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Atmospheric variability modes and trends in the UTLS from the RO recordA. K. Steiner1, B. Scherllin-Pirscher1, F. Ladstädter1,
R. Biondi1, L. Brunner1, G. Kirchengast1,2, and the ARSCliSys group
1 Wegener Center for Climate and Global Change (WEGC) and 2 IGAM/Inst. of Physics, University of Graz, Austria
.SPARC Temperature Trends Workshop, Victoria, BC, Canada, April 9-10, 2015
2
Courtesy: T. Rieckh
Global Positioning System (GPS) radio signals at 2 frequencies1575.42 MHz (~19 cm)1227.60 MHz (~24 cm)
Receiver on LEO satellite
Occultation geometry
Atmospheric refraction of signals
Measurements of phase pathbased 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
GPS RO Data Availability and Products
3
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)
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 resolutionabout 100 km to 300 km in the UTLS, synoptic scales, climate
Long-term stabilitymeasurements 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
RO Data Consistency
Consistency of different satellitesOne 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 centersStructural uncertainty CHAMP
[Ho et al. JGR 2009, 2012; Foelsche et al. TAO 2009, AMT 2011; Steiner et al. RS 2009, ACP 2013]
6
Cal/Val – Comparison with other Observations (1)
Envisat MIPAS and GOMOS (here global 10/20km–30km)ESA project MMValRO Multi-Mission Validation against RORO is a valuable reference record over Envisat period 2002–2012MIPASv6.0 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
Cal/Val – Comparison with other Observations (2)
Radiosonde Data Vaisala 90/92 vs RO
[Ladstädter et al. AMT 2015]
RAOBs V90/92 and GRUAN vs RO for day and nightAnnual-mean temp differences (global, 10hPa–30hPa, day/night)
[Ladstädter et al. AMT 2015] 8
Cal/Val – Comparison with other Observations (3)
9
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]
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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 SO2 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 SO2 cloud
○Caliop data
Monitoring Climate Variability with RO
RO Temperature anomalies 05/2001-12/2013 in UTLS (SE subtracted)
QBO signal above the tropical tropopauseENSO signal below the tropical tropopause
11
Principal Component Analysis – QBO
Principal component analysis in LS (20km-30km): PC1, PC2 → QBO
12
Regression Results (1)
Regression Results Tropical LS: altitude levels 25 km and 20 km
13
Regression Results (2)
Regression Results Tropical UT: altitude levels 15 km and 10 km
14
QBO and ENSO Variability
15
QBO & ENSO Residual Variance
Deseasonalized Temp. Anomalies Kelvin Waves
[Scherllin-Pirscher et al. EGU 2015]
RO Temperature Trends in Tropical UTLS
16
Trend in Tropics Variance
Trend not significant, residual variance large near TP, above 28 km
Ozone and Temperature Evolution
17
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 stratosphereMerged 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
Ozone and Temperature Regression Results
18
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.
Ozone and Temperature Trends 2002–2012
19
GOZCARDS Ozone SBUV Ozone ERA-Interim Ozone
Agreement RO and ERA-Int, temperature trends not significant, large nat. variability O3 increase in mid- and upper stratosphere at mid- and high latitudes
O3 decline in tropics near 30 km to 35 km, consistent with literature
Anti-correlation of O3 and temperature above ~30 km, points to indirect effects/feedbacks
RO Temperature ERA-Interim Temperature HARMOZ Ozone
[L. Brunner, MSc Thesis, WEGC Rep. 2014]
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 UTLSGPS RO long-term stable within ~0.1 K/decade (not for horizontal target resolution <100 km, and not yet globally)
20
21
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]
www.atmos-meas-tech.net/special_issue68.htmlwww.atmos-meas-tech-discuss.net/special_issue48.html
Activities – AMT Special Issue – Results OPAC 2013
23
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 WorkshopApril 16–22, 2015 Melbourne, Australiahttp://cawcr.gov.au/events/IROWG-4/
www.irowg.org
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Announcement OPAC-IROWG 2016 Workshop
(www.uni-graz.at/opacirowg2013)
Joint OPAC-6 & IROWG-5
8–14 September 2016
2016 6
THANK YOU !
Thanks for funds to
Note: WEGC publications available at www.wegcenter.at/en/arsclisys-publ
RAOBs V90/92 and GRUAN vs RO CHAMP, GRACE, COSMICAnnual-mean temp differences (global, altitude range 100hPa–30hPa)
[Ladstädter et al. AMT 2015] 26
Cal/Val – Comparison with other Observations (3)
[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 Ocean27