Energy balance climate modelling: An alternative dynamic parameterization

Carbon dioxide (CO2) concentrations have increased in the atmosphere as a direct result of human activity and are at their highest level over the last 2-3 million years, with profound impacts on the Earth system. However, the magnitude and future dynamics of land and ocean carbon sinks are not well understood; therefore, the amount of anthropogenic fossil fuel emissions that remain in the atmosphere (the airborne fraction) is poorly constrained. This work aims to quantify the sources and controls of atmospheric CO2, the fate of anthropogenic CO2 over time, and the trend and robustness of the airborne fraction. We use Hector v3.0, a coupled simple climate and carbon cycle model, with the novel ability to explicitly track carbon as it flows through the Earth system. We use a priori probability distribution functions for key model parameters in a Monte Carlo analysis of 10,000 coupled carbon-climate model runs from 1750 to 2300. Results are filtered for physical realism against historical observations and CMIP6 projection data, and we calculate the relative importance of parameters controlling how much CO2 ends up in the atmosphere. Unsurprisingly, we find that anthropogenic emissions are the dominant source of near- and long-term atmospheric CO2, composing roughly 45% of the atmosphere, which is consistent with observational studies of the airborne fraction. The overwhelming majority of model runs exhibited a negative trend in the airborne fraction from 1960-2020, implying that current-day land and ocean sinks are proportionally taking up more carbon than the atmosphere. Furthermore, when looking at the destination of anthropogenic fossil fuel emissions, only a quarter ends up in the atmosphere while more than half of emissions are taken up by the land sink on centennial timescales. This study evaluates the likelihood of airborne fraction trends and provides insights into the dynamics and destination over time of anthropogenic CO2 in the Earth system.

On the relationship between clear-sky planetary and surface albedos: a parameterization for simple energy balance climate models

Coupled climate model simulations designed to isolate the effects of Arctic sea ice loss often apply artificial heating, either directly to the ice or through modification of the surface albedo, to constrain sea ice in the absence of other forcings. Recent work has shown that this approach may lead to an overestimation of the climate response to sea ice loss. In this study, we assess the spurious impacts of ice-constraining methods on the climate of an idealized aquaplanet general circulation model (GCM) with thermodynamic sea ice. The true effect of sea ice loss in this model is isolated by inducing ice loss through reduction of the freezing point of water, which does not require additional energy input. We compare the results of freezing point modification experiments with experiments where sea ice loss is induced using traditional ice-constraining methods and confirm the result of previous work that traditional methods induce spurious additional warming. Furthermore, additional warming leads to an overestimation of the circulation response to sea ice loss, which involves a weakening of the zonal wind and storm-track activity in midlatitudes. Our results suggest that coupled model simulations with constrained sea ice should be treated with caution, especially in boreal summer, where the true effect of sea ice loss is weakest, but we find the largest spurious response. Given that our results may be sensitive to the simplicity of the model we use, we suggest that devising methods to quantify the spurious effects of ice-constraining methods in more sophisticated models should be an urgent priority for future work. Significance Statement The potential effects of Arctic sea ice loss on midlatitude weather have been investigated using climate models where sea ice loss is artificially induced, without increasing greenhouse gas concentrations. Recently, this approach has been challenged because the artificial heating used to melt sea ice may also have direct effects on climate, which are not caused by sea ice loss. We run simulations with a simplified climate model that allows us to separate the “spurious” direct effects of the artificial heating from the “true” effect of sea ice loss. In our simulations, the responses of temperature and atmospheric circulation are amplified by spurious effects. Consequently, we argue that previous studies using more complex climate models may overestimate the effect of sea ice loss on climate.

Climate modelling requires spending an extensive amount of time programming, which means reading, learning, testing, and evaluating source code. Fortunately, many climate models have been developed within the past decades, making it easier for climate studies to be conducted on a global scale. However, some climate models have millions of code lines, making the introduction of new parameterizations a laborious task that demands teamwork. While it is true that the high complexity models perform realistic climate simulations, some researchers perform their studies using simplified climate models in the preliminary test phases. This realisation motivated the development of the Daily INSOLation (DINSOL) model, a robust computer program to support the simplified climate models, performing solar radiation calculations while considering Milankovitch cycles and offering various simulation options for its users. DINSOL was intended to function as a model which supplies data (e.g., daily insolation, instantaneous solar radiation, orbital parameters of the Earth, and the calendar dates), such as the PMIPII. While preparing the boundary conditions of solar radiation for climate models, it was realised that the DINSOL model could also be a helpful tool for use in classrooms. Thus, it was decided that an intuitive graphical user interface would be required to cater to this educational purpose. The model was written in the Fortran 90 language, while its graphical user interface would be built using PyGTK, a Python application programming interface (API) based on GIMP ToolKit (GTK). Furthermore, the R language would also be used to generate a panel containing contour fields and sketches of the orbital parameters to support the graphical execution. The model evaluation made use of data from PMIPII and other models, and the data analysis was performed through statistical methods. Once all tests were concluded, an insignificant difference between the DINSOL-obtained results and the results obtained from other models validated the viability of DINSOL as a tool.

Climate modelling requires spending an extensive amount of time programming, which means reading, learning, testing, and evaluating source code. Fortunately, many climate models have been developed within the past decades, making it easier for climate studies to be conducted on a global scale. However, some climate models have millions of code lines, making the introduction of new parameterizations a laborious task that demands teamwork. While it is true that the high complexity models perform realistic climate simulations, some researchers perform their studies using simplified climate models in the preliminary test phases. This realisation motivated the development of the Daily INSOLation (DINSOL) model, a robust computer program to support the simplified climate models, performing solar radiation calculations while considering Milankovitch cycles and offering various simulation options for its users. DINSOL was intended to function as a model which supplies data (e.g., daily insolation, instantaneous solar radiation, orbital parameters of the Earth, and the calendar dates), such as the PMIPII. While preparing the boundary conditions of solar radiation for climate models, it was realised that the DINSOL model could also be a helpful tool for use in classrooms. Thus, it was decided that an intuitive graphical user interface would be required to cater to this educational purpose. The model was written in the Fortran 90 language, while its graphical user interface would be built using PyGTK, a Python application programming interface (API) based on GIMP ToolKit (GTK). Furthermore, the R language would also be used to generate a panel containing contour fields and sketches of the orbital parameters to support the graphical execution. The model evaluation made use of data from PMIPII and other models, and the data analysis was performed through statistical methods. Once all tests were concluded, an insignificant difference between the DINSOL-obtained results and the results obtained from other models validated the viability of DINSOL as a tool.

We will assemble and analyse instrumental climate data and to develop simple climate models as a basis for (1) detecting greenhouse-gas-induced climatic change, and (2) validation of General Circulation Models. In addition to variations in greenhouse gas concentrations, climate responds to a number of other forcing factors, changes in ocean circulation, volcanic activity, solar irradiance, etc. To detect the greenhouse effect, its signal must be isolated from the noise'' of natural climatic variability, a significant part of which is due to these other factors. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics. Available land and marine data bases will updated and expanded, and differences between different data sets will be reconciled where possible. The data will be analysed to determine the potential effects on climate of greenhouse gas concentration changes and other factors. Analyses will be guided by simple energy balance climate models. These analyses are oriented toward obtaining early evidence of greenhouse-gas-induced climatic change that would lead either to confirmation, rejection or modification of model projections, and toward the statistical validation of General Circulation Model control runs. 23 refs., 4 figs.

The objective of this research is to assembly and analyze instrumental climate data and to develop and apply climate models as a basis for detecting greenhouse-gas-induced climatic change, and validation of General Circulation Models. In addition to changes due to variations in anthropogenic forcing, including greenhouse gas and aerosol concentration changes, the global climate system exhibits a high degree of internally-generated and externally-forced natural variability. To detect the anthropogenic effect, its signal must be isolated from the ``noise`` of this natural climatic variability. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics. To facilitate this, available land and marine data bases will be updated and expanded. The data will be analyzed to determine the potential effects on climate of greenhouse gas and aerosol concentration changes and other factors. Analyses will be guided by a variety of models, from simple energy balance climate models to coupled atmosphere ocean General Circulation Models. These analyses are oriented towards obtaining early evidence of anthropogenic climatic change that would lead either to confirmation, rejection or modification of model projections, and towards the statistical validation of General Circulation Model control runs and perturbation experiments.

Abstract. Many tasks in climate science, including climate impact assessment, scenario analysis, and end-to-end attribution, require efficient methods to translate a wide range of emissions scenarios into regional-scale climate indicators while explicitly accounting for uncertainty. Climate and impact model emulators are statistical models that approximate selected outputs of comprehensive models and can perform this translation. The Rapid Impact Model Emulator (RIME) uses individual simulations from climate or impact models to empirically relate global mean surface air temperature (GMT) levels to regional-scale indicators, enabling the conversion of GMT trajectories, commonly derived from Simple Climate Models (SCMs), into time series of regional climate impacts. Here, we present the Rapid Impact Model Emulator Extended (RIME-X), an extension of the RIME framework that replaces deterministic emulation of individual models along single GMT trajectories with a probabilistic approach. RIME-X combines ensemble simulations of GMT derived from SCMs with warming-level-dependent regional indicator distributions estimated from weighted Model Intercomparison Project (MIP) data. This results in scenario-dependent, time-evolving probability distributions of regional indicators. By jointly quantifying global and regional sources of uncertainty from the start, RIME-X enables systematic exploration of the full space of plausible regional climate impact trajectories under different emissions scenarios. The method is applicable to regional indicators whose distributions are predominantly determined by warming level and provides a computationally efficient framework for uncertainty-aware regional indicator emulation. We provide an open-source Python implementation of RIME-X, including preprocessing workflows for data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) and support for user-defined indicators.

Future air traffic management systems are required to balance the conflicting objectives of maximizing safety and efficiency of traffic flows while minimizing the climate impact of aviation emissions and contrails. Integrating emission and climate models together with air traffic simulations improve the understanding of the complex interaction between the physical climate system, carbon and other greenhouse gas emissions and aviation activity. This paper integrates a national-level air traffic simulation and optimization capability with simple climate models and carbon cycle models, and climate metrics to assess the impact of aviation on climate. The capability can be used to make trade-offs between extra fuel cost and reduction in global surface temperature change. The parameters in the simulation can be used to evaluate the effect of various uncertainties in emission models and contrails and the impact of different decision horizons. Alternatively, the optimization results from the simulation can be used as inputs to other tools that monetize global climate impacts like the FAA’s Aviation Environmental Portfolio Management Tool for Impacts.

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 sixth major <scp>IPCC</scp> assessment report and its implications: 15 September 2021

The classic vertical advection‐diffusion (VAD) balance is a central concept in studying the ocean heat budget, in particular in simple climate models (SCMs). Here we present a new framework to calibrate the parameters of the VAD equation to the vertical ocean heat balance of two fully‐coupled climate models that is traceable to the models' circulation as well as to vertical mixing and diffusion processes. Based on temperature diagnostics, we derive an effective vertical velocity w∗ and turbulent diffusivity for each individual physical process. In steady state, we find that the residual vertical velocity and diffusivity change sign in middepth, highlighting the different regional contributions of isopycnal and diapycnal diffusion in balancing the models' residual advection and vertical mixing. We quantify the impacts of the time evolution of the effective quantities under a transient 1% CO2 simulation and make the link to the parameters of currently employed SCMs.

Regional climate modeling is a dynamical downscaling technique applied to the results of global climate models (GCMs) in order to acquire more information on climate simulations and climate change projections. GCMs and regional climate models (RCMs) have undergone considerable development over the past few decades, and both have increased in resolution. The higher‐resolution edge of RCMs compared to GCMs still remains, however. This has been demonstrated in a number of specific studies. As GCMs operate on relatively coarse resolutions, they do not resolve more variable land forms and similar features that shape regional‐scale climates. RCMs operate on higher resolutions than GCMs, by a factor of 2–10. Some RCMs now explore resolutions down to 1–5 km. This adds value in regions with variable orography, land–sea and other contrasts, as well as in capturing sharp, short‐duration and extreme events. In contrast, large‐scale and time‐averaged fields, not least over smooth terrain and on scales that have been already skillfully resolved in GCMs, are not much affected. RCMs also generate additional detail compared to GCMs when in climate projection mode. Compared to the present‐day climate for which observations exist, here the added value aspect is more complex to evaluate. Nevertheless, added value is meaningfully underlined when there is a clear physical context for it to appear in. In addition to climate modeling and model evaluation‐related added value considerations, a significant relevant aspect of added value is the provision of regional scale information, including climate change projections, for climate impact, adaptation, and vulnerability research. WIREs Clim Change 2016, 7:145–159. doi: 10.1002/wcc.378This article is categorized under: Climate Models and Modeling &gt; Knowledge Generation with Models

The frequency properties of the spectra of sea surface temperature (SST) and land surface air temperature (SAT) anomalies are analyzed based on a simple energy-balance climate model taking into account the fluctuations of the radiation balance, the latent and sensible heat flux, and the near-surface wind velocity in two particular cases: when the statistical properties of the model parameters correspond to white noise (small-scale and mesoscale subintervals) and the combined case when the properties of the synoptic subinterval of these parameters are taken into account in the SAT block. It is found that, in the first case, the spectra have no features, while, in the second case, the spectra contain selected frequencies in the synoptic and low-frequency intervals. The dependence of their frequencies on the model parameters is analyzed. The properties of the standard deviations of SST and SAT anomalies are investigated.

This paper introduces the use of information theory in characterizing climate predictability. Specifically, the concepts of entropy and transinformation are employed. Entropy measures the amount of uncertainty in our knowledge of the state of the climate system. Transinformation represents the information gained about an anomaly at any time t with knowledge of the size of the initial anomaly. It has many desirable properties that can be used as a measure of the predictability of the climate system. These concepts when applied to climate predictability are illustrated through a simple stochastic climate model (an energy balance model forced by noise). The transinformation is found to depict the degradation of information about an anomaly despite the fact that we have perfect knowledge of the initial state. Its usefulness, especially when generalized to other climate models, is discussed.