Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model

Abstract

Global climate Models are key tools for predicting the future response of the climate system to a variety of natural and anthropogenic forcings. Here we show how to use statistical mechanics to construct operators able to flexibly predict climate change. We perform our study using a fully coupled model-MPI-ESM v.1.2-and for the first time we prove the effectiveness of response theory in predicting future climate response to co 2 increase on a vast range of temporal scales, from inter-annual to centennial, and for very diverse climatic variables. We investigate within a unified perspective the transient climate response and the equilibrium climate sensitivity, and assess the role of fast and slow processes. the prediction of the ocean heat uptake highlights the very slow relaxation to a newly established steady state. the change in the Atlantic Meridional overturning circulation (AMoc) and of the Antarctic circumpolar current (Acc) is accurately predicted. the AMoc strength is initially reduced and then undergoes a slow and partial recovery. the Acc strength initially increases due to changes in the wind stress, then undergoes a slowdown, followed by a recovery leading to a overshoot with respect to the initial value. finally, we are able to predict accurately the temperature change in the north Atlantic. Climate change is arguably one of the greatest contemporary societal challenges 1 and one of the grand contemporary scientific endeavours 2. The provision of new and efficient ways to understand its mechanisms and predict its future development is one of the key goals of climate science. Global climate models (GCMs) are currently the most advanced tools for studying future climate change; their future projections are key ingredients of the reports of the Intergovernmental Panel on Climate Change (IPCC) and are key for climate negotiations 3. For IPCC-class GCMs, future climate projections are usually constructed by defining a few climate forcing scenarios, given by changes in the composition of the atmosphere and in the land use, each corresponding to a different intensity and time modulation of the equivalent anthropogenic forcing. Typically, for each scenario an ensemble of simulations is performed, with each member differing in terms of initial conditions, applied forcing or chosen physical parametrizations. Subsequent phases of the Coupled Model Intercomparison Project (CMIP, currently the sixth phase CMIP6 is active 4), which is part of the Program for Climate Model Diagnosis and Intercomparison (PCMDI), allowed the definition of standardized experimental protocols for numerical simulations performed with GCMs and for the evaluation of the GCMs runs 5,6. A bottleneck of this approach is that the consideration of an additional forcing scenario requires running a new ensemble of simulations. Additionally, for each forcing scenario, it is hard to disentangle the impact of each component of the forcing, e.g. different greenhouse gases with their concentration pathways and land surface alterations in geographical...

Relating climate sensitivity indices to projection uncertainty

“…However, the analytical set of solutions for the temperature response to a step change in forcing is the same in either case -a superposition of decaying exponential modes with different timescales varying between a few years and a few centuries (Proistosescu and Huybers, 2017). It has been shown that the implications of these additional degrees of freedom and ambiguity over contributions from different timescales of response might imply that EffCS may not be strongly constrained by temperature change over the last century (Proistosescu and Huybers, 2017;Andrews et al, 2018), and that the long-term equilibrium (LTE) sensitivity may be greater than that implied by EffCS (Otto et al, 2013;Lewis, 2013).…”

Identification of linear response functions from arbitrary perturbation experiments in the presence of noise – Part II. Application to the land carbon cycle in the MPI Earth System Model

“…In all of the mentioned examples, the generality of the RFI method allows for the derivation of the appropriate linear response functions for any model by taking only data from an arbitrary perturbation experiment and a control experiment. Such generality opens the possibility of combining the linear response framework, which has been gaining increasing attention due its wide applicability in climate science (e.g., Lucarini, 2009;Lucarini and Sarno, 2011;Lucarini et al, 2014;Ragone et al, 2016;Lucarini et al, 2017;Aengenheyster et al, 2018;Ghil and Lucarini, 2020;Lembo et al, 2020;Bódai et al, 2020), with model intercomparison studies, hopefully leading to a deeper understanding of critical differences encountered across models.…”

Correcting for model changes in statistical postprocessing – an approach based on response theory

“…This assumption is called the Gallavotti-Cohen hypothesis (Gallavotti and Cohen, 1995a, b). With this assumption, response theory has been successfully used in various weather and climate-related problems (Demaeyer and Vannitsem, 2018;Vissio and Lucarini, 2018;Lembo et al, 2019;Bódai et al, 2020). Indeed, the systems used to produce weather forecasts are typically not uniformly hyperbolic, but thanks to the aforementioned hypothesis, one can still use what will follow and compare with the results obtained with experiments.…”