Earth System Models (ESMs) are invaluable tools to study the climate system’s response to greenhouse gas emissions. But their projections are affected by three major sources of uncertainty: (i) internal variability, i.e., natural climate variability, (ii) ESM structural uncertainty, i.e., uncertainty in the response of the climate system to given greenhouse gas concentrations, and (iii) emission scenario uncertainty, i.e., which emission pathway the world chooses. The large computational cost of running full ESMs limits the exploration of this uncertainty phase space since it is only feasible to create a limited number of ESM runs. However, climate change impact and integrated assessment models, which require ESM projections as their input, could profit from a more complete sampling of the climate change uncertainty phase space. In this contribution, we present MESMER (Beusch et al., 2020), a Modular ESM Emulator with spatially Resolved output, which allows for a computationally efficient exploration of the uncertainty space of yearly temperatures. MESMER approximates ESM land temperature fields at a negligible computational cost by expressing grid-point-level temperatures as a function of global mean temperature and an overlaid spatio-temporally correlated variability term. Within MESMER all three major sources of uncertainty can be accounted for. Stochastic simulation of natural climate variability allows to account for internal variability. ESM structural uncertainty can be addressed by calibrating MESMER on different ESMs from the Coupled Model Intercomparison Project (CMIP) archives. Finally, emission scenario uncertainty can be accounted for by ingesting forced global mean temperature trajectories from global climate model emulators, such as MAGICC or FaIR. MESMER is a flexible statistical tool which is under active development and in the process of becoming an open-source software.

Beusch, L., Gudmundsson, L., and Seneviratne, S. I. (ESD, 2020): https://doi.org/10.5194/esd-11-139-2020

How to cite: Beusch, L., Gudmundsson, L., and Seneviratne, S. I.: Emulating Earth System Model land temperature fields with MESMER, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8640, https://doi.org/10.5194/egusphere-egu21-8640, 2021.

Emulators are computationally cheap statistical devices that derive simplified relationships from otherwise complex climate models. A recently developed Earth System Model (ESM) emulator, MESMER (Beusch et al. 2020), uses a combination of pattern scaling and a variability emulator to emulate ESM initial-condition ensembles. Linear scaling provides the spatially resolved yearly temperature trend projections from global mean temperature trend values. In addition, the variability emulator stochastically models spatio-temporally correlated local variability, yielding a convincing imitation of the internal climate variability displayed within a multi-model initial condition ensemble. The work presented here extends MESMER’s framework to have a monthly downscaling module, so as to provide spatially resolved monthly temperature values from spatially resolved yearly temperature values. For this purpose, a harmonic model is trained on monthly ESM output to capture monthly cycles and their evolution with changing temperature. Once the mean monthly cycle is sufficiently emulated, a process based understanding of the biases within the harmonic model is undertaken. Such entails employing a Gradient Boosting Regressor tree model (GBR) to explain the residuals from the harmonic model using biophysical climate variables such as albedo and thermal fluxes as explanatory variables. These variables can be rated according to their explanatory power when categorising residuals which furthermore elucidates the main physical processes driving biases in the harmonic model within seasons at the grid point level. Finally we add residual variability ontop of the harmonic model outputs to provide convincing imitations of ESM monthly temperature realisations. The residual variability is generated using an AR(1) process coupled to a multivariate trans-gaussian process so as to maintain spatio-temporal correlations and the non-stationarity in monthly variability with increasing yearly temperatures.

Beusch, L., Gudmundsson, L., & Seneviratne, S. I. (2020). Emulating Earth System Model temperatures: from global mean temperature trajectories to grid-point level realizations on land. Earth System Dynamics, 11(1), 139–159. https://doi.org/10.5194/esd-11-139-2020

How to cite: Nath, S., Lejeune, Q., Beusch, L., Schleussner, C., Gudmundsson, L., and Seneviratne, S.: Building an Earth Sytem Model emulator for local monthly temperature, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5706, https://doi.org/10.5194/egusphere-egu21-5706, 2021.

Reduced-complexity climate models form part of the climate model hierarchy and are increasingly relied upon at the science-policy interface. Historically, evaluation of reduced-complexity climate models has been limited to a number of independent studies. Here we present the reduced-complexity model intercomparison project (RCMIP), the first systematic, community-organised evaluation of reduced-complexity climate models. We introduce the motivation behind RCMIP, where to find information about it and key insights arising from its first two scientific outputs. Future phases of RCMIP will examine specific behaviour of reduced-complexity climate models in more detail, for example their carbon cycle response. We are particulalry keen to hear from users of reduced-complexity models to discuss their use cases, how we can evaluate our models in the way most relevant to them and where key model improvements can be made.

How to cite: Nicholls, Z. and the Reduced complexity model intercomparison project contributors: Reduced Complexity Model Intercomparison Project (RCMIP), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3707, https://doi.org/10.5194/egusphere-egu21-3707, 2021.

The Empirical Model of Global Climate (EM-GC) (Canty et al., ACP, 2013, McBride et al., ESDD, 2020) is a multiple linear regression, energy balance model that accounts for the natural influences on global mean surface temperature due to ENSO, the 11-year solar cycle, major volcanic eruptions, as well as the anthropogenic influence of greenhouse gases and aerosols and the efficiency of ocean heat uptake. First, we will analyze the human contribution of global warming from 1975-2014 based on the climate record, also known as the attributable anthropogenic warming rate (AAWR). We will compare the values of AAWR found using the EM-GC with values of AAWR from the CMIP6 multi-model ensemble. Preliminary analysis indicates that over the past three decades, the human component of global warming inferred from the CMIP6 GCMs is larger than the human component of warming from the climate record. Second, we will compare values of equilibrium climate sensitivity inferred from the historical climate record to those determined from CMIP6 GCMs using the Gregory et al., GRL, 2004 method. Third, we will use the future abundances of greenhouse gases and aerosols provided by the Shared Socioeconomic Pathways (SSPs) to project future global mean surface temperature change. We will compare the projections of future temperature anomalies from CMIP6 GCMs to those determined by the EM-GC. We will conclude by assessing the probability of the CMIP6 and EM-GC projections of achieving the Paris Agreement target (1.5°C) and upper limit (2.0°C) for several of the SSP scenarios.

How to cite: McBride, L., Hope, A., Canty, T., Tribett, W., Bennett, B., and Salawitch, R.: Simulation and Projection of Global Mean Surface Temperature Using the Empirical Model of Global Climate and Comparison to CMIP6 GCMs, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6491, https://doi.org/10.5194/egusphere-egu21-6491, 2021.

One of the key applications of simple climate models is probabilistic climate projections to assess a variety of emission scenarios in terms of their compatibility with global warming mitigation goals. The second phase of the Reduced Complexity Model Intercomparison Project (RCMIP) compares nine participating models for their probabilistic projection methods through scenario experiments, focusing on consistency with given constraints for climate indicators including radiative forcing, carbon budget, warming trends, and climate sensitivity. The MCE is one of the nine models, recently developed by the author, and has produced results that well match the ranges of the constraints. The model is based on impulse response functions and parameterized physics of effective radiative forcing and carbon uptake over ocean and land. Perturbed model parameters are generated from statistical models and constrained with a Metropolis-Hastings independence sampler. A parameter subset associated with CO₂-induced warming is assured to have a covariance structure as diagnosed from complex climate models of the Coupled Model Intercomparison Project (CMIP). The model's simplicity and the successful results imply that a method with less complicated structures and fewer control parameters has an advantage when building reasonable perturbed ensembles in a transparent way despite less capacity to emulate detailed Earth system components. Experimental results for future scenarios show that the climate sensitivity of CMIP models is overestimated overall, suggesting that probabilistic climate projections need to be constrained with observed warming trends.

How to cite: Tsutsui, J.: Probabilistic climate projections with Minimal CMIP Emulator (MCE), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13697, https://doi.org/10.5194/egusphere-egu21-13697, 2021.

The Simple Climate Model for Optimization version 2.0 (SCM4OPT v2.0) is one of the contributors to the Reduced Complexity Model Intercomparison Project Phase 2 (RCMIP2). However, low effective radiative forcing is emulated in SCM4OPT v2.0, which is driven by the strong negative aerosol effective radiative forcing and considered to be an outlier compared to other models. In addition, the carbon cycles and climate system in SCM4OPT v2.0 are calibrated based on the outputs from Coupled Model Intercomparison Project Phase 5 (CMIP5), which cannot reflect the latest Earth system model results. In this study, we update the reduced-complexity model to SCM4OPT v3.0. First, we re-calibrate the carbon cycles, including land carbon-cycle and ocean carbon-cycle, and the climate system according to 32 coupled atmosphere-ocean general circulation models (AOGCMs) with selected experimental outputs in the latest CMIP6; Second, we fix the aerosol forcing by introducing a constrain in the light of the IPCC AR5 aerosol forcing. We retain the lightweight and efficient nature of this model, in order to make it suitable to be involved in a large-scale optimization process. Using SCM4OPT v3.0, we produce a new set of scenario simulations by using the dataset of harmonized emissions used in CMIP6 and compare with other reduced-complexity models. SCM4OPT v3.0 is expected to simulate climate-related uncertainties regarding the latest understanding of climate change.

How to cite: Su, X.: Develop a reduced-complexity model – SCM4OPT v3.0 for integrated assessment-optimization, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14657, https://doi.org/10.5194/egusphere-egu21-14657, 2021.

We present the Fractional Energy Balance Equation (FEBE): a generalization of the standard EBE.  The key FEBE novelty is the assumption of a hierarchy of energy storage mechanisms: scaling energy storage.  Mathematically the storage term is of fractional rather than integer order.  The special half-order case (HEBE) can be classically derived from the continuum mechanics heat equation used by Budyko and Sellers simply by introducing a vertical coordinate and using the correct conductive-radiative surface boundary conditions (the FEBE is a mild extension).

How to cite: Procyk, R., Lovejoy, S., Hébert, R., and Del Rio Amador, L.: Global and Regional Temperature Projections Using the Fractional Energy Balance Equation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12229, https://doi.org/10.5194/egusphere-egu21-12229, 2021.

Since Hasselmann and Leith, stochastic Energy Balance Models (EBMs) have allowed treatment of climate fluctuations, and at least the possibility of fluctuation-dissipation relations.   However, it has recently been argued that observations motivate heavy-tailed temporal response functions in global mean temperature. Our complementary approach  (arXiv:2007.06464v2[cond-mat.stat-mech]) exploits the correspondence  between Hasselmann’s EBM and  Langevin’s equation (1908).  We propose mapping the Mori-Kubo Generalised Langevin Equation (GLE) to generalise the Hasselmann EBM. If present, long range memory then simplifies the GLE to a fractional Langevin equation (FLE).  We describe the EBMs that correspond to the GLE and FLE,  and relate them to  Lovejoy et al’s FEBE [NPG Discussions, 2019; QJRMS, to appear, 2021].

How to cite: Watkins, N. W., Chapman, S. C., Chechkin, A., Ford, I., Klages, R., and Stainforth, D.: On Generalized Langevin Dynamics and the Modelling of Global Mean Temperature, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12121, https://doi.org/10.5194/egusphere-egu21-12121, 2021.

While Earth system models (ESM) provide spatially detailed process-based outputs, they present heavy computational costs. Reduced complexity models such as OSCAR are calibrated on those complex models and provide an alternative with faster calculations but lower resolutions. Yet, reduced-complexity models need to be evaluated and validated. We diagnose the newest version of OSCAR (v3.1) using observations and results from ESMs and the current Coupled Model Intercomparison Project 6. A total of 99 experiments are selected for simulation with OSCAR v3.1 in a probabilistic framework, reaching a total of 567,700,000 simulated years. Here, we showcase these results. A first highlight of this exercise is the unstability of the model for high-warming scenarios, which we attribute to the ocean carbon cycle module. The diverging runs caused by this unstability were discarded in the post-processing. The ensuing main results were further obtained by weighting each physical parametrizations based on their performance to replicate a set of observations. Overall, OSCAR v3.1 qualitively behaves like complex ESMs, for all aspects of the Earth system, although we observe a number of quantitative differences with state-of-the-art models. Some specific features of OSCAR contribute in these differences, such as its fully interactive atmospheric chemistry and endogenous calculations of biomass burning, wetlands and permafrost emissions. Nevertheless, the low sensitivity of the land carbon cycle to climate change, the unstability of the ocean carbon cycle, the seemingly over-constrained climate module, and the strong climate feedback over short-lived species, all call for an improvement of these aspects in OSCAR. Beyond providing a key diagnosis of the model in the context of the reduced-complexity models intercomparison project (RCMIP), this work is also meant to help with the upcoming calibration of OSCAR on CMIP6 results, and to provide a large set of CMIP6 simulations all run consistently with a probalistic model.

How to cite: Quilcaille, Y. and Gasser, T.: Showcasing the compact Earth system model OSCAR v3.1 with CMIP6 simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15630, https://doi.org/10.5194/egusphere-egu21-15630, 2021.

Projecting the global climate feedback and surface warming responses to anthropogenic forcing scenarios remains a key priority for climate science. Here, we explore possible roles for efficient climate model ensembles in contributing to quantitative projections of future global mean surface warming and climate feedback within model hierarchies. By comparing complex and efficient (sometimes termed ‘simple’) model output to data we: (1) explore potential Bayesian approaches to model ensemble generation; (2) ask what properties an efficient climate model should have to contribute to the generation of future warming and climate feedback projections; (3) present new projections from efficient model ensembles.

Climate processes relevant to global surface warming and climate feedback act over at least 14 orders of magnitude in space and time; from cloud droplet collisions and photosynthesis up to the global mean temperature and carbon storage over the 21^st century. Due to computational resources, even the most complex Earth system models only resolve around 3 orders of magnitude in horizontal space (from grid scale up to global scale) and 6 orders of magnitude in time (from a single timestep up to a century).

Complex Earth system models must therefore contain a great many parameterisations (including specified functional forms of equations and their coefficient values) representing sub grid-scale and sub time-scale processes. We know that these parameterisations affect the quantitative model projections, because different complex models produce a range of historic and future projections. However, complex Earth system models are too computationally expensive to fully sample the plausible combinations of their own parameterisations, typically being able to realise only several tens of simulations.

In contrast, efficient climate models are able to utilise computational resources to resolve their own plausible combinations of parameterisations, through the construction of very large model ensembles. However, this parameterisation resolution occurs at the expense of a much-reduced resolution of relevant climate processes. Since the relative simplicity of efficient model representations may not capture the required complexity of the climate system, the qualitative nature of their simulated projections may be too simplistic. For example, an efficient climate model may use a single climate feedback value for all time and for all sources of radiative forcing, when in complex models (and the real climate system) climate feedbacks may vary over time and may respond differently to, say, localised aerosol forcing than to well mixed greenhouse gases.

By far the dominant quantitative projections of global mean surface warming in the scientific literature, as used in the Intergovernmental Panel on Climate Change Assessment Reports, derive from relatively small ensembles of complex climate model output. However, computational resources impose an inherent trade-off between model resolution of relevant climate processes (affecting the qualitative nature of the model framework) and model ensemble resolution of plausible parameterisations (affecting the quantitative exploration of projections within that model framework). This computationally imposed trade-off suggests there may be a significant role for efficient model output, within a hierarchy of model complexities, when generating future warming projections.

How to cite: Goodwin, P. and Cael, B. B.: The role of efficient climate models in the projection of future climate feedback and surface warming, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-852, https://doi.org/10.5194/egusphere-egu21-852, 2021.

Observations and models indicate a varying radiative response of the Earth system to CO₂ forcing. This variation introduces large uncertainties in the climate sensitivity estimates to increasing atmospheric CO₂ concentration. This variation is represented as an additional feedback mechanism in energy-balance models, which depends on more than only the surface temperature change. Models and observations also indicate that a spatio-temporal pattern in the surface warming controls this additional contribution to the radiative response. However, several authors picture this effect as a feedback change in the atmosphere, reducing the role of the ocean's enthalpy-uptake variations. I use a widely-known linearised conceptual energy-balance model and its analytical solutions to find an explicit expression of the radiative response and its temporal evolution. This explicit expression provides another timescale in the Earth system, as the ocean-atmosphere coupling modulates the radiative response. Thus, to understand the variation of the climate feedback parameter, we need not only to know its relation to the spatio-temporal warming pattern but an improved picture of the ocean-atmosphere coupling that generates the pattern.

How to cite: Jiménez-de-la-Cuesta, D.: Revisiting the variation of the climate feedback parameter, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8142, https://doi.org/10.5194/egusphere-egu21-8142, 2021.

Stochastic weather generators (WGs) are tools for producing weather series, which are statistically similar to the real world weather series. The synthetic series may represent both present and changed (not only the future) climate. In the latter case, WG parameters derived from the observed weather series are modified with climate change scenario, which is typically based on RCM or GCM simulations. As the GCM/RCM simulations are very demanding on computer resources, the numbers of simulations made for individual possible emission scenarios are limited, especially for some (mostly the less probable ones) emission scenarios (e.g. RCP 2.6). Still, many climate change impact studies try to give projections of the CC impacts assuming uncertainties coming from all possible sources, including the modeling uncertainty and  uncertainties in emissions & climate sensitivity. To allow generation of weather series fitting the projection of any GCM forced by any emission scenario, we use a pattern scaling approach, in which the standardized climate change scenario (consisting of changes in climatic characteristic related to 1ºC change in global mean temperature) derived from a given GCM is multiplied by a change in global mean temperature (dTg) projected (for a selected emission scenario and climate sensitivity) by a simple climate model MAGICC.

In our contribution, we will demonstrate the use of the generator (using SPAGETTA WG, which is our multi-site multi-variate parametric daily WG) in probabilistic projection of future changes in selected climatic characteristics of temperature (T) and precipitation (P); we will focus on spatial hot/cold/dry/wet/hot-dry/hot-wet/cold-dry/cold-wet spells). Standardized climate change scenarios will be derived from multiple GCMs (taken from CMIP5 database) and scaled by dTg projected by MAGICC. Effects of the three above-named sources of uncertainty, as well as the effects of changes in individual statistical characteristics (the means & the site-specific variabilities & the characteristics of the temporal and spatial variability of both T and P) will be assessed.

Acknowledgements: Projects GRIMASA (Czech Science Foundation, project no. 18-15958S) and SustES (European Structural and Investment Funds, project no. CZ.02.1.01/0.0/0.0/16_019/0000797).

How to cite: Dubrovsky, M., Lhotka, O., Miksovsky, J., Stepanek, P., and Meitner, J.: SPAGETTA Weather Generator Linked with Climate Models May Produce Weather Series for Future Climate , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12704, https://doi.org/10.5194/egusphere-egu21-12704, 2021.

In atmospheric and climate sciences, research and development is often first conducted with a simple idealized system like the Lorenz-N models (N ∈ {63, 84, 96}) which are toy models of atmospheric variability. On the other hand, reduced-order spectral quasi-geostrophic models of the atmosphere with a sufficient number of modes offer a good representation of the dry atmospheric dynamics. They allow one to identify typical features of the atmospheric circulation, such as blocked and zonal circulation regimes, and low-frequency variability. However, these models are less often considered in literature, despite their demonstration of more realistic behavior.

qgs (Demaeyer et al., 2020) aims to popularize these systems by providing a fast and easy-to-use Python framework for researchers and teachers to integrate this kind of model. The documentation makes it clear and efficient to handle the model, by explaining the equations and parameters and linking these to the code. 

The choice to use Python was specifically made to facilitate its use in Jupyter Notebooks and with the multiple recent machine learning libraries that are available in this language.

In this talk, we will present the modeling capabilities of qgs and show its usage in a varieties of didactical and research use cases.

Reference

Demaeyer, J., De Cruz, L., & Vannitsem, S. (2020). qgs: A flexible Python framework of reduced-order multiscale climate models. Journal of Open Source Software, 5(56), 2597, https://doi.org/10.21105/joss.02597 .

How to cite: De Cruz, L., Demaeyer, J., and Vannitsem, S.: qgs: A flexible Python framework of reduced-order multiscale climate models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1091, https://doi.org/10.5194/egusphere-egu21-1091, 2021.

The global hydrological cycle is of crucial importance for life on Earth. Hence, it is a focus of both future climate projections and paleoclimate modeling. The latter typically requires long integrations or large ensembles of simulations, and therefore models of reduced complexity are needed to reduce the computational cost. Here, we study the hydrological cycle of the the Planet Simulator (PlaSim) [1], a general circulation model (GCM) of intermediate complexity, which includes evaporation, precipitation, soil hydrology, and river advection.

Using published parameter configurations for T21 resolution [2, 3], PlaSim strongly underestimates precipitation in the mid-latitudes as well as global atmospheric water compared to ERA5 reanalysis data [4]. However, the tuning of PlaSim has been limited to optimizing atmospheric temperatures and net radiative fluxes so far [3].

Here, we present a different approach by tuning the model’s atmospheric energy balance and water budget simultaneously. We argue for the use of the globally averaged mean absolute error (MAE) for 2 m temperature, net radiation, and evaporation in the objective function. To select relevant model parameters, especially with respect to radiation and the hydrological cycle, we perform a sensitivity analysis and evaluate the feature importance using a Random Forest regressor. An optimal set of parameters is obtained via Bayesian optimization.

Using the optimized set of parameters, the mean absolute error of temperature and cloud cover is reduced on most model levels, and mid-latitude precipitation patterns are improved. In addition to annual zonal-mean patterns, we examine the agreement with the seasonal cycle and discuss regions in which the bias remains considerable, such as the monsoon region over the Pacific.

We discuss the robustness of this tuning with regards to resolution (T21, T31, and T42), and compare the atmosphere-only results to simulations with a mixed-layer ocean. Finally, we provide an outlook on the applicability of our parametrization to climate states other than present-day conditions.

[1] K. Fraedrich et al., Meteorol. Z. 14, 299–304 (2005)
[2] F. Lunkeit et al., Planet Simulator User’s Guide Version 16.0 (University of Hamburg, 2016)
[3] G. Lyu et al., J. Adv. Model. Earth Syst. 10, 207–222 (2018)
[4] H. Hersbach et al., Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020)

How to cite: Mehling, O., Ziegler, E., Andres, H., Werner, M., and Rehfeld, K.: Parameterization dependence of the hydrological cycle in a general circulation model of intermediate complexity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1328, https://doi.org/10.5194/egusphere-egu21-1328, 2021.

As an Earth system model of intermediate complexity (EMIC), the University of Victoria Earth system climate model (UVic-ESCM) has a comparably low computational cost (4.5–11.5 h per 100 years on a simple desktop computer). It is therefore a well-suited tool to perform experiments that are not yet computationally feasible in a state-of-the-art Earth system model. For example, the UVic-ESCM can be used to perform large perturbed parameter ensembles to constrain uncertainties, but also run a multitude of scenarios while at the same time simulating a well resolved carbon cycle. Thanks to its representation of many important components of the carbon cycle and the physical climate and its ability to simulate dynamic interactions between them, the UVic-ESCM is additionally a more comprehensive tool for process level uncertainty assessment compared to integrated assessment models (IAMs).

The coupling of this EMIC with an atmospheric chemistry module based on the FAIR simple climate model, now allows to directly implement GHG emission files as an input to the model, which makes it a valuable tool for many ‘what-if’ questions about climate turnaround times. Especially in the context of assessing the carbon cycle responses to future long-term climate change scenarios including e.g. marine CDR or terrestrial CDR implementations. In this presentation we will introduce this new model setup and show examples of first applications of this novel tool, while showcasing the advantages that it brings about. 

How to cite: MacIsaac, A. J., Mengis, N., Zickfeld, K., and Nzotungicimpaye, C.-M.: New applications for an EMIC coupled to an atmospheric chemistry model - The University of Victoria Earth system climate model version 2.10 + FAIR chemistry module, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13072, https://doi.org/10.5194/egusphere-egu21-13072, 2021.

The shelf represents a relatively small fraction of global oceanic area but plays an important role in the global carbon cycle because of high production and burial of organic matter and calcium carbonate. Biological processes on the shelf can greatly alter the partial pressure of dissolved CO2, causing disequilibrium with the atmosphere and fluxes significantly larger than those in the open ocean. Also the transport of major ions from land to open ocean is mediated by shelf processes. Available models resolving the governing processes are typically designed to simulate specific regions. Global carbon cycle models typically implement all shelf processes in one simple box. Global earth system models typically impose a flux of riverine export products from land directly into the open ocean without accounting for processes in the coastal zone. However, the global role of the coastal zone in the carbon cycle on various time scales remains poorly quantified, partly due to the large variability in continental margin environments, hampering proper understanding of past, present and future global carbon cycle dynamics.
We develop a new coastal zone model that links river biogeochemistry with open ocean models, focusing on the transfer of carbon. Our first approach represents a box model in which number, size and depth of boxes can be varied. We apply global fluxes of carbon into the system and include functions describing first order organic and inorganic carbon processes in each of the boxes. With this conceptual model of the coastal zone we aim to test the effect of changes in bathymetry, temperature and light attenuation on the way carbon is transferred through the coastal interface, suitable for paleo and future applications.

How to cite: Kruijt, A., Middelburg, J., and Sluijs, A.: Coastal carbon transfer in the past - a box model study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8700, https://doi.org/10.5194/egusphere-egu21-8700, 2021.

The Atlantic Meridional Overturning Circulation (AMOC) plays an important role in regulating the climate of the Northern Hemisphere. Several studies have shown that the AMOC can be in two stable states under equal forcing. This bistability, and associated tipping behavior, has been suggested as a mechanism for climate transitions in the past such as the Dansgaard-Oescher events. The relationship between AMOC variability and that in atmospheric pCO2 concentration is still unclear since different studies provide  contradictory results. Here, we investigate this  relationship using the Simple Carbon Project Model v1.0 (SCP-M), which we extended to represent a suite of nonlinear  carbon cycle feedbacks. By implementing SCP-M in the continuation and bifurcation software AUTO-07p, we can efficiently explore the multi-dimensional parameter space to address the AMOC - pCO2 relationship while varying the strengths of the carbon cycle feedbacks. We do not find multiple equilibria in the carbon-cycle dynamics, with fixed AMOC, but there are  intrinsic oscillations due to Hopf bifurcations with multi-millennial periods. The mechanisms of  this variability,  related to biological production and to calcium carbonate compensation, will be presented and their relevance  is addressed. 

How to cite: Boot, D. and Dijkstra, H.: Effect of the Atlantic Meridional Overturning Circulation on atmospheric pCO2, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8960, https://doi.org/10.5194/egusphere-egu21-8960, 2021.

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.

How to cite: Gering, S., Bond-Lamberty, B., and Woodard, D.: Tracking carbon flows through the biosphere: a new capability for the simple climate model Hector, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10395, https://doi.org/10.5194/egusphere-egu21-10395, 2021.

Cost-benefit integrated assessment models (IAMs) include a simplified representation of both the anthropogenic and natural components of the Earth system, and of the interactions and feedbacks between them. As such, they embed economic- and physics-based equations, and the uncertainty in one domain will inevitably affect the other. Most often, however, the physical uncertainty is explored by testing the sensitivity of the optimal mitigation pathway to a few key physical parameters; but for robust decision-making, the optimal pathway itself should ideally embed the uncertainty.

Here, we present a new physical module for cost-benefit IAMs that is based on state-of-the-art climate sciences. The module follows well-established formulations that were deemed a good trade-off between simplicity and accuracy. Therefore, its overall complexity remains low, as is necessary to be used with optimisation algorithms, but able to reproduce the behaviour of more complex CMIP models. It is made of four components that all exhibit a degree of non-linearity: global climate response, ocean carbon cycle, land carbon cycle, and permafrost carbon system. (Two impact components were also developed: surface ocean acidification, and sea-level rise response.)

The calibration of this new module is done through Bayesian inference. Prior distributions of the module’s parameters are taken from CMIP multi-model ensembles, and prior distributions of historical constraints are taken from observational datasets (such as global mean surface temperature) and other synthesis exercises (such as IPCC reports or the global carbon budget). The Bayesian calibration itself is done with a full-rank automatic differentiation variational inference (ADVI) algorithm, which leads to posterior distributions of parameters that are consistent with observations. Additionally, the full-rank ADVI algorithm also finds correlations between parameters (i.e. co-distributions) that tend to further reduce the uncertainty in projected climate change.

We then implement this new module within the DICE model (that is likely the most widely used cost-benefit IAM), and we demonstrate a significant improvement of the physical modelling, and thus of the IAM’s results. We run a Monte Carlo ensemble of 4000 elements taken from the Bayesian calibration, to properly sample the physical uncertainty in the optimal mitigation pathway simulated by DICE. Notably, our new module leads to a social cost of carbon (SCC) of 26 USD / tCO2 (90% range: 13–43), which is lower than 37 USD / tCO2 in the original model.

This Monte Carlo approach is not a robust one, however, and a final simulation is run to estimate one unique mitigation pathway shared across all 4000 states of the world (by maximizing the total welfare). This robust mitigation pathway is therefore a unique solution that embeds the physical uncertainty, and it is different from the average pathway of the Monte Carlo ensemble. The unicity of the solution (and its lack of explicit uncertainty) makes it very attractive for decision-making and communication purposes. We posit this robust approach could be applied with the cost-optimal IAMs that are used by the IPCC to create and investigate climate change scenarios.

How to cite: Gasser, T., Baklanov, A., Rezai, A., Chéritel, C., and Obersteiner, M.: A Bayesian-inferred physical module to estimate robust mitigation pathways with cost-benefit IAMs, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15363, https://doi.org/10.5194/egusphere-egu21-15363, 2021.

Simple climate models enable not only rapid simulation of a large number of climate scenarios, especially in connection with the integrated assessment models of economy and environment, but also provide chances for outreach and education. En-ROADS, (Energy Rapid Overview and Decision Support)[1], is a publicly available, online policy simulation model designed to complement integrated assessment models for rapid simulation of climate solutions. En-ROADS is a globally aggregated energy-economy-climate model based on a simple climate model, and supports outreach and education about the causes and effects of climate change.  It has an intuitive user interface and runs essentially instantly on ordinary laptops and tablets, providing policymakers, other leaders, educators, and the public with the ability to learn for themselves about the likely consequences of energy and climate policies and uncertainties.

En-ROADS is a behavioral system dynamics model consisting of a system of nonlinear ordinary differential equations solved numerically from 1990-2100, with a time step of one-eighth year. En-ROADS extends the C-ROADS model, which has been used extensively by officials and policymakers around the world to inform positions of parties to the UNFCCC[2][3]. In En-ROADS’ climate module, the resulting emissions from the energy system, from forestry and land use, and carbon removal technologies, determine the atmospheric concentrations of each GHG, radiative forcing, and climate impacts including global surface temperature anomaly, heat and carbon transfer between the surface and deep ocean, sea level rise, and ocean acidification. It is calibrated to fit historical data of temperature change and carbon cycle elements, as well as the projections within the RCP-SSP framework. Both En-ROADS and C-ROADS are further developed to account for the details of the terrestrial carbon cycle.

How to cite: Eker, S., Siegel, L., Jones, C., Sterman, J., Kapmeier, F., Fiddaman, T., Homer, J., Rooney-Varga, J., Franck, T., and Jones, A.: Public Outreach and Interactive Learning with En-ROADS Global Energy and Climate Simulator, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7334, https://doi.org/10.5194/egusphere-egu21-7334, 2021.