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Chemistry-Climate Model Validation
Activity for SPARC (CCMVal)
Status and Workshop Goals
Veronika Eyring (DLR), Ted Shepherd (Univ. Toronto),
Darryn Waugh (JHU), Andrew Gettelman (NCAR),
and Steven Pawson (NASA)
CCMVal Workshop 2009; Toronto, 1-5 June 2009
Motivation
CCMVal-1 simulations
CCMVal approach to CCM Evaluation and analysis
Process-oriented
CCM evaluation
CCMVal Evaluation Table
Eyring et al., BAMS, 2005
Example: CCMVal Diagnostic Table For Transport
Process
Diagnostic
Variables
Subtropical and
polar mixing
barriers
PDFs of long-lived
tracers
N2O, CH4, CFC-11, etc.; Satellite and in situ
Potential Vorticity (PV)
(aircraft, balloons)
chemical measurements
and Meteorological
Analyses
Latitudinal gradients of
long-lived tracers
Correlations of long-lived
tracers
Meridional
circulation
Data
Phase and amplitude of
tropical CO2 or H2O
annual cycle in lower
stratosphere (tape
recorder)
CO2, H2O or idealized
annually repeating
tracer
Satellite and in situ
measurements
Mean age
Conserved tracer with
linearly increasing
concentration, SF6 or
CO2;
in situ measurements
Correlation of interannual Total ozone and heat
anomalies of total ozone flux at 100 hPa, zonal
and Planetary wave flux
and monthly means
Satellite measurements,
Meteorological Analyses
Entire CCMVal Evaluation table: http://www.pa.op.dlr.de/CCMVal/CCMVal_EvaluationTable.html
Eyring
will be updated as part of the SPARC CCMVal Report
et al., BAMS, 2005
CCMVal-1: Model Assessment (13 CCMs)
mainly focused on transport and polar dynamics diagnostics
Temperature Bias
Water Vapor
Eyring et al.,
JGR, 2006
Inorganic Chlorine
Mean Age
CCMVal-1: Ozone Projections
There are substantial differences in
the date at which Cly returns to
1980 values varying from before
2030 to after 2050. (Primarily
transport related.)
There is a similar large variation in
the timing of recovery of Antarctic
spring-time column ozone back to
1980 values.
Value of Process-oriented
evaluation of CCMs
Eyring et al., JGR, 2007
Unsatisfying from an assessment point of view ...
WMO, 2007:
TWENTY
QUESTIONS AND
ANSWERS ABOUT
THE OZONE
LAYER: 2006
UPDATE
Lead Author:
D.W. Fahey
Quantitative Performance Metrics:
Inorganic Chlorine Cly
Grades:
Waugh & Eyring,
ACP, 2008
bad
good
CCMVal-1 developed
performance metrics
1
0
Waugh and Eyring, ACP, 2008
Potential benefits:
 Allow visualization of performance for
multiple aspects of the simulations.
 Allow identification of incompletely modeled
processes.
 Enable quantitative assessment of model
improvements for different versions of
individual CCMs and for different generations
(e.g. CCMVal-1 vs CCMVal-2).
 Make it possible to explore the value of
weighting the projections.
Potential improvements:
 Explore other metrics.
 Consider other diagnostics (radiation &
chemistry).
 Better consider uncertainty in
observations by using observations from
multiple platforms and instruments.
 Metrics for seasonal and interannual
variability and trends.
 Statistically more robust grading.
Weighting Ozone
Projections
For diagnostics and ozone
projections considered
there is, generally, only
small differences between
weighted and unweighted
multi-model mean
projections.
IPCC, WGNE and WGCM
• The missing set of metrics that might be used to narrow
the range of plausible climate projections through a
comparison of model simulations with observations is one
of the current principal source of uncertainty in model
evaluation [IPCC, AR4, WG1, 2007].
• WGNE/WGCM metrics panel established (Members: Karl
Taylor, Beth Ebert, Veronika Eyring, Peter Gleckler, Robert
Pincus, Richard Wood)
• IPCC Expert meeting on "Metrics" and "Assessing and
Combining Multi-model Climate Projections", January 2010,
NCAR, Boulder, USA
CCMVal Diagnostic Tool (requires NCL & Python installed)
Andrew Gettelman (NCAR), Veronika Eyring (DLR), Greg Bodeker (NIWA), Irene Cionni
(DLR), Chris Fischer (NCAR), Mike Neish (Univ. Toronto), Hamish Struthers (NIWA), Ted
Shepherd (Univ. Toronto), Hisako Shiona (NIWA) & Charlotte Pascoe (BADC)
Facilitate the model evaluation for CCMVal, e.g.
 Allow quick looks at standard diagnostic plots & output diagnostic variables
 Produce climatology files from CCMVal CF-compliant model output.
Include the diagnostics of the previous round of CCMVal evaluation so that we don't
have to start from scratch each time
 ensures progress
 allows to assess quickly where we stand with the new CCMVal simulations
 helps to increase the standard for CCM evaluation
Expandable and extensible
 Useful for multiple model groups & those analyzing models
 SPARC CCMVal report can extend tool (i.e. provide diagnostics once report is finalized)
 Extend with tropospheric diagnostics (in collaboration with Jean-Francois Lamarque)
E06FIG01.ncl: Figure1 of Eyring et al., JGR, 2006
./var_att/ta_att.ncl
info@fig01_lat_max = (/90.,90.,-60.,-60./)
info@fig01_lat_min = (/60.,60.,-90.,-90./)
info@fig01_season = (/"DJF","MAM","JJA","SON"/)
info@fig01_refModel = (/"ERA-40"/)
info@fig01_climObs =(/"NCEP","UKMO"/)
;Climatological Observation file
info@fig01_climObs_file
=(/"input_data/OBS/CCMVal1_1980_2000_NCEP_Obs
_T2Mz_ta.nc","input_data/OBS/CCMVal2_19922001_UKMO_Obs_C2Mz_ta.nc"/) ;C2Mz file
Climatological mean temperature biases for latitudes
between info@fig01_lat_max and info@fig01_lat_min
for the
info@fig01_season
seasons. Biases are
calculated relative to info@fig01_refModel. The grey
area shows info@fig01_refModel plus and minus 1
standard deviation about the climatological mean. The
climatological means info@fig01_climObs are included.
CCMVal-2 (in support of WMO/UNEP 2010 and AR5)

18 CCMs participate in the 2nd round of CCMVal (CCMVal-2).

CCMVal-2 reference and sensitivity simulations defined (Eyring et al., 2008)
 CCMVal-2 simulations of the future begin in 1960, and most continue to 2100.
 Earlier starting date allows a more accurate determination of the milestone when total
ozone returns to pre-1980 levels
 Extended simulations allow multi-model ozone projections and an analysis of the
causes of these projected changes throughout the 21st century.

CCMVal-2 data request output is collected in Climate and Forecast (CF) standard
compliant NetCDF format at BADC.
 Allows automatic software to work on the output (e.g. CCMVal diagnostic tool and
netCDF operators).
 Same format as for CMIP5 simulations.
 Base output for core diagnostics 3D (lat, lon, p) monthly mean fields. In addition,
instantaneous output for a subset of diagnostics is collected (e.g. UTLS).

CCMVal Data Policy:
 To release the model data to the SPARC CCMVal author teams at an early stage, a
PHASE 0 has been added to the existing Phase 1 and 2 of the CCMVal data policy.
 In PHASE 0 CCMVal-2 data are only accessible for the authors and models PIs.
CCMVal-2 Models
Model
Group and Location
Horiz. resolution
Vert. Layers / Upper
Boundary
1
AMTRAC3
GFDL, USA
2˚ (lat) x 2.5˚(lon)
48 L / 0.002 hPa
2
CAM3.5
NCAR, USA
1.9(lat)x2.5(lon)
26 L / 2.2 hPa
3
CCSRNIES
NIES, University of Tokyo, Japan
T42
34 L / 0.012 hPa
4
CMAM
MSC, Univ. of Toronto, York Univ., Canada
T31
71 L / 0.000637 hPa
5
CNRM-ACM
Meteo-France, France
T42(GCM), T21(CHEM)
60L / 0.07 hPa
6
E39CA
DLR, Germany
T30
39 L / 10 hPa
7
EMAC
MPI Mainz, Germany
T42
90 L/ 0.01hPa
8
GEOSCCM
NASA/GSFC, USA
2˚ x 2.5˚
72 L / 0.01hPa
9
LMDZrepro
IPSL, France
2.5˚ x 3.75˚
50 L / 0.07 hPa
10
MRI
MRI, Japan
T42
68 L / 0.01 hPa
11
NIWA-SOCOL
NIWA, NZ
T30
39 L / 0.01 hPa
12
SOCOL
PMOD/WRC and IAC ETHZ, Switzerland
T30
39 L / 0.01 hPa
13
ULAQ
University of L'Aquila, Italy
10˚ x 22.5˚ (CHEM)
26 L / 0.04 hPa
14
UMETRAC
NIWA, NZ
2.5˚ x 3.75˚
64 L / 0.01 hPa
15
UMSLIMCAT
University of Leeds, UK
2.5˚ x 3.75˚
64 L / 0.01 hPa
16
UMUKCA-METO
MetOffice, UK
2.5˚ x 3.75˚
64 L / 84 km
17
UMUKCA-UCAM
University of Cambridge, UK
2.5˚ x 3.75˚
64 L / 84 km
18
WACCM (v.3)
NCAR, USA
4˚ x 5˚
66 L / 4.5 x 10-6 hPa
Documentation of Model Improvements
Inorganic
Chlorine
CCMVal-1
CCMVal-2
SPARC CCMVal Report on Evaluation of CCMs
18 CCMs in support of WMO/UNEP 2010 and IPCC AR5


In the past there has been insufficient time to evaluate CCM performance thoroughly while preparing
the Ozone Assessments.
The goal of the SPARC CCMVal report is to provide useful and timely information for the
WMO/UNEP 2010 & IPCC AR5 and an up-to-date evaluation of CCMs, a reassessment of the
projections of ozone and UV radiation through the 21st century, and the impact of stratospheric
changes on climate.
Structure and Authors (around 100 authors are analyzing the CCMVal-2 data):
 Executive Summary {Eyring, Shepherd, Waugh plus chapter Lead Authors}
 Chapter 1: Introduction {Eyring, Shepherd, Waugh}
 Part A: Chapter 2: Chemistry Climate Models and Scenarios {Morgenstern, Giorgetta, Shibata}
 Part B: Process evaluation

Chapter 3: Radiation {Fomichev, Forster}

Chapter 4: Dynamics {Butchart, Charlton}

Chapter 5: Transport {Neu, Strahan}

Chapter 6: Chemistry and microphysics {Chipperfield, Kinnison}

Chapter 7: UTLS {Gettelman, Hegglin}
 Part C: Chemistry-Climate Coupling

Chapter 8: Natural Variability {Manzini, Matthes}

Chapter 9: Long-term Projection of Stratospheric Ozone {Austin, Scinocca}

Chapter 10: Effect of the Stratosphere on Climate {Baldwin, Gillett}
Timelines: Currently under final external review, published Jan-March 2010; JGR Special Issue
Chapter 7: The UTLS in chemistryclimate models
Michaela I. Hegglin & Andrew Gettelman
Seok-Woo Son, Masatomo Fujiwara, Simone Tilmes, Laura Pan, Peter Hoor, Huikyo
Lee, Gloria Manney, Thomas Birner, Gabriele Stiller, Markus Rex, Stefanie
Kremser, Don Wuebbles
•
•
CCMVal-2 included for the first time the comprehensive validation of CCM
performance in the tropical and extra-tropical UTLS.
Major efforts were needed in order to
 find and process suitable observations for comparison
 establish robust diagnostics for the CCMs (which operate in a

•
free-running mode and are multi-dimensional)
turn the diagnostic into a quantitative grade
Trend analyses for tropopause measures and chemical species were also
included.
The global UTLS
Seasonal cycle in H2O
The right representation of the water vapour is critical for both the radiative and chemical
properties of the UTLS (and the stratosphere in general), and should be mainly
determined by the
seasonal cycle in the tropical
cold point temperatures.
• The models show major
difficulties in reproducing
the H2O seasonal cycle.
• Model means, phase, and
amplitude of the seasonal
cycle exhibit a wide range.
• At lower altitudes, the
models indicate too
strong mixing across the
tropopause.
GLOBAL UTLS METRICS TABLE
KEY FINDINGS
• Despite their relatively low horizontal resolution, the CCMs perform reasonably well in
resolving the complex processes and structures in the UTLS. Models with semi- Lagrangian
transport schemes tend to be overly diffusive and perform worst.
• The models perform least in representing the amplitude and phase in water vapour, both in
the tropics and the extra-tropics. This seems particularly worrisome in the light of the expected
feedback role of water vapour.
•
•
KEY ISSUES AND RECOMMENDATIONS
The UTLS is still relatively sparsely sampled by observations. This limits our
confidence in the quantitative evaluation of model performance in the UTLS. New
observations are needed especially for O3 and H2O with a vertical resolution
better than 1 km and a horizontal resolution better than 100 km, especially in
the SH and the tropics.
In this round of CCMVal, our main focus was on evaluating the representation of
dynamics, and transport and mixing in the UTLS.
However, testing chemistry should be part of future model validation efforts,
including tropospheric CCMs used for the IPCC. Future model development is
needed that brings together tropospheric and stratospheric chemistry
climate models.
SPARC CCMVal Report
Chapter 8: Natural Variability of Stratospheric Ozone
Lead Authors: Elisa Manzini and Katja Matthes
Co-Authors:
Christian Blume, Greg Bodeker, Chiara Cagnazzo, Natalia
Calvo, Andrew Charlton-Perrez, Anne Douglass, Pier
Giuseppe Fogli, Lesley Gray, Junsu Kim, Kuni Kodera,
Markus Kunze, Cristina Pena Ortiz, Bill Randel, Thomas
Reichler, Gera Stenchikov, Claudia Timmreck, Matt Toohey,
and Shigeo Yoden
Motivation
Stratospheric ozone is known to vary in response to a number of
natural factors, such as the seasonal and the 11-year cycles in solar
irradiance, the QBO, ENSO, variations in transport associated with
large-scale circulations (i.e., Brewer Dobson circulation) and
dynamical variability associated with the annular modes. Aerosols
from volcanic eruptions can also affect stratospheric ozone, although
their effects depend on the background atmospheric composition.
Ozone observations have demonstrated variations on a large number
of spatial and temporal scales.
Objective:
The goal of this Chapter is to evaluate how well CCMs simulate natural
stratospheric ozone variability, based on our current knowledge about
links between ozone variations and natural forcings.
Tropical variability measure: Detrended, deseasonalized and filtered
(9-48 months) time series. Monthly zonal mean standard deviation (5oS-5oN).
Zonal wind (m/s)
Ozone density (DU/km)
TOP PANELS:
The QBO (via
nudging of the zonal
winds or vorticity) is
assimilated in the
chemistry climate
models (8 models out
of 17 models)
CCMVal-2 simulations: in color, ERA40 (left) and SAGE (right) black.
QBO, key results:
•The modeling of the QBO in the CCMVal-2 models is judged to be still at
a primitive stage. A very few models are capable to spontaneously
simulate the salient features of QBO ozone variations.
•Some AGCM in recent years have been able to simulate a quite realistic
QBO in zonal winds and related dynamical quantities, but it does not
seem that this expertise has passed to the CCMs, possibly also because
of the computational and/or developmental constrain of the additional
chemical modelling.
•The nudging of the QBO induces substantial errors in QBO ozone
variations, notably in the amplitude of the column ozone.
RECOMMENDATIONS:
Advance the development of the modeling of the QBO is called for. This
modeling problem is outstanding. It is an open issue that cannot be
tackled by assimilating selected properties of the observed QBO in the
CCMs.
SPARC CCMVal Report
Chapter 9: Long-term projections of stratospheric ozone
Lead Authors: John Austin and John Scinocca
Co-Authors:
Trevor Bailey, Luke Oman, David Plummer, David
Stephenson, and Hamish Struthers
Ozone Changes in CCMVal-2
Vertical profile results of the
MLR analysis for the CCMVal-2
models in the latitude band
10°S-10°N.
(a) Ozone in the year 2000
(b) Ozone change from 2000 to
2100
(c) EESC change from 2000 to
2100,
(d) Contribution of the EESC
change to the ozone change.
(e) and (f) are the same as (c)
and (d), except for temperature.
From Chapter 9, Figure 9.4 of
SPARC CCMVal (2010).
Ozone Changes in CCMVal-2
1980 baseline-adjusted multi-model
trend (MMT) estimates of annually
averaged total ozone for the latitude
range 60-90°S (heavy dark grey
line) (upper panels).
The baseline-adjusted IMT
estimates, and unadjusted fit to the
observations are additionally plotted.
The lower panel shows the same
analysis of CCMVal-2 data but for a
baseline adjustment employing a
1960 reference date
From Chapter 9, Figure 9.11 of
SPARC CCMVal (2010).
Ozone Changes in CCMVal-2
Date of return to 1960 (left)
and 1980 (right) values for
the annual average (tropical
and midlatitude) and spring
(polar) total ozone column
derived from the IMT
(colored symbols) and MMT
(large black triangles)
estimates for CCMVal-2 (left
and right respectively in
each latitude band).
From Chapter 9, Figure
9.21 of SPARC CCMVal
(2010).
SPARC CCMVal Report
Chapter 10: Effects of the stratosphere on the troposphere
Lead Authors: Mark Baldwin & Nathan Gillett
Co-Authors:
Piers Forster, Ed Gerber, Michaela Hegglin, Alexey
Karpechko, Junsu Kim, Paul Kushner, Olaf Morgenstern,
Thomas Reichler, Seok-Woo Son, Kleareti Tourpali
Effect of ozone hole depletion/recovery on climate
y-axis is SH:
polar cap 50 hPa ozone
x-axis is:
(a) SH polar cap 100
hPa temperature
(b) TP pressure
poleward of 50S
(c) latitude of 850 hPa
zonal wind maximum
(d) SH Hadley cell
boundary at 500 hPa
Effect of climate change on clear-sky UV radiation
High northern latitudes will see a long-term reduction in UV
Tropics will see a long-term increase
Effect of ozone depletion/recovery and climate change on
stratosphere-to-troposphere ozone flux
Effect of ozone depletion/recovery evident in SH
Strengthening Brewer-Dobson circulation will increase ozone flux
CCMVal models on the high side of AR4 range, also of the observational estimates
AR5 WG1 draft outline
• Ch.1: Introduction
• Ch.2: Observations: Atmosphere and Surface
• Ch.3: Observations: Ocean
• Ch.4: Cryosphere
• Ch.5: Information from climate archives
• Ch.6: Carbon and other biogeochemical cycles
• Ch.7: Clouds and aerosols
• Ch.8: Anthropogenic and natural radiative forcing
• Ch.9: Evaluation of climate models
• Ch.10: Detection and attribution of climate change: from global to regional
• Ch.11: Near-term climate change: projections and predictability
• Ch.12: Long-term climate change: projections, commitments, reversibility
• Ch.13: Sea-level change
• Ch.14: Climate phenomena and their relevant for future regional climate change
SPARC and IGAC possible points of entry
Ch.2: Observations: Atmosphere and Surface
 Changes in atmospheric circulation
 Spatial-temporal patterns of climate variability
Ch.7: Clouds and aerosols
 Aerosol types, including black carbon
 Geoengineering involving aerosols
Ch.8: Anthropogenic and natural radiative forcing
 Radiative forcing changes: solar and volcanic
 Effects of atmospheric chemistry and composition, incl. GHGs
 Radiative forcing due to emissions from aviation and shipping
Ch.9: Evaluation of climate models
 Performance metrics, ensembles and their use
 New model components and coupling
 Representation of processes and feedbacks in climate models
 Simulation of recent and longer-term records
 Simulation of regional patterns, variability and extremes
Ch.10: Detection and attribution of climate change: from global to regional
Atmospheric and surface changes
Ch.11: Near-term climate change: projections and predictability
 Climate change projections for the next few decades
 Predictability of decadal climate variations and change
 Regional climate change, variability and extremes
 Atmospheric composition and air quality
 Possible effects of geoengineering
Ch.12: Long-term climate change: projections, commitments, reversibility
 Projections for the 21st century
Ch.14: Climate phenomena and their relevant for future regional climate
change
 Patterns of variability
 Monsoons
 Interconnections among phenomena
AR5 WG2 draft outline (30 chapters!)
SPARC and IGAC point of entry
Ch.11: Human health
 Air quality and human health [with WG1 authors on air quality]
I asked Chris Field (WG2 co-chair) about climate/ozone/UV aspects, and
he said that climate/ozone/health issues would be part of this section. He
said there are considering a separately identified group of atmospheric
chemistry authors.