Modeling tools: The Norwegian Earth System Model

In this study the global climate model NorESM [Bentsen et al. 2013, Iversen et al. 2013]

has been used to calculate the climate effects of BC. The model is to a large extent based on the CESM4.0 [Gent et al. 2011] developed at the National Centre for Atmospheric Research (NCAR). The atmospheric module of NorESM, CAM-Oslo includes a comprehensive treatment of aerosols and their interactions with radiation and clouds [Kirkevåg et al. 2013]. Emitted primary particles include sulphate, BC, organic matter, sea salt and mineral dust. Model calculated gas-phase components are DMS and SO

. Figure 10 illustrates the aerosol processes in CAM-Oslo.

Figure 10: The aerosol-particle processing in CAM4-Oslo [Kirkevåg et al. 2013]. The source terms are labeled Q and the source labels bb, ff and bio indicate biomass burning, fossil fuel combustion, and biogenic sources, respectively. The emitted primary particles are indicated in red dashed-dotted arrows. There are four different modes; nucleation (n), aitken (a), accumulation (ac) and course (c).

Dotted yellow arrows indicate the transformation from gaseous sulphate (SO4(gas)) to nucleation-mode sulphate (SO4(n). Solid yellow arrows indicate condensation of SO4. Long-dashed black arrows represent coagulation.

BC from fossil fuel is mostly emitted as nucleation/aitken mode and 10 % is assumed

emitted as accumulation mode conglomerates created by self-coagulation in the exhaust.

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BC from biomass burning is emitted as internally mixed with organic matter. Once emitted, BC grows in size by condensation of sulphate and/organic matter, or by coagulation. NorESM only take into account coagulation of nucleation and aitken mode particles with accumulation and coarse mode particles. Coagulation of aerosol particles with cloud droplets takes place when there is liquid water present in a grid square.

Sulphuric acid gas condenses on all particle surfaces available in a grid volume.

The concentrations of aerosols are tagged according to the size modes and production mechanisms given in Figure 10. There are 11 components for externally mixed particles that are calculated in the life cycle scheme (and transported in the model). In addition there are 9 components that are tagged according to production mechanisms in air or clouds droplets. The transformation from externally mixture to internal mixture for the 11 components is estimated by use of look-up tables. The look-up tables contain values for aerosol optical properties and CCN calculations. The values have been tabulated for a large range of input values for process-tagged concentrations, relative humidity and supersaturation. CCN activation is estimated based on supersaturations calculated from Köhler theory. The main advantage by this method is that the degree of external vs. the internal mixing can be estimated based on physicochemical processes.

Figure 11: Biases in estimating annual mean aerosol optical depth (AOD) (left) and aerosol absorption optical depth (AAOD) (right) in NorESM compared to AERONET stations 2000-2009.

Figure from Kirkevåg et al. [2013].

Figure 11 shows biases in the estimated AOD and AAOD in NorESM (with IPCC

emission inventory [Lamarque et al. 2010]) compared to observations from the

ground-based AERONET stations. The AOD and AAOD are underestimated in large parts of the

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tropics and sub-tropics, with the highest underestimation in South Asia. On the other hand, AOD is overestimated in several remote regions at high latitudes.

When using fully-coupled climate models, it is a challenge to separate the climate signal of increased BC from the internal variability. In the climate simulations we performed, it was necessary to scale up the BC concentrations or emissions significantly in order to get a significant signal. We have to assume that the perturbations we have done are linearly scalable. However, there are non-linearities in the climate system that add additional uncertainties in our estimates, but we think nevertheless there are substantial knowledge to be gained from the experiments. The global forcing we achieved by the scaling factors in paper I and II was in order of 1 W m

. This value is considered acceptable, even though it is not desirable [Hansen et al. 2005].

We have used a fully coupled ocean model, which require thousands of model years to reach full equilibrium. Many equilibrium model studies replace the ocean model with a slab-ocean model that has a simple thermodynamic mixed-layer. However, changes in the ocean heat transport, which may influence atmospheric feedbacks and temperatures, are not fully captured in slab-ocean models. A method to calculate the equilibrium climate response in fully-coupled model simulations without running the models to equilibrium, is to use a linear regression method that assumes a linear dependence between the TOA radiative flux and the global-mean surface temperatures [Gregory et al. 2004]. In paper IV we apply this method to two experiments with perturbed BC and CO

, respectively.

For BC, this method is insufficient, due to fast responses in the climate system during the first year, as also have been pointed out in other studies [Hansen et al. 1997, Andrews et

Chapter 7.3 in Iversen et al. [2013] is part of this Ph. D thesis. The paper concerns the

climate response and scenario projections of NorESM, and chapter 7.3 focus on the

low-frequency variability in the model climate system. To investigate the variability an

empirical orthogonal function (EOF) analysis [Hannachi 2004] has been applied to the

historical runs and the RCP scenarios from NorESM. The EOF analysis can be used to

extract coherent variations that are dominant in a time series. It is commonly used to

study spatial patterns and how they evolve with time, for instance the North Atlantic

Oscillation pattern [Hurrell and Deser 2009]. The EOF patterns are found by computing

the eigenvalues and the eigenvectors of the anomaly covariance matrix.

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