short-term aerosol trends: reality or myth?€¦ · trend computation artifact? • need a better...

SHORT-TERM AEROSOL TRENDS: REALITY OR MYTH?

Gregory Leptoukh1 and Viktor Zubko2,1

1NASA Goddard Space Flight Center2Wyle IS

https://ntrs.nasa.gov/search.jsp?R=20100003357 2020-07-12T20:26:58+00:00Z

Gregory Leptoukh, IGARSS '09 2

Spatial Distribution of AOT (Terra) Annual Anomaly Trend (Time range Sep 2002 – Aug 2008)

Annual Anomaly: Deviation of monthly AOT time-series in each 1 x 1 grid (72 points for 6 years) from the AOT averaged over 6 years

Least squares linear trend: slopes (per year) for each grid box 7/17/2009

Gregory Leptoukh, IGARSS '09 3

Spatial Distribution of AOT (Terra) Anomaly Percentage Change per Year

(Time range Sep 2002 – Aug 2008)

7/17/2009

Main questions addressedShort-term Trends of MODIS AOT over six years

– Why are the trends different in different regions?

– How these trends be so high?

– Why are they “coherent” in many areas?

– Are these changes in aerosol concentrations real, i.e., are they monotonic changes in emissions?

7/17/2009 4Gregory Leptoukh, IGARSS '09

Possible alternative explanations

• Trend computation artifact?

• Need a better deseasonalization?

• Related to changes in meteorology patterns?

7/17/2009 Gregory Leptoukh, IGARSS '09 5

MODIS Terra AOT Anomaly Trend (Least Squares)

7/17/2009 Gregory Leptoukh, IGARSS '09 6

2003 Siberian Fires and “negative” AOT trend

Outlier

Negative LS trend

The regular Least Squares (LS) linear trend is simple but sensitive to outliersAn alternative (Sen) linear trend is less sensitive to outliers

7/17/2009 7Gregory Leptoukh, IGARSS '09

Eastern China: real positive trend

7/17/2009 8Gregory Leptoukh, IGARSS '09

MODIS Terra AOT Anomaly Trend Alternative (Sen) computation

7/17/2009 Gregory Leptoukh, IGARSS '09 9

An alternative linear trend based on Kendall-Sen non-parametric statistics is more robust and less sensitive to outliers

60

40

20 "

o

-20

-40

Orig. Time Series: B(Sen), Av~era~ge~A~O~T~~X~~~

-60 ~~~~.,~~~~~~~~~~~,-~~~~~~~~

-LSO -LOO -50 o 50 LOO LSO

MODIS Terra AOT Anomaly Trends:Difference between LS and Alternative (Sen)

7/17/2009 Gregory Leptoukh, IGARSS '09 10

SenLS

Difference

Characteristics of outlier hot and cold spots

7/17/2009 Gregory Leptoukh, IGARSS '09 11

PDFs for four case study regions (Bezier approximations are shown in black)

MODIS Terra AOT Percentage of change per year

Sen shrinks outliers’ PDFs and pushes them closer to zero

SenLS

SenLS

Possible alternative explanations

• Trend computation artifact?

• Need a better deseasonalization?

• Related to changes in meteorology patterns?

7/17/2009 Gregory Leptoukh, IGARSS '09 12

MODIS AOT Annual Anomaly Linear Trends

7/17/2009 Gregory Leptoukh, IGARSS '09 13Terra

Sen Sen

LS LS

Difference Difference

Sen Sen

LS LS

Difference Difference

Aqua

Deseasonalized AOT Monthly Anomaly Linear Trends

7/17/2009 Gregory Leptoukh, IGARSS '09 14AquaTerra

Sen Sen

LS LS

Difference Difference

Possible alternative explanations of AOT short-term changes in some areas • Trend computation artifact?

• May disappear after deseasonalization?

• Related to changes in meteorology patterns?

7/17/2009 Gregory Leptoukh, IGARSS '09 15

Similarities in MODIS Cloud Properties Anomaly trends with the Coincident 52-Months of CERES and AIRS-V5 OLR

From Joel Susskind, IGARSS’08

?

7/17/2009 16Gregory Leptoukh, IGARSS '09

AIRS OLR CERES OLR

MODIS Cloud Fraction MODIS Cloud Optical Thickness

SST

AOT

Cloud Fraction

Western Pacific Warm Pool displacement (5S-5N, 160E-160W)

7/17/2009 17Gregory Leptoukh, IGARSS '09

MODIS AOT Standardized Anomaly Trend (LS)

Standardized or normalized anomaly: divided by anomaly std

60 J?Wj~

40 '

""' 20 o '-'

~ ° a .~

'oi ...:i -20

-60 -150 -100 -50 0 50 100 150 -150

Longitude e) -100

0.2

0.15

0, I

0,05

° -0,05

-0, I

-0,15

-0,2

Western Pacific Warm Pool contraction cloud pattern displacement

7/17/2009 19Gregory Leptoukh, IGARSS '09

MODIS Cloud Fraction Standardized Anomaly TrendWestern Pacific Warm Pool case

60 ~~

40 ''''-'''0

o '-"

.g 0 B . ~ ... Ol

....l -20

-40

-150 -100 -50 0 50 100 150 Longitude (0)

-150 -100

0.2

0.15

0.1

0.05

o -0.05

-0.1

-0.15

-0.2

7/17/2009 Gregory Leptoukh, IGARSS '09 20

0

Displacement leads to artificial trend

Negative “trend”

Positive “trend”

This explains spatial coherence, similarity in shape of adjacent areas with opposite trends, and magnitude of trend

NAO and SO cause precipitation trends

7/17/2009 Gregory Leptoukh, IGARSS '09 21

From: Kyte, E.A., G.D. Quartly, M.A. Srokosz and M.N. Tsimplis, 2006, 'Interannual variations in precipitation: The effect of the North Atlantic and Southern Oscillations as seen in a satellite precipitation data set and in models', J. Geophys. Res., 111, art. no. D24113

a) 80~~~-'C-~~~--~ "\ "''''~~~~-.~'-----~r-~"

70

60

50

40

60

50

40

30~~~:=~~~~~~~~~ C) 80; 70

60

50

40

- 20 - IS - 10 - S 0 5 10 15 20 - 20 - IS - 10 -s 0 S 10 15 20

mm month-' / NAO unit mm month-' / SO unit

Conclusions (trends)• There is a broad spatial inhomogenueity in AOT trends

over 6 years of MODIS Terra and Aqua• Some of the areas demonstrate clear positive trends

related to increase of emission (e.g., Eastern China)• Strong trends in some other areas are superficial and

might be attributed, in part, to:– Least squares linear trend sensitivity to outliers (need to use

more robust linear fitting method)– Spatial and temporal shifts or trends in meteorological

conditions, especially in wind patterns responsible for aerosol transport

• Aerosol trends should be studied together with changes in meteorology patterns as they might closely linked together

7/17/2009 22Gregory Leptoukh, IGARSS '09