1. Introduction
Aerosols influence the climate in a number of ways that constitute a large source of uncertainty in climate models (Flato et al. 2013). Light-absorbing aerosols, such as black carbon (BC), influence climate differently than most other anthropogenic aerosols, which predominantly scatter sunlight. BC absorbs solar radiation efficiently, causing a positive aerosol direct effect and a warming of the atmosphere (e.g., Bond et al. 2013; Ramanathan and Carmichael, 2008). Atmospheric absorption and scattering both reduce solar radiation at the surface (Ramanathan et al. 2001b), which may influence the rate of evaporation and rainfall (Ming et al. 2010). By mixing with hygroscopic particles, BC can also change cloud properties such as increasing the number and decreasing the size of cloud droplets, and increase the lifetime of clouds. These are indirect aerosol effects (Albrecht 1989; Twomey 1977). In this way, BC can also contribute to a cooling effect on climate (Ramanathan et al. 2001a). Furthermore, BC deposited and accumulated on sea ice and snow reduces the surface albedo, which may lead to a surface warming (Warren and Wiscombe 1980; Hansen and Nazarenko 2004; Flanner et al. 2007).
In addition to the direct and indirect effects, the immediate local temperature increase brought about by absorption of solar radiation may cause BC to efficiently perturb relative humidity, static stability, and the local and vertical profile of cloudiness and cloud water. These are referred to as semidirect effects (e.g., Koch and Del Genio 2010), which may be positive or negative (Ackerman et al. 2000; Hansen et al. 2000; Lohmann and Feichter 2001). The net effect on clouds often depends on the vertical profile of the atmospheric heating. Upper-level BC may cause evaporation of local upper-level cloud water and the associated increased static stability may reduce the vertical extension of deep convective clouds while increasing lower-level stratiform clouds (Penner et al. 2003; Johnson et al. 2004; Yoshimori and Broccoli 2008; Allen and Sherwood 2010).
Several studies have investigated the climate response of adding absorbing aerosols at different altitudes (Hansen et al. 1997; Hansen et al. 2005; Ming et al. 2010; Hwang et al. 2011; Ban-Weiss et al. 2012). Ban-Weiss et al. (2012) injected prescribed BC aerosols in different layers in a general circulation model and found that BC near the surface caused surface warming due to the absorption of solar energy, while BC in the upper troposphere and in the stratosphere cooled the surface. The absorbed solar energy by BC at high altitudes was rapidly lost to space by increased longwave (LW) radiation due to the local temperature increase, and without heating the lower layers. This altitude dependence also influenced the hydrological cycle. While BC injected close to the ground increased precipitation, BC added at upper levels decreased precipitation. Ming et al. (2010) found similar results and showed that suppression of precipitation due to heating in the free troposphere can outweigh the enhanced precipitation due to of surface warming, resulting in a net decrease in precipitation.
A simple conceptual measure of how an anthropogenic or natural process may influence the global climate is the equilibrium climate sensitivity (ECS). ECS relates a global estimate of the net energy input to the Earth system (the forcing) to the difference in global average surface air temperatures between equilibrium climates before and after the forcing is imposed (e.g., Boucher et al. 2013). The radiative forcing (RF) of BC and other climate agents is commonly defined by the radiation imbalance at the top of the atmosphere (TOA), the “instantaneous forcing,” before any change in climate variables or rapid responses in the troposphere, stratosphere, and land surface (Hansen et al. 1997). The indirect, semidirect, and surface albedo effects of BC can be viewed as rapid responses in the sense that they occur before any significant change in surface temperatures (Gregory et al. 2004; Andrews et al. 2010; Bala et al. 2010). Given the range of fast tropospheric responses (Andrews and Forster 2008) to increased atmospheric BC, the instantaneous forcing is clearly not a fully valid measure of how BC may influence climate (Hansen et al. 1997; Cook and Highwood 2004; Hansen et al. 2005), and the globally averaged surface temperature is not an adequate measure of its climate response (Hansen et al. 1997; Penner et al. 2003). To keep ECS as a fruitful concept, the TOA RF has been largely replaced by an effective radiative forcing (ERF) in the latest IPCC report (Boucher et al. 2013). ERF includes rapid feedbacks. In this paper we have not calculated the ERF in the feedback analysis, yet it is important to bear in mind that fast feedbacks are very important for BC-driven climate impacts. Also CO2-driven forcing incurs fast feedbacks (Gregory and Webb 2008; Andrews and Forster 2008), although a considerably smaller fraction than caused by BC.
This study includes numerical experiments that emphasize the importance of fast responses to changes in BC concentrations. They are designed to discuss how BC may impact the climate in a different manner than greenhouse gases (CO2), as well as to discuss the role of possible feedbacks between airborne particulate BC and climate processes influencing the atmospheric distribution of BC. The study is entirely based on one single, yet fully coupled, global climate (Earth system) model, the Norwegian Earth System Model (NorESM; Bentsen et al. 2013; Iversen et al. 2013), which includes an advanced module for aerosols and aerosol–cloud–radiation interactions (Kirkevåg et al. 2013). In the experiments BC emissions from fossil and biofuel combustion are multiplied by a factor of 25. This (unrealistically large) factor is chosen to obtain an instantaneous TOA RF comparable to that obtained by a quadrupling of the CO2 concentrations in air (7 W m−2). A parallel experiment with prescribed BC concentrations is made to distinguish, to first order, climate feedbacks that alter the BC concentrations calculated from emissions. These prescribed concentrations are calculated from a 10-yr offline run without feedbacks to the dynamic and physical climate processes. The two BC experiments are performed as analogs (in terms of the instantaneous TOA RF) to the standard abrupt 4×CO2 experiment used in CMIP5.
A few studies with similarities to ours have been published in recent years. Allen and Sherwood (2011) used a low-resolution (T42) version of the NCAR CAM3 atmospheric model coupled to a slab ocean or run with prescribed SSTs. Impacts of anthropogenic aerosols (not only BC) were introduced by importing forcing fields based on observations, and with all aerosol forcing uniformly distributed in the lower 3 km of the troposphere. The study found a significant effect on the cloud cover, mostly due to a vertical redistribution of heating influenced by the vertical profile of the prescribed forcing.
Randles et al. (2013) studied the aerosol–climate response by using prognostic aerosols and prescribed aerosols from a climatology in the NASA GEOS5 model. The study did not address BC in particular. They found that the feedback of meteorology on the aerosol distribution can significantly impact the climate response, especially in regions remote from major emissions. Mahajan et al. (2013) conducted experiments with a range of prescribed levels of BC concentrations in the CAM4 model coupled to a slab ocean model. Their experiments were entirely concentration driven, and thus without interactive feedbacks between climate processes and BC. They found a linear relationship between BC-driven TOA RF and surface temperature response.
In this study we have estimated climate feedbacks associated with physical processes by applying a feedback radiative kernel method. This method, along with the model experiments, is described in section 2. The climate responses and feedbacks are presented in section 3. The differences between the two BC runs are discussed in more detail in section 4 and a summary and conclusions are given in section 5.
2. Methods and data
a. NorESM
The version of NorESM used in this study is the NorESM1-M developed and applied for CMIP5 (Bentsen et al. 2013, Iversen et al. 2013). It is largely based on the Community Climate System Model CCSM4.0 (Gent et al. 2011) developed by the National Center for Atmospheric Research. The system couples (by CPL7) the atmospheric component (CAM4-Oslo; Kirkevåg et al. 2013) which includes online aerosol–cloud–radiation interactions, with a land model (CLM4), a dynamic-thermodynamic sea ice model (CICE4), and an ocean model with isopycnic coordinate surfaces [a considerably developed version of the Miami Isopycnic Coordinate Ocean Model (MICOM); Bentsen et al. 2013]. The ocean model also includes a module for ocean carbon cycling [Hamburg Ocean Carbon Cycle (HAMOCC); Tjiputra et al. 2013]. The sea ice model and the land model in NorESM are the same as in CCSM4.0, except that the deposition of BC and mineral dust aerosols onto snow and sea ice are given by CAM4-Oslo instead of using precalculated deposition fields. The resolution in CAM4-Oslo is 1.9° latitude and 2.5° longitude with 26 levels in the vertical with a hybrid sigma-pressure coordinate and model top at 2.917 hPa, and the time step is 1800 s.
The aerosols in CAM4-Oslo are sea salt (SS), mineral dust, particulate sulfate (SO4), BC, and particulate organic matter (OM), in addition to the gaseous aerosol precursors dimethyl sulfide (DMS) and sulfur dioxide (SO2). The life cycle scheme calculates mass concentrations of 20 aerosol components, which are tagged according to production mechanisms in clear and cloudy air, and there are up to four size modes for each of these (nucleation, Aitken, accumulation, and coarse mode). The processes are gas phase and aqueous phase chemical production, gas to particle nucleation, condensation on preexisting aerosol surfaces, and coagulation of smaller particles onto preexisting Aitken, accumulation, and coarse mode particles. Water is mixed into the particles based on their hygroscopicity and the ambient relative humidity. Thus a range of internal and external particle mixtures is calculated. Look-up tables for size-distribution parameters for cloud droplet activation and for optical properties are precalculated, and interpolation between predefined input values enables CAM-Oslo to estimate the aerosol direct effect and the first and second aerosol indirect effects associated with pure water (warm) clouds.
The “present day” emissions of aerosols and precursor gases are taken from the IPCC AR5 datasets (Lamarque et al. 2010) and are valid for the year 2000. BC primary particles from fossil fuel and biofuel combustion are emitted as externally mixed large nucleation (12-nm modal radius with a lognormal distribution) and accumulation mode particles. BC from biomass burning is assumed internally mixed with OM in an Aitken mode (40 nm). Externally mixed BC is assumed hydrophobic, but transforms into hydrophilic when internally mixed by condensation of gaseous sulfate or by coagulation with any hydrophilic particles. BC is removed from the atmosphere by dry and wet deposition. The dry deposition velocity depends on particle size, which is influenced by the relative humidity for hygroscopic particles. The wet deposition is calculated in full integration with the cloud and precipitation schemes. The local precipitation production rate determines the rainout.
The aerosols and their interaction with clouds are described in more detail in Kirkevåg et al. (2013). Here we only note that processes in convective clouds may efficiently scavenge aerosol mass and contribute to rapid vertical transport of a minor fraction. The air volume available for convective scavenging and vertical transport is available directly from the parameterization. The dilute plume approximation in the convective clouds calculation, permits detrainment at all level (Gent et al. 2011). Mixing of aerosols between updrafts and downdrafts in convective clouds are not accounted for, however, which may contribute to overestimated concentrations in the free troposphere (e.g., Kirkevåg et al. 2013; Samset et al. 2013; Allen and Landuyt 2014).
The semidirect effect is calculated online in the model, but as heating of air and associated increases in water vapor saturation pressures are involved, this effect is counted as a response and is not included in the estimates of either the instantaneous TOA forcing or any effective RF. Calculated in this way, BC may therefore influence climate even without a net TOA forcing or a change in the surface air temperature.
Many of the climate properties of NorESM are evaluated by Bentsen et al. (2013) and Iversen et al. (2013), while the aerosol modeling is thoroughly presented and discussed by Kirkevåg et al. (2013). Some aspects of the BC modeling in NorESM compared to other global models are discussed by Samset et al. (2013).
b. Model experiments
Four sets of experiments were initiated from a 700-yr preindustrial (year 1850) spinup and ran for 150 years each. The first run is a control run with preindustrial levels of greenhouse gases (GHG) and aerosols (CTRL). The second run is the instantaneous quadrupling of CO2 concentrations compared to preindustrial levels (4×CO2). The third and fourth runs are inspired by 4×CO2, but concern BC. In the emission-driven experiment the fossil fuel and biofuel emissions of BC for year 2000 were multiplied by 25 (25×BCem). This factor was chosen to achieve a similar instantaneous TOA forcing averaged over the first year (7.0 W m−2) as in the abrupt 4×CO2 experiment (relative to preindustrial levels). The actual TOA forcing after multiplying with 25 is 7.3 W m−2. In the concentration-driven experiment, monthly mean 25×BC concentrations were prescribed as a perpetual annual cycle during the integration (25×BCconc). The prescribed concentrations were taken from averages over years 8–10 of a 10-yr run with 25×BC emissions in which the BC concentrations did not influence the climate dynamics or physics (offline calculation; see below). In the concentration-driven experiment, BC could influence the climate, but no BC-induced climate change could influence the BC concentrations.
The reason for taking the BC concentrations from offline BC calculations instead of from the already available run 25×BCem [which would be similar to Randles et al. (2013)] is that the BC from 25×BCem is already strongly influenced by the changes in climate variables forced by the airborne BC. With our approach, we secure that the BC in the concentration-driven run is no way influenced by such changes in climate variables. In the emission-driven run, however, the changes in climate influence the BC fields, which then again influence the climate and so on, since feedbacks are allowed.
The difference in climate variables (and BC) between the emission-driven and concentration-driven runs is thus a valid estimate of these feedback effects. Climate statistics are estimated over the years 121–150. A summary of the climate simulations is given in Table 1.
Short description of the experiments in this study.
To calculate the direct RF of 25×BCem in order to find the scaling factor of 25, two separate 10-yr offline simulations were performed with the same experimental setup as 25×BCem and CTRL, respectively, but with no climate adjustments to the calculated BC. In these offline runs, the meteorological fields were calculated in the model and were identical in both simulations following the method described in detail in Kirkevåg et al. (2013). The RF was then calculated as the difference in the net instantaneous radiation fluxes at TOA between 25×BCem and CTRL.
c. Feedback radiative kernel method
It should be noted that all responses in feedback variables are calculated with respect to the change in global mean surface temperature. Black carbon causes changes in some atmospheric climate variables with relatively minor changes in surface temperature compared to CO2. These changes mainly takes place during a fraction of the first year and are therefore called fast (or rapid) feedbacks. Since the kernels of Soden et al. (2008) link all changes to changes in surface temperatures, the results for BC may appear confusing when the sensitivities are compared with those of CO2 (e.g., Gregory and Webb 2008; Andrews and Forster 2008). Even though fast feedbacks may dominate the response to BC-driven forcing, this is not evident a priori, and the feedbacks involving the surface temperature, the evaporation from the ground surface, and warming of the oceans are potentially important. The results of the feedback analysis are presented in section 3d below.
3. Results
Major aspects of the climate response to 4×CO2, 25×BCem, and 25×BCconc relative to CTRL are presented and compared. We emphasize that the BC concentrations in 25×BCem and 25×BCconc do not have identical statistics such as monthly averages; see details in sections 2 and 5. A summary of the global mean climate responses is presented in Table 2.
Summary of annual mean climate response to 4×CO2, 25×BCem, and 25×BCconc relative to CTRL. The responses are averaged over the last 30 years of the 150-yr simulations. The numbers in brackets are interannual standard deviations.
a. Surface air temperature
Figure 1 shows the 150-model-year-long time series of the global surface temperature response. Initial rapid increases are evident for all runs, but while the CO2-induced response continues to increase over the entire period, the BC-induced temperature increase levels off after it reaches ~1.2 K (25×BCem) and ~0.8 K (25×BCconc) after less than 10 years. The geographical distribution of the annual mean response in surface air temperatures for the three experiments averaged over the last 30 simulation years in shown in Fig. 2. For 4×CO2 there is a global mean warming of 4.3 K, with an Arctic amplification concurrent with a decrease in the sea ice cover (not shown). There are also clear signs of more warming over the continents than over the ocean. The temperature increase is considerably smaller in all regions of both BC-driven runs, but the difference between the Northern and Southern Hemispheres is more evident. The largest warming is seen over Northern Hemisphere (NH) continents with a secondary maximum in the high Arctic. As opposed to the situation for CO2, cooling of surface air is estimated over considerable regions, mainly over high-latitude oceans in the Southern Hemisphere (SH). For the concentration-driven 25×BCconc run the temperature response is only 0.8 K globally, with the largest warming in the Arctic regions.
Global average surface temperature response (in K) for 25×BCem (blue), 4×CO2 (red), and 25×BCconc (green). The line denotes the 5-yr running mean. The tick marks on the x axis represent January each 10th model year.
Citation: Journal of Climate 28, 6; 10.1175/JCLI-D-14-00050.1
Change in annual mean surface air temperature (in K) for (left) 4×CO2, (middle) 25×BCem, and (right) 25×BCconc compared to CTRL, averaged over the last 30 model years. White areas are not significant on a 95% confidence level.
Citation: Journal of Climate 28, 6; 10.1175/JCLI-D-14-00050.1
A large part of the SW absorption in 25×BCem is counteracted by the LW radiative cooling in the atmosphere, which is brought about by the evident atmospheric temperature responses seen in Figs. 2 and 3. For 25×BCem the warming is largest in the upper troposphere/lower stratosphere. The 25×BCconc experiment does not show such a large atmospheric warming. A warming pattern similar to Arctic amplification is evident in 25×BCconc and 4×CO2, possibly linked to a decrease in the sea ice fraction, which is larger for 25×BCconc compared to 25×BCem (not shown).
Annual average of the zonal mean atmospheric temperature response (in K) for (left) 4×CO2, (middle) 25×BCem, and (right) 25×BCconc compared to CTRL, averaged over the last 30 model years.
Citation: Journal of Climate 28, 6; 10.1175/JCLI-D-14-00050.1
b. Energy fluxes
Figure 4 shows the responses in annually averaged global mean energy fluxes at the top of the model atmosphere and at the ground surface. As seen in Table 2, after a few decades, increased CO2 leads to a positive net TOA radiative flux (1.8 W m−2) that persistently contributes to heating of the Earth system. The net positive flux is a result of a positive net shortwave (SW) and a negative but smaller net LW radiative flux. The fluxes for 25×BCem are much larger, but the positive net fluxes for SW radiation are considerably closer to being balanced by the negative LW radiation. The seasonal variations are in agreement with the seasonal cycle of solar insolation in the NH where BC concentrations dominate (not shown), but they average to almost 0.3 W m−2 annually.
Annual averages of the global mean energy budget at the (top) TOA and (bottom) surface for (left) 25×BCem and (right) 4×CO2; SW indicates net (downward) shortwave radiation and LW net (downward) longwave radiation. Downward turbulent fluxes of sensible and latent heat are shown at the surface. Black solid lines are the net downward fluxes of energy.
Citation: Journal of Climate 28, 6; 10.1175/JCLI-D-14-00050.1
The response in energy fluxes at the surface includes turbulent fluxes of sensible and latent heat. For the 25×BCem run, the huge negative response in the flux of SW radiation is caused by the absorption by BC in the atmosphere and possibly changes in total cloudiness. This flux is almost entirely compensated by the increased positive fluxes of LW radiation, sensible heat, and latent heat. While the response driven by the long-lived greenhouse gas CO2 produces a net downward flux of energy into the ground surface of 1.7 W m−2 annually after 150 years, the BC-driven response has a vanishing surface energy flux already after the first decade. For the concentration-driven 25×BCconc (not shown), the results for the net energy fluxes are similar to the emission-driven runs. The different components also have the same sign, but with considerably smaller amplitudes in agreement with the smaller upper tropospheric BC burdens. Table 2 shows that the net downward surface flux is 0.2 W m−2 for emission-driven and 0.3 W m−2 for concentration-driven runs averaged over the last 30 years.
In consequence of the net surface energy fluxes, the CO2-driven climate change involves deep oceans (which is already well known), while the BC-driven climate change does not, or at least to a considerably smaller (and negligible) degree. This is clearly evident from Fig. 1.
It is also worth noting that while the CO2-driven response produces a net evaporation into the atmosphere from the ground (negative latent heat flux), the BC-driven response produces a net removal of water vapor from the atmosphere deposited on the ground surface (positive latent heat flux). These changes are in agreement with the calculated precipitation response.
c. Clouds and precipitation
The net cloud forcing (SW+LW) is negative (i.e., contributes to a cooling effect) for all experiments due to a large negative LW cloud forcing offsetting the positive SW cloud forcing (−1.0 W m−2 for 4×CO2, −2.8 W m−2 for 25×BCem, and −0.5 W m−2 for 25×BCconc); see Table 2. Low-level cloudiness increases in all experiments. The CO2-induced response for low-level cloudiness is slightly positive (+1.4%) because there is a large increase in Arctic cloud cover, possibly due to reduced sea ice in the Arctic (see Fig. 5). The increase in low-level cloudiness for BC (+4.4%) is mostly seen over oceans. The resulting change in global SW cloud forcing is positive for both experiments: +1.3 and +0.4 W m−2 for BC and CO2, respectively. The reason why the SW cloud forcing is positive is partly due to reductions in midlevel cloudiness and liquid water path above 700 hPa. High-level cloudiness is reduced for the BC experiments (−5.3% for 25×BCem and −1.2% for 25×BCconc).
Annual mean cloud cover change (in %) for (left) 4×CO2, (middle) 25×BCem, and (right) 25×BCconc compared to CTRL, for (top) high clouds and (bottom) low clouds, averaged over the last 30 model years. White areas are not significant on a 95% confidence level.
Citation: Journal of Climate 28, 6; 10.1175/JCLI-D-14-00050.1
The BC-induced decrease in high-level cloudiness counteracts some of the direct atmospheric warming by the absorbing aerosols. Ban-Weiss et al. (2012) found similar results for BC located above 4 km, and Randles et al. (2013) found similar results for the direct and semidirect effects of prognostic aerosols. An increased occurrence of marine stratocumulus clouds in combination with BC located above was found in Johnson et al. (2004). Whether the cloud feedback is a result of the direct effect of the aerosols and/or the changes in albedo or surface temperatures cannot be determined from this fully coupled experiment.
The emission-driven BC-induced response includes a northward shift in the ITCZ, which is evident from Fig. 6 and is in agreement with results found in other studies (Roberts and Jones 2004; Wang 2007). This shift is probably both caused by the general asymmetric distribution of fossil fuel BC combustion, which contributes to larger BC burdens in the NH, and the more local influences by increased BC concentrations, which absorb more solar radiation at low levels close to the ITCZ and in adjacent subtropical areas. This is seen, for example, in and to the north of Australia. The influence on the concentration-driven BC experiment shows much weaker signals in general for precipitation, but impacts on low-latitude precipitation and the ITCZ are apparent. In comparison, the impacts by CO2 are much more symmetric across the equator.
Annual mean precipitation response (in mm day−1) for (left) 4×CO2, (middle) 25×BCem, and (right) 25×BCconc compared to CTRL, averaged over the last 30 model years. White areas are not significant at a 95% confidence level.
Citation: Journal of Climate 28, 6; 10.1175/JCLI-D-14-00050.1
Precipitation is reduced in the NH extratropical storm tracks for 25×BCem (Fig. 6), contributing to increased atmospheric residence times of both the water substance in any phase and the aerosols. The annual global mean change in precipitation for 25×BCem is negative (−0.18 mm day−1). A decrease in precipitation due to BC has also been shown in other studies using slab ocean models (e.g., Yoshimori and Broccoli 2008; Mahajan et al. 2013; Ming et al. 2010). For 4×CO2 the global mean precipitation response is positive (+0.18 mm day−1). The CO2-induced net surface heating favors more evaporation and, hence, more precipitation (e.g., Allen and Ingram 2002; Trenberth 2011).
Allen et al. (2012) suggested that atmospheric heating from BC and tropospheric ozone at the midlatitudes may generate a poleward shift of the tropospheric jet and a tropical widening. In this study we also find a BC-induced poleward shift of the storm tracks. Actually, the BC response in the 500-hPa geopotential height bears a strong signal reminiscent of a positive-phase NAM pattern in NorESM, but with a considerable distortion of the pattern probably caused by the unrealistically large forcing implied by the factor 25 (not shown). This makes these aspects less suitable for analysis for our experiments. Allen and Sherwood (2011) found, however, similar results in a study with more realistic experiments.
d. Feedback analysis
The results from the radiative kernel method summarized in Table 3 emphasize why the model rapidly reestablish climate equilibrium for BC. The feedback parameter for a given process indicates how much the TOA radiative flux changes, due to the feedback process alone, per unit change in global average surface air temperature. With our sign convention, a negative (positive) feedback parameter indicates that the associated process counteracts (reinforces) the direct influence of the forcing. A negative total feedback is required to reestablish a new equilibrium.
Feedback parameters of the radiative kernel method for the three experiments relative to CTRL.
The Planck feedback is a dominating contributor to re-equilibration of all experiments (Table 3), but for the 25×BCem the lapse-rate feedback also contributes considerably to equilibration, while the cloud feedback in total is considerably smaller. The lapse-rate feedback is strongly influenced by the enhanced BC concentrations and thus the increased temperatures in the upper tropospheric and lower stratospheric levels in 25×BCem. This is in contrast with the concentration-driven experiment for which the BC concentrations are more confined to lower levels and kept invariant with respect to the climate change taking place. While the water vapor feedback is positive for the three experiments, the net cloud feedback is negative for the BC-driven experiments and positive for the CO2-driven. The emission-driven cloud feedback is a residual between a large and negative LW feedback and a large and positive SW feedback, also in agreement with the effects of the deep dispersion of BC in the emission-driven run.
The heating of the air due to absorption of sunlight by BC aerosols results in a strong negative lapse-rate feedback, which also can be seen in Fig. 3. This is often counteracted by the water vapor feedback (e.g., Soden and Held 2006; Soden et al. 2008; Gettelman et al. 2012; Vial et al. 2013), although this is not the case for the concentration-driven experiment. The sum of the water vapor and lapse-rate feedbacks shows a smaller difference between the simulations (1.0 W m−2 for 4×CO2, 0.0 for 25×BCem, 1.2 for 25×BCconc) than between the feedbacks separately, although these combined feedbacks for 25×BCem are neutral whereas it is positive for the other experiments. The large water vapor feedback for the 25×BCem simulation is due to the temperature increasing more with height than in the other simulations, with an increased capacity for holding water vapor (see also Table 2). In fact, the 25×BCem simulation reduces the global precipitation while 4×CO2 increases it (and 25×BCconc is neutral). The albedo feedback for the BC simulations is higher because in addition to effects due to the increase of surface temperature, there are effects of deposited BC on snow and ice.
The total values of the 4×CO2 simulation (−1.2 W m−2 K−1) are in range with or slightly lower than other findings [e.g., Soden et al. 2008 (−1.3 to −1.2 W m−2 K−1); Shell et al. 2008 (−1.5 W m−2 K−1); Previdi 2010 (−1.8 W m−2 K−1); and Vial et al. 2013 (−1.6 to −1.5 W m−2 K−1)] even though some studies include small changes in their calculations. The estimates of BC-driven responses are new in this paper. Overall it is clear that the smaller net feedback in 25×BCconc than in 4×CO2 does not translate into a smaller change in global surface air temperature. The feedback factor does not take into account that the fast response does not influence the surface air temperature. Since the fast responses are much larger for BC than CO2, the feedback factors are also smaller (i.e., sensitivities are larger) for BC for equal changes in surface air temperatures.
We have not calculated the total feedback factor independently; hence the sums presented in the lower row in Table 3 presume linearity. Both Shell et al. (2008) and Vial et al. (2013) estimated smaller effects of nonlinearity than 10% for CO2. We have not made specific tests to investigate if nonlinear effects can be larger for BC.
There are also large differences between the emission-driven (where climate change may change the BC concentrations which causes the change in the first place) and the concentration-driven BC experiments. It should be noted there that the considerably larger differences in our experiments than found by, for instance, Randles et al. (2013) are due to the differences in experimental design. While Randles et al. (2013) used concentrations produced by the online BC calculations, we have used concentrations from an offline model.
The net feedback for the concentration-driven run is much smaller than for the emission-driven run, even though the resulting surface air temperature response is smaller for the concentration-driven run (Fig. 1). This apparent inconsistency can be a result of nonlinear effects, but it is also influenced by the fact that the missing feedback between BC and climate processes in the concentration-driven experiment implies that the fast feedbacks constitute a larger fraction of the total feedbacks than for the emission-driven run. These aspects need to be investigated in further studies, and complemented with studies using models that are less efficient than NorESM in distributing BC and other aerosols vertically. According to the study by Samset et al. (2013), the presently used NorESM is likely too efficient in transporting BC into the upper troposphere. Also studies with smaller multiplication factors for BC (i.e., smaller than 25) are recommended.
4. Discussion
A key to the difference between the climate responses to forcing driven by BC and CO2 is the rapid response which in a different manner compensates the added energy input. The instantaneous RF for an abrupt quadrupling of atmospheric CO2 is independently estimated at 7.0 W m−2 (Kay et al. 2012). By using the Gregory et al. (2004) linear regression, the SW+LW fluxes at TOA are estimated at 6.3 W m−2. This ~10% smaller value is interpreted as being due to the fast feedbacks that do not involve changes in the average surface temperature, and takes place over time periods much shorter than a year.
The fast feedbacks for the BC experiments are considerably larger. Furthermore, the full climate response produces a new quasi-equilibrium already after 1–2 decades and a linear regression between TOA fluxes and surface temperature changes is not as suitable as it is for CO2. Instead, we estimate the fast feedbacks as the difference between the TOA net radiative flux from the run with BC concentrations calculated offline (7.3 W m−2 annually) and the net value (from the coupled online run) of the TOA net radiative flux averaged over the first year (1.7 W m−2). Figure 1 furthermore indicates that the emission-driven impact of BC on the surface air temperature reaches equilibrium after only a few years, when the surface air temperature response fluctuates around 1.2 K (0.8 K for the concentration-driven run). This suggests that instantaneous forcing is a poor first indicator of the global climate response represented by global mean surface air temperature. It has further been shown by several studies that the nature of the forcing agent is important in predicting the climate’s surface and hydrological response (e.g., Hansen et al. 1997; Allen and Sherwood 2011; Yoshimori and Broccoli 2008; Ming et al. 2010; Shiogama et al. 2010).
There are differences in BC distribution between the emission-driven (25×BCem) and the concentration-driven (25×BCconc) runs; see Fig. 7. As described in section 2b, the BC concentrations in 25×BCconc were taken from an offline calculation of BC concentrations. Three aspects of the relation between the prescribed BC and the calculated emission-driven BC are important (Figs. 7a,b): the lower prescribed concentrations in the upper troposphere and in the stratosphere, the lower prescribed concentrations in the NH midlatitudes, and the higher prescribed concentrations in the NH subtropics and at lower levels in the Arctic. These differences can be linked to the BC-induced changes in the emission-driven run regarding precipitation and wet deposition of BC, and the mass transport by updrafts in the deep convective clouds of the ITCZ; see Fig. 7. Figure 7c shows that the ITCZ is displaced northward in the BC-driven run, which can be expected since the BC emissions predominantly takes place in the NH and the absorption of solar radiation by the BC leads to more warming in the Northern than in the Southern Hemisphere. The northward displaced precipitation in the ITCZ leads to more wet deposition (Fig. 7d) and more efficient vertical transport on the convective updrafts (Figs. 7e,f), as well as reduced BC concentrations in the lower levels in the NH subtropics.
Ratio between the emission-driven online run (25×BCem) and the offline run (offl) for annual mean (a) BC total column burden, (b) BC zonal mean concentrations, (c) precipitation, (d) BC wet deposition, (e) convective mass flux (649 hPa), and (f) zonal mean convective mass flux. Note that the BC values for the offline run and the concentration-driven run are identical. The values for 25×BCem are the 30-yr mean, while values for the offline run are the 3-yr mean (of total 10-yr run).
Citation: Journal of Climate 28, 6; 10.1175/JCLI-D-14-00050.1
The northward shift of ITCZ is particularly relevant in South and Southeast Asia where there are large emissions of BC. In addition to increased wet removal of BC from the emissions in the region, the efficient vertical transport in convective updrafts brings a larger fraction of this emitted BC into the upper levels in the troposphere. Even though this is a minor fraction of the emitted BC, once it is brought to the upper troposphere its atmospheric residence time is considerably increased, and it spreads to higher latitudes with the meridional advection and eddies. Once reaching higher latitudes at upper levels, the direct absorption of sunlight partly heats the air, stabilizes the column, and partly evaporates upper-level clouds. This explains why precipitation is smaller at midlatitudes (Fig. 7c). The reduced concentrations in the emission-driven run in the Arctic are probably linked to higher convective activity with increased vertical mass transport and wet deposition, associated with a decrease in the Arctic sea ice extent.
It should be noted that NorESM is among the more efficient models in distributing aerosols (including BC) vertically (Samset et al. 2013; Allen and Landuyt 2014). From the vertical profiles of BC discussed by Samset et al. (2013), there are reasons to believe that the presently used CMIP5 version of NorESM has too much BC in the free troposphere. Allen and Landuyt (2014) also argue that most CMIP5 models overestimate the convective mass fluxes, and in particular in the upper levels of the troposphere. Sensitivity experiments in Kirkevåg et al. (2013) point to the efficient vertical transport in deep convective clouds and the assumed fraction of hydrophobic fossil fuel BC emitted in the accumulation mode as important contributors to this.
Although we have not investigated the importance of this efficient, it is likely that fast feedbacks, as well as feedbacks changing the surface temperature, are enhanced by the elevated BC layers. It is difficult to say to what extent the fast feedbacks in our calculations are exaggerated, or heat fluxes into the oceans are underestimated, without performing sensitivity experiments. Ocko et al. (2014) performed an experiment with different assumptions for the vertical distribution of BC, but with entirely concentration-driven experiments. Confining the BC to lower levels only, the TOA RF was more than halved while the global surface air temperature increased by ~50%. This indicates that fast feedbacks are sensitive to the vertical profile of BC.
The increased upper-level BC in the emission-driven run contributes to further absorption of solar radiation there, which influences the northward displacement of the ITCZ. The increased upper tropospheric BC also tends to warm the upper troposphere locally and destabilize the lower stratosphere with increased convective updrafts in the upper troposphere at low latitudes (Fig. 7e). The tropopause tends to rise higher, and thus support a further vertical mixing of BC and further absorption of solar radiation. These positive feedbacks are eventually counteracted by the semidirect effects on tropospheric clouds.
Another sensitivity experiment which was performed by Kirkevåg et al. (2013) produced a considerably larger BC burden when the run included feedbacks between aerosol concentrations and the climate variables than when they were not included. This resulted in increased atmospheric residence times of present-day BC from 8.3 to 9.5 days. That preliminary result is in agreement with the results in this paper; however, the large multiplication factor (25) for the fossil fuel BC emissions further exacerbates the importance of the feedbacks, with extended residence time and deeper vertical dispersion of BC, and eventually a considerably enhanced impacts on the climate.
5. Summary and conclusions
The two experiments 4×CO2 and 25×BCem are designed to yield the same instantaneous RF at TOA (7 W m−2). However, 25×BCem leads to much less surface warming than 4×CO2 and fast feedbacks are much more evident for BC that for CO2. After the adjustments during the first few model years, the temperature response to BC forcing is in equilibrium, while for CO2 it continues to increase due to a net downward heat transport to the ocean.
Our study includes both emission-driven and concentration-driven BC runs with a fully coupled global climate model. The results show that the slow climate response associated with heating of the world oceans is considerably smaller than for CO2-driven forcing, due to vanishing energy fluxes into the ocean. The differences between concentration-driven and emission-driven runs, however, emphasize that feedbacks between dynamics and BC are crucial. When atmospheric dynamics and physical processes are allowed to change the BC distribution (in the emission-driven run), the feedbacks cause more BC in the upper troposphere and lower stratosphere, where it may efficiently influence the climate. Here, we need to emphasize that our radical upscaling of BC emissions by a factor of 25 may exaggerate this feedback. Nevertheless, our experiments demonstrate that simply prescribing BC concentrations may lead to seriously inaccurate conclusions.
The feedbacks we have analyzed point to the vertical profile of the resulting BC distribution as important. The BC vertical profile causes efficient absorption of solar radiation at high levels and swift Planck and lapse-rate feedbacks, as well as feedbacks associated with the changed vertical profile of clouds and the cloud feedbacks.
The results concerning the fast attainment of new equilibria after a BC-driven change, as well as the importance of feedbacks between BC and climate processes, emphasize the need of further experiments like this with Earth system models. The model we have used (NorESM) is efficient in transporting BC (and other aerosols) vertically. Other models with less efficient vertical transport are likely to produce results with smaller differences between concentration-driven and emission-driven results. The relative importance of fast feedbacks is probably sensitive to the vertical profile of BC for which vertical transport in deep convective clouds is crucial, and this may be a key factor in obtaining the quick re-equilibration seen in the present experiments. Allen and Landuyt (2014) point to convective mass fluxes as likely too efficient generally in the CMIP5 global climate models.
We have applied a large upscaling factor (25) to obtain an initial TOA RF comparable to quadrupling of CO2. Experiments with smaller factors [e.g., as suggested by Mahajan et al. (2013)] should be pursued. We encourage that multimodel experiments similar to those presented in this paper be carried out during the upcoming phase 6 of the Coupled Model Intercomparison Project (CMIP6).
Acknowledgments
This work was supported by the Norwegian Research Council through the EarthClim and EVA projects, the Norwegian Meteorological Institute, the Programme for Supercomputing (NOTUR), NCoE CRAICC, and the EU FRP7 projects PEGASOS and ACCESS. We thank two anonymous reviewers and Dr. Robert J. Allen for very constructive comments and suggestions that greatly improved this paper.
REFERENCES
Ackerman, A. S., O. B. Toon, D. E. Stevens, A. J. Heymsfield, V. Ramanathan, and E. J. Welton, 2000: Reduction of tropical cloudiness by soot. Science, 288, 1042–1047, doi:10.1126/science.288.5468.1042.
Albrecht, B. A., 1989: Aerosols, cloud microphysics, and fractional cloudiness. Science, 245, 1227–1230, doi:10.1126/science.245.4923.1227.
Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 224–232, doi:10.1038/nature01092.
Allen, R. J., and S. C. Sherwood, 2010: Aerosol–cloud semi-direct effect and land–sea temperature contrast in a GCM. Geophys. Res. Lett., 37, L07702, doi:10.1029/2010GL042759.
Allen, R. J., and S. C. Sherwood, 2011: The impact of natural versus anthropogenic aerosols on atmospheric circulation in the Community Atmosphere Model. Climate Dyn., 36, 1959–1978, doi:10.1007/s00382-010-0898-8.
Allen, R. J., and W. Landuyt, 2014: The vertical distribution of black carbon in CMIP5 models: Comparison to observations and the importance of convective transport. J. Geophys. Res. Atmos., 119, 4808–4835, doi:10.1002/2014JD021595.
Allen, R. J., S. C. Sherwood, J. R. Norris, and C. S. Zender, 2012: Recent Northern Hemisphere tropical expansion primarily driven by black carbon and tropospheric ozone. Nature, 485, 350–354, doi:10.1038/nature11097.
Andrews, T., and P. M. Forster, 2008: CO2 forcing induces semi-direct effects with consequences for climate feedback interpretations. Geophys. Res. Lett., 35, L04802, doi:10.1029/2007GL032273.
Andrews, T., P. M. Forster, O. Boucher, N. Bellouin, and A. Jones, 2010: Precipitation, radiative forcing and global temperature change. Geophys. Res. Lett., 37, L14701, doi:10.1029/2010GL043991.
Bala, G., K. Caldeira, and R. Nemani, 2010: Fast versus slow response in climate change: Implications for the global hydrological cycle. Climate Dyn., 35, 423–434, doi:10.1007/s00382-009-0583-y.
Ban-Weiss, G., L. Cao, G. Bala, and K. Caldeira, 2012: Dependence of climate forcing and response on the altitude of black carbon aerosols. Climate Dyn., 38, 897–911, doi:10.1007/s00382-011-1052-y.
Bentsen, M., and Coauthors, 2013: The Norwegian Earth System Model, NorESM1-M—Part 1: Description and basic evaluation of the physical climate. Geosci. Model Dev., 6, 687–720, doi:10.5194/gmd-6-687-2013.
Bond, T. C., and Coauthors, 2013: Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos., 118, 5380–5552, doi:10.1002/jgrd.50171.
Boucher, O., and Coauthors, 2013: Clouds and aerosols. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 571–657.
Cook, J., and E. J. Highwood, 2004: Climate response to tropospheric absorbing aerosols in an intermediate general-circulation model. Quart. J. Roy. Meteor. Soc., 130, 175–191, doi:10.1256/qj.03.64.
Flanner, M., C. Zender, J. Randerson, and P. Rasch, 2007: Present-day climate forcing and response from black carbon in snow. J. Geophys. Res., 112, D11202, doi:10.1029/2006JD008003.
Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866.
Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 4973–4991, doi:10.1175/2011JCLI4083.1.
Gettelman, A., J. E. Kay, and K. M. Shell, 2012: The evolution of climate sensitivity and climate feedbacks in the Community Atmosphere Model. J. Climate, 25, 1453–1469, doi:10.1175/JCLI-D-11-00197.1.
Gregory, J., and M. Webb, 2008: Tropospheric adjustment induces a cloud component in CO2 forcing. J. Climate, 21, 58–71, doi:10.1175/2007JCLI1834.1.
Gregory, J., and Coauthors, 2004: A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett., 31, L03205, doi:10.1029/2003GL018747.
Hansen, J., and L. Nazarenko, 2004: Soot climate forcing via snow and ice albedos. Proc. Natl. Acad. Sci. USA, 101, 423–428, doi:10.1073/pnas.2237157100.
Hansen, J., M. Sato, and R. Ruedy, 1997: Radiative forcing and climate response. J. Geophys. Res., 102, 6831–6864, doi:10.1029/96JD03436.
Hansen, J., M. Sato, R. Ruedy, A. Lacis, and V. Oinas, 2000: Global warming in the twenty-first century: An alternative scenario. Proc. Natl. Acad. Sci. USA, 97, 9875–9880, doi:10.1073/pnas.170278997.
Hansen, J., and Coauthors, 2005: Efficacy of climate forcings. J. Geophys. Res., 110, D18104, doi:10.1029/2005JD005776.
Hwang, Y. T., D. M. W. Frierson, and J. E. Kay, 2011: Coupling between Arctic feedbacks and changes in poleward energy transport. Geophys. Res. Lett., 38, L17704, doi:10.1029/2011GL048546.
Iversen, T., and Coauthors, 2013: The Norwegian Earth System Model, NorESM1-M—Part 2: Climate response and scenario projections. Geosci. Model Dev., 6, 389–415, doi:10.5194/gmd-6-389-2013.
Johnson, B. T., K. P. Shine, and P. M. Forster, 2004: The semi-direct aerosol effect: Impact of absorbing aerosols on marine stratocumulus. Quart. J. Roy. Meteor. Soc., 130, 1407–1422, doi:10.1256/qj.03.61.
Kay, J. E., M. M. Holland, C. M. Bitz, E. Blanchard-Wrigglesworth, A. Gettelman, A. Conley, and D. Bailey, 2012: The influence of local feedbacks and northward heat transport on the equilibrium Arctic climate response to increased greenhouse gas forcing. J. Climate, 25, 5433–5450, doi:10.1175/JCLI-D-11-00622.1.
Kirkevåg, A., and Coauthors, 2013: Aerosol–climate interactions in the Norwegian Earth System Model—NorESM1-M. Geosci. Model Dev., 6, 207–244, doi:10.5194/gmd-6-207-2013.
Koch, D., and A. D. Del Genio, 2010: Black carbon semi-direct effects on cloud cover: Review and synthesis. Atmos. Chem. Phys., 10, 7685–7696, doi:10.5194/acp-10-7685-2010.
Lamarque, J., and Coauthors, 2010: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application. Atmos. Chem. Phys., 10, 7017–7039, doi:10.5194/acp-10-7017-2010.
Lohmann, U., and J. Feichter, 2001: Can the direct and semi-direct aerosol effect compete with the indirect effect on a global scale? Geophys. Res. Lett., 28, 159–161, doi:10.1029/2000GL012051.
Mahajan, S., K. J. Evans, J. J. Hack, and J. E. Truesdale, 2013: Linearity of climate response to increases in black carbon aerosols. J. Climate, 26, 8223–8237, doi:10.1175/JCLI-D-12-00715.1.
Ming, Y., V. Ramaswamy, and G. Persad, 2010: Two opposing effects of absorbing aerosols on global-mean precipitation. Geophys. Res. Lett., 37, L13701, doi:10.1029/2010GL042895.
Ocko, I. B., V. Ramaswami, and Y. Ming, 2014: Contrasting climate responses to the scattering and absorbing features of anthropogenic aerosol forcings. J. Climate, 27, 5329–5345, doi:10.1175/JCLI-D-13-00401.1.
Penner, J. E., S. Y. Zhang, and C. C. Chuang, 2003: Soot and smoke aerosol may not warm climate. J. Geophys. Res., 108, 4657, doi:10.1029/2003JD003409.
Previdi, M., 2010: Radiative feedbacks on global precipitation. Environ. Res. Lett., 5, 025211, doi:10.1088/1748-9326/5/2/025211.
Ramanathan, V., and G. Carmichael, 2008: Global and regional climate changes due to black carbon. Nat. Geosci., 1, 221–227, doi:10.1038/ngeo156.
Ramanathan, V., P. J. Crutzen, J. T. Kiehl, and D. Rosenfeld, 2001a: Aerosols, climate, and the hydrological cycle. Science, 294, 2119–2124, doi:10.1126/science.1064034.
Ramanathan, V., and Coauthors, 2001b: Indian Ocean Experiment: An integrated analysis of the climate forcing and effects of the great Indo-Asian haze. J. Geophys. Res., 106, 28 371–28 398, doi:10.1029/2001JD900133.
Randles, C. A., P. R. Colarco, and A. da Silva, 2013: Direct and semi-direct aerosol effects in the NASA GEOS-5 AGCM: Aerosol–climate interactions due to prognostic versus prescribed aerosols. J. Geophys. Res. Atmos., 118, 149–169, doi:10.1029/2012JD018388.
Roberts, D. L., and A. Jones, 2004: Climate sensitivity to black carbon aerosol from fossil fuel combustion. J. Geophys. Res., 109, D16202, doi:10.1029/2004JD004676.
Samset, B. H., and Coauthors, 2013: Black carbon vertical profiles strongly affect its radiative forcing uncertainty. Atmos. Chem. Phys., 13, 2423–2434, doi:10.5194/acp-13-2423-2013.
Shell, K. M., J. T. Kiehl, and C. A. Shields, 2008: Using the radiative kernel technique to calculate climate feedbacks in NCAR’s Community Atmospheric Model. J. Climate, 21, 2269–2282, doi:10.1175/2007JCLI2044.1.
Shiogama, H., S. Emori, K. Takahashi, T. Nagashima, T. Ogura, T. Nozawa, and T. Takemura, 2010: Emission scenario dependency of precipitation on global warming in the MIROC3.2 model. J. Climate, 23, 2404–2417, doi:10.1175/2009JCLI3428.1.
Soden, B. J., and I. M. Held, 2006: An assessment of climate feedbacks in coupled ocean–atmosphere models. J. Climate, 19, 3354–3360, doi:10.1175/JCLI3799.1.
Soden, B. J., I. M. Held, R. Colman, K. Shell, and J. T. Kiehl, 2008: Quantifying climate feedbacks using radiative kernels. J. Climate, 21, 3504–3520, doi:10.1175/2007JCLI2110.1.
Tjiputra, J. F., C. Roelandt, M. Bentsen, D. M. Lawrence, T. Lorentzen, J. Schwinger, Ø. Seland, and C. Heinze, 2013: Evaluation of the carbon cycle components in the Norwegian Earth System Model (NorESM). Geosci. Model Dev., 6, 301–325, doi:10.5194/gmd-6-301-2013.
Trenberth, K. E., 2011: Changes in precipitation with climate change. Climate Res., 47, 123–138, doi:10.3354/cr00953.
Twomey, S., 1977: The influence of pollution on the shortwave albedo of clouds. J. Atmos. Sci., 34, 1149–1152, doi:10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2.
Vial, J., J.-L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Climate Dyn., 41, 3339–3362, doi:10.1007/s00382-013-1725-9.
Wang, C., 2007: Impact of direct radiative forcing of black carbon aerosols on tropical convective precipitation. Geophys. Res. Lett., 34, L05709, doi:10.1029/2006GL028416.
Warren, S. G., and W. J. Wiscombe, 1980: A model for the spectral albedo of snow. II: Snow containing atmospheric aerosols. J. Atmos. Sci., 37, 2734–2745, doi:10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2.
Yoshimori, M., and A. J. Broccoli, 2008: Equilibrium response of an atmosphere-mixed layer ocean model to different radiative forcing agents: Global and zonal mean response. J. Climate, 21, 4399–4423, doi:10.1175/2008JCLI2172.1.
