COSMIC-10, Estes Park, Sep. 25, 2017.

Stratospheric temperature trends fromGPS-RO and Aqua AMSU measurements

Hans Gleisner, Johannes K. Nielsen, Stig Syndergard, Kent B. Lauritsen

DMI & ROM SAF

COSMIC-10, Estes Park, Sep. 25, 2017.

Contents

 Trends in the 15-year ROM SAF dry-temperature data record:- construction of the time series, estimation of uncertainties- trends from linear regression- impact of serial correlations- impact of length of time series

 Results from study on stratospheric temperature trends in RO and Aqua AMSU. Done in collaboration with Sergey Khaykin, Beatriz Funatsu, and colleagues, at the LATMOS laboratory in France.

COSMIC-10, Estes Park, Sep. 25, 2017.

15-year RO climate data record ––

ROM SAF reprocessed data set basedon UCAR excess-phase data (atmPhs).

CHAMP, GRACE, COSMIC, and Metop. Metop-A available from Mar 2008, and Metop-B from Feb 2013.

Large step in data numbers from CHAMP-only to COSMIC.

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Level 2aREFRACTIVITY

DRY TEMP, PRESS, GEOPOT

1D-VAR RETRIEVAL

Level 2bTEMPERATURE

SPECIFIC HUMIDITYGEOPOTENTIAL HEIGHT

Level 1bBENDING ANGLES (L1, L2, LC)

GEOMETRIC OPTICS > 25 kmWAVE OPTICS < 20 km

Level 1aPHASES, AMPLITUDES,SATELLITE POS & VEL

ROM SAF reprocessing chain– processing steps and algorithms –

BINNING & AVERAGINGSAMPLING ERROR CORRECTION

Level 3BENDING ANGLEREFRACTIVITY

DRY TEMP, PRES, GEOPOTTEMPERATURE

SPECIFIC HUMIDITYGEOPOTENTIAL HEIGHT

IONOSPHERIC CORRECTIONSTATISTICAL OPTIMIZATION

ABEL TRANSFORMHYDROSTATIC INTEGRAL

DATA PROCESSING STEPS ALGORITHMS

Canonical Transform (CT2) below 20 km.Transition to Geometric Optics above 25 km.

Using ERA-Interim short-term forecasts as a priori.

Optimal Linear Combination of L1 and L2 BAs.2-parameter fit of observed BA to a best fitting BAprofile found by global search in BAROCLIM.Downward integration of hydrostatic integral from150 km. Upper boundary conditions from d(log(N))/dh.

Weighted averaging into 5o latitude grids.Sampling error estimation based on 2.5ox2.5o

ERA-Interim fields.

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Monthly means––

The MULTI data set combines all 4 missions.We use the sampling error corrected means.

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Monthly mean anomalies––

– =

Monthly means for June 2008 Means over all June months 2007-2016 Monthly anomalies for June 2008

We construct monthly mean temperature anomalies by subtracting a mean seasonal cyclefrom the observed monthly mean temperatures.

Temperature anomalies can be up to 5-10 K in the zonal monthly means.

We estimate trends from time series of anomaly data – directly in the zonal grid, or with theanomaly data averaged over latitudes.

COSMIC-10, Estes Park, Sep. 25, 2017.

The visual impression is a cooling in the stratosphere and a warming in the troposphere.QBO oscillations are clearly seen also in global data.

90S – 90N

Monthly mean anomalies averaged over latitudes––

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5S – 5N

Monthly mean anomalies averaged over latitudes––

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Zonal monthly uncertainties––

MEASUREMENT ERRORS:

RESIDUAL SAMPLING ERRORS:

SYSTEMATIC ERRORS:

TOTAL UNCERTAINTY:

from Scherllin-Pirscher et al. (2011).

COSMIC-10, Estes Park, Sep. 25, 2017.

Measurement errors––

COSMIC+METOP PERIODCHAMP ONLY PERIOD

The measurement errors for the individual profiles are assumed purely random.Uncertainties of the monthly means scale as the square root of the data numbers.

COSMIC-10, Estes Park, Sep. 25, 2017.

Residual sampling errors––

CHAMP ONLY PERIOD COSMIC+METOP PERIOD

The residual sampling error of the monthly means are assumed purely random.Uncertainties scale with the data numbers, but in a more complicated way.

COSMIC-10, Estes Park, Sep. 25, 2017.

Trends from linear regression––

𝑇 = 𝐴 + 𝐵𝑡 + 𝜖()* + 𝜖+,

: atmospheric variability in the monthly mean anomaly time series𝜖()*𝜖+, : RO observational errors in the monthly mean anomaly time series

The eatm time series is obviously serially correlated.

One way to handle eatm is through multi-variate regression.

If using ordinary linear regression, the standard estimated regression parameters may still be unbiased estimators. But the uncertainties of the estimated parameters(A, B) must be corrected for the reduced number of degrees of freedom. A consequence of serial correlation is that it is more difficult get significant results.

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Linear trends in monthly mean TDRY

Trends from standard linear regression.Global trends (90N-90S) in 1-km vertical layers to the left.

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Significances assuming no serial correlations

Sigma values from standard error of the regression, assuming no auto-correlation in time series.p values from two-sided t-test.

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Significances assuming serial correlations

Here accounting for auto-correlation in time series by decreasing the number of degrees of freedom.p values from two-sided t-test.

COSMIC-10, Estes Park, Sep. 25, 2017.

Significances assuming serial correlations

Here accounting for auto-correlation in time series by decreasing the number of degrees of freedom.p values from two-sided t-test.

COSMIC-10, Estes Park, Sep. 25, 2017.

Global trends in 5 km height layers

15 – 20 km

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20 – 25 km

Global trends in 5 km height layers

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25 – 30 km

Global trends in 5 km height layers

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30 – 35 km

Global trends in 5 km height layers

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Impact of length of time series

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Monthly averaged GPS-RO data:- RO dry-temperature profiles from the 15-year ROM SAF CDR;- Vertical weighted averaging of dry-temperature profiles, using fixed AMSU weighting functions;

- Monthly averaging of AMSU-averaged temperatures into 5-degreelatitude bands + sampling error correction;

- Anomaly time series by subtraction of mean seasonal cycle.

Study on GPS-RO and Aqua AMSU

Monthly averaged AMSU data:- Aqua AMSU data from NOAA STAR;- Using the inner 10 fields (swath width 500 km) to avoid limb effects;- Monthly averaging of near-nadir data into 5 degree latitude bands;- Anomaly time series by subtraction of mean seasonal cycle.

Computation of trends- Robust linear regression on the anomaly time series, providingstatistical uncertainties of the trends;

Study results published in Khaykin et al., GRL, 2017.

Purpose of study- To compare trends in GPS-RO and Aqua AMSU;- To detect stratospheric temperature trends over a 15-year period.

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Global temperature anomalies: RO and AMSU

channel 985S – 85N

COSMIC-10, Estes Park, Sep. 25, 2017.

channel 1085S – 85N

Global temperature anomalies: RO and AMSU

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channel 1185S – 85N

Global temperature anomalies: RO and AMSU

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channel 1285S – 85N

Global temperature anomalies: RO and AMSU

• Excellent agreement between RO and Aqua AMSU global temperature anomalies• Somewhat larger differences during CHAMP-only period • Stronger cooling in first part of period present in both AMSU and RO

COSMIC-10, Estes Park, Sep. 25, 2017.

Main findings in Khaykin et al. [GRL, 2017]

Temperature trends over 14 years for GS-RO and AMSU channels 9 to 12. GPS-RO data have been verticallyaveraged to símulate AMSU temperatures.

Temperature trends by month for Aqua AMSU channel 10, and the corresponding RO data.a) high northern latitudes (60-90 N)b,c) low latitudes (0-30 S, 0-30 N)d) high southern latitudes (60-90 S)

Conclusions: - excellent agreement between RO and AMSU trends- global cooling trends statistically significant in channels 11 and 12; - cooling trends in 30 degree latitude bands are not significant;

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