Detecting Abrupt Shifts and Coherent Spatial Domains in Earth System Data with TOAD

Abstract. Large-scale, nonlinear, abrupt, and potentially irreversible transitions in major Earth-system components are becoming increasingly likely under human pressures, with far-reaching consequences for ecosystems, climate stability, and human societies. Yet detecting and comparing such transitions across Earth System Model ensembles remains fragmented and inconsistent, hindering systematic assessment of tipping-point risks. Here we present the first release of the Tipping and Other Abrupt events Detector (TOAD v1.0), an open-source, user-oriented Python framework for detecting abrupt changes in gridded Earth-system data. TOAD implements a modular three-stage pipeline consisting of 1) grid-level abrupt shift detection, 2) spatio-temporal clustering of co-occurring changes, and 3) consensus synthesis to identify statistically robust regions across ensemble members, variables, models, or methodological configurations and quantifies agreement in transition timing. The framework addresses key practical challenges of large-scale spatio-temporal clustering on geographic grids and provides diagnostic statistics and visualisation tools. Detection, clustering, and synthesis algorithms can be flexibly exchanged, supporting systematic method comparison and extensibility. TOAD functions as a data-introspection tool that reveals potentially tipping-relevant dynamics across spatial and temporal scales for subsequent, process-based analysis. We apply TOAD to a synthetic benchmark, domain models of the Antarctic Ice Sheet and the global terrestrial biosphere, and a global Earth System Model ensemble of the North Atlantic Subpolar Gyre. Together, these demonstrations illustrate TOAD's applicability across diverse systems and establish a structured foundation for investigating where and when potentially tipping-relevant changes occur and for quantifying associated uncertainties, supporting coordinated assessment efforts such as the Tipping Points Modelling Intercomparison Project (TIPMIP).

<p>Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long-term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different phasing of internal climate variability. We further demonstrate the validity of our statistical approach by recreating a ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long-term trends in the presence of internal variability. Our results suggest the potential to explore this approach for other ocean biogeochemical variables.</p>

There has been increasing interest in the possibility of abrupt climatic changes in the North Atlantic and their impacts on northwestern Europe. Here, we investigate such abrupt changes in a large ensemble of CMIP6 climate models. We define two potentially observable metrics based on subpolar sea surface temperatures (SSTs) or mixed layer depths (MLDs), to explore the link between temperature changes and convection collapse. The two metrics yield similar numbers of abrupt events but suggest that several types of abrupt event are possible. Abrupt MLD changes appear related to ongoing warming. Abrupt SST changes mostly consist of decadal cooling followed by warming, apparently related to coupled dynamics involving the North Atlantic Oscillation (NAO). Models with more realistic NAO variability show more such events. However, several more persistent SST events are also found. Both cooling and warming phases have important implications for impacts and adaptation, particularly over the British Isles.

Combining large model ensembles with extreme value statistics to improve attribution statements of rare events

Identifying disease risk factors, characterizing their effects, and forecasting disease risk across space and time are crucial tasks in human, animal, and plant epidemiology. Statistical and machine learning models have largely superseded purely descriptive analyses of data in handling these tasks. In addition, these models have demonstrated their full potential in the current era, characterized by an unprecedented abundance of data. However, applying these models to real-world, large-scale data sets raises critical questions: Which model should be used? Which explanatory variables should be selected? What data should be allocated for training and validation? The answers to these questions often have a significant impact on the analysis outcomes. One way to address some of these challenges is to analyze risk factors and predict risk by using an ensemble of models rather than relying on a single model. This approach is developed in this article and implemented in the case of virus yellows in sugar beet in France. Among the explanatory variables correlated with the severity of virus yellows, we identified winter and spring temperatures (positive correlation), spring humidity and precipitation (negative correlation), the proportion of cereal crops (positive correlation), the proportion of grasslands (negative correlation), and the distance to sugar beet seed production fields (negative correlation). Additionally, we found that predictions are generally more robust when using a spatial aggregation of models compared with relying on the best individual model. Our approach is highly versatile and can be applied to characterize and predict the spatiotemporal distributions of diverse diseases.

GATE: A guided approach for time series ensemble forecasting

The Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1) contains 50‐member ensembles of bias‐adjusted near‐surface global and regional climate model variables on a 0.5° grid over North America for historical and future scenarios (1950–2100). Canadian Earth System Model Large Ensembles (CanESM2 LE) and Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) datasets are bias‐corrected using a multivariate quantile‐mapping algorithm for statistical consistency – in terms of marginal distributions and multivariate dependence structure – with two observationally constrained historical meteorological forcing datasets. For each observational dataset, bias‐adjusted variables are provided for two sets of 50‐member initial‐condition CanESM2 ensembles (historical plus RCP8.5 scenarios, 1950–2005 and 2006–2100, respectively; and historicalNAT scenario, 1950–2020, which excludes anthropogenic forcings), and one 50‐member CanRCM4 ensemble (historical plus RCP8.5). The archive includes daily minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation and incoming longwave radiation. Intended uses include hydrological and land surface impact modelling, as well as related event attribution studies.

Changes in temperature variability affect the frequency and intensity of extreme events, as well as the regional range of temperatures that ecosystems and society need to adapt to. While accurate projections of temperature variability are vital for understanding climate change and its impacts, they remain highly uncertain. We use rank-frequency analysis to evaluate the performance of eleven single model initial-condition large ensembles (SMILEs) against observations in the historical period, and use those that best represent historical regional variability to constrain projections of future temperature variability. Constrained projections from the best-performing SMILEs still show large uncertainties in the intensity and the sign of the variability change for large areas of the globe. Our results highlight poorly modelled regions where observed variability is not well represented such as large parts of Australia, South America, and Africa, particularly in their local summer season, underscoring the need for further modelling improvements over crucial regions. In these regions, the constrained projected change is typically larger than in the unconstrained ensemble, suggesting that in these regions, multi-model mean projections may underestimate future variability change.

ABSTRACTAimClimate change poses a challenge to the Azores' biodiversity, with consequences that remain unexplored. To shed light on the potential impacts of climate change, we have developed a large ensemble of species distribution models (SDMs) for species found in the coastal marine environments and examined their spatiotemporal turnover and stability.LocationThe Azorean archipelago.TaxonCoastal marine species (mammals, fish, turtles, seabirds, kelp forest and corals).MethodsSDMs were fitted a large ensemble comprising 10 machine learning algorithms and a fivefold cross‐validation resampling procedure, thus yielding a maximum number of 50 models fitted per species. These models were then utilised for projecting species distribution under different future scenarios. The projected distributions of the species were employed to assess changes in the stability of their ranges throughout the entire modelled period (2030–2100) and in their community compositions by examining changes in alpha diversity and beta diversity over 10‐year periods.ResultsWe show that under our model assumptions over 12% of the modelled units could lose suitable climate by the end of the century, with this number increasing up to 25% under a high carbon emissions scenario. Climate change refugia, which are areas of long‐term species range stability, are expected to be mainly located in the coastal areas in the northernmost part of the archipelago. A substantial loss of suitable climate is anticipated for mammals and birds, which is likely to trigger a major loss of species on the islands of Santa Maria, São Miguel, Pico and Faial. For fish, the loss of suitable climates is less pronounced. However, climate change is expected to cause a major reshuffling of the pelagic fish assemblage, with important consequences for local fisheries on each island.Main ConclusionsOur models provide insights into how climate change may alter the distribution of Azorean marine coastal species, offering important guidance for conservation and management efforts in these important North Atlantic ecosystems.

Demonstrating the effect that climate change is having on regional weather is a subject which occupies climate scientists, government policy makers and the media. After an extreme weather event occurs, the question is often posed, ‘Was the event caused by anthropogenic climate change?’ Recently, a new branch of climate science (known as attribution) has sought to quantify how much the risk of extreme events occurring has increased or decreased due to climate change. One method of attribution uses very large ensembles of climate models computed via volunteer distributed computing. A recent advancement is the ability to run both a global climate model and a higher resolution regional climate model on a volunteer's home computer. Such a set‐up allows the simulation of weather on a scale that is of most use to studies of the attribution of extreme events. This article introduces a global climate model that has been developed to simulate the climatology of all major land regions with reasonable accuracy. This then provides the boundary conditions to a regional climate model (which uses the same formulation but at higher resolution) to ensure that it can produce realistic climate and weather over any region of choice. The development process is documented and a comparison to previous coupled climate models and atmosphere‐only climate models is made. The system (known as weather@home) by which the global model is coupled to a regional climate model and run on volunteers' home computers is then detailed. Finally, a validation of the whole system is performed, with a particular emphasis on how accurately the distributions of daily mean temperature and daily mean precipitation are modelled in a particular application over Europe. This builds confidence in the applicability of the weather@home system for event attribution studies.

Regional Climate Models (RCMs) are able to simulate small-scale processes that are missing in their coarser resolution driving data and thereby provide valuable climate information for climate impact assessments. Less attention has been paid to the ability of RCMs to capture large-scale weather types (WTs). An inaccurate representation of WTs can result in biases and uncertainties in current and future climate simulations that cannot be easily detected by standard model evaluation metrics. Here we define 12 hydrologically important WTs in the Contiguous United States (CONUS). We test if RCMs from the North American CORDEX (NA-CORDEX) and the Weather Research and Forecasting (WRF) model large physics ensembles (WRF36) can capture those WTs in the current climate and how they simulate changes in the future. Our results show that the NA-CORDEX RCMs are able to simulate WTs more accurately than members of the WRF36 ensemble. The much larger WRF36 domain in combination with not constraining large-scale conditions by spectral nudging results in lower WT skill. The selection of the driving global climate model (GCM) has a large effect on the skill of NA-CORDEX simulations but a smaller impact on the WRF36 runs. The formulation of the RCM is of minor importance except for capturing the variability within WTs. Changing the model physics or increasing the RCM horizontal grid spacing has little effect. These results highlight the importance of selecting GCMs with accurate synoptic-scale variability for downscaling and to find a balance between large domains that can result in biased WT representations and small domains that inhibit the realistic development of mesoscale processes. At the end of the century, monsoonal flow conditions increase systematically by up to 30 % and a WT that is a significant source of moisture for the Northern Plains during the growing seasons decreases systematically up to -30 %.

The ongoing and projected future climate change impacts the heating, cooling, and air-conditioning sectors both directly and indirectly. The consideration of heating, cooling, and energy degree-days is a consistent, robust, and widely used approach for quantitatively estimating the energy demand of closed environments based on outdoor thermal conditions. Hence, the spatial distribution and the long-term changes in this demand depend on on the quantity of final users for such services; it is essential to consider demographic data in the assessment. The paper presents a comprehensive analysis of the population-weighted degree-days for the near past and the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) scenario-driven future over Southeast Europe for all four ‘Tier 1’ Shared Socioeconomic Pathways (SSPs) based on the methodology of the United Kingdom Meteorological Office and performed using large NEX-GDDP CMIP6 ensemble of global circulation models (GCMs) and up to date population dynamics data from the NASA’s SEDAC. As an expression of regional warming tendencies, the study reveals an overall reduction in heating and an increase in cooling degree-days, confirming the leading role of the climate. We also provide evidences for the influence of the population factor, which significantly alters the region’s degree-day climatology in both space and time. The resulting overall picture on country-wide and regional level is complex; in some cases, the population dynamics is projected to outbalance the thermal-induced changes.

<p>Recent studies have introduced methods to simultaneously calibrate model structure choices and parameter values to identify an appropriate (conceptual) model structure for a given catchment. This can be done through mixed-integer optimization to identify the graph structure that links dominant flow processes (Spieler et al., 2020) or, likewise, by continuous optimization of weights when blending multiple flux equations to describe flow processes within a model (Chlumsky et al., 2021). Here, we use the combination of the mixed-integer optimization algorithm DDS and the modular modelling framework RAVEN and refer to it as Automatic Model Structure Identification (AMSI) framework.</p><p>This study validates the AMSI framework by comparing the performance of the identified AMSI model structures to two different benchmark ensembles. The first ensemble consists of the best model structures from the brute force calibration of all possible structures included in the AMSI model space (7488+). The second ensemble consists of 35+ MARRMoT structures representing a structurally more divers set of models than currently implemented in the AMSI framework. These structures stem from the MARRMoT Toolbox introduced by Knoben et al. (2019) providing established conceptual model structures based on hydrologic literature.</p><p>We analyze if the model structure(s) AMSI identifies are identical to the best performing structures of the brute force calibration and comparable in their performance to the MARRMoT ensemble. We can conclude that model structures identified with the AMSI framework can compete with the structurally more divers MARRMoT ensemble. In fact, we were surprised to see how well we do with a simple two storage structure over the 12 tested MOPEX catchments (Duan et al.,2006). We aim to discuss several emerging questions, such as the selection of a robust model structure, Equifinality in model structures, and the role of structural complexity.</p><p> </p><p>Spieler et al. (2020). https://doi.org/10.1029/2019WR027009</p><p>Chlumsky et al. (2021). https://doi.org/10.1029/2020WR029229</p><p>Knoben et al. (2019). https://doi.org/10.5194/gmd-12-2463-2019</p><p>Duan et al. (2006). https://doi.org/10.1016/j.jhydrol.2005.07.031</p>

Uncertainties in hydro‐climatic projections are (in part) related to various components of the production chain. An ensemble of numerous projections is usually considered to characterize the overall uncertainty; however in practice a small set of scenario combinations are constructed to provide users with a subset that is manageable for decision‐making. Since projections are unavoidably uncertain, and multiple projections are typically informationally redundant to a considerable extent, it would be helpful to identify an informationally representative subset in a large model ensemble. Here a framework rooted in the information theoretic Maximum Information Minimum Redundancy concept is proposed for identifying a representative subset from an available ensemble of hydro‐climatic projections. We analyze an ensemble of 16 precipitation and temperature projections for Sweden, and use these as inputs to the HBV hydrological model to project river discharge until the mid of this century. Representative subsets are judged in terms of different statistical properties of three essential climate variables (precipitation, temperature and discharge), whilst we further assess the sensitivity of the optimized subset for different seasons and future periods. Our results indicate that a quarter to a third of the available set of projections can represent more than 80% of the total information of hydro‐climatic changes. We find that the representative subsets are sensitive to the regional hydro‐climatic characteristics and the choice of variables, seasons and periods of interest. Therefore we recommend that any selection process should not be solely driven by climatic variables but, rather, should also consider variables of the impact model.

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS