Transcript Rachel Law

Interannual inversions with continuous data
and
recent inversions with the CSIRO CC model
Rachel Law, CSIRO Atmospheric Research
•
Another set of pseudodata inversions (Law, 2004, ACP)
•
Setting up to use real data
 Changing models (to use ‘real’ winds)

Redefining regions

Testing with monthly data

Trying to add continuous data
Continuous data at Cape Grim (purple) and Aspendale (blue)
ASPENDALE selected CO2
440
100
420
400
50
380
Cape Grim CO2
360
Aspendale CO2
Asp WS(m/s)
Asp wd/50+10
340
28-Jun
0
18-Jul
7-Aug
27-Aug
16-Sep
6-Oct
26-Oct
Wind speed > 2.5 m/s
ASPENDALE selected CO2
ws>2.5 m/s
395
100
Cape Grim CO2
Aspendale CO2
Asp WS(m/s)
385
Asp wd/50+10
375
50
365
355
2-Aug
0
4-Aug
6-Aug
8-Aug
10-Aug
12-Aug
14-Aug
16-Aug
18-Aug
20-Aug
AIM: Perform a T3L3 type inversion using pseudodata generated from
interannually-varying fluxes (Friedlingstein land, LeQuere ocean) and
compare results using monthly and 4 hourly data at 35 sites for 1982-1997
BUT problem of size
T3-IAV base case (78 sites, solve 86-02): nsrc=4557, ndat=14040
116 region inversion, 35 sites with 4hr data, if solved over 86-02:
nsrc=23665, ndat=1149750
SOLUTION: Solve in 2 year overlapping segments adding 1 new year of
data each time. Predicted sources from previous step used as priors in
next step.
SHORT-CUT: Full covariance matrix not kept from one step to the next
(but tests showed this wasn’t a problem)
Two example regions
Comparison with correct sources (black)
and between 3 year inversion (red) and 1
year inversions (green, blue)
Difference in 1982-1997 mean source from correct source in gCm-2yr-1
(yellow/green good, blue/red bad)
Using 4 hr data
Some large biases over
land regions – no
significant improvement
over monthly data
Using monthly data
Root mean square bias in the mean seasonal cycle in gCm-2yr-1
(blue good, red bad)
Using 4 hr data
Regions with sites
nearby generally better
than with monthly data
Using monthly data
Correlation of interannual variability between estimated and
correct (red good, blue bad)
Using 4 hr data
Higher correlations over
more regions (53 > 0.6)
than with monthly data
(17 > 0.6). Some low
correlations over ocean –
leakage of land signal.
Using monthly data
Magnitude of interannual variability compared to correct
(red variability too large, blue variability too small)
Using 4 hr data
Variability too small or too
large away from sites
Using monthly data
Magnitude of variability
underestimated for land
regions away from sites
CSIRO Conformal-Cubic Atmospheric Model (CCAM)
Stand-alone GCM or nudged with NCEP winds.
Uniform or stretched grid. Currently using ~200 km.
Trace gas transport using prescribed sources and on-line
biospheric CO2.
Inversion set-up: 94 land regions, (42 also day-time flux),
46 (48) ocean regions.
6 month responses (hourly resolution) currently run for 1995,
1996, 1997.
Inversion using 1992-2002 monthly data only – 88 sites (81 GV, 7 WDCGG),
data uncertainty: 1.5*rsd
Prior source: Fos95+CASA NEP+Taka (but no presub fields used)
Prior source uncertainty: ocean, area*150 gCm-2yr-1
land, tree area*1500+grass area*800+desert area*100 gCm-2yr-1
Total land – green
Total ocean – blue
Solid CCAM, dashed TC mean
Australian region source (12 month running mean)
compared to TransCom mean
Note: CCAM case is sum
of 16 regions, TransCom is
one region and includes
New Zealand. TC sources
are sensitive to Baring
Head
CCAM NZ source
Standard deviation of interannual component of fluxes in gCm-2yr-1
Prior source uncertainty in
gCm-2yr-1
Adding continuous data
A small first step: use estimated sources and uncertainties from
inversion with monthly data as prior sources for this inversion. Run
inversion for 1 site (Cape Grim, 1997 hourly data).
What data uncertainty is appropriate?
Blue – obs
green – unc 1.4 ppm
red – unc 28 ppm
Change in 1997 mean source and uncertainty estimates with data
uncertainty for four example regions
Blue – SE coast, mainland Australia
Red – ocean to SW of Cape Grim
Blue – Western Brazil
Red – Western Siberia
Ratio of estimated to prior uncertainty (for 1997 mean source) due
to addition of Cape Grim hourly data with 7 ppm data uncertainty
Tasmania source estimate
Blue – original prior source, red – source from inversion of monthly data,
green – set of inversions using hourly CGA with different data uncertainty,
pale blue – smallest data uncertainty (1.4 ppm)
Source uncertainty shown by
dashed line, mid-range
(14ppm) case shown for set
of inversions
Monthly data added little
information about Tasmanian
source, CGA data tends to
weaken seasonal cycle.
Note some months much
better observed (e.g. Feb)
than others (e.g. May).
Larger shifts from prior in
better observed months.
SE Australia source estimate
Blue – prior, red – monthly inversion, green - CGA hourly data
Monthly data moves sources
closer to zero, reduces spring
uptake, but only small reduction
in uncertainty
Hourly data reduces
seasonality further, tendency to
shift uptake from Nov to SepOct. Substantial decrease in
uncertainty.
Ocean to south west of Cape Grim
Blue – prior, red – monthly inversion, green – hourly CGA data
Monthly data put large
seasonality into region
compared to Takahashi prior
Hourly data tends to moderate
seasonality and make more
consistent with Takahashi
Australian continental source
Red – monthly inversion, blue – addition of hourly Cape Grim
Thick dashed line – 12 month running mean
Lose spring uptake and
consequently shift annual
mean. Result seems
unlikely.
Future plans
• ‘Clean up’ method where possible e.g. monthly and continuous
data in same step
• Use full covariance matrix in 2nd step
• Add more continuous sites
• Extend number of years
• Think about how to assess results – how do we know what’s a
good result?
• Test temporal averaging of data
• Are there any ways to deal with transport model error?
Future plans – part 2