1. Atmospheric variability modes and trends in the UTLS from the RO record A. K. Steiner 1, B. Scherllin-Pirscher 1, F. Ladstädter 1, R. Biondi 1, L. Brunner 1, G. Kirchengast 1,2, and the ARSCliSys group 1 Wegener Center for Climate and Global Change (WEGC) and 2 IGAM/Inst. of Physics, University of Graz, Austria andi.steiner@uni-graz.at. SPARC Temperature Trends Workshop, Victoria, BC, Canada, April 9-10, 2015

2. 1 Courtesy: T. Rieckh  Global Positioning System (GPS) radio signals at 2 frequencies 1575.42 MHz (~19 cm) 1227.60 MHz (~24 cm)  Receiver on LEO satellite  Occultation geometry  Atmospheric refraction of signals  Measurements of phase path based on precise atomic clocks  Retrieval of key atmospheric/ climate parameters e.g., refractivity N, pressure p, geopotential height Z, temperature T, humidity q GPS–LEO satellite constellations GPS Radio Occultation

3. GPS RO Data Availability and Products 2  Bending angle  Refractivity  Pressure  Geopotential height  Temperature  Humidity  Tropopause parameters  Geostrophic/gradient wind Number of Observations over Time (Fig. courtesy: R. Biondi/WEGC) (Fig. courtesy: U. Foelsche/WEGC)

4. Summary of RO Data Characteristics  Global coverage  All weather capability  Best data quality in upper troposphere–lower stratosphere (UTLS)  Vertical resolution ~0.3 km to ~1.5 km in the UTLS  Horizontal resolution about 100 km to 300 km in the UTLS, synoptic scales, climate  Long-term stability measurements based on accurate&precise clocks (SI-traceable to time)  No need of inter-satellite calibration  Error characterization of profiles and climatological fields  Structural uncertainty estimates

5. 4 RO Data Consistency  Consistency of different satellites One processing center WEGC  meeting GCOS climate monitoring targets in UTLS  long-term stable within ~0.1 K/decade (not for horizontal target resolution <100km, and not yet globally ) Temperature  different processing centers Structural uncertainty CHAMP [Ho et al. JGR 2009, 2012; Foelsche et al. TAO 2009, AMT 2011; Steiner et al. RS 2009, ACP 2013]

6. 5 Cal/Val – Comparison with other Observations (1) Envisat MIPAS and GOMOS (here global 10/20km–30km)  ESA project MMValRO Multi-Mission Validation against RO  RO is a valuable reference record over Envisat period 2002–2012  MIPASv6.0 was ‘test-reprocessing’; the slight bias from Q4/2006 is from stronger bias < 17 km; official re-processing on-going to ~May 2015 MIPAS – v6.0 GOMOS – v6.01 [Schwaerz et al. OPAC-IROWG 2013; TR ESA-ESRIN 2013] http:// validate.globclim.org

7. 6 Cal/Val – Comparison with other Observations (2) Radiosonde Data Vaisala 90/92 vs RO [Ladstädter et al. AMT 2015]

8. RAOBs V90/92 and GRUAN vs RO for day and night  Annual-mean temp differences (global, 10hPa–30hPa, day/night) [Ladstädter et al. AMT 2015] 7 Cal/Val – Comparison with other Observations (3)

9. 8 Atmospheric Variability – Volcanoes (1) Thermal structure before and after volcanic eruptions  Detection of volcanic cloud top height: Nabro volcano eruption before (1-11 June 2011) after (12-14 June 2011) [Biondi et al., A novel technique including GPS RO for detecting and monitoring volcanic clouds, GRL 2015, in revision]

10. 9 Atmospheric Variability – Volcanoes (2) [Biondi et al., A novel technique including GPS RO for detecting and monitoring volcanic clouds, GRL 2015, in revision] Thermal structure after volcanic eruptions  Temporal evolution of volcanic cloud top & thermal structure from RO Nabro, Eritrea (13.37°N, 41.70°E), 12 Jun 2011, mainly SO 2 cloud) Puyehue Chile (40.35°S, 72.07°W), 5 Jun 2011, mainly ash cloudand Ejafjöll Iceland (63.63°N, 19.60°W), March and April 2010, ash and SO 2 cloud ○ Caliop data

11. Monitoring Climate Variability with RO RO Temperature anomalies 05/2001-12/2013 in UTLS (SE subtracted)  QBO signal above the tropical tropopause  ENSO signal below the tropical tropopause 10

12. Principal Component Analysis – QBO Principal component analysis in LS (20km-30km): PC1, PC2 → QBO 11

13. Regression Results (1) Regression Results Tropical LS: altitude levels 25 km and 20 km 12

14. Regression Results (2) Regression Results Tropical UT: altitude levels 15 km and 10 km 13

15. QBO and ENSO Variability 14 QBO & ENSO Residual Variance Deseasonalized Temp. Anomalies Kelvin Waves [Scherllin-Pirscher et al. EGU 2015]

16. RO Temperature Trends in Tropical UTLS 15 Trend in Tropics Variance  Trend not significant, residual variance large near TP, above 28 km

17. Ozone and Temperature Evolution 16 Ozone observations – vertically resolved:  HARMOZ: Harmonized dataset of ozone profile occultation and limb sounders: GOMOS, MIPAS, OSIRIS, ACE-FTS [Sofieva et al. ESSD 2013]  GOZCARDS: Global ozone and related trace gas records for the stratosphere Merged SAGE, HALOE, MLS, ACE-FTS [Froidevaux et al. AMT 2013]  SBUV: Solar Backscatter UltraViolet instruments (nadir) [Bhartia et al. AMT 2013]  ERA-Interim: ECMWF reanalysis-Interim Temperature data: RO and ERA-Interim [L. Brunner, MSc Thesis, WEGC Rep. 2014] HARMOZ ozone anomalies GOSZCARDS ozone anomalies

18. Ozone and Temperature Regression Results 17 Multiple Standard Linear Regression: QBO winds, ENSO SST indices [L. Brunner, MSc Thesis, WEGC Rep. 2014] HARMOZ QBO coeff. SBUV QBO coeff. RO QBO coeff. RO ENSO coeff.

19. Ozone and Temperature Trends 2002–2012 18 GOZCARDS Ozone SBUV Ozone ERA-Interim Ozone  Agreement RO and ERA-Int, temperature trends not significant, large nat. variability  O 3 increase in mid- and upper stratosphere at mid- and high latitudes  O 3 decline in tropics near 30 km to 35 km, consistent with literature  Anti-correlation of O 3 and temperature above ~30 km, points to indirect effects/feedbacks RO Temperature ERA-Interim Temperature HARMOZ Ozone [L. Brunner, MSc Thesis, WEGC Rep. 2014]

20. Conclusions GPS RO – a unique resource:  high accuracy and vertical resolution, consistency, long-term stability  reference standard in the troposphere/stratosphere - for validating and calibrating data from other observing systems - as absolute reference within assimilation system - for climate model evaluation  for monitoring climate variability and climate change  meeting GCOS climate monitoring targets in the UTLS GPS RO long-term stable within ~0.1 K/decade (not for horizontal target resolution <100 km, and not yet globally) 19

21. 20 Outlook  Reference records with integrated uncertainty estimation (SI-traceable)  Structural uncertainty assessment (RO Trends Working Group)  Improving the maturity of RO climate records (SCOPE-CM* project RO-CLIM)  Contribution to the WMO Integrated Global Observing System (WIGOS)  Scientific applications in support of WCRP grand challenges  Essential: continuous global observations “Ensure the continuity of the constellation of GNSS RO satellites.” (Action A21 [A20 IP-04], GCOS-138, 2010) *SCOPE-CM (Sustained and coordinated processing of Environmental Satellite data for Climate Monitoring) [GCOS-154, 2011]

22. www.atmos-meas-tech.net/special_issue68.html www.atmos-meas-tech-discuss.net/special_issue48.html Activities – AMT Special Issue – Results OPAC 2013

23. 22 Activities – IROWG-4 Workshop Upcoming The IROWG was established as a permanent Working Group of the Coordination Group for Meteorological Satellites (CGMS) at the 37th meeting in October 2009 (Jeju Island, South Korea). The IROWG is co-sponsored by CGMS and the World Meteorological Organization (WMO). The IROWG serves as a forum for operational and research users of radio occultation data. UPCOMING next week: IROWG-4 Workshop April 16–22, 2015 Melbourne, Australia http://cawcr.gov.au/events/IROWG-4/ www.irowg.org

24. 23 Announcement OPAC-IROWG 2016 Workshop (www.uni-graz.at/opacirowg2013) Joint OPAC-6 & IROWG-5 8–14 September 2016 2016 6

25. THANK YOU ! Thanks for funds to Note: WEGC publications available at www.wegcenter.at/en/arsclisys-publ

26. RAOBs V90/92 and GRUAN vs RO CHAMP, GRACE, COSMIC  Annual-mean temp differences (global, altitude range 100hPa–30hPa) [Ladstädter et al. AMT 2015] 25 Cal/Val – Comparison with other Observations (3)

27. [Biondi et al. 2010, 2011, 2014] Thermal structure of strong convective systems – Cyclones  Detection of cloud top height using RO bending angle and temperature Atmospheric Variability – Convective Clouds West Pacific Ocean South Pacific Ocean 26