The evolution of the Earth’s climate from the past to the future is explored by a hierarchy of models ranging from conceptual models to full-complexity, high-resolution Earth System Models (ESMs) (Claussen et al., 2002). The strength of conceptual models lies in the clarity of representing the concept of interactions between different climate processes, while ESMs  offer greater realism when it comes to spatial or temporal detail. Intermediate complexity models are somewhere in between, they are able to provide a big picture for long timescales. A common pattern throughout the model hierarchy, except for conceptual models illustrating multiple steady states, is often linearity of model responses to external forcing. This linearity can be visible in transient experiments, but also in equilibrium simulations. The question arises: is this linearity an artefact of our models, or is it reflective of reality?

In most cases, the linear response is likely representative of reality. As an example, we will focus on the linearity of land-related processes, such as climate-carbon feedbacks and permafrost-hydrology interactions. Permafrost systems have thresholds at 0°C, leading to nonlinearities at the local scale, but the combined response at large spatial scales tends to be more linear. However, nonlinear and abrupt changes are evident in geological records. For instance, the abrupt onset of the Bölling/Alleröd warming about 14.8 thousand years ago indicates that nonlinear changes on large spatial scales are indeed a real, albeit very rare, phenomenon. We will discuss possible reasons for the predominant linearity of the models and explore whether high-resolution models might show more nonlinear responses than coarse-grid models.

Reference:

Claussen, M., Mysak, L., Weaver, A. et al. Earth system models of intermediate complexity: closing the gap in the spectrum of climate system models. Climate Dynamics 18, 579–586 (2002). https://doi.org/10.1007/s00382-001-0200-1

How to cite: Brovkin, V., Stacke, T., de Vrese, P., Kleinen, T., and Winkler, A.: From conceptual to complex earth system models: why are models so linear?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22102, https://doi.org/10.5194/egusphere-egu24-22102, 2024.

The climate system can be numerically represented by a set of physically-based dynamical equations whose solution requires substantial computational resources. This makes computationally efficient, low dimensional emulators that simulate trajectories of the underlying dynamical system an attractive alternative for model evaluation and diagnosis. We suggest that since such an emulator must adequately capture anomaly evolution, its construction should employ a grid search technique where maximum forecast skill determines the best reference model. In this study, we demonstrate this approach by testing different bases used to construct a Linear Inverse Model (LIM), a stochastically-forced multivariate linear model that has often been used to represent the evolution of coarse-grained climate anomalies in both models and observations. LIM state vectors are typically represented in a basis of the leading Empirical Orthogonal Functions (EOFs), but while dominant large-scale climate variations often are captured by a subset of these statistical patterns, key precursor dynamics involving relatively small scales are not. An alternative approach is balanced truncation, where the dynamical system is transformed into its Hankel space, whose modes span both precursors and their subsequent responses. Constructing EOF- and Hankel-based LIMs from monthly observed anomalous Pacific sea surface temperatures, both for the 150-yr observational record and a perfect model study using 600 yrs of LIM output, we find that no balanced truncation model of any dimension can outperform an EOF-based LIM whose dimension is chosen to maximize independent skill. However, the dynamics of a high-dimensional EOF-based LIM can be efficiently reproduced by far fewer Hankel modes.

How to cite: Xu, T., Newman, M., Alexander, M., and Capotondi, A.: A Forecast Test for Reducing Dynamical Dimensionality of Model Emulators, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14067, https://doi.org/10.5194/egusphere-egu24-14067, 2024.

The Atlantic Meridional Overturning Circulation (AMOC) exerts a major influence on global climate. There is much debate about whether the current strong AMOC may collapse as a result of anthropogenic forcing and/or natural variability. Here, we ask whether internal decadal variability could affect the likelihood of AMOC collapse. We examine natural variability of basin-scale salinities and temperatures in four CMIP6 pre-industrial runs. We fit the CMIP6 variability to several empirical, linear noise models, and to a nonlinear, process-based AMOC model. The variability is weak and its processes inconsistent among the CMIP6 models considered. Based on the CMIP6 variability levels we find that noise-induced AMOC collapse is unlikely in the pre-industrial climate, but plausible if external forcing has shifted the AMOC closer to a threshold, which can be identified for the non-linear model using bifurcation analysis. However the CMIP6 models may systematically underestimate current Atlantic Ocean variability, and we find that substantially stronger variability would increase the likelihood of noise-induced collapse.

How to cite: Chapman, R., Ashwin, P., Wood, R., and Baker, J.: Quantifying risk of a noise-induced AMOC collapse from northern and tropical Atlantic Ocean variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8729, https://doi.org/10.5194/egusphere-egu24-8729, 2024.

Earth-system Models of Intermediate Complexity (EMICs) are climate models featuring a simplified representation of climate processes and a much lower computation cost. This makes them particularly suitable for exploring phenomena with a large ensemble simulation approach. Here we use the coupled atmosphere-ocean PlaSIM-LSG EMIC to study the possibility of Atlantic Meridional Overturning Circulation (AMOC) spontaneous collapses and how this is altered in the presence of external anthropogenic forcing. Understanding the stability of the AMOC and its response to anthropogenic forcing is of key importance for advancing climate science. The idea of a “safe-operating space” has been proposed in order to define a threshold on anthropogenic forcing within which the AMOC does not lose stability. This requires understanding the combined action of CO2-driven and noise-induced processes in climate tipping events

 First, we address the occurrence of noise-induced AMOC collapses, i.e. spontaneous abrupt weakening  events induced by chaotic internal climate variability in absence of any external forcing. We address the problem of finding these extreme events via the application of a Rare Event Algorithm, which - via a selective cloning of the most interesting model trajectories -  allows a faster exploration of the model phase space in the direction of an AMOC decrease. The algorithm is applied to a PlaSIM-LSG ensemble simulation run at T21 spectral resolution in the atmosphere, and 3.5 degrees in the ocean, with fixed pre-industrial conditions. A number of collapse events, unseen in the pre-industrial control run, are sampled by the algorithm. Looking at the mechanisms causing the AMOC spontaneous collapse, we find that zonal wind stress over the North Atlantic is the main driver of the initial AMOC slowdown, while the suppression of surface convection in the Labrador sea is the likely cause of the subsequent AMOC collapse. Then, we investigate the influence of increasing CO2 levels on the frequency of these spontaneous AMOC collapses. We show that a higher CO2 not only leads to the well-known weakening of the AMOC mean state, but it also increases the possibility of incurring in abrupt noise-induced transitions. The employment of EMICs, combined with the proposed approach, samples a large number of rare phenomena. This procedure allows us to explore statistical properties that are not accessible with a deterministic approach in state-of-the-art high resolution models.

How to cite: Cini, M., Zappa, G., Ragone, F., and Corti, S.: Exploring Noise-induced and CO2-driven AMOC collapses in the PlaSIM-LSG climate model with a Rare Event Algorithm., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1036, https://doi.org/10.5194/egusphere-egu24-1036, 2024.

A fully-coupled general circulation model of intermediate complexity is documented. The study presents an overview of the model climatology and variability, with particular attention for the phenomenology of processes that are relevant for the predictability of the climate system on seasonal-to-decadal time-scales. It is shown that the model can realistically simulate the general circulation of the atmosphere and the ocean, as well as the major modes of climate variability on the examined time-scales: e.g. El Niño-Southern Oscillation, North Atlantic Oscillation, Tropical Atlantic Variability, Pacific Decadal Variability, Atlantic Multi-decadal Variability. We demonstrate the ability of the model in simulating non-stationarity of coupled ocean-atmosphere modes of variability. Potential applications of the model are discussed, with emphasis on the possibility to generate sets of low-cost large-ensemble retrospective forecasts. We argue that the presented model is suitable to be employed in traditional and innovative model experiments that can play a significant role in future developments of seasonal-to-decadal climate prediction.

How to cite: Ruggieri, P., Abid, M. A., Garcia-Serrano, J., Grancini, C., Kucharski, F., Pascale, S., and Volpi, D.: SPEEDY-NEMO: performance and applications of a fully-coupled intermediate-complexity climate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14831, https://doi.org/10.5194/egusphere-egu24-14831, 2024.

We utilize ideas from signal processing to demonstrate a novel methodology for climate emulation based on the response of the climate system to effective radiative forcing (ERF). While previous work has demonstrated the efficacy of impulse response functions as a tool for climate emulation, these methods are largely non-generalizable to new scenarios and are inaccessible to more general audiences. To remedy this, we propose a generalizable framework for emulation of climate variables such as near-surface air temperature, representing the climate system through the surrogate of spatially resolved impulse response functions. These response functions are derived through the deconvolution of ERF and near-surface air temperature profiles, treating ERF and near-surface air temperature as input and output signals, respectively. Using this framework, new scenarios can be quickly and easily emulated through convolution and other sets of impulse response functions can be derived from any pair of climate variables. We present results from an application to near-surface air temperature based on ERF and temperature data taken from experiments in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). We evaluate the emulator using additional experiments taken from the CMIP6 archive, including the Shared Socioeconomic Pathways (SSPs), demonstrating accurate emulation of global mean and spatially resolved temperature change with respect to the outputs of the CMIP6 ensemble. Global absolute error in emulated temperature averages 0.25 degrees Celsius with a bias ranging from -0.14 to -0.04 degrees Celsius. We additionally show how our emulator can be implemented as a tool for climate education through integration with the En-ROADS platform, providing fast visualizations of spatially resolved temperature change for a number of policy-relevant scenarios. While it is unable to capture state-dependent climate feedbacks, such as the non-linear effects of Arctic sea ice melt in high-warming scenarios, our results show that the emulator is generalizable to any scenario independent of the specific forcings present.

How to cite: Womack, C., Eckley Selin, N., and Eastham, S.: Rapid Emulation of Spatially Resolved Temperature Response Functions to Effective Radiative Forcing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18412, https://doi.org/10.5194/egusphere-egu24-18412, 2024.

The Amazon rainforest, one of the most important biomes in the world, and recognized as a potential tipping element in the Earth system, has received increasing attention in recent years. Theory and observations suggest that regional climate change from greenhouse gas emissions and deforestation may push the remaining forest toward a catastrophic tipping point.

Despite the urgency to assess the future fate of the Amazon, it remains unclear if state-of-the-art Dynamic Global Vegetation Models (DGVMs) can capture the highly nonlinear dynamics underlying such potentially abrupt dynamics and there is a noticeable scarcity of DGVM evaluations regarding their potential to predict forthcoming tipping points.

In our manuscript, we systematically investigate how the Amazon forest responds in idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero, using the state-of-the-art model LPJmL. We investigate whether large-scale abrupt changes and tipping points occur, and whether early warning signals as expected from theory can be detected. 

Our results indicate a pronounced nonlinearity but reversible behavior between vegetation aboveground biomass (AGB) and mean annual precipitation (MAP) in the LPJmL simulations. In particular, there exists a threshold at a critical rainfall level below which there is a rapid decrease in forest biomass. The value of the threshold is determined by seasonality, evapotranspiration and the adaptive capacity of roots. Significant "early warning signs" can be detected before the transition.

How to cite: Nian, D., Bathiany, S., Sakschewski, B., Drüke, M., Blaschke, L., Ben-Yami, M., von Bloh, W., and Boers, N.: The critical precipitation threshold for the Amazon forest biomass in the LPJmL vegetation model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5570, https://doi.org/10.5194/egusphere-egu24-5570, 2024.