Abstract
The effect of observational constraint on the ranges of uncertain physical and chemical process parameters was explored in a global aerosol-climate model. The study uses 1 million variants of the Hadley Centre General Environment Model version 3 (HadGEM3) that sample 26 sources of uncertainty, together with over 9000 monthly aggregated grid-box measurements of aerosol optical depth, PM2:5, particle number concentrations, sulfate and organic mass concentrations. Despite many compensating effects in the model, the procedure constrains the probability distributions of parameters related to secondary organic aerosol, anthropogenic SO2 emissions, residential emissions, sea spray emissions, dry deposition rates of SO2 and aerosols, new particle formation, cloud droplet pH and the diameter of primary combustion particles. Observational constraint rules out nearly 98% of the model variants. On constraint, the1 (standard deviation) range of global annual mean direct radiative forcing (RFari) is reduced by 33% to ±0:14 to ±0:26Wm±2, and the 95% credible interval (CI) is reduced by 34% to ±0:1 to ±0:32Wm±2. For the global annual mean aerosol-cloud radiative forcing, RFaci, the1 range is reduced by 7% to ±1:66 to ±2:48Wm±2, and the 95% CI by 6% to ±1:28 to ±2:88Wm±2. The tightness of the constraint is limited by parameter cancellation effects (model equifinality) as well as the large and poorly defined "representativeness error" associated with comparing point measurements with a global model. The constraint could also be narrowed if model structural errors that prevent simultaneous agreement with different measurement types in multiple locations and seasons could be improved. For example, con straints using either sulfate or PM2:5 measurements individually result in RFari1 ranges that only just overlap, which shows that emergent constraints based on one measurement type may be overconfident.
| Original language | English |
|---|---|
| Pages (from-to) | 9491-9524 |
| Number of pages | 34 |
| Journal | Atmospheric Chemistry and Physics |
| Volume | 20 |
| Issue number | 15 |
| DOIs | |
| Publication status | Published - 13 Aug 2020 |
Funding
For the sulfate data used in this study, we acknowledge the EMEP (http://ebas.nilu.no/, last access: 17 September 2019; Tørseth et al., 2012), IMPROVE (http://views.cira.colostate. edu/fed/, last access: 17 September 2019) and EANET (https: //www.eanet.asia/, last access: 17 September 2019) measurement networks for making their measurement data available, along with all data managers involved in data collection. Additional ground station observations from the SOR-PES (Station for Observing Regional Processes of the Earth System) monitoring station in Nanjing, China (Ding et al., 2016), are also included. Data on the acid deposition in the east Asian region were provided from the Network Center for EANET, https://monitoring.eanet.asia/document/public/ index (last access: 7 June 2018). IMPROVE is a collaborative association of state, tribal and federal agencies and international partners. The US Environmental Protection Agency is the primary funding source, with contracting and research support from the National Park Service. The Air Quality Group at the University of California, Davis, is the central analytical laboratory, with ion analysis provided by Research Triangle Institute and carbon analysis provided by Desert Research Institute. The N3 data used in this study were obtained from the EBAS ACTRIS database (Asmi et al., 2013; https://www. actris.eu/, last access: 17 September 2019; http://ebas.nilu. no/, last access: 17 September 2019), collated via the Global Aerosol Synthesis and Science Project, GASSP (Redding-ton et al., 2017, http://gassp.org.uk/, last access: 17 September 2019), and public data on the EBAS database. The EBAS database has largely been funded by the UNECE CLRTAP (EMEP) and AMAP and through NILU internal resources. Specific developments have been possible due to projects like EUSAAR (EU-FP5; EBAS web interface), EBAS Online (Norwegian Research Council INFRA; upgrading the database platform) and HTAP (European Commission DG-ENV; import and export routines to build a secondary repository in support of http://www.htap.org; last access: 4 April 2019). A large number of specific projects have supported development of data and metadata reporting schemes in dialogue with data providers (EU; CREATE, ACTRIS and others). Through ACTRIS, the research leading to the these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 654109. For a complete list of programmes and projects for which EBAS serves as a database, please consult the information box in the Framework filter of the web interface. These are all highly acknowledged for their support. The observations of OC used in this study are collated from seven observational campaigns and supplemented by additional ground station observations. The campaign data were collated via GASSP and are derived from size distribution measurements taken during the following campaigns: VOCALS (NERC grant NE/F019874/1; Allen et al., 2011; Hawkins et al., 2010; Wood et al., 2011), Cal-Nex (Ryerson et al., 2013), WACS (Quinn et al., 2014), ICEALOT (Frossard et al., 2011), DYNAMO (DeWitt et al., 2013), NEAQS-2004 (Quinn et al., 2006; Wang et al., 2007), TEXAQS06 (Bates et al., 2008), RHaMBLe (NERC grants NE/D006570/1, NE/E011454/1; Allan et al., 2009) and ACCACIA (NERC grant NE/I028696/1; Allan et al., 2015). The OC ground station observations used are from the AMS Global Database which has a worldwide cover- age (Zhang et al., 2017), along with data for further European sites from ACTRIS (https://www.actris.eu/, last access: 17 September 2019; http://ebas.nilu.no/, last access: 17 September 2019; collated via GASSP) and data from individual stations including Chilbolton (England; Crippa et al., 2014), COPS (NERC grant NE/E016200/1; Hor-nisgrinde, Germany; Irwin et al., 2010; Jones et al., 2011), Holme Moss (England; Liu et al., 2011), OP3 (NERC grant NE/D004624/1; southeast Asia; Hewitt et al., 2010) and MC4 (NERC grant NE/H008136/1; Weybourne, England; Liu et al., 2013) collated via GASSP, the SORPES site in Nanjing, China (Ding et al., 2016), and AMF stations in the US and northeast Atlantic (Atmospheric Radiation Measurement (ARM) user facility, 2014a, b). The OC data at the AMF stations (US and northeast Atlantic) were obtained from the ARM programme sponsored by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, Climate and Environmental Sciences Division. The AMS Global Database ground station data cover the following sites: Barcelona (Minguillón et al., 2011; Mohr et al., 2012), Beijing (Sun et al., 2010), Blodgett Forest (Farmer et al., 2011), Boulder (Nemitz et al., 2008), Cape Hedo, Chebogue Point, Cheju Island (Topping et al., 2004), Chilbolton, Cool (Setyan et al., 2012), Duke Forest (Stroud et al., 2007), Edinburgh (Allan et al., 2003a), Fi-nokalia (Hildebrandt et al., 2010), Fukue Island (Takami et al., 2005), Helsinki (Timonen et al., 2013), Houston (Cana-garatna et al., 2007), Hyytiälä (Allan et al., 2006), Jungfrau-joch (Ng et al., 2010), Komaba (Takegawa et al., 2006), K-Puszta, Mace Head (Dall’Osto et al., 2010), Mainz (Ng et al., 2010), Manaus (Chen et al., 2009), Manchester (Allan et al., 2003b), Melpitz (Poulain et al., 2011), Mexico City (Aiken et al., 2009), Montseny, New York City (Drewnick et al., 2004; Sun et al., 2011; Weimer et al., 2006), Pasadena (Hayes et al., 2013), Pinnacle State Park (Bae et al., 2007), Pittsburgh (Zhang et al., 2005), Point Reyes National Seashore – ARM Mobile Facility (AMF) (Ervens et al., 2010), Puy de Dome (Freney et al., 2011), Riverside – SOAR field site (Docherty et al., 2011; Williams et al., 2010), San Pietro Capofiume, Storm Peak, Trinidad Head (Millet et al., 2004), Vancouver (Alfarra et al., 2004; Boudries et al., 2004), Wey-bourne Atmospheric Observatory, Whistler Mountain (Sun et al., 2009), Whiteface Mountain (Hogrefe et al., 2004) and Writtle Agricultural College. The observations of N50 used in this study are collated from 19 observational campaigns and supplemented by additional ground station observations. The campaign data were collated via GASSP and derived from size distribution measurements taken during the following campaigns: ACE1 (Bates et al., 1998; Clarke et al., 1998), VOCALS (NERC grant NE/F019874/1; Allen et al., 2011; Hawkins et al., 2010; Wood et al., 2011), DOE ARM MAGIC (Lewis and Teixeira, 2015), CalNex (Ryerson et al., 2013), WACS (Quinn et al., 2014), NEAQS-2002 (Bates et al., 2005; Quinn and Bates, 2005), ARCTAS (McNaughton et al., 2011), AS-COS (Heintzenberg and Leck, 2012; Tjernström et al., 2014), ICEALOT (Frossard et al., 2011), AEROSOL99 (Bates et al., 2001), DYNAMO (DeWitt et al., 2013), INDOEX (Quinn and Bates, 2005; Ramanathan et al., 2001), PEM-Tropics-A (Fenn et al., 1999), PEM-Tropics-B (Raper et al., 2001), PASE (Hudson and Noble, 2009), NAURU99 (Long and Mc-Farlane, 2012), ACE-ASIA (Bates et al., 2004; Huebert et al., 2003), NEAQS-2004 (Quinn et al., 2006; Wang et al., 2007) and TEXAQS06 (Bates et al., 2008). The N50 ground station observations used, collated via GASSP and public data on the EBAS database, are from Canada (Jeong et al., 2010; Leaitch et al., 2013; Takahama et al., 2011), South Africa (Vakkari et al., 2013), the Russian Arctic (Asmi et al., 2016), India (Hyvärinen et al., 2010), Antarctica (Fiebig et al., 2009) and European sites (Asmi et al., 2011). ASCOS (the Arctic Summer Cloud Ocean Study) was funded by the Knut and Alice Wallenberg Foundation and DAMOCLES (EU Sixth Framework Programme). The Swedish Polar Research Secretariat provided access to the icebreaker Oden and logistical support. Acknowledgements. We made use of the N8 HPC facility funded from the N8 consortium, an Engineering and Physical Sciences Research Council Grant to use ARCHER (EP/K000225/1) and the JASMIN facility (http://www.jasmin.ac.uk/; last access: 17 September 2019) via the Centre for Environmental Data Analysis funded by NERC and the UK Space Agency and delivered by the Science and Technology Facilities Council. We acknowledge the following additional funding: the Royal Society Wolfson Merit Award (Carslaw); a doctoral training grant from NERC and a CASE studentship with the Met Office Hadley Centre (Regayre). Financial support. This research has been supported by the Nat- ural Environment Research Council (grant nos. NE/J024252/1, NE/I020059/1, NE/P013406/1, NE/F019874/1, NE/D004624/1, NE/H008136/1, NE/D006570/1, NE/E011454/1, NE/E016200/1 and NE/I028696/1), the Newton Fund (grant no. UK–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China), the National Centre for Atmospheric Science, the Engineering and Physical Sciences Research Council (grant no. EP/K000225/1), the European Union (grant no. ACTRIS-2 (262254)) and the Horizon 2020 (grant no. INTAROS (727890)).
| Funders | Funder number |
|---|---|
| China Research and Innovation Partnership Fund | |
| DAMOCLES | |
| Environment Research Council | NE/I020059/1, NE/H008136/1, NE/E016200/1, NE/P013406/1, NE/I028696/1, NE/J024252/1, NE/D006570/1, NE/E011454/1, NE/F019874/1, NE/D004624/1 |
| Met Office Hadley Centre | |
| Office of Biological and Environmental Research, Climate and Environmental Sciences Division | |
| US Department of Energy | |
| U.S. Environmental Protection Agency | |
| Office of Science | |
| National Park Service | |
| Horizon 2020 Framework Programme | 262254, 727890, 654109 |
| Newton Fund | |
| UK Space Agency | |
| Engineering and Physical Sciences Research Council | EP/K000225/1 |
| Science and Technology Facilities Council | |
| Royal Society | |
| National Eye Research Centre | |
| National Centre for Atmospheric Science | |
| European Commission | ACTRIS-2 |
| Knut och Alice Wallenbergs Stiftelse | |
| Sixth Framework Programme | |
| Horizon 2020 |
