Model Documentation: Chalmers Climate Calculator (CCC)

Transcription

1 Model Documentation: Chalmers Climate Calculator (CCC) Model development: Christian Azar, Daniel Johansson Data collection: Christian Azar, Paulina Essunger, Daniel Johansson Web interface: Claes Andersson Graphics design: Andreas Davidsson Introduction The aim of this web-based model is to provide anyone interested (journalists, policymakers, teachers and students at different levels, and the public at large) with a tool for exploring how emissions of greenhouse gases (primarily CO 2 ) lead to changes in atmospheric concentration of CO 2 and the impact on this on global average surface temperature. Model users control future emissions of CO 2, in the model. Model users also choose the climate sensitivity value and the net aerosol effect on radiative forcing in year Climate sensitivity is a measure of the equilibrium temperature increase, following a doubling of the CO 2 equivalent concentration in the atmosphere. In more complex models, this value is a result of the model run. However, different models get different results (typically in the range degrees per CO 2 equivalent doubling). The net aerosol effect on radiative forcing is a measure of the extent to which aerosols affect the energy balance of the Earth s climate system. Model users can let the net aerosol forcing value be set automatically; in this case, it is calculated so as to give a good fit between the historic and modeled global mean surface temperature; or else, users can set the value themselves. Our aim is to let the user vary these parameters in order to get a better understanding of the uncertainty in the future temperature response, even for the same emissions trajectory. Below, we present briefly the underlying geophysical model and the baseline emissions. Emissions Baseline emissions for the well-mixed greenhouse gases CO 2, CH 4, and N 2 O for the period are taken from the IIASA A2r scenario, which is an updated version of the SRES A2; see Riahi et al. (2006) and IIASA (2009). The regions/countries adopted in Chalmers Climate Calculator do in some instances not coincide with those in the IIASA GGI database. In order to deal with this we weight the data from IIASA GGI with data from the WRI CAIT (2009) data base. The weighting is done as follows (take the US as an example: the US is included in the IIASA GGI as a part of the region North America (NA)): Emissions_US(t)=Emissions_NA_IIASA(t)*Emissions_US_WRI(2000)/Emissions_NA_WRI(2000) where t denotes the time index. The CO 2 emissions for year 2010 have been updated so that the emissions in 2010 are set equal to the estimated emissions in 2008 according to CDIAC (2009). Since the estimated emissions for 2008 (according to CDIAC ) in general are higher (much due to the high growth rate of China s CO 2

2 emissions) than those reported by the IIASA GGI database A2r scenario for year 2010, we add the difference between the scenarios to those reported by the IIASA GGI database throughout the century. Emissions reductions In the one-region version, the user controls which year the global emissions of CO 2, including emissions from deforestation, start to decline and the annual rate at which emissions decline beyond that year. These reduction rates also apply for methane and nitrous oxide. In the two-region version, the user specifies emissions reduction targets for Annex-1 and non-annex- 1, respectively, for the years 2020, 2050 and Emissions in the intervening years are calculated by linear interpolation. The emissions reduction targets are set relative to the emissions level in 1990 for Annex 1 countries and relative to expected emissions in 2010 for non-annex-1 countries. Note that negative emissions can be achieved by setting values below zero. (In reality, negative emissions may be achieved by, for instance, using biomass energy with carbon capture and storage, direct air capture or by reforestation projects). Emissions of methane and nitrous oxide are calculated so that the relative reduction level between baseline emissions and the controlled emissions are the same as for CO 2 emissions in each year. In the two-region version, the user is also asked to set targets for emissions caused by deforestation. These are set the same way as emissions from non-annex 1. The net future aerosol forcing (from sulphur emissions and black soot) is assumed to be linked to energy-related CO 2 emissions (the forcing is assumed to be proportional to CO 2 emissions, but the proportionality constant is reduced by 1.5 % per year). Other greenhouse gases are included in the model but not controlled by the user. Carbon cycle and other greenhouse gases To model the sink of atmospheric CO 2 in oceans, we use a non-linear response function presented in Joos et al. (1996). We adopt the use of the response function estimated for the Princeton 3-D carbon cycle model. This approach is an efficient representation of the carbon cycle that takes into account the change in CO 2 buffer factor (Revelle factor) of the ocean surface layer when the partial pressure of CO 2 is altered. For the terrestrial sink we use the non-linear response function presented in Tanaka (2008). The carbon fertilization factor β is set to 0.3, and the carbon fertilization is assumed to depend logarithmically on atmospheric CO 2 concentration. CH 4 and N 2 O concentrations are modeled using the global mean mass-balance equations in Prather & Ehhalt, et al. (2001), taking into account the feedback effect CH 4 has on its own atmospheric lifetime. Radiative forcing The equations for radiative forcing are the expressions given in IPCC s Third Assessment Report (Ramaswamy et al., 2001), available here: We also

3 include the indirect forcing effect of methane on tropospheric ozone and stratospheric water vapor concentrations by adding another 40 % to the direct radiative forcing of methane (Forster et al., 2007). The radiative forcing for industrial greenhouse gases is assumed to remain constant at the present value over the model time horizon reflecting the rather long life-time of some of the CFC s and the increasing concentration of many other industrial greenhouse gases, e.g., HFCs. The radiative forcing of aerosols can either be set by the user or set automatically, in which case it is determined through a good fit between the modeled and measured global average surface temperature. The procedure for calculating the net radiative forcing of aerosols in the model is described in the section Calibration, below. Energy balance model and global average surface temperature The energy balance model that calculates the temperature response is a globally averaged box model with three boxes. This three-box model consists of a mixed ocean-atmosphere layer, an intermediate ocean layer and a bottom ocean layer. This model is calibrated to emulate a linear Upwelling Diffusion Energy Balance Model (UDEBM) with polar overturning. The assumptions for the UDEBM include a climate sensitivity of X K per CO 2 equivalent doubling which the user decides on, a heat diffusivity of 2 cm 2 /s, upwelling rate of 4 m/year and a ratio of polar water warming to average ocean warming of 0.2, in line with standard assumptions for UDEBMs, see Raper et al. (2001), Meinshausen et al. (2008), Baker & Roe (2009), Hoffert et al. (1980), and Shine et al. (2005). The response time of our UDEBM has been assessed, and it gives e-folding times almost identical to those of MAGICC (as reported in Richels et al. (2007)) for the same levels of heat diffusivity and climate sensitivity. This is an indication that our model works well in the sense that it reproduces results from more advanced climate models (MAGICC is calibrated to emulate three-dimensional AOGCMs). Calibration When the the aerosol forcing value is set automatically, the global average surface temperature and the heat uptake in the three-box energy balance model are initiated so as to fit the historical temperature by adjusting the radiative forcing contribution from aerosols (the sum of direct and indirect radiative forcing from both reflecting and absorbing aerosols) given the exogenously set value for climate sensitivity. This is a simple approach to estimating the radiative forcing strength of aerosols. (For a climate sensitivity of 3 degrees per CO 2 doubling, we get a value that lies very close to the IPCC median estimate of the aerosol forcing). This approach makes the modeled global average temperature in line with historical measured global mean temperature. For this calibration of net aerosol forcing and initialization of the energy balance model, the historical radiative forcing contributions from CO 2, CH 4 (both direct and indirect effects), O 3, N 2 O, Halocarbons (both direct and indirect effects), changes in solar activity, and the radiative impacts of volcanoes are considered. Data on these forcings are from NASA GISS (2009a); those on the global average surface temperature are from NASA GISS (2009b). The fact that some radiative forcing agents may have different efficacies, i.e., some agents have a stronger/smaller effect on the global average surface equilibrium temperature than CO 2 for a similar level of radiative forcing is not considered; see Forster et al. (2007). Other forcing changes since pre-industrial times, such as non-ch 4 -induced O 3 leading to an increase in radiative forcing, and changes in surface albedo leading to a net decrease in radiative forcing, are assumed to balance; see Forster (2007).

4 References Baker M.B., Roe G.H. (2009) The shape of things to come: why is climate change so predictable?. Journal of Climate, Early Online Releases, CAIT (2009) The Climate Analysis Indicators Tool (CAIT). World Resources Institute, available at CDIAC (2009) Fossil-Fuel CO 2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, available at Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van Dorland (2007) Changes in Atmospheric Constituents and in Radiative Forcing. In: Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (ed) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Hoffert, M. I., Callegari A. J, Hsieh C.T. (1980) The Role of Deep Sea Heat Storage in the Secular Response to Climatic Forcing. Journal of Geophysical Research 85 (C11): IIASA (2009) GGI Scenario Database. Available at: Joos, F., Bruno M., Fink R., Siegenthaler U.; Stocker T.F., Le Quéré C.; Sarmiento, J.L. (1996) An efficient and accurate representation of complex oceanic and biospheric models of anthropogenic carbon uptake. Tellus B, 48(3): Meinshausen, M., S. C. B. Raper and T. M. L. Wigley (2008). Emulating IPCC AR4 atmosphereocean and carbon cycle models for projecting global-mean, hemispheric and land/ocean temperatures: MAGICC 6.0. Atmospheric Chemistry and Physics Discussions 8: NASA Goddard Institure for Space Studies (GISS) (2009a) Forcings in GISS Climate Model downloaded via at NASA Goddard Institure for Space Studies (GISS) (2009b) GISS Surface Temperature Analysis. downloaded via downloaded Prather, M. and Ehhalt, D. et al. (2001) Atmospheric chemistry and greenhouse gases, Climate Change 2001: The Scientific Basis, J.T. Houghton et al. (eds.), Cambridge University Press, Cambridge. Ramaswamy, V. et al.: (2001) Radiative forcing of climate change, Climate Change 2001: The Scientific Basis, J.T. Houghton et al., eds., Cambridge University Press, Cambridge. Raper S.C.B., Gregory J.M., Osborn T.J. (2001) Use of an upwelling-diffusion energy balance climate model to simulate and diagnose A/OGCM results. Climate Dynamics 17: Riahi K., Grubler A., Nakicenovic N. (2006) Scenarios of Long-term Socio-economic and Environmental Development under Climate Stabilization. Technological Forecasting and Social Change 74(7): , doi: /j.techfore

5 Shine K.P., Fuglestvedt J.S., Hailemariam K., Stuber N. (2005) Alternatives to the Global Warming Potential for Comparing Climate Impacts of Emissions of Greenhouse Gases. Climatic Change 68(3): Tanaka K. (2008) Inverse Estimation for the Simple Earth System Model ACC2 and its Applications, Ph.D. thesis, International Max Planck Research School on Earth System Modelling, Hamburg, Germany. Downloadable at