Documentation of CAMS Climate Forcing products, version 0

Transcription

1 ECMWF COPERNICUS REPORT Documentation of CAMS Climate Forcing products, version 0 Issued by: University of Reading / Nicolas Bellouin and co-authors Date: 29/12/2016 Ref: CAMS74_2016SC1_D74.1-1_201612_Documentation_v1

2 This document has been produced in the context of the (CAMS). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of CAMS on behalf of the European Union (Delegation Agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission and the European Centre for Medium-Range Weather Forecasts has no liability in respect of this document, which is merely representing the authors view.

3 Contributors UNIVERSITY OF READING N. Bellouin W. Davies K. Shine UNIVERSITY OF LEIPZIG K. Block J. Muelmenstaedt J. Quaas UNIVERSITY OF LEEDS P. Forster L. Regayre C. Smith Forcing products, version 0 Page 3 of 41

4 Table of Contents 1. Alphabetical list of products 9 2. File access and naming convention Carbon dioxide and methane Product specifications Carbon dioxide Methane Methods Radiative transfer calculations Inputs to the radiative transfer code Pre-industrial atmospheric concentrations Uncertainties Tropospheric and stratospheric ozone Product specifications Methods Radiative transfer calculations Inputs to the radiative transfer code Pre-industrial atmospheric concentrations Uncertainties Speciated aerosol optical depths Product specifications Methods Identification of aerosol origin Inputs to the algorithm Uncertainties Aerosol-radiation interactions Product specifications Methods Inputs to the algorithm Pre-industrial aerosol optical depths Uncertainties Aerosol-cloud interactions Product specifications Methods Cloud condensation nuclei 35 Forcing products, version 0 Page 4 of 41

5 7.2.2 Cloud droplet number concentrations Calculation of RFaci Uncertainties List of acronyms References User support and contacts 40 Forcing products, version 0 Page 5 of 41

6 Introduction Please refer to section 8 for the definition of all acronyms. Radiative forcing (RF) measures the imbalance in the Earth s energy budget caused by a perturbation of the climate system, for example changes in atmospheric composition driven by human activities (Myhre et al., 2013). RF is a useful predictor of globally-averaged temperature change, especially when rapid adjustments of atmospheric temperature and moisture profiles are taken into account (Boucher et al., 2013). RF has therefore become a quantitative metric to compare the potential climate response to different perturbations. Increases in greenhouse gas concentrations over the industrial era exerted a positive RF, causing a gain of energy in the climate system. In contrast, concurrent changes in atmospheric aerosol concentrations are thought to exert a negative RF leading to a loss of energy. Chapter 8 of the IPCC AR5 estimates net total industrialera RF at +2.3 W m 2 with a broad confidence interval of +1.1 to +3.3 W m 2 (Myhre et al., 2013). Estimates for key forcing agents from the IPCC AR5 are summarised in Table I.1 below. The methods used to estimate RF of different species in the IPCC AR5 are diverse: global modelling of atmospheric composition, line-by-line radiative transfer calculations, simplified calculations, or observational-based calculations. The CAMS Climate Forcing service aims at refining those estimates by providing in a consistent way the distributions, global averages, and uncertainties of the RF of key atmospheric constituent. Table I.1 - Estimates of Radiative Forcing and Effective Radiative Forcing for changes in atmospheric composition between 1750 and 2011, taken from Tables 8.2 and 8.6 of the IPCC AR5 (Myhre et al., 2013). Forcing agent Radiative Forcing RF (W m 2 ) Effective Radiative Forcing ERF (W m 2 ) CO ± 0.19 CH ± 0.05 N 2 O ± 0.03 Halocarbons ± Total well-mixed greenhouse gases (2.54 to 3.12) (2.26 to 3.40) Tropospheric ozone (0.20 to 0.60) Stratospheric ozone 0.05 ( 0.15 to +0.05) Stratospheric water vapour from (+0.02 to +0.12) methane Aerosol-radiation interactions 0.35 ( 0.85 to +0.15) 0.45 ( 0.95 to +0.05) Aerosol-cloud interactions 0.45 ( 1.2 to 0.0) CAMS Climate Forcing provides RF separately for: carbon dioxide methane tropospheric ozone Forcing products, version 0 Page 6 of 41

7 stratospheric ozone interactions between anthropogenic aerosols and radiation interactions between anthropogenic aerosols and clouds and their uncertainties. For aerosols, CAMS Climate Forcing also produces distributions of aerosol optical depths, distinguishing natural from anthropogenic aerosols. This documentation describes version 0 RF estimates, which are essentially a first draft of the actual products, which will be published in mid Version 0 products include several simplifying assumptions that will be improved upon in the future: needs for further improvements are clearly listed in the boxes labelled Future improvements in this document. Therefore, version 0 products should be used with caution, because they are not necessarily based on state-of-the-art methods, they are not yet at the level of consistency targeted by CAMS, and may contain mistakes. CAMS Climate Forcing estimates follow the definitions for instantaneous and stratosphericallyadjusted RF given in the IPCC AR5 (Myhre et al., 2013): Instantaneous RF is the instantaneous change in net (down minus up) radiative flux (shortwave plus longwave; in W m 2 ) due to an imposed change. Stratospherically-adjusted RF, hereafter referred to simply as adjusted RF, is the change in net irradiance at the tropopause after allowing for stratospheric temperatures to readjust to radiative equilibrium, while holding surface and tropospheric temperatures and state variables such as water vapour and cloud cover fixed at the unperturbed values. The reference state is taken by CAMS Climate Forcing products to be year 1750, except for aerosol RF, which is defined with respect to present-day natural aerosol and scaled to pre-industrial conditions. (See section 6.) Radiative effect (RE) is also quantified for aerosols: the difference with RF is that RE is defined with respect to an atmosphere that contains no aerosols. CAMS instantaneous forcing is quantified in terms of flux changes at the top of the atmosphere (TOA) and the surface for all species, but also at the climatological tropopause for carbon dioxide, methane, and ozone. CAMS adjusted RF is currently only quantified at the climatological tropopause for carbon dioxide, methane, and ozone. Adjusted RF is not yet given for aerosol perturbations because it differs only slightly from instantaneous RF at the TOA. CAMS Climate Forcing products are quantified by default in all-sky conditions, meaning that the radiative effects of clouds are included in the radiative transfer calculations. Products denoted clear-sky are computed by excluding clouds in the radiative transfer calculations. IPCC AR5 further defines effective radiative forcing (ERF) as the change in net TOA downward radiative flux after allowing for atmospheric temperatures, water vapour and clouds to adjust, but with surface temperature or a portion of surface conditions unchanged. ERF estimates by the IPCC AR5 are given in Table I.1 above. ERF estimates by CAMS Climate Forcing are planned as part of an evolution of RF products in Forcing products, version 0 Page 7 of 41

8 Figure I.1 illustrates the RF production chain that will be in place for version 1 RF products in Version 0 products are based on a developmental version of that production chain. Figure I.1 - Diagram of the radiative forcing production chain (light orange), which takes inputs from the the CAMS Global Reanalysis (blue) and produces radiative forcing estimates and their uncertainties (dark orange). Green boxes indicate observational constraints. BB stands for biomass burning, AOD for aerosol optical depth, and CCN for cloud condensation nuclei. RRTMG is the Rapid Radiative Transfer Model for General Circulation Models. Red boxes and lines indicate aspects that differ or are yet to be implemented in version 0, as discussed in the text. The source of atmospheric composition data is a Global Reanalysis performed by the ECMWF IFS. That reanalysis includes assimilation of satellite retrievals of atmospheric composition, thus improving RF estimates compared to free-running models. Improvements derive directly from observational constraints on reactive gas columns and aerosol optical depths and, for ozone, vertical profiles. Data assimilation also constrains gaseous and biomass-burning aerosol emissions, leading to indirect improvements in the simulation of atmospheric concentrations. The RF production chain therefore relies in priority on variables improved by the data assimilation process (gas mixing ratios, total aerosol optical depth). However, it is not possible to solely rely on assimilated variables because other characteristics of the model affect RF directly (vertical profiles of aerosols and gases, speciation of total aerosol mass) or indirectly (cloud cover and cloud type, surface albedo, temperature and moisture profiles). In addition, parameters required by the RF estimate but not simulated by the Global Reanalysis (e.g. aerosol size distributions) are provided by ancillary datasets. Forcing products, version 0 Page 8 of 41

9 Version 0 differs from the final production chain depicted in Figure I.1 in four important aspects. Those differences are highlighted in red in Figure I.1, and described as follows. The main objective of developments towards version 1 products will be to remove those differences. 1. Version 0 takes most distributions of atmospheric properties from the MACC Global Reanalysis dataset, which covers Concentrations of carbon dioxide and methane are taken from third-party datasets because the MACC reanalysis is strongly biased in those two variables. The version 1 production chain will use the new CAMS Global Reanalysis, and will be able to rely on simulated carbon dioxide and methane distributions because of improvements in the modelling and data assimilation systems. 2. Version 0 defines RF against a pre-industrial state that is specified in a rather ad-hoc way, with no consistency between forcing agents. The version 1 production chain will rely on dedicated simulations of the pre-industrial atmosphere, consistent for all species and using the model that provides the present-day CAMS Global Reanalysis. 3. Version 0 computes RF using three different radiative transfer codes: o Streamer (Key and Schweiger 1988), a wrapper to DISORT, for aerosol-radiation RF; o the ECHAM radiative transfer code (Stevens et al. 2015), for aerosol-cloud RF; o the IFS radiative transfer code (see section 3.2.1), which is a dedicated version of RRTMG (Mlawer et al. 1997). The version 1 production chain will use the IFS RRTMG for all forcing agents. 4. Version 0 uses a simplified uncertainty analysis. For aerosol-radiation and aerosol-cloud RF, uncertainties are based on a perturbed physics ensemble applied to a global aerosol model. It is here assumed that those uncertainties are representative of uncertainties associated with the IFS Global Reanalysis. For greenhouse gas and ozone RF, uncertainties are taken from the IPCC AR5. Uncertainties in the version 1 production chain will be more representative of the radiative transfer code used, and made consistent across species. 1. Alphabetical list of products As of December 2016, the CAMS Climate Forcing service publishes 47 different products: - 13 for carbon dioxide; - 7 for methane; - 22 for aerosol-radiation interactions, including speciated aerosol optical depths and radiative effects; - 5 for aerosol-cloud interactions. Table 1.1 lists the main characteristics of the CAMS Climate Forcing products and the section in this document where more detailed information can be found about each product. Forcing products, version 0 Page 9 of 41

10 Table Alphabetical list by variable name of CAMS Climate Forcing products, as of December # Variable name Forcing agent Description RE/RF Period Section type and covered spectrum 1 abs550anth Aerosols Anthropogenic absorption AOD N/A abs550dust Aerosols Mineral dust N/A absorption AOD 3 abs550landnat Aerosols Land-based N/A natural absorption AOD 4 abs550marine Aerosols Marine absorption AOD N/A arf_ch4_trop_lw Methane RF at Adjusted, tropopause LW 6 arf_co2_trop_lw Carbon dioxide RF at Adjusted, tropopause LW 7 od550aer Aerosols Total AOD N/A od550anth Aerosols Anthropogenic AOD N/A od550dust Aerosols Mineral dust N/A AOD 10 od550landnat Aerosols Land-based N/A natural AOD 11 od550marine Aerosols Marine AOD N/A reari_anth_srfcs Aerosols Clear-sky anthropogenic aerosolradiation RE at surface 13 reari_anth_toacs Aerosols Clear-sky anthropogenic aerosolradiation RE at TOA 14 reari_dust_srfcs Aerosols Clear-sky mineral dust aerosolradiation RE at surface 15 reari_dust_toacs Aerosols Clear-sky mineral dust aerosol- Instant, SW Instant, SW Instant, SW Instant, SW Forcing products, version 0 Page 10 of 41

11 radiation RE at TOA 16 reari_landnat_srfcs Aerosols Clear-sky landbased natural aerosolradiation RE at surface 17 reari_landnat_toacs Aerosols Clear-sky landbased natural aerosolradiation RE at TOA 18 reari_marine_srfcs Aerosols Clear-sky marine aerosolradiation RE at surface 19 reari_marine_toacs Aerosols Clear-sky marine aerosolradiation RE at TOA 20 rfaci_srf_lw Aerosols Aerosol-cloud RF at surface 21 rfaci_srf_sw Aerosols Aerosol-cloud RF at surface 22 rfaci_toa_lw Aerosols Aerosol-cloud RF at TOA 23 rfaci_toa_sw Aerosols Aerosol-cloud RF at TOA 24 rfari_srfcs Aerosols Clear-sky aerosolradiation RF at surface 25 rfari_srf Aerosols Aerosolradiation RF at surface 26 rfari_toacs Aerosols Clear-sky aerosolradiation RF at TOA 27 rfari_toa Aerosols Aerosolradiation RF at TOA 28 rf_ch4_srf_lw_cs Methane Clear-sky RF at surface Instant, SW Instant, SW Instant, SW Instant, SW Instant a, LW Instant a, SW Instant a, LW Instant a, SW Instant a, SW Instant a, SW Instant a, SW Instant a, SW Instant, LW Forcing products, version 0 Page 11 of 41

12 29 rf_ch4_srf_lw Methane RF at surface Instant, LW 30 rf_ch4_toa_lw_cs Methane Clear-sky RF at Instant, TOA LW 31 rf_ch4_toa_lw Methane RF at TOA Instant, LW 32 rf_ch4_trop_lw_cs Methane Clear-sky RF at Instant, tropopause LW 33 rf_ch4_trop_lw Methane RF at Instant, tropopause LW 34 rf_co2_srf_lw_cs Carbon dioxide Clear-sky RF at Instant, surface LW 35 rf_co2_srf_lw Carbon dioxide RF at surface Instant, LW 36 rf_co2_srf_sw_cs Carbon dioxide Clear-sky RF at Instant, surface SW 37 rf_co2_srf_sw Carbon dioxide RF at surface Instant, SW 38 rf_co2_toa_lw_cs Carbon dioxide Clear-sky RF at Instant, TOA LW 39 rf_co2_toa_lw Carbon dioxide RF at TOA Instant, LW 40 rf_co2_toa_sw_cs Carbon dioxide Clear-sky RF at Instant, TOA SW 41 rf_co2_toa_sw Carbon dioxide RF at TOA Instant, SW 42 rf_co2_trop_lw_cs Carbon dioxide Clear-sky RF at Instant, the tropopause LW 43 rf_co2_trop_lw Carbon dioxide RF at the Instant, tropopause LW 44 rf_co2_trop_sw_cs Carbon dioxide Clear-sky RF at Instant, the tropopause SW 45 rf_co2_trop_sw Carbon dioxide RF at the Instant, tropopause SW 46 std_rfaci_toa Aerosols Standard Instant a, deviation of SW aerosol-cloud RF at TOA 47 std_rfari_toa Aerosols Standard Instant a, deviation of SW aerosolradiation RF at TOA Forcing products, version 0 Page 12 of 41

13 Notes: a. Stratospheric adjustment is negligible for tropospheric aerosol perturbations, so for aerosols instantaneous and adjusted RFs are equal. 2. File access and naming convention Files are currently stored on ECMWF systems. As of December 2016, a web-based download tool is being finalised. Its address is likely to be As of December 2016, there are 47 different variables ( products ) available. (See Section 1 for full list.) Products are made from the MACC Global Reanalysis of atmospheric composition, and therefore cover the period that the reanalysis provides. Aerosol-cloud RF products are limited to the period however. (See section 8.) Spatial and temporal resolutions depend on the variable. There is one file per variable and per month. For variables given at monthly temporal resolution, there is only one distribution per file. For variables given at daily temporal resolution, there are as many distributions in a monthly file as there are days in that month. Files names follow the World Meteorological Organization s recommendation available at The file name convention of CAMS Climate Forcing version 0 products: z_cams_l_uor_<yyyy><mm>_v0_<varname>.nc where YYYY is the 4-digit year corresponding to the distribution stored in the file; MM is the 2-digit year month corresponding to the distribution stored in the file; varname is the name of the main netcdf variable stored in the file. Files are in netcdf format and follow the Climate and Forecast metadata convention version 1.6 (CF-1.6). Version 0 files are in netcdf3 format, except for carbon dioxide and methane RF files, which are in netcdf4 format. Future improvement: All version 1 products will be in netcdf4 format. The netcdf header for methane LW instantaneous RF at the surface for the month of December 2012 is given below as an example for the file structure used by CAMS Climate Forcing products. Aspects that may be different for other products are highlighted in red: refer to the corresponding note for details. netcdf z_cams_l_uor_201212_v0_rf_ch4_srf_lw a { dimensions: latitude = 61 b ; longitude = 120 b ; time = UNLIMITED ; // (1 c currently) Forcing products, version 0 Page 13 of 41

14 variables: float d latitude(latitude) ; latitude:units = "degrees_north" ; latitude:long_name = "latitude" ; latitude:standard_name = "latitude" ; latitude:point_spacing = "even e " ; float d longitude(longitude) ; longitude:units = "degrees_east" ; longitude:long_name = "longitude" ; longitude:standard_name = "longitude" ; longitude:modulo = ; longitude:point_spacing = "even e " ; float rf_ch4_srf_lw a (time, latitude, longitude) ; rf_ch4_srf_lw a :units = "W m-2 f " ; rf_ch4_srf_lw a :long_name = "Methane_lw_instantaneous_radiative_forcing_at_surface g " ; rf_ch4_srf_lw a :standard_name = "surface_instantaneous_longwave_forcing h " ; rf_ch4_srf_lw:species = "ch4 i " ; float d time(time) ; time:units = "hours since 1900 j :00:0.0" ; time:long_name = "time" ; time:standard_name = "time" ; time:calendar = "gregorian" ; // global attributes: :Conventions = "CF-1.6" ; :title = " 74 Climate Forcings" ; :description = "CAMS 74 production chain : Monthly mean Methane radiative forcing for year Will Davies UoR 2016, w.h.davies@reading.ac.uk" ; k :references = "Update to Bellouin et al., doi: /acp , 2013 l " ; :source = "model-generated, CAMS74 using MACC reanalysis, version 0. $Rev: 52 $. 1 m " ; :institution = "Department of Meteorology, University of Reading n " ; :history = "Thu Nov 3 14:18: : ncks -d time,11 /export/cloud/cams74/users/gn907779/outputs/rev52/cams74_rf_ch4_srf _lw_2012.nc /glusterfs/cams74/users/pv904464/version0/z_cams_l_uor_201212_v0_rf _ch4_srf_lw.nc\nwritemonthlymean.py -o ch4 -y 2012 run on :25:13 o " ; :NCO = "4.2.0" ; p } Forcing products, version 0 Page 14 of 41

15 Notes: a. Each product is associated with a different netcdf variable name; b. The number of grid boxes depends on the product: 61x120 for carbon dioxide and methane products, 320x161 for aerosol products, 96x73 for aerosol RF uncertainty distributions; c. Aerosol-radiation products are at daily resolution, giving from 28 to 31 distributions per file. Other products are at monthly resolution, giving 1 distribution per file; d. Dimension variables in aerosol-cloud RF products are double precision; e. The horizontal grid used for aerosol RF uncertainty products is uneven; f. Radiative effects and forcings and their uncertainties are in W m 2. Absorption and extinction AODs are dimensionless. g. The long name is a free-form name that tries to give as much information as possible on the variable s meaning. h. The standard name is taken, as much as possible, from the CMIP standard output table available at When a new name needed to be created, it follows the CMIP naming convention. i. Variable attributes species and, for aerosol RF, forcing_mechanism, are used to identify the forcing agent and mechanism when different variables happen to share the same standard name; j. The reference year varies depending on product: 1900 for carbon dioxide and methane RF products, current year for the other products; k. The short description and contact address are currently available only for carbon dioxide and methane RF products. See section 10 for contact information for all products; l. Products are made by applying a series of different methods and their updates, some of which are published, other not. Where possible, a reference is given; m. The main source of data for version 0 products is the MACC Global Reanalysis, but other datasets have been used. See sections 3 to 7 for details about each products; n. The CAMS Climate Forcing service is delivered by three main partners: the University of Reading (UK), University of Leeds, and Universität Leipzig (Germany). Each partner is responsible for specific products. The service is led by the University of Reading, which is the main contact for users (see section 10); o. The history attribute lists the commands issued to generate the products and the corresponding time stamps; p. Some products have undergone slight post-processing using the netcdf operators ( Future improvement: - Latitude, longitude, and time boundaries will be added to clarify gridbox dimensions and time averaging periods. - Differences in file structure between the products will be removed. Forcing products, version 0 Page 15 of 41

16 3. Carbon dioxide and methane 3.1 Product specifications Carbon dioxide arf_co2_trop_lw 3 x 3 Monthly ( ) W m 2 CMIP Standard name tropopause_adjusted_longwave_forcing Carbon Dioxide_lw_adjusted_radiative_forcing_at_tropopause rf_co2_srf_lw_cs 3 x 3 Monthly ( ) W m 2 CMIP Standard name surface_instantaneous_longwave_forcing_assuming_clear_sky Carbon Dioxide_lw_instantaneous_radiative_forcing_at_surface_ assuming_clear_sky rf_co2_srf_lw 3 x 3 Monthly ( ) W m 2 CMIP Standard name surface_instantaneous_longwave_forcing Carbon Dioxide_lw_instantaneous_radiative_forcing_at_surface rf_co2_srf_sw_cs 3 x 3 Monthly ( ) W m 2 CMIP Standard name surface_instantaneous_shortwave_forcing_assuming_clear_sky Carbon Dioxide_sw_instantaneous_radiative_forcing_at_surface_ assuming_clear_sky rf_co2_srf_sw 3 x 3 Monthly ( ) W m 2 CMIP Standard name surface_instantaneous_shortwave_forcing Carbon Dioxide_sw_instantaneous_radiative_forcing_at_surface rf_co2_toa_lw_cs 3 x 3 Monthly ( ) W m 2 CMIP Standard name toa_instantaneous_longwave_forcing_assuming_clear_sky Carbon Dioxide_lw_instantaneous_radiative_forcing_at_toa_ assuming_clear_sky rf_co2_toa_lw 3 x 3 Monthly ( ) W m 2 CMIP Standard name toa_instantaneous_longwave_forcing Carbon Dioxide_lw_instantaneous_radiative_forcing_at_toa Forcing products, version 0 Page 16 of 41

17 rf_co2_toa_sw_cs 3 x 3 Monthly ( ) W m 2 CMIP Standard name toa_instantaneous_shortwave_forcing_assuming_clear_sky Carbon Dioxide_sw_instantaneous_radiative_forcing_at_toa_ assuming_clear_sky rf_co2_toa_sw 3 x 3 Monthly ( ) W m 2 CMIP Standard name toa_instantaneous_shortwave_forcing Carbon Dioxide_sw_instantaneous_radiative_forcing_at_toa rf_co2_trop_lw_cs 3 x 3 Monthly ( ) W m 2 CMIP Standard name tropopause_instantaneous_longwave_forcing_assuming_clear_sky Carbon Dioxide_lw_instantaneous_radiative_forcing_at_tropopause_ assuming_clear_sky rf_co2_trop_lw 3 x 3 Monthly ( ) W m 2 CMIP Standard name tropopause_instantaneous_longwave_forcing Carbon Dioxide_lw_instantaneous_radiative_forcing_at_tropopause rf_co2_trop_sw_cs 3 x 3 Monthly ( ) W m 2 CMIP Standard name tropopause_instantaneous_shortwave_forcing_assuming_clear_sky Carbon Dioxide_sw_instantaneous_radiative_forcing_at_tropopause_ assuming_clear_sky rf_co2_trop_sw 3 x 3 Monthly ( ) W m 2 CMIP Standard name tropopause_instantaneous_shortwave_forcing Carbon Dioxide_sw_instantaneous_radiative_forcing_at_tropopause Methane arf_ch4_trop_lw 3 x 3 Monthly ( ) W m 2 CMIP Standard name tropopause_adjusted_longwave_forcing Methane_lw_adjusted_radiative_forcing_at_tropopause Forcing products, version 0 Page 17 of 41

18 rf_ch4_srf_lw_cs 3 x 3 Monthly ( ) W m 2 CMIP Standard surface_instantaneous_longwave_forcing_assuming_clear_sky name Methane_lw_instantaneous_radiative_forcing_at_surface_assuming_clear_sky rf_ch4_srf_lw 3 x 3 Monthly ( ) W m 2 CMIP Standard name surface_instantaneous_longwave_forcing Methane_lw_instantaneous_radiative_forcing_at_surface rf_ch4_toa_lw_cs 3 x 3 Monthly ( ) W m 2 CMIP Standard name toa_instantaneous_longwave_forcing_assuming_clear_sky Methane_lw_instantaneous_radiative_forcing_at_toa_assuming_clear_sky rf_ch4_toa_lw 3 x 3 Monthly ( ) W m 2 CMIP Standard name toa_instantaneous_longwave_forcing Methane_lw_instantaneous_radiative_forcing_at_toa rf_ch4_trop_lw_cs 3 x 3 Monthly ( ) W m 2 CMIP Standard name tropopause_instantaneous_longwave_forcing_assuming_clear_sky Methane_lw_instantaneous_radiative_forcing_at_tropopause_ assuming_clear_sky rf_ch4_trop_lw 3 x 3 Monthly ( ) W m 2 CMIP Standard name tropopause_instantaneous_longwave_forcing Methane_lw_instantaneous_radiative_forcing_at_tropopause 3.2 Methods Radiative transfer calculations The radiative transfer model used is a standalone version of the ECMWF IFS, configured like in cycle 36r1, which is the version used to create the MACC Global Reanalysis. Gaseous optical properties are computed by RRTMG (Mlawer et al., 1997) while cloud and aerosol optical properties are computed by schemes developed at ECMWF. The LW and SW solvers are based on McICA (Pincus et al., 2003). Cloud overlap is assumed to be exponential-random. Scattering by clouds and aerosols in the LW spectrum is neglected. The calculations of radiative fluxes by the CAMS Climate Forcing Forcing products, version 0 Page 18 of 41

19 radiative transfer code have been compared against observational estimates (Kato et al., 2013) and found to be accurate within a few percents (Table 3.2). Fluxes with sizeable aerosol contributions, such as surface and clear-sky fluxes, are less accurate because aerosols were not included in those benchmark tests. Table 3.1 Comparison of globally- and annually-averaged radiative fluxes computed by the standalone ECMWF IFS radiative transfer code against observational estimates by Kato et al. (2013). Radiative flux Kato et al. (2013) (W m 2 ) ECMWF IFS radiative transfer calculations (W m 2 ) Difference (W m 2, %) TOA incoming solar (0.5%) All sky TOA outgoing SW 99 to (1.4%) TOA outgoing LW 237 to (0.0%) Surf SW downward (4.5%) Surf SW upward 23 to (7.5%) Surf LW downward 342 to (0.0) Surf LW upward (0.3) Clear sky (cloud free) TOA outgoing SW (8.7%) TOA outgoing LW 264 to (0.0%) Surf SW downward 242 to (4.4%) Surf SW upward 29 to (6.0%) Surf LW downward (0.2%) Surf LW upward 397 to (0.2%) Radiative fluxes are calculated at 61 model half-levels but for RF purposes, three levels are retained: surface, TOA, and tropopause. The tropopause is currently set to be at 200 hpa at all points. Forcing products, version 0 Page 19 of 41

20 Future improvement: In version 1 products, the tropopause level will be defined according to local conditions. As of version 0, adjustment of LW radiative fluxes to account for changes in stratospheric temperatures is done in a very simplified way, following Shine et al. (1995) and Hansen et al. (2005). For carbon dioxide, adjusted LW RF is computed at the tropopause by decreasing instantaneous LW RF at the tropopause by 7.5%. For tropospheric ozone, the decrease is 23% (Table 7 of Berntsen et al. 1997; Table 4 of Stevenson et al. 2013). For methane, the impact of stratospheric adjustment is much smaller and is neglected, so adjusted methane LW RF at the tropopause is equal to instantaneous RF at the tropopause. For stratospheric ozone, adjustment cannot be done in such a simplified way and is not imposed for version 0 products. Future improvement: Adjustment in version 1 products will be achieved using seasonallyvarying fixed-dynamical heating (Forster et al., 1997). Methane RF is only given in the LW spectrum, although it is now known that the SW contribution can be as large as 15% (Etminan et al., 2016). The decision not to publish methane RF estimates in the SW stems from the inability of RRTMG and most other radiative transfer codes to properly handle methane absorption bands in the SW part of the spectrum. Future improvement: For version 1 products, it will be attempted to compute methane RF in the SW by improving RRMTG or using scaling factors based on line-by-line calculations Inputs to the radiative transfer code The radiative transfer code is run on distributions of atmospheric variables simulated by the MACC Global Reanalysis (Table 3.1) and taken from ECMWF MARS and corresponding to data available from the download tool at The distributions are used as the mean of 4 time steps (0Z, 6Z, 12Z, and 18Z) for the reanalysis dated 0Z daily. The distributions are used at the degraded horizontal resolution of 3.0 by 3.0, down from 0.75 x 0.75, to reduce computational cost. That decrease in resolution causes negligible (third decimal place) changes in globally-averaged RF. Forcing products, version 0 Page 20 of 41

21 Table 3.2 Variables taken from the MACC Global Reanalysis and used as input to the radiation scheme. Variable name MARS Parameter Number Levels Fraction of Cloud Cover levels Forecast Albedo Surface only GEMS ozone levels Logarithm of surface pressure Surface only Specific cloud ice water content levels Specific cloud liquid water content levels Skin temperature Surface only Snow depth Surface only Specific humidity levels Pressure on 61 half levels is calculated from the logarithm of the surface pressure following the definition and coefficients of the 60-level IFS configuration, as described at The radiative transfer code also requires temperature on 61 half levels, which are computed from temperature on 60 model levels using pressure-weighted linear interpolation following the equation: T " P " = 1 P " P ( ) P ( * P ( ) P ( ) T ( ) + P " P ( ) P ( * P ( ) P ( * T ( * where T " and P " are temperature and pressure at the half-level, T ( ) and P ( ) are the pressure and temperature for the model level above, and T ( * and P ( * are the pressure and temperature for the level below. Temperature at the top-most half-level is obtained by linear extrapolation. LW surface emissivity is computed as done in the ECMWF IFS based on simulated snow and sand covers. Its value is computed by averaging the emissivity of four surface tiles in proportion to their coverage of each gridbox. Surface emissivities used in that calculation are listed in Table 3.3. Outside the window region, the value for sea is used. Table 3.3 Values of LW surface emissivity used in radiative transfer calculations. Surface type LW emissivity Land 0.96 Sand 0.93 Sea 0.99 Snow 0.98 RF is integrated diurnally by integrating over 6 solar zenith angles, computed as a function of local latitude and day of the year and symmetrically distributed around local noon. Concentrations of carbon dioxide and methane are taken from third-party datasets because the MACC reanalysis is strongly biased in those two variables. Present-day carbon dioxide Forcing products, version 0 Page 21 of 41

22 concentrations are taken from the globally-averaged marine surface monthly mean record maintained by NOAA ESRL at and specifically the file at ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_gl.txt The resulting time series for is shown in Figure 3.2.a. Present-day methane concentrations are taken from the globally-averaged monthly mean record maintained by AGAGE at and specifically the file at The resulting time series for is shown in Figure 3.2.b. Figure 3.2 Time series of globally- and monthly-averaged mole fractions of carbon dioxide (a, ppm) and methane (b, ppb) for the MACC Global Reanalysis period Future improvement: Version 1 products will be based on the CAMS Global Reanalysis, which is expected to have much reduced biases in its simulation of carbon dioxide and methane distributions. For the moment, the radiative transfer calculations for carbon dioxide and methane RF do not include aerosol. The effective radius of cloud liquid droplets and ice crystals are fixed at 10 μm and 50 μm, respectively. Optically active gases are set to their PI values, see section Future improvement: Radiative transfer calculations for version 1 products will use consistent representations of aerosols and clouds for all forcing agents. The 3D radiative transfer capabilities of the code are not used, and there are no plans to use them in the future. Forcing products, version 0 Page 22 of 41

23 3.2.3 Pre-industrial atmospheric concentrations Pre-industrial volume mixing ratios of carbon dioxide, methane, and nitrous oxide are taken from the IPCC AR5 (Myhre et al., 2013) and given in Table 3.3. Those mixing ratios are used as global and annual single numbers, i.e. are uniform horizontally and vertically and without monthly cycles. Future improvement: Version 1 products will be based on several estimates of the preindustrial atmosphere and will resolve horizontal, vertical, and monthly variations of methane, and if possible also carbon dioxide, volume mixing ratios. Table Volume mixing ratios used to represent pre-industrial conditions in radiative transfer calculations. Taken from IPCC AR5 Table 8.2 (Myhre et al., 2013). Species Volume mixing ratio CO ppm CH ppb N 2 O 270 ppb CFC11 0 CFC Uncertainties For version 0 products, uncertainties in carbon dioxide and methane RF are taken at ±10% following IPCC AR5 (Myhre et al., 2013), which assessed uncertainties due to spectroscopy, radiative transfer calculations, and representation of clouds. Future improvement: Version 1 product uncertainties will be derived for the radiative transfer code used in the service, and will include both structural and parametric uncertainties. 4. Tropospheric and stratospheric ozone 4.1 Product specifications As of December 2016, tropospheric and stratospheric ozone products have not been finalised because of difficulties in correcting for biases in MACC Global Reanalysis ozone against both present-day and pre-industrial states (Section 4.2.3). Ozone products exist at version 0 at an experimental stage but are not distributed publicly. Forcing products, version 0 Page 23 of 41

24 4.2 Methods Radiative transfer calculations The radiative transfer calculations made for computing ozone RF are identical to those described in section for carbon dioxide and methane RF, with the exception of stratospheric adjustment of LW tropospheric ozone RF, which is achieved by decreasing instantaneous LW tropospheric ozone RF by 25%. Future improvement: Adjustment in version 1 products will be achieved using seasonallyvarying fixed-dynamical heating (Forster et al., 1997) Inputs to the radiative transfer code Calculations are made in a similar way to those for carbon dioxide and methane, as described in section In version 0 products, the ozone distributions are taken from the MACC Global Reanalysis (variable GEMS ozone in Table 3.1). The distinction between tropospheric and stratospheric ozone is made by setting the tropopause level following Hansen et al. (2005), where the tropopause level is set at 100 hpa for latitudes within ±30, (100+(110/15)*( latitude 30)) for 30 < latitude < 45, and 210 hpa for latitudes larger than ±45. Future improvement: In version 1 products, the tropopause level will be defined according to local conditions Pre-industrial atmospheric concentrations Pre-industrial ozone conditions are taken as three-dimensional, monthly-averaged distributions from the CMIP6 dataset derived from CCMI models with representations of stratospheretroposphere chemistry. The year represented by that dataset is 1850 and it is assumed here that ozone distributions have not changed between 1750 and That assumption is reasonable, although contributions from wildfires and anthropogenic activities linked to the start of the industrial revolution may have introduced small variations. CMIP6 ozone files used by CAMS Climate Forcing were obtained from The CMIP6 dataset is given as volume mixing ratios and are converted to mass-mixing ratios by multiplying by To ensure that this PI distribution of ozone is consistent with the PD distribution taken from the MACC Global Reanalysis (see previous section), a bias correction is applied where monthly CMIP PI averages are scaled by the PD MACCto-CMIP6 ratios for the same month. Unfortunately, that bias correction does not reduce known biases in the MACC Global Reanalysis (Inness et al. 2013). Future improvement: Pre-industrial ozone distributions for version 1 products will be based on several simulations of the pre-industrial atmosphere using the ECMWF IFS. Forcing products, version 0 Page 24 of 41

25 4.3 Uncertainties For version 0 products, uncertainties in ozone RF are taken at ±50% following IPCC AR5 (Myhre et al., 2013), which assessed uncertainties due to the pre-industrial ozone concentrations, inter-model spread in present-day ozone distributions, and radiative transfer calculations. Future improvement: Version 1 product uncertainties will be derived for the radiative transfer code used in the service, and will include both structural and parametric uncertainties. 5. Speciated aerosol optical depths 5.1 Product specifications abs550anth x Daily ( ) 1 CMIP Standard atmosphere_absorption_optical_thickness_due_to_ambient_aerosol_particles name anthropogenic_aerosol_absorption_optical_depth_at_550_nm abs550dust x Daily ( ) 1 CMIP Standard atmosphere_absorption_optical_thickness_due_to_ambient_aerosol_particles name mineral_dust_aerosol_absorption_optical_depth_at_550nm abs550landnat x Daily ( ) 1 CMIP Standard atmosphere_absorption_optical_thickness_due_to_ambient_aerosol_particles name non-dust_land_natural_aerosol_absorption_optical_depth_at_550nm abs550marine x Daily ( ) 1 CMIP Standard atmosphere_absorption_optical_thickness_due_to_ambient_aerosol_particles name marine_aerosol_absorption_optical_depth_at_550_nm od550aer x Daily ( ) 1 CMIP Standard name atmosphere_optical_thickness_due_to_ambient_aerosol_particles total_aerosol_optical_depth_at_550_nm Forcing products, version 0 Page 25 of 41

26 od550anth x Daily ( ) 1 CMIP Standard name atmosphere_optical_thickness_due_to_ambient_aerosol_particles anthropogenic_aerosol_optical_depth_at_550_nm od550dust x Daily ( ) 1 CMIP Standard name atmosphere_optical_thickness_due_to_ambient_aerosol_particles mineral_dust_aerosol_optical_depth_at_550nm od550landnat x Daily ( ) 1 CMIP Standard name atmosphere_optical_thickness_due_to_ambient_aerosol_particles non-dust_land_natural_aerosol_optical_depth_at_550nm od550marine x Daily ( ) 1 CMIP Standard name atmosphere_optical_thickness_due_to_ambient_aerosol_particles marine_aerosol_optical_depth_at_550_nm 5.2 Methods Identification of aerosol origin To obtain aerosol RF, it is necessary to distinguish between aerosols of natural origin and aerosols of anthropogenic origin. The ECMWF IFS does not keep track of the aerosol origin mainly to keep computational cost reasonable but also because aerosol origin is not always given in emission inventories, because the same aerosol particle may be an internal mixture with anthropogenic and natural contributions, and because data assimilation cannot constrain natural and anthropogenic aerosols independently. Instead the aerosol origin is obtained using the algorithm described by Bellouin et al. (2013) where aerosol size is used as a proxy for aerosol origin. The algorithm identifies four aerosol origins: anthropogenic, mineral dust, marine, and land-based fine-mode natural aerosol. That last origin mostly includes biogenic aerosols. The reader is referred to section 3 of Bellouin et al. (2013) for details of the algorithm. The present documentation describes two updates made to the algorithm since the publication of Bellouin et al. (2013). The first update is the replacement of continental-wide anthropogenic fractions used over land surfaces by a fully gridded dataset that includes seasonal variations. Over land, identification of component AODs starts with removing the contribution of mineral dust aerosols from total AOD. The remaining non-dust AOD, τ non-dust, is then distributed between anthropogenic and fine-mode natural components, noted τ anth and τ fine-natural, respectively, following: τ anth = f anth. τ non-dust τ fine-natural = (1 f anth ). τ non-dust Forcing products, version 0 Page 26 of 41

27 where fanth is the anthropogenic fraction of the non-dust AOD. In version 0 products, fanth is given by monthly distributions on a 1x1-degree grid projected onto the 1.1 x1.1 grid used by the MACC Global Reanalysis. This new dataset derives from an analysis of AeroCom 2 numerical models (Kinne et al., 2013). Its annual average is shown in Figure 5.1. Anthropogenic fractions show a North-South gradient, as expected from the location of population and industrial activities. Anthropogenic fractions are larger than 0.8 over most industrialised regions of North America, Europe, and Asia. The largest fractions are located over China, where more than 90% of non-dust AOD is attributed to anthropogenic aerosols. In the southern hemisphere, anthropogenic fractions are typically smaller than 0.7 on an annual average. In terms of seasonality, anthropogenic fractions remain larger than 0.7 throughout the year in the northern hemisphere, with a peak in winter when energy consumption is high. In the southern hemisphere, seasonality is driven by biomass-burning aerosols, which are considered purely anthropogenic in the CAMS Climate Forcing products. Anthropogenic fractions therefore peak in late summer in South America and southern Africa. Figure Annually-averaged anthropogenic fraction of non-dust aerosol optical depth over land. The second change concerns the fine-mode fraction (FMF) of marine AOD at 0.55 μm, which gives the fraction of marine AOD that is exerted by particles with radii smaller than 0.5 μm. In Bellouin et al. (2013), that fraction was set to a fixed value of 0.3. In version 0 products, that fraction is determined by a gridded dataset that includes monthly variations. The dataset is obtained by applying the method of Yu et al. (2009) to daily MODIS Collection 6 aerosol retrievals of AOD and FMF. First, the marine aerosol background is isolated by selecting only ocean-based scenes where total AOD at 0.55 μm is between 0.03 and Then, an AOD-weighted averaged FMF is computed. The analysis has been applied to retrievals from MODIS instruments on both the Terra (dataset covering ) and Aqua (dataset covering ) platforms. Both instruments yield very similar marine FMF distributions, and the distributions used in version 0 product are the multiannual monthly averages of the two instruments. Figure 5.2 shows the marine FMF derived from MODIS/Terra for the months of January and July. It suggests that marine FMF varies over a wide range of values. Regions of high wind speeds, around in both hemispheres, are associated with large FMFs indicating that the marine aerosol size distribution includes a sizeable fraction of smaller particles there. There are indications of contamination by fine-mode anthropogenic and Forcing products, version 0 Page 27 of 41

28 mineral dust aerosols in coastal areas, but the impact on speciated AODs is small because the aerosol identification algorithm uses broad FMF categories rather than absolute values. Figure 5.1 Fine-mode fraction of marine aerosol optical depth at 0.55 μm as derived from MODIS/Terra Collection 6 aerosol retrievals for the months of January (left) and July (right). In fact, the impact of using monthly-varying distributions instead of a global, annual marine FMF is small. Anthropogenic AOD decreases slightly in the roaring forties in the Southern Ocean, but tends to increase slightly in the Northern Atlantic and Pacific oceans. On a global average, the change in anthropogenic AOD due to the improved specification of marine FMF is (+1.4%) Inputs to the algorithm The list of input distributions from the MACC Global Reanalysis used by the aerosol origin identification algorithm is given in Table 5.1. Table 5.1 Variables taken from the MACC Global Reanalysis and used as input to the aerosol origin identification algorithm. Variable name MARS Parameter Number Levels 10 metre U wind component Surface only 10 metre V wind component Surface only Black Carbon Aerosol Optical Depth at 550nm Column Dust Aerosol Optical Depth at 550nm Column Land-sea mask Surface only Organic Matter Aerosol Optical Depth at 550nm Column Sea Salt Aerosol Optical Depth at 550nm Column Sulphate Aerosol Optical Depth at 550nm Column Total Aerosol Optical Depth at 550nm Column 5.3 Uncertainties Uncertainties on speciated AODs are given in Table 2 of Bellouin et al. (2013) and correspond to a relative uncertainty of about 20% on a global average. Forcing products, version 0 Page 28 of 41

29 6. Aerosol-radiation interactions 6.1 Product specifications reari_anth_srfcs x Daily ( ) W m 2 CMIP Standard surface_shortwave_aerosol_radiative_effect_assuming_clear_sky name anthropogenic_sw_radiative_effect_of_ari_at_surface_assuming_clear_sky reari_anth_toacs x Daily ( ) W m 2 CMIP Standard name toa_shortwave_aerosol_radiative_effect_assuming_clear_sky anthropogenic_sw_radiative_effect_of_ari_at_toa_assuming_clear_sky reari_dust_srfcs x Daily ( ) W m 2 CMIP Standard name surface_shortwave_aerosol_radiative_effect_assuming_clear_sky mineral_dust_sw_radiative_effect_of_ari_at_surface_assuming_clear_sky reari_dust_toacs x Daily ( ) W m 2 CMIP Standard name toa_shortwave_aerosol_radiative_effect_assuming_clear_sky mineral_dust_sw_radiative_effect_of_ari_at_toa_assuming_clear_sky reari_landnat_srfcs x Daily ( ) W m 2 CMIP Standard name surface_shortwave_aerosol_radiative_effect_assuming_clear_sky non-dust_land_natural_aerosol_sw_radiative_effect_of_ari_ at_surface_assuming_clear_sky reari_landnat_toacs x Daily ( ) W m 2 CMIP Standard name surface_shortwave_aerosol_radiative_effect_assuming_clear_sky non-dust_land_natural_aerosol_sw_radiative_effect_of_ari_ at_toa_assuming_clear_sky reari_marine_srfcs x Daily ( ) W m 2 CMIP Standard name surface_shortwave_aerosol_radiative_effect_assuming_clear_sky marine_sw_radiative_effect_of_ari_at_surface_assuming_clear_sky Forcing products, version 0 Page 29 of 41

30 reari_marine_toacs x Daily ( ) W m 2 CMIP Standard name surface_shortwave_aerosol_radiative_effect_assuming_clear_sky marine_sw_radiative_effect_of_ari_at_toa_assuming_clear_sky rfari_srfcs x Daily ( ) W m 2 CMIP Standard surface_instantaneous_shortwave_forcing_assuming_clear_sky name anthropogenic_sw_radiative_forcing_of_ari_at_surface_assuming_clear_sky rfari_srf x Daily ( ) W m 2 CMIP Standard name surface_instantaneous_shortwave_forcing sw_radiative_forcing_of_ari_at_surface_allsky rfari_toacs x Daily ( ) W m 2 CMIP Standard name toa_instantaneous_shortwave_forcing_assuming_clear_sky anthropogenic_sw_radiative_forcing_of_ari_at_toa_assuming_clear_sky rfari_toa x Daily ( ) W m 2 CMIP Standard name toa_instantaneous_shortwave_forcing sw_radiative_forcing_of_ari_at_toa_allsky std_rfari 2.5 x 3.75 Monthly ( )* W m 2 CMIP Standard name standard_deviation_of_toa_instantaneous_shortwave_forcing standard_deviation_of_sw_radiative_forcing_of_ari_at_toa_allsky * Standard deviations are determined for a given year and assumed to apply to all years. 6.2 Methods Radiative effect and forcing of aerosol-radiation interactions are computed by radiative transfer calculations that combine the speciated AODs derived in Section 5 with prescriptions of aerosol size distribution and single-scattering albedo. The methods are as described in Section 4 of Bellouin et al. (2013) with one exception: the prescription of single-scattering has been updated from a few, continental-wide numbers to gridded monthly climatologies. This updated dataset introduces two major improvements compared to Bellouin et al. (2013). First, the new dataset provides the monthly cycle of fine-mode absorption. Second, the data set is provided on a 1x1-degree grid, later projected onto the coarser grid used by the MACC re-analysis. The method used to produce the dataset is described in Kinne et al. (2013). First, distributions of fine-mode extinction and absorption Forcing products, version 0 Page 30 of 41

31 AODs are obtained from a selection of global aerosol numerical models that participated in the AeroCom 1 simulations using a common set of aerosol and precursor emissions for present-day conditions (Kinne et al. 2006). In order to include an observational constraint, those modelled distributions are then merged with retrievals of aerosol SSA for the period at more than 300 AERONET sites. The merging is based on a subjective assessment of the quality of the measurements at each of the AERONET sites used, along with their ability to represent aerosols in a wider region around the site location. The main impact of merging observed SSAs is to make aerosols in Africa and South Asia more absorbing than numerical models predicted. The distribution of annual-averaged aerosol SSA is shown in Figure 6.1. The dataset represents the local maximum of absorption over California and the change in absorption as biomass-burning aerosols during transport, which is visible over Africa. Over Asia, Europe, and South America, absorption is also larger near source regions, with less absorption elsewhere. Figure 6.1 Annually-averaged distribution of single-scattering albedo at 0.55 μm used to characterize absorption of anthropogenic aerosols. It is worth noting that the SSA distribution characterises absorption of fine-mode aerosols but is used to provide the absorption of anthropogenic aerosols, which is not fully consistent. The inconsistency is however mitigated by two factors. First, fine-mode aerosols are the main proxy for anthropogenic aerosols in the MACC algorithm that identifies aerosol origin, and their distributions are broadly similar. Second, regions where natural aerosols such as marine and mineral dust may contaminate the fine-mode AOD often correspond to minima in anthropogenic AOD. Like in Bellouin et al. (2013), REari and RFari are estimated in clear-sky (cloud-free sky) then scaled by the complement of the cloud fraction in each gridbox to represent all-sky conditions, thus assuming that cloudy-sky aerosol-radiation interactions are zero. However, experimental products that include estimates of cloudy-sky RF exist but are based on a simplified account of cloud albedo, which limits their usefulness. For 2003, globally-averaged above-cloud anthropogenic and mineral dust AODs weighted by cloud fraction, are and 0.003, respectively, or 8% of their clear-sky counterparts. Above-cloud marine and fine-mode natural AODs are negligible. Those above-cloud aerosols exert a positive REari because of their absorbing nature and the high reflectance of clouds. REari commonly reach +5 to +10 Wm 2 during the biomass-burning season that lasts from late Forcing products, version 0 Page 31 of 41

32 August to October over the south-eastern Atlantic stratocumulus deck. That would translate into a cloudy-sky anthropogenic RFari of Wm 2. Neglecting above-cloud aerosols therefore introduces a small uncertainty on the global average, but leads to larger errors regionally and seasonally. Future improvement: Version 1 products will use the ECMWF IFS radiation code and will therefore properly account for impact of clouds on all-sky radiative forcing of aerosolradiation interactions Inputs to the algorithm The list of input distributions from the MACC Global Reanalysis used by the radiative transfer calculations to obtain REari and RFari is given in Table 6.1. Table 6.1 Variables taken from the MACC Global Reanalysis and used to compute aerosolradiation interactions. Variable name Levels Near IR albedo for diffuse radiation Surface only Near IR albedo for direct radiation Surface only Total cloud cover Column UV visible albedo for diffuse radiation Surface only UV visible albedo for direct radiation Surface only Pre-industrial aerosol optical depths Version 0 products are not defined with respect to PI conditions. Rather, RFari is defined with respect to PD aerosols, which is a different reference to PI so a correction is required. That correction factor is taken from Bellouin et al. (2013) and is equal to 0.8, i.e. RFari defined with respect to PI is 80% of RFari defined with respect to PD aerosols. Future improvement: Pre-industrial aerosol optical depth distributions for version 1 products will be based on several simulations of the pre-industrial atmosphere using the ECMWF IFS. 6.3 Uncertainties Uncertainties are derived from a Perturbed Parameter Ensemble (PPE) run at the University of Leeds with the GLOMAP global aerosol model embedded in HadGEM3 (Yoshioka et al. 2017). It is therefore assumed that the uncertainties that characterise RFari in a numerical aerosol model are the same than the uncertainties that would characterise the combination of modelling, data assimilation, and radiative transfer calculations that make up the CAMS RFari estimates. Forcing products, version 0 Page 32 of 41

33 Future improvement: For version 1 products, this assumption will be supported by a PPE derived from the ECMWF IFS and accounting for uncertainties in input distributions. For the GLOMAP PPE, prior probability distributions of uncertain parameters were generated using an expert elicitation process that lasted from February 2015 to October To avoid overly centralising the sample for individual parameters compared to expert beliefs, trapezoidal distributions are used here to represent parameter uncertainty. Experts also converged on agreement about parameter uncertainty using trapezoidal distributions faster than they did when attempting to use other types of distribution. For each elicited distribution a literature review and analysis of the model sensitivity to individual parameter perturbations was conducted. The set of uncertain parameters is given in Table 6.3. It is an update and extension of the set used in Lee et al. (2013). Uncertainties are calculated for a generic year and assumed to apply to all years. Table 6.3 List of parameters perturbed in the HadGEM3-GLOMAP Perturbed Physics Ensemble used to estimate RFari and RFaci uncertainties # Parameter # Parameter 1 Boundary layer nucleation 14 Sea spray mass flux (coarse/accumulation) 2 Ageing rate from insoluble to soluble 15 SO 2 emission flux (anthropogenic) 3 Modal width (accumulation 16 SO 2 emission flux (volcanic) soluble/insoluble) 4 Modal width (Aitken soluble/insoluble) 17 Biogenic monoterpene production of SOA 5 ph of cloud drops 18 DMS emission flux 6 BC/OC mass emission rate (fossil fuel) 19 Dry deposition velocity of Aitken mode Aerosol 7 BC/OC mass emission rate (biomass 20 Dry deposition velocity of accumulation burning) mode aerosol 8 BC/OC mass emission rate (biofuel) 21 Dry deposition velocity of SO 2 9 BC/OC emitted mode diameter (fossil fuel) 22 Hygroscopicity parameter Kappa for organic aerosols 10 BC/OC emitted mode diameter (biomass 23 Standard deviation of updraft velocity. burning) 11 BC/OC emitted mode diameter (biofuel) 24 Tuning factor to the dust emission flux. 12 Mass fraction of SO 2 converted to new 25 Fraction of the cloudy part of the gridbox 2 SO 4 particles in sub-grid power plant where rain is forming and hence scavenging plumes 2 13 Mode diameter of new sub-grid SO 4 particles takes place. 26 Scavenging threshold for both cloud liquid and ice water scavenging Forcing products, version 0 Page 33 of 41

34 7. Aerosol-cloud interactions 7.1 Product specifications rfaci_srf_lw x Monthly ( ) W m 2 CMIP Standard name surface_instantaneous_longwave_forcing lw_radiative_forcing_of_aci_at_surface_allsky rfaci_srf_sw x Monthly ( ) W m 2 CMIP Standard name surface_instantaneous_shortwave_forcing sw_radiative_forcing_of_aci_at_surface_allsky rfaci_toa_lw x Monthly ( ) W m 2 CMIP Standard name toa_instantaneous_longwave_forcing lw_radiative_forcing_of_aci_at_toa_allsky rfaci_toa_sw x Monthly ( ) W m 2 CMIP Standard name toa_instantaneous_shortwave_forcing sw_radiative_forcing_of_aci_at_toa_allsky std_rfaci 2.5 x 3.75 Monthly ( )* W m 2 CMIP Standard name standard_deviation_of_toa_instantaneous_shortwave_forcing standard_deviation_of_sw_radiative_forcing_of_aci_at_toa_allsky * Standard deviations are determined for a given year and assumed to apply to all years. 7.2 Methods The aerosol-cloud RF estimation algorithm proceeds in three steps: 1. Derive CCN concentrations from the MACC Global Reanalysis, using aerosol properties as prescribed in the IFS radiative transfer code; 2. Estimate CDNC from cloud properties derived from A-Train satellite instruments and assumptions of updraft velocity distributions; 3. Compute the anthropogenic fraction of CDNC based on AOD speciation. The four steps are described below. Aerosol-cloud RFs are estimated as monthly distributions because of limitations of the method above. Forcing products, version 0 Page 34 of 41

35 Future improvement: In version 1 products, cloud properties will be taken from the CAMS Global Reanalysis, allowing daily estimates of aerosol-cloud RF Cloud condensation nuclei Three-dimensional, daily distributions of CCN are computed for 3 supersaturations (0.2, 0.4, and 1%) for the period CCN are calculated diagnostically in a box model (Davies et al. 2005) as function of temperature, pressure, specific humidity and aerosol mass mixing ratios. Those input distributions are all taken from the MACC Global Reanalysis. Aerosol mass mixing ratios are converted to aerosol number concentrations by assuming that size distributions are lognormal, with the parameters given in Table 7.1. Note that CCN concentrations result only from hydrophilic black carbon and organic matter, sulfate and sea-salt components. Dust is treated entirely as an insoluble aerosol species and no ageing or coating effects are considered in the model. Table 7.1 Properties of the lognormal size distributions of MACC Global Reanalysis aerosol components: median radius r 0 (μm) and geometric standard deviation σ g. Also given is the dry density ρ p (kg m 3 ). From Benedetti et al. (2009) and Reddy et al. (2005). Aerosol component r 0 (μm) σ g ρ p (kg m 3 ) Dust (small) Dust (medium) Dust (large) Sea-salt (small) Sea-salt (medium) Sea-salt (large) Sulfate Black carbon Organic matter Cloud droplet number concentrations Two-dimensional, daily distributions of CDNC at cloud base height are computed for 6 categories of liquid water clouds over the period This dataset is produced using a joint satellitereanalysis approach. The MACC Global Reanalysis aerosol distributions are first collocated to the track of the CALIPSO lidar using a nearest neighbour approach. The data is then filtered for liquid, single-layer and non-precipitating clouds using the satellite retrievals gathered in CCCM (Kato et al. 2014). MACC Global Reanalysis aerosol mass mixing ratios are taken at cloud base height, which is determined approximately from the CALIOP lidar retrievals within the CCCM dataset. Finally, colocated aerosol mass mixing ratios at cloud base heights are used in a box model, which diagnostically computes CDNC using a parameterization of aerosol activation, as described in the next subsection. Forcing products, version 0 Page 35 of 41

36 CDNC is estimated from aerosol composition, size distribution, and hygroscopicity (see Table 7.1). The parameterization of maximum supersaturation, S max, which depends on temperature, pressure, humidity, and updraft velocity, is taken from Abdul-Razzak and Ghan (2000). This parameterisation accounts for the competition between aerosol particles for available water vapour and for the dependence of this competition on particle sizes, chemical properties and also on the supersaturation forcing rate which is determined by the updraft velocity. An important aspect in the computation of S max is the updraft velocity. To account for sub-grid scale variability within the CERES footprint (20 by 20 km), the updraft velocity is assumed to be distributed according to the following PDF: * 1 w w f w = exp 2π σ 1 2σ* 1 where w is the mean large-scale vertical velocity, taken at 0 m s 1 and σ w is the standard deviation. The PDF is composed of 20 updraft bins from 0 to 2 m s 1. Only the positive part of the vertical velocity distribution is taken into account to ensure that only updrafts and not downdrafts are used for the computation of S max. The value of σ w depends on cloud regime, which itself depends on cloud base height and cloud top height variability. Cloud regimes and corresponding σ w values are given in Table 7.2. Table 7.2 Standard deviation, in m s 1, of cloud updraft velocity used for each cloud regime. H cb is the cloud base height (m). V Htop (%) is the variability in cloud top height. Cloud base / variability V Htop < 11% (homogeneous / stratiform) V Htop > 11% (inhomogeneous / cumuliform) H cb < 350 m m < H cb < 950 m H cb > 950 m Calculation of RFaci Radiative fluxes are calculated using the standalone version of the RRTM-G (Iacono et al., 2008) implementation in the ECHAM6 general circulation model (Stevens et al., 2013). Input distributions to the code are the fixed-σ w CDNC described in section 7.2.2; distributions of surface temperature, surface emissivity, atmospheric temperature and pressure profiles, cloud cover, cloud liquid and ice water mixing ratios, specific humidity, and ozone mixing ratio taken from the MACC Global Reanalysis, and climatological globally-averaged carbon dioxide, methane, and nitrous oxide mixing ratios. The calculation of the anthropogenic perturbation in CDNC uses the anthropogenic AOD fraction f anth derived in Section 5. ΔCDNC = f anth x CDNC Forcing products, version 0 Page 36 of 41

37 RFaci is then calculated as RFaci = F(CDNC) F(CDNC ΔCDNC) where F is the TOA all-sky radiative flux calculated by the radiative transfer code. CDNC is the only difference between the calculations of the two sets of radiative fluxes. Radiative transfer calculations are performed for each CERES footprint using the CDNC calculated for that footprint and the CAMS f anth of the grid box and at the time corresponding to the CERES footprint. If no CDNC has been retrieved, the radiative flux perturbation for that footprint is set zero. RFaci is therefore that due to liquid, non-precipitating clouds only. To produce the monthly gridded CAMS RFaci, the radiative flux in each CERES footprint is corrected for the diurnal cycle in insolation. This correction is performed by scaling the flux by the ratio cos θ / <cos θ> where θ is the instantaneous zenith angle at the CERES overpass time and brackets indicate the diurnal mean at the location of the CERES footprint. The RFaci product is then regridded to the grid used by the CAMS anthropogenic AOD product Pre-industrial reference state Like for RFari (section 6.2.2), version 0 products of RFaci are not defined with respect to PI conditions. Rather, RFaci is defined with respect to PD aerosols, which is a different reference to PI so a correction is required. That correction factor is assumed to be the same as for RFari at 0.8 (Bellouin et al. 2013). So RFaci defined with respect to PI is 80% of RFaci defined with respect to PD aerosols. Future improvement: Pre-industrial aerosol optical depth distributions for version 1 products will be based on several simulations of the pre-industrial atmosphere using the ECMWF IFS. 7.3 Uncertainties Uncertainties on RFaci are calculated in the same way as uncertainties on RFari. See section 6.3. Uncertainties are calculated for a generic year and assumed to apply to all years. Forcing products, version 0 Page 37 of 41