Stratospheric temperature trends from GPS-RO and Aqua AMSU measurements

Transcription

1 Stratospheric temperature trends from GPS-RO and Aqua AMSU measurements Hans Gleisner, Johannes K. Nielsen, Stig Syndergard, Kent B. Lauritsen DMI & ROM SAF

2 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.

3 15-year RO climate data record ROM SAF reprocessed data set based on UCAR excess-phase data (atmphs). CHAMP, GRACE, COSMIC, and Metop. Metop-A available from Mar 2008, and Metop-B from Feb Large step in data numbers from CHAMP-only to COSMIC.

4 ROM SAF reprocessing chain processing steps and algorithms DATA PROCESSING STEPS ALGORITHMS Level 1a PHASES, AMPLITUDES, SATELLITE POS & VEL Level 1b BENDING ANGLES (L1, L2, LC) Level 2a REFRACTIVITY DRY TEMP, PRESS, GEOPOT Level 2b TEMPERATURE SPECIFIC HUMIDITY GEOPOTENTIAL HEIGHT GEOMETRIC OPTICS > 25 km WAVE OPTICS < 20 km IONOSPHERIC CORRECTION STATISTICAL OPTIMIZATION ABEL TRANSFORM HYDROSTATIC INTEGRAL 1D-VAR RETRIEVAL Canonical Transform (CT2) below 20 km. Transition to Geometric Optics above 25 km. Optimal Linear Combination of L1 and L2 BAs. 2-parameter fit of observed BA to a best fitting BA profile found by global search in BAROCLIM. Downward integration of hydrostatic integral from 150 km. Upper boundary conditions from d(log(n))/dh. Using ERA-Interim short-term forecasts as a priori. Level 3 BENDING ANGLE REFRACTIVITY DRY TEMP, PRES, GEOPOT TEMPERATURE SPECIFIC HUMIDITY GEOPOTENTIAL HEIGHT BINNING & AVERAGING SAMPLING ERROR CORRECTION Weighted averaging into 5 o latitude grids. Sampling error estimation based on 2.5 o x2.5 o ERA-Interim fields.

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

6 Monthly mean anomalies Monthly means for June 2008 Means over all June months Monthly anomalies for June 2008 = We construct monthly mean temperature anomalies by subtracting a mean seasonal cycle from 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 the anomaly data averaged over latitudes.

7 Monthly mean anomalies averaged over latitudes 90S 90N The visual impression is a cooling in the stratosphere and a warming in the troposphere. QBO oscillations are clearly seen also in global data.

8 Monthly mean anomalies averaged over latitudes 5S 5N

9 Zonal monthly uncertainties MEASUREMENT ERRORS: RESIDUAL SAMPLING ERRORS: SYSTEMATIC ERRORS: from Scherllin-Pirscher et al. (2011). TOTAL UNCERTAINTY:

10 Measurement errors CHAMP ONLY PERIOD COSMIC+METOP 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.

11 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.

12 Trends from linear regression T = A + Bt + ε ()* + ε +, ε ()* : atmospheric variability in the monthly mean anomaly time series ε +, : RO observational errors in the monthly mean anomaly time series The e atm time series is obviously serially correlated. One way to handle e atm 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.

13 Linear trends in monthly mean T DRY Trends from standard linear regression. Global trends (90N-90S) in 1-km vertical layers to the left.

14 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.

15 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.

16 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.

17 Global trends in 5 km height layers km

18 Global trends in 5 km height layers km

19 Global trends in 5 km height layers km

20 Global trends in 5 km height layers km

21 Impact of length of time series

22 Study on GPS-RO and Aqua AMSU Purpose of study - To compare trends in GPS-RO and Aqua AMSU; - To detect stratospheric temperature trends over a 15-year period. 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-degree latitude bands + sampling error correction; - Anomaly time series by subtraction of mean seasonal cycle. 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, providing statistical uncertainties of the trends; Study results published in Khaykin et al., GRL, 2017.

23 Global temperature anomalies: RO and AMSU channel 9 85S 85N

24 Global temperature anomalies: RO and AMSU channel 10 85S 85N

25 Global temperature anomalies: RO and AMSU channel 11 85S 85N

26 Global temperature anomalies: RO and AMSU channel 12 85S 85N 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

27 Main findings in Khaykin et al. [GRL, 2017] 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) Temperature trends over 14 years for GS-RO and AMSU channels 9 to 12. GPS-RO data have been vertically averaged to símulate AMSU temperatures. 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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