Role of climate model dynamics in estimated climate responses to anthropogenic aerosols

Abstract. Significant discrepancies remain in estimates of climate impacts of anthropogenic aerosols between different general circulation models (GCMs). Here, we demonstrate that eliminating differences in model aerosol or radiative forcing fields results in close agreement in simulated globally averaged temperature and precipitation responses in the studied GCMs. However, it does not erase the differences in regional responses. We carry out experiments of equilibrium climate response to modern day anthropogenic aerosols using an identical representation of anthropogenic aerosol optical properties and aerosol-cloud interactions, MACv2-SP, in two independent climate models (NorESM and ECHAM6). We find consistent global average temperature responses of −0.48 K and −0.50 K and precipitation responses of −1.69 % and −1.79 % in NorESM1 and ECHAM6, respectively, compared to modern-day equilibrium climate without anthropogenic aerosols. However, significant differences remain between the two GCMs regional temperature responses around the Arctic circle and the equator and precipitation responses in the tropics. The scatter in the simulated globally averaged responses is small in magnitude when compared against literature data from modern GCMs using model intrinsic aerosols but same aerosol emissions (−(0.5–1.1) K and −(1.5–3.1) % for temperature and precipitation, respectively). The Pearson correlation of regional temperature (precipitation) response in these literature model experiments with intrinsic aerosols is 0.79 (0.34). The corresponding correlation coefficients for NorESM1 and ECHAM6 runs with identical aerosols are 0.78 (0.41). The lack of improvement in correlation coefficients between models with identical aerosols and models with intrinsic aerosols implies that the spatial distribution of regional climate responses is not improved via homogenizing the aerosol descriptions in the models. Rather, differences in the atmospheric dynamic and high latitude cloud and snow/sea ice cover responses dominate the differences in regional climate responses. Hence, further improvements in the model aerosol descriptions can be expected to have a limited value in improving our understanding of regional aerosol climate impacts, unless the dynamical cores of the climate models are improved as well.

[1]  Dong Eun Lee,et al.  Fast Adjustments of the Asian Summer Monsoon to Anthropogenic Aerosols , 2018, Geophysical research letters.

[2]  M. Salzmann,et al.  Robust response of Asian summer monsoon to anthropogenic aerosols in CMIP5 models , 2014 .

[3]  Ivar A. Seierstad,et al.  The Norwegian Earth System Model, NorESM1-M – Part 2: Climate response and scenario projections , 2012 .

[4]  G. Faluvegi,et al.  Spatial patterns of radiative forcing and surface temperature response , 2014 .

[5]  J. Lamarque,et al.  Regional and global temperature response to anthropogenic SO2 emissions from China in three climate models , 2016 .

[6]  V. Ramaswamy,et al.  Anthropogenic Aerosols and the Weakening of the South Asian Summer Monsoon , 2011, Science.

[7]  J. Gregory,et al.  Irreducible uncertainty in near-term climate projections , 2016, Climate Dynamics.

[8]  V. Ramanathan,et al.  Aerosols, Climate, and the Hydrological Cycle , 2001, Science.

[9]  Jochem Marotzke,et al.  Quantifying the irreducible uncertainty in near‐term climate projections , 2018, WIREs Climate Change.

[10]  Sarah M. Kang,et al.  Anthropogenic sulfate aerosol and the southward shift of tropical precipitation in the late 20th century , 2013 .

[11]  Anthropogenic aerosols and the distribution of past large‐scale precipitation change , 2015, Geophysical research letters.

[12]  A. Ekman.  Do sophisticated parameterizations of aerosol‐cloud interactions in CMIP5 models improve the representation of recent observed temperature trends? , 2014 .

[13]  David S. Lee,et al.  Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application , 2010 .

[14]  T. Shepherd.  Atmospheric circulation as a source of uncertainty in climate change projections , 2014 .

[15]  Hans von Storch,et al.  Taking Serial Correlation into Account in Tests of the Mean. , 1995 .

[16]  A. Kirkevåg,et al.  The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate , 2013 .

[17]  C. Deser,et al.  Does ocean coupling matter for the northern extratropical response to projected Arctic sea ice loss? , 2016 .

[18]  A. P. Siebesma,et al.  Clouds, circulation and climate sensitivity , 2015 .

[19]  M. Chin,et al.  Host model uncertainties in aerosol radiative forcing estimates: results from the AeroCom Prescribed intercomparison study , 2012 .

[20]  J. Kay,et al.  Global Climate Impacts of Fixing the Southern Ocean Shortwave Radiation Bias in the Community Earth System Model (CESM) , 2015 .

[21]  Stefano Tarantola,et al.  A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output , 1999, Technometrics.

[22]  G. Mann,et al.  Large contribution of natural aerosols to uncertainty in indirect forcing , 2013, Nature.

[23]  U. Lohmann,et al.  Anthropogenic aerosol forcing – insights from multiple estimates from aerosol-climate models with reduced complexity , 2019, Atmospheric Chemistry and Physics.

[24]  P. Forster,et al.  Climate Impacts From a Removal of Anthropogenic Aerosol Emissions , 2018, Geophysical research letters.

[25]  P. Rasch,et al.  Climate response of the South Asian monsoon system to anthropogenic aerosols , 2012 .

[26]  C. Bitz,et al.  Influence of high latitude ice cover on the marine Intertropical Convergence Zone , 2005 .

[27]  H. Storch,et al.  Regional climate models add value to global model data, A Review and Selected Examples , 2011 .

[28]  E. Fischer,et al.  Midlatitude atmospheric circulation responses under 1.5 and 2.0 °C warming and implications for regional impacts , 2017 .

[29]  B. Stevens,et al.  MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6 , 2017 .

[30]  Kenneth S. Carslaw,et al.  Quantifying sources of inter‐model diversity in the cloud albedo effect , 2015 .

[31]  G. Hegerl,et al.  Decreased monsoon precipitation in the Northern Hemisphere due to anthropogenic aerosols , 2014 .

[32]  Kai Zhang,et al.  MAC‐v1: A new global aerosol climatology for climate studies , 2013 .

[33]  Raquel V. Francisco,et al.  Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM , 2000 .

[34]  R. Stouffer,et al.  Response of the ITCZ to Northern Hemisphere cooling , 2006 .

[35]  A. OHagan,et al.  Bayesian analysis of computer code outputs: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[36]  B. Stevens,et al.  Atmospheric component of the MPI‐M Earth System Model: ECHAM6 , 2013 .

[37]  Michael Schulz,et al.  Aerosol–climate interactions in the Norwegian Earth System Model – NorESM1-M , 2012 .

[38]  G. Myhre,et al.  Climate responses to anthropogenic emissions of short-lived climate pollutants , 2015 .