CL5.1.2

Exploration of future emissions scenarios mostly relies on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Such approaches are unable to reliably predict climate variables which respond non-linearly to emissions or forcing (such as precipitation) and must rely on heavily simplified representations of e.g., aerosol, neglecting important spatial dependencies.

Here we present ClimateBench - a benchmark dataset based on a suite of CMIP, AerChemMIP and DAMIP simulations performed by NorESM2, and a set of baseline machine learning models that emulate its response to a variety of forcers. These surrogate models can skilfully predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and spatially resolved aerosol. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, consistency and mathematical tractability.

We hope that by defining a clear baseline with appropriate metrics and providing a variety of baseline models we can bring the power of modern machine learning techniques to bear on the important problem of efficiently and robustly sampling future climates.

How to cite: Watson-Parris, D., Rao, Y., Olivié, D., Seland, Ø., Nowack, P., Camps-Valls, G., Stier, P., Bouabid, S., Dewey, M., Fons, E., Gonzalez, J., Harder, P., Jeggle, K., Lenhardt, J., Manshausen, P., Novitasari, M., Ricard, L., and Roesch, C.: ClimateBench: A benchmark for data-driven climate projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3961, https://doi.org/10.5194/egusphere-egu22-3961, 2022.

Glacier modelling of the Alpine region during past ice ages has received increasing attention in the recent years. Considering the complexity of the Alpine topography, high spatial resolution climate information is required for running glacier models over the area. However, continuous climatic signal at resolution higher than 10 km and covering several hundred thousand of years cannot be directly derived using dynamical climate models. Alternative strategies must be considered.

Here, a climate emulator providing monthly temperature and precipitation over the Alpine region, with a horizontal resolution of 2x2 km and covering the last 400’000 years at intervals of 100 years, is presented. The emulatorcombines a dynamical modelling chain of Earth  System Models (ESMs) and a Regional Climate Model (RCM) with different statistical modelling methods. The dynamical modelling chain delivers climate information at highest accuracy based upon physical prognostic equations for specific time slices. The subsequent statistical modelling uses these time slices as physically consistent boundaries and estimates the climate conditions in between them, thus generatinga long-term climate evolution.

A total of 19 climate model experiments are conducted for different time-slices of the considered study period, including also sensitivity tests with changes in the ice sheet height of the Northern Hemisphere and land cover type. Starting from a simple linear regression, a series of different statistical approaches is tested for building the emulator. An evaluation of the different versions is then conducted against one of the RCM time-slice experiments, left out in turns from the training set of the statistical model.

Results show robust skills of the emulator in the representation of temperature, whose changes are mainly driven by smooth variations in the seasonal pattern of insolation at different time-steps, already using a simple linear regression. For precipitation, non-linearities associated to changes in the large-scale atmospheric circulation seem to dominate, making the use of more complex statistical approaches more appropriate. Additional evaluation tests conducted using glacier modelling driven by the outputs of the developed emulator confirm its potential for reconstructing the ice extent over the Alpine region during the last ice ages.

How to cite: Russo, E., Buzan, J., Jouvet, G., Cohen, D., and Raible, C. C.: A regional climate emulator to estimate glacial-interglacial changes over the Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11514, https://doi.org/10.5194/egusphere-egu22-11514, 2022.

The Absolute Global Temperature change Potential (AGTP) and Absolute Global Precipitation change Potential (AGPP) are widely used climate change indices.  They can be applied quickly and easily to estimate the global mean temperature and precipitation responses to a pulse emission of a long-lived climate pollutant at a given time horizon, making them invaluable policy-relevant metrics.  They can also be extended to short-lived climate pollutants - where a sustained emission is more useful to consider than a pulse emission - by using their time-integrated forms (iAGTP and iAGPP).

However, these metrics are only useful when taking a global-average perspective, and do not allow us to account for the regional nature of either emissions or their climate response.  Although long-lived greenhouse gases induce a relatively homogeneous radiative forcing (RF) which is not sensitive to emission location, nonetheless due to transport of heat there is not a one-to-one correspondence between the RF in a region and the local temperature response.  Moreover when considering short-lived pollutants such as aerosols, the region of emission is potentially critical because the short lifetime of such pollutants results in an inhomogeneous distribution of RF.  Therefore, for both long-lived and short-lived pollutants the AGTP/AGPP (or iAGTP/iAGPP) are not adequate when looking at climate responses on a regional scale, even though this would be the most relevant when evaluating different policy scenarios or climate change impacts.

Here, we combine the results of simulations from the Precipitation Driver Response Model Intercomparison Project (PDRMIP) where emissions (or concentrations) of multiple long- and short-lived climate pollutants were perturbed globally in nine different climate models, with the results of simulations using the HadGEM3 model where sulfate aerosol emissions are perturbed one at a time in several key geopolitical regions: the United States, Europe, India, East Asia, or the whole Northern Hemisphere Mid-Latitudes.  We use these results to adapt the (i)AGTP/(i)AGPP to the case where both the emission and the response are regional.  Data from the regional HadGEM3 simulations allow us to estimate normalised regional forcing-response relationships for aerosols, whilst the PDRMIP multi-model means and ensemble spread are used to derive estimates of radiative efficiency for both long- and short-lived pollutants and their corresponding uncertainties, as well as the regional climate sensitivities for long-lived pollutants.

Finally, using these regional temperature and precipitation change potentials, we produce a simple model in Python which allows the user to specify arbitrary combinations of different future emission scenarios for different pollutants from different regions, allowing rapid projections of the regional climate responses to diverse emissions policies.

How to cite: Kasoar, M., Corsaro, C., and Voulgarakis, A.: Metrics for Regional Climate Responses to Regional Pollutant Emissions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11069, https://doi.org/10.5194/egusphere-egu22-11069, 2022.

A statistical framework to assess the long-term climatic water balance changes includes the following phases of the data analysis and predictions: (1) Preparation of daily, monthly, and yearly averaged time series of meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), and an evaluation of the temporal structural breakpoints (breakthroughs) of meteorological parameters trends, (2) calculations of potential evapotranspiration, aridity index, actual evapotranspiration (ET), Standard Precipitation Index (SPI), and Standard Precipitation-Evapotranspiration Index (SPEI), as well as an evaluation of breakthroughs of their trends, (3) climatic zonation based on the application of the hierarchical k-means and Principal Component Analysis (PCA) clustering of temporal trends of ET and SPEI for the periods before and after the breakthroughs, and (4) simple forecasting hierarchical time series for different forecasting situations.

The statistical framework was applied to 17 locations at the East River watershed for the period from 1966 to 2020. Structural changes of time trends of measured and calculated water balance parameters are used to determine the time of abrupt climatic changes and breakthroughs. Calculations of the evapotranspiration are conducted using the Budyko model, with the potential/reference evapotranspiration (ETo) calculated using the Penman-Monteith (PM) equation. The results of calculations of ETo based on the PM model were compared to the ETo calculated using the Thornthwaite and Hargreaves equations. The results of the hierarchical clustering using ET and SPEI are illustrated using the tree dendrograms and the PCA plots of clusters of the studied sites for the periods of before and after the breakthroughs. A significant shift in the cluster arrangements for the time periods before and after the temporal structural breakpoints indicate that zonation patterns are driven by dynamic climatic processes, which are variable through time, and the watershed zonation requires periodic re-evaluation. Examples of time series forecasting are also shown.

How to cite: Faybishenko, B., Arora, B., Dwivedi, D., and Brodie, E.: A statistical framework to assess time trend and predict near-future climatic conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12999, https://doi.org/10.5194/egusphere-egu22-12999, 2022.

Earth System Models (ESMs) are essential for understanding the dynamics of our climate system, but their computational costs make nuanced investigations of future climatic conditions difficult. Using statistical techniques, computationally efficient tools known as emulators, which mimic ESM simulations, can be built. Emulators allow to (i) project the regional climate change for a broad variety of emission scenarios and to (ii) thoroughly sample the uncertainty space associated with natural variability as well as structural model uncertainties. Both tasks would be computationally infeasible with actual ESMs. In this contribution, we introduce a probabilistic, bivariate ESM emulation framework that produces joint monthly spatial fields of temperature and precipitation for a given global mean temperature trajectory. This contribution adds to the existing modular MESMER framework developed by Beusch et al. (2020). The building blocks of the new emulator are: (i) A module for approximating the annual global mean temperature trajectory from ESM output. This module is adapted from the existing MESMER framework. (ii) A module capturing the deterministic local response of monthly temperature and precipitation to global mean temperature. The response function is assumed to be linear with coefficients fitted independently for each month, grid-cell and variable. (iii) A module capturing the residual variability, that follows a probabilistic, non-parametric approach to reproduce spatial and temporal variance, covariance and cross-covariance structures of both variables. The emulator is trained and tested on ESM ensembles generated during CMIP6. Near-term development steps include the quantification of inter-ESM differences through the trained parameters and the coupling of the emulator to the simple climate model MAGICC (Meinshausen et al., 2020) to explore the emission scenario space.

Beusch, Lea, Lukas Gudmundsson, and Sonia I. Seneviratne. "Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land." Earth System Dynamics 11.1 (2020): 139-159.

Meinshausen, Malte, et al. "The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500." Geoscientific Model Development 13.8 (2020): 3571-3605.

How to cite: Schöngart, S., Lejeune, Q., Schleußner, C.-F., Seneviratne, S., Gudmundsson, L., and Beusch, L.: A spatially explicit approach for joint temperature-precipitation emulation of Earth System Model simulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11049, https://doi.org/10.5194/egusphere-egu22-11049, 2022.

In this paper, we propose a new, fully statistical, reduced complexity climate model. The starting point for our model is a number of physical equations for the global climate system, which we show how to cast in non-linear state-space form. The resulting model incorporates measurement errors, capturing the fact that observations of physical quantities might be contaminated by error, as well as internal stochastic error processes, capturing the fact that the physical equations used are approximations to the true underlying  climate system. The state-space formulation allows for statistical estimation of the parameters in the model, using the method of maximum likelihood, as well as filtering and smoothing of latent quantities in the model, such as ocean and surface temperatures. Further, the explicit statistical formulation of the model allows for conducting a number of useful analyses, such as the estimation of parameter uncertainty, model selection, and probabilistic scenario analysis. 

By considering a range of different scenarios for greenhouse gas emissions, we set up simulation studies that can be used to investigate  the effect that a given scenario has on parameter estimates. We find substantial differences in the performance of the estimation procedure, depending on the precise scenario considered. These investigations can help decide what kind of data are best suited for estimating/calibrating the parameters of reduced complexity climate models, e.g. to what extend the historical data record can be used to reliably estimate parameters and/or which CMIP experiments are best suited for calibrating such models.

Using a data set of historical observations from 1959-2020, we estimate the model and report key parameter estimates and associated standard errors. A likelihood ratio test sheds light on the most appropriate equation for converting the atmospheric concentration of carbon dioxide (GtC) into forcings (W/m2). We then use the estimated model and assumptions on future greenhouse gas emissions to project global mean surface temperature out to the year 2100. The statistical nature of the model allows us to attach uncertainty bands to the projections, as well as quantify how much of the uncertainty is "aleatoric" (uncertainty arising from the internal variability of the climate system) and how much is "epistemic" (uncertainty arising from unknown model parameters). We find that epistemic uncertainty is by far the most important contributor to the uncertainty on the projected future global temperature increase.

How to cite: Bennedsen, M., Hillebrand, E., and Koopman, S. J.: A New Statistical Reduced Complexity Climate Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6346, https://doi.org/10.5194/egusphere-egu22-6346, 2022.

The Earth system response to climate forcers can be broken down to multiple timescales, with the land surface responding within a few years to a change in forcing while the deep ocean layers have only fully equilibrated after several hundreds to thousands of years. In this work we assume that there is a number of distinct timescales represented in the thermal response to pulse injections of different climate forcers in the Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth System Models (ESMs), which can be estimated by fitting a sum of decaying exponential responses  to a set of non-noisy Empirical Orthogonal Functions of each model. Using these exponential decay functions and a regression-based pattern scaling approach we are able to emulate the gridded transient surface temperature response to an input forcing timeseries. We determine that for the abrupt-4xCO2 experiment the thermal response in most CMIP6 ESMs can be represented by a similar set of timescales, but early results suggest diverse spatial warming patterns. This work introduces the concept that the evolving spatial patterns associated with the thermal response on different timescales for pulse injections of different climate forcers can be simply and accurately emulated and ultimately be used to predict transient simulations.

How to cite: Baur, S., Sanderson, B., Séférian, R., and Terray, L.: Thermal response timescales and associated spatial patterns in ESMs to pulse injections of climate forcers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5538, https://doi.org/10.5194/egusphere-egu22-5538, 2022.

Scientifically rigorous guidance to policy makers on mitigation options for meeting the Paris Agreement long-term temperature goal requires an evaluation of long-term global-warming implications of greenhouse gas emissions pathways. Here, we present a uniform and transparent methodology to evaluate the climate outcome, and hence the Paris Agreement consistency of influential institutional emission scenarios from the grey literature, including those from the International Energy Agency^1,2, BP³, and Shell⁴. We first identify challenges to performing such an assessment and then proceed to outline a sequence of steps to address these challenges by harmonizing⁵ all emissions to a consistent base-year (2010), extending all pathways to 2100, and filling in missing emission species⁶. We employ two simple climate models, MAGICC⁷ and FaIR^8,9 to assess peak and end-of-century temperatures, and find that few published scenarios that claim to be compatible with the Paris Agreement are so.

References

 1. International Energy Agency. World Energy Outlook 2020. (2020).

2. International Energy Agency. Net Zero by 2050 - A Roadmap for the Global Energy Sector. (2021).

3. BP. Global Energy Outlook 2020. (2020).

4. Shell. The Energy Transformation Scenarios. (2021) 

5. Gidden, M. J. et al. A methodology and implementation of automated emissions harmonization for use in Integrated Assessment Models. Environ. Model. Softw. 105, 187–200 (2018)

6. Lamboll, R. D., Nicholls, Z. R. J., Kikstra, J. S., Meinshausen, M. & Rogelj, J. Silicone v1.0.0 : an open-source Python package for inferring missing emissions data for climate change research. Geosci. Model Dev 13, 5259–5275 (2020)

7. Meinshausen, M., Raper, S. C. B. & Wigley, T. M. L. Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 1: Model description and calibration. Atmos. Chem. Phys. 11, 1417–1456 (2011).

8. Smith, C. J. et al. FAIR v1.3: A simple emissions-based impulse response and carbon cycle model. Geosci. Model Dev. 11, 2273–2297 (2018)

9. Millar, J. R., Nicholls, Z. R., Friedlingstein, P. & Allen, M. R. A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. Atmos. Chem. Phys. 17, 7213–7228 (2017).

How to cite: Ganti, G., J. Brecha, R., D. Lamboll, R., Nicholls, Z., Hare, B., Lewis, J., Meinshausen, M., Schaeffer, M., J. Smith, C., and J. Gidden, M.: Assessing the consistency of institutional pathways with the Paris Agreement, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9739, https://doi.org/10.5194/egusphere-egu22-9739, 2022.

The physical reality of the Earth system implies that there are clear conditions to respect the Paris agreement, or to limit any climate impact below a certain level. To be policy relevant, these conditions should be expressed in terms of emission targets, such as peaking date, budget, or annual value of global CO2 emissions. They have been explored by the IPCC using integrated assessment models. However, past work has focused on bottom-up scenarios, and on temperature as the only metric evaluating climate impacts, even though not all impacts are linearly related to it.

Here, we show that for these emission targets, across thousands of scenarios we have generated, there are points of no return after which limiting a given climate impact becomes geophysically infeasible. In addition to the Paris Agreement objectives (consisting of a 1.5 °C temperature target above pre-industrial (PI) era possibly overshot by no more than 0.5°C), we investigate three other climate targets:  ocean acidification, sea-level elevation rate, and Arctic sea-ice melting. We use a newly developed model called PathFinder; a reduced-form carbon-climate model that also emulates the three global climate impacts we investigate. The model is calibrated through Bayesian inference, using outputs from the state-of-the-art CMIP6 models as prior parameters, and the latest IPCC assessment and observations of the Earth system as constraints. This advanced calibration is enabled by the model’s capacity of using temperature and atmospheric CO2 concentration as inputs (instead of anthropogenic emissions and non-CO2 radiative forcing).

Thanks to this backward approach, we demonstrate that, for every emission target considered, the combination of climate impact targets is non-linear. While the Paris Agreement insists on the importance of reaching a carbon neutral world in 2050, our results show that global CO2 emissions must peak before 2030 but do not have to reach net-zero to keep all targets reachable with at least 50% chances. We also highlight the inevitable role of geoengineering technologies in reaching the Paris Agreement, as chances to keep it reachable goes from at least 69% if SRM or CDR are available to 10% if none of them is.

How to cite: Bossy, T., Gasser, T., Ciais, P., Tanaka, K., and Lecocq, F.: Points of no return to respect the Paris Agreement, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1075, https://doi.org/10.5194/egusphere-egu22-1075, 2022.

Simple climate models (SCMs) are most often composed of ad hoc parametric laws that emulate the behaviour of more complex Earth system models (ESMs). The emulation allows investigating experiments or scenarios that would be too costly to compute with ESMs. However, the “SCM” denomination refers to a fairly broad range of models whose complexity can go from a couple of boxes that only emulate one part of the climate system (e.g. a global temperature impulse response function) to hundreds or thousands of boxes representing the different cycles of greenhouse gases and induced climate change (e.g. the compact Earth system model OSCAR). Simpler models are easier and faster to solve, but they may not adequately represent physical processes. Therefore, finding the “simplest but not simpler” model depends on a study’s precise goals.

We developed the Pathfinder model to remedy a deficiency within the spectrum of existing SCMs. Pathfinder is a compilation of existing formulations describing the climate and carbon cycle systems, chosen for their balance between mathematical simplicity and physical accuracy. The resulting model is simple enough that it can be used with Bayesian inference algorithms for calibration, which enables integration of the latest data from CMIP6 Earth system models and the IPCC AR6, as well as a yearly update using observations of global temperature and atmospheric CO2. The model’s simplicity also enables coupling with integrated assessment models (IAMs) and their optimization algorithms, or simply running the model in a backward temperature-driven fashion. In spite of this simplicity, the model accurately reproduces behaviours and results from complex models – including uncertainty ranges – when ran following standardized diagnostic experiments.

Here, we will briefly describe the Pathfinder model, demonstrate its performance, and illustrate its strengths and potential with two example studies. The first one combines a very large-scale ensemble of climate change scenarios generated procedurally, and the physical uncertainty sampling extracted from the Bayesian calibration, to determine which future CO2 emissions pathways remain compatible with the Paris agreement. The second one couples Pathfinder with a stylized IAM and climate impact emulators, to generate cost-effective pathways that limit permafrost carbon thaw, sea level rise speed, and ocean surface acidification.

How to cite: Gasser, T.: Pathfinder: a simple yet accurate carbon-climate model to explore climate change scenarios, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12530, https://doi.org/10.5194/egusphere-egu22-12530, 2022.