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Page 1Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
The ASSET Intercomparison project (ASSIC)
Author: W.A. Lahoz
Thanks to ASSET partners
Data Assimilation Research Centre, University of Reading RG6 6BB, UK
Page 2Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
You want to do chemical DA… You ask:
What model should I use?What DA scheme should I use?What data should I assimilate?How complex must chemistry be?
Note implicit assumption: chemical DA adds value
To answer: Test DA in the context of constituent DA
The ASSIC project: Comparison of ozone analysesGeer et al. (2006), ACPD (accepted)http://darc.nerc.ac.uk/assset/assic/index.html
Page 3Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Paradigm:Two approaches: NWP models (GCMs) & chemical models (CTMs):
1) NWP + tracer (stratospheric methane)NWP + parametrization of chemistry (Cariolle + cold tracer)
NWP + chemical module (coupled system)
2) CTM + tracer (no chemistry)CTM + parametrization (Cariolle + cold tracer)CTM + complex chemistry
Note: approaches converging (e.g. coupled NWP/CTM)
Page 4Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
DA options:
(1) Var:
3D-Var: DARC/Met Office4D-Var: ECMWF; BIRA-IASB3D-FGAT: MF/CERFACS
(2) KF methods:
KF + parametrization: KNMI
(3) other:
Direct inversion
Options re observations:
(1) Retrievals (L2):
Constituents:NWP (DARC/Met Office)CTMs (Several groups)
(2) Radiances (L1):
Nadir: NWP systemsLimb: ECMWF
Not investigated here
Page 5Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Model:• NWP GCM: DARC/ Met Office, ECMWF• CTM: KNMI, BASCOE, Meteo-France/CERFACS• Isentropic CTM: MIMOSA, Juckes (2006)
Data:• MIPAS: all except KNMI• SCIAMACHY: KNMI; columns / profiles
Ozone chemistry:• None: MIMOSA, Juckes• Cariolle scheme: (linearized ozone chemistry)• Full chemistry: Reprobus (reasonable troposphere),
BASCOE (diurnal cycle in mesosphere)Assimilation techniques:
• 3D-Var, 4D-Var, sub-optimal KF, Direct Inversion
ASSIC: analyses
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Page 8Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
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Upper stratosphere
Page 10Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Mid stratosphere – ECMWF PV at 850K
Page 11Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Mid stratosphere: ozone at 10hPa, 17th October 2003
Page 12Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Ozone hole
ECMWF assimilate MIPAS ozone
Page 13Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Ozone hole: heterogeneous chemistry schemes
Correctly depleting ozone to near-zero:• T < 195K term (Cariolle v1.2, v2.1)• MOCAGE-PALM Reprobus – detailed scheme• BASCOE v3q33 – PSC parametrization
0.5 ppmm ozone remains (incorrect):• KNMI, DARC – cold tracer
• Cariolle v1.0 adds too much ozone in vortex• KNMI?
1-2 ppmm ozone remains (incorrect):• BASCOE v3d24 – detailed PSCBox scheme
• Affected by ECMWF temperature biases?• MIMOSA, Juckes
• No chemistry; MIPAS data limitations
Page 14Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Midlatitude UTLS: Payerne ozonesonde profiles
Sonde at fullresolution
Sonde at analyses
resolution
Page 15Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Through most of stratosphere (50 hPa-1 hPa) biases within +/-10% and standard deviations less than 10% vs sonde & HALOEAreas with worse agreement:
• Upper stratosphere and mesosphere• Ozone hole• UTLS• Tropical tropopause• Troposphere
These areas have issues about fidelity of transport, chemistry and observations
First results
Page 16Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Ozone hole• A variety of heterogeneous chemistry schemes work well
Upper stratosphere / mesosphere• Care needed to avoid biases in linear chemistry• Newer linear chemistry schemes work well• Diurnal variability?• Are there remaining uncertainties in chemistry and
instrument calibration?Troposphere
• Relaxation to climatology?Tropical tropopause
• Improvements needed in modelled transport and in observations
Extratropical UTLS• Needs further investigation and case studies
Problem areas Green -> amber -> red
Page 17Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
MIPAS profiles:If good data quality & coverage:Similarly good results obtained no matter DA method or system
MIPAS ~5% higher than HALOE in mid/upper stratosphere & mesosphere (above 30 hPa), & O(10%) higher than ozonesonde & HALOE in lower stratosphere (100 hPa - 30 hPa)
SCIAMACHY total columns:Almost as good as MIPAS analyses; analyses based on SCIAMACHY
limb profiles are worse in some areas -> problems in SCIAMACHY retrievals
Conclusions from ASSIC
Page 18Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Choice of system:
• Systems based on different approaches, i.e. 3D-Var, 4D-Var, KF or direct inversion, CTM or GCM, show broadly similar agreement vs independent data
Potential improvements:
• Improved ozone analyses can be achieved via better modelled chemistry & transport & better observations
• Likely that improvements will come through better modelling of background errors (B)
Page 19Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
Chemistry complexity:
• Schemes with no chemistry do well vs. independent observations; extremely fast; depend on quality & availability of MIPAS data
• These schemes are not as good for ozone hole -> limitations with MIPAS observations; CTMs & GCMs with chemistry do better
• Studies of choice of linear chemistry parametrization (Geer et al)
Costs:
• GCM-based analyses -> substantially more computer power than CTMs; ozone DA relatively small additional cost when included in NWP system
Page 20Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
• Weather forecasts -> add ozone (usually with simplified chemistry) to enhance pre-existing NWP system (GCM-based): DARC/MO, ECMWF
• Chemistry-> strong arguments to build ozone DA into CTM with sophisticated chemistry, taking met. input as given: BIRA-IASB
• Ozone -> middle way: use transport model, driven by pre-existing dynamical fields, in combination with simplified chemistry scheme (e.g. Cariolle): KNMI, GMAO
• Coupled system -> chemical module embedded in GCM – still early days: BIRA-IASB and MSC Canada
CHOICE DEPENDS ON APPLICATION
Conclusions about chemistry applications
Page 21Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
What comes from these studies: key ideas in chemical DA
•Ozone DA key driver in NWP & chemical models; not as much benefit as originally hoped in NWP
Key reason: relatively poor information content of ozone data available from current operational satellites -> Need more data (IASI, GOME-2,…)
•Important area for NWP & chemical model DA: estimation of B
Needs to take account of physical & chemical principles & statistical dataOne of biggest challenges in DA
•Bias an important issue in DA -> estimation of biases a major challenge
Observations & models confronted to identify & attribute biasesImplementation of bias correction schemes active field of research
Page 22Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
•Model deficiencies in transport & chemistry limit value of DA
V. difficult for assimilated data to represent processes on timescales much longer than typical assimilation cycle – O(weeks to months)
•Expanding area for future DA activities is likely to be air quality
Experience gained in stratospheric chemical DA -> starting point for tackling technical problems associated of tropospheric chemical DA
•Currently a wealth of constituent data from research satellites
Danger that this will diminish just as DA techniques to exploit those data reaching maturity -> What happens after Envisat & Eos Aura?
Page 23Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
•DA invaluable for use of stratospheric constituent measurements:
•Fills gaps between observations•Allows use of heterogeneous measurements
•Numerical model -> information to be propagated forward in time: combination of measurements available at different times & locations
•Properly applied, DA can add value to observations & models, compared to information that each can supply on their own
•DA underpins evaluation of impact of current observation types using OSEs, and future global observing system using OSSEs
DA ADDS VALUE
BUT, for proper use, limitations must be borne in mind
Finally…
Page 24Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
BE VERY CAREFUL HOW YOU USE DA…
Page 25Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
EXTRA SLIDES
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i. DA fills in observational gaps and can do so more accurately than linear interpolation of observations
ii. By filling in observational gaps in an intelligent and statistically justifiable manner, DA provides initial conditions of constituent fields, consistent with observations & model, for chemical forecasts
iii. DA (using inverse modelling ideas) allows quantification of sources & sinks of constituents (see, e.g., Meirink et al. 2005). A model cannot obtain initial conditions nor quantify sources & sinks objectively
iv. DA allows better use of observations by assimilation of radiances –this should result in superior constituent retrieval capabilities by providing better a priori information for the retrieval process
Added value
Page 27Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
v. DA confronts observations & models objectively by introducing error statistics (B & R) and allow the possibility of identifying problems with model or observations. By contrast, direct comparison of a model with observations does not necessarily have this same rigorous foundation
vi. Errors & biases of both model & observations can be quantified in the process of tuning a DA scheme for internal consistency, e.g., Chapnik et al. (2006); Dee & da Silva (1999)
vii. DA, using OSEs and OSSEs, can be used to evaluate incremental value of current observations and of (often expensive) future missions – models cannot be readily used for this purpose
Page 28Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
viii. Because CTMs can be very sensitive to the forcing fields (winds and temperature), their representation of constituent fields could be in error if forcing fields are in error (R. Ménard, pers. comm., 2005)
ix. Dispersive nature of winds (Schoeberl et al. 2003) means CTMs will tend to provide inadequate budget distributions if, e.g., mean age of air is too young (Meijer et al. 2004)
In both cases, DA keeps the model state on track, i.e., as consistent as possible with information from both constituent observations & forcing fields
See Lahoz, Fonteyn & Swinbank, submitted to QJ
Page 29Atmospheric Sciences Conference, ESRIN, 8th – 12th May 2006
The SH polar vortex split of Sep 2002: ozone at one level
MIPAS ozone
DARC analyses
Blue: low ozone; Red: high ozone; 10 hPaCourtesyAlan Geer
HOW DA CAN ADD VALUE