Tropospheric ozone variations revealed by high resolution lidar

Tropospheric ozone variations revealed by high resolution lidar

M. J. Newchurch1, John Burris2, Shi Kuang1, Guanyu Huang1, Wesley Cantrell1, Lihua Wang1,

Patrick I. Buckley1, Xiong Liu3, Debra Hopson4

1 University of Alabama in Huntsville2 Goddard Space Flight Center, NASA

3Harvard Smithsonian Astrophysical Observatory4 Huntsville Department of Natural Resources and Environmental Management

The 3rd Asia Pacific Radiation SymposiumSeoul, South Korea25-28 August 2010

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Introduction

• High frequency of the ozone layer occurs in ozonesonde and Lidar profiles.

• The ozone layer has high potential implications for a variety of dynamic, chemical atmospheric processes and energy budgets (Newell et. al,. 2001).

• Due to their significance, dynamics and chemistry models should reproduce the ozone laminar structure (Stoller et al., 1999, Newell et al., 2001, Thouret et al., 2001).

• However, we have little understanding of the mechanisms of ozone layers. (Stoller et al., 1999, Newell et al., 2001, Thouret et al., 2001).

• In this study, we use two independent methods (Gradients and Wavelets) to study the mechanisms of ozone layer and its applications to models and satellite retrievals.

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The 3rd Asia Pacific Radiation Symposium

Seoul, South Korea25-28 August 2010

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2007

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Using Difference Quotients to Find Extreme Points of Ozone Profiles

• Difference quotients are used to find extreme points of the mixing ratio.

• Local minima and maxima are filtered through to distinguish significant layers based on the threshold percent difference value.

• The threshold is defined as 15% difference between max and min.

• A 3-point boxcar average is applied to data before difference quotients are applied. Huntsville Ozonesonde Data

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• The CWT coefficient is defined as:

• a is the spatial extent or dilation of the function.• b is the location at which the wavelet function

is centered—the translation of the function. • f(z) is the signal of interest, in this case, an

ozone profile. • and are the top and the bottom of the

profile. means wavelet function.

t

b

z

zf dza

bzzf

abaW )()(

1),(

tZ bZ

)(z

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• Where there is a large gradient in the profile, the absolute value of CWT coefficient will be large.

• Therefore, we use CWT to detect the upper and lower boundaries of the ozone layer.

• In order to delete the “noisy” layer, two thresholds are set:

• The max of these two should > 10.0% and the min should > 3.0%. is the max ozone mixing ratio (MR) within the layer. and are the ozone MR at the upper and lower boundaries of the layer.

%100/)( maxmax b

%100/)( maxmax u

max

u b

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Fairbanks

Pellston

Trinidad Head

Houston

Huntsville

Sable Island

Lidar and Ozonesonde Facilities used in this investigation

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Seasonal Variations Occur in Altitudinal Distributions- Layer Height WRT to Tropopause Height

Gradient Wavelet

Spring

Summer High frequency of layers below tropopause

Low frequency of layers near tropopause

Trinidad Head

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Layer Characteristics Vary Between Locations-Layer Height WRT to Tropopause Height (Wavelet)

Dec

reas

ing

Latit

ude

Fairbanks (1996-1997, 2001; 64.86, -147.85)

Pellston (2004; 45.59, -84.7)

Sable Island (1997; 43.96, -60.05) Houston (2000; 29.75, -95.43 )

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The latitudinal trend of layer

frequency below the tropopause is captured by

both methods of analysis

The frequency of layers above the tropopause decreases as

latitude decreases; this is consistent for both methods

as well



Seasonal Peak Above Ground Level (Wavelets)

Huntsville

Spring Summer

Fall Winter

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Layer B ThicknessMax: 4.8 km Min: 3.0 km

Mean Thickness: 0.3 km /10min

+0.9 km /10min

-0.3 km /10min

Layer A Max-MinMax: 50.1 ppbv Min: 36.6 ppbv

Mean max-min : 2.5 ppbv / 10min

+7.9 ppbv / 10min

-2.4 ppbv / 10min

A

B

•Temporal variability from other layer attributes can be similarly quantified.

•For example: O3 peak altitude, mixing ratio at peak.

Fine structure in the temporal variations of layer attributes can be quantified by Wavelet and Gradient methods from Lidar observations.

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1. Intense STE (~500ppbv at 7km) to reach top of the PBL (~2km) within 48 hours

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10min, 500m resolution

O3 lidar retrieval

sonde

500ppbv

Cloud

CloudCloud

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Cold front passage, clouds at 4-6km

sonde

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Apr. 23, 2010 Apr. 27, 2010 May 1, 2010

Dry stratospheric air

Tropopause

Co-located ozonesonde measurements

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15http://nomads.ncdc.noaa.gov/

Huntsville

Dry air tongue

NAM RH pressure-time cross-section above Huntsville

http://nomads.ncdc.noaa.gov/

12Z 26 April~ 00Z 30 April, 2010

Dry air intrusion

lat:34.73, long: -86.65

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2. PBL ozone maximum due to post-front air stagnation. High surface ozone was also observed by the EPA station in Huntsville.

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Sonde, 1PM, May 8

May 5, 2010

May 7, 2010

May 6, 2010

May 3, 2010 May 4, 2010

EPA surface O3

Decoupling of surface and residual layer

Sonde

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OzoneLidar (CNRS) during theESCOMPTE fieldCampaign (Marseilles area,summer 2001)

MOCAGE (Météo-France) equivalent to Lidar observations

Current models, even run at high resolution (10km and below) tend to underestimate above surface horizontal and vertical gradients as well as variability. This is a fundamental concern in the context of a changing climate : to what extent can we assess future evolutions (Air Quality, regional-scale radiative forcings,…)?

[email protected] on behalf of the MAGEAQ consortium

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3. Correlation between ozone and aerosol

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Positively correlated due to transport (from the same source)

Aerosol ext.coeff. at 291nm from O3 DIAL

Co-located ceilometer backscatter

Low-level jet

Co-located wind profiler

Positively correlated due to transport

Oct. 4, 2008

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Ozone mixing ratio, August 4, 2010

Aerosol ext.n coeff. At 291nm from O3 DIALDiurnal variation

Different variation structures for ozone and aerosol suggest local photochemistry dominates the ozone production

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4. Potential for using lidar measurements to address ozone variability captured by satellite



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Lidar observation, Aug. 4, 2010

Lidar convolved with OMI kernel

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Convolution of lidar ozone measurements between the surface and 10 km altitude at Huntsville, AL during August 4, 2010 with OMI ozone averaging kernel and a priori indicates that OMI is unable to capture the highly variable ozone structure in PBL, but captures a significant portion of the mid-tropospheric layer

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1. High spatio-temporal ozone variations are associated with different dynamic and photochemical processes from PBL to upper troposphere.

2. The ozone variations and structures sometimes are closely correlated with aerosol and sometimes not.

3. Nocturnal residual ozone layers often exist decoupled from the surface.

4. The lidar observations will be very helpful for addressing the ozone variability captured by geostationary satellites and forecast with regional air-quality forecasts.

Conclusions

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Tropospheric ozone variations revealed by high resolution lidar