Preliminary results of tracer transport simulation with satellite experiment

protocol

S. Maksyutov1,3

S. Houweling2, M. Naja1, P. Patra3

1NIES, Japan, 2SRON, Netherlands, 3FRCGC, Japan

Progress in CO2 remote sensing since 2005?

• Ground based FTS – in 2006 progressed to retrieving CO2 columns with a noise of an order of 0.1 ppm (near WLEF), capable of resolving hourly variability (was 0.5 ppm in 2005)

• M. Barkley et al, and SCIAMACY group retrieved CO2 from SCIAMACY using DOAS approach

• Recent: GOSAT validation team retrieved column averaged CO2 data with noise of an order of 1 ppm in reflected light (from top of Mt. Tsukuba)

• Conclusion: usable observations from space more likely than was previously expected

What satellite/column observations are expected in near future?

• In 2008, OCO, GOSAT (NIR), observe total column absorption of reflected light (NIR), accuracy – not known expected to be better than 1%

• AIRS, GOSAT(TIR) - thermal infrared spectra –atmosphere – contributed mostly by mid-upper troposphere, 1 ppm can be realistic (ask Prof. Imasu Univ. Tokyo)

• Networks of ground based FTS – see incoming solar radiation spectra in NIR (observe column CO2)

• JAL aircrafts (5 planes B-747/B-777 around the clock), get profiles at take off/landings (more than 400 profiles per year at Tokyo/Narita, ~50 at Paris, London, Milan, Singapore, ~100 in Shanghai)

What transport model properties are important for simulating and analyzing satellite CO2 data?

• Concentration fields highly variable: diurnal cycle, seasonal cycle and synoptic scale variability introduce biases

• Biases in retrieval are also related to aerosols, cirrus clouds, have spatial and temporal structure

• Quantification of weather bias – observations taken under no-cloud conditions.

• Need to simulate profile and total column together with point data

What should we understand about model errors by doing multiple model simulations.

• Effect of the model transport difference on inverse model fluxes, when satellite observations are used in the inversion. – When using column average CO2– When looking at CO2 profile

• Quantification of the column data specific properties of CO2 variability – Seasonal cycle (Olsen & Randerson)– Synoptic scale variability (e.g. Naja & Maksyutov AGU Fall 2005)– Duirnal cycle (S. Denning & CSU group)

What sort of simulations we should do (with flux field available to us)?

• Diurnal tracer Sib – hourly Carbontracker• CASA 3hourly• Seasonal/daily tracer • Slowly varying fluxes Fossil 1998, Ocean, (radon/SF6)• Other data collected: pressure- surface, level centers, level interfaces

• Benefits of using continuous experiment fluxes: – validated in Transcom continuous experiment project– easy to run for modelers– compared to upper-air experiment have diurnal, synoptic variability

• Disadvantages as compared to inverse model fluxes– Wrong seasonal cycle in Southern hemisphere

Progress with satellite experiment:• Plan was suggested at Tsukuba 2004 meeting

• Protocol (v.1.1) prepared and request to collect model grid data was made during Paris 2005 meeting– Response mostly positive, but some requested submission by ftp, rather than

mail.– Proposed to sample noon data from full 3D output

• Slow progress since 2005– Logistical problems

• Need to manage large capacity server.• In 2006 ftp site @nies was approved (limit to 5 Gb), but unable to make

large capacity dedicated server for net security reasons• At the end of 2006 ftp site was moved to new server (200 Gb limit).

• Before 2007 meeting, opinions from modelers and satellite retrieval group at GOSAT collected and protocol revised

Time step, spatial resolution, time period?• Time period – same as continuous experiment 2002, 2003

– Time resolution – 1 hour at model grid, 3 hourly at common grid

• Full output at all model levels– CO2 at model levels, model level interfaces

• Limited output 2D at model grid– column average CO2– surface pressure (dry and wet)

• Fields at common grid – 2D and 3D– CO2 at pressure levels – 3D– column average CO2 – 2D– surface pressure (dry and wet) - 2D

• Pressure levels: as in NCEP, ECMWF standard products• 1000 925 850 700 600 500 400 300 250 200 150 100• Horizontal layout: latitude from -90 to 90, 73 values, 2.5 degree; longitude 0 to 357.5,

144 values, 2.5 degree

• Proposed change from previous version of the protocol. – 3D output: Proposed to add pressure levels, and common grid. – Benefits of common grid: compact size, meteo data, winds, clouds available from NCEP,

JMA, (ECMWF $$), at exactly same grid

Plan and timetable

• Time table:

• May 1st, 2007 Protocol distributed and sample program available by ftp.• May 1st - June 1, 2007 Corrections to the software requested.• June 1 – Nov 1, 2007 Software, file format finalized. Output submission.• Nov 12-16, 2007, Analysis group meeting (NIES, Tsukuba)

• Submission instructions.

• The NIES group ( shamil@nies.go.jp ) provides data handling. • Ftp site for downloads and submitting modest amount of data (up to 200 Gb) is

available at NIES.

• Site address: fxp.nies.go.jp• User name: transcom• User password: provided by e-mail.

• A sample program that writes netcdf files in the required format is available in the directory nc-write (based on one prepared by Dr. Manish Naja at NIES).

Prototype simulation with the protocol

• In 2005 and 2006 we conducted simulations with the suggested protocol and found an overlooked effect of the synoptic scale dynamics on the column average CO2 concentration

• Cyclones and anticyclones are accompanies by vertical motion – high pressure – with large scale subsidence– low pressure – vertical ascent

• As a result:– Low pressure systems in extra tropics enhance the seasonal

surface flux signals– High pressure systems dilute seasonal surface flux signal.

Output at model grid, column average CO2

Simulation of the column average CO2 (Sib hourly + fossil + ocean)

• NIES’05 model

• 0.25 deg resolution,

• 1 hour output

Vertical and lateral motions in synoptic scale weather systems enhance surface flux signals in low pressure systems and dilute it in high pressure systems.

Seasonal weather bias

August (average of 1988-2002) surface pressure bias

Seasonal and interannual variability

• Simulation with NIES(99) model and combination of Fossil + CASA (monthly) + ocean + inversion fluxes

• Naja et al, AGU Fall 2005

Summary and conclusions

• Transcom satellite experiment protocol has been revised according to suggestions from modelers

• Logistics issues resolved by downgrading the size requirements, and creating a project ftp site at NIES.

• Feasibility and usefulness of the protocol based simulation conformed with synoptic scale bias study.

Plans for GOSAT data distribution

• The data distribution by GOSAT project will proceed according the following steps

• Data policy is decided by 3 agencies contributing to the project: MOE, JAXA, NIES – Data policy is likely to establish 3 levels of data access

• Early access to invalidated data• Early access to validated data• General distribution

• Research announcement (RA) will be made, calling for proposals on data use in research.

• Proposals reviewed and access at various levels granted to investigators.

• Combined proposal by Transcom modeling groups such as multi-model satellite inversion experiment may be a possible approach.