Tracking carbon flows through the biosphere: a new capability for the simple climate model Hector

<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>

The Pause End and Major Temperature Impacts during Super El Niños are Due to Shortwave Radiation Anomalies

The hiatus or temperature pause during the 21st century has been the subject of numerous research studies with very different results and proposals. In this study, two simple climate models have been applied to test the causes of global temperature changes. The climate change factors have been shortwave (SW) radiation changes, changes in cloudiness and ENSO (El Niño Southern Oscillation) events assessed as the ONI (Oceanic Niño Index) values and anthropogenic climate drivers. The results show that a simple climate model assuming no positive water feedback follows the satellite temperature changes very well, the mean absolute error (MAE) during the period from 2001 to July 2019 being 0.073°C and 0.082°C in respect to GISTEMP. The IPCC’s simple climate model shows for the same period errors of 0.191°C and 0.128°C respectively. The temperature in 2017-2018 was about 0.2°C above the average value in 2002–2014. The conclusion is that the pause was over after 2014 and the SW anomaly forcing was the major reason for this temperature increase. SW anomalies have had their greatest impacts on the global temperature during very strong (super) El Niño events in 1997-98 and 2015-16, providing a new perspective for ENSO events. A positive SW anomaly continued after 2015-16 which may explain the weak La Niña 2016 temperature impacts, and a negative SW anomaly after 1997-98 may have contributed two strong La Niña peaks 1998-2001. No cause and effect connection could be found between the SW radiation and temperature anomalies in Nino areas.

The Monash Simple Climate Model experiments (MSCM-DB v1.0): an interactive database of mean climate, climate change, and scenario simulations

This study introduces the Monash Simple Climate Model (MSCM) experiment database. The simulations are based on the Globally Resolved Energy Balance (GREB) model to study three different aspects of climate model simulations: (1) understanding processes that control the mean climate, (2) the response of the climate to a doubling of the CO2 concentration, and (3) scenarios of external forcing (CO2 concentration and solar radiation). A series of sensitivity experiments in which elements of the climate system are turned off in various combinations are used to address (1) and (2). This database currently provides more than 1300 experiments and has an online web interface for fast analysis and free access to the data. We briefly outline the design of all experiments, give a discussion of some results, put the findings into the context of previously published results from similar experiments, discuss the quality and limitations of the MSCM experiments, and also give an outlook on possible further developments. The GREB model simulation is quite realistic, but the model without flux corrections has a root mean square error in the mean state of the surface temperature of about 10 ∘C, which is larger than those of general circulation models (2 ∘C). It needs to be noted here that the GREB model does not simulate circulation changes or changes in cloud cover (feedbacks). However, the MSCM experiments show good agreement to previously published studies. Although GREB is a very simple model, it delivers good first-order estimates, is very fast, highly accessible, and can be used to quickly try many different sensitivity experiments or scenarios. It builds a basis on which conceptual ideas can be tested to first order and it provides a null hypothesis for understanding complex climate interactions in the context of response to external forcing or interactions in the climate subsystems.

The Monash Simple Climate Model Experiments (MSCM-DB v1.0): An interactive database of mean climate, climate change and scenario simulations

This study introduces the Monash Simple Climate Model (MSCM) experiment database. The model simulations are based on the Globally Resolved Energy Balance (GREB) model. They provide a basis to study three different aspects of climate model simulations: (1) understanding the processes that control the mean climate, (2) the response of the climate to a doubling of the CO2 concentration, and (3) scenarios of external CO2 concentration and solar radiation forcings. A series of sensitivity experiments in which elements of the climate system are turned off in various combinations are used to address (1) and (2). This database currently provides more than 1,300 experiments and has an online web interface for fast analysis of the experiments and for open access to the data. We briefly outline the design of all experiments, give a discussion of some results, and put the findings into the context of previously published results from similar experiments. We briefly discuss the quality and limitations of the MSCM experiments and also give an outlook on possible further developments. The GREB model simulation of the mean climate processes is quite realistic, but does have uncertainties in the order of 20–30 %. The GREB model without flux corrections has a root mean square error in mean state of about 10 °C, which is larger than those of general circulation models (2 °C). However, the MSCM experiments show good agreement to previously published studies. Although GREB is a very simple model, it delivers good first-order estimates, is very fast, highly accessible, and can be used to quickly try many different sensitivity experiments or scenarios.

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

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.