The present work addresses two persistent quandaries of the climate sciences: (i) the existence of global oscillatory modes in the coupled ocean–atmosphere system; and (ii) solar effects on this coupled system. Interannual oscillatory modes, atmospheric and oceanic, are present in several large regions of the globe. We examine here interannual-to-decadal variability over the entire globe in the Community Earth System Model (CESM) and in the NCEP-NCAR reanalysis, and apply multichannel singular spectrum analysis (MSSA) to these two datasets.

In the fully coupled CESM1.1 model, with its resolution of 0.1 × 0.1 degrees in the ocean and 0.25 × 0.25 degrees in the atmosphere, the fields analyzed are surface temperatures, sea level pressures and  the 200-hPa geopotential. The simulation is 100-yr long and the last 66 yr are used in the analysis. The two statistically significant periodicities in this IPCC-class model are 11 and 3.4 yr.

In the reanalysis, the fields of sea level pressure and of 200-hPa geopotential are analyzed at its resolution of 2.5 × 2.5 degrees over the 68-yr interval 1949–2016. Oscillations with periods of 12 and 3.6 yr are found to be statistically significant in this dataset. The spatio-temporal patterns  of the oscillations in the two datasets are quite similar. The spatial pattern of these  global oscillations over the North Pacific and North Atlantic resemble the Pacific Decadal Oscillation and the interannual variability found in the western North Atlantic, respectively.

The two global modes, with their 11–12-yr and 3.4–3.6-yr periodicities, are quite robust, suggesting potential contributions of both to predictability at 1–3-yr horizons. On the other hand, the CESM run has no year-to-year changes in the prescribed insolation, excluding any role of the solar cycle in the model’s 11-yr mode. The solar cycle is present, however, in the reanalysis, since it is present in nature and hence it does affect the observations. We speculate, therefore, that regional oscillations — with their distinct near-periodicities and spatial patterns — are synchronized  over the globe, thus yielding both the global oscillatory modes found in CESM. In nature, the decadal mode could be further synchronized with the solar cycle, but that does not seem to be the case, given the slight difference in period — 12 yr for the reanalysis and 11 yr for the solar cycle, which makes them drift in and out of phase.

The work’s tentative conclusion is, therefore: (i) yes, there are global oscillatory modes in the climate system, especially a decadal mode; but (ii) no, this mode has little or nothing to do with the solar cycle.

How to cite: Ghil, M., Feliks, Y., and Small, J.: A global decadal mode in a high-end climate model and in observations: Any connection to the solar cycle?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4961, https://doi.org/10.5194/egusphere-egu21-4961, 2021.

A stochastic model for daily-spatial mean stratospheric temperature over a given area is suggested. The model is a sum of a deterministic seasonality function and a Lévy driven vectorial Ornstein-Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the model is an order 4 continuous-time autoregressive (CAR(4)) process, derived from data analysis suggesting an order 4 autoregressive (AR(4)) process to model the deseasonalized stochastic temperature data empirically. In this analysis, temperature data as represented in ECMWF re-analysis model products are considered. The residuals of the AR(4) process turn out to be normal inverse Gaussian distributed random variables scaled with a time dependent volatility function. In general, it is possible to show that the discrete time AR(p) process is closely related to CAR(p) processes, its continuous counterpart. An equivalent effort is made in deriving a dual stochastic model for stratospheric temperature, in the sense that the year is divided into summer and winter seasons. However, this seems to further complicate the modelling, rather than obtaining a simplified analytic framework. A stochastic characterization of the stratospheric temperature representation in model products, such as the model proposed in this paper, can be used in geophysical analyses to improve our understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modelling and prediction of large-scale middle atmospheric events like sudden stratospheric warmings. Through stratosphere-troposphere coupling, this is important in the work towards an extended predictability of long-term tropospheric weather forecasting.

How to cite: Eggen, M., Rognlien Dahl, K., Näsholm, S. P., and Mæland, S.: Stochastic modelling of stratospheric temperature, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-239, https://doi.org/10.5194/egusphere-egu21-239, 2020.

Forced-dissipative beta-plane turbulence in a single-layer shallow-water fluid has been widely considered as a simplified model of planetary turbulence as it exhibits turbulence self-organization into large-scale structures such as robust zonal jets and strong vortices. In this study we perform a series of numerical simulations to analyze the characteristics of the emerging structures as a function of the planetary vorticity gradient and the deformation radius. We report four regimes that appear as the energy input rate ε of the random stirring that supports turbulence in the flow increases. A homogeneous turbulent regime for low values of ε, a regime in which large scale Rossby waves form abruptly when ε passes a critical value, a regime in which robust zonal jets coexist with weaker Rossby waves when ε passes a second critical value and a regime of strong materially coherent propagating vortices for large values of ε. The wave regime which is not predicted by standard cascade theories of turbulence anisotropization and the vortex regime are studied thoroughly. Wavenumber-frequency spectra analysis shows that the Rossby waves in the second regime remain phase coherent over long times. The coherent vortices are identified using the Lagrangian Averaged Deviation (LAVD) method. The statistics of the vortices (lifetime, radius, strength and speed) are reported as a function of the large scale parameters. We find that the strong vortices propagate zonally with a phase speed that is equal or larger than the long Rossby wave speed and advect the background turbulence leading to a non-dispersive line in the wavenumber-frequency spectra.

How to cite: Bakas, N.: Waves, jets and vortices: Regime transitions in shallow water turbulence, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1025, https://doi.org/10.5194/egusphere-egu21-1025, 2021.

The increase in atmospheric CO2 driven by anthropogenic emissions is the main radiative forcing causing climate change. But this increase is not only a result from emissions, but also from changes in the global carbon cycle. These changes arise from feedbacks between climate and the carbon cycle that drive CO2 into or out of the atmosphere in addition to the emissions, thereby either accelerating or buffering climate change. Therefore, understanding the contribution of these feedbacks to the global response of the carbon cycle is crucial in advancing climate research. Currently, this contribution is quantified by the α-β-γ framework (Friedlingstein et al., 2003). But this quantification is only valid for a particular perturbation scenario and time period. In contrast, a recently proposed generalization (Rubino et al., 2016) of this framework for weak perturbations quantifies this contribution for all scenarios and at different time scales. 

Thereby, this generalization provides a systematic framework to investigate the response of the global carbon cycle in terms of the climate-carbon cycle feedbacks. In the present work we employ this framework to study these feedbacks and the airborne fraction in different CMIP5 models. We demonstrate (1) that this generalization of the α-β-γ framework consistently describes the linear dynamics of the carbon cycle in the MPI-ESM; and (2) how by this framework the climate-carbon cycle feedbacks and airborne fraction are quantified at different time scales in CMIP5 models. Our analysis shows that, independently of the perturbation scenario, (1) the net climate-carbon cycle feedback is negative at all time scales; (2) the airborne fraction generally decreases for increasing time scales; and (3) the land biogeochemical feedback dominates the model spread in the airborne fraction at all time scales. This last result therefore emphasizes the need to improve our understanding of this particular feedback.

References:

P. Friedlingstein, J.-L. Dufresne, P. Cox, and P. Rayner. How positive is the feedback between climate change and the carbon cycle? Tellus B, 55(2):692–700, 2003.

M. Rubino, D. Etheridge, C. Trudinger, C. Allison, P. Rayner, I. Enting, R. Mulvaney, L. Steele, R. Langenfelds, W. Sturges, et al. Low atmospheric CO2 levels during the Little Ice Age due to cooling-induced terrestrial uptake. Nature Geoscience, 9(9):691–694, 2016.

How to cite: Torres Mendonça, G., Pongratz, J., and Reick, C.: Time-scale dependence of climate-carbon cycle feedbacks for weak perturbations in CMIP5 models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14427, https://doi.org/10.5194/egusphere-egu21-14427, 2021.

Discovering causal dependencies from observational time series datasets is a major problem in better understanding the complex dynamical system Earth. Recent methodological advances have addressed major challenges such as high-dimensionality and nonlinearity (PCMCI, Runge et al. Sci. Adv. 2019), as well as instantaneous causal links (PCMCI+, Runge UAI, 2020) and hidden variables (LPCMCI, Gerhardus and Runge, 2020), but many more remain. In this presentation I will give an overview of challenges and methods and present a recent approach, Ensemble-PCMCI, to analyze ensembles of climate time series. An example for this are initialized ensemble forecasts. Since the individual samples can then be created from several time series instead of different time steps from a single time series, such cases allow to relax the assumption of stationarity and hence to analyze whether and how the underlying causal relationships change over time. We compare Ensemble-PCMCI to other methods and discuss preliminary applications.

Runge et al., Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances eeaau4996 (2019).

Runge, J. Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020, Toronto, Canada, 2019, AUAI Press, 2020

Gerhardus, A. & Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders. Advances in Neural Information Processing Systems, 2020, 33

How to cite: Runge, J. and Gerhardus, A.: Causal discovery in climate research: Overview and recent progress, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10231, https://doi.org/10.5194/egusphere-egu21-10231, 2021.

The concept of weather or climate 'regimes' have been studied since the 70s, to a large extent because of the possibility they offer of truncating complicated dynamics to vastly simpler, Markovian, dynamics. Despite their attraction, detecting them in data is often problematic, and a unified definition remains nebulous. We argue that the crucial common feature across different dynamical systems with regimes is the non-trivial topology of the underlying phase space. Such non-trivial topology can be detected in a robust and explicit manner using persistent homology, a powerful new tool to compute topological invariants in arbitrary datasets. We show some state of the art examples of the application of persistent homology to various non-linear dynamical systems, including real-world climate data, and show how these techniques can shed light on questions such as how many regimes there really are in e.g. the Euro-Atlantic sector. Future directions are also discussed.

How to cite: Strommen, K., Otter, N., Chantry, M., and Dorrington, J.: Persistent Homology, Regimes and Climate Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12462, https://doi.org/10.5194/egusphere-egu21-12462, 2021.

Climate can be interpreted as a complex, high dimensional non-equilibrium stationary system characterised by multiple time and space scales spanning various orders of magnitude. Statistical mechanics and dynamical system theory have been key mathematical frameworks in the study of the climate system. In particular, unstable periodic orbits (UPOs) have been proven to provide relevant insight in the understanding of its statistical properties. In a recent paper Lucarini and Gritsun [1] provided an alternative approach for understanding the properties of the atmosphere.

In general, UPOs decomposition plays a relevant role in the study of chaotic dynamical systems. In fact, UPOs densely populate the attractor of a chaotic system, and can therefore be thought as building blocks to construct the dynamic of the system itself. Since they are dense in the attractor, it is always possible to find a UPO arbitrarily near to a chaotic trajectory: the trajectory will remain close to the UPO, but it will never follow it indefinitely, because of its instability. Loosely speaking, a chaotic trajectory is repelled between neighbourhoods of different UPOs and can thus be approximated in terms of these periodic orbits. The statistical properties of the system can then be reconstructed from the full set of periodic orbits in this fashion.

The numerical study of UPOs thus represents a relevant problem and an interesting research topic for Climate Science and chaotic dynamical systems in general. In this presentation we address the problem of sampling UPOs for the paradigmatic Lorenz-63 model. First, we present results regarding the measure of the system, thus its statistical properties, using UPOs theory, namely with the trace formulas. Second, we introduce a more innovative approach, considering UPOs as global states of the system. We approximate the exact dynamics by a suitable Markov chain process, describing how the system hops on different UPOs, and we compare the two different approaches.  

[1] V. Lucarini and A. Gritsun, “A new mathematical framework for atmospheric blocking events,” Climate Dynamics, vol. 54, pp. 575–598, Jan 2020.

How to cite: Maiocchi, C. C. and Lucarini, V.: Unstable Periodic Orbits Sampling and Its Applications to Climate Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5457, https://doi.org/10.5194/egusphere-egu21-5457, 2021.

Factor separation is widely used in the analysis of numerical simulations.  It allows changes in properties of a system to be attributed to changes in multiple variables associated with that system.  There are many possible factor separation methods; here we discuss three previously-proposed methods that have been applied in the field of climate modelling: the linear factor separation, the Stein and Alpert (1993) factor separation, and the Lunt et al (2012) factor separation.  We show that, when more than two variables are being considered, none of these three methods possess all four properties of 'uniqueness', 'symmetry', 'completeness', and 'purity'.  Here, we extend each of these methods so that they do possess these properties for any number of variables, resulting in three factor separation methods -- the 'linear-sum' , the 'shared-interaction', and the 'scaled-total'.  We show that the linear-sum method and the shared-interaction method reduce to be identical in the case of four or fewer variables, and we conjecture that this holds for any number of variables.  We present the results of the factor separations in the context of studies that used the previously-proposed methods.  This reveals that only the linear-sum/shared-interaction factor separation method possesses a fifth property -- `boundedness', and as such we recommend the use of this method in applications for which these properties are desirable.   The work described here is in review in Geoscientific Model Development - see https://gmd.copernicus.org/preprints/gmd-2020-69 .

How to cite: Lunt, D., Chandan, D., Schmidt, G., Rougier, J., and Lunt, G.: Multi-variate factor separation of numerical simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4643, https://doi.org/10.5194/egusphere-egu21-4643, 2021.

The analysis of nonlinear and nonstationary processes is, in general, a challenging task.
One way to tackle it is to first decompose the signal into simpler components and then analyze them separately. This is the idea behind the Empirical Mode Decomposition (EMD) method, published originally in 1998. EMD had a big impact in many filed of research as testified by the more than 15300 citations (based on Scopus). However, the mathematical properties of EMD and its generalizations, like the Ensemble EMD, are still under investigation. For this reason an alternative technique, called Iterative Filtering (IF), was proposed in 2009.

In this talk we introduce the IF method and present new insights in its mathematical properties. In particular, we show its robustness to noise, its ability to avoid mode mixing, and its speed up in what is called the Fast Iterative Filtering (FIF).
Both IF and FIF have been extened to handle multivariate and multidimensional data sets, outperforming, in terms of computational time, any alternative method proposed so far in the literature for the decomposition of nonstationary signals.

This is a joint work with H. Zhou (Georgia Tech).

How to cite: Cicone, A. and Zhou, H.: Fast Iterative Filtering: a new, fast and robust decomposition method for nonlinear and nonstationary processes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3303, https://doi.org/10.5194/egusphere-egu21-3303, 2021.

Approximations in the moist thermodynamics of atmospheric/weather models are often inconsistent. Different parts of numerical models may handle the thermodynamics in different ways, or the approximations may disagree with the laws of thermodynamics. In order to address these problems we may derive all relevant thermodynamic quantities from a defined thermodynamic potential; approximations are then instead made to the potential itself --- this guarantees self-consistency. This concept is viable for vapor and liquid water mixtures in a moist atmospheric system using the Gibbs function but on extension to include the ice phase an ambiguity exists at the triple-point. In order to resolve this the energy function must be used instead; constrained maximisation methods may be used on the entropy in order to solve the system equilibrium state. Once this is done however, a further extension is necessary for atmospheric systems. In the Earth's atmosphere many important non-equilibrium processes take place; for example, freezing of super-cooled water, evaporation, and precipitation. To fully capture these processes the equilibrium method must be reformulated to involve finite rates of approach towards equilibrium. This may be done using various principles of non-equilibrium thermodynamics, principally Onsager reciprocal relations. A numerical scheme may then be implemented which models the competing finite processes in a moist thermodynamic system.

How to cite: Bowen, P. and Thuburn, J.: Consistent Modelling of Non-Equilibrium Thermodynamic Processes in the Atmosphere, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6114, https://doi.org/10.5194/egusphere-egu21-6114, 2021.

Earth’s long-term carbonate-silicate cycle is continuously perturbed by processes of mountain building and erosion. Mountain uplift near convergent plate boundaries causes steep slopes, which in turn imply high rates of continental erosion. Erosion rates ultimately affect the weatherability and thereby the regulation of Earth’s climate. Using a simple 1D-model that includes the outlines processes, I investigate the resulting climate oscillations over timescales from thousands to millions of years. With a simple model of the long-term carbon cycle that includes biological enhancement of weathering and marine biogenic calcite precipitation, I study the role of Earth’s biosphere in damping these oscillations [1]. I show that both mechanisms play a role: Biological enhancement of weathering damps oscillations mainly on timescales > 1 Ma and marine calcification mainly on shorter timescales. Altogether, the results indicate that Earth’s biosphere contributes to a stable climate over a wide range of timescales.

In the context of anthropogenic emissions, a dramatic elevation in the atmospheric CO₂ and related temperature is known to damage Earth’s biosphere [2] and may even trigger runaway processes [3]. The results presented here indicate that a damaged biosphere may furthermore cause the Earth system to react more sensitive to oscillations from geological forcing and may also affect climate recovery.

References:

[1] Höning 2020, Geochem. Geophys. Geosyst. 21(9), e2020GC009105
[2] Sully et al. 2019, Nat. Comm. 10, 1264
[3] Lenton 2013, Annu. Rev. Environ. Resour. 38, 1-29

How to cite: Höning, D.: Climate Oscillations from Mountain Uplift and Erosion are Damped by Bioactivity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13627, https://doi.org/10.5194/egusphere-egu21-13627, 2021.

Landslides are common in the mid-hill region of Nepal where the terrain slopes are steep and consist of fragile geo-morphology. In Nepal, the casual and triggering factors of the landslides are respectively the underlying geology, intense rainfall and unplanned construction of rural roads is highly recognized, which is however less known and limited in study. Establishment of rainfall threshold for landslides at the watershed landscape is data driven, which is scared in the context of Nepal. The only available long term daily rainfall and sparsely available historical landslides date has been used to develop the rainfall threshold model for the two watersheds in central and western mid-hill regions respectively the Sindhukhola and Sotkhola in Bagmati and Karnali Provinces of Nepal. The watersheds are located in two distinct hydro-climatic regions in terms of rainfall amount and intensity. Historical daily (monsoonal) rainfall data of over four decades (1970-2016) were analyzed available from the Department of Hydrology and Meteorology (DHM)/Government of Nepal and five days’ antecedent rain was calculated. With the limitedly available temporal landslides data, correlation was examined among the 5-days antecedent rain (mm/5days) and daily rainfall (mm/day) portrayed the rainfall threshold (RT) model (Sindhukhola=180-1.07R_T5adt and Sotkhola = 110-0.83*R_T5adt). Utilizing the five days’ antecedent rain fitted into the model, results the threshold rainfall. Deducting the five days’ antecedent rains to the RT described the threshold exceedance (R) for the landslides. The model can be plotted in simple spreadsheet (landslides date in Y-axis and threshold exceedance R in X-axis) to visualize the changes in the threshold exceedance over time, whenever the threshold exceedance progressively and rapidly increased and crossed the threshold line and reached to the positive (> 0) zone, the plots allows for the landslides warning notice. In case of the threshold exceedance is further increased there is likely to have landslides in the watersheds. The model was validated with the 35 dated landslides recorded in monsoon 2015 in Sotkhola watershed. The result indicated that the model preserves 72% coefficient of determination (R²) where there were landslides in the watershed during 2015 monsoon. Due to the simplicity and at the data scarce situation, the model was found to be useful to forecast the landslides during the monsoon season in the region. The model; however, can be improved for better performance whenever the higher resolution time-series landslides data and automated weather stations are available in the watersheds. Linking this model to the proper landslide susceptibility map, and the real time rainfall data through mobile communication techniques, landslide early warning system can be established.

KEYWORDS: landslide, rainfall threshold, data-scare, antecedent rainfall

References:

Aleotti, P. (2004). A warning system for rainfall-induced shallow failures. Engineering Geology, 73(3-4), 247-265.

Jaiswal, P. and van Westen, C.J., 2009. Estimating temporal probability for landslide initiation along transportation routes based on rainfall thresholds. Geomorphology, 112(1-2): 96-105.

Acknowledgement: Comprehensive Disaster Risk Management Programme – UNDP in Nepal for the opportunity to conduct this research.

How to cite: Devkota, S., Kc, D., Jaboyedoff, M., and Acharya, G.: Development of Rainfall threshold model for the watershed/sub-watershed landscape at data scarce situation – a case study of mid-hill region, Nepal., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4797, https://doi.org/10.5194/egusphere-egu21-4797, 2021.

The impact of the El Niño-Southern Oscillation (ENSO) on the extratropics is investigated in an idealized, reduced-order model that has a tropical and an extratropical module. Unidirectional forcing is used to mimic the atmospheric bridge between the tropics and the extratropics. The variability of the coupled ocean--atmosphere extratropical module is then investigated through the analysis of its pullback attractors (PBA). This analysis focuses on two ENSO-type forcings generated by the tropical module, one periodic and one aperiodic.

For a substantial range of coupling parameters, multiple chaotic PBAs are found to coexist for the same set of parameter values. Different types of extratropical low-frequency variability are associated with each PBA over the parameter ranges explored. For periodic ENSO forcing, the coexisting PBAs are nonlinearly stable, while for the chaotic forcing, they are unstable and certain extratropical perturbations induce transitions between the PBAs. These distinct stability properties may have profound consequences for extratropical climate predictions, provided they are confirmed by studies using comprehensive climate models. Thus, for instance, ensemble averaging may no longer be a valid approach to isolate the low-frequency variability signal.

How to cite: Vannitsem, S., Demaeyer, J., and Ghil, M.: Extratropical low-frequency variability with ENSO forcing: a reduced-order coupled model study  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1036, https://doi.org/10.5194/egusphere-egu21-1036, 2021.

2020 was exceedingly difficult for humans. As the world was experienced surge waves of COVID19, East Asia was also facing a one in a century, record-breaking flood,  as the result of a super 47-day Meiyu/Baiu stage of East Asian summer monsoon. As East Asian monsoons (EAM) follow a yearly cyclical pattern, we wonder which stage(s) were collateral damages of the extended Meiyu. Was it an early termination of the anomalous dry Spring, or was it a delayed northward propagation of the rain belt, i.e. late Mid-summer? The hypothesis stems from our recent finding (Dai et al., 2020) that the duration of the Spring stage is informative for the onset of Meiyu, while the duration of Meiyu is negatively correlated with that of Mid-summer, i.e., the longer the Meiyu, the shorter the Mid-summer. To verify this, we first positioned the 2020 pre-Meiyu, Meiyu, Mid-summer stages in the 40-year climatology annual cycle (Dai et al., 2020). Although neither the onset nor the termination was beyond the 40-year variance, Meiyu indeed hastened to arrive but postponed its departure. Rain belt stalled over the Yangtze river basin and southern Japan since mid-June; until the end of July, a planetary-scale anomalous high pressure band was in place encompassing the Arabian sea and north Pacific. It hindered the South Asian monsoonal flow to the South China Sea, curbing the northward propagation of the rain belt with assistance by both southeast-ward shift of South Asian High and lower level high pressure system persistent over the northern China. With these observations, we put forward a framework of ocean-atmosphere coupled mechanisms that traces back to the summer in 2019, and reveal the climate teleconnection and circulation systems that pave the road to the 2020 super Meiyu. With this study, we address the question of whether the 2020 super Meiyu was a “black swan” or a manifestation of ongoing systematic changes of the EAM annual cycle?

Reference

Dai, L., Cheng, T. F., & Lu, M. (2020). Define East Asian monsoon annual cycle via a self‐organizing map‐based approach. Geophysical Research Letters, 47. e2020GL089542. https://doi.org/10.1029/2020GL089542

How to cite: Lu, M., Pan, M., Dai, L., and Cheng, T. F.: Is 2020 super Meiyu a result of changing annual cycle of East Asian monsoon?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1996, https://doi.org/10.5194/egusphere-egu21-1996, 2021.

We use a LGM setup of the CESM with marine and terrestrial biogeochemistry. This free-running  set-up (i.e., no freshwater hosing) exhibts Dansgaard-Oeschger events and Antarctic Isotope Maxima with time-lags and amplitudes that are consistent with paleo reconstructions. The CO2 signal associated DO events is also consistent with reconstructions: a 10 ppm/kyr increase during stadials, with the increase continuing some 400 years after Antarctica has started to cool again. An analysis of the modelled air-sea/land carbon fluxes reveals that some 3ppm of the stadial increase are due to shifting rain and temperature patterns that reduce growth of land vegetation. This adjustment is largely concluded after 3 centuries. The remainder of the signal is due to reduced ocean uptake. It turns out that reduced subduction of carbon in the Southern Ocean is mostly compensated by reduced upwelling in the equatorial oceans. Thus, as found in previous studies, much of the extra carbon is due to reduced uptake in the North Atlantic, partly directly due to reduced deep convection, and partly due to a reduced biological productivity because much of the North Atlantic nutrients are supplied by the AMOC. A big surprise is the emergence of the North Pacific as a major contributor to the changes in the air-fluxes of carbon. It is the reorganization of its wind-driven circulation that explains why global net-outgassing of carbon continues long after the interstadial has begun.

How to cite: Jochum, M., Chase, Z., Nutermn, R., Pedro, J., Rasmussen, S., and Vettoretti, G.: Carbon cycle response to Dansgaard-Oeschger events in a freely oscillating LGM setup of the Community Earth System Model., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1556, https://doi.org/10.5194/egusphere-egu21-1556, 2021.

The resilience of the Amazon rainforest to both climate and land use change is of critical importance for biodiversity, regional climate, and the global carbon cycle. Some models project future climate-driven Amazon rainforest dieback (Cox et al. 2000) and others argue that land-use and climate change have already pushed the Amazon close to a tipping point of rainforest dieback and transition to savanna (Lovejoy & Nobre 2018, 2019). But competing effects between rising temperatures, changing precipitation patterns, and CO₂ fertilization, make the future of the Amazon uncertain. An alternative approach is to look for direct observational signals of changing rainforest resilience from timeseries analysis - here of remotely-sensed vegetation optical depth (VOD) (Moesinger et al. 2018), which correlates well with changes in broadleaf tree fraction coverage. Our results indicate that the Amazon rainforest has been losing resilience since the early 2000s, with statistical characteristics evolving consistently with critical slowing down on the way to a bifurcation-induced transition. Specifically, changes in lag-1 autocorrelation of VOD show that resilience is lost faster in regions with less mean annual rainfall. Parts of the rainforest that are closer to human activity are also losing resilience more quickly. Given observed increases in dry-season length, and expanding areas of land use change, the loss of Amazon rainforest resilience is likely to continue. Our results provide direct empirical evidence that the Amazon rainforest is losing stability, risking a sudden dieback that would have profound implications for biodiversity, carbon storage and climate change.

References

Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A. & Totterdell, I. J. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 184-187, doi:10.1038/35041539 (2000).

Lovejoy, T. E. & Nobre, C. Amazon Tipping Point. Science Advances 4, eaat2340, doi:10.1126/sciadv.aat2340 (2018).

Lovejoy, T. E. & Nobre, C. Amazon tipping point: Last chance for action. Science Advances 5, eaba2949, doi:10.1126/sciadv.aba2949 (2019).

Moesinger, L. et al. The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth System Science Data 12, 177-196, doi:10.5194/essd-12-177-2020 (2020).

This work was funded by the Volkswagen foundation and the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 820970.

How to cite: Boulton, C., Lenton, T., and Boers, N.: Loss of Amazon rainforest resilience since the early 2000s, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2286, https://doi.org/10.5194/egusphere-egu21-2286, 2021.

The driver of the Dansgaard-Oeschger (DO) events remains uncertain, in part because many models do not show similar behaviour of a climate system tipped into a DO oscillatory state. Here we present results from glacial simulations of the HadCM3 GCM that show stochastic DO-scale variability. This is driven by variations in AMOC strength in response to North Atlantic salinity fluctuations. This represents a salt oscillator, driven by the salinity gradient between the subtropical gyre and Nordic seas. We give a mechanistic explanation of the feedbacks that drive this oscillator, particular the interplay between surface fluxes and advection. We identify that the key trigger that pushes the system into this oscillatory mode is the height of the North American ice sheet, which alters atmospheric and subsequently ocean circulation patterns. Our results highlight that glacial background conditions and ice sheet height act to push the system past a tipping point and into an oscillatory state on a timescale comparable to the DO events.

How to cite: Armstrong, E., Valdes, P., and Izumi, K.: Identifying the drivers of stochastic Dansgaard–Oeschger scale variability in a GCM, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2379, https://doi.org/10.5194/egusphere-egu21-2379, 2021.

A quasi-geostrophic, low-order model of the wind-driven ocean circulation is used to illustrate tipping points induced by time-dependent forcing in excitable chaotic systems. When the wind stress amplitude G is constant in time, our model has a bifurcation from low-amplitude oscillations to high-amplitude relaxation oscillations (ROs) at a wind intensity value G_c. In the presence of time-dependent wind stress, the corresponding tipping point time t_tp is defined as the time at which ROs arise. Numerical experiments are carried out using ensemble simulations in the presence of different drift rates of monotonically increasing forcing. Additional experiments include small periodic perturbations of such forcing. The results indicate substantial sensitivity of t_tp and G(t_tp) Rate-induced tipping, coexisting pullback attractors and total independence from initial states are found for subsets of parameter space. Besides, nonlinear resonance occurs in the presence of periodic perturbations for periods comparable to the RO time scale. The small periodic perturbation can be thought of as the seasonal-to-interannual variability in the wind stress, while the monotonically increasing component stands for the effect of amplification in the midlatitude winds due to anthropogenic warming.

How to cite: Pierini, S. and Ghil, M.: Tipping points induced by parameter drift in an excitable low-order model of the wind-driven ocean circulation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2462, https://doi.org/10.5194/egusphere-egu21-2462, 2021.

Paleoclimatic records document large-scale shifts in the Earth’s climate history. Among other possibilities, these transitions might have been caused by bifurcations in the leading dynamical modes. Such bifurcation-induced critical transitions are typically preceded by characteristic early-warning signals (EWS), for example in terms of rising standard deviation and lag-one autocorrelation. These EWS are caused by the phenomenon of critical slowing down (CSD) in response to a widening of the underlying basin of attraction as the bifurcation is approached. The presence of EWS prior to an observed transition therefore provides evidence that the transition is caused by a bifurcation. We reveal significant EWS prior to several critical transitions within a paleoclimate record spanning the Cenozoic Era, i.e., the last 67M years. We employed the CENOzoic Global Reference benthic foraminifer carbon and oxygen Isotope Dataset (CENOGRID), comprising two time series of isotope variations of δ¹⁸O and δ¹³C. The standard deviation and lag-one autocorrelation are estimated in sliding windows for both records, to reveal whether CSD occurs ahead of the major abrupt transitions in these records. Specifically, we detect significant EWS for five out of nine previously identified transitions in at least one of the two available records. EWS are recognized for significant increases in both CSD indicators prior to the transition. Our results hence suggest that at least five major climate transitions of the last 67 Ma were triggered by bifurcations in leading modes of variability, indicating bifurcations have likely played a key role in the deep-time evolution of the Earth's climate system.

How to cite: Klinghammer, G. and Böttner, C.: Early-Warning Signals For Cenozoic Climate Transitions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2520, https://doi.org/10.5194/egusphere-egu21-2520, 2021.

The Dansgaard-Oeschger (D-O) oscillation recorded in isotopic analyses of Greenland ice cores is a climate oscillation with millennial scale variability alternating between cold stadial climate and warm interstadial climate states. Using a series of long comprehensive climate model integrations of the glacial climate system under different levels of radiative forcing, we formulate a simple heuristic model to emulate the D-O oscillation. We demonstrate that the D-O oscillation has properties that are consistent with an internal unforced oscillation as well as displaying interesting behaviour that is consistent with noise induced transitions. Therefore, the D-O oscillation is more aptly characterized as a stochastic oscillator with stadial and interstadial durations that are more dependent upon a control parameter and internal climate variability rather than an intrinsic characteristic timescale.

How to cite: Vettoretti, G., Ditlevsen, P., Jochum, M., and Rasmussen, S.: The Structure of Millennial Scale Glacial Climate Variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4264, https://doi.org/10.5194/egusphere-egu21-4264, 2021.

Methane mitigation is a key component of limiting the extent of global warming. However, little is known about how methane mitigation can benefit other critical aspects of the climate system, such as tipping elements. This study explores how reducing methane emissions can avert an approaching and concerning climate event: the loss of Arctic summer sea ice. We show that early deployment of feasible methane mitigation measures is essential to delaying and potentially even avoiding the loss of Arctic summer sea ice. Whether or not the sea ice is preserved beyond this century will ultimately depend on the level of concomitant carbon dioxide mitigation, but it is clear that sea ice will be at risk in the absence of methane mitigation. This analysis provides further justification of the value of early methane mitigation and supports the need to consider climate benefits beyond temperature when evaluating mitigation pathways.

How to cite: Sun, T., Ocko, I., and Hamburg, S.: Early Methane Mitigation Critical to Preserving Arctic Summer Sea Ice, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-740, https://doi.org/10.5194/egusphere-egu21-740, 2021.

Due to non-linearities in the dynamics of crucial elements in the climate system, Earth’s safe operating space is limited. Beyond a certain level of a control parameter, such as the atmospheric Greenhouse gas concentration, qualitative regime shifts in one or more sub-systems may take place. Additionally, theoretical studies suggest that abrupt, irreversible change can happen already prior to the crossing of a critical threshold in a control parameter.

In these so-called rate-induced transitions, the effective parameter level to induce tipping depends on the rate of change, or more generally the precise trajectory of the changing control parameter. Here we show rate-induced tipping points of the overturning circulation in a global ocean model. Due to the chaotic dynamics of the system, whether there will be tipping or not depends both on the rate and initial conditions in a very sensitive, non-smooth way. This raises questions about whether the safe operating space is still well-defined, and whether an approach of its boundary can be predicted.

For tipping points associated with slow passages across a bifurcation, generic early-warning signals have been developed for these purposes. Due to the necessarily fast parameter changes involved in rate-induced tipping, early-warning is more challenging. In many cases the tipping involves a saddle escape, which results in a delay of the actual transition and can be exploited for early-warning. Here this is demonstrated in the context of low-dimensional models. While due to the sensitivity of the dynamics around the saddle one might not be able to predict with certainty whether and when the system will tip, the indicators presented here may allow issuing a warning as the system gets close to tipping.

How to cite: Lohmann, J. and Ditlevsen, P.: Predictability of tipping points with rate-dependent effects, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5536, https://doi.org/10.5194/egusphere-egu21-5536, 2021.

When modelling potential tipping elements of the earth system, one conventionally distinguishes "bifurcation-induced" and "noise-induced" tipping. The former occurs when an internal system parameter slowly crosses a critical threshold and external noise is negligible. The latter arises from forcing by noise well before a critical threshold for the internal dynamics is reached. The former comes with early warning signals, due to "critical slowing down" in the internal dynamics; but the latter occurs randomly without warning. However, these descriptions typically assume that the noise is Gaussian white noise, which arises as a limit of fast-timescale chaotic driving. We will instead consider, through a simple discrete-time prototype, finite-timescale bounded chaotic driving; this is a more suitable description of the subgrid forcing of turbulent geophysical fluid dynamics than uncorrelated noise. We will see that the phenomenon previously known as "noise-induced tipping" now corresponds to a deterministic bifurcation-induced tipping of the joint dynamics of the tipping element and the driving. Although "critical slowing down" does not occur in this bifurcation, early warning and near-exact prediction of the tipping event may still be possible. We also discuss the phenomenon of "noise-induced" prevention or delay of a tipping event, which cannot occur under conventional memoryless noise.

How to cite: Newman, J. and Ashwin, P.: Re-thinking noise-induced tipping, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5918, https://doi.org/10.5194/egusphere-egu21-5918, 2021.

Most layer-counting based paleoclimate proxy records have non-negligible uncertainties that arise from both the proxy measurement and the dating processes. Proper knowledge of the dating uncertainties in paleoclimatic ice core records is important for a rigorous propagation to further analyses; for example for identification and dating of stadial-interstadial transitions during glacial intervals, for model-data comparisons in general, or to provide a complete uncertainty quantification of early warning signals. We develop a statistical model that incorporates the dating uncertainties of the Greenland Ice Core Chronology 2005 (GICC05), which includes the uncertainty associated with layer counting. We express the number of layers per depth interval as the sum of a structural component that represents both underlying physical processes and biases in layer counting, described by a linear regression model, and a noise component that represents the internal variation of the underlying physical processes, as well as residual counting errors. We find the residual components to be described well by a Gaussian white noise process that appear to be largely uncorrelated, allowing us to represent the dating uncertainties using a multivariate Gaussian process. This means that we can easily produce simulations as well as incorporate tie-points from other proxy records to match the GICC05 time scale to other chronologies. Moreover, this multivariate Gaussian process exhibits Markov properties which grants a substantial gain in computational efficiency.

How to cite: Myrvoll-Nilsen, E., boers, N., Rypdal, M., and Riechers, K.: A statistical model for dating uncertainties in Greenland ice core records, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7807, https://doi.org/10.5194/egusphere-egu21-7807, 2021.

Early evidence of abrupt transitions in Camp Century and Dye 3 Greenland ice cores (Dansgaard et al. 1982) has recently been reinforced by the identification of additional abrupt transitions in the NGRIP ice core (Rasmussen et al. 2014). These additional events correspond to changes of either short duration or amplitude of d¹⁸O that visual or statistical inspections do not necessarily validate. Abrupt transitions have been described for marine (Bond et al. 1992) and continental (Wang et al. 2001) records as well, and they provide a broader spatial perspective. Finally, abrupt transitions have also been documented over much deeper timescales (Zachos et al., 2001, Hodell & Channel, 2016, Westerhold et al. 2020). In spite of the variable time resolution of all these records, the abrupt transitions seem to reflect the individual impact of external forcing, of internal climate variability, or a combination of the two on Earth’s climate system. To illustrate this, we have analyzed 4 reference datasets with timescales ranging from one glacial cycle — i.e., the last 130,000 years — to the last 70 Ma. We show patterns that repeat within a single glacial cycle and seem to be related to internal variability, along with patterns associated with longer time periods and possibly related to external forcing; such forcing may arise from changes in either Earth’s orbit or its dynamics. This study is supported by the H2020-funded Tipping Points in the Earth System (TiPES) project.

How to cite: Rousseau, D.-D., Bagniewski, W., and Ghil, M.: Abrupt transitions in past climates: How reliable are they and what do they mean?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7857, https://doi.org/10.5194/egusphere-egu21-7857, 2021.

We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model and analyze their interplay. First, drawing from the theory of quasipotentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states, and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, specifically manifold learning, we characterize  the data landscape of the simulation output to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earth's climate.

How to cite: Margazoglou, G., Lucarini, V., Grafke, T., and Laio, A.: Dynamical Landscape and Multistability of a Climate Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8059, https://doi.org/10.5194/egusphere-egu21-8059, 2021.

Dansgaard-Oeschger (DO) events are abrupt, large climate swings that punctuated the last glacial period. There is uncertainty whether current IPCC-relevant models can effectively represent the processes that cause DO events. This has implications for whether these models are also capable of simulating future TEs,  and more in general, for the delivery of accurate climate change projections. Here we present progress on possible pathways to a DO Paleoclimate Modelling Intercomparison Project (PMIP) protocol. This is broad interest to the climate community since (1), there is currently no PMIP common guidance to investigate DO events, (2) it could help carry out simulations in Earth system models under a common framework, and (3) it will help guide a more methodical search for DO events in current models. A protocol could help investigate cold-period TEs through a range of insolation-, freshwater-, green-house-gas-, and Northern Hemisphere ice sheet-related forcings, as well as evaluating the possibility of spontaneous TEs. MIS3 was a period of noticeable millennial-scale climate variability, characterised by the most regular incidence of DO events (Schulz et al., 1999). Although most abrupt DO events happened during MIS3, only few studies investigate TEs in coupled general circulation models under MIS 3 conditions (e.g., Kawamura et al., 2017; Zhang and Prange, 2020). Here, we therefore suggest that the MIS3 period could be the focus of such a DO-event focussed modelling protocol. Experiments performed under MIS 3 boundary conditions may help (1) explore variability under intermediate glacial conditions, (2) better understand the mechanisms behind millennial-scale TEs, (3) look for spontaneous DO-type oscillations, and (4) help answer the question: “are models too stable?”.

How to cite: Sime, L., Malmierca Vallet, I., and Valdes, P.: Dansgaard-Oeschger Tipping Events (TEs): Towards determining if IPCC-relevant models represent these TEs., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8503, https://doi.org/10.5194/egusphere-egu21-8503, 2021.

The Amazon rainforest is widely recognised as a potential tipping element in the Earth's climate system. While several studies suggest a sudden dieback of the rainforest ecosystem after partial deforestation [e.g., 1, 2], there is still a lack of understanding where to search for early-warning signals that might precede such a dieback. In this work we employ a non-linear model of the moisture transport across the Amazon Basin to propose several statistical and physical early warning signals for a critical transition in the coupled dynamics of the Amazon rainforest and the atmospheric circulation of the South American monsoon. 

Widespread deforestation and its effects on evapotranspiration and radiation have been shown to potentially trigger a collapse of the positive feedback related to latent heat release over the rainforest [3], resulting in substantially reduced rainfall amounts. The model includes a nonlinear contribution representing the acceleration of low-level moisture flow caused by condensational latent heating.  

Guided by our modelling results, we associate characteristic changes in the hydrological cycle as well as statistical indicators in observed data with deforestation-induced circulation changes that are consistent with the identified early-warning signals. Our findings indicate that in response to deforestation, the coupled atmosphere-vegetation system is destabilising and that further deforestation could trigger a transition of the Amazon rainforest to a savanna state. 

[1] Nobre, C. A., & Borma, L. D. S. (2009). “Tipping points” for the Amazon forest. Current Opinion in Environmental Sustainability. https://doi.org/10.1016/j.cosust.2009.07.003

[2] Hirota, M., Holmgren, M., Van Nes, E. H., & Scheffer, M. (2011). Global resilience of tropical forest and savanna to critical transitions. Science, 334(6053), 232–235. https://doi.org/10.1126/science.1210657

[3] Boers, N., Marwan, N., Barbosa, H. M. J., & Kurths, J. (2017). A deforestation-induced tipping point for the South American monsoon system. Scientific Reports, 7. https://doi.org/10.1038/srep41489

How to cite: Bochow, N.: Early-Warning Signals for a Critical Transition in the Coupled Amazon Rainforest-South American Monsoon System, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2023, https://doi.org/10.5194/egusphere-egu21-2023, 2021.

Ice core records from Greenland provide evidence for multiple abrupt warming events recurring at millennial time scales during the last glacial interval. Although climate transitions strongly resembling these Dansgaard-Oeschger (DO) transitions have been identified in several speleothem records, our understanding of the climate and ecosystem impacts of the Greenland warming events in lower latitudes remains incomplete.

Here, we investigate the influence of DO transitions on the global atmospheric circulation pattern. We comprehensively analyse d18O changes during DO transitions in a globally distributed dataset of speleothems (SISALv2; Comas-Bru et al., 2020). Speleothem d18O signals mostly reflect changes in precipitation amount and moisture source. Thereby this proxy allows us to infer spatially resolved changes in global atmospheric dynamics that are characteristically linked to DO transitions. We confirm the previously proposed shift of the Intertropical Convergence Zone towards more northerly positions. In addition, we find evidence for a similar northward shift of the westerly winds of the Northern Hemisphere. Furthermore, we identify a decreasing trend in the transition amplitudes with increasing distances from the North Atlantic region. This confirms previous suggestions of this region being the core and origin of these past abrupt climate changes.

References:

Comas-Bru et al., 2020, Earth System Science Data 12, 2579–2606

How to cite: Fohlmeister, J., Sekhon, N., Columbu, A., Rehfeld, K., Sime, L., Veige-Pires, C., Marwan, N., and Boers, N.: Global reorganization of atmospheric circulation during Dansgaard-Oschger cycles, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9433, https://doi.org/10.5194/egusphere-egu21-9433, 2021.

The Dansgaard-Oeschger (DO) events are one of the most striking examples of abrupt climate change in the Earth's history, representing temperature oscillations of about 8 to 16 degrees Celsius within a few decades. DO events have been studied extensively in paleoclimatic records, particularly in ice core proxies. Examples include the Greenland NGRIP record of oxygen isotopic composition.
This work addresses the anticipation of DO events using machine learning algorithms. We consider the NGRIP time series from 20 to 60 kyr b2k with the GICC05 timescale and 20-year temporal resolution. Forecasting horizons range from 0 (nowcasting) to 400 years. We adopt three different machine learning algorithms (random forests, support vector machines, and logistic regression) in training windows of 5 kyr. We perform validation on subsequent test windows of 5 kyr, based on timestamps of previous DO events' classification in Greenland by Rasmussen et al. (2014). We perform experiments with both sliding and growing windows.
Results show that predictions on sliding windows are better overall, indicating that modelling is affected by non-stationary characteristics of the time series. The three algorithms' predictive performance is similar, with a slightly better performance of random forest models for shorter forecast horizons. The prediction models' predictive capability decreases as the forecasting horizon grows more extensive but remains reasonable up to 120 years. Model performance deprecation is mostly related to imprecision in accurately determining the start and end time of events and identifying some periods as DO events when such is not valid.

How to cite: Moniz, N. and Barbosa, S.: Prediction of Dansgaard-Oeschger events using machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9699, https://doi.org/10.5194/egusphere-egu21-9699, 2021.

The Pleistocene climate was dominated by alternating retreat and regrowth of massive ice sheets accompanied by large variations in the global mean temperature and sea level. Partial agreement between the power spectra of global ice volume proxies and high-latitude summer insolation provides evidence that quasi-periodic changes in the earth’s orbital configuration affect the timing of glaciations and deglaciations. It remains, however, a topic of active debate whether the main cause of glacial cycles is an internal self-sustained oscillation of the climate system that merely phased locked, more or less, to orbital forcing or whether glacial cycles could not exist at all in the absence of orbital forcing. Furthermore, it is unclear whether past ice volume records should be regarded as the result of a purely deterministic process or as a randomly selected trajectory of a stochastic process. To study plausible paths of the earth’s climate system given the orbital forcing, we compute the pullback attractors of several conceptual Pleistocene models. The results are confronted with the power spectra, as well as the time series of proxy records and conclusions will be drawn about the role of internal vs. forced variability and the possible contribution of stochastic processes to the mix of causes. We argue, moreover, that the explanatory power of either a deterministically chaotic or a dynamic-stochastic model cannot be assessed by comparing the model output to observations in the time domain alone.

How to cite: Riechers, K., Boers, N., Ghil, M., and Mitsui, T.: Glaciation cycle models and their pullback attractors, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9733, https://doi.org/10.5194/egusphere-egu21-9733, 2021.

The climate can be regarded as a stationary non-equilibrium statistical system (Gallavotti 2006): a continuous and spatially inhomogeneous input of solar energy enters at the top-of-atmosphere and compensates the action of non-conservative forces, mainly occurring at small scales, to give rise to a statistically steady state (or attractor) for the whole climate.

Depending on the initial conditions and the range of forcing, all other parameters being the same, some climate models have the property to settle down on different attractors. Multi-stability reflects how energy, water mass and entropy can be re-distributed in multiple ways among the climate components, such as the atmosphere, the ocean or the ice, through a different balance between nonlinear mechanisms.

Starting from a configuration where competing climate attractors occur under the same forcing, we have explored their robustness performing two kinds of numerical experiment. First, we have investigated the impact of frictional heating on the overall energy balance and we have shown that such contribution, generally neglected in the atmospheric component of climate models, has crucial consequences on conservation properties: it improves the energy imbalance at top-of-atmosphere, typically non negligible in coarse simulations (Wild et al. 2020), strengthens the hydrological cycle, mitigates the mechanical work associated to atmospheric circulation intensity and reduces the heat transport peaks in the ocean. Second, we have compared two bulk formulas for the cloud albedo, one where it is constant everywhere and the other where it increases with latitude, as implemented in the new version of the atmospheric module SPEEDY in order to improve comparisons with observational data (Kucharski 2013). We have checked that this new parameterization does not affect energy and water-mass imbalances, while reduces global temperature and water-mass transport on the attractor, giving rise to a larger conversion of heat into mechanical work in the atmosphere.

In order to perform such studies, we have run the climate model MITgcm on coupled aquaplanets at 2.8 horizontal resolution until steady states are reached (Brunetti el al. 2019) and we have applied the Thermodynamic Diagnostic Tool (TheDiaTo, Lembo et al. 2019).

References:

Brunetti, Kasparian, Vérard, Climate Dynamics 53, 6293 (2019)

Gallavotti, Math. Phys. 3, 530 (2006)

Kucharski et al., Bulletin of the American Meteorological Society 94, 25 (2013)

Lembo, Lunkeit, Lucarini, Geoscientific Model Development 12, 3805 (2019)

Wild, Climate Dynamics 55, 553 (2020)

How to cite: Ragon, C., Lembo, V., Lucarini, V., Vérard, C., Kasparian, J., and Brunetti, M.: Testing robustness of co-existing climate states, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9902, https://doi.org/10.5194/egusphere-egu21-9902, 2021.

Tipping points (TPs) in the Earth system have been studied with growing interest and concern in recent years due to the potential risk of anthropogenic forcing causing abrupt, and possibly irreversible, climate transitions. Paleoclimate records are essential for identifying TPs in the Earth’s past and to properly understand the climate system’s underlying bifurcation mechanisms. Due to their varying quality, resolution, and dating methods, it is often necessary to select the records that give the best representation of past climate. Furthermore, as paleoclimate records vary in their origin, time spans, and periodicities, an objective, automated methodology is crucial for identifying and comparing TPs.

To reach this goal, here we present the PaleoJump database of carefully selected, high-resolution records originating in ice, marine sediments, speleothems, loess, and lake sediments. These records, which include tipping elements, cover long time intervals and represent a global distribution from all continents and ocean basins. For every record, a transition detection methodology based on an augmented Kolmogorov-Smirnov test is applied to identify abrupt transitions. The PaleoJump database highlights these automatically detected transitions for every record together with other essential information, including location, temporal scale and resolution, as well as temporal plots; it therefore represents a valuable resource for researchers investigating TPs in past climates. This study is supported by the H2020-funded TiPES project.

How to cite: Bagniewski, W., Rousseau, D.-D., and Ghil, M.: PaleoJump database for research on rapid climate transitions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9968, https://doi.org/10.5194/egusphere-egu21-9968, 2021.

The future of ski resorts in the Swiss Alps is highly uncertain. Being dependent on snow cover conditions, winter sport tourism is highly susceptible to changes in temperature and precipitation. With the observed warming of the European Alps being well above global average warming, snow cover in Switzerland is projected to shrink at a rapid pace. Climate uncertainty originates from greenhouse gas emission trajectories (RCPs) and differences between climate models. Beyond climate uncertainty, the snow conditions are strongly subject to intra-annual variability. Series of unfavorable years have already led to the financial collapse of several low-altitude ski resorts. Such abrupt collapses with a large impact on the regional economy can be referred to as climate change induced socio-economic tipping points. To some degree, tipping points may be avoided by adaptation measures such as artificial snowmaking, although these measures are also subject to physical and economical constraints. In this study, we use a variety of exploratory modeling techniques to identify tipping points in a coupled physical-economic model applied to six representative ski resorts in the Swiss Alps. New high-resolution climate projections (CH2018) are used to represent climate uncertainty. To improve the coverage of the uncertainty space and accounting for the intra-annual variability of the climate models, a resampling technique was used to produce new climate realizations. A snow process model is used to simulate daily snow-cover in each of the ski resorts. The likelihood of survival of each resort is evaluated from the number of days with good snow conditions for skiing compared to the minimum thresholds obtained from the literature. Economically, the good snow days are translated into the total profit of ski resorts per season of operation. Multiple unfavorable years of total profit may lead to a tipping point. We use scenario discovery to identify the conditions under which these tipping points occur, and reflect on their implications for the future of snow tourism in the Swiss Alps.

How to cite: Ashraf Vaghefi, S., Muccione, V., van Ginkel, K. C. H., and Haasnoot, M.: The future of ski resorts in the Swiss Alps: using DMDU to identify tipping points, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12302, https://doi.org/10.5194/egusphere-egu21-12302, 2021.

Ancient peat deposits provide valuable and complementary insight into the biogeochemical response of wetlands to climate perturbations, including potential tipping points in such systems. The combination of temperature (GDGTs) and hydrology (leaf wax hydrogen isotopic compositions) proxies with qualitative proxies for methanogenesis (archaeal lipid abundances) and methanotrophy (bacterial lipid carbon isotopic compositions) has revealed dramatic perturbations to the carbon cycle during transient warming events, including the Palaeocene Eocene Thermal Maximum.  Bacterially-derived hopanes in at least two PETM-spanning lignite sequences record negative carbon isotope excursions of near-unprecedented magnitude in response to rapid global warming.  The warming – either directly or indirectly – clearly caused a fundamental reorganisation of the carbon cycle in those ancient wetlands. Intriguingly however, these excursions persist for a far shorter duration than the PETM warming. Similarly, hopane δ¹³C values in lignites of the Early Eocene Climate Optimum, the warmth of which was reached more gradually, are similar to those of today. This suggests that these unusually isoptopically light hopanoids represent a transient disruption to the methane cycle associated with a climate perturbation rather than an equilibrium response to warmer temperatures.  Such an interpretation is consistent with Deglacial and Holocene peat-derived records, in which hopane δ¹³C values exhibit large responses to transient drying events and modest responses to longer-term change. Such findings could have implications for future climate change feedbacks, with the wetland methane cycle being particularly sensitive to the rate of climatic change.

How to cite: Pancost, R., Naafs, D., Inglis, G., and Lauretano, V.: Ancient warming pushed wetlands across biogeochemical tipping points, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12477, https://doi.org/10.5194/egusphere-egu21-12477, 2021.

Studies with global climate models over the past 25 years have shown a range of long-term responses of the Atlantic Meridional Overturning Circulation (AMOC), in response to scenarios in which greenhouse gases are increased then eventually stabilised at some value, possibly after temporarily overshooting that value. AMOC responses include stabilisation at weaker than the pre-industrial level, rapid recovery following a period of quasi-steady weak circulation, overshooting to stronger than pre-industrial strength, or tipping to a quasi-permanent weak state. While many of these studies have gained insight into the mechanisms behind their individual model behaviour, no overarching understanding exists of what determines how a particular model will respond.   

We present a simple AMOC model suitable to characterise the different possible long-term responses, and use it as a (partially) unifying framework to show how the different behaviours can arise from a competition between thermal and haline feedbacks. The results are relevant to defining ‘safe mitigation pathways’ that avoid or reduce the risk of AMOC tipping or potentially dangerous overshoots.

How to cite: Wood, R.: A simple model to assess the long term fate of the AMOC, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14691, https://doi.org/10.5194/egusphere-egu21-14691, 2021.

The last millennium was characterised by a cooling from the Medieval Warm Period into the Little Ice Age. While strong volcanic eruptions could have triggered the onset of the Little Ice Age by reducing solar irradiance, modelling experiments suggest that it was amplified and maintained by sea ice-ocean feedbacks, including a potential abrupt weakening of the subpolar gyre. The weakening of negative feedbacks that maintain a system in a stable state, prior to an abrupt transition, can be detected as an increase in temporal autocorrelation and variability. Here we use an annually-resolved and absolutely dated shell-derived record from the North Icelandic Shelf that spans the last millennium, to detect such a loss of resilience in the marine environment leading up to the Little Ice Age transition. We find a significant increase in autocorrelation and variance in bivalve growth increments and oxygen isotopes before the transition, providing evidence consistent with loss of stability in the marine environment. This supports the idea that internal feedbacks played an important role in the Little Ice Age onset.

How to cite: Arellano Nava, B., Halloran, P. R., Boulton, C. A., and Lenton, T. M.: Bivalves indicate that the North Atlantic was under stress before the onset of the Little Ice Age, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16401, https://doi.org/10.5194/egusphere-egu21-16401, 2021.

The science of Earth system and climate tipping points has evolved and matured as a disciplined approach to understanding anthropogenic and non-anthropogenic stresses on the Earth’s subsystems in the 21^st century. However, tipping points is strongly interlinked with the science of bifurcations and dynamical systems, which received a seminal and resonant illumination by the great French mathematician Henri Poincare (1854-1912). Thus, quite a few historically minded tipping point scientists mention Poincare as the seminal, path-setting thinker for tipping point understandings.

Moreover, Poincare’s bifurcation and dynamical systems-pertinent science is also linked to his seminal role in chaos theory, which illuminates today’s understanding of climate stochasticity. Poincare famously said, "A very small cause which escapes us determines a considerable effect that we cannot see; so, we say this effect is random," which provided grounding for the chaos notion of critical sensitivity to initial conditions. Since Poincare, great strides in abrupt change understanding as linked to chaos (and within an examination of turbulence) have taken place in the science that informs tipping points, such as with the work of Ed Lorenz and David Ruelle. Additionally, the Russian mathematicians (e.g., Andronov and Arnold) have contributed greatly with the refining of differential equations for bifurcation understandings that Poincare began.  

This EGU presentation is a history of science presentation on Henri Poincare's commencement of bifurcation, dynamical system and chaos understandings as presented by a journalist who has done both interviews and general historical research. The presentation sets key points in Poincare’s biography and pertinent career and sketches the legacy of this Poincare focus up from Henri Poincare through Russian bifurcation scientists, catastrophe theorist Rene Thom, and ultimately Lorenz and current bifurcation theorists, such as Michael Ghil and Valerio Lucarini. It offers light on the ancestry of one of the most important examinations of the Anthropocene, climate change tipping points.  

How to cite: Blaustein, R.: Henri Poincare’s legacy for tipping points, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16427, https://doi.org/10.5194/egusphere-egu21-16427, 2021.