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Motivation

Identifying Modes of Temperature Variability Using AIRS Data Alexander Ruzmaikin, Hartmut H. Aumann and Yuk Yung Jet Propulsion Laboratory & California Institute of Technology. Motivation.

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Motivation

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  1. Identifying Modes of Temperature Variability Using AIRS DataAlexander Ruzmaikin, Hartmut H. Aumann and Yuk YungJet Propulsion Laboratory & California Institute of Technology

  2. Motivation The canonical global warming at the 100mK/decade rate is largely based on the rise in the ocean surface temperature. One would expect that temperature trends in the mid-troposphere follow the surface due to convection. Measuring such temperature trends is a challenge. It requires extremely stable radiometric performance over many years. An additional challenge is the effect of natural interannual variability. We use the first five years of Airs data and a new data analysis method to address this challenge.

  3. Data • Airs: daily zonal means at 2388 1/cm in the CO2 R-branch with • weighting function peaking in the mid-troposphere (5 km), • clear sky over tropical ocean • AMSU at 57 GHz Oxygen band independent of CO2 at roughly the • same altitude for the same data

  4. Empirical Mode Decomposition Huang et al., (1998)

  5. Application of EMD to Airs Data: An Example

  6. Annular Modes

  7. CO2 at Mauna Loa Linear trend 1.695 ± 0.005 ppmv/year 2.001 ± 0.014 ppmv/year in 2002-2007 Sensitivity at 400 hPa is 40 mK/ppmv. Expect 80 mK/year in 2002-2007

  8. CO2 Signal at Airs The CO2 signal is calculated as TB(Airs at 2388.2 1/cm) - TB(AMSU5 at 57 GHz) in 0 - 20°N latitudinal band over tropical ocean

  9. The EMD Modes of CO2 Signal at Airs Linear trend 0.046 ± 0.006 K/year day 0.044 ± 0.012 K/year night 1 sigma confidence intervals found by Monte-Carlo simulation with white noise

  10. Comparison of 2 Methods - 45 ± 9 mK /year -- Airs 2388 1/cm using EMD -43 ± 7 mK/year (day) -- AIRS 2388 1/cm using method Santer(2001), -50 ± 8 mK/year (night) corrected for autocovariance, ± one sigma The EMD trend and the anomaly trend agree, but the EMD gives tighter error bars

  11. Observed Trend with AIRS Data - 45 ± 9 mK /year -- Airs 2388 1/cm data +10 ± 1 mK /year -- frequency shift (instrumental) ----------------------- -56 ± 10 mK/year -- spectral shift corrected The observed cooling is due to the effect of increasing CO2, which causes the 2388 1/cm weighting function to shift to higher (colder) altitudes. The radiometric stability of AIRS for 5 years is better than 8 mK/year.

  12. Trend interpretation We expected - 80 ± 2 mK/year based on the 2 ppmv/year trend in the CO2 column abundance. +10 mK /year is expected from sea surface bulk measurements assuming that the mid troposphere follows the moist adiabat The observed trend can be explained if the temperature at 5 km additionally increased by 24 ± 11 mK/year ------------------------------------------------------------------ 14 ± 11 mK/year discrepancy Possible explanations: 1. The SST increased faster than 10 mK/year during 2002-2007 2. The mid-troposphere is warming faster than the surface

  13. Conclusions • Five years of Airs data have climate quality and can be used to identify modes of natural variability and temperature trends in the mid-troposphere • There is a possible discrepancy between the expected trend in the mid-troposphere and the observed trend, which may be due to enhanced convection. • This is work in progress and is continuing as more AIRS data become available. The next step will be the extension of this work to lower and higher altitudes.

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