The Bern Simple Climate Model

Geosci. Model Dev., 11, 1887–1908, 2018 https://doi.org/10.5194/gmd-11-1887-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. The Bern Simple Climate Model (BernSCM) v1.0: an extensible and fully documented open-source re-implementation of the Bern reduced-form model for global carbon cycle–climate simulations Kuno M. Strassmann1,a and Fortunat Joos1,2 1Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland 2Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland anow at: Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland Correspondence: Kuno Strassmann ([email protected]) Received: 22 September 2017 – Discussion started: 2 November 2017 Revised: 2 March 2018 – Accepted: 19 March 2018 – Published: 25 May 2018 Abstract. The Bern Simple Climate Model (BernSCM) is 1 Introduction a free open-source re-implementation of a reduced-form carbon cycle–climate model which has been used widely in pre- Simple climate models (SCMs) consist of a small number vious scientific work and IPCC assessments. BernSCM rep- of equations, which describe the climate system in a spa- resents the carbon cycle and climate system with a small set tially and temporally highly aggregated form. SCMs have of equations for the heat and carbon budget, the parametriza- been used since the pioneering days of computational cli- tion of major nonlinearities, and the substitution of complex mate science to analyze the planetary heat balance (Budyko, component systems with impulse response functions (IRFs). 1969; Sellers, 1969) and to clarify the role of the ocean and The IRF approach allows cost-efficient yet accurate substitu- land compartments in the climate response to anthropogenic tion of detailed parent models of climate system components forcing through carbon and heat uptake (e.g., Oeschger et al., with near-linear behavior. Illustrative simulations of scenar- 1975; Siegenthaler and Oeschger, 1984; Hansen et al., 1984). ios from previous multimodel studies show that BernSCM is Due to their modest computational demands, SCMs en- broadly representative of the range of the climate–carbon cy- abled pioneering research using the limited computational cle response simulated by more complex and detailed mod- resources of the time and continue to play a useful role in els. Model code (in Fortran) was written from scratch with the hierarchy of climate models today. transparency and extensibility in mind, and is provided open Recent applications of SCMs are often found in research source. BernSCM makes scientifically sound carbon cycle– in which computational resources are still limiting. Exam- climate modeling available for many applications. Support- ples include probabilistic or optimization studies involving ing up to decadal time steps with high accuracy, it is suit- a large number of simulations, or the use of a climate compo- able for studies with high computational load and for cou- nent as part of a detailed interdisciplinary model. SCMs are pling with integrated assessment models (IAMs), for exam- also much easier to understand and handle than large climate ple. Further applications include climate risk assessment in models, which makes them useful as practical tools that can a business, public, or educational context and the estimation be used by non-climate experts for applications for which of CO2 and climate benefits of emission mitigation options. detailed spatiotemporal physical modeling is not essential. This applies to interdisciplinary research, educational applications, or the quantification of the impact of emission re- ductions on climate change. An important application of SCMs is in integrated assessment models (IAMs). IAMs are interdisciplinary models that couple a climate component with an energy-economy model Published by Copernicus Publications on behalf of the European Geosciences Union. 1888 K. Strassmann and F. Joos: The Bern Simple Climate Model 1 1 1 1m mS1 mS2 mSk Sn τO1 τO2 τOk τΟn Carbon change a . f in surface ocean m m m m L1 NPP 1m S1 S2 Sk Sn mL1 L1 τL1 mS = ∑ k mSk aO1 fO aO2 fO aOk fO aOn fO a . f Net air-to-sea L2 NPP 1 mL2 mL2 Net primary τL2 carbon ux, fO productivity, f Emissions NPP Carbon dioxide Biomass decay , a . f Lk NPP 1 Non-CO₂ agents f mLk mLk decay τLk Regional climate change Radiative forcing Δv(x,t) a . f Net air-to-sea Ln NPP 1m mLn Ln Pattern scaling H τLn heat ux, fO H H H H aO1 f O aO2 f O aOk f O aOn f O Carbon in Global mean land biosphere SAT change ΔT1cS ΔT2cS ΔTkcS ΔTncS mL = ∑k mLk ΔT = ∑k ΔTk 1ΔT c 1ΔT c 1ΔT c 1ΔT c τO1 1 S τO2 2 S τOk k S τOn n S Figure 1. BernSCM as a box-type model of the carbon cycle–climate system based on impulse response functions. Heat and carbon taken up by the mixed ocean surface layer and the land biosphere, respectively, is allocated to a series of boxes with characteristic timescales for surface-to-deep ocean transport (τO) and of terrestrial carbon overturning (τL). The total perturbations in land and surface ocean carbon inventory and in surface temperature are the sums over the corresponding individual perturbations in each box (mSk;1Tk;mLk). Using pattern scaling, the response in SAT can be translated to regional climate change for fields v.x;t/ of variables such as SAT or precipitation. to simulate emissions and their climate consequences. An- models with detailed process descriptions is captured using other application of simple models (e.g., Boucher and Reddy, impulse-response functions (IRFs). These IRF-based substi- 2008; Bruckner et al., 2003; Enting et al., 1994; Good et al., tute models are combined with nonlinear parametrizations 2011; Hooss et al., 2001; Huntingford et al., 2010; Joos and of carbon uptake by the surface ocean and the terrestrial Bruno, 1996; Oeschger et al., 1975; Raupach, 2013; Siegen- biosphere as a function of atmospheric CO2 concentration thaler and Oeschger, 1984; Smith et al., 2017; Tanaka et al., and global mean surface temperature. Pulse response models 2007; Urban and Keller, 2010; Wigley and Raper, 1992) is to have been shown to accurately emulate spatially resolved, compare, analyze, or emulate more complex models (Geof- complex models (Joos et al., 1996; Joos and Bruno, 1996; froy et al., 2012a,b; Meinshausen et al., 2011; Raper et al., Meyer et al., 1999; Joos et al., 2001; Hooss et al., 2001). 2001; Thompson and Randerson, 1999). Simple models also BernSCM (Fig.1) is designed to compute decadal- to play a significant role in previous assessments of the Inter- millennial-scale perturbations in atmospheric CO2, in cli- governmental Panel on Climate Change (e.g., Harvey et al., mate and in fluxes of carbon and heat relative to a refer- 1997). The comprehensive scope and interdisciplinarity of ence state, typically preindustrial conditions. The uptake of such models raise the challenge of maintaining a high and excess, anthropogenic carbon from the atmosphere is de- balanced scientific standard across all model components, es- scribed as a purely physicochemical process (Prentice et al., pecially when human resources are limited. This may apply 2001). As in pioneering modeling approaches with box-type particularly to the climate component, as IAMs are mostly (Oeschger et al., 1975; Revelle and Suess, 1957) and gen- used within the economic and engineering disciplines. Cli- eral ocean circulation models (Maier-Reimer and Hassel- mate and carbon cycle representation are central parts of an mann, 1987; Sarmiento et al., 1992), modification of the IAM and have been critically assessed in the literature (Joos natural carbon cycle through potential changes in circula- et al., 1999a; Schultz and Kasting, 1997; van Vuuren et al., tion and the marine biological cycle (Heinze et al., 2015) 2011). are not explicitly considered. While such modifications and BernSCM is a zero-dimensional global carbon cycle– their potential socioeconomic consequences are vividly dis- climate model built around impulse-response representations cussed in the literature (Gattuso et al., 2015), associated of the ocean and land compartments, as described previ- climate–CO2 feedbacks are likely of secondary importance. ously in Joos et al.(1996) and Meyer et al.(1999). The Estimated uncertainties in the marine carbon uptake due to linear response of more complex ocean and land biosphere climate change, including warming-driven changes in CO2 Geosci. Model Dev., 11, 1887–1908, 2018 www.geosci-model-dev.net/11/1887/2018/ K. Strassmann and F. Joos: The Bern Simple Climate Model 1889 solubility, are found to be smaller in magnitude than uncer- capsulates the finite volume of the entire ocean. It also rep- tainties arising from imperfect knowledge of surface-to-deep resents the range of transport timescales associated with ad- physical transport (see Fig. 2d and e in Friedlingstein et al., vection, diffusion, and convection ranging from decades for 2006). The exchange of CO2 between the atmosphere and the ventilation of thermocline to more than a millennium for the surface ocean is described by two-way fluxes, from the deep Pacific ventilation as evidenced by transient tracers such atmosphere to the surface ocean and vice versa, and the net as chlorofluorocarbons and radiocarbon (Olsen et al., 2016). flux of CO2 into the ocean is proportional to the air–sea par- The simulated surface ocean temperature perturbation, taken tial pressure difference. CO2 reacts with water to form car- as a measure of global mean surface air temperature (SAT) bon and bicarbonate ions (Dickson et al., 2007; Orr et al., change, may be combined with spatial patterns of change in 2015), and acid–base equilibria are described here using the temperature, precipitation, or any other variable of interest to well-established Revelle factor formalism (Siegenthaler and compute regionally explicit changes (Hooss et al., 2001; Joos Joos, 1992; Zeebe and Wolf-Gladrow, 2001).

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