Top Banner

Click here to load reader

Investigating Climate Trends in 14 Years of AERI Data Climate Trends in 14 Years of AERI Data at the ARM SGP Site Jonathan Gero1, David Turner2 1Space Science and Engineering Center,

Jun 15, 2018

ReportDownload

Documents

duongduong

  • Investigating Climate Trends in 14 Years of AERI Data at the ARM SGP SiteJonathan Gero1, David Turner2

    1Space Science and Engineering Center, University of Wisconsin Madison2Department of Atmospheric and Oceanic Sciences, University of Wisconsin Madison

    Introduction

    150 200 250 3000

    2000

    4000

    6000

    8000

    10000

    985 cm1 Radiance temperature (K)

    N

    AERI Observations by Scene Type

    All skyClear skyThin cloudThick cloud

    1996 1998 2000 2002 2004 2006 20080.10

    0.12

    0.14

    0.16

    0.18

    0.20

    0.22

    0.24

    0.26

    Year

    Rad

    ianc

    e (m

    W /

    (m2

    sr c

    m1

    ))

    2510 cm1 Deseasonalized Monthly Radiance Timeseries for Thin Cloud

    Trend Results

    The trends for each scene type for a selection of 30 microwindows are shown in Figure 7. Few significant climactic trends emerge from the overall time series. As the data are parsed seasonally (Figures 9-12), however, significant trends become evident. For example, thick clouds in the winter have a positive trend, suggesting that the clouds may be getting warmer or lower. Clear sky scenes in the winter are getting colder, which can be attributed a decreasing trend in water vapor. The strong positive trend clear sky autumn radiance at shorter wavelengths, but not at higher ones, may be attributed to a changing aerosol layer.

    Ground-based measurements of downwelling infrared radiance have a rich information content: H2O and CO2 absorptions bands, the 8-12 mm atmospheric window and the far-infrared regions (Figure 1) provide data on profiles of atmospheric temperature, water vapor and aerosol and cloud microphysics. Furthermore, a long term time series of such observations can be used to observe trends in the climate, given that the measurements are made with demonstrable accuracy. The ARM program has collected infrared spectra from the Atmospheric Emitted Radiance Interferometer (AERI) at the SGP site since the mid 1990s. The AERI regularly views high-accuracy blackbody calibration targets that have been tested against NIST standards. Thus the accuracy of the AERI observed infrared radiance is robust over the past decades. Any statistically significant trend in the AERI data over this time can be attributed to changes in the atmospheric composition, and not to changes in the sensitivity or response of the instrument. 500 1000 1500 2000 2500 3000

    100

    150

    200

    250

    300

    350

    Wavenumber (cm1)

    Rad

    ianc

    e te

    mpe

    ratu

    re (K

    )

    Typical AERI radiance spectra

    Clear skyThin cloudThick cloud

    20 10 6.7 5 4 3.3

    675 560 900 2510 fraction2.5

    2.0

    1.5

    1.0

    0.5

    0

    0.5

    1.0

    1.5

    Wavenumber (cm1)

    Rad

    ianc

    e Tr

    end

    (% /

    year

    )

    Overall Radiance Trend

    Clear skyThin cloudThick cloudAll sky

    675 560 900 2510 fraction2.5

    2.0

    1.5

    1.0

    0.5

    0

    0.5

    1.0

    1.5

    Wavenumber (cm1)

    Rad

    ianc

    e Tr

    end

    (% /

    year

    )

    Winter Radiance Trend

    Clear skyThin cloudThick cloudAll sky

    675 560 900 2510 fraction2.5

    2.0

    1.5

    1.0

    0.5

    0

    0.5

    1.0

    1.5

    Wavenumber (cm1)

    Rad

    ianc

    e Tr

    end

    (% /

    year

    )

    Summer Radiance Trend

    Clear skyThin cloudThick cloudAll sky

    675 560 900 2510 fraction2.5

    2.0

    1.5

    1.0

    0.5

    0

    0.5

    1.0

    1.5

    Wavenumber (cm1)

    Rad

    ianc

    e Tr

    end

    (% /

    year

    )

    Spring Radiance Trend

    Clear skyThin cloudThick cloudAll sky

    1996 1998 2000 2002 2004 2006 20080.05

    0.10

    0.15

    0.20

    0.25

    0.30

    0.35

    0.40

    Year

    Rad

    ianc

    e (m

    W /

    (m2

    sr c

    m1

    ))

    2510 cm1 Monthly Radiance Timeseries for Thin Cloud

    AERI observationMean seasonal cycle

    0.0 0.2 0.4 0.6 0.8 1.0Network Output

    0

    1000

    2000

    3000

    4000

    Cou

    nt

    33.5% 3.9% 62.7% Network status

    0 2104 4104 6104Pattern Number

    -1.0

    -0.5

    0.0

    0.5

    1.0

    1.5

    Net

    wor

    k ou

    tput

    True 0

    True 1

    NN 0 NN 1

    94.7 0.6

    5.4 91.1

    675 560 900 2510 fraction2.5

    2.0

    1.5

    1.0

    0.5

    0

    0.5

    1.0

    1.5

    Wavenumber (cm1)

    Rad

    ianc

    e Tr

    end

    (% /

    year

    )

    Autumn Radiance Trend

    Clear skyThin cloudThick cloudAll sky

    0.8

    0.6

    0.4

    0.2

    0

    0.2

    0.4

    0.6

    Microwindow (cm1)

    Rad

    ianc

    e Tr

    end

    (% /

    year

    )

    Summer Diurnal Radiance Trend for Thin Cloud

    530

    560

    675

    700

    775

    790

    810

    820

    830

    845

    860

    875

    895

    900

    935

    960

    990

    1080

    1095

    1115

    1125

    1145

    1160

    2050

    2130

    2285

    2295

    2455

    2510

    2610

    fract

    ion

    DaytimeNighttimeOverall

    1.5

    1.0

    0.5

    0

    0.5

    1.0

    Microwindow (cm1)

    Rad

    ianc

    e Tr

    end

    (% /

    year

    )

    Overall Radiance Trend

    530

    560

    675

    700

    775

    790

    810

    820

    830

    845

    860

    875

    895

    900

    935

    960

    990

    1080

    1095

    1115

    1125

    1145

    1160

    2050

    2130

    2285

    2295

    2455

    2510

    2610

    fract

    ion

    Clear skyThin cloudThick cloudAll sky

    We have analyzed the AERI time series from 1996 through 2008, which is comprised of 751,208 reliable spectra. A histogram of the 985 cm-1 radiance temperature shows a trimodal distribution (Figure 2) corresponding to various cloud regimes. We have used a neural network, trained using Raman lidar observations over a 14 month period in 2007-2008, to identify clear vs. cloudy conditions in the AERI radiance data (Figure 3). We have further broken down the cloudy data into optically thin and thick classifications. Typical spectra from each classification are shown in Figure 1.

    Significant climactic trends are obtained from the AERI radiance dataset when looking at the data on a seasonal or diurnal scale. Further work can be done to study and attribute physical mechanisms to the observed trends. Given the decadal timespan of the dataset, effects from natural variability should be considered when drawing broader conclusions. The high value of these accurate spectral observations reinforces the importance of maintaining the AERI time series at SGP and other sites worldwide, as its value for climate studies will appreciate as the dataset grows with time.

    Trend Detection

    Scene Type Selection

    Seasonal Trends

    We took monthly averages of the dataset. Of the 156 months of data, only 3 had less than 2500 reliable spectra (Figure 6). The data from these 3 months were not used in the trend analysis, as they did not contain sufficient synoptic variability. Specific microwindows were selected from the spectra (Figure 1, black lines). A resulting radiance time series is shown in Figure 4. The data were deseasonalized and the trend was calculated using a least squares regression weighted by the variance and number of data points (Figure 5). The 95% confidence interval for the trends was computed using the method of Weatherhead et al. (JGR 1998). 96 97 98 99 00 01 02 03 04 05 06 07 08 09

    0

    1000

    2000

    3000

    4000

    5000

    6000

    Year

    N

    Number of AERI observations per month

    Summary

    While the trends in the summer are not large, separation of the thin cloud results (for example) into diurnal components reveals two distinct physical phenomena (Figure 13): The slope of the trends increasing towards higher wavelengths is indicative of a trend towards clouds with smaller effective radii, whereas the overall vertical shifting of the trends reveals diurnal dependence in the cloud radiance.

    Diurnal Trends

    5

    2

    6

    1

    4

    3

    7

    10

    9

    8

    13

    12

    11

    Error bars signify 95% (2s) confidence intervals

    Wavelength (mm)

Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.