Global upper tropospheric/lower stratospheric water vapor from

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Global upper tropospheric/lower stratospheric water
vapor from satellites
G.P. Stiller, S. Lossow, M. Kiefer, W. Read, K.H. Rosenlof, M. García-Comas, M.E. Hervig,
G. Nedoluha, E.E. Remsberg, J.M. Russell III, T. Schieferdecker, L. W. Thomason, J.
Urban, K.A. Walker, M. Weber, J.M. Zawodny
Institute for Meteorologie and Climate Research – Atmospheric Trace Gases and Remote Sensing
HALOE tropical H2O time series,
from Rosenlof and Reid, 2008
KIT – University of the State of Baden-Wuerttemberg and
National Research Center of the Helmholtz Association
www.kit.edu
Motivation
Water vapor is the most powerful greenhouse gas in the atmosphere
The temporal variation of lower stratospheric water vapor is not yet fully
understood
Water vapor transport into the stratosphere seems to be largely, but not
fully ruled by the tropical tropopause temperatures
Hurst et al., 2011
2
18 June 2013
7th Atmospheric Limb Conference, Bremen, 17-19 June 2013
Dameris et al.
Global UTLS H2O from satellites,
G. Stiller, IMK-ASF, KIT
Motivation: Why is UTLS water vapor important?
Figure by P. de
Forster, from Riese et
al., JGR, 2012
Solomon et al., Science, 2010
3
18 June 2013
SPARC WAVAS-II satellite data quality
assessment: Aims and scope
Update of WAVAS-I report published in 2000 (SPARC report No. 2)
Analysis of the quality of water vapor satellite records:
Compare satellite records to ground-truth (CFH, LIDAR, MW)
measurements on basis of co-incident observations
Compare satellite records among each other on basis of co-incident
observations; for special regions and latitude bands/seasons
Compare satellite records on basis of climatologies; collect available
material and complete
Compare representation of temporal variation on basis of climatologies
and within co-incident comparison to ground-truth instruments (seasonal,
QBO, longer-term)
Compare upper tropospheric specific humidity from satellites (TES,
IASI, AIRS, TOVS)
Compare available satellite data records on water vapor isotopologues
(HDO, δD) and compare to ground-truth observations
General approach: review of literature completed by dedicated studies
where necessary
4
18 June 2013
today
Available Satellite Data Records
1980-1990
SAGE II+III
1990-2000
2000-2010
2010-2020
SAGE III
HALOE
MLS(UARS+Aura)
limb sounders
POAM III
SMR
MIPAS
SCIAMACHY
GOMOS
ACE-FTS
SOFIE
CLAES
ISAMS
UTH
IASI
5
TES
TOVS
18 June 2013
AIRS
SPARC Data Requirement Workshop, ESRIN, Frascati, 20-22 February 2013
Examples
6
18 June 2013
Latitude cross
sections of H20
MLS v3
HALOE
MIPAS
ACE-FTS
AIRS
SMR
HALOE is dry for most
altitudes
SMR has a pronounced dry
bias in the tropics below 38
hPa
Sparse statistics for ACEFTS
AIRS not to be used above
100 hPa
MIPAS dryer than MLS
below 200hPa and wetter
around tropopause
316 hPa
DJF 2004/05
261 hPa
215 hPa
178 hPa
147 hPa
121 hPa
100 hPa
83 hPa
68 hPa
46 hPa
56 hPa
38 hPa
Figures by Bill Read
7
18 June 2013
Consistency of satellite records: extra-tropics
Figures by Jo Urban
8
18 June 2013
Bias among the
satellite data
sets
SPARC-DI: Hegglin et al.,
submitted
9
18 June 2013
Amplitudes and phases of the seasonal cycles
SPARC-DI:
Hegglin et al.,
submitted
10 18 June 2013
Anomalies (de-biased and de-seasonalized)
20S-20N 10 hPa, 1997-2010
20S-20N 70 hPa, 1997-2010
60N-70N 10 hPa, 1997-2010
60N-70N 80 hPa, 1997-2010
SPARC-DI: Hegglin et al., submitted
11 18 June 2013
Combination of HALOE and MIPAS to one
harmonized data set
For one
altitude/latitude bin:
Resulting shift for
all bins:
Not resolved yet:
differences in the
amplitudes of
seasonal/QBO/…
variations
12 18 June 2013
SWOOSH
data set
(S. Davis
and K.
Rosenlof,
NOAA)
13 18 June 2013
14 18 June 2013
From MEASURES-GOZCARDS README documentation, L.Froidevaux, et al., 2013
Confrontation of CCMs to observational data
Kunze et al., 2010
1.Understanding of
processes, here:
WV transport
through
monsoon
systems
2. Understanding of temporal variation and trends: no single trend in observational data;
CCMs: check piecewise linear trends, ascent rates of the tape recorder, volcanic
signals, etc.. How realistic are model predictions then?
E39CA-REF-B1
(DLR)
ECHAM/MESSY
REF-B1(FUB)
HALOE/MIPAS
15 18 June 2013
1980
50-60N 15 hPa
2010
G. Stiller,
IMK-ASF,
KIT
Schieferdecker
et al.,
in prep.
Water vapor isotopologues: Comparison of HDO from
Lossow et al., ACP,
SMR, ACE-FTS and MIPAS
2011
16 18 June 2013
Water vapor isotopologues: Tape recorder and
monsoon systems
JJA
90
H2O JA2003 16 km
8
ppmv
MAM
30
SON
DJF
0.5
-90
HDO JA2003 16 km
15
-800
NP
-500 δD
ppbv
SP
1.5
-200
0.1
-200
δD permil
δD JA2003 16 km
Steinwagner et al., ngeo, 2010
Randel et al., 2012
17 18 June 2013
-800
S. Lossow et al., in prep.
Future perspectives
Satellite missions covering water vapor observation still in orbit:
MLS
ACE-FTS
SOFIE
SAGE III-ISS
SMR
AIRS
TES
IASI
Most of them are aged missions with limited remaining lifetime
In Europe: no plans for limb sounders before 2020
18 18 June 2013
Summary
The last decade was a ‘golden age’ for global water vapor observation
from satellites
The SPARC WAVAS-II activity plans a thorough satellite data quality
assessment
Satellite data records have large biases (lat/alt dependent) and
reproduce seasonality differently (amplitude and phase of the tape
recorder)
Water vapor isotopologues (HDO/δD) shed light on
condensation/freezing processes during TST of water vapor
Three available data sets – ACE-FTS and MIPAS largely consistent in
HDO, but disagree in δD.
A gap of vertically resolved UTS water vapor observations is to be
expected for the next decade
19 18 June 2013
Acknowledgment
Stefan Lossow, Michael Kiefer – KIT (MIPAS)
William Read – JPL (MLS)
Karen H. Rosenlof – NOAA (WAVAS activity leader)
Jean-Loup Bertaux – LATMOS (GOMOS)
Maya García-Comas – IAA, CSIC (MIPAS)
Mark E. Hervig – GATS Inc. (SOFIE)
Gerald Nedoluha – NRL (POAM)
Ellis E. Remsberg – NASA Langley (HALOE)
James M. Russell III – Hampton University (LIMS, HALOE, AIM)
Larry W. Thomason – NASA Langley (SAGE)
Jo Urban – Chalmers University (SMR)
Kaley A. Walker – University of Toronto (ACE-FTS)
Mark Weber – University of Bremen (SCIAMACHY)
Joseph M. Zawodny – NASA Langley (SAGE)
Thank you for your attention!
20 18 June 2013
Additional slides
21 18 June 2013
Temporal variation in satellite records and
Boulder time series
Hurst et al., 2011
HALOE+MIPAS, 0-10deg N, 18 km
Days after 01/01/2001
500
Altitude [km]
30
Date of H2O drop from H+M
HALOE+MIPAS 35-45 deg N
0
-500
15
90S
22 18 June 2013
Latitude
90N
Schieferdecker et al.,
in prep.
1992
2010
Consistency of satellite records: tropics
HALOE SAGE SCIAMACHY SMR MIPAS MLS
 Large biases
 Strong variability in the
amplitude of the tape recorder
 Data records agree much
better after de-seasonalization
Figures by Mark Weber
23 18 June 2013
Confrontation of CCMs to observational data
Kunze et al., 2010
1.Understanding of
processes, here:
WV transport
through
monsoon
systems
2. Understanding of temporal variation and trends: no single trend in observational data;
CCMs: check piecewise linear trends, ascent rates of the tape recorder, volcanic
signals, etc.. How realistic are model predictions then?
E39CA-REF-B1
(DLR)
ECHAM/MESSY
REF-B1(FUB)
HALOE/MIPAS
24 18 June 2013
1980
50-60N 15 hPa
2010
G. Stiller,
IMK-ASF,
KIT
Schieferdecker
et al.,
in prep.

Global upper tropospheric/lower stratospheric water vapor from

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