The three main models used are HadGEM3, ECHAM6-HAM2 and NorESM1-M. HadGEM3 and NorESM1-M have interactive aerosols and chemistry; ECHAM6-HAM2 has interactive aerosols but does not include interactive chemistry. Therefore in HadGEM3 and NorESM1-M, changes in the aerosols can affect the chemistry via changes in oxidation of SO2 and changing the available surface for heterogeneous chemistry; these processes will directly and indirectly affect O3 and OH. Photolysis is not affected by the aerosols in these models. The fact that ECHAM6-HAM2 does not include interactive chemistry is expected to lead to only minor differences from the other two models with interactive chemistry with regard to the radiative and climate effects of aerosol and aerosol precursor emissions. For the BC perturbation experiments some additional simulations were performed: one extra ensemble member was run by each of HadGEM3 and NorESM1-M, and three ensemble members were run by NCAR CESM 1.0.4/CAM4. The extra BC simulations were included in order to explore the BC results further, as this work was part of a larger project of which BC was a key focus.

HadGEM3 is the Hadley Centre Global Environment Model version 3 . The atmosphere component has a horizontal resolution of 1.875∘ × 1.25∘ and 85 vertical levels extending to 85 km in height (of which 50 are below 18 km). The atmosphere is coupled to the NEMO ocean modelling framework with a horizontal resolution of 1.0∘ and 75 vertical levels, and to the CICE sea-ice model . The UKCA TropIsop scheme is used to model gas-phase chemistry in the troposphere. This treats 55 chemical species (37 of which are transported) including hydrocarbons up to propane, and isoprene and its degradation products . Atmospheric gas and aerosol tracers are advected using the same semi-Lagrangian advection scheme as used for the physical climate variables. Parameterized transport such as boundary layer mixing and convection is also as used for the physical climate variables. Aerosols are modelled by the UKCA GLOMAP-mode aerosol scheme . This models the internal mixing of SO4, OC, BC, dust and sea-salt using a two-moment modal approach and dynamically evolving particle size distributions. There are seven modes: four soluble (nucleation to coarse) and three insoluble (Aitken to coarse). Aerosol processes are simulated in a size-resolved manner, including primary emissions, secondary particle formation by binary homogeneous nucleation of sulphuric acid and water, particle growth by coagulation, condensation, and cloud-processing, and removal by dry deposition, in-cloud and below-cloud scavenging. The effects of aerosols on clouds are modelled using an aerosol activation parameterization scheme . The radiative impact from aerosols is calculated using the Edwards–Slingo radiation scheme .

ECHAM6-HAM2 is the European Centre for Medium-Range Weather Forecasts Hamburg model version 6 . The atmospheric simulations were made using the ECHAM6 GCM with a horizontal resolution of T63 (about 1.8∘ × 1.8∘) and a vertical resolution of 47 levels (extending from the surface to 0.01 hPa). The atmospheric model is coupled to the Max Planck Institute Global Ocean/Sea-Ice Model (MPIOM) with a bipolar grid with 1.5∘ resolution (near the equator) and 40 vertical levels . The atmospheric model is extended with the Hamburg aerosol model (HAM2) version 2 . The main components of HAM are the microphysical module M7, which predicts the evolution of an ensemble of seven internally mixed lognormal aerosol modes , an emission module, a sulfate chemistry scheme , a deposition module, and a radiative transfer module to account for sources, transport, and sinks of aerosols as well as their radiative impact. Five aerosol components, namely SO4, OC, BC, sea-salt, and mineral dust, are considered in this model. Aerosol effects on liquid-water and ice clouds are considered following . Oxidant fields for the sulphate aerosol production were a 2003–2010 average from the MACC reanalysis .

NorESM1-M is the Norwegian Earth System Model version 1 , with horizontal atmospheric resolution of 1.9∘ × 2.5∘, and 26 levels in the vertical with a hybrid sigma pressure coordinate and model top at 2.19 hPa. The ocean module is an updated version of the isopycnic ocean model MICOM (with a 1.1∘ resolution near the equator and 53 layers), while the sea-ice (CICE4) and land (CLM4) models and the coupler (CPL7) are basically the same as in CCSM4 . The atmosphere module CAM4-Oslo is a version of CAM4 with advanced representation of aerosols, aerosol–radiation and aerosol–cloud interactions. It uses the finite volume dynamical core for transport calculations. CAM4-Oslo calculates mass-concentrations of aerosol species that are tagged according to production mechanisms in clear and cloudy air and four size classes (nucleation, Aitken, accumulation, and coarse modes). These processes are primary emission, gaseous and aqueous chemistry (cloud processing), nucleation, condensation, and coagulation. Loss terms are dry deposition, in-cloud and below-cloud scavenging. The aerosol components included are SO4, BC, organic matter (OM), sea-salt, and mineral dust, and are described by 20 tracers. In the model version used in this study, the aerosol module of CAM4-Oslo is coupled with the tropospheric gas-phase chemistry from MOZART , which treats around 80 gaseous species. This coupling allows for a more explicit description of the formation of secondary aerosol (SO4 and secondary OM). The radiative forcing from aerosols is calculated using the radiation scheme. In the fully coupled NorESM1-M, albedo-effects of BC and mineral dust aerosols deposited on snow and sea-ice are also taken into account; this process is not represented in the other three models.

An additional model, NCAR CESM 1.0.4/CAM4, was used for the BC analysis only. NCAR CESM 1.0.4/CAM4 is the National Center for Atmospheric Research Community Earth System Model run with the Community Atmosphere Model version 4 . The atmospheric component is set up here with a horizontal resolution of 1.9∘ × 2.5∘, and 26 vertical layers (extending from the surface to 2.19 hPa). CAM4 is coupled to a full ocean model , which is based on the Parallel Ocean Program version 2 , to the CICE4 sea ice model , and the CLM4 land model . Here, the model has been run without interactive chemistry and aerosols, and instead used prescribed 3-D monthly mean concentrations of ozone and aerosols (BC, OC and SO4) from the Oslo Chemistry-Transport model version 2 (OsloCTM2) . OsloCTM2 is driven by meteorological data from the ECMWF-IFS model, and has been run with T42 (approximately 2.8∘ × 2.8) horizontal resolution and 60 vertical layers (extending from the surface to 0.1 hPa). In CAM4, the direct and semi-direct aerosol effects of BC are included, while indirect aerosol effects and the effect of BC deposited on snow and ice are not included.

Hereafter we refer to the four models discussed above as HadGEM, ECHAM-HAM, NorESM and CESM-CAM4, respectively.

Each of the three main models (HadGEM, ECHAM-HAM and NorESM) ran a control simulation and a set of three perturbation experiments in which the total land-based anthropogenic component of a single aerosol emission species was removed globally. In addition, HadGEM and NorESM ran a second control and perturbed BC experiment, and CESM-CAM4 ran three control and three perturbed BC experiments.

The control simulations were first run for several decades using an initial ocean state based on present-day CMIP5 conditions for all models except for ECHAM-HAM, which used a pre-industrial control state (see below). The control and perturbed simulations were then run from the same point in this spun-up state for 50 years, in order to separate a robust signal from the interannual variability. The climate is not necessarily expected to be stationary after the spin up, but any underlying climate trends are expected to be present in the control and perturbations. By taking the difference between the control and the perturbations we are therefore removing any underlying trends not associated with the changes in aerosol emission. The 50-year integration length was deemed sufficient based on previous studies, e.g. performed integrations of length 40 years after 10 years of spin-up, and performed integrations of length 30 years after 30 years of spin-up. Furthermore, showed that most of the temperature response to a step CO2 perturbation in AOGCMs is achieved within around the first 10 years or so (the Cx2 case in their Fig. 1), after which the temperature remains relatively constant, with only a very gradual continued increase towards the equilibrium response temperature.

We focus on global mean and zonal mean values of the surface temperature and precipitation. We also examine the top-of-atmosphere (TOA) short-wave (SW) fluxes to aid understanding of these results. This is not the same as the TOA SW forcing in prescribed-SST simulations, since in the coupled simulations it includes the feedbacks from snow and ice albedo changes and cloud responses to surface temperature, so it is a combination of SW radiative forcing and these feedback changes on the SW flux. It is useful in understanding the causes of changes in climate variables, particularly on regional scales.

The control simulations have present-day anthropogenic emissions of SLCP species from the ECLIPSE emission data set V4.0a for the year 2008 , for all models except CESM-CAM4 which used ECLIPSE V5.0 emissions for the year 2000. Non-anthropogenic biomass burning emissions are from the GFED3 emissions data set (http://www.globalfiredata.org) for the year 2005 (in ECHAM-HAM and NorESM) and 2008 (in HadGEM and CESM-CAM4), and are not perturbed. Agricultural biomass burning emissions are included in the anthropogenic component of emissions which are perturbed. Sea-salt and dust aerosol emissions are interactive in HadGEM and ECHAM-HAM; in NorESM, dust emissions are prescribed from a climatology but sea-salt emissions are interactive; and in CESM-CAM4 both dust and sea-salt concentrations are prescribed from a climatology. Other natural emissions, including DMS and volcano emissions, are included and are not perturbed. The concentrations of WMGHGs are also kept fixed at present-day levels in HadGEM, NorESM and CESM-CAM4, and in ECHAM-HAM are fixed at pre-industrial (1850) levels. The surface methane concentration is also prescribed at present-day levels in HadGEM, NorESM and CESM-CAM4, and at pre-industrial levels in ECHAM-HAM. For ECHAM-HAM, the pre-industrial greenhouse gas concentrations were chosen since the model was spun up to equilibrium for this case, and a new spin-up for increased levels of greenhouse gas concentrations would have been computationally too costly. Since only differences between experiments and control simulations are considered here, no large effect caused by the differences in greenhouse gas concentrations is expected.

Figure shows the emissions of BC, OC and SO2, divided into the anthropogenic emissions that are perturbed in the experiments (left column) and other emissions that are input to the model (natural, biomass burning and shipping; right column). The strongest anthropogenic emissions of all three species are mostly concentrated over China, India, Europe, the eastern US and parts of Africa and South America.

Despite all the models having the same emissions input, there is a large discrepancy between models in the vertical distribution of aerosols in the atmosphere, and in the total aerosol burden, which is typical for current global aerosol models . HadGEM and ECHAM-HAM have relatively low total burdens of BC, and short atmospheric lifetimes, compared with NorESM and CESM-CAM4 (Table ). Figure  shows vertical sections of the annual average, zonal mean BC mass mixing ratio in the control simulation for each of the models considered. HadGEM and ECHAM-HAM (Fig. a and b) have low concentrations of BC at high altitude, which means there is less BC above clouds. In contrast, NorESM and CESM-CAM4 show high BC concentrations extending to above 200 hPa throughout most of the Northern Hemisphere and Southern Hemisphere tropics (Fig. c and d). This has implications for the impact that removing anthropogenic BC emissions may have. BC at high altitude can have very strong direct effects if it is located above high-albedo cloud surfaces. In the models with higher concentrations of BC at high levels in the control simulations, more of this high-level BC can be removed in the BC perturbation experiment, leading to a larger change in BC direct forcing. The larger amount of high-level BC in NorESM and CESM-CAM4 (which uses aerosol input from OsloCTM2) is consistent with the AeroCom models discussed by and who found that these models have too much BC at high altitudes when compared with observations over the Pacific in the HIPPO campaign , and overestimate the BC lifetime. At lower levels, the models underestimate BC concentrations due to the emissions being too low: found that increasing emissions of BC and decreasing the BC lifetime in models gave a better agreement with observations. In HadGEM, the lower concentrations of BC at high altitudes and shorter BC lifetimes are likely due to recent modifications to the convective scavenging scheme, which were implemented in order to improve the correspondence with these observations. However, the BC lifetime of 3.4 days is shorter than the AeroCom average. The true BC distribution may therefore lie somewhere in between that of HadGEM and NorESM/CESM-CAM4. The OC burden in NorESM is considerably higher than in the other three models, and its lifetime is correspondingly longer. The range of OC burdens between models is expected due to differences in OA burdens and OA / OC ratios between models . NorESM and CESM-CAM4 have relatively low burdens of SO4, and short lifetimes, compared with HadGEM and ECHAM-HAM. There are also differences in the vertical distribution of OC and SO4 between models (not shown) but as these are scattering, rather than absorbing, aerosols the impact of the vertical distribution of the aerosol will have less of an impact on the results. More detailed evaluations of the models used here against observations are given in , and .

Figure shows the annual average global mean surface temperature in the control simulations for each of the models. ECHAM-HAM has a lower mean temperature than the other models due to its pre-industrial WMGHG and methane concentrations. CESM-CAM4 has a higher mean temperature than the others. ECHAM-HAM has a slight negative drift in surface temperature over the integration period, while both NorESM ensemble members have a slight positive drift; the other two models remain relatively stable, although the second HadGEM member has a decrease in temperature over the first 10 years or so. These drifts in the global mean surface temperature are also present in the perturbation experiments since these start from the control simulations at the beginning of the 50-year period analysed. Therefore we do not expect any drift in the signal, i.e. in the difference between the perturbed and control simulations.

There are some differences between models in the precipitation patterns, particularly in the tropics (Fig. S1 in the Supplement). All models suffer from the “double ITCZ” problem (i.e. there is an overly strong band of precipitation to the south of the equator) which is a known problem in CMIP5 AOGCMs . This is most pronounced in ECHAM-HAM (Fig. S1d). ECHAM-HAM and HadGEM also have regions of very low precipitation around the equator in the Pacific (Fig. S1c and d). There is some variation in the north-eastward extent of the North Atlantic storm track: in NorESM it extends too far north-east, while in CAM4 it does not extend far enough (Fig. S1e and f); in HadGEM and ECHAM-HAM it matches the observations well. All models have too much precipitation over the Himalayas and the Andes, which is probably due to inaccuracies in their representation of precipitation over high orography.

All three models show an increase in global mean surface temperature as a result of removing anthropogenic SO2 emissions: HadGEM and ECHAM-HAM show almost equal temperature increases while NorESM warms by approximately half this value (Fig. a and Table ). The multi-model mean global mean surface temperature increases by 0.69 K. The zonal mean temperature change is positive at all latitudes and increases with increasing latitude, with a multi-model mean increase of around 2.5 K at the North Pole (Fig. b). Figure a shows warming over almost all areas of the globe, including all land areas. As shown by the stippling, these temperature responses are significant throughout almost all the Northern Hemisphere, and much of the Southern Hemisphere. Most of the Northern Hemisphere land shows warming of at least 1 K, with some northern regions exceeding 2 K.

These temperature responses can be understood further by comparison with the TOA SW flux changes. The global mean TOA SW flux change is positive for all three model simulations (Fig. c). HadGEM, which has the strongest temperature response, also has the largest change in TOA SW flux, while NorESM, which has the weakest temperature response, has the smallest change in TOA SW flux. The ratio of temperature change to SW flux change is similar between the models (0.33–0.40 K (W m-2)-1, Table ). The strongest increase in TOA SW flux change occurs in the Northern Hemisphere mid-latitudes, where the anthropogenic emissions are largest (Fig. b). There is good agreement between the three models in the zonal distribution of TOA SW flux change, although NorESM shows smaller values in the Northern Hemisphere, which may explain the weaker temperature increase in this model compared to the others. The multi-model mean changes are significant throughout most of the Northern Hemisphere (Fig. a). There are regions of strong TOA SW flux change over Europe, the eastern USA and China, which correspond to locations with the largest anthropogenic emissions. Over Europe and the eastern USA, this explains the relatively strong warming in these regions (Fig. a). The positive TOA SW flux change over China also extends in a band over the North Pacific. This is consistent with the decreased aerosol concentrations in this region due to the reduced emissions in China. As well as the direct radiative effects, the reduced aerosols would also cause changes in cloud cover. It was shown by that Chinese aerosol emissions increased cloud cover over the North Pacific, so removing these aerosols would reduce cloud cover. A similar region of positive TOA SW flux change also occurs over the North Atlantic, which is similarly due to weaker aerosol–radiation and aerosol–cloud effects over this region resulting from the aerosol emissions reductions over the eastern USA. The regions of negative TOA SW flux change in the Pacific and Atlantic just north of the equator relate to a northward shift in the ITCZ, which increases the cloud cover north of the equator and decreases it to the south. This northward ITCZ shift is expected due to the hemispherically asymmetric warming .

At high Northern Hemisphere latitudes there are regions of enhanced warming and corresponding increased TOA SW flux (Figs. a and a), the most pronounced being over the ocean north of Europe. These correspond to regions with large reductions in sea-ice (not shown). All three models agree on a large loss of Arctic sea-ice, due to the strong Northern Hemisphere warming. In the Southern Hemisphere, all three models actually show a region of increased sea-ice east of the Antarctic Peninsula, which explains the reduced temperatures and decreased TOA SW flux there.

The removal of anthropogenic SO2 emissions results in an increase in global mean precipitation (Fig. e). This increase is expected due to the increased surface temperature. The multi-model mean percentage precipitation change per unit warming can be calculated from Table  as 2.50 % K-1, which is consistent with the value for SO4 found by (2.46±0.11 % K-1). While there is a global increase in precipitation, the Southern Hemisphere actually shows an overall decrease in precipitation (Fig. b). This is mostly due to the northward shift in the ITCZ (discussed above), which can be seen as a clear dipole in precipitation change about the equator (Fig. b). All three models agree on the northward shift in tropical precipitation over the ITCZ regions and the corresponding pattern of precipitation change is significant in much of the tropics (Fig. a). There is a relatively strong increase in precipitation over India and China, collocated with regions of high anthropogenic emissions of SO2. There is a clear increase in precipitation in the Indian monsoon region, which is consistent with the findings that anthropogenic aerosol has caused a weakening of the summer monsoon . There is a large increase in precipitation over the Sahel. This is consistent with the results of who found that present-day anthropogenic sulphate aerosol had contributed to reduced precipitation in the Sahel. There are broad regions over Russia and Canada with increased precipitation collocated with regions of increased surface temperature. The increased temperature will provide more available moisture through evaporation. The reduced aerosols in these regions may also cause an increase in precipitation. Over much of Europe and the USA there is a decrease in precipitation. While these changes are mostly not statistically significant, we hypothesize that this is linked to the northward ITCZ shift and corresponding changes in circulation.

Overall the models agree qualitatively on the climate response to removing anthropogenic SO2 emissions, showing Northern Hemisphere warming and a northward shift in the ITCZ. HadGEM and ECHAM-HAM show very good quantitative agreement in the response.

For the BC perturbation experiments, we consider, in addition to the original simulations from HadGEM, ECHAM-HAM and NorESM, one extra ensemble member from each of HadGEM and NorESM, and three ensemble members from CESM-CAM4. For the calculations of multi-model means, we take the mean of the mean values for each model.

The temperature response to removing anthropogenic BC emissions is much smaller overall than the response to perturbing SO2 emissions (Fig.  and Table ). All the models except HadGEM show a net decrease in global mean surface temperature (Fig. a). This results in a small negative multi-model mean value for the global surface temperature response. However, we note the results of which indicate that climate models may underestimate the forcing from BC by around 10 %. Figure b shows the temperature response in the individual model members. This shows that HadGEM member 1 has a significant increase in global mean surface temperature, while the other simulations all show a decrease, although the sign of this response is uncertain in the cases of HadGEM member 2 and CESM-CAM4 member 2.

A similar pattern is seen for the change in TOA SW flux (Fig. c). For the majority of the simulations the ratio of temperature change to SW flux change is between 0.21 and 0.28 K (W m-2)-1 (Table ) which is smaller than for SO2; however the HadGEM member 1 and CESM-CAM4 member 2 simulations are outliers with ratios of 0.78 and 0.03 K (W m-2)-1 respectively.

The multi-model mean temperature response is within ±0.5 K everywhere (Fig. c). These temperature changes are significant in large parts of the Southern Hemisphere ocean and the tropical Pacific but less so in the Northern Hemisphere. The TOA SW flux change is also relatively small everywhere,with the strongest TOA SW flux decrease over northern India (Fig. c). However, the changes in TOA SW flux are significant over large areas of land in the Northern Hemisphere, in general over areas with high anthropogenic BC emissions.

The small multi-model mean temperature and TOA SW flux responses are the result of conflicting regional responses in the different models, rather than weak responses in each model. This can be seen in Fig. d, which shows the range of zonal mean temperature responses between models. NorESM shows relatively strong cooling, which is stronger towards high latitudes, reaching around -0.4 K at the North Pole. In contrast, HadGEM shows warming in the Northern Hemisphere, but to different degrees in the two ensemble members: in member 1 the temperature increases towards the North Pole, reaching 0.4 K; in contrast member 2 shows only slight warming, and a decrease in temperature at the pole. ECHAM-HAM shows a weak response in general but a small increase towards the North Pole. The three CESM-CAM4 members show different behaviour: all three show weak cooling at most latitudes, but north of around 60∘ N member 2 shows warming, which increases towards the pole and reaches 0.6 K. The zonal mean TOA SW flux change also shows large differences between models (Fig. d), which helps to explain the range of temperature responses in each model in the Northern Hemisphere.

The spatial responses in each of the model simulations can be seen in Figs. S2–S6, and can explain some of the differences between models discussed above. HadGEM member 1 shows warming in the Arctic and over most of the Northern Hemisphere mid-latitudes, including Europe, which is unexpected since anthropogenic BC emissions are relatively large there (Fig. S2a). This is due to increased TOA SW flux over Europe (Fig. S2c). Inspection of cloud and snow cover fields (not shown) shows that this is in fact a result of a combination of reduced cloud cover and reduced snow cover over northern Europe; these changes are likely due to circulation changes, and their combined effect is enough to more than balance the negative forcing from local removal of BC. The warming in the Arctic is linked to decreases in sea-ice (Fig. S2e) and collocated increases in TOA SW flux (Fig. S2c). HadGEM member 2 shows warming over much of Russia but cooling over North America (Fig. S2b). There is also strong warming along the south-eastern edge of Greenland and in the Barents Sea, which is linked to increased TOA SW flux (Fig. S2d) and large decreases in sea-ice (Fig. S2f). Both HadGEM members show strong decreases in TOA SW flux over India due to the emissions reductions, but these do not translate to strong temperature decreases. ECHAM-HAM also shows some localized warming in the Arctic, but cooling in much of the rest of the Northern Hemisphere (Fig. S3a), although most of this is not significant. As in HadGEM, the regions of Arctic warming are collocated with increased TOA SW flux (Fig. S3b) and decreased sea-ice (Fig. S3c). There are regions with decreased TOA SW flux over India, China and the eastern USA, which correspond to large reductions in BC emissions (Fig. S3b). In contrast, both NorESM members show cooling in the Arctic and significant cooling over much of the globe (Fig. S4a and b). In both members this corresponds to decreased TOA SW flux over much of the Arctic and most of the northern hemispere land area (Fig. S4c and d). There are regions with increases in sea-ice, such as in the Barents Sea in Fig. S4f, but also small regions where the sea-ice decreases, although these decreases are generally not significant (Fig. S4e and f). The three CESM-CAM4 members show different temperature responses in the Arctic: member 1 shows very little temperature response in the Arctic (Fig. S5a), while member 2 shows significant warming over much of the Arctic (Fig. S5b); member 3 shows cooling of a similar magnitude over the Arctic (Fig. S6a). Corresponding to the warmer Arctic temperatures in member 2, there are also widespread decreases in Arctic sea-ice (Fig. S5f), while member 1 shows more mixed sea-ice changes (Fig. S5e) and member 3 shows some regions with increase sea-ice (Fig. S6c). Member 1 shows significant cooling in much of the Southern Hemisphere ocean, while members 2 and 3 show only weak temperature responses. Both members show significant cooling in the North Pacific, linked to the reduction in aerosol emissions from China. Compared to members 2 and 3, member 1 shows stronger decreases in TOA SW flux over China and Europe in response to the emissions reductions, which could explain the stronger overall temperature reduction in member 1 (Figs. S5c, d and 6b).

The global mean precipitation response to removing anthropogenic BC emissions is relatively small (Fig. e and f). Despite the different signs of temperature response, the global precipitation increases in all the model simulations. This is not surprising since the removal of BC from the atmosphere will lead to a negative atmospheric forcing, which in turn is expected to lead to increased precipitation . NorESM shows a pronounced southward shift in the position of the ITCZ, which is consistent with the cooling in the Northern Hemisphere in these simulations (Fig. d). HadGEM member 1 shows a weak northward shift in the ITCZ, while the other model simulations do not show a coherent shift in its position. The opposing direction of the ITCZ shift in HadGEM member 1 and NorESM partly explains why the model-mean responses are generally relatively weak everywhere (Fig. c). It is interesting to note that over India, where the anthropogenic BC emissions are large, the removal of the BC emissions results in a decrease, rather than an increase, in precipitation. These precipitation changes are driven by circulation changes (e.g. the southward shift in the ITCZ) which dominate over the local effects on precipitation due to BC removal causing destabilization of the atmosphere.

Overall, the climate response to removing anthropogenic BC emissions is weaker than the response to removing SO2 emissions. Although there is a mean global temperature decrease, there is a large variation between models in the temperature response, particularly in the Northern Hemisphere high latitudes. All models agree on an increase in precipitation globally, although there is some variation between models in the patterns of precipitation response.

The multi-model mean response to removing anthropogenic OC emissions is an increase in global mean surface temperature (Fig. a and Table ). HadGEM and NorESM show a clear increase in surface temperature, with the largest response in HadGEM; ECHAM-HAM shows a weak reduction in global mean surface temperature, although the error bars indicate some uncertainty in the sign of this response. HadGEM and NorESM show an increase in the zonal mean surface temperature throughout the Northern Hemisphere, increasing towards the pole; ECHAM-HAM shows almost no change in the zonal mean surface temperature (Fig. f). Despite the different behaviour in ECHAM-HAM compared with the other models, there are broad areas in the Northern Hemisphere where the temperature changes are significant, including over much of Europe (Fig. e).

The TOA SW flux change is weakly positive over most of the Northern Hemisphere, and is mostly not significant (Fig. e). HadGEM and NorESM show an increase in zonal mean TOA SW flux over the Northern Hemisphere (Fig. f), and in particular show increased TOA SW flux over the mid-latitudes, which have the largest anthropogenic OC emissions (Fig. e). In contrast, ECHAM-HAM shows a decrease in TOA SW flux over the Northern Hemisphere mid-latitudes (Fig. f). Inspection of spatial maps (not shown) indicate that this is due to decreased SW flux over Europe and the eastern USA, despite the reduced OC emissions in these regions. This may be due to natural variability in cloud cover over these regions driven by changes in atmospheric circulation patterns. The TOA SW flux change from the OC emissions perturbation seems to be much weaker in ECHAM-HAM than in the other models, so natural variability may dominate.

The global mean precipitation changes in each model are consistent with their respective temperature responses: HadGEM and NorESM show an increase in global precipitation, while ECHAM-HAM shows a decrease (Fig. e). Despite the variation in temperature responses, all three models show a northward shift in the ITCZ (Fig. f). The changes in precipitation patterns are similar to those for the SO2 experiments but with weaker magnitude (compare Figs. c and e).

Overall the response to removal of anthropogenic OC emissions is an increase in surface temperature and precipitation, primarily in the Northern Hemisphere. The spatial patterns of changes in these quantities are broadly similar to those for the SO2 emissions perturbation, but with smaller magnitude.