convective cloud properties from satellite 61

retrievals [Kaufman et al., 2005; Koren et al., 2005; 2010; Niu and Li, 2012; Dey and Girolamo, 62

2010; Dey et. al. 2012; Mace and Abernathy, 2016] and the reported deepening of warm mixed 63

phase clouds with increasing aerosol in numerical models [Khain et al., 2005; Tao et al., 2007; 64

Storer and van den Heever, 2013; Fan et al., 2013] might suggest observational and numerical 65

evidence of aerosol-cloud invigoration. However, the factors like meteorological covariations, and 66

the uncertainties in the satellite retrievals, can strongly affect both the aerosol and cloud properties 67

and hence can possibly be misinterpreted as an aerosol influence on clouds. 68

In particular, satellite observations of aerosol often suffer cloud contamination due to 69

incomplete cloud screening [Kaufman et al., 2005; Zhao et al., 2013], three-dimensional radiative 70

effect due to the illumination from the adjacent clouds [Marshak et al., 2006], and hygroscopic 71

growth due to aerosol humidification [Boucher and Quaas, 2013; Altaratz et al., 2013]. The 72

spurious enhancement of aerosol optical depth in similar proportion to the clouds due to the 73

retrieval errors could be responsible for a large part of the correlations between aerosol and cloud 74

[Zhang et al., 2005; Chand et al., 2012]. 75


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Apparent aerosol effects on clouds due to covariations generated by local meteorology has also 76

been a suggested explanations or partial explanation for the observed correlations. Engstrom and 77

Ekman, [2010] using satellite observations and reanalysis data reported significant weakening in 78

the correlation between aerosol optical depth and cloud fraction when the impact of 10-meter wind 79

speed was statistically removed. Similarly, aerosol humidification due to high relative humidity 80

(which is a very strong correlate to cloud fraction) has been suggested to explain the correlation 81

between aerosol optical depth and cloud fraction in the satellite-data-based study of Twohy et al., 82

[2009]. Koren et al., [2010] also reported a correlation between relative humidity, aerosol optical 83

depth and cloud top pressure, but ultimately concluded that it did not strongly influence their 84

invigoration results. Their environmental data were from an atmospheric reanalysis, which is 85

unable to resolve small-scale variability that could be important. 86

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Efforts have been undertaken to untangle meteorological effects from the aerosol-cloud 88

relationship in both observations and numerical models. Gryspeerdt et al., [2014], using satellite 89

data, showed that the large part of the relationship between aerosol optical depth and cloud top 90

pressure is mediated by the aerosol optical depth and cloud fraction relationship. They inferred 91

from this result a strong influence of meteorological covariations on the aerosol vs. cloud-top-92

pressure relationship, as there is less reason for cloud fraction to be affected by CCN in comparison 93

to the other cloud properties. Studies involving general circulation models have reported a 94

dominant contribution of relative humidity to the models’ aerosol optical depth vs. cloud cover 95

relationship [Quaas et al., 2010; Grandey et al., 2013]. 96

One of the limitations of such global model studies is the coarse resolution. Due to the need 97

for parameterisations in these coarse-resolution models, representation of cloud scale processes is 98


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highly uncertain, and representation of aerosol-cloud interactions is even more so. Therefore, in 99

this study we examine the issue using a cloud-system resolving model with observed large-scale 100

meteorological conditions imposed. Specifically, we re-examine the findings of Koren et al., 101

[2014], hereafter KDA14, whose study is a prominent example of those using satellite retrievals 102

to support aerosol-cloud invigoration. KDA14 reported “aerosol-limited invigoration” of warm 103

convective clouds over a small oceanic region of Southern Pacific, using cloud and aerosol data 104

from the Moderate Resolution Imaging Spectro-radiometer (MODIS). They argued that 105

meteorological factors had no significant effect on the observed relationship between cloud and 106

aerosol properties, implying that the invigoration is primarily driven by the aerosol-cloud 107

interaction. 108

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2 Materials and Methods 110

111 We choose the same study area and time period as KDA14. The boundaries of the oceanic region 112

over the southern Pacific extend from 13 S to 22 S and from 121 W to 130 W. For observations, 113

the time period considered is from June 1 to August 31 2007; however, due to computational 114

expense the simulation is performed only for the first 40 days of this time period (during which 115

the most interesting meteorological events occur although the relationship between aerosol and 116

cloud is somewhat weaker than over the entire time period). Once-daily observed cloud and 117

aerosol, data are taken from the MODIS instrument on board the Aqua satellite [Platnick et al., 118

2003], Collection 6. As aerosol retrievals from space may be affected by clouds, therefore in order 119

to estimate aerosol-cloud covariations on short time and spatial scales we also examine the 120

Monitoring Atmospheric Composition and Climate (MACC) II dataset which is now a part of 121


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Copernicus Atmospheric Monitoring Service (CAMS) global atmospherics composition data 122

[Inness, 2012]. CAMS use a four-dimensional variational data assimilation technique to combine 123

satellite observational with chemistry-aerosol modelling to obtain a gridded continuous 124

representation of the mass mixing ratios of atmospheric gases and aerosol. The global model and 125

data assimilation system of CAMS is based on the ECMWF’s integrated forecast system, and the 126

atmospheric chemical system is represented by the Model of Ozone and Related Chemical Tracers 127

(MOZART) [Emmons et al., 2010] chemical transport model. We also use 10-m wind, mean 128

surface pressure and specific humidity data (these fields are essentially the same as those of the 129

parent reanalysis product from ECMWF) and surface 80% sea-salt mass mixing ratio (expressed 130

at 80% relative humidity) from the CAMS for our meteorological analysis. We also use dry mass 131

mixing ratio of sulphate, organic matter, dust and black carbon from CAMS. 132

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Model-simulated cloud properties can differ from observational ones due to limitations in data 134

assimilation or the model (in particular, parameterizations of cloud processes). Nonetheless, 135

numerical models are the most comprehensive tool by which the observed weather/climate 136

phenomenon can be reproduced. Therefore, the cloud variations during the first 40 days are 137

simulated using the Weather Research and Forecasting (WRF) regional meteorological model 138

[Skamarock et al., 2005], version 3.7.1. The initial and boundary conditions for the large-scale 139

atmospheric fields are taken from 6-hourly ERA-Interim reanalysis data [Dee et al., 2011]. The 140

domain comprises 3-way nests at 9, 3 and 1 km horizontal resolution. The size of the outermost to 141

innermost domains are 1000´1000, 840´840 and 660´660 km2. The simulation is free-running 142

after initialization, without any nudging except at the boundaries of the outer domain. All results 143

presented here are from the innermost domain, as it is run at cloud resolving resolution making it 144


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capable of better representation of clouds. In the vertical, 50 pressure levels are defined, with the 145

model top at 50 mbar. The Thompson and Eidhammer [2014] (TE14) scheme is used for the 146

microphysics, and second-order horizontal turbulent diffusion. TE14 is a bulk microphysics 147

scheme which treats five separate water species: cloud water, cloud ice, rain, snow, and a hybrid 148

graupel–hail category. Fixed profiles of cloud condensation nuclei and ice nuclei are used, with 149

near-surface and free-troposphere CCN values of 300 and 50 cm3 respectively, and 1.5 and 0.5 cm 150

-3 respectively for ice nuclei. The Rapid Radiative Transfer Model [Mlawer et al., 1997] and 151

Goddard [Chou and Suarez, 1994] schemes are used for longwave and shortwave radiation, 152

respectively. For the boundary layer, the Yonsei University [Hong et al., 2006] scheme is used. 153

The outer and middle domains employ the Betts-Miller-Janiac [Betts and Miller, 1993] convective 154

parameterization, whereas the innermost domain has no convective parameterization. 155

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3 Results 157

158 From the time-series (calculated as the daily and spatial mean of the variable) of MODIS 159

cloud fraction (CF) and cloud-top pressure (CTP), it is evident that the domain is mostly covered 160

by shallow clouds except for a strong convective event which occurs from day 6 to 15 (Fig. 1a,b). 161

Also, the highest concentration of the aerosol optical depth (AOD) is observed during this 162

convective event, with a second maximum during the last 10 days of the time period (Fig. 1c). The 163

two aerosol products yield reasonable agreement on the time variations of aerosol over the period. 164

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Figure 1. The time-series (spatial and daily means) of (a) cloud fraction (CF), (b) cloud top pressure 167

(CTP), (c) aerosol optical depth (AOD) and the surface 80% sea-salt mass mixing ratio and (d) 10-168

meter wind speed. Red, blue and green lines represent data from WRF, MODIS and CAMS, 169

respectively. Green solid and green marked line in (c) represent AOD and surface 80% sea salt 170

mass mixing ratio, respectively. 171

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Since the cost of running the entire 90 days simulation is high, we run the WRF simulation 173

only for the first 40 days, so that it captures the strong event (between the 6th and 15th day as 174

observed in MODIS data) and few other smaller events (weaker convection between 12th and 19th 175

day) (Fig. 1a,b). After the first 5 days, WRF-simulated cloud properties (CF and CTP) look 176

comparable to those from MODIS, however, the simulated cloud tops do not reach as high as 177

MODIS, and the CF shows larger variability. For the last 10 days of simulation the model is able 178

to capture the cloud pattern, though slightly overestimating the CF. The rough agreement between 179

simulated and observed variations in cloud amount and altitude suggests that WRF is simulating 180

the observed clouds satisfactorily. 181

For the statistical analysis, CF and CTP are sorted as a function of AOD and averaged to 182

create 100 lumped data points, following KDA14. We use the AOD values from CAMS for 183

analysing the aerosol-cloud relationship because we think it may provide more accurate cloud-184

aerosol relationship due to use of wind and sea level pressure data to help constrain aerosol. All 185

data from both CAMS and WRF are regridded to the scale of MODIS and daily means of variables 186

from WRF and CAMS are compared to the once-daily variables of MODIS. 187

As in KDA14, we find a strong correlation between CF and AOD observations and between 188

CTP and AOD, with clouds becoming more prevalent and deeper at increasing aerosol 189

concentration (Fig. 2 (a,b). For the entire 90 days, a strong positive correlation (r = 0.90±0.05) at 190

2s) between CF and AOD and a strong negative correlation (r= -0.81±0.08) between CTP and 191

AOD is seen. This could suggest that the clouds are invigorating due to their interaction with the 192

aerosol. However, it is mostly the convective clouds which invigorate: shallow CF increases 193

during the invigoration event, but the clouds do not ascend higher. The two correlations noted 194


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above are reduced to r = 0.71±0.09 and r = -0.50±0.12 respectively when the deep convective 195

event (vigorous growth of the deep convective cloud between day 6 and 15) is removed from 196

computation, showing that this event is important but not solely responsible for the observed 197

relationship. 198

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Figure 2. Relationship between the aerosol optical depth (AOD) from CAMS and (a, c) cloud 200

fraction (CF) and (b, d) cloud top pressure (CTP). Top panel and bottom panel show data from 201

MODIS and WRF, respectively. 202

The correlation between simulated cloud properties and observed AOD is weaker than with 203

observed properties, as the WRF-simulated CF and CTP do not completely agree with those 204


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observed. This is likely a limitation of our model in simulating the observed cloud properties. CTP 205

and AOD show a strong correlation (r= -0.82±0.07) whereas the correlation between CF and AOD 206

is comparatively weaker (r = 0.31± 0.11) (Fig. 2 (c,d)). Hence the WRF simulation could suggest 207

a similar invigoration of clouds as do the observations (Fig. 2c,d). However, the fact that we have 208

run WRF without any aerosol component contradicts the interpretation of this correlation as being 209

the result of an aerosol-cloud interaction. 210

Investigation of the aerosol type from the CAMS reanalysis data revealed that nearly 90% 211

of the aerosol (Fig. 3) is inferred to be sea salt throughout the entire domain. Sea salt is formed 212

by the bursting of entrained air bubbles during whitecap formation [Blanchard, 1983; Monahan et 213

al., 1986]. Therefore, the concentration of sea-salt aerosol is sensitive to the wind speed and 214

pattern. Additionally, strong winds can have a potential effect on the developing clouds, either 215

directly by increasing surface fluxes or indirectly by their relationship with synoptic storm 216

patterns. Hence, it is important to examine the winds and evaluate their relationships with both 217

aerosol and clouds. 218

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Figure 3. Mass mixing ratio of the different aerosol types from CAMS over the study region. 222

The ERA-Interim wind imposed as the WRF boundary condition is similar to the CAMS 223

wind, but the WRF-simulated winds after initial 10 days diverge from the imposed boundary 224

conditions (Fig. 1 (d)). Nonetheless the spatial patterns of wind from either CAMS or the WRF 225

simulation vary together with both AOD and CF. Both the simulated and the re-analysis domain-226

average wind speeds are well correlated (WRF: r = 0.39 ± 0.10), CAMS: (r= 0.95 ± 0.03) with 227

AOD. This indicates advection or local source due to the wind is the possible pathway for the 228

growth and transport of observed sea-salt aerosol. 229

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Figure 4. Spatial distribution of aerosol optical depth (AOD), 10-meter wind vector and sea level 232

pressure (a,b) and specific humidity at 700 hPa (c,d) from CAMS over the Southern Ocean. Here 233

(a,c) and (b,d) represent the synoptic conditions for days 6 and 13 respectively, and the box 234

represents the study area. 235


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The CAMS reanalysis meteorological data (which are nearly identical to ERA-I) show the 236

formation of a subtropical depression (Fig. 4 (a,b)) in the Southern Ocean (with its centre around 237

100 W and 25 S) starting from day 7. The subtropical depression grows with time and brings strong 238

winds (>15 m/s) to the study area. Strong southerly wind in the study area advects the high 239

background sea salt concentration over the Southern Ocean into the study region and also enhances 240

the local production of sea salt (Fig. 4). Past studies have too reported enhanced production of sea 241

salt due to strong wind [Gong et al., 2003; Fan and Toon, 2010; Dunne et al., 2014]. However, 242

here, we observe both local source enhancement and advection from high-loading regions farther 243

south contributing towards the reason for the observed variations in the AOD. 244

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Strong wind can also affect the intensity of shallow and deep convection. Enhanced 246

evaporation due to strong wind produces a greater number of shallow cumuli, where each cumulus 247

contributes to the formation of a deeper precipitating cloud system [Nuijens and Stevens, 2012]. 248

Convergence due to the strong anomalous winds ahead of the subtropical trough has been reported 249

for the intensification of clouds and its associated precipitation by bringing moisture and lifting of 250

air masses leading to the development of organised deep convection [Ziv, 2001; Hart et al., 2010; 251

Tu and Chen, 2011]. We see a strong correlation in the variations of wind, specific humidity and 252

clouds throughout the time period (Figs. 2,4) confirming that the development of clouds in this 253

case is an outcome of the moisture transport due to the strong winds ahead of the subtropical 254

depression. 255

Hygroscopic growth of aerosol in the presence of moisture has been reported by Boucher 256

and Quaas [2013], and the strong correlation between AOD and cloud has been assigned for this 257

reason by Quaas et al., [2010] and Grandey et al., [2013]. In our case, we see similar changes in 258


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AOD and surface 80% sea-salt mass mixing ratio throughout the time period (Fig. 1c) suggesting 259

no anomalous growth of AOD. Thus, the AOD changes are (at least in the aerosol reanalysis) due 260

to changes in sea salt mass and not hygroscopic growth as the 80%-RH mass is directly related to 261

the dry mass. 262

Therefore, the strong correlation between aerosol and cloud properties over the domain, at 263

least for this region and time period, is the result of the subtropical synoptic activity in the Southern 264

Hemisphere, which leads to advection and enhanced local production of sea salt, and formation of 265

deep convective event coincidentally. The results indicate that correlations between cloud 266

properties (CF or CTP) and AOD can be caused primarily by the wind-cloud and wind-aerosol 267

interactions, and should not be taken as evidence of an aerosol-cloud interaction. 268

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4 Conclusions and Discussion 270

271 Many previous studies have noted that strong convective events in subtropical regions 272

coincide with higher aerosol loadings. Our analysis of a previously reported event (KDA14) 273

confirms this relationship in observations. Our detailed simulations however suggest that this 274

relationship can be explained by coexisting but independent wind-aerosol and wind-cloud 275

relationships. Reproduction of the apparent convective invigoration, in the absence of any aerosol 276

variations in the model, proves that the aerosol-cloud correlations can be explained by the wind-277

cloud and wind-aerosol relationship and no CCN effect is required at least to leading order. The 278

numerical simulation and re-analysis data indicate that subtropical storm activity can control both 279

the cloud and sea-salt aerosol amounts in the pristine conditions over the Southern Pacific, with 280


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associated winds advecting sea-salt aerosol from the Southern Ocean, and associated 281

meteorological conditions (but not the sea salt itself) producing stronger cloud development. 282

Past global model-based studies have also argued that apparent invigoration is an effect of 283

meteorological covariation [Quaas et al., 2010; Gryspeerdt et al., 2014], but ours adds to this list 284

by reproducing observed weather variations in a cloud-resolving simulation, and by clarifying that 285

wind-induced sea-salt production is the dominant mechanism at least in this region. While our 286

results indicate that the leading-order cause of these correlations is wind, a meteorological variable, 287

this does not rule out an important role of aerosols in invigorating clouds, it only means that more 288

sophisticated means would be needed to infer the effect from observations. 289

Our study selected a small oceanic region and relatively short time period; therefore, it will 290

be beneficial if future studies could examine bigger domains and longer time periods. This will 291

help in capturing the synoptic activities which may have a strong implication for results. It would 292

be useful in particular to check the relevance of the results over other oceanic regions and different 293

time of year as our conclusions may not hold in all seasons and over all maritime regions. It will 294

also be beneficial to test the wind-driven sea-salt production mechanism for this region, as we did 295

not specifically test this mechanism yet. 296

297

Acknowledgment 298

299 We acknowledge the European Centre for Medium-Range Weather Forecasts (ECMWF) and 300

National Center for Atmospheric Research, for providing the ERA-Interim Reanalysis data and 301

WRF model. We also acknowledge NASA LAADS DAAC for providing the MODIS data and 302


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ECMWF for providing the CAMS atmospheric composition data. The global CAMS project is 303

built within the European Union’s Global Monitoring of Environment and Security program and 304

a series of Monitoring Atmospheric Composition and Climate (MACC) projects at the ECMWF. 305

We would also like to thank Olivier Boucher for his help in accessing the CAMS data. In addition, 306

we would also like to thank the Australian NCI and ARC Discovery Project DP140101104 for 307

providing computational resources for this work. 308

309

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