Lecture 2: Variability and trends in stratospheric temperature and water vapor• Stratospheric temperature• Climate change and the stratosphere• Stratospheric temperature trends:
observations (balloons and satellites) and model simulations• Recent results from the upper stratosphere• Stratospheric water vapor• Seasonal cycle and the ‘tape recorder’• Interannual changes• Links to tropical tropopause temperatures

Simple view: climate change in the stratospherestratospherecools

tropospherewarms

WMO Ozone Assessment, 1985

United States CCSP 2006 Assessment:
Temperature Trends in the Lower Atmospheremodel
results

temp trends
for 1958-1999
totalradiosondedata sets

note that radiosondedata give stronger
cooling than models

ozone
CO2 increases andO3 decreases act tocool the stratosphere

CO2Model calculated stratospheric temperature trends
Shine et al 2003

modelsstratosphere cooling:
about ½ from CO2 increase
and ½ from O3 decrease
satellites

radiosondes

Data sources for stratospheric temperature trends:
mainly for
research
lidars>1980

rockets~1960-1990

satellites> 1979radiosondes~1960

Fundamental problem: data are intended for weather forecasting,not climate variability and trends

Operational satellites (nadir sounders)nadir sounders are sensitive totemperatures over deep layers (~10-15 km)

50 km

GPSradiosondes

2001-present

30 km

10 km

Global radiosonde network(~30 km)Characteristics:•

Majority of measurements
over continents•

Poorer coverage at upper levels•

Radiosonde sensors change over time(~21 km)

Problem: inhomogeneities in historical radiosonde data due to
instrumentation changes, radiation corrections, etc.

radiosonde record
from Naimey
Lanzante et al 2003

Corrections can be made using different techniques:••••Manual adjustments for ~80 key stations (RATPAC, Free et al , 2005)
Statistical adjustments (HADAT2; Thorne et al, 2005)
Statistical identification of ‘break points’ (IUK, Sherwood et al, 2008)
Using meteorological data assimilation increments to identify break points(Raobcore, RICH; Haimberger et al, 2008)

Example of radiosonde station with artificial change
deseasonalizedanomalies
satellite

note that jumps are
not evident in anomaly
time series

radiosonde

difference

jump due to change
in radiosondeCommon problem; many
historical radiosondedata have similar jumps

Historical radiosonde results now available from
6 separate homogenized data sets:
RATPAC (Free et al, 2005)(expert judgement for 85 stations)

RATPAC-lite (Randel and Wu, 2006)(subset of RATPAC stations)

HadAT2 (Thorne et al, 2005)(use near neighbors to identify breaks)

IUK (Sherwood, 2007)(statistical fits to identify break points)

RAOBCORE 1.4 (Haimberger, 2007)
RICH (Haimberger et al., 2008)(use ERA40 assimilation
increments to identify breaks)

differences provide a measure of ‘structural uncertainty’JGR, 2009

Comparison of time series
from different homogenizedradiosonde data sets

Tropics (30o N-S) 50 hPa
deseasonalizedanomaliesTemperature trends from radiosonde data

results from different
homogenized data sets

Randel et al., 2009, J. Geophys. Res.

Lower stratosphere temps: MSU4 satellite and radiosondes, 60 N-S

Volcanoes:

El Chichon

Pinatubo

MSU4 satellite

Radiosondes, using
RATPAC-lite stations

note relatively constanttemps after ~1995

Randel, 2010, American Geophysical Union

Global average time series from RICH radiosonde dataAElarge
volcanoes

Randel, 2010, American Geophysical Union

PReasonable overall agreement among radiosonde and satellite data setslargevolcanoes

lowerstratosphere

black: satellite
colors: radiosondes

lowertroposphere

IPCC AR5 2014

Quantifying temperature variability using multiple linear regression
From experience, stratospheric temperature is known to be influenced by the QBO,
the 11-year solar cycle, volcanoes, ENSO, plus changes in CO2 and O3 and H2O

𝑂3 𝑡 = 𝐴1 ∗ 𝑄𝐵𝑂1 𝑡 + 𝐴2 ∗ 𝑄𝐵𝑂2 𝑡 + 𝐴3 ∗ 𝑠𝑜𝑙𝑎𝑟 𝑡 + 𝐴4 ∗ 𝑡
Use two orthogonal proxies
for QBO

Long-term changeor linear tend

Could also include other proxies, such as for ENSO, volcanoes or EP fluxes

JAS 1993

Other proxies:
Key point: two orthogonal EOF’s explain almost allof the variance tied to the QBO

QBO1 and QBO2:
orthogonal proxies

Solar cycle (F10.7 flux)

ENSO

Temperature trends and ENSO signal derived from
RICH radiosonde data 1970-2010

stratospherecools(K/decade)(K/MEI index).3

tropospherewarms

Randel, 2010, American Geophysical Unionchanges focusedIn tropics

Regression fits of QBO using GPS temperatures
there is a corresponding QBO2 pattern (orthogonal to QBO1)

QBO1-.2

Randel and Wu, 2014, J. Atmos. Sci.

•Signals confined to stratosphere•Out-of-phase patterns in subtropics
reflect meridional circulation

Variability in the tropical lower stratosphere:time series and regression fits
at 70 hPa, 10o N-S

note that the volcanic signal
is clear if you first remove‘other’ variability

Randel, 2010, American Geophysical Union

Volcanic signals derived as ‘residuals’
to regression fits
Temperature anomalies for 2 years
after volcanic eruptions

El Chichon

Pinatubo

StratospherewarmsTropospherecools
Randel, 2010, American Geophysical Union

Polar stratosphere temperatures

Large ‘natural’
year-to-year
variability during
winterRandel et al., 2009, J. Geophys. Res.

Polar temperature trends

Cooling due
to Antarctic
ozone holeRandel, 2010, American Geophysical Union

In the middle and upper stratosphere, satellite measurements
are the primary data set for variability and trends• Broad layer temperatures• Derived from many separate
operational instruments• Long-term records need to beconstructed for trend studies

Satellite records are constructed from many separate instrumentsOperational NOAA satellites

MSU + SSU
AMSU(>1998)

MSU only

HALOE (>45 km)

Research satellites:
Randel, 2010, American Geophysical UnionMIPAS, GOMOS, SABER, MLSLower stratosphere temperatures (MSU4) are well characterizedradiosondes

MSU4 satelliteEP

Randel, 2010, American Geophysical Union

Several groups haveconstructed climate
records from MSU

Middle-upper stratosphere temperatures from SSU

Constructed byJohn Nashfrom UK Met OfficeSSU340-50 km

SSU235-45 km

SSU125-35 km

But:• Details not wellunderstood• No independentanalyses ofSSU data

MSU413-22 km

CO2 increases and the SSU weighting functionTemp

effect ofincreased CO2(exaggerated)

SSU26
weightingfunction

Higher CO2 raises SSU weighting function, withresulting (apparent) positive temperature trend

SSU pressure modulator cells leak over time. These leaks cause a change in themodulator frequency over time, which can be used to monitor the gas leakage.

these effects on measured temperatures canbe estimated using SSU radiative transfer model

Zou et al, 2014

Recent independent
analysis of SSU dataJ. Climate 2012

SSU3
SSU3

SSU2
SSU2SSU1SSU1

Global-average Stratospheric Temperature

Yikes!

Thompson et al 201232

Comparisons with Models
Chemistry-Climate Models
SSU 40-50 km

Altitude

SSU 35-45 km

SSU 25-35 km

MSU 15-25 km

Thompson et al., 2012, Nature33

Comparisons with Models
Chemistry-Climate Models

Atmosphere-Ocean Global Climate Models
SSU 40-50 km

Altitude

SSU 35-45 km

SSU 25-35 km

MSU 15-25 km

Thompson et al, Nature, 201234

Latitudinal profile of trends

Stronger cooling in tropics
for NOAA data

Thompson et al., 2012, Nature

Not the last word: new, updated versions of NOAA and UKMO SSU dataUKMO v2
NOAA v2
NOAA v1

Zou et al, JGR 2014, in pressUpper stratospheretemperature trends
1979-2006

relatively ‘flat’latitudinal structure

Zou et al, JGR 2014, in press

A similar situation exists for measurements of stratospheric ozone:
Global ozone anomalies derived from combined SBUV measurements

Globalcolumn ozone

anomalies

McPeters et al 2014

Some important points:• Radiosondes and satellites primarily intended for weather forecasting,not climate monitoring• Historical radiosonde data have artificial cooling biases, but thesehave been corrected using different techniques• Long-term temperature changes are small, and correcting/merging
data sets is difficult• Valuable to have different groups evaluate and homogenize data sets
(examples: radiosondes and MSU satellite data, and now SSU)
• Upper stratosphere satellite data (SSU) still a work in progress• Meteorological reanalyses rely on satellite data, and can be affectedby the same problems

Global temperature anomalies from reanalyses
older generation

newer generation

0-50 km

ERA interim

MERRA

JRA-25JRA-55jumps due to satellite changes

Extra slides

11-year solar cycle in temperature derived from SSU dataObserved

WACCM model

Marsh et al, 2007

Comparison of SSU data with lidar measurements at OHP
SSU27

Seasonal (3-month avg.) anomalies

Randel et al 2009

trendsStratospheric water vapor• Measurements of stratospheric H2O• Global variability and seasonal cycle• Simulations of H2O: trajectory models and global models• Long-term variability, trends and links to tropical tropopause temperatures

Measurements of stratosphere water vapor
JGR, 2009
cryogenic
frostpointhygrometerballoon CFH

MLS
satellite

JGR, 2011

balloon frostpoint
hygrometer measurements
at Boulder (40o N)
1980 – present(~ 1 per month)aircraft measurements

AustraliaACP, 2009

BrazilAfrica

JGR 2010
Tropical balloon
measurements

HALOE
satellitedata

HALOE solar
occultation
Measurements•

Good vertical resolution ~2 km•

Limited space-time sampling•

Observations 1992-2005

HALOE samplingfor one year

Aura Microwave Limb Sounder (MLS)•Vertical resolution ~3 km•

Daily global sampling•

Observations 2004-present

MLS daily
orbital data

QJRMS, 1949
The stratosphere is
extremely dry because
air is dehydrated
passing the cold
tropical tropopause

Workshop on Brewer-Dobson circulation, Oxford University, December 1999

HALOE global climatology

QJRMS, 1949

Climatological‘tape recorder’

HALOE
cold point
tropopause

MLSTropical tape recorder observed by MLS 2004-2012

cold point
tropopause

2005

2006

2007

2008

2009

2010

2011

Interannual variations in tropopause
temperature reflected in H2O

2012

Lower stratosphere horizontal tape recorder

390 K

HALOE

3 years from MLS observations

quasi-horizontal transport
in lower stratosphere, approximately
following 400 K isentrope

dehydration in
Antarctic polar vortex

Tropical dehydration
zone is ~20 N-S

Climatology at Boulder (40o N)
HALOEBalloon

tropopause

seasonal minimum due to transport from tropics

Trajectory simulation of transport on 400 K isentropecalculations for June-August 2001

Summertime lower stratosphere maxima linked to monsoon circulations
Asianmonsoon

HALOE climatology

HH

North Americanmonsoon

Climatological circulation and OLR

MLS climatology

monsoon anticyclones,
much stronger over Asia

white lines: OLR (deep convection)

100 hPa H2O aligned
more with circulation
than with deep convection

Trajectory simulations of seasonal cycle
so-called advection-condensation paradigm* dehydration at Lagrangian cold point *modelobs

Brewer, 1949

Note that results are sensitive
to many details of the calculations:kinematic vs. diabatic trajectories,
temperature data, supersaturation,….
Fueglistaler et al 2005 JGR
also Liu, Fueglistaler, Haynes, JGR 2010

Trajectory calculations based on different data sets
Schoeberl et al 2012 ACP

MLS obs.
MERRA

CFSR

ERAinterim

Details are sensitive to the meteorological data

Water vapor in summer monsoons simulated in trajectory models

observations

trajectory model

Schoeberl et al 201363

Trajectory simulation of dehydration in Asian monsoon
Wright et al 2011 JGRColddehydration
regionInterannual changes in stratospheric water vapor

HALOE global mean, 82 hPa

deseasonalized

decrease after 2001
Randel et al., 2004, J. Atmos. Sci.

Extending the satellite record: HALOE + Aura MLS data

MLS

Variability tied tothe QBO. What else?

HALOE
overlap during
2004-2005

H2O anomalies originate near the tropical tropopause,and propagate coherently with time
vertical propagation

vertical propagation

latitudinalpropagationfrom tropics

Comparisons with the Boulder balloon recordMLSppm

near-global
HALOE

satelliteppm

measurements
near Boulder (40o N)
balloons

Correlated variations in stratospheric H2O and cold point temperatures
Deseasonalized anomalies

H2O

most recent plot, updated through 2013

near-global mean (60o N-S)
water vapor at 82 hPafrom combined HALOE-MLS dataTCP
cold-point tropical
tropopause temperatures
black: radiosondes
red: GPS (after 2001)
r=0.78Same data, 3-month averages

Very strong correlation
during cold season
Winter-spring

H2O

r=0.89temp

Summer-fall

r=0.76

r=0.58

Seasonalcorrelations

Chemistry-climate model simulations from WACCM

‘tape recorder’
HALOE vs. WACCM
HALOE

WACCM

EP

In the model, volcanoes dominate interannual variability

Observations: HALOE + MLS
very different
variability
after 1992

Observations: HALOE + MLS
very different
variability
after 1992

Key points:• Stratospheric H2O seasonal cycle is well understood. Tropicaldehydration mainly during boreal winter (cold season).
Tape recorder, rapid global transport in lower stratosphere,monsoons in UTLS during NH summer. Also Antarctic dehydration.

• Interannual changes for satellite record (1992-2013) in good(quantitative) agreement with tropical cold point. Cold point
controls stratospheric water vapor; what controls the cold point?
•What controls water vapor in summer monsoon regions?
Is deep overshooting convection important?

• Simulation of seasonal cycle in trajectory calculations andchemistry-climate models is reasonable. Interannual variability
in models is different from observations.

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