NP1.5 | The climate model hierarchy: Improving process understanding by bridging the gap between conceptual and earth system models
EDI
The climate model hierarchy: Improving process understanding by bridging the gap between conceptual and earth system models
Co-organized by AS5/CL4/OS4
Convener: Reyk BörnerECSECS | Co-conveners: Oliver MehlingECSECS, Raphael RoemerECSECS, Maya Ben YamiECSECS, Richard Wood
Orals
| Tue, 16 Apr, 08:30–10:15 (CEST)
 
Room 0.94/95
Posters on site
| Attendance Mon, 15 Apr, 16:15–18:00 (CEST) | Display Mon, 15 Apr, 14:00–18:00
 
Hall X4
Orals |
Tue, 08:30
Mon, 16:15
Projections of future climate rely on increasingly complex, high-resolution earth system models (ESMs). At the same time, nonlinearities and emergent phenomena in the climate system are often studied by means of simple conceptual models, which offer qualitative process understanding and allow for a broad range of theoretical approaches. Simple climate models are also widely used as physics-based emulators of computationally expensive ESMs, forming the basis of many probabilistic assessments in the IPCC 6th Assessment.

Between these two approaches, a persistent “gap between simulation and understanding” (Held 2005, see also Balaji et al. 2022) challenges our ability to transfer insight from simple models to reality, and distill the physical mechanisms underlying the behavior of state-of-the-art ESMs. This calls for a concerted effort to learn from the entire model hierarchy, striving to understand the differences and similarities across its various levels of complexity for increased confidence in climate prediction.

In this session, we invite contributions from all subfields of climate science that showcase how modeling approaches of different complexity advance our process understanding, and/or highlight inconsistencies in the model hierarchy. We also welcome studies exploring a single modeling approach, as we aim to foster exchange between researchers working on different rungs of the model hierarchy. Contributions may employ dynamical systems models, physics-based low-order models, explainable machine learning, Earth System Models of Intermediate Complexity (EMICs), simplified or idealized setups of ESMs (radiative-convective equilibrium, single-column models, aquaplanets, slab-ocean models, idealized geography, etc.), and full ESMs.

Processes and phenomena of interest include, but are not limited to:
* Earth system response to forcing scenarios (policy-relevant, extreme, counterfactual)
* Tipping points and abrupt transitions (e.g. Dansgaard-Oeschger events)
* Coupled modes of climate variability (e.g. ENSO, AMV, MJO)
* Emergent and transient phenomena (e.g. cloud organization)
* Extreme weather events

Session assets

Orals: Tue, 16 Apr | Room 0.94/95

Chairpersons: Reyk Börner, Maya Ben Yami, Raphael Roemer
08:30–08:35
08:35–08:45
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EGU24-22102
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NP1.5
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solicited
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Highlight
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On-site presentation
Victor Brovkin, Tobias Stacke, Philipp de Vrese, Thomas Kleinen, and Alexander Winkler

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.

08:45–08:55
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EGU24-4679
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NP1.5
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Virtual presentation
Stefano Pierini

Within the climate model hierarchy, simple models usually play the important role of highlighting dynamical processes that can possibly govern climate phenomena. If, in addition, their results are in significant agreement with observations, the processes thus identified are even more likely to regulate the actual phenomena. In this context, the dynamical process of intrinsic variability paced by a deterministic forcing (also called deterministic excitation, DE [1]) is highlighted here by two simple models of different degrees of complexity and set in the different contexts of paleoclimate and physical oceanography. In both cases, despite the simplicity of the models, the results show significant agreement with observations.

The DE mechanism requires the system (i) to possess intrinsic nonlinear relaxation oscillations (ROs) and (ii) to be in the excitable state (i.e., ROs do not emerge spontaneously but can be excited, and therefore paced, by a suitable forcing); moreover, (iii) ROs are excited by a deterministic forcing if a given tipping point is passed.

In the first case [1], the abrupt late Pleistocene glacial terminations are shown by a conceptual model to correspond to the excitation, by the astronomical forcing, of ROs describing glacial-interglacial transitions (e.g., [2]). In the second case [3], ROs describing the Kuroshio Extension low-frequency variability [4] are shown, by a primitive equation ocean model, to be excited remotely by the North Pacific Oscillation. These results show how simple modeling approaches of different complexity advance process understanding and can, therefore, provide theoretical guidelines for interpreting state-of-the-art ESM results.

[1] Pierini S., 2023: The deterministic excitation paradigm and the late Pleistocene glacial terminations. Chaos, 33, 033108.

[2] Gildor H. and E. Tziperman, 2001: A sea ice climate switch mechanism for the 100-kyr glacial cycles. J. Geophys. Res., 106, 9117–9133.

[3] Pierini S., 2014: Kuroshio Extension bimodality and the North Pacific Oscillation: a case of intrinsic variability paced by external forcing. J. Climate, 27, 448-454.

[4] Pierini S., 2006: A Kuroshio Extension System model study: decadal chaotic self-sustained oscillations. J. Phys. Oceanogr., 36, 1605-1625.

How to cite: Pierini, S.: Simple oceanographic and paleoclimate modeling highlights the same dynamical process: intrinsic variability paced by a deterministic forcing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4679, https://doi.org/10.5194/egusphere-egu24-4679, 2024.

08:55–09:05
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EGU24-14067
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NP1.5
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ECS
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On-site presentation
Tongtong Xu, Matthew Newman, Michael Alexander, and Antonietta Capotondi

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.

09:05–09:15
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EGU24-16958
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NP1.5
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On-site presentation
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Nicholas Wynn Watkins, Raphael Calel, Sandra Chapman, Aleksei Chechkin, Rainer Klages, and David Stainforth

Hasselmann’s paradigm, introduced in 1976 and recently honoured with the Nobel Prize, can, like many key innovations in the sciences of climate and complexity, be understood on several different levels, both technical and conceptual. It can be seen as a mathematical technique to add stochastic variability into pioneering energy balance models (EBMs) of Budyko and Sellers. On a more conceptual level, it used the mathematics  of Brownian motion to provide an  abstract superstructure linking slow climate variability to fast weather fluctuations, in a context broader than EBMs, leading Hasselmann to posit the need for negative feedback in climate modelling.

Hasselmann's paradigm itself has much still to offer us [e.g. Calel et al, Naure Communications, 2020], but naturally, since the 1970s a number of newer developments have built on his pioneering ideas. One important one has been the development of a rigorous mathematical hierarchy that embeds Hasselmann-type models in the more comprehensive Mori-Zwanzig (MZ) framework  (e.g.  Lucarini and Chekroun, Nature Reviews Physics, 2023). Another has been the interest in long range memory in stochastic EBMs, notably Lovejoy et al’s Fractional Energy Balance Equation [FEBE, discussed in this week’s Short Course SC5.15 ]. These have a memory with slower decay and thus longer range than the exponential form seen in Hasselmann’s EBM. My presentation [based on Watkins et al, in review at Chaos] attempts to build a bridge between MZ-based extensions of  Hasselmann, and the fractional derivative-based FEBE model.  I will argue that the Mori-Kubo overdamped Generalised Langevin Equation, as widely used in statistical mechanics, suggests the form of a relatively simple stochastic EBM with memory for the global temperature anomaly, and will discuss how this relates to FEBE.

How to cite: Watkins, N. W., Calel, R., Chapman, S., Chechkin, A., Klages, R., and Stainforth, D.: The Challenge of Non-Markovian Energy Balance Models in Climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16958, https://doi.org/10.5194/egusphere-egu24-16958, 2024.

09:15–09:25
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EGU24-8397
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NP1.5
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ECS
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On-site presentation
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Sacha Sinet, Anna S. von der Heydt, Peter Ashwin, and Henk A. Dijkstra

The Atlantic Meridional Overturning Circulation (AMOC) and polar ice sheets are considered susceptible to critical transitions under climate change. Identified as core tipping elements, their collapse would have global and drastic consequences. Furthermore, the AMOC and polar ice sheets form a complex interacting system, where the collapse of one component can heavily impact the stability of others. In the worst case, this could result in a large-scale domino effect, otherwise known as a cascading tipping event.

In this presentation, our focus is on assessing the stability of the AMOC in the presence of tipping Greenland ice sheet (GIS) and West Antarctica ice sheet (WAIS). While most existing studies agree on the destabilizing impact of a GIS collapse on the AMOC, the consequences of a WAIS collapse remain uncertain. A previous conceptual study suggested that a WAIS tipping event might actually prevent an AMOC collapse against both climate warming and increased GIS meltwater fluxes. Using a better conceptual model of the AMOC, we demonstrate that both the melting rate and natural variability associated with surface meltwater fluxes are decisive factors for this phenomenon to occur. Finally, we present preliminary findings in which the relevance of this stabilizing effect is investigated in the model of intermediate complexity CLIMBER-X.

How to cite: Sinet, S., von der Heydt, A. S., Ashwin, P., and Dijkstra, H. A.: AMOC Stability amid Tipping Ice Sheets from Conceptual to Intermediate Complexity Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8397, https://doi.org/10.5194/egusphere-egu24-8397, 2024.

09:25–09:35
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EGU24-8729
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NP1.5
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ECS
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On-site presentation
Ruth Chapman, Peter Ashwin, Richard Wood, and Jonathan Baker

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.

09:35–09:45
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EGU24-1036
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NP1.5
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ECS
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On-site presentation
Matteo Cini, Giuseppe Zappa, Francesco Ragone, and Susanna Corti

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.

09:45–09:55
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EGU24-14831
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NP1.5
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ECS
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On-site presentation
Paolo Ruggieri, Muhammad Adnan Abid, Javier Garcia-Serrano, Carlo Grancini, Fred Kucharski, Salvatore Pascale, and Danila Volpi

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.

09:55–10:05
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EGU24-18412
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NP1.5
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ECS
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On-site presentation
Christopher Womack, Noelle Eckley Selin, and Sebastian Eastham

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.

10:05–10:15
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EGU24-5570
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NP1.5
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ECS
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On-site presentation
Da Nian, Sebastian Bathiany, Boris Sakschewski, Markus Drüke, Lana Blaschke, Maya Ben-Yami, Werner von Bloh, and Niklas Boers

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.

Posters on site: Mon, 15 Apr, 16:15–18:00 | Hall X4

Display time: Mon, 15 Apr 14:00–Mon, 15 Apr 18:00
Chairpersons: Reyk Börner, Oliver Mehling, Maya Ben Yami
X4.113
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EGU24-4539
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NP1.5
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ECS
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Ignacio del Amo, George Datseris, and Mark Holland

Extreme value theory provides a universal limit for the extremes of continuous independent and identically distributed random variables and has proven to be robust to generalisation to wider classes of random variables, including stationary processes, some nonstationary processes and even trajectories on deterministic chaotic systems. This universality, together with the fact that these methods require data from only one realization of the system, has been exploited in applications to study many series of climate data.

Fitting a probability distribution to the extreme events of a data series generated by a chaotic dynamical system gives us not only probabilistic predictions of the intensity and return time of the events themselves, but also geometrical information about the local structure of the attractor and the predictability and persistence of the extreme events.

However, these methods are sensitive to the mathematical properties of the dynamical system that generates the data, and are seldomly even mentioned when they are applied to real climate data. One further caveat of these methods is that they are hard to falsify, i.e. we cannot verify easily if an answer is wrong. For these reasons, we explore how these methods respond to different systems with different complexity and different mathematical properties, trying to understand which of the results on the literature could meaningful and which could be numerical artifacts.

How to cite: del Amo, I., Datseris, G., and Holland, M.: Extreme value methods in dynamical systems of different complexity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4539, https://doi.org/10.5194/egusphere-egu24-4539, 2024.

X4.114
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EGU24-2167
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NP1.5
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ECS
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Paolo Bernuzzi, Christian Kuehn, and Henk Dijkstra

The search of early-warning signs able to predict the approach of a parameter to a deterministic bifurcation threshold is relevant in climate as it aims to enable a proper prediction of qualitative changes in the studied models. The observation of such objects in SPDEs (stochastic partial differential equations) permits the consideration of space variables and the ensuing heterogeneity in the behaviour of their solutions.

The presence of Gaussian noise on the boundary of the studied space is used in order to build the signals, whose properties are discussed thoroughly. An example in the form of application of such tools on a climate model is presented and justified. The utility and appropriate use of the results on a more applied perspective are shown.

How to cite: Bernuzzi, P., Kuehn, C., and Dijkstra, H.: Early-Warning Signs for SPDEs under Boundary Noise, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2167, https://doi.org/10.5194/egusphere-egu24-2167, 2024.

X4.115
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EGU24-2867
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NP1.5
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ECS
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Masoud Rostami and Stefan Petri

Aeolus 2.0 is an open-source numerical atmosphere model with intermediate complexity designed to capture the dynamics of the atmosphere, especially extreme weather and climate events. The model's dynamical core is built on a novel multi-layer pseudo-spectral moist-convective Thermal Rotating Shallow Water (mcTRSW) model, and it utilizes the Dedalus algorithm, renowned for its efficient handling of spin-weighted spherical harmonics in solving pseudo-spectral problems. Aeolus 2.0 comprehensively characterizes the temporal and spatial evolution of key atmospheric variables, including vertically integrated potential temperature, thickness, water vapor, precipitation, and the influence of bottom topography, radiative transfer, and insolation. It provides a versatile platform with resolutions ranging from smooth to coarse, enabling the exploration of a wide spectrum of dynamic phenomena with varying levels of detail and precision.

The model has been utilized to investigate the adjustment of large-scale localized buoyancy anomalies in mid-latitude and equatorial regions, along with the nonlinear evolution of key variables in both adiabatic and moist-convective environments. Our findings highlight the triggering mechanisms of phenomena such as the Madden-Julian Oscillation (MJO) and the circulation patterns induced by temperature anomalies and buoyancy fields. Furthermore, our simulations of large-scale localized temperature anomalies reveal insights into the impact of perturbation strength, size, and vertical structure on the evolution of eddy heat fluxes, including poleward heat flux, energy, and meridional elongation of the buoyancy field. We observe the initiation of atmospheric instability, leading to precipitation systems, such as rain bands, and asymmetric latent heat release due to moist convection in diabatic environments. This study identifies distinct patterns, including the formation of a comma cloud pattern in the upper troposphere and a comma-shaped buoyancy anomaly in the lower layer, accompanied by the emission of inertia gravity waves. Additionally, the role of buoyancy anomalies in generating heatwaves and precipitation patterns is emphasized, particularly in mid-latitude regions.

In summary, Aeolus 2.0, with its specific capabilities, contributes to our understanding of the complex interactions of moist convection, buoyancy anomalies, and atmospheric dynamics, shedding light on the dynamics of extreme weather events and their implications for climate studies.

References

1. Rostami, M., Zhao, B., & Petri, S. (2022). On the genesis and dynamics of MaddenJulian oscillation-like structure formed by equatorial adjustment of localized heating. Quarterly Journal of the Royal Meteorological Society, 148 (749), 3788-3813. Retrieved from https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.4388 doi: https://doi.org/10.1002/qj.4388

2. Rostami, M., Severino, L., Petri, S., & Hariri, S. (2023). Dynamics of localized extreme heatwaves in the mid-latitude atmosphere: A conceptual examination. Atmospheric Science Letters, e1188. Retrieved from https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/asl.1188 doi: https://doi.org/10.1002/asl.1188

How to cite: Rostami, M. and Petri, S.: Exploring Extreme Weather and Climate Events with Aeolus 2.0: A Multi-layer moist-convective Thermal Rotating Shallow Water (mcTRSW) Dynamical Core, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2867, https://doi.org/10.5194/egusphere-egu24-2867, 2024.

X4.116
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EGU24-8104
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NP1.5
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ECS
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Swinda Falkena and Anna von der Heydt

The subpolar gyre (SPG) is one of the climate tipping elements which could have a large impact on the climate in the northern hemisphere. Improving our understanding of its dynamics is key to assessing the likelihood of it passing a tipping point. Some CMIP6 models exhibit abrupt transitions in the sea surface temperature in the SPG region, but the majority does not. The differences in the model response can be related to the stratification bias, with many models having a too strong stratification preventing them from exhibiting bistable gyre dynamics.

To better understand the SPG we study the (lagged) partial correlations between the relevant aspects of its dynamics in the CMIP6 ensemble. In contrast to standard correlations, partial correlations correct for the effect of autocorrelation and the effect of (the past of) other relevant variables. Therefore, it gives a better indication of there being a causal relation. Based on the partial correlation between the sea surface temperature and mixed layer depth we split the ensemble into two groups (strong or negligible relation) and for each select one model to study its dynamics in detail. In addition, we discuss the interaction of the SPG with the Atlantic Meridional Overturning Circulation (AMOC) using the same methods. These results can help in better informing more conceptual climate models of the SPG, AMOC and their interactions, which can be used to study potential tipping dynamics.

How to cite: Falkena, S. and von der Heydt, A.: Disentangling the dynamics of the subpolar gyre and its interaction with the AMOC in the CMIP6 ensemble, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8104, https://doi.org/10.5194/egusphere-egu24-8104, 2024.

X4.117
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EGU24-18360
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NP1.5
Valerio Lucarini, Melanie Kobras, and Maarten Ambaum

We introduce a minimal dynamical system derived from the classical Phillips two-level model with the goal of elucidating the essential mechanisms responsible for the interaction between eddies and mean flow. The choice of a two-level model as starting points allows for appreciating the relative role of barotropic and baroclinic processes. Specifically, we wish to explore the eddy saturation mechanism, whereby, when average conditions are considered, direct forcing of the zonal flow increases the eddy kinetic energy, while the energy associated with the zonal flow does not increase. The eddy-driven jet stream and storm tracks in the mid-latitude atmosphere are known to shift in latitude on various timescales, but the physical processes that cause these shifts are still unclear. Using our low-order model, we aim to understand the link between the structure of the eddies and the shift of the latitudinal maximum of the zonal flow in the mid-latitude atmosphere. Our findings elucidate the basic mechanisms behind baroclinic adjustment and provide insights into the properties of the storm track change between the jet entrance and jet exit regions of the North Atlantic.

How to cite: Lucarini, V., Kobras, M., and Ambaum, M.: Eddy Saturation and Latitudinal Storm Track Shift in a Reduced Two-level Model of the Atmosphere, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18360, https://doi.org/10.5194/egusphere-egu24-18360, 2024.

X4.118
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EGU24-8117
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NP1.5
F. Hugo Lambert, Claire Zarakas, Monisha Natchiar S. R., Abigail L. S. Swann, and Charles D. Koven

Complex numerical models of climate consist of simulation of fluid dynamics and thermodynamics on a discrete grid, and parameterizations, which are algorithms that approximate processes smaller than gridscale. Because parameterizations of a given process may be written as different functions of different, potentially non-observable variables, it can be difficult to quantify the process differences between individual climate models and between climate models and the real world.

Here, we attempt to write down a simple linear model that represents the response of the Earth's tropical land surface to atmospheric forcing on monthly timescales in terms of the same observable variables using a technique called continuous structural parameterization. Simulated data are taken from complex General Circulation Models (GCMs) run under the AMIP protocol and a CESM2 perturbed physics ensemble (PPE) of our own devising;  observed measurements are taken from FLUXNET flux tower sites. We find that the simple model captures land surface behaviour well except in mountainous regions.

Establishing a generalised parameter space, we see that most GCMs are in reasonable agreement with FLUXNET at FLUXNET sites, although there is evidence that GCMs consistently slightly overestimate the response of surface turbulent fluxes to downward radiation. Further, it is found that the differences between structurally different AMIP models are considerably greater than the differences between CESM2 PPE members -- even though the PPE parameters are varied across their realistic domain. If the simple model is trained only at GCM spatial gridpoints that contain a FLUXNET site, there is little degradation in simple model performance compared with global training, suggesting that even the few available tropical FLUXNET sites are useful for constraining land surface model response throughout the tropics. This is of course contingent on whether or not point measurements taken by FLUXNET are representative of the wider area around FLUXNET sites.

How to cite: Lambert, F. H., Zarakas, C., Natchiar S. R., M., Swann, A. L. S., and Koven, C. D.: Using a simple model to measure the differences between climate model land surface simulations and FLUXNET observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8117, https://doi.org/10.5194/egusphere-egu24-8117, 2024.

X4.119
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EGU24-9253
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NP1.5
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ECS
Oisín Hamilton, Jonathan Demaeyer, Anupama Xavier, and Stéphane Vannitsem

Reduced order quasi-geostrophic land-atmosphere coupled models display qualitatively realistic mid-latitude atmosphere behaviour, meaning that such models can produce typical atmospheric dynamical features such as atmospheric blocking. At the same time, due to a low number of degrees of freedom, they are still simple enough to allow for analysis of the system dynamics. These features mean that these models are well suited to investigating bifurcations in atmospheric dynamics, and use a dynamical systems approach to better understand the corresponding atmospheric behaviour. 

This project introduces a symbolic python workflow for using the flexible  land-atmosphere (qgs, 2020) spectral model with the continuation software AUTO. This work builds on the results of Xavier et al. (2023) to understand how the model variability and predictability is impacted by the model resolution. We also use bifurcation diagrams to better understand how parameters such as atmosphere-land friction impact the atmospheric blocking, and in turn the model atmosphere predictability. This is done for a range of model resolutions to investigate how the number of degrees of freedom impacts both the realism of the model, but also the structures found in the dynamics.

 

Demaeyer, Jonathan & De Cruz, Lesley & Vannitsem, S.: qgs: A flexible Python framework of reduced-order multiscale climate models. Journal of Open Source Software. 5. 2597. 10.21105/joss.02597, 2020. 

 

Xavier, A. K., Demaeyer, J., and Vannitsem, S.: Variability and Predictability of a reduced-order land atmosphere coupled model, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2257, 2023.

How to cite: Hamilton, O., Demaeyer, J., Xavier, A., and Vannitsem, S.: The impact of model resolution on variability in a coupled land atmosphere model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9253, https://doi.org/10.5194/egusphere-egu24-9253, 2024.

X4.120
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EGU24-11984
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NP1.5
Elisa Ziegler, Nils Weitzel, Jean-Philippe Baudouin, Marie-Luise Kapsch, Uwe Mikolajewicz, Lauren Gregoire, Ruza Ivanovic, Paul Valdes, Christian Wirths, and Kira Rehfeld

Climate variability is crucial to our understanding of future climate change and its impacts on societies and the natural world. However, the climate records of the observational era are too short to explore long-term variability. Conversely, an exploration of long transient simulations from state-of-the-art Earth System Models (ESMs) poses high computational demands. It is therefore pertinent to identify the level of complexity sufficient to simulate the variability of surface climate from annual to centennial and longer timescales.

To this end, we use an ensemble of transient simulations of the Last Deglaciation, the last period of significant global warming. The ensemble covers an energy balance model (EBM), models of intermediate complexity (EMICs), general circulation models (GCMs) and ESMs. This constitutes a hierarchy that we categorize based on employed atmosphere and ocean components and their resolution, as well as implemented radiation, land hydrology, vegetation and aerosol schemes.

To investigate the simulated variability of surface temperature and precipitation, we analyze changes in the shapes of their distributions as characterized by their higher order moments – variance, skewness, kurtosis – with warming. These higher order moments relate the tails to the extremes of the distributions. We identify spatial and temporal patterns and how they depend on model complexity. The EMICs can generally match the global and latitudinal changes in temperature variability found in more complex models. However, they lack in precipitation variability. We further find that the EMICs fail to simulate the tails of the precipitation distributions. We observe dependency of variability on the background state, generally increasing with model complexity. However, there is still a large spread between models of similar complexity, some of which can be related to differences in forcings. Furthermore, questions remain on the abilities of models of any complexity to simulate a magnitude of long-term variability similar to that found regionally in proxy reconstructions. Our analysis offers implications as to the complexity needed and sufficient for capturing the full picture of climate change and we offer some first insights into how the findings translate to future projections of climate change.

How to cite: Ziegler, E., Weitzel, N., Baudouin, J.-P., Kapsch, M.-L., Mikolajewicz, U., Gregoire, L., Ivanovic, R., Valdes, P., Wirths, C., and Rehfeld, K.: Dependence of simulated variability of surface climate on model complexity – insights from an ensemble of transient simulations of the Last Deglaciation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11984, https://doi.org/10.5194/egusphere-egu24-11984, 2024.

X4.121
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EGU24-14439
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NP1.5
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ECS
dustin lebiadowski and shaun Lovejoy

We introduce a stochastic, energy-balance, climate model defined over the macroweather regime (approximately 15 days or longer). Together, the energy balance principle, combined with the model’s natural scaling, demonstrate quite promising results despite the relative simplicity. A special case of the model can also be derived from a very classical basis, and, because of some similarities, we propose this model as a development upon the work of Budyko and Sellers.

When the classical Budyko-Sellers energy balance model is updated by using the (correct) radiative-conductive surface boundary conditions, one obtains the Fractional Energy Balance Equation (FEBE). The FEBE involves fractional space-time operators and its generic solutions are scaling, in agreement with much atmospheric and oceanic data. In time, it implies long range memories that have been successfully used to make both multi-decadal climate projections as well as monthly and seasonal (long range) forecasts. In space, the FEBE is nonlocal so that energy flux imbalances at any location can affect the balance in locations far away. This is possible because the model operates over monthly and longer time scales; over these scales, energy can be both stored and transported in the atmosphere, ocean, and subsurface.

Until now, the FEBE’s full nonlocal space-time interaction operator has been only approximated. Here, by introducing a numerical model, the nonlocal dynamics of the FEBE and corresponding Earth-system FEBE energy flows over the 2D Earth surface are fully detailed.

We propose the FEBE as an alternative to more conventional, deterministic, weather-regime-based climate models. Given the generality of the ideas pursued here - the use of fractional operators; the use of stochasticity and the macroweather regime - there seems a great potential for these to be used much more widely. Hopefully this research, and possibly related works, will encourage a greater diversity of pursuits and be inspiring to others in their own work.

How to cite: lebiadowski, D. and Lovejoy, S.: Nonlocal energy fluxes and fractional operators in updated, stochastic, Budyko-Sellers models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14439, https://doi.org/10.5194/egusphere-egu24-14439, 2024.

X4.122
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EGU24-10070
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NP1.5
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ECS
Clara Hummel

Every year, the area of the Arctic sea-ice decreases in the boreal spring and summer and reaches its yearly minimum in the early autumn. Due to global warming, Arctic summer sea ice will most probably disappear. As the sea ice cover decreases, its border is retreating northwards towards the central Arctic. This retreat is not uniform in space and the variability of the border’s movement further North could yield an early warning signal for summer sea ice loss. Here, we track the sea ice border from time series obtained from models of various complexity and observations to study the spatial variability of the border’s movement as Arctic summer sea ice approaches its disappearance.

How to cite: Hummel, C.: Spatial fluctuations of the Arctic sea ice border, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10070, https://doi.org/10.5194/egusphere-egu24-10070, 2024.

X4.123
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EGU24-12421
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NP1.5
Paul Hall, Christopher Horvat, Baylor Fox-Kemper, Samuel Brenner, and Alper Altuntas

We report on the development of a novel process model created to study ocean-sea ice interaction and the dynamics of the upper ocean in the marginal ice zone (MIZ), built using the Community Earth System Model (CESM). Our model uses the MOM6 ocean model and CICE6 sea-ice model as active components within CESM, on a custom ~50km x ~50km grid with a horizontal resolution of ~50m, extending to a depth of 75m (30 vertical layers). The model allows for either reflecting or zonally re-entrant boundary configurations. Atmospheric forcing is imposed through a simplified data atmosphere component that provides constant forcing over the model domain. Results from several simple scenarios are presented and compared to results obtained using the MITgcm.

By working within CESM, we are able to leverage CESM’s existing infrastructure and capabilities, including the use of the Community Mediator for Earth Prediction Systems (CMEPS) for coupling between active components. Furthermore, additional model components that are already available within CESM (e.g., waves, atmosphere) can be incorporated into the process model in a straightforward way. Future work will include incorporation of a modified sea-ice component that allows tracking of individual floes utilizing a discrete element method approach.

How to cite: Hall, P., Horvat, C., Fox-Kemper, B., Brenner, S., and Altuntas, A.: A Novel Process Model of Ocean-Sea-Ice Interaction Using CESM, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12421, https://doi.org/10.5194/egusphere-egu24-12421, 2024.

X4.124
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EGU24-18330
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NP1.5
Crossing planetary boundaries could lead to Hothouse Earth
(withdrawn)
Erik Chavez, Michael Ghil, and Jan Rombouts