Atmosphere Monitoring

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1 Atmosphere Reanalyses of Atmospheric Composition at ECMWF: From MACC to CAMS Antje Inness (ECMWF) With thanks to J. Flemming, M. Ades, A. Agusti-Panareda, R. Engelen, Z. Kipling, M. Parrington, S. Massart, M. Suttie & the rest of the CAMS team

2 Atmosphere CAMS SERVICE Service CHAIN Chain Space Agencies In-situ observations National agencies SMEs Citizens Scientists 15

3 Atmosphere Reanalyses of atmospheric composition Cross section through ozone hole ECMWF s IFS has been extended to include aerosols, reactive gases (e.g. CO, NO2,, ) & GHG Model and assimilation system have evolved with time We also produce reanalyses of atmospheric composition (e.g. aerosols, CO, NO2,,, CO2, CH4) from 2003 onwards as part of the Copernicus Atmosphere Service (CAMS) Surface NO2 ( included in ERA-40, ERA-Interim, ERA-5 ) Long experience of atmospheric composition at ECMWF: GEMS -> MACC -> CAMS

4 Our previous reanalyses (CHEM, AER & GHG) Atmosphere Coupled system GEMS reanalysis: eac1, CY32R3 MACC reanalysis: rean, CY36R1 Inness et al. 2013, ACP CHEM & AER IFS CAMS interim reanalysis: eac3, CY40R2, CY41R1 Flemming et al. 2017, ACP CAMS reanalysis: eac4, CY42R1 CHEM & AER 2003 April Today

5 Atmosphere Datasets used in CAMS reanalysis GOME2 MetopB GOME2 MetopA TANSO GOSAT OMI Aura SCIAMACHY Envisat IASI MetopA SCIAMACHY Envisat MOPITT Terra NO2 NO2 CO2, CH4 NO2 CO2, CH4 CO2, CH4 NO2 CO AATSR Envisat GOME2 MetopB MIPAS Envisat SCIAMACHY Envisat GOME2 MetopA SBUV/2 NOAA-19 SBUV/2 NOAA-18 SBUV/2 NOAA-17 SBUV/2 NOAA-16 SBUV/2 NOAA-14 OMI Aura MLS Aqua MODIS Terra MODIS Aqua 2003 AOD AOD AOD 2013

6 Improvements in newer reanalyses Atmosphere good score at Neumayer station CAMS reanalysis CIRA (CAMS Interim Reanalysis) MACC reanalysis GEMS reanalysis bad sonde data positive CO bias (obs-model) hpa at Frankfurt IAGOS data Improvements in CAMS reanalysis compared to our older MACC and GEMS reanalyses negative

7 CAMS interim reanalysis Impact of the assimilation of MOPITT CO Atmosphere CAMSiRA CAMSiRA-CR We also produce a control run (CR) without data assimilation that allows us to assess the impact of the assimlation Differences between CAMS interim reanalysis (CAMSiRA) CO and the control run without assimilation of MOPITT The differences are an indication of the model bias. The model is too low in the NH and to high in Tropics and SH. This could be related to biases in anthropogenic and biomass burning emissions and an increased CO lifetime

8 Atmosphere CAMS interim reanalysis Trends and significance ( ) Robust linear trend % Classic linear trend % Classic linear trend & significance 95% CAMSiRA (MOPITT + GFAS + MACCITY) CR (MACCITY +GFAS) Trends in the CR (emissions) are less pronounced than in CAMSiRA (emissions & MOPITT). Global CO trends are about -1%/year in period Negative trends mainly over North-America, Europe and South-America

9 Atmosphere CAMS interim reanalysis GLOBAL Trends of CO burden CAMS interim Reanalysis Anomaly (%) 2015 GLOBAL CO Burden in Tg Indonesian Fires 2015 Anomaly (%) 2016 Flemming and Inness, BAMS State of Climate 2015

10 Atmosphere More products from CAMS interim RA (& CR & MACCRA) Tropospheric ozone Stratospheric ozone Aerosols GHG to come in CAMS reanalysis

11 Summary Atmosphere ECMWF produces reanalysis of atmospheric composition as part of CAMS The CAMS interim re-analysis ( ) is the latest available long global 3D data set of CO, ozone and aerosols produced by assimilating satellite observations with IFS Global CO trends are about -1%/year in period Negative trends mainly over North-America, Europe and South-America Indonesian Fires (2015) significantly increased global CO in 2 nd half of 2015 and 1 st half of 2016 Production of a new CAMS reanalysis (CHEM, AER, GHG) has started is available and more years are to be released in 2018 See to obtain the data

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