M Michou

P Nabat

D Saint-Martin

J Bock

B Decharme

M Mallet

R Roehrig

R Séférian

S Sénési

A Voldoire

Present-Day and Historical Aerosol and Ozone Characteristics in CNRM CMIP6 Simulations

Characteristics and radiative forcing of the aerosol and ozone fields of two configurations of the Centre National de Recherches Météoroglogiques (CNRM) and Cerfacs climate model are analyzed over the historical period (1850-2014), using several Coupled Model Intercomparison Project 6 (CMIP6) simulations. CNRM-CM6-1 is the atmosphere-ocean general circulation model including prescribed aerosols and a linear stratospheric ozone scheme, while the Earth System Model CNRM-ESM2-1 has interactive tropospheric aerosols and chemistry of the midtroposphere aloft. The representations of aerosols and ozone in CNRM-CM6-1 are issued from simulations of CNRM-ESM2-1, and this ensures some comparability of both representations. In particular, present-day anthropogenic aerosol optical depths are similar (0.018), and their spatial patterns correspond to those of reference data sets such as MACv2 and MACv2-SP despite a negative bias. Effective radiative forcings (ERFs) have been estimated using 30-year fixed sea surface temperature simulations (piClim) and several calls to the radiative scheme. Present-day anthropogenic aerosol ERF, aerosol-radiation ERF, and aerosol cloud ERF are fully within CMIP5 estimates and, respectively, equal to -1.10, -0.36, and -0.81 W m -2 for CNRM-CM6-1 and -0.21, -0.61, and -0.74 W m -2 for CNRM-ESM2-1. Additional CMIP6-type piClim simulations show that these differences are mainly due to the interactivity of the aerosol scheme whose impact is confirmed when assessing the response of both climate model configurations to rising CO 2 . Present-day stratospheric ozone ERF, equal to -0.04 W m -2 , is in agreement with that of the CMIP6 ozone. No trend appears in the ozone ERF over the historical period although the evolution of the total column ozone is correctly simulated.

Introduction

In the design of the Coupled Model Intercomparison Project 6 (CMIP6) presented in Eyring et al. (2016), several Model Intercomparison Projects (MIPs), including AerChemMIP (Aerosol Chemistry; see [START_REF] Collins | AerChemMIP: Quantifying the effects of chemistry and aerosols in CMIP6[END_REF] and RFMIP (Radiative Forcing; see [START_REF] Pincus | The radiative forcing model intercomparison project (RFMIP): Experimental protocol for CMIP6[END_REF], address the question of the quantification of the climate impacts of aerosols and various chemically reactive gases including ozone. Aerosols and ozone are part of the near-term climate forcers defined as those compounds whose impact on climate occurs primarily within the first decade of their presence in the atmosphere [START_REF] Myhre | Anthropogenic and natural radiative forcing[END_REF]. With regard to these two forcings, the variety of climate models involved in CMIP6 will range from models with both prescribed aerosols and ozone to models with fully interactive ozone chemistry and aerosol schemes. Therefore, official ozone and aerosol forcings have been developed (see http://goo.gl/r8up31 and [START_REF] Stevens | MACv2-SP: A parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6[END_REF] and will be used by some of the CMIP6 models, notably in some specific CMIP6 simulations, for example, in RFMIP simulations [START_REF] Pincus | The radiative forcing model intercomparison project (RFMIP): Experimental protocol for CMIP6[END_REF].

Forcing estimates of ozone and aerosols for the 1750-2011 period have been reported in the Fifth Assessment Report (AR5) of the International Panel on Climate Change [START_REF] Myhre | Anthropogenic and natural radiative forcing[END_REF] with various confidence levels that range from high confidence with medium agreement for tropospheric and 10.1029/2019MS001816 [START_REF] Voldoire | Evaluation of CMIP6 deck experiments with CNRM-CM6-1[END_REF] in order to generate the AOD fields needed to run the model. TACTIC_v2 simulates the physical evolution of five tropospheric aerosols that are supposed externally mixed: dust (DD; three size bins), primary sea salt (SS; three size bins), black carbon (BC; two bins separating hydrophilic and hydrophobic particles), organic matter (OM; two bins as for BC), and sulfate (SO 4 ; one bin). The representation of the sulfate aerosol involves several gaseous precursors, namely, SO 2 , H 2 S, and dimethylsulfide (DMS), which are gathered into a single gaseous variable.

Compared to the version used in [START_REF] Michou | Development and basic evaluation of a prognostic aerosol scheme (v1) in the CNRM climate model CNRM-CM6[END_REF] and [START_REF] Nabat | Dust aerosol radiative effects during summer 2012 simulated with a coupled regional aerosol-atmosphere-ocean model over the Mediterranean[END_REF], the main new features of the aerosol scheme come from changes in the SS scheme. To correct for a systematic low bias of AOD over oceans, the SS emissions are now calculated according to the formulation of [START_REF] Grythe | A review of sea-spray aerosol source functions using a large global set of sea salt aerosol concentration measurements[END_REF]Grythe et al. ( ) (p. 1286, equation 7, equation 7) that includes a dependence on SST given by [START_REF] Jaeglé | Global distribution of sea salt aerosols: New constraints from in situ and remote sensing observations[END_REF]. Tested against observed concentrations in [START_REF] Grythe | A review of sea-spray aerosol source functions using a large global set of sea salt aerosol concentration measurements[END_REF], this formulation of sea-salt emissions has shown to give the best estimates of sea-salt concentrations compared to other formulations.

)] 2 ) , ( 1
)
where dF dD p is the source function in terms of number of aerosols by particle size by area by time, D p the dry diameter, and U 10 the wind speed at 10 m. The dependence to SST is T W (SST) = 0.3 + 0.1 SST -0.0076 SST 2 + 0.00021 SST 3 .

(2)

The dust emission module relies on the studies of [START_REF] Marticorena | Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme[END_REF] and [START_REF] Kok | A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle[END_REF], as described in [START_REF] Nabat | Dust aerosol radiative effects during summer 2012 simulated with a coupled regional aerosol-atmosphere-ocean model over the Mediterranean[END_REF]. Dust particles are emitted in function of surface wind and land surface characteristics. The latter characteristics include soil textures, surface roughness length, and superficial soil moisture and are given by the land surface module embedded in the SURFEX surface modeling platform, detailed in [START_REF] Decharme | Recent changes in the ISBA-CTRIP land surface system for use in the CNRM-CM6 climate model and in global off-line hydrological applications[END_REF].

With regard to anthropogenic and biomass burning emissions, TACTIC_v2 considers the static monthly mean 1750-2014 emissions inventories of BC, OM, and SO 2 recommended for the CMIP6 simulations. Anthropogenic sources (horizontal resolution of 0.5 degrees) are described in [START_REF] Hoesly | Historical (1750-2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)[END_REF] and biomass burning sources (0.25 degrees) in van [START_REF] Van Marle | Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750-2015)[END_REF]. [START_REF] Hoesly | Historical (1750-2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)[END_REF] indicate that their inventories, which benefited from improved methodologies and source information, are in general slightly higher than previous inventories. van [START_REF] Van Marle | Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750-2015)[END_REF] analyze that global biomass emissions, with 1997-2015 GFED4s data as an anchor point, have been relatively constant over the entire 1750-2014 period, with regions of higher emissions due to deforestation and regions of lower emissions linked to changes in agricultural practices. All these inventories have been interpolated with a bilinear method to the CNRM grid at 1.4 degrees resolution. It is worth mentioning that, as in [START_REF] Michou | Development and basic evaluation of a prognostic aerosol scheme (v1) in the CNRM climate model CNRM-CM6[END_REF], a multiplier coefficient of 1.5 has been applied to organic carbon emissions in order to take into account the conversion of organic carbon into organic matter. Such a conversion factor is a common practice and was originally based on analysis of fresh urban emissions [START_REF] Turpin | Species contributions to PM2.5 mass concentrations: Revisiting common assumptions for estimating organic mass[END_REF]. In particular, this ratio ranges from 1.5 to 1.8 in the case of fresh particles emissions from biomass burning [START_REF] Ng | Organic aerosol components observed in northern hemispheric datasets from aerosol mass spectrometry[END_REF][START_REF] Tiitta | Chemical composition, main sources and temporal variability of PM 1 aerosols in southern African grassland[END_REF]. Secondary organic aerosols (SOA) are taken into account through a monthly inventory given by [START_REF] Dentener | Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom[END_REF], and DMS emissions follow the data set of [START_REF] Kettle | A global database of sea surface dimethylsulfide (DMS) measurements and a procedure to predict sea surface DMS as a function of latitude, longitude, and month[END_REF]. In addition, in order to avoid unrealistic large AOD peaks, emissions have been leveled off: SO 2 and BC emissions are limited to 5 • 10 -10 kg•m -2 •s -1 , while OM emissions are limited to 3 • 10 -9 kg•m -2 •s -1 . A threshold of 5 • 10 -10 kg m -2 s -1 is applied to SO 2 emissions coming from gaseous volcanic emissions [START_REF] Andres | A time-averaged inventory of subaerial volcanic sulfur emissions[END_REF]. All emissions are injected into the lowest model level, and AODs are those of the grid-box mean conditions.

The TACTIC_v2 interactive aerosol scheme is used in all CMIP6 simulations carried out with CNRM-ESM2-1.

The Aerosol Data Set for CNRM-CM6-1

In the standard GCM configuration of CNRM-CM6-1, this interactive aerosol scheme is not activated and is replaced by monthly AOD fields of BC, OM, SO 4 , DD, and SS varying each year. As described in [START_REF] Voldoire | Evaluation of CMIP6 deck experiments with CNRM-CM6-1[END_REF], these AOD fields have been calculated in preliminary CNRM-CM6-1 AMIP-type (atmosphere only) historical and future simulations using the interactive aerosol scheme described above and observed SSTs and sea ice (CMIP6 data). This historical simulation is named amip-hist-aod in the present study (see Table 2). Its AOD outputs for each aerosol type have been smoothed out using an 11-year running mean. The resulting AOD data set is used as the source of aerosol forcing in all CNRM-CM6-1 CMIP6 simulations.

The Aerosol-Radiation-Cloud Interactions

CNRM-CM6-1 and CNRM-ESM2-1 use the same parameterizations for representing interactions between aerosols, clouds, and radiation. The radiative transfer scheme receives 3-D distributions of aerosol extinction, either directly calculated by the interactive aerosol scheme in CNRM-ESM2-1 or coming from the vertical distribution of AODs in CNRM-CM6-1. This vertical distribution in CNRM-CM6-1 depends only on the aerosol type and is therefore unchanged in time and space. It is calculated from a pressure-dependent function using a scale height specific to each aerosol type as described in [START_REF] Bozzo | An aerosol climatology for global models based on the tropospheric aerosol scheme in the Integrated Forecasting System of ECMWF[END_REF]. Other aerosol optical properties used as input in the radiative transfer scheme, both in CNRM-CM6-1 and CNRM-ESM2-1, namely, the single scattering albedo (SSA; 0 to 1 range) and the asymmetry parameter (ASY; -1 to 1 range), are prescribed depending on the aerosol type, relative humidity (except for DD and BC), and wavelength. All our aerosol properties have been calculated with the Mie theory using refractive indexes of [START_REF] Krekov | Aerosol single-scattering albedo over the global oceans: Comparing PARASOL retrievals with AERONET, OMI, and AeroCom models estimates[END_REF] for each aerosol type and have been published in Table 6 of [START_REF] Nabat | A 4-D climatology (1979-2009) of the monthly tropospheric aerosol optical depth distribution over the Mediterranean region from a comparative evaluation and blending of remote sensing and model products[END_REF]. For the sake of brevity, Table 1 only shows values at 550 nm and 80% relative humidity, together with MACv2 values, based in part on AERONET observations, and MERRA-2 Aerosol Reanalysis values. MACv2 [START_REF] Kinne | The MACv2 aerosol climatology[END_REF] and MERRA-2 (see Randles et al., 2017, supplementary material) describe their choices of refractive indices and/or aerosol size distributions which lead to their aerosol optical properties. Differences in these properties highlight uncertainties on these parameters, especially for BC.

With regard to aerosol-cloud interactions, CNRM-CM6-1 and CNRM-ESM2-1 only represent the first aerosol indirect (or Twomey) effect whereby aerosols increase the number of cloud droplets at constant liquid water content thus reducing cloud droplet size and increasing cloud albedo. No secondary aerosol indirect effects (impact of particles on precipitation) are included in either models. The Twomey effect is parameterized through the calculation of cloud droplet number concentration (CDNC; cm -3 ) which is related to changes in aerosol concentrations (μg m -3 ). The parameterization of [START_REF] Menon | GCM, simulations of the aerosol indirect effect: Sensitivity to cloud parameterization and aerosol burden[END_REF] has been chosen in order to take into account the Twomey effect caused by SO 4 , OM (hydrophilic part), and SS (first size bin), as it had been done in the ECMWF IFS model [START_REF] Michou | Development and basic evaluation of a prognostic aerosol scheme (v1) in the CNRM climate model CNRM-CM6[END_REF][START_REF] Morcrette | Aerosol-cloud-radiation interactions and their impact on ECMWF[END_REF]. Note that the effects of DD and BC are not taken into account in this formulation that reads CDNC = 10 [b+a SO 4 log 10 (m SO 4 )+a SS log 10 (m SS )+a OM log 10 (m OM )] .

(3)

Several values of the associated coefficients have been tested based on the uncertainty ranges given in several studies [START_REF] Boucher | The sulfate-CCN-cloud albedo effect[END_REF][START_REF] Menon | GCM, simulations of the aerosol indirect effect: Sensitivity to cloud parameterization and aerosol burden[END_REF][START_REF] Quaas | Constraining the first aerosol indirect radiative forcing in the LMDZ GCM using POLDER and MODIS satellite data[END_REF][START_REF] Quaas | Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data[END_REF]. In particular, we varied a SO 4 from 0.2 to 0.5 and b from 1.7 to 2.41. These sensitivity tests resulted in estimates of the ERF due to aerosol-cloud interactions of the anthropogenic aerosols ranging from -1.9 to 0.1 W m -2 (the Overall, differences between the current and the [START_REF] Michou | A new version of the CNRM chemistry-climate model, CNRM-CCM: Description and improvements from the CCMVAL-2 simulations[END_REF] versions of the chemistry have been motivated by taking into account existing updates (e.g., new kinetics) and evolutions of the climate model, in particular evolution with regard to its vertical extension that required small adjustments both at the top and at the bottom of the atmosphere to simulate satisfactory concentrations of chemical species and evolution toward an Earth system model that considers the full carbon cycle. These differences consist in

• Kinetics and photolysis rates are now those of [START_REF] Sander | Chemical kinetics and photochemical data for use in atmospheric studies, evaluation no[END_REF].

• Monthly distributions of stratospheric sulfate aerosols follow the CMIP6 recommendations: Concentration, surface area density, and volume density are based on the work of [START_REF] Thomason | A global space-based stratospheric aerosol climatology: 1979-2016[END_REF]. • Solar information also follows the CMIP6 recommendations [START_REF] Matthes | Solar forcing for CMIP6 (v3.2)[END_REF]. The CNRM climate model uses monthly averages to modulate photolysis rates. • Photolysis rates are no longer modified according to cloudiness as we could not see any impact of this correction factor on the diagnostics of the upper troposphere and of the stratosphere covered by our chemistry scheme and as the correction factor was developed for a CTM aimed at studying tropospheric ozone and its precursors [START_REF] Brasseur | Mozart, a global chemical transport model for ozone and related chemical tracers: 1. Model description[END_REF]. • A different processing between the surface and the 560 hPa level as detailed above.

• The chemistry scheme vertical extension has been updated following that of the climate model, which now covers the atmospheric layer going from the surface to about 80 km in the mesosphere with 91 vertical levels. This did not require any specific adjustment except for the relaxation of H 2 O performed toward 6.5 ppmv over the seven highest model levels (above 0.5 hPa). • The chemistry variables increased to 63, with 44 fields transported by the CNRM-ESM2-1 dynamical core.

The remaining chemical fields are supposed in chemical equilibrium. • The scheme has been extended to consider CO 2 emissions (anthropogenic and natural sources) and transport, so that CNRM-ESM2-1 can be driven by CO 2 emissions and thus contribute to the associated CMIP6 experiments.

The Ozone Linear Scheme in CNRM-CM6-1

The ozone chemistry scheme used in CNRM-CM6-1 is a modified version of the one used in CNRM-CM5.1 [START_REF] Voldoire | The CNRM-CM5.1 global climate model: Description and basic evaluation[END_REF]. [START_REF] Eyring | Long-term ozone changes and associated climate impacts in CMIP5 simulations[END_REF] emphasized the main weaknesses of the CNRM-CM5.1 version, in particular a large underestimation of past ozone depletion. Therefore, the authors had excluded CNRM-CM5.1 from the multimodel analysis of the future evolution of ozone. The simplicity and low computational cost of this scheme was retained for CNRM-CM6-1. The scheme is a linearized version of the

𝜕r O 3 𝜕t = A 1 + A 2 (r O 3 -A 3 ) + A 4 (T -A 5 ) + A 6 (Σ O 3 -A 7 ), (4)
with A 1 = P -L where P and L are the respective ozone production and loss,

A 2 = 𝜕(P -L)∕𝜕r O 3 , A 3 = r O 3 the monthly average of r O 3 , A 4 = 𝜕(P -L)∕𝜕T, A 5 = T (temperature), A 6 = 𝜕(P -L)∕𝜕Σ O 3 , and A 7 = Σ O 3 .
All A i coefficients are monthly evolving values computed in the course of AMIP simulations with the CNRM climate model in which the REPROBUS-C_v2 scheme was activated. For the CMIP6 historical period, an ensemble of three AMIP simulations with CMIP6 forcings have been performed over 1950-2014, considering that A i coefficients could be set to 1950 ones prior to 1950. By construction, the CNRM-CM6-1 ozone concentrations should closely follow those of CNRM-ESM2-1. A similar strategy based on AMIP simulations has been set up for the scenarios.

Experimental Settings

All the simulations used here are presented in Table 2. They cover the 1850-2014 historical period (165 years). The number of members is also noted in Table 2, as well as the figures and tables of this article where they are used. By construction, there is no difference in the aerosol loadings of the various members of an ensemble of CNRM-CM6-1 simulations. Additional diagnostics come from the amip-hist-aod simulation, performed to produce the AOD used in CNRM-CM6-1. Most simulations concern the CNRM-CM6-1 model, but a few simulations with CNRM-ESM2-1 are also analyzed to highlight specific similarities or differences between the two models.

Reference Data Sets

Our assessment is fully placed in the CMIP6 context, with the objective to provide information to relate our results with those obtained with other models using "official" CMIP6 prescribed ozone and/or aerosol fields (see CMIP6 input4MIPs page, https://esgf-node.llnl.gov/projects/input4mips/). So our initial reference data sets are these official CMIP6 data sets, and then we enlarged slightly our analysis to closely related data sets.

More specifically, two aerosol data sets are used as references. The first one provides aerosol optical properties and an estimate of the Twomey effect computed with an off-line version of the MACv2-SP (Simple Plume) code [START_REF] Stevens | MACv2-SP: A parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6[END_REF], run on a T63 grid (courtesy of S. Fiedler). It is thereafter referred to as the MACv2-SP aerosol data set. The Simple Plume code has been developed to be implemented in climate models to investigate some of the aerosol effects on the climate system (e.g., within RFMIP; see [START_REF] Pincus | The radiative forcing model intercomparison project (RFMIP): Experimental protocol for CMIP6[END_REF]. It has already been used in several publications [START_REF] Fiedler | On the sensitivity of anthropogenic aerosol forcing to model-internal variability and parameterizing a Twomey effect[END_REF][START_REF] Fiedler | First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect[END_REF][START_REF] Nordling | Role of climate model dynamics in estimated climate responses to anthropogenic aerosols[END_REF]. and used in a number of climate studies [START_REF] Michou | Development and basic evaluation of a prognostic aerosol scheme (v1) in the CNRM climate model CNRM-CM6[END_REF][START_REF] Toll | Impacts of the direct radiative effect of aerosols in numerical weather prediction over Europe using the ALADIN-HIRLAM NWP system[END_REF] and which is the baseline MACv2-SP attempts to approximate. This data set provides monthly aerosol optical properties on a 1 degree grid, derived from a combination of observations and model outputs. Observations include the AERONET ground-based sun photometers network [START_REF] Holben | AERONET-A federated instrument network and data archive for aerosol characterization[END_REF]; http://aeronet.gsfc.nasa.gov), as well as measurements over ocean provided by the Maritime Aerosol Network [START_REF] Smirnov | Maritime aerosol network as a component of aerosol robotic network[END_REF]. Model outputs are derived from the AeroCom global modeling initiative (e.g., [START_REF] Kinne | An AeroCom initial assessment optical properties in aerosol component modules of global models[END_REF][START_REF] Koffi | Evaluation of the aerosol vertical distribution in global aerosol models through comparison against CALIOP measurements: AeroCom phase II results[END_REF]; https:// aerocom.met.no/references.html, for the list of AeroCom related references). MACv2 covers the 1850-2100 period and includes interannual variability for the anthropogenic aerosols, while natural aerosols consider only monthly variations. Optical properties for the main aerosol types (i.e., DD,SS, BC, OM, and SO 4 ) are provided.

One could argue that as MACv2-SP is a fit to MACv2, there is not much to learn by comparing to MACv2-SP in addition to comparing to MACv2. However, as MACv2-SP is an official CMIP6 forcing, the comparison of CNRM results against this data set is a basic pillar of the present study. Second, MACv2 appears as a rather obvious data set to enlarge to other aerosol properties an analysis using MACv2-SP which describes only anthropogenic aerosol properties. Lastly, the evaluation of our model outputs in terms of aerosols against observations from the real world is not presented here as it had already been done in a previous version of TACTIC [START_REF] Michou | Development and basic evaluation of a prognostic aerosol scheme (v1) in the CNRM climate model CNRM-CM6[END_REF] and then in several other papers following the evolution of the scheme [START_REF] Drugé | Model simulation of ammonium and nitrate aerosols distribution in the Euro-Mediterranean region and their radiative and climatic effects over 1979-2016[END_REF][START_REF] Nabat | Dust aerosol radiative effects during summer 2012 simulated with a coupled regional aerosol-atmosphere-ocean model over the Mediterranean[END_REF][START_REF] Watson | Assessment of CNRM coupled ocean-atmosphere model sensitivity to the representation of aerosols[END_REF], as well as "in-house," all along model developments.

With regard to ozone, the official CMIP6 ozone forcing (see http://goo.gl/r8up31) is referred to, in this paper, as CMIP6-OZ. CMIP6-OZ provides monthly 3-D ozone mixing ratios from 1850 to 2014 on a 96 × 44 lat-lon grid and on 66 pressure levels from 1,000 to 0.0001 hPa. The tropospheric and stratospheric ozone forcings computed with CMIP6-OZ are analyzed in [START_REF] Checa-Garcia | Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database[END_REF].

Finally, we considered NIWA3.

Evaluation of PD Characteristics of Aerosols and Ozone

Aerosol Optical Properties

Characteristics of aerosol loads and distributions are determined to a large extent by characteristics of their respective emissions. Table 3 presents 10 year means of the total yearly emissions over the globe for the various aerosols in the amip-hist-aod simulation, for the beginning and the end of the 1850-2014 period, together with other total emissions reported in the literature for PD emissions, derived from observations or from various modeling exercises. The TACTIC_v2 emission budget agrees well with current estimates that can be found in the literature or at least falls within the large uncertainty ranges reported in Table 3.

The only notable exceptions concern SS and volcanic SO 2 emissions. We identified two main causes for this overestimation of SS emissions (as well as boundary layer SS mixing ratios, not shown): First, SS masses are expressed at 80% humidity, unlike the other aerosol masses which are expressed as dry masses, and thus include hygroscopic growth and change in density. Second, the largest SS particles with a bin of 20 μm (radius at 80% humidity) are quite large compared to other models often limited to 10 μm. However, ultimately, optical properties of SS aerosols are in the range of observations.

Journal of Advances in Modeling Earth Systems

10.1029/2019MS001816 (2017) report that the five CMIP5 models they have considered specifically in their study have mean AOD differences with MODIS observations (2000)(2001)(2002)(2003)(2004)(2005) ranging from 0.01 (overestimation) to -0.09. The largest biases in CNRM models occur over the Gulf of Guinea and the Equatorial Atlantic, with larger exports of dust and biomass burning aerosols for MACv2, over Asia including China and India, and to a lesser extent, over Europe. Over Central Africa and Amazonia, CNRM-CM6-1 bias could be reduced by scaling up biomass burning emissions, as commonly done in other models [START_REF] Johnson | Evaluation of biomass burning aerosols in the HadGEM3 climate model with observations from the SAMBBA field campaign[END_REF]; not scaling the official CMIP6 emissions appeared to us as preferable to better assess the intrinsic performance of our aerosol model. Over China and Europe, the negative bias could be due, for a part, to the absence of nitrate aerosols in TAC-TIC_v2. Indeed, such aerosols can cause an additional AOD of about 0.1 over China [START_REF] Bellouin | Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate[END_REF] and of about 0.08-0.1 over Europe [START_REF] Drugé | Model simulation of ammonium and nitrate aerosols distribution in the Euro-Mediterranean region and their radiative and climatic effects over 1979-2016[END_REF]. Over South Asia, the AOD negative bias is an identified issue detected in different GCMs [START_REF] Pan | A multi-model evaluation of aerosols over South Asia: Common problems and possible causes[END_REF]. Their mean annual biases range from 15% to 44% compared to MISR.

Figures 2 and 3 emphasize the anthropogenic and natural contributions to the AOD. The MACv2 and MACv2-SP data sets suppose that 1850 conditions are the reference conditions for anthropogenic aerosols. For a fair comparison, anthropogenic optical properties for the CNRM models are computed using the same hypothesis: For instance, anthropogenic AOD of a given year is the anomaly of the (SO 4 + BC + OM) AOD relative to 1850, noting that TACTIC_v2 assumes that DD is completely natural, and natural AOD includes the residual (SO 4 + BC + OM) AOD in addition to SS and DD AOD. Over India, differences are due to differences in natural aerosols, possibly exports of dust from the Arabian peninsula, while over China, they are essentially linked to anthropogenic sources. Overall, the NRMSE of CNRM-CM6-1 is larger for the anthropogenic aerosols than for the natural or total aerosols (0.87, 0.53, and 0.51, respectively). Furthermore, one can note that anthropogenic AOD differences between MACv2 and MACv2-SP are also large over China, while the two data sets are within 0.05 over the rest of the globe, highlighting the difficulty in representing aerosols over China. The PD proportion of anthropogenic AOD over total AOD is 25% for MACv2, against 18% for CNRM-CM6-1. Both proportions are within the range of the AeroCom Phase II models, estimated at 24% ± 0.06 [START_REF] Myhre | Radiative forcing of the direct aerosol effect from AeroCom phase ii simulations[END_REF], although these figures hide much larger regional differences. The latter potentially impacts the aerosol radiative forcing, as argued in [START_REF] Carslaw | Large contribution of natural aerosols to uncertainty in indirect forcing[END_REF] who demonstrated that uncertainties in natural aerosols are the major contributor to the uncertainty of the aerosol indirect forcing. Description and analysis of radiative forcing appear in section 5.1. Table 5 presents additional statistics for the AOD and AAOD, namely, their means calculated separately over land and ocean for CNRM-CM6-1, MACv2, the MERRA-2 aerosol reanalysis [START_REF] Randles | The MERRA-2 aerosol reanalysis, 1980 onward. Part I: System description and data assimilation evaluation[END_REF], and the CAMS reanalysis project [START_REF] Inness | The CAMS reanalysis of atmospheric composition[END_REF]. It confirms the too low CNRM-CM6-1 AOD, and it indicates that most of the global bias in AOD comes from a bias over land.

AAOD, which has been shown to be the biggest contributor to the aerosol direct effect uncertainty (see [START_REF] Krekov | Aerosol single-scattering albedo over the global oceans: Comparing PARASOL retrievals with AERONET, OMI, and AeroCom models estimates[END_REF], and references therein), is larger in MACv2 than in CNRM-CM6-1 over all the equatorial continents (see Figures 2 and3). This is related to the differences in SSA, whose global mean is smaller in MACv2 (0.95) than in CNRM-CM6-1 (0.98). Note that this CNRM-CM6-1 value is slightly outside the range of the AeroCom models (0.95 ± 0.02; [START_REF] Myhre | Radiative forcing of the direct aerosol effect from AeroCom phase ii simulations[END_REF]. Differences are the largest over South America and may be linked to inaccurate optical properties of biomass burning particles.

Recent in situ observations over the main biomass burning regions, obtained in the frame of the ORACLES (Southern Atlantic; [START_REF] Zuidema | The ascension island boundary layer in the remote Southeast Atlantic is often smoky[END_REF] and SAMBBA (South America; [START_REF] Johnson | Evaluation of biomass burning aerosols in the HadGEM3 climate model with observations from the SAMBBA field campaign[END_REF] experiments, reveal SSA of about 0.85 and between 0.80 and 0.88, respectively. The BC emissions and the assumption of external mixing state [START_REF] Cappa | Radiative absorption enhancements due to the mixing state of atmospheric black carbon[END_REF], as well as the absence of brown carbon particles [START_REF] Zhang | Modeling global radiative effect of brown carbon: A larger heating source in the tropical free troposphere than black carbon[END_REF], could possibly explain part of the TACTIC_v2 SSA overestimation for smoke aerosols. In contrast, SSA values are the closest over the Southern ocean and the Sahara, where optical properties of dust and sea-salt 2015), which showed that models generally overestimate SSA while they underestimate AAOD. In addition, a number of characteristics of the AAOD distribution appear more realistic in MACv2 than in CNRM-CM6-1, including higher spatial variability over the oceans in part due to the transport of continental aerosols [START_REF] Krekov | Aerosol single-scattering albedo over the global oceans: Comparing PARASOL retrievals with AERONET, OMI, and AeroCom models estimates[END_REF]. Most of these differences in total AAOD between MACv2 and CNRM-CM6-1 are due to differences in natural AAOD (Figure 3 and Table 5), notably over ocean where a large bias is noted, due to underestimates of transported mineral dust and biomass burning particles. It is also worth mentioning that biases of the anthropogenic AAOD are of the same order of magnitude in MACv2-SP and CNRM-CM6-1. The NRMSE of CNRM-CM6-1 is larger for AAOD than for AOD (7.2 × 10 -1 vs. 5.0 × 10 -1 ), while those for SSA and ASY are of a smaller order of magnitude. The MACv2 ASY parameter is smaller than the CNRM-CM6-1 one over all oceans and larger over roughly a 0-30 • N latitude band over the continents.

Ozone

Zonal plots of mean 1990-1999 (DJF and JJA seasons) ozone mixing ratios (mol mol -1 ) appear in Figure 4. CNRM-CM6-1 and CNRM-ESM2-1 historical ensembles (see Table 2), as well as CMIP6-OZ, are shown, together with their relative differences. CNRM-CM6-1 and CNRM-ESM-2.1 are within 10% throughout most the atmosphere, with some larger differences around the tropopause and in the mesosphere. Differences are larger between the CMIP6-OZ and the CNRM ozone mixing ratios, with maximum differences in the same regions of the tropopause and of the mesosphere and near the surface. However, through the major part of the stratosphere where ozone mixing ratios are the highest, differences are smaller than 10% with the exception of the high latitudes of the winter hemisphere. In the troposphere, differences show also an annual cycle with reduced differences in JJA. In both seasons, however, the CMIP6 tropospheric ozone is higher than the CNRM-CM6-1 one in the Northern Hemisphere and smaller in the Southern Hemisphere (with biases around 15 ppb). The CNRM tropospheric ozone lacks the very marked hemispheric difference with mixing ratios higher that 40 ppb in the whole northern troposphere and mixing ratios lower than 30 ppb in the entire southern troposphere, reflecting industrialized latitudes.

Figure 5 presents the 1990-1999 monthly mean latitudinal evolution of the total column ozone (TCO) for the CMIP6-OZ data set and the CNRM-CM6-1 and CNRM-ESM2-1 historical simulations. All these three data sets are compared to the NIWA3.3 columns, as another reference data set, and the respective differences are shown. The annual cycles of the three data sets are quite similar at all latitudes, as is the latitudinal gradient. However, biases against NIWA3.3 of the CMIP6-OZ and the CNRM-CM6-1 ozone have opposite signs, with too high columns in the tropics for CNRM-CM6-1 and too low columns elsewhere especially in the 60-90 • N band (see also Figure 10). The global mean of the differences is smaller for CNRM-CM6-1 (-1.2 DU) than for CMIP6-OZ (-2.3 DU) and almost null (0.1 DU) for CNRM-ESM-2.1, with a reduced negative bias in the tropical band. The largest biases appear at high latitudes, in both hemispheres, and timing of the ozone depletion is better represented in CNRM-CM6-1 than in CNRM-ESM2-1 with a too long duration in CNRM-ESM2-1.

Sensitivity to CO 2 and Historical Evolutions

Tracking the Response of the Ozone and Aerosol Schemes to Rising CO 2

With the description and the PD evaluation of the various aerosols and ozone schemes completed, we now investigate how these schemes may interact with climate change. For this goal, we use a well-established global-scale climate metric, the Equilibrium Climate Sensitivity (ECS), namely, the global mean surface-air temperature response to an abrupt doubling of CO 2 relative to the PI. The use of this idealized approach has the advantage of constraining the role of anthropogenic forcing agents other than CO 2 , such as greenhouse gases, aerosols, and land-cover changes.

The first objective of the simulations analyzed in this paragraph is to evaluate whether the linear ozone scheme is responding correctly in an abrupt-4xCO2 experiment. Indeed, one could be concerned from equation ( 4) that evolution of ozone in this simulation, with all monthly coefficients of the linear scheme fixed at their 1850 value, would not match the atmospheric changes due to the quadrupling of CO 2 and would, for instance, result in nonphysical high ozone in the upper troposphere with the rising of the tropopause. An excess in upper-tropospheric tropical ozone leads to increased water entry into the stratosphere, all this in the end increases the ECS [START_REF] Nowack | A large ozone-circulation feedback and its implications for global warming assessments[END_REF]. The second objective is to assess if and how much the difference in ECS between CNRM-CM6-1 and CNRM-ESM2-1 (5.06 and 4.55 K, respectively; see [START_REF] Séférian | Evaluation of CNRM Earth-system model, CNRM-ESM 2-1: Role of Earth system processes in present-day and future climate[END_REF] is related to the representation of ozone and of aerosols.

For those purposes, triplets of 20 year long SST forced simulations (piClim-control, piClim-4xCO2, and 4xCO2Clim-4xCO2), along the lines described in [START_REF] Andrews | The dependence of eadiative forcing and feedback on evolving patterns of surface remperature change in climate models[END_REF] and [START_REF] Ringer | Global-mean radiative feedbacks and forcing in atmosphere-only and coupled atmosphere-ocean climate change experiments[END_REF], have been performed with several configurations of the model. The 4xCO2Clim-4xCO2 simulations have SST and sea-ice conditions of a 30 year mean monthly climatology from the abrupt-4xCO2 simulations. The various configurations consist in ( 1) a CNRM-ESM2-1 one, (2) CNRM-CM6-1 ones with the ozone linear scheme either as in equation ( 4) or with one or several coefficients of the linear ozone scheme set to zero, (3) CNRM-CM6-1 with the chemistry scheme REPROBUS-C_v2, and (4) CNRM-CM6-1 with the aerosol scheme TACTIC_V2, either for all aerosols or anthropogenic aerosols only (in the latter case, monthly AODs of DD and SS are prescribed).

Figure 6 illustrates the relative change in ozone mixing ratios (mean over last 10 years of the simulations) between 4xCO2Clim-4xCO2 and piClim-control, for CNRM-CM6-1 (and the linear ozone scheme) and CNRM-ESM2-1. Spatial distribution and intensity of these changes look very similar over all tropical latitudes and most of the stratosphere. They agree to expected changes in ozone under abrupt-4xCO2 experiments (see, for instance, [START_REF] Nowack | The impact of stratospheric ozone feedbacks on climate sensitivity estimates[END_REF]. Setting the A 4 coefficient (dependence of ozone evolution to temperature) to 0 resulted in a very different ozone field under 4xCO2Clim-4xCO2 conditions through most of the stratosphere, with smaller evolutions from the control simulation than those of CNRM-CM6-1 (see Figure 6). The dependence to the A 6 coefficient (not shown) is of less importance than the dependence to A 4 , but it, however, plays a role in ensuring a correct distribution of ozone, particularly in the middle to upper stratosphere at midlatitudes. We conclude from this short analysis on the satisfactory behavior of the ozone representation in CNRM-CM6-1 abrupt-4xCO2 experiments.

Figure 7 is a [START_REF] Gregory | A new method for diagnosing radiative forcing and climate sensitivity[END_REF] type plot in which the regression of the change in net TOA radiation (ΔN) against the change in surface temperature (Δtas) gives an estimate of the ECS in the case of 4xCO2 perturbed simulations (ECS = 0.5 × Δtas intercept when ΔN = 0 and ΔN = F -𝜆Δtas, with 𝜆 the climate feedback parameter and F the radiative forcing. In addition to the yearly dots of the CNRM-CM6-1 and CNRM-ESM2-1 CMIP6 abrupt-4xCO2 experiments (black and gray small dots, respectively), Figure 7 includes colored dots. For a given color, a star reflects changes in TOA fluxes under 4xCO2 conditions corresponding to the beginning of the abrupt-4xCO2 simulation, while a large colored dot reflects changes corresponding to its end. Therefore, both points enable also to estimate the ECS of the model in a given configuration.

Figure 7 shows that as the CNRM-CM6-1 red and green dots almost overlap, the ECS values of CNRM-CM6-1 with the linear scheme and with the REPROBUS-C_v2 scheme are very similar. Table 6, which provides the numbers associated with the various SST-forced regression lines in Figure 7, confirms this statement. On the contrary, the ECS of CNRM-CM6-1 with TACTIC_v2 (yellow) is noticeably different from that of CNRM-CM6-1 (red) and quite close to the ECS of CNRM-ESM2-1 (blue). Several candidates could explain these results, such as the overall aerosol content that is different in CNRM-CM6-1 and in CNRM-CM6-1 with TACTIC_v2 because of differences in natural aerosols or the vertical distribution of aerosols that change at each time step of the physics when TACTIC_v2 is activated. The additional group of simulations, CNRM-CM6-1 with TACTIC_v2 for anthropogenic aerosols (CNRM-CM6-1 + TACTICant, orange dots), allows to confirm that aerosols feedbacks, that is, changes of natural aerosols due to changes in meteorology (see simulations CNRM-CM6-1 + TACTIC and CNRM-CM6-1 + TACTICant), are slightly smaller, but comparable, than feedbacks in link with the interactivity of aerosols (see simulations CNRM-CM6-1 and CNRM-CM6-1 + TACTICant).

Table 6 Parameters of the Various Regression Lines in Figure 7, T eq (K) Intercept With

𝑦 = 0, 𝜆 (W•m -2 •K -1 ), F (W m -2 ) (

Evolution of Aerosol Optical Properties

At this stage of the paper, we remind the reader that differences in CNRM-CM6-1 and CNRM-ESM1-1 aerosols may be due, in particular, to differences in meteorology (CNRM-CM6-1 aerosols are those of an AMIP-type simulation, while CNRM-ESM2-1 historical simulations include coupling with the ocean) and also to differences in land cover as CNRM-ESM2-1 considers anthropogenic land use changes that impact land cover characteristics. Figure 8 shows AOD time series between 1850 and 2014, with separate plots for anthropogenic and natural aerosols. Trends of the annual global mean anthropogenic AODs (first line) are comparable in MACv2, MACv2-SP, and CNRM models over 1850-1950 but then are smaller for the CNRM models over 1950-1980. This trend is scaled in MACv2-SP on the CMIP6 SO 2 and NH 3 anthropogenic emissions [START_REF] Hoesly | Historical (1750-2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)[END_REF][START_REF] Stevens | MACv2-SP: A parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6[END_REF], while in CNRM-CM6-1, it reflects both sources and sinks of the anthropogenic aerosols of TACTIC_v2 (SO 4 , BC, and OM). [START_REF] Flato | Evaluation of climate models[END_REF] note that despite the use of common anthropogenic aerosol emissions for the historical simulations, ACCMIP models (17 in total) show very different trends in aerosol loading since 1850. Variability of the sinks resulting from model internal variability appears in the CNRM-ESM2-1 outputs (five realizations of CMIP6 historical simulations). This variability appears small with regard to the overall trend, and the time series of CNRM-CM6-1 and CNRM-ESM2-1 anthropogenic aerosols coincide, both for total anthropogenic aerosols and for their individual components.

Differences in the level of natural aerosols between MACv2 and CNRM-CM6-1 (not shown) are around 10% and lower at the end of the period. Indeed, the MACv2 AOD of natural aerosols remains constant (global mean of 0.09), whereas CNRM-CM6-1 AOD of sea-salt aerosols increases by about 5%, both due to an increase in winds over some equatorial regions and over the southern ocean and to an increase in SST.

There is no such noticeable increase in the CNRM-CM6-1 dust aerosols. Another interesting feature is that in CNRM-CM6-1, the level of the sum of BC, OM, and SO 4 aerosols in 1850 (not shown) is almost as high as the level of dust aerosols (respective AOD of 0.012 and 0.014), although their spatial distributions are different. Lower SS and DD AODs in CNRM-ESM2-1 than in CNRM-CM6-1 throughout 1850-2014 denote the influence of the climate on emissions of these compounds. For the DD aerosols, emissions have also been lowered because of a smaller fraction of bare soil due to the use of the CMIP6 Land used Harmonized data 10.1029/2019MS001816

Figure 10. Time series of area-weighted total column ozone from 1960 to 2014 for the annual mean for different latitude bands (a to d) and for March and October in (e) and (f) (as in [START_REF] Eyring | Long-term ozone changes and associated climate impacts in CMIP5 simulations[END_REF]. Data sets presented here are NIWA3.3 (in black, ± uncertainty in gray), CMIP6-OZ (in green), CNRM-CM6-1 ensembles (in blue), and CNRM-ESM2-1 ensembles (in red). For the CNRM models, full lines for CMIP6 historical ocean-atmosphere simulations and dotted lines for CMIP6 amip-hist simulations.

(see http://goo.gl/r8up31). The interannual variability of the dust aerosol AOD in CNRM-ESM2-1 is larger than the one of the sea-salt aerosols (≃5% vs. ≃2% relative standard deviations, respectively), and they are both comparable to the interannual variability of their respective emissions (not shown). Impact of aerosol composition on total aerosols results in very small variations in the global ASY or SSA parameters between 1850 and 2014 (0.5% and 0.3% for the relative standard deviations, respectively). The main peaks in these two parameters are related to volcanic eruptions that brought stratospheric aerosols with higher SSA (0.999) and lower ASY (0.72). The weak historical trend in SSA and ASY is related to the increase in anthropogenic aerosols. Note that the MACv2-SP data set does not consider anthropogenic aerosol composition changes over time. The Nat. aerosols Anthro. aerosols ratios for CNRM-CM6-1 and MACv2 are close, 25% versus 30%, respectively.

Aerosol-Cloud Interactions

As the only indirect effect represented in CNRM-CM6-1 is the Twomey effect, the change in CDNC between PI and PD can be seen as a relevant proxy of the anthropogenic aerosol-cloud interactions in CNRM-CM6-1. This change in CDNC has been calculated from a pair of dedicated 10 year fixed SST CNRM-CM6-1 simulations with interactive aerosols, one with 1850 static anthropogenic aerosol emissions (PI) and one with 2000 emissions (PD). Ratios of CDNC between PD and PI simulations are shown in Figure 9 for the MACv2-SP dataset and CNRM-CM6-1 at the cloud top, as well as at various pressure levels (950, 800, and 700 hPa) for CNRM-CM6-1 Although the magnitude of the change depends on the altitude to a certain extent, at all levels, the largest changes in CNRM-CM6-1 appear over Asia in agreement with changes in MACv2-SP (see equation 15 of Stevens et al., 2017, and Figure 9). Two features however are clearly different between CNRM-CM6-1 and MACv2-SP. First, the CNRM-CM6-1 ratios show areas of doubling or more CDNC between PI and PD, especially over continents, while the MACv2-SP ratios are limited to 1.37. This results in a lower global mean value (1.07) for MACv2-SP than for CNRM-CM6-1 (1.20). The relatively low value in MACv2-SP is discussed in [START_REF] Stevens | MACv2-SP: A parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6[END_REF] and seems also to be low compared to a number of observations; [START_REF] Mccoy | The global aerosol-cloud first indirect effect estimated using MODIS, MERRA, and AeroCom[END_REF] report that the change is estimated to be larger than three over highly polluted regions such as China. Second, since the MACv2-SP data set is derived from satellite observations which 10.1029/2019MS001816

only see clouds at their top, the MACv2-SP CDNC is independent from the model level and is more representative of the cloud top properties. This assumption has been adopted in the parameterization of this field in certain climate models [START_REF] Quaas | Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data[END_REF]. In reality, CDNC at the cloud top is smaller than inside the cloud due to cloud entrainment [START_REF] Zeng | Study of global cloud droplet number concentration with A-Train satellites[END_REF]. In CNRM-CM6-1, the mean change in CDNC from PI to PD is maximum at 850 hPa. Finally, uncertainties in this CNRM-CM6-1 CDNC change might be related to the [START_REF] Menon | GCM, simulations of the aerosol indirect effect: Sensitivity to cloud parameterization and aerosol burden[END_REF] formulation itself that could be questioned when used in high aerosol loadings or in PI conditions.

Evolution of Ozone

As there is no trend in the evolution of the historical TCO of CNRM-ESM2-1 prior to 1960 (not shown), Figure 10 shows this evolution over the period 1960-2014. Although tropospheric ozone weights only about 10% of the TCO, we emphasize again that we do not model the increase in tropospheric ozone related to the increase in emissions of ozone precursors. Hence, changes in the CNRM-CM6-1 and CNRM-ESM2-1 tropospheric ozone are only due to changes in upper tropospheric and stratospheric ozone and follow the evolution of stratospheric ozone.

Figure 10 shows 1960-2014 time series of TCOs as modeled in CNRM-CM6-1 and CNRM-ESM2-1 CMIP6 coupled (historical ensembles; see Table 2) and forced (amip-hist ensemble of 10 members for CNRM-CM6-1 and 1 member only for CNRM-ESM2-1) simulations, averaged over various latitude bands, and different periods of the year (annual mean, March, and October). The CMIP6-OZ data set is also shown, as is the NIWA3.3 data set to get further insight in the CNRM performance. In general, the CMIP6 and NIWA3.3 TCO compare relatively well, except however in 1990-1999 60-90 • N (see Figures 10e and5).

Averaged over 90 • S to 90 • N (see Figure 10a), CNRM TCOs are higher than CMIP6-OZ TCO over 1960-1980 (∼5 DU), in link with higher TCOs over the tropical band (∼15 DU; see Figure 10b). After 1980, all evolutions are within the uncertainty of NIWA3.3 (gray lines), reflecting a correct trend over 1980-2000 driven by the trends in the southern latitudes (Figures 10d and10f). In the tropical band, the CNRM positive bias is smaller for CNRM-ESM2-1 than for CNRM-CM6-1, but the modulation of the CNRM TCOs by the solar cycle are too weak. In contrast, in the 35-60 • N and 60-90 • N bands (Figures 7c and7e), CNRM TCOs are too low, again outside of the uncertainties of NIWA3.3. These negative biases could be the signature of a too weak Brewer-Dobson circulation, but as there is an excess of ozone in the model in the 60-35 • S band (Figure 7d), this affirmation could be modulated, keeping in mind the intrinsic hemispheric differences in the stratospheric transport. In the 60-90 • N band (Figure 7e), the interannual variability of the CNRM-ESM2-1 historical ensemble (red full line) is closer to that of NIWA3.3 than the variability of both CMIP6-OZ data (green line) and CNRM-CM6-1 historical ensemble (blue full line). In the 60-90 • S band, ozone depletion of the CNRM models is within the uncertainty of the observations, with the 2000-2005 higher columns better represented by CNRM-CM6-1. Finally, TCOs of the CNRM coupled (ocean-atmosphere) and forced (atmosphere only, dotted lines) simulations differ, with generally higher TCOs in the coupled simulations (e.g., Figures 10a and10c).

Aerosol and Ozone Forcings

Forcing Calculations

In order to estimate the aerosol and ozone forcings over the historical period, we have calculated their respective ERFs. The aerosol ERF can be differentiated between aerosol-radiation interactions (ERFari) and aerosol-cloud interactions (ERFaci). Anthropogenic aerosol ERFari and ERFaci can be computed as in [START_REF] Ghan | Technical note: Estimating aerosol effects on cloud radiative forcing[END_REF]. ERFari is thus defined as Δ(F -F clean ), where Δ refers to the difference between an aerosol-perturbed simulation and a PI control simulation, F to the TOA radiation flux (net, SW, or LW) and F clean to the TOA radiation flux (net, SW, or LW) neglecting both scattering and absorption by aerosols. [START_REF] Ghan | Technical note: Estimating aerosol effects on cloud radiative forcing[END_REF] compute ERFaci as Δ(F clean -F clear,clean ), where F clear,clean is the TOA radiative flux neglecting scattering and absorption by both clouds and aerosols. Other techniques to estimate ERFari and ERFaci could lead to slightly different estimates, but [START_REF] Zelinka | Quantifying components of aerosol-cloud-radiation interactions in climate models[END_REF] emphasize that the technique of [START_REF] Ghan | Technical note: Estimating aerosol effects on cloud radiative forcing[END_REF] is the most accurate one. Total anthropogenic aerosol ERF then is written as

ERF = ERFari + ERFaci + ERFres,
where ERFres is a residual term defined as Δ(F clear,clean ) and representing mainly the change in surface albedo [START_REF] Ghan | Technical note: Estimating aerosol effects on cloud radiative forcing[END_REF]. All these terms are issued from multiple calls to the climate model radiation scheme whereby the model radiation scheme is called several times consecutively with one or more radiative species set to a different value.

ERFari and ERFaci have been calculated with two protocols, along the lines of the AerChemMIP protocols [START_REF] Collins | AerChemMIP: Quantifying the effects of chemistry and aerosols in CMIP6[END_REF]. One protocol is the one of the CMIP6 fixed-SST piClim simulations to derive PD ERFs; [START_REF] Forster | Recommendations for diagnosing effective radiative forcing from climate models for CMIP6[END_REF] indicate that in this method, the 5-95% confidence level of the global ERF is reduced to 0.1 W m -2 for 30 year integrations. The second protocol is used to derive transient ERFs; the corresponding CMIP6 simulations are the so-called histSST/histSST-piNTCF ones (see Table 7). In addition to RFMIP and/or AerChemMIP piClim-type simulations (see Table 7), we performed additional piClim-type simulations in order to disentangle different aspects of the anthropogenic aerosol ERF. Table 8 summarizes the characteristics of these simulations with regard to their aerosol forcing, separating anthropogenic and natural aerosols. CNRM-CM6-1 and CNRM-ESM2-1 * use prescribed aerosol AODs, and all piClim-aerxx simulations come along with a corresponding piClim-controlxx simulation. The objectives here are to estimate, in the anthropogenic aerosol ERF, the impact of the interactivity of aerosols, the impact of the natural aerosols, and the impact of the configuration of the model (atmosphere-ocean general circulation model vs. ESM).

As far as the ozone forcing is concerned, previous studies have derived it from calculations with off line radiative codes (e.g., [START_REF] Checa-Garcia | Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database[END_REF][START_REF] Cionni | Ozone database in support of CMIP5 simulations: Results and corresponding radiative forcing[END_REF][START_REF] Szopa | Aerosol and ozone changes as forcing for climate evolution between 1850 and 2100[END_REF]. However, in the present work, we chose to compute differences in radiative fluxes at the TOA from pairs of simulations, along the protocols defined by AerChemMIP and RFMIP, even though there was a possible weakness of these protocols owing to the small amplitude of the signal [START_REF] Forster | Recommendations for diagnosing effective radiative forcing from climate models for CMIP6[END_REF][START_REF] Myhre | Anthropogenic and natural radiative forcing[END_REF]. This Table 10 lists several simulations of the two CNRM climate models that, compared to their piClim-control corresponding simulation, enable to disentangle ERF components and total ERFs of various aerosols configurations over the period 1850-2014. More specifically, a comparison between CNRM-CM6-1 piClim-aer and piClim-aerant shows that the change in natural aerosols between 1850 and 2014 results in a change of ERF of only -0.06 W m -2 , compared to an ERF of -1.10 W m -2 for the anthropogenic aerosols. This change is mainly explained by the ERFari term. In the CNRM-CM6-1 piClim-aerAODESM simulation where monthly AODs of the anthropogenic aerosols are those derived from the CNRM-ESM2-1 simulation, the total ERF is now of -1.03 W m -2 to be compared to the CNRM-ESM2-1 piClim-aerAODESM ERF, -0.97 W m -2 . This shows the small impact of the level of natural aerosols on the anthropogenic aerosol ERF as these simulations share the same anthropogenic AODs but have a different level of natural aerosols (see Figure 8). For the sake of clarity, we repeat here that TACTIC does not take into account changes in natural aerosol other than those in DD and SS, as, for instance, changes in biogenic SOA which might vary substantially between PI and PD due to deforestation and rising temperatures. A similar small impact appears from the results of the CNRM-CM6-1 piClim-aerant and CNRM-ESM2-1 piClim-aerAODCM (total ERF of -1.10 and -1.08, respectively). A larger difference in ERFs appears between the CNRM-ESM2-1 piClim-aer and piClim-aerAODESM, which tests the impact of the interactivity of the aerosols. Indeed, here in piClim-aer, 3-D concentrations of aerosols change at each time step of the physics, while in piClim-aerAODESM, monthly climatology of AODs from the piClim-aer simulation, vertically distributed by aerosol type, are considered by the radiative scheme. The total ERFs of these two simulations are, respectively, of -0.74 and -0.97, with the largest change in ERFari (-0.21 and -0.35, respectively). These results reveal the importance of the interactivity of the aerosols in deriving ERFs.

Simulations that test the change in an individual aerosol type indicate that the ERFari components appear relatively additive, with ERFari SO 2 + OM + BC (-0.23) almost equal to ERFari in piClim-aer (-0.21). On the contrary, ERFaci is less additive with respect to the different aerosol species (-0.70 against -0.61).

Total ERF of sulfate alone is even larger than that of the total anthropogenic aerosols (-0.75 vs. -0.74).

Finally, ERFs of the natural and biomass burning aerosols are dominated by the ERF of sea salt (-1.04), followed by DMS (-0.37) and biomass burning aerosols (-0.22).

Overall, while the mean CNRM-CM6-1 total anthropogenic aerosol ERF is within the range of [START_REF] Zelinka | Quantifying components of aerosol-cloud-radiation interactions in climate models[END_REF], the mean CNRM-ESM2-1 total anthropogenic aerosol ERF appears as weak (-0.74). However, CNRM individual components ERFaci and ERFari are all within the range of [START_REF] Zelinka | Quantifying components of aerosol-cloud-radiation interactions in climate models[END_REF], -0.25 ± 0.22 and -0.92 ± 0.34, respectively. Table 10 shows also results from using the MACv2-SP parameterization in the MPI ESM [START_REF] Stevens | MACv2-SP: A parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6[END_REF], where total ERF is -0.50 and ERFari -0.23. Various effects explain the larger ERF in CNRM models than in MACv2-SP despite a smaller increase in anthropogenic aerosol AOD (see Figure 8). Indeed, the ERFaci term is larger in CNRM models, as expected given the larger change in CDNC. In addition, the mean SSA (0.98), which affects roughly linearly the clear-sky ERF [START_REF] Stevens | MACv2-SP: A parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6[END_REF], is also larger in CNRM climate models. [START_REF] Fiedler | First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect[END_REF] compute a multimodel mean ERF of -0.59 W m -2 from five Earth system models with the MACv2-SP parameterization.

As for the ERF for each aerosol species, the comparison with literature shows (1) an agreement with the large SO 4 ERF of [START_REF] Zelinka | Quantifying components of aerosol-cloud-radiation interactions in climate models[END_REF], larger than the AR5 estimate, (2) a slightly too low negative OC ERF (-0.17), and (3) a BC ERF rather at the low end of [START_REF] Myhre | Anthropogenic and natural radiative forcing[END_REF] estimates. As for the results of [START_REF] Myhre | Anthropogenic and natural radiative forcing[END_REF] for dust (-0.10 ± 0.20) and fire (-0.00 ± 0.20), they have no direct comparative value with CNRM-ESM2-1. Indeed, the latter gives ERF representing doubling dust and fire emissions, while [START_REF] Myhre | Anthropogenic and natural radiative forcing[END_REF] ones correspond to a change from 1750 to 2011.

Table 10 also reports the clear-sky SW ERFari (ERFari CSSW ). As expected, the change in clear-sky flux is a negatively biased measure of ERFari, as a presence of clouds lessens the cooling effect of aerosols. As for the absorbing aerosols, BC, and dust, their positive ERFari is slightly increased in the presence of clouds.

Ozone

Figure 12 shows both the change in ozone in CNRM-CM6-1 from the PI to the PD (ca. 2014, last two lines) and the corresponding ERF due to this change in ozone (first line). All plots present 30 year mean differences between the piClim-control simulation and the piClim-stratO3 simulation. The change in total ozone column is minimal in the tropical band (between 5 and 10 DU or about 3% for the annual means) and is maximal over the southern high latitudes (up to 60 DU or 22%). Separating MAM and SON plots shows the annual cycle of the ozone change, and in particular the higher ozone changes in the high latitudes of the winter hemisphere than those averaged annually (first column). Overall, if the change of ozone is clear, the related ERF appears relatively noisy, with however larger continuous areas of negative ERF over areas with larger changes in ozone (i.e., high latitudes) and with a mean global value of -0.035 W m -2 . This is consistent with recent literature that reports mainly on the RF measure of the ozone radiative forcing, noting that the global mean ozone RF and ERF are within 10% (Shindell et al., and references therein) and that the ozone ERF is anticipated to be quite noisy (Shindell et al., 2013). According to [START_REF] Myhre | Anthropogenic and natural radiative forcing[END_REF] (see Table 8.3), the total AR5 ozone RF from 1750 to 2011 is 0.35 W m -2 , with a RF due to tropospheric ozone changes of 0.40 W m -2 (ranging from 0.20 to 0.60) and due to stratospheric ozone changes of -0.05 W m -2 (ranging from -0.15 to 0.05). Quite recently, the WMO scientific assessment of ozone depletion: 2018 (2018) indicate that there has been no recent update on the estimated PI to PD radiative forcing due to changes in stratospheric ozone and report only on one study, dedicated to the specific impact of short-lived ODS [START_REF] Hossaini | Efficiency of short-lived halogens at influencing climate through depletion of stratospheric ozone[END_REF]. However, more recently, Checa-Garcia et al.

(2018) provide information of specific interest for this study as they detail both the stratospheric and tropospheric ozone forcing of the CMIP6-OZ data set, here again computed with an off-line radiative transfer model. Circa 2010, the Checa-Garcia et al. ( 2018) ERF of the stratospheric ozone is -0.029 W m -2 , with LW ERF of -0.109 W m -2 and SW ERF of 0.08 W m -2 . In CNRM-CM6-1, total ERF is -0.036 W m -2 , with LW ERF of 0.140 W m -2 and SW ERF of -0.18W m -2 (see also Figure 14). Although the total (LW + SW) forcing in CNRM-CM6-1 is quite similar to that of [START_REF] Checa-Garcia | Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database[END_REF], the individual LW and SW contributions are of opposite signs. The negative SW ERF in CNRM-CM6-1 can be explained by radiative transfer theory and reveals that the SW impact of the reduction of ozone in the troposphere is larger than that of the reduction of ozone in the stratosphere. The positive sign of the LW ERF could appear surprising as a reduction in ozone should lead both in the stratosphere and in the troposphere to a negative LW radiative effect (see, for instance, Iglesias-Suarez et al., 2018, and references therein). We can add that the LW ERF clear sky (not shown) presents features similar to that of the LW ERF and that the signal is quite noisy as shown in Figure 12. This change in LW ERF in CNRM simulations reflects complex interactions within the whole system.

The interannual variability computed over 30 years is of 0.121 W m -2 (standard deviation 𝜎 of yearly annual means), which corresponds to a 5-95% confidence interval (CI) of 0.045 W m -2 computed as in equation ( 3) where (𝜎∕ √ 30) is the standard error on the mean and t value is provided by the t distribution of 29 degrees of freedom. Over 70 years of the same simulation, as recommended in Shindell et al. (2013) to diagnose the stratospheric forcing, the mean ERF is -0.040±0.032 W m -2 . These uncertainties are somehow smaller that reported in the literature, for instance, 0.07 W m -2 in Checa-Garcia et al. ( 2018) and Shindell et al. (2013). [START_REF] Forster | Recommendations for diagnosing effective radiative forcing from climate models for CMIP6[END_REF] have shown that this uncertainty is largely determined by the variability of the TOA fluxes in the baseline simulation.

Historical Evolution of the Aerosol and of the Ozone Forcings 5.3.1. Aerosols

Historical values of the anthropogenic aerosol ERF have been computed from two sets of CMIP6 CNRM-ESM2-1 simulations. The first method consists in using outputs of a transient simulation (histSST-piNTCF) with all forcings fixed at 1850 levels except for the anthropogenic aerosol emissions that are the CMIP6 ones [START_REF] Hoesly | Historical (1750-2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)[END_REF]. Annual global mean values of the total ERF (SW + LW) are shown in Figure 13 (small dots). Interannual variations reveal the uncertainties in this anthropogenic aerosol ERF, much larger of course for ERFaci. The second method is based on outputs of fixed SST (1850) simulations (piClim*) repeated 30 years for specific years with their respective anthropogenic aerosol emissions. In this configuration, the sea-salt and dust aerosols are therefore always the PI ones. The 30 year mean ERF for 1900ERF for , 1950ERF for , 1970ERF for , 1990ERF for , 2000ERF for , and 2010 appear as large dots in Figure 13. The two methods provide quite consistent results. The maximum of the anthropogenic aerosol ERF occurs around 1990, with a global mean of -0.98 W m -2 , divided into -0.76 W m -2 for ERFaci and -0.27 W m -2 for ERFari. However, this global mean has been relatively stable from 1970 onward. [START_REF] Forster | Recommendations for diagnosing effective radiative forcing from climate models for CMIP6[END_REF] indicate that in this ERF_fSST method, the 5-95% CI of the global mean ERF is reduced to 0.1 W m -2 . Our corresponding CIs are even smaller (see Table 11).

Trends in this 150 year period reveal distinct periods for the ERFari with ruptures in 1900, 1950, 1970, and 1990. These ruptures are less obvious for the ERFaci that shows almost no trend in the last 30 year period in agreement with results of [START_REF] Szopa | Aerosol and ozone changes as forcing for climate evolution between 1850 and 2100[END_REF]. Global evolutions hide distinct regional evolutions: ERF components over Asia (5-40 • N; 60-125 • E) and Europe (5-40 • N; 60-125 • E) as in [START_REF] Szopa | Aerosol and ozone changes as forcing for climate evolution between 1850 and 2100[END_REF] are shown in the second and third lines of Figure 13. Differences in the evolution of ERF over these regions are striking; ERFaci over Asia also reveals a negative trend. In total, anthropogenic aerosol ERF over Asia reaches about -4 W m -2 .

When zooming over the 1990-2010 period as in [START_REF] Myhre | Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990-2015[END_REF], we derive changes in global mean anthropogenic aerosol ERF very comparable to those of the model multimean from [START_REF] Myhre | Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990-2015[END_REF], that is, changes of 0.08 W m -2 (0.1 ± 0.08) and 0.03 (0.04) for total ERF and ERFari, respectively.

Ozone

Figure 14 shows the 1950-2014 time series of the yearly global ERF due to ozone, computed from the simulations summarized in Table 9. The piClim-1950histstratO3 simulation follows the RFMIP protocol with transient 1950-2014 ozone and all other forcings as in the piClim-control simulation, including fixed SST and sea-ice conditions which are those of a climatology of the CMIP6 pi-Control simulation. On the contrary, the amip-hist-1950stratO3 simulations (three members) hold ozone at its PI value, with everything else as in the amip-hist corresponding simulation (AerChemMIP protocol). In addition, the ensemble of the three members is shown (red thick line). Figure 14

Conclusions

This paper documents characteristics of the aerosol and ozone fields of the CNRM-CM6-1 and CNRM-ESM2-1 climate models used in the course of the CMIP6 initiative. PD and historical (1850-2014) features of these fields are analyzed against reference data sets, and their respective PD and transient ERFs are discussed. These ERFs result from diagnostics of a number of CMIP6 or CMIP6-like simulations designed in the RFMIP and AerChemMIP projects.

It is important to note first that CNRM-CM6-1 and CNRM-ESM2-1 share a common physical-dynamical core and have been run with the same vertical and horizontal resolutions. Even the calibration achieved for CNRM-CM6-1 (see [START_REF] Voldoire | Evaluation of CMIP6 deck experiments with CNRM-CM6-1[END_REF] has been kept unchanged in CNRM-ESM2-1. CNRM-ESM2-1 adds an interactive representation of the land and ocean carbon cycle, in addition to a more complex description of the atmospheric chemistry and of the aerosols, of specific interest to this article. Second, we ensured 10.1029/2019MS001816 further consistency between the two models, notably insofar as CNRM-CM6-1 prescribed monthly AODs and monthly coefficients of the linear ozone scheme have been computed with the interactive aerosol and chemistry schemes of CNRM-ESM2-1. The main developments concerning chemistry and aerosols from previous descriptions that appear in literature concern the linear ozone scheme of CNRM-CM6-1 and the parameterization of the emissions of sea-salt aerosols in CNRM-ESM2-1.

A particular attention has been paid to compute radiative forcings in terms of ERF, as recommended in [START_REF] Myhre | Anthropogenic and natural radiative forcing[END_REF]. For aerosols, the separation of ERF between ERFari, ERFaci, and a residual term (ERFres) has been carried out using the methodology of [START_REF] Ghan | Technical note: Estimating aerosol effects on cloud radiative forcing[END_REF] that requires an additional call to the radiative scheme during the course of the simulation without scattering and absorption of the aerosols, in full-sky and clear-sky conditions. For the ERF of ozone, the linear ozone scheme of CNRM-CM6-1 allows simulations with ozone coefficients for a specific year, all other forcings remaining unchanged, and this offers a quite unique way to compute this ERF term. Such a methodology cannot be applied in a model with full chemistry like CNRM-ESM2-1.

Compared to reference data sets, MACv2 and MACv2-SP, and to MERRA-2, the PD CNRM-CM6-1 total AOD (0.10) is biased low, although in the range of aerosol models, with a too small transport away from source regions. Most of this bias comes from the anthropogenic aerosol AOD (0.018) over land. The CNRM-CM6-1 AAOD (4.510 -3 ) appears also weak, with the largest biases over biomass burning regions. The change in CDNC at cloud top from 1850 to 2014, used to characterize the Twomey effect, is larger (1.20) than that of MACv2-SP. However, in the end, total anthropogenic aerosol PD ERF and its two terms ERFari and ERFaci lie within the estimates of [START_REF] Zelinka | Quantifying components of aerosol-cloud-radiation interactions in climate models[END_REF] (-1.10, -0.36, and -0.81 W m -2 , respectively). These global means hide large spatial and seasonal differences.

Several piClim-aer-type simulations have been carried out both with CNRM-CM6-1 and CNRM-ESM2-1 to better understand their ERF values. In summary, these simulations reveal the small impact of the level of natural aerosols on the anthropogenic aerosol ERF but conversely the large change in computing the ERF from a simulation with monthly prescribed AODs and a simulation with interactive aerosols (ERFs of -0.74 and -0.97 W m -2 , respectively). Simulations to derive the ERF of an individual aerosol type indicate the predominant part of the sulfate ERF.

Over the historical period, CNRM-CM6-1 and CNRM-ESM2-1 have similar trends of anthropogenic aerosols, and these trends are lower than the MACv2 and MACv2-SP ones, possibly because of differences in emissions but also in overall aerosol representation (see [START_REF] Flato | Evaluation of climate models[END_REF]. Transient ERFs computed from a pair of AMIP-type simulations are confirmed when evaluated from pairs of 30 year fixed SSTs simulations. Zooming over Asia, the large negative trend that started in 1950 is very similar in both ERFaci and ERFari.

PD ozone mixing ratios of CNRM-CM6-1, CNRM-ESM2-1, and the CMIP6-OZ reference data set are within 10% throughout most of the stratosphere, with exceptions around the tropopause and in the mesosphere. The TCO is overall well simulated by the CNRM climate models, with compensating biases depending on the latitude though. Its historical evolution over 1960-2014 is, globally averaged, very close to that of the NIWA3.3 data set. Biases over this period reflect those of the PD, and latitude-dependent year-to-year variability of the model outputs is relatively realistic.

The PD CNRM-CM6-1 ozone ERF that represents a stratospheric ozone ERF, computed with a 70 year fixed SST simulation, is -0.040 ± 0.032 W m -2 , in coherence with literature. The transient ERFs, computed with two different methods, produce very similar results for the global mean total ERF: no trend over the whole period and opposite trends for the LW (positive trend) and SW (negative trend) ERF components.

Finally, interesting results in relation to ECS estimates include the satisfactory behavior of the CNRM-CM6-1 linear ozone scheme under 4xCO2 conditions, while the ECS estimate of CNRM-CM6-1 with TACTIC_v2 differs noticeably from that of CNRM-CM6-1 with prescribed aerosols.

Results presented here give confidence that overall, the aerosol and ozone representations of both CNRM climate models are suitable for further studies by the scientific community that will analyze CMIP6 experiments. They also point out features that will require specific attention in future developments. In particular, attention will concern anthropogenic aerosols whose negative biases should be reduced by considering the nitrate and ammonium aerosols, as described in [START_REF] Drugé | Model simulation of ammonium and nitrate aerosols distribution in the Euro-Mediterranean region and their radiative and climatic effects over 1979-2016[END_REF] who performed an evaluation of this extension of TACTIC over the Euro-Mediterranean region, and by introducing an absorbing part (brown carbon) in the organic aerosol. Adoption of recent developments, in particular concerning natural aerosols over oceans, should also improve the realism of the aerosol representation. Finally, integration of a lower-troposphere chemistry, simple enough to be suitable for climate simulations, would benefit both to aerosol representation, allowing the parameterization of SOAs, and to lower tropospheric ozone.

Acronyms

  dF(D p , U 10 , SST)

Figure 1 .

 1 Figure 1. Mean optical properties (550 nm) of total aerosols around 2005. From left to right MAC-v2, CNRM-CM6-1, and difference MAC-v2-CNRM-CM6-1. And from first line to last line annual means of AOD, AAOD, SSA, and ASY.

Figure 2 .

 2 Figure 2. Mean annual AOD and AAOD around 2005 of anthropogenic aerosols. From first to last lines MACv2, MACv2-SP, CNRM-CM6-1, difference MACv2-MACv2-SP, and difference MACv2-CNRM-CM6-1. Means of 2000-2009 for CNRM-CM6-1 and MACv2 Ant. AOD, 2005 for the other MACv2 fields, and 2000-2005 for MACv2-SP.

Figure 3 .

 3 Figure 3. Mean annual AOD and AAOD around 2005 of natural aerosols. From first to last lines MACv2, CNRM-CM6-1, and difference MACv2-CNRM-CM6-1. Means of 2000-2009 for CNRM-CM6-1 and 2005 for MACv2 fields.

Figure 6 .

 6 Figure6. Zonal mean ozone percentage differences between the 4xCO2Clim-4xCO2 and the piClim-control simulations (last 10 years), from 300 to 1 hPa. (left) CNRM-CM6-1, (middle) CNRM-ESM2-1, and (right) CNRM-CM6-1 with the A 4 coefficient of the linear ozone scheme (see equation (4)) set to 0.

Figure 7 .

 7 Figure7.[START_REF] Gregory | A new method for diagnosing radiative forcing and climate sensitivity[END_REF] type plot, x axis change with the corresponding pi-Control simulation in global average surface temperature, 𝑦 axis change in net downward radiative flux. CNRM-CM6-1 CMIP6 abrupt-4xCO2 (black dots) and CNRM-ESM2-1 CMIP6 abrupt-4xCO2 (gray dots). All other colored dots are 20 year mean changes from piClim-type simulations (fixed SST). "4xClim-4xCO2": 4xCO2Clim-4xCO2 simulations. See text for additional details.

Figure 8 .

 8 Figure 8. Time series between 1850 and 2014 of yearly global mean aerosol optical properties. First line anthropogenic AODs, including biomass burning, for MACv2, MACv2-SP, and CNRM-CM6-1 and CNRM-ESM2-1 CMIP6 historical simulations. The three anthropogenic aerosol types of TACTIC_v2 (BC, OM, and SO 4 ) are also presented. Second and third lines show outputs from CNRM-CM6-1 and CNRM-ESM2-1 CMIP6 historical simulations. (second line) SS AODs (left) and DD AODs (right). (third line) SSA (left) and ASY (right) of total aerosols.

Figure 9 .

 9 Figure 9. Proportional change in CDNC between 2000 and 1850, with scale as in figure 12 of Stevens et al. (2017). (first line) MACv2-SP and CNRM-CM6-1 amip-hist-aod simulation (see text for details) at cloud top. (second line) amip-hist-aod at 925, 850, and 700 hPa.

Figure 11 .

 11 Figure 11. (lines) Components of the ERF due to the change of aerosols from 1850 to 2014 from the CNRM-CM6-1 piClim-aer simulation (30 year means). (first line) Total ERF (SW+LW), (second line) LW ERF, (third line) SW ERF, (fourth line) ERFaci, (fifth) line ERFari, and (sixth) line ERFres term[START_REF] Ghan | Technical note: Estimating aerosol effects on cloud radiative forcing[END_REF]. With annual means in left column, DJF means in middle column, and JJA means in right column, areas in gray are below the 90 % level of significance using a Student's t-test.

Figure 12 .

 12 Figure 12. (first line) ERF due to ozone in CNRM-CM6-1, computed from the 30 years of the piClim-stratO3 simulation; areas in gray are below the 90% level of significance using a Student's t-test. (second line) Absolute difference between the 1850 (piClim-control simulation) and the 2014 (piClim-stratO3 simulation) column ozone (30 year means). (third line) The same as second line but with relative differences. Annual means on the left, MAM means in the middle, and SON means on the right.

Table 1

 1 Aerosol Optical Properties by Species at 550 nm and 80% Relative Humidity for TACTIC_v2, MACv2, and the MERRA-2 Reanalysis MACv2 and MERRA-2 values represent the range for a given species.

		Single scattering albedo	Asymmetry parameter
	Species	TACTIC_v2	MACv2	MERRA-2	TACTIC_v2	MACv2	MERRA-2
	SO 4	0.997	1.00	1.00	0.74	0.67-0.70	0.78
	OM	0.997	0.97	0.97-0.98	0.74	0.56-0.62	0.58-0.68
	SS	0.996	0.999	1.00	0.78	0.75	0.50-0.86
	DD	0.90	0.86-0.96	0.77-0.96	0.785	0.73-0.83	0.71-0.87
	BC	0.32	0.21-0.27	0.21-0.25	0.43	0.27-0.33	0.33-0.40
	Stratospheric	0.999	NA	NA	0.73	NA	NA
	Note.

Table 2

 2 List of Simulations Used in the Present Study, Along With Their Characteristics

	Name	Model	CMIP6	Members	Oce/atm	Figures	Tables
	amip-hist-aod	CNRM-CM6-1+TACTIC_v2	n	1	No	2, 3, and 9	4 and 5
	historical	CNRM-CM6-1	y	6	Yes	1, 4, 5, 8, and 10
	historical	CNRM-ESM2-1	y	5	Yes	4, 5, 8, and 10
	amip-hist	CNRM-CM6-1	y	10	No	10
	amip-hist	CNRM-ESM2-1	y	1	No	10
	ERF simulations (see Tables 7, 8 and 9)				11, 12, 13, and 14	10 and 11
	Note. Oce/Atm indicates ocean-atmosphere coupled simulations.
	REPROBUS-C_v2 ozone component and describes the ozone net production as a linear function of the ozone
	mixing ratio (r O 3 ), the temperature T, and the ozone column above the grid point Σ O 3 . In CNRM-CM5.1, a
	fourth variable was used, namely, the equivalent chlorine content of the stratosphere, to account for ozone
	heterogeneous chemistry. This term proved to be problematic in CNRM-CM5.1 and resulted in an inaccurate
	treatment of heterogeneous ozone chemistry, as in other models (Monge-Sanz et al., 2011). Therefore, after
	unfruitful attempts to retain this term, we chose to keep only the first three variables of the linear scheme, as
	in Monge-Sanz et al. (2011), and we embedded in the set of new A i coefficients the heterogeneous chemistry
	scheme. Evolution of the ozone mixing ratio r O 3 in CNRM-CM6-1 now reads

  The second aerosol data set used in this paper is the 2017 version of the Max Planck Institute Aerosol Climatology (MACv2;[START_REF] Kinne | The MACv2 aerosol climatology[END_REF], which is an update of the MAC-v1 climatology described in[START_REF] Kinne | Mac-v1: A new global aerosol climatology for climate studies[END_REF]

  Note. BB refers to biomass burning emissions. BC, OM, SOA, and DD emissions are in Tg year -1 , all sulfur species in Tg(SO 2 ) year -1 , and SS in Pg year -1 . a AeroCom mean ±𝜎 (intermodel)(Textor et al., 2006, Table 10). b Mean ±𝜎 (intermodel)(Huneeus et al., 2012, Table 5). c[START_REF] Tsigaridis | The AeroCom evaluation and intercomparison of organic aerosol in global models[END_REF] mean and range from models. d Boucher et al. (2013) (Table 7.1 range). e Dentener et al. (2006). f Huneeus et al. (2012) (AeroCom median and range). g Grythe et al. (2014) range.

	Table 3
	Emission Totals (Means ±𝜎), for the Beginning (1850-1859) and the End (2005-2014) of the CMIP6
	Historical Period, as well as Present-Day Estimates From the Literature
			amip-hist-aod	Literature
	Species	Source	1850-1859	2005-2014	present day
	BC	Total	2.50 ± 0.05	8.77 ± 0.33	12 ± 3 a , 15 ± 14 b
		BB	1.61 ± 0.03	1.75 ± 0.17
	OM	Total	58.97 ± 0.51	78.86 ± 2.26	97 ± 25 a , 119 ± 111 b
		BB	20.12 ± 0.41	18.07 ± 1.39
		SOA	32.16 e	32.16 e	19 (13-121) c
	All SO 4 prec.	Total	45.67 ± 0.33	134.99 ± 4.65	119 ± 26 a
	SO 2	BB	2.02 ± 0.04	2.15 ± 0.15
		Volcanoes	2.93	2.93	29.2 e
	DMS	Total	39.78 h	39.78 h	20-80 d
	SO 4	Total	3.27 ± 0.02	9.96 ± 0.35
	DD	Total	3,485.48 ± 82.04	3,537.16 ± 100.45	1,123 (514:4,313) f
	SS	Total	288.4 ± 4.1	299.7 ± 5.8	1.8 to 2,444 g

h

[START_REF] Kettle | A global database of sea surface dimethylsulfide (DMS) measurements and a procedure to predict sea surface DMS as a function of latitude, longitude, and month[END_REF]

.

Table 4

 4 Global Mean Values Around 2005 of the Aerosol Optical Properties Presented in

		MACv2	MACv2-SP	CNRM-CM6-1	MACv2-SP	CNRM-CM6-1
	Parameter	mean	mean	mean	NRMSE	NRMSE
	Total AOD	0.12		0.10		0.51
	Total AAOD	0.0076		0.0045		0.72
	SSA	0.95		0.98		0.034
	ASY	0.73		0.75		0.035
	Ant. AOD	0.030	0.023	0.018	0.63	0.87
	Ant. AAOD	0.0027	0.0018	0.0017	0.79	0.95
	Nat. AOD	0.090		0.083		0.53
	Nat. AAOD	0.0021		0.0027		1.4

Figure

1

shows the mean 2000-2009 optical properties of the MACv2 and CNRM-CM6-1 historical sets, namely, the AOD, Aerosol Absorption Optical Depth (AAOD = (1 -SSA) × AOD), SSA, and ASY parameters at 550 nm for all aerosols, and Table

4

presents the global mean values for these different parameters and the normalized spatial root mean square error (NRMSE) defined as the ratio between the root mean square error (rmse) and the global mean of the reference data set (MACv2). The 2000-2009 period is centered on Year 2005 which is the anchor year of MACv2. The AOD is larger for MACv2 (0.12 global mean) than for CNRM-CM6-1 (0.10).

[START_REF] Flato | Evaluation of climate models[END_REF]

and more recently

[START_REF] Sockol | Intercomparison between CMIP5 model and MODIS satellite-retrieved data of aerosol optical depth, cloud fraction, and cloud-aerosol interactions: CMIP5 and MODIS AOD-CF interactions[END_REF]

allow to put this difference into perspective.

[START_REF] Flato | Evaluation of climate models[END_REF]

indicate that CMIP5 models (21) underestimate the mean observed 2001-2005 AOD by at least 20% over most land surfaces, and Sockol and Small Griswold

  See Text for Details)

	Simulation	T eq	𝜆	F
	CNRM-ESM2-1	8.21	1.02	8.39
	CNRM-CM6-1	9.20	0.92	8.47
	CNRM-CM6-1 + TACTICant	8.80	0.96	8.46
	CNRM-CM6-1 + TACTIC	8.50	0.99	8.45
	CNRM-CM6-1 + REPROBUS	9.30	0.90	8.42

Table 7

 7 Summary of RFMIP and/or AerChemMIP Fixed SST Simulations Used to Calculate the ERF of Aerosols With CNRM-ESM2-1 (Noted ESM2-1 in This Table) and CNRM-CM6-1 (noted CM6-1 in This Table)All piClim* simulations have forcings set to 1850 values, except for aerosols when specified above. int. for interactive TACTIC aerosols. TYPE refers to the following species: SO 2 , BC, and OM. EMIS refers to the following emissions: dust, DMS, SS, and fire.

	Simulations	MIP	Model	Period	SO 4 , BC, OM	DD, SS
	piClim-control RFMIP/AerChemMIP CM6-1 30 years	1850 CM6-1 AOD	1850 CM6-1 AOD
	piClim-control RFMIP/AerChemMIP ESM2-1 30 years	emis. 1850	int.
	piClim-aer	RFMIP/AerChemMIP CM6-1 30 years	2014 CM6-1 AOD	2014 CM6-1 AOD
	piClim-aer	RFMIP/AerChemMIP ESM2-1 30 years	emis. 2014	int.
	piClim-TYPE	AerChemMIP	ESM2-1 30 years	2014 for TYPE emis. only	int.
	piClim-2xEMIS	AerChemMIP	ESM2-1 30 years 1850 with double emis. for EMIS	int.
	histSST	AerChemMIP	ESM2-1 1850-2014	trans. emis 1850-2014	int.
	histSST-piNTCF	AerChemMIP	ESM2-1 1850-2014	emis. 1850	int.
	Note.

Table 8

 8 Summary of Supplementary Fixed SST Simulations Used to Calculate the ERF of Aerosols With CNRM-ESM2-1 (Noted ESM2-1 in This Table)and

	Simulations	Model	Period	SO 4 , BC, OM	DD, SS
	piClim-aerYY	ESM2-1	30 years	YY emis	int.

Table 10

 10 Aerosol ERF (in W m -2 ), for thePeriod 1850-2014, Separated Into ERFari CSSW , ERFaci, ERFari, ERFres, and total ERF (=ERFaci + ERFari + ERFres) for Various CNRM-CM6-1 and CNRM-ESM2-1 Simulations (ESM2-1* Indicates CNRM-ESM2-1 Without the Interactive Aerosol Scheme) Presented in Tables7 and 8Zelinka et al. (2014) for CMIP5 models, ±intermodel standard deviation. b[START_REF] Stevens | MACv2-SP: A parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6[END_REF]. c[START_REF] Myhre | Anthropogenic and natural radiative forcing[END_REF] for Table8.4 AR5 RF between 1750 and 2011 (mean and uncertainty).

	Simulation	Model	ERFari CSSW	ERFari	ERFaci	ERFres	ERF	Ref.
	piClim-aer	CM6-1	-0.63	-0.42	-0.79	0.06	-1.16
	piClim-aerant	CM6-1	-0.53	-0.36	-0.81	0.07	-1.10
	piClim-aerAODESM	CM6-1	-0.48	-0.33	-0.79	0.09	-1.03	-1.17 ± 0.30 a
	piClim-aer	ESM2-1	-0.41	-0.21	-0.61	0.08	-0.74	-0.50 b
	piClim-aerAODESM	ESM2-1*	-0.52	-0.35	-0.69	0.08	-0.97
	piClim-aerAODCM	ESM2-1*	-0.57	-0.39	-0.80	0.11	-1.08
	piClim-SO 2	ESM2-1	-0.42	-0.29	-0.53	0.08	-0.75	-0.40 ± 0.20 c
	piClim-OC	ESM2-1	-0.10	-0.07	-0.14	0.04	-0.17	-0.09 ± 0.07 c
	piClim-BC	ESM2-1	0.10	0.13	-0.03	0.01	0.11	0.40 ± 0.40 c
	piClim-2xdust	ESM2-1	0.02	0.05	-0.04	0.08	0.09	/
	piClim-2xss	ESM2-1	-1.59	-1.03	-0.04	0.03	-1.04	/
	piClim-2xDMS	ESM2-1	-0.10	-0.06	-0.34	0.03	-0.37	/
	piClim-2xfire	ESM2-1	-0.11	-0.06	-0.20	0.03	-0.22	/

a

  Time series from 1950 to 2014 of the yearly global ERF due to ozone in CNRM-CM6-1 simulations (amip-hist-1950stratO3r1, r2, r3 red dotted lines; amip-hist-1950stratO3 ensemble red thick line; piClim-1950histstratO3 blue thick line; see text for details), in the order of reading LW, SW, and LW + SW ERF.

				10.1029/2019MS001816
	Figure 14.
	Table 11
	Confidence Intervals (W m -2 , Minimum/Maximum) for Aerosol ERF,
	ERFaci, and ERFari From the Six ERF_fSST Simulations Shown in
	Figure 13
		Globe	Europe	Asia
	ERF	0.07/0.08	0.23/0.29	0.28/0.37
	ERFaci	0.05/0.06	0.27/0.33	0.36/0.49
	ERFari	0.00/0.00	0.02/0.05	0.02/0.04

Note. Columns correspond to the three regions shown in Figure

13

.

  indicates comparable transient ERFs obtained with the two protocols, with no trend over 1950-2014 and with a smaller interannual variability (𝜎 = 0.07 W m -2 ) in the amip-hist-1950stratO3 ensemble. However, as for the present ozone forcing, the separated LW and SW ERFs do not agree with what appears in literature (e.g.,[START_REF] Checa-Garcia | Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database[END_REF]. In the CNRM simulations, LW ERF slightly increases and SW ERF slightly decreases over1960-2010.

Acknowledgments

A number of components of the

CNRM-CM6-1 and CNRM-ESM2-1 code are open source. The SURFEX code is available (Open-SURFEX) using a CECILL-C Licence (http:// www.cecill.info/licences/Licence_ CeCILL-C_V1-en.txt) at http://www. umr-cnrm.fr/surfex; NEMO-GELATO is also available at https://opensource. umr-cnrm.fr/; OASIS3-MCT can be downloaded at https://verc.enes.org/ oasis/download; and XIOS can be downloaded at https://forge.ipsl. jussieu.fr/ioserver. For the

10.1029/2019MS001816

stratospheric ozone ERF has been diagnosed from a pair of simulations of CNRM-CM6-1, the baseline being the official piClim-control CMIP6 simulation and the second simulation being piClim-stratO 3 (see Table 9), which is identical to piClim-control except for the ozone coefficients fixed to their 2014 value. This simulation is not an official AerChemMIP simulation. A number of other simulations have been performed to diagnose the transient ERF due to ozone. Table 9 provides a summary of these additional CMIP6 AMIP-type simulations, together with their respective reference simulation and the corresponding protocol.

PD Forcing 5.2.1. Aerosols

Figure 11 shows various aspects of the PD aerosol ERF computed from the CNRM-CM6-1 piClim-aer (and piClim-control) simulation. It should be noted that by construction, all aerosols in CNRM-CM-6-1 piClim-aer are those of 2014, including the sea-salt and dust ones. Therefore, the ERF presented include aspects of natural aerosol changes between 1850 and 2014 (see also section 4.2). An additional piClim-aer type simulation has been performed, named piClim-aerant, in which the natural aerosols are fixed to 1850, whose main results are shown below in Table 10, together with results of the CNRM-ESM2-1 piClim-aer simulation. In CNRM-ESM2-1 piClim-aer, the interactive sea-salt and dust aerosols should be very close to those of the corresponding piClim-control simulation, as SSTs and land surface are the same in both simulations (1850 conditions).

The mean total ERF (LW + SW) is -1.16 W m -2 , dominated by the ERFaci term (-0.79 W m -2 ). The spatial distribution reveals larger heterogeneity in the southern oceans, largely due to aerosol-cloud interactions.

In the Northern Hemisphere, the negative forcing over Southeast Asia appears clearly, mostly due to the ERFari term. ERFari is larger over source regions, negative over Asia, and positive over desert areas. The LW ERF (global mean > 0), quite heterogeneous, is an order of magnitude smaller than the SW ERF (global mean < 0), and the mean global residual term ERFres is more than half the LW ERF mean. DJF and JJA maps highlight the differences in the seasonal patterns, with however negative forcings in most cases over China. ARPEGE-Climat v6 code and exact version applied to each component, in particular the aerosol and chemistry components, please contact the authors. CMIP6 CNRM-CM6-1 and CNRM-ESM2-1 experiments assessed in this paper are available on the Earth System Grid Federation (ESGF). Diagnostics from the other simulations shown are available upon request to the authors. We particularly acknowledge the support of the entire team in charge of the CNRM climate models and especially that of Antoinette Alias and Laurent Franchisteguy for their technical assistance. Supercomputing time was provided by the Météo-France/DSI supercomputing center. We would like to thank Bodeker Scientific, funded by the New Zealand Deep South National Science Challenge, for providing the combined NIWA-BS total column ozone database. We thank Stefan Kinne for providing the MACv2 data and Stephanie Fiedler for providing the MACv2-SP data. The ESMValTool software has been used to produce figures in this paper (http://doi.org/10. 17874/ac8548f0315, Version 1.1; Eyring et al., 2016). We acknowledge support from the European Unions Horizon 2020 Research and Innovation Programme under Grant Agreements 641816 (CRESCENDO) and 820829 (CONSTRAIN).