scholarly journals The Bern Simple Climate Model (BernSCM) v1.0: an extensible and fully documented open source reimplementation of the Bern reduced form model for global carbon cycle-climate simulations

2017 ◽  
Author(s):  
Kuno Strassmann ◽  
Fortunat Joos

Abstract. The Bern Simple Climate Model (BernSCM v1.0) is a free open source reimplementation of a reduced form carbon cycle-climate model which has been used widely in previous scientific work and IPCC assessments. BernSCM represents the carbon cycle and climate system with a small set of equations for the heat and carbon budget, the parametrization of major nonlinearities, and the substitution of complex component systems with impulse response functions (IRF). The IRF approach allows cost-efficient yet accurate substitution of detailed parent models of climate system components with near linear behaviour. Illustrative simulations of scenarios from previous multi-model studies show that BernSCM is broadly representative of the range of the climate-carbon cycle response simulated by more complex and detailed models. Model code (in Fortran) was written from scratch with transparency and extensibility in mind, and is provided as open source. BernSCM makes scientifically sound carbon cycle-climate modeling available for many applications. Supporting up to decadal timesteps with high accuracy, it is suitable for studies with high computational load, and for coupling with, e.g., Integrated Assessment Models (IAM). Further applications include climate risk assessment in a business, public, or educational context, and the estimation of CO2 and climate benefits of emission mitigation options.

2018 ◽  
Vol 11 (5) ◽  
pp. 1887-1908 ◽  
Author(s):  
Kuno M. Strassmann ◽  
Fortunat Joos

Abstract. The Bern Simple Climate Model (BernSCM) is a free open-source re-implementation of a reduced-form carbon cycle–climate model which has been used widely in previous scientific work and IPCC assessments. BernSCM represents the carbon cycle and climate system with a small set of equations for the heat and carbon budget, the parametrization of major nonlinearities, and the substitution of complex component systems with impulse response functions (IRFs). The IRF approach allows cost-efficient yet accurate substitution of detailed parent models of climate system components with near-linear behavior. Illustrative simulations of scenarios from previous multimodel studies show that BernSCM is broadly representative of the range of the climate–carbon cycle response simulated by more complex and detailed models. Model code (in Fortran) was written from scratch with transparency and extensibility in mind, and is provided open source. BernSCM makes scientifically sound carbon cycle–climate modeling available for many applications. Supporting up to decadal time steps with high accuracy, it is suitable for studies with high computational load and for coupling with integrated assessment models (IAMs), for example. Further applications include climate risk assessment in a business, public, or educational context and the estimation of CO2 and climate benefits of emission mitigation options.


2014 ◽  
Vol 7 (5) ◽  
pp. 7075-7119
Author(s):  
C. A. Hartin ◽  
P. Patel ◽  
A. Schwarber ◽  
R. P. Link ◽  
B. P. Bond-Lamberty

Abstract. Simple climate models play an integral role in policy and scientific communities. They are used for climate mitigation scenarios within integrated assessment models, complex climate model emulation, and uncertainty analyses. Here we describe Hector v0.1, an open source, object-oriented, simple global climate carbon-cycle model. This model runs essentially instantaneously while still representing the most critical global scale earth system processes. Hector has three main carbon pools: an atmosphere, land, and ocean. The model's terrestrial carbon cycle includes respiration and primary production, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector's actively solves the inorganic carbon system in the surface ocean, directly calculating air–sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO2] and surface temperatures. The model simulates all four Representative Concentration Pathways with high correlations (R>0.7) with current observations, MAGICC (a well-known simple climate model), and the Coupled Model Intercomparison Project version 5. Hector is freely available under an open source license, and its modular design will facilitate a broad range of research in various areas.


2011 ◽  
Vol 11 (4) ◽  
pp. 1417-1456 ◽  
Author(s):  
M. Meinshausen ◽  
S. C. B. Raper ◽  
T. M. L. Wigley

Abstract. Current scientific knowledge on the future response of the climate system to human-induced perturbations is comprehensively captured by various model intercomparison efforts. In the preparation of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), intercomparisons were organized for atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models, named "CMIP3" and "C4MIP", respectively. Despite their tremendous value for the scientific community and policy makers alike, there are some difficulties in interpreting the results. For example, radiative forcings were not standardized across the various AOGCM integrations and carbon cycle runs, and, in some models, key forcings were omitted. Furthermore, the AOGCM analysis of plausible emissions pathways was restricted to only three SRES scenarios. This study attempts to address these issues. We present an updated version of MAGICC, the simple carbon cycle-climate model used in past IPCC Assessment Reports with enhanced representation of time-varying climate sensitivities, carbon cycle feedbacks, aerosol forcings and ocean heat uptake characteristics. This new version, MAGICC6, is successfully calibrated against the higher complexity AOGCMs and carbon cycle models. Parameterizations of MAGICC6 are provided. The mean of the emulations presented here using MAGICC6 deviates from the mean AOGCM responses by only 2.2% on average for the SRES scenarios. This enhanced emulation skill in comparison to previous calibrations is primarily due to: making a "like-with-like comparison" using AOGCM-specific subsets of forcings; employing a new calibration procedure; as well as the fact that the updated simple climate model can now successfully emulate some of the climate-state dependent effective climate sensitivities of AOGCMs. The diagnosed effective climate sensitivity at the time of CO2 doubling for the AOGCMs is on average 2.88 °C, about 0.33 °C cooler than the mean of the reported slab ocean climate sensitivities. In the companion paper (Part 2) of this study, we examine the combined climate system and carbon cycle emulations for the complete range of IPCC SRES emissions scenarios and the new RCP pathways.


2008 ◽  
Vol 21 (9) ◽  
pp. 1891-1910 ◽  
Author(s):  
L. Mark Berliner ◽  
Yongku Kim

Abstract The authors develop statistical data models to combine ensembles from multiple climate models in a fashion that accounts for uncertainty. This formulation enables treatment of model specific means, biases, and covariance matrices of the ensembles. In addition, the authors model the uncertainty in using computer model results to estimate true states of nature. Based on these models and principles of decision making in the presence of uncertainty, this paper poses the problem of superensemble experimental design in a quantitative fashion. Simple examples of the resulting optimal designs are presented. The authors also provide a Bayesian climate modeling and forecasting analysis. The climate variables of interest are Northern and Southern Hemispheric monthly averaged surface temperatures. A Bayesian hierarchical model for these quantities is constructed, including time-varying parameters that are modeled as random variables with distributions depending in part on atmospheric CO2 levels. This allows the authors to do Bayesian forecasting of temperatures under different Special Report on Emissions Scenarios (SRES). These forecasts are based on Bayesian posterior distributions of the unknowns conditional on observational data for 1882–2001 and climate system model output for 2002–97. The latter dataset is a small superensemble from the Parallel Climate Model (PCM) and the Community Climate System Model (CCSM). After summarizing the results, the paper concludes with discussion of potential generalizations of the authors’ strategies.


2021 ◽  
Author(s):  
Skylar Gering ◽  
Benjamin Bond-Lamberty ◽  
Dawn Woodard

<p>Simple climate models focusing on the global climate and carbon cycle are valuable tools for large-ensemble sensitivity studies, model coupling experiments, and policy analyses. One example is Hector, an open-source model with multiple biomes, ocean chemistry, and a novel permafrost implementation. However, Hector does not currently have the capability to reconstruct the flow of carbon from one carbon pool (e.g., atmosphere and ocean) to another or report, at the end of a model run, the origin of the carbon within each pool. We developed a novel ‘trackedval’ C++ class and integrated it into Hector’s codebase. In addition to keeping track of a pool’s total carbon, the trackedval class also records the origin pools of the carbon, determined at the start of a run. If carbon tracking is enabled, this record is updated every timestep to reflect carbon fluxes (pool-to-pool transfers). To demonstrate this capability, we reconstruct and visualize the movement of carbon for several example model runs. Hector is the only simple climate model that we are aware of with the ability to reconstruct the carbon-cycle in detail through carbon tracking. The addition of the trackedval class to Hector opens up opportunities for deeper exploration of the effects of climate change on the global carbon cycle and can be used to track carbon isotopes or other elements in the future.</p>


2015 ◽  
Vol 8 (4) ◽  
pp. 939-955 ◽  
Author(s):  
C. A. Hartin ◽  
P. Patel ◽  
A. Schwarber ◽  
R. P. Link ◽  
B. P. Bond-Lamberty

Abstract. Simple climate models play an integral role in the policy and scientific communities. They are used for climate mitigation scenarios within integrated assessment models, complex climate model emulation, and uncertainty analyses. Here we describe Hector v1.0, an open source, object-oriented, simple global climate carbon-cycle model. This model runs essentially instantaneously while still representing the most critical global-scale earth system processes. Hector has a three-part main carbon cycle: a one-pool atmosphere, land, and ocean. The model's terrestrial carbon cycle includes primary production and respiration fluxes, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector actively solves the inorganic carbon system in the surface ocean, directly calculating air–sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO2], radiative forcing, and surface temperatures. The model simulates all four Representative Concentration Pathways (RCPs) with equivalent rates of change of key variables over time compared to current observations, MAGICC (a well-known simple climate model), and models from the 5th Coupled Model Intercomparison Project. Hector's flexibility, open-source nature, and modular design will facilitate a broad range of research in various areas.


2008 ◽  
Vol 8 (2) ◽  
pp. 6153-6272 ◽  
Author(s):  
M. Meinshausen ◽  
S. C. B. Raper ◽  
T. M. L. Wigley

Abstract. Current scientific knowledge on the future response of the climate system to human-induced perturbations is comprehensively captured by various model intercomparison efforts. In the preparation of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), intercomparisons were organized for atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models, named "CMIP3" and "C4MIP", respectively. Despite their tremendous value for the scientific community and policy makers alike, there are some difficulties in interpreting the results. For example, key radiative forcings have not been considered or standardized in the majority of AOGCMs integrations and carbon cycle runs. Furthermore, the AOGCM analysis of plausible emission pathways was restricted to only three SRES scenarios. This study attempts to address these issues. We present an updated version of MAGICC, the simple carbon cycle-climate model used in past IPCC Assessment Reports with enhanced representation of time-varying climate sensitivities, carbon cycle feedbacks, aerosol forcings and ocean heat uptake characteristics. This new version of MAGICC (6.0) is successfully calibrated against the higher complexity AOGCM and carbon cycle models. Parameterizations of MAGICC 6.0 are provided. Previous MAGICC versions and emulations shown in IPCC AR4 (WG1, Fig. 10.26, page 803) yielded, in average, a 10% larger global-mean temperature increase over the 21st century compared to the AOGCMs. The reasons for this difference are discussed. The emulations presented here using MAGICC 6.0 match the mean AOGCM responses to within 2.2% for the SRES scenarios. This enhanced emulation skill is due to: the comparison on a "like-with-like" basis using AOGCM-specific subsets of forcings, a new calibration procedure, as well as the fact that the updated simple climate model can now successfully emulate some of the climate-state dependent effective climate sensitivities of AOGCMs. The mean diagnosed effective climate sensitivities of the AOGCMs is 2.88°C, about 0.33°C cooler than the reported slab ocean climate sensitivities. Finally, we examine the combined climate system and carbon cycle emulations for the complete range of IPCC SRES emission scenarios and some lower mitigation pathways.


2013 ◽  
Vol 31 (3) ◽  
pp. 413 ◽  
Author(s):  
André Becker Nunes ◽  
Gilson Carlos Da Silva

ABSTRACT. The eastern region of Santa Catarina State (Brazil) has an important history of natural disasters due to extreme rainfall events. Floods and landslides are enhancedby local features such as orography and urbanization: the replacement of natural surface coverage causing more surface runoff and, hence, flooding. Thus, studies of this type of events – which directly influence life in the towns – take on increasing importance. This work makes a quantitative analysis of occurrences of extreme rainfall events in the eastern and northern regions of Santa Catarina State in the last 60 years, through individual analysis, considering the history of floods ineach selected town, as well as an estimate through to the end of century following regional climate modeling. A positive linear trend, in most of the towns studied, was observed in the results, indicating greater frequency of these events in recent decades, and the HadRM3P climate model shows a heterogeneous increase of events for all towns in the period from 2071 to 2100.Keywords: floods, climate modeling, linear trend. RESUMO. A região leste do Estado de Santa Catarina tem um importante histórico de desastres naturais ocasionados por eventos extremos de precipitação. Inundações e deslizamentos de terra são potencializados pelo relevo acidentado e pela urbanização das cidades da região: a vegetação nativa vem sendo removida acarretando um maior escoamento superficial e, consequentemente, em inundações. Desta forma, torna-se de suma importância os estudos acerca deste tipo de evento que influencia diretamente a sociedade em geral. Neste trabalho é realizada uma análise quantitativa do número de eventos severos de precipitação ocorridos nas regiões leste e norte de Santa Catarina dos últimos 60 anos, por meio de uma análise pontual, considerandoo histórico de inundações de cada cidade selecionada, além de uma projeção para o fim do século de acordo com modelagem climática regional. Na análise dos resultados observou-se uma tendência linear positiva na maioria das cidades, indicando uma maior frequência deste tipo de evento nas últimas décadas, e o modelo climático HadRM3P mostra um aumento heterogêneo no número de eventos para todas as cidades no período de 2071 a 2100.Palavras-chave: inundações, modelagem climática, tendência linear.


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