Climate change is a consequence of the perturbation of the global carbon cycle through emission of CO₂ and other greenhouse gases through fossil fuel burning, large-scale deforestation and other human activities since the industrial revolution. These emissions have been buffered by about 50% by the land and ocean carbon sinks, which have increased in pace with anthropogenic emissions. At the same time, changes in temperature and precipitation patterns and extremes due to climate change influence the efficiency of the land and ocean carbon sinks, so that their efficiency is projected to decline as negative consequences of climate change become more important.

Recent studies have pointed out that the global land carbon sink might be already slowing down and even declining in some regions, e.g., Europe. Understanding to which extent these recent trends are driven by climate change and extremes, policy changes or other factors is key to predict how the land sink will evolve in the coming decades and better constrain the potential for land-based climate change mitigation. However, at time scales of few years to decades, the role of internal climate variability on trends and extremes in climatic drivers of ecosystem carbon cycling cannot be ignored, and may mask or amplify changes in carbon sinks due to anthropogenic activities.

Here, we argue that extending Detection and Attribution (D&A) to the carbon cycle realm is crucially needed to support both climate science and policy assessments. We will discuss a number of conceptual and practical hurdles that make this exercise arguably even more challenging than climate D&A. We further present developments that can open the way towards a D&A for the carbon cycle, including improved quantification of carbon fluxes over land, the progress towards fast-track assessments of carbon flux anomalies following weather extremes, and the use of D&A techniques to study recent trends in the carbon cycle.

How to cite: Bastos, A., Li, N., and Dunkl, I.: The need for a detection and attribution system for the carbon cycle, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4529, https://doi.org/10.5194/egusphere-egu24-4529, 2024.

The sensitivity of annual atmospheric CO₂ growth rate (AGR) variations to tropical temperature has almost doubled between 1959 and 2011, a trend that has been linked to increasing drought in tropical ecosystems. This sensitivity metric has been used to suggest an emergent constraint of the future land carbon sink in response to climate change. However, a recent study showed that this sensitivity has decreased since then. Here, we investigate whether this doubling sensitivity reflects a forced response to climate change, or if it may arise due to internal climate variability. 

We show that, first, several similar events have occurred in individual simulations of Earth System Model Large Ensembles since 1851, but without changes in the ensemble mean's forced signal, suggesting the possibility of the doubling sensitivity being an internally-driven signal. Second, these observed doubling sensitivity events are linked to few strong El Niño events, such as 1982/83 and 1997/98. Such extreme events result in enhanced carbon release in tropical and extratropical terrestrial ecosystems, thus increasing the variance of the global land sink. Third, the doubling event is mostly explained by an increase in the variance of global AGR (rather than variance of tropical temperature or changes in the covariance), so that the signal constitutes only an "apparent" sensitivity change. In conclusion, the doubling sensitivity is not necessarily caused by forced climate change, but may arise from tropical and northern land sinks associated with internal climate variability.

Wang, X., Piao, S., Ciais, P. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014). https://doi.org/10.1038/nature12915

Luo, X., Keenan, T. F. Tropical extreme droughts drive long-term increase in atmospheric CO₂ growth rate variability. Nat Commun 13, 1193 (2022). https://doi.org/10.1038/s41467-022-28824-5

How to cite: Li, N., Sippel, S., Linscheid, N., Rödenbeck, C., Winkler, A. J., Reichstein, M., Mahecha, M. D., and Bastos, A.: Enhanced global carbon cycle sensitivity to tropical temperature linked to internal climate variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12127, https://doi.org/10.5194/egusphere-egu24-12127, 2024.

The era of anthropogenic climate change can be described by defined climate milestones. These milestones mark changes in the historic trajectory of change, and include peak greenhouse gas emissions, peak CO₂ concentration, deceleration of warming, net-zero emissions, and a transition to global cooling. However, given internal variability in the Earth system and measurement uncertainty, definitively saying that a milestone has passed requires rigour, with the statistical illusion of the 2010s global warming hiatus being a recent cautionary tale of the need for robust methods.

Here we use CMIP6 simulations of peak-and-decline scenarios to examine the time needed to robustly detect three climate milestones: 1) the slowdown of global warming; 2) the end of global surface temperature increase; and 3) peak concentration of CO₂. To detect these climate milestones we use a modified version of the Monte-Carlo based method of Rahmstorf et al. 2017, developed to test whether the global warming hiatus was an illusion. The method has been modified to account for auto-correlated noise characteristic of the climate system.

We estimate that it will take 40 to 60 years after a simulated slowdown in warming rate, to robustly detect the signal in the global average temperature record. Detecting when warming has stopped will also be difficult and for the one peak-and-decline scenario that has model simulations extended to the year 2300, it takes until the mid 22nd century to have enough data to conclude that  warming has stopped. Detecting that CO₂ concentration has peaked is far easier, and a drop in CO₂ concentration of 3 ppm is consistent with a greater than 99% chance that CO₂ has peaked in all scenarios examined.  Overall it is sobering that even under aggressive mitigation scenarios a conclusive end to global warming is at the very outer edge of the living future, with only a small number of the very youngest children alive today likely to witness detection of the end of global warming.

How to cite: MacDougall, A. H., Rogelj, J., Jones, C. D., Liddicoat, S. K., and Grassi, G.: Detecting climate milestones on the path to climate stabilization, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5703, https://doi.org/10.5194/egusphere-egu24-5703, 2024.

How to cite: Faranda, D. and the The ClimaMeter Team: ClimateMeter: Putting Extreme Weather Phenomena in Climate Perspective , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10870, https://doi.org/10.5194/egusphere-egu24-10870, 2024.

Interest in the question of how anthropogenic climate change is affecting extreme weather has grown considerably over the past few years - and 2023 has been no exception. This increase in interest has brought a need for robust approaches that are able to quantitatively answer this question rapidly after an event occurs. However, conventional attribution frameworks using statistical or dynamical climate models have been challenged by several recent events that lay well beyond the historical record.

While such events have proven difficult to attribute using conventional methodologies, many were surprisingly well forecast by high-resolution numerical weather prediction systems. These systems generally lie at the state-of-the-art in the spectrum of earth system modelling, and their deficiencies are well documented and understood. We suggest that they therefore represent an opportunity for answering attribution — and other weather and climate risk-related — questions, based on models that are demonstrably able to simulate the often non-linear physics of the extremes that we are most interested in. This can increase the confidence in any attributable changes assessed since such changes can be explained in terms of the underlying physical processes. Further, as attribution science extends beyond purely physical assessments and into socioeconomic impacts, this opportunity will grow: weather models are already widely used by risk and emergency management professionals as inputs to hazard models. A final advantage of basing attribution statements on weather forecast models is that it is not only apparent when a forecast model can be used — but also when the model has a crucial deficiency as indicated by a forecast bust. In this case it would be clear that making a quantitative attribution statement would not be appropriate.

We have previously used a global high-resolution and coupled ensemble prediction system to quantify human influence on the Pacific Northwest Heatwave and Storm Eunice. Here, we move from event-centric to pseudo-operational experiments. We present a season of perturbed forecasts for attribution, initialised twice per week during the 2022-23 winter in both pre-industrial and future climates, using the same operational ECMWF model as before. A number of high-impact extreme events took place during this winter, and we will present preliminary results from some of these.

We suggest that this large set of simulations may be of interest to a wide range of users both inside and outside the attribution community, and we therefore aim to make them publicly available. In addition, we are keen to overcome the limitation imposed by our use of a single model within these experiments, and therefore invite other weather forecasting groups to run comparable experiments.

How to cite: Leach, N., Ermis, S., Vashti Ayim, O., Sparrow, S., Lott, F., Zhou, L., Hope, P., Mitchell, D., Weisheimer, A., and Allen, M.: Towards an operational forecast-based attribution system - beyond isolated events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20081, https://doi.org/10.5194/egusphere-egu24-20081, 2024.

Detection and attribution of anthropogenic influence on extreme events has always been one of the focuses of climate research. A number of studies have been undertaken that employed different approaches (such as the risk-based, Boulder, and circulation-based ones) for attributing individual extreme events of various types over the globe. While many of these extreme events are attributable to anthropogenic or natural factors, some still remain inconclusive. To this end, a super attribution framework is proposed, in which multiple extreme events occurring in one region within a predefined timeframe are considered as a whole instead of individually. The rationale is that climate change may influence large-scale circulation over a region, which subsequently alters the frequency of extreme events in multiple locations in this area. Specifically, the supervariable is proposed to characterize how severely a region is affected by extreme precipitation in terms of area. It is defined as the fraction of area in a region that experiences extreme precipitation of over 99.9^th percentile in each day. The trends in the supervariable in the 20 Italian regions are examined. For regions with positive but not significant trends, there could be an anthropogenic signal present, but it could be too weak to be detected. Therefore, regions with positive trends are selected, and a super attribution is undertaken on them simultaneously. It is accomplished by calculating the combined supervariable, which is obtained by pooling the stations/grids of the selected regions together. Simultaneous events that occur in the autumn of each year are then considered. The results show that a statistically significant increasing trend can be identified in the combined supervariable for the selected regions, which suggests an increase in the area affected by extreme precipitation. In parallel to the statistical analysis, dynamical attribution is also carried out using the analog method, and the type of pattern that is both significantly influenced by climate change and associated with significant increases in precipitation is identified.

How to cite: Lu, C., Coppola, E., Pichelli, E., and Faranda, D.: A framework for the super attribution of multiple extreme events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2525, https://doi.org/10.5194/egusphere-egu24-2525, 2024.

Historical archives and climate model simulations show that there can be multi-century periods with no local extreme precipitation, referred to as disaster gaps, followed by intense temporal clusters of extreme precipitation. The irregular occurrence of extreme precipitation represents a major challenge for detection and attribution of climate signals, adaptation planning and for insurance pricing. Here we use the first large ensemble of a convection permitting model (including twelve 100-yr simulations) and multi-century GCM simulations to study the irregular occurrence of local precipitation extremes.

We show that local extreme precipitation events occur highly irregularly, with potential clustering (11% probability of five or more 100-year events in 250 years) or long disaster gaps with no events (8% probability for no 100-year events in 250 years). Even for decadal precipitation records, there is almost a 50% chance of a complete absence of any tail events in a 70-year period, the typical length of observational or reanalysis data. This generally causes return levels – a key metric for infrastructure codes or insurance pricing – to be underestimated.

We then explore whether the occurrence of extreme events is purely random (“white noise”) or induced by low-frequency modes of internal variability, such as the multi-decadal variability in the North Atlantic. Surprisingly, we find based on millennial climate simulations that long-term variability in extreme precipitation is largely random, with no clear indication of low-frequency decadal to multidecadal variability.

We also evaluate the potential of employing information across neighbouring locations, which substantially improves the estimation of return levels by increasing the robustness against potential adverse effects of long-term internal variability. The irregular occurrence of events makes it challenging to estimate return periods for planning and for extreme event attribution.

How to cite: Fischer, E. and Zeder, J.: Multi-century disaster gaps followed by strong clusters of extreme precipitation – understanding the irregular occurrence of local heavy rainfall, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19934, https://doi.org/10.5194/egusphere-egu24-19934, 2024.

In 2022, Europe faced an extensive summer drought that resulted in severe socio-economic consequences. Combining observations and climate model outputs with hydrological and land-surface simulations, we show that central and southern Europe experienced the highest observed total water storage deficit since the observations started in 2002, likely marking the highest and most widespread soil moisture deficit since 1960. While precipitation deficits primarily drove the soil moisture drought, global warming contributed to over 30% of the drought intensity and its spatial extent via enhancing evaporation. We reveal that about 15-40% of the climate change contribution was mediated by the warming that started drying the soil before the hydrological year of 2022, indicating the importance of considering lagged climate change effects to avoid underestimating risks. Qualitatively similar effects were observed in river discharges. These findings highlight that global warming impacts on droughts are widespread, long-lasting, and already underway, and that drought risk may escalate in the future. 

How to cite: Bevacqua, E., Schumacher, D. L., Rakovec, O., Kumar, R., Thober, S., Schweppe, R., Samaniego, L., Seneviratne, S. I., and Zscheischler, J.: Direct and lagged climate change effects intensified the widespread 2022 European drought, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3100, https://doi.org/10.5194/egusphere-egu24-3100, 2024.

During the 2017-2019 period, a large region of the Murray Darling Basin in Australia received the lowest three-year rainfall resulting in an unprecedented drought, known as the Tinderbox Drought. The cool season (Apr-Sep) rainfall declined by more than 54% of the 1901-1960 average. An analysis of the observed rainfall records (1900 – 2020) shows that it was exceptionally unlikely that a decline of this magnitude could occur from internal climate variability alone. In this study, we analysed outputs from CMIP5 and CMIP6 climate models under different forcing conditions (i.e., pre-industrial, historical-all forcings and different future emissions pathways) to estimate the relative contribution of anthropogenic forcing and internal variability to the observed 2017-2019 cool season rainfall reduction and the future likelihood of three-year rainfall change as dry or drier than the Tinderbox Drought under different emission pathways. According to the models, taken at face value, the Tinderbox Drought is an extremely unlikely event, but the likelihood of its occurrence is being increased from virtually impossible to extremely unlikely by the anthropogenic forcing. This suggests that the Tinderbox Drought was largely dominated by internal climate variability, however, it would not have been as dry without the influence of anthropogenic forcing. We found that the likelihood of a three-year drought as dry or drier than the Tinderbox Drought is going to increase by 15% towards the end of the twenty-first century under a high-emission scenario. Even with a marked reduction in emissions, its likelihood will be still around 5 % which is 10 times higher than the pre-industrial climate. Only a few ensemble members simulate a drying as large or larger than the observed 2017-2019 drying. The inability of most models to fully replicate the large drying seen so far leads to two possible conclusions: the rainfall in this region is more sensitive to greenhouse gas concentrations than is currently modelled, or factors other than climate change have coincidentally reduced rainfall during the recent period of anthropogenic climate change. 

How to cite: Rauniyar, S., Power, S., Hope, P., and Bende-Michl, U.: The role of anthropogenic forcing on Australia’s Tinderbox (2017-19) Drought and its future likelihood , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8892, https://doi.org/10.5194/egusphere-egu24-8892, 2024.

How to cite: Romanovska, P., Undorf, S., Schauberger, B., Duisenbekova, A., and Gornott, C.: Human-induced climate change has decreased wheat production in northern Kazakhstan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1917, https://doi.org/10.5194/egusphere-egu24-1917, 2024.

The United States (US), Brazil, and Argentina collectively produce about 75% of the world's soybean supply. In 2012, soybean crops failed in these three major producing regions due to spatially compound hot and dry weather across North and South America. This led to unprecedented shortages in the global supply, resulting in record-high market prices. Despite the severity of this event, the role of historical and future anthropogenic warming in influencing such occurrences remains unknown. Here, we present different impact storylines of the 2012 event by imposing the same seasonally evolving atmospheric circulation in a pre-industrial, present day (+1°C above pre-industrial), and future (+2°C above pre-industrial) climate. We use so-called nudged atmospheric simulations and train a statistical model to estimate yield losses from meteorological conditions. While the drought intensity is rather similar under different warming levels, our results show that anthropogenic warming strongly amplifies the impacts of such a large-scale circulation pattern on global soybean production, driven not only by warmer temperatures, but also by stronger heat-moisture interactions. We estimate that 51% (47-55%) of the global soybean production deficit in 2012 is attributable to climate change. Future warming (+2°C above pre-industrial) would further exacerbate production deficits by 58% (46-67%), compared to present-day 2012 conditions. This highlights the increasing intensity of global soybean production shocks linked to similar atmospheric conditions with warming and thus requires urgent adaptation strategies.

How to cite: Hamed, R., Lesk, C., Shepherd, T. G., M.D Goulart, H., van Garderen, L., Van den Hurk, B., and Coumou, D.: Half of the unprecedented global soybean production failure in 2012 is attributable to climate change., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11020, https://doi.org/10.5194/egusphere-egu24-11020, 2024.

Compound hot and dry extremes like the recent summers of 2015, 2018, and 2022 have an impact on a wide range of sectors in Europe, including health, transport, energy production, ecology, agriculture, and forestry. Current research suggests that climate change will increase the intensity, frequency, and duration of joint hot and dry extreme summers in Europe.

However, how robust and skilful are assessments of these compound events?

Here, we understand compound hot and dry extreme summers as the joint exceedance of temperature and (negative) precipitation thresholds (thresholds: 2001-2020 summer 95^th percentiles). Since this definition results in particularly rare events, a robust climatology of these extreme events can hardly be obtained from observational time series alone. To investigate these events and their variability, larger sample sizes are required. Some studies so far focus on temporally limited observational records and regional multi-model ensembles that both do not allow for robust climate variability assessment. Others address internal climate variability by using single-model initial condition large ensembles (SMILEs), but based on global models and thus truncating spatial heterogeneity. In an attempt to meet these limitations, we exploit a 50-member SMILE of the Canadian Regional Climate Model, version 5, at 12 km resolution (CRCM5-LE, RCP 8.5 from 2006 onwards, driven by the Canadian Earth System Model Version 2 large ensemble, CanESM2-LE) in this study. Owing to its large size and high spatial resolution, the CRCM5-LE is a yet unique source for analyzing compound events on a regional scale.

We consider detrended ERA5-Land data during 1955-2023 for evaluation purposes. In general, comparing single observational time series to SMILEs remains challenging due to internal climate variability. By using, among others, a bootstrapping approach, we find a very good agreement of the local compound event frequency distribution in the CRCM5-LE and ERA5-Land. Also, regional hotspots of event frequencies agree in the SMILE and reanalysis data. Going one step further, we also statistically disentangle climate change signals and internal variability in event frequencies at two global warming levels (+2 °C, + 3°C) by means of, e.g., signal-to-noise ratios.  

In general, the regional model (re)produces fine-scale spatial patterns of hot and dry compound events (e.g., mountains, land-sea contrast). These are found in event frequencies and change signals at impact-relevant scales. Furthermore, the application of a SMILE provides an extensive database of events. The latter is crucial for assessing trends and climatologies of highly variable events like compound hot and dry extremes. In combining a regional climate model and the SMILE approach, we thus show the benefits of a regional SMILE for addressing the uncertainty in compound event assessment.

How to cite: Böhnisch, A., Felsche, E., Mittermeier, M., Poschlod, B., and Ludwig, R.: Compound events drowned in climate noise? The benefits of employing a regional SMILE in compound hot and dry summer assessment , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14889, https://doi.org/10.5194/egusphere-egu24-14889, 2024.

Anthropogenic climate change is unfolding rapidly, yet its regional manifestation is often obscured by naturally occurring variability internal to the atmosphere and ocean system. A primary goal of climate science is to identify the forced response, i.e., spatiotemporal changes in climate in response to greenhouse gases, anthropogenic aerosols, and other external forcing, amongst the noise of internal climate variability. Separating the forced response from internal variability can be addressed in climate models by taking the average over a large ensemble, where the same model is run multiple times with small differences in initial conditions leading to different realizations of internal variability. However, there is only one realization of the real world, making it a major challenge to isolate the forced response in observations, as is needed for accurate attribution of historical climate changes, for characterizing and understanding observed internal variability, and for climate model evaluation.

In the Forced Component Estimation Intercomparison Project (ForceSMIP), contributors utilized existing and newly developed statistical and machine learning methods to estimate the forced response during the historical period within individual ensemble members and observations, across nine key climate variables (sea-surface temperature, surface air temperature, precipitation, sea-level pressure, sea-ice concentration, zonal-mean atmospheric temperature, monthly maximum and minimum temperature, and monthly maximum daily precipitation). Participants had access to five CMIP6 large ensembles on which to train their methods, but they then had to apply their methods to individual evaluation members, the identity of which was hidden to all participants. Participants used methods including regression methods, dynamical adjustment, convolutional neural networks, linear inverse models, fingerprinting methods, and low-frequency component analysis, and all codes have been collected to make an open-access repository. The ForceSMIP submission period ends on March 1, 2024, and we will present first results showing how the different methods performed on climate models, what they assessed to the be the forced response in observations, and how the estimate of the forced response in observations compares with that in climate models.

How to cite: Jnglin Wills, R., Deser, C., McKinnon, K., Phillips, A., and Po-Chedley, S.: Forced Component Estimation Statistical Methods Intercomparison Project (ForceSMIP): First Results, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1962, https://doi.org/10.5194/egusphere-egu24-1962, 2024.

The Detection and Attribution Model Intercomparison Project (DAMIP) coordinates single forcing climate model simulations for detection and attribution analysis and other applications. DAMIP simulations were carried out with fifteen climate models as part of CMIP6, and these simulations were used in at least 270 published articles. These simulations were also used directly in at least five chapters of the IPCC Sixth Assessment Working Group I Report, and they underpinned the estimate of anthropogenic attributable warming highlighted in the Summary for Policymakers of that report, and quoted directly in the UNFCCC Glasgow Climate Pact. For CMIP7, natural-only, well-mixed greenhouse gas-only, and aerosol-only simulations have been proposed as fast track DAMIP simulations, and planning of a broader set of experiments is currently underway. This talk will highlight key DAMIP results from CMIP6, and will discuss plans for the CMIP7 version of DAMIP. Comments and suggestions regarding the CMIP7 DAMIP experimental design will be welcomed.

How to cite: Gillett, N., Simpson, I., Hegerl, G., Knutti, R., Ribes, A., Shiogama, H., Stone, D., Tebaldi, C., Wolski, P., and Zhang, W.: The Detection and Attribution Model Intercomparison Project: CMIP6 highlights and plans for CMIP7, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13472, https://doi.org/10.5194/egusphere-egu24-13472, 2024.

To detect, quantify and attribute the effects of climate change in the context of rising carbon emissions, analyses often pinpoint individual variables. The aim is to find a signal of the externally forced response amidst internal climate variability. This becomes more challenging when examining regional shifts or variables with high internal variability like evapotranspiration, which in addition is affected by observational and modeling uncertainty. However, the interconnection of climate variables provides an advantage in considering them together, allowing us to explore how their relationships evolve over time and a better understanding of the underlying drivers.

Here, we investigate the combined effects of energy and water availability on evapotranspiration in climate models. Using a simple linear model, we quantify the contributions of these variables, which vary regionally. Water availability is more important in dry regions, whereas in wetter regions energy is the more dominant constraint on evapotranspiration. Moreover, we also find regions in which water availability dominates inter-annual variability, while evapotranspiration trends are better predicted by energy availability. This suggests that different causal factors may drive variations in the short and long term, which bears implications for the interpretation and potential constraint of projected future trends. In such a case, a signal of climate change is much more easily detected in a multi variate space, as the signal emerges in a direction where there is little internal variability. Finally, this approach provides insights into the complex influences shaping evapotranspiration and opens the door to possible constraints on future changes.

How to cite: Egli, M., Sippel, S., Humphrey, V., and Knutti, R.: Exploring drivers of evapotranspiration in CMIP6: A Multivariate Perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18512, https://doi.org/10.5194/egusphere-egu24-18512, 2024.

As global climate change accelerates a spectrum of extreme events (e.g., heatwaves, droughts, etc.) are occurring in many parts of the world at an increasing frequency and intensity threatening the socio‐economic fabric of our modern civilisation. The boreal summer (JJA) 2023 was globally the warmest, while July and August 2023 were the two warmest months on the observational record. Embedded in these global conditions were series of strong heatwaves that in the Mediterranean region often reached above 40deg.C in daily maximums of surface (2m) air temperature (SAT). We apply multi-method attribution approach to illuminate the role of climate change in setting this expectational monthly and seasonal SAT conditions in the Mediterranean.

We utilise a collection of observations and reanalysis products combined with large ensembles of CMIP5 and CMIP6 historical and future simulations to analyse the role of atmospheric circulation and anthropogenic factors leading to these extreme events on monthly and seasonal timescales. We also use large ensembles of historical and counterfactual simulations of weather@home2 (climateprediction.net numerical experiments) globally distributed to and executed by volunteers on their home computers to assesses to what extent anthropogenic forcing altered the probability and magnitude of these extremes. We explore conditional perspective of the atmospheric circulation in this attribution analysis. The initial results indicate a significant role of the global climate change in modifying likelihood and intensity of these boreal SAT summer extreme events.

How to cite: Fuckar, N.-S., Allen, M., and Obersteiner, M.: Multi-method attribution of the 2023 boreal summer temperature extremes in the Mediterranean region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9233, https://doi.org/10.5194/egusphere-egu24-9233, 2024.

Event attribution has significantly developed over the past years, with an increasing number of events being attributed to human-induced climate change. Typical event attribution studies focus on the assessment of individual events of high societal relevance. While this allows for a detailed analysis and a comprehensive interpretation, it also implies that the influence of anthropogenic climate change is not assessed for many extreme events. Here, we present the first collective attribution of 149 historical heatwaves reported over the 2000-2021 period. We apply a well-established extreme weather attribution approach (Philip et al., 2020; van Oldenborgh et al., 2021) to heatwaves in the EM-DAT database (EM-DAT, 2023). Each of these heatwaves were reported for severe societal impacts, making them relevant for attribution. For each listed heatwave, we identify the event in observational data (ERA5, BEST) and CMIP6 data, then we estimate the probability distribution conditional on global mean surface temperature, deduce the occurrence probabilities of the events for present and pre-industrial climate conditions. We discuss the method and the choices made to systematize the definition of the event, the evaluation of the probabilities and the selection of the datasets. The results of this framework is consistent with existing attribution studies, albeit with limits. This work calls for a more systematic reporting of heatwaves, and paves the way for the use of these results in climate litigation cases.

Furthermore, we calculate the contributions in global mean surface temperature of 110 fossil fuels and cement companies using their CO₂ and CH₄ emissions (Heede, 2014) and the reduced-complexity Earth system model OSCAR (Gasser et al., 2017). This collective attribution allows to extend these contributions to the analyzed historical heatwaves. The majority of heatwaves are made substantially more probable and intense due to six carbon majors that represent 0.30°C of global mean surface temperature. Though, other carbon majors cannot be neglected, as their sole contribution may be enough to make some heatwaves possible. We suggest that extending attribution studies to the actors could consolidate their applicability for climate litigation.

EM-DAT, CRED / UCLouvain: www.emdat.be, last access: 09.01.2024.

Gasser, T., Ciais, P., Boucher, O., Quilcaille, Y., Tortora, M., Bopp, L., and Hauglustaine, D.: The compact Earth system model OSCAR v2.2: Description and first results, Geoscientific Model Development, 10, 271-319, 10.5194/gmd-10-271-2017, 2017.

Heede, R.: Tracing anthropogenic carbon dioxide and methane emissions to fossil fuel and cement producers, 1854–2010, Climatic Change, 122, 229-241, 10.1007/s10584-013-0986-y, 2014.

Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M.: A protocol for probabilistic extreme event attribution analyses, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177-203, 10.5194/ascmo-6-177-2020, 2020.

van Oldenborgh, G. J., van der Wiel, K., Kew, S., Philip, S., Otto, F., Vautard, R., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M.: Pathways and pitfalls in extreme event attribution, Climatic Change, 166, 13, 10.1007/s10584-021-03071-7, 2021.

How to cite: Quilcaille, Y., Gudmundsson, L., Gasser, T., and Seneviratne, S. I.: Collective attribution of historical heatwaves to anthropogenic climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17768, https://doi.org/10.5194/egusphere-egu24-17768, 2024.

Global warming results in increase in the intensity and frequency of extreme temperature events across the world. This study used multivariate copula approach to comprehend variations in intensity and frequency of extreme temperature events over 54 urban agglomerations in India. The current study uses the Coupled Model Intercomparison Phase 6 (CMIP6) framework to explore the relationship between temperature intensity duration frequency for 1.5°C, 2°C, and 3°C global warming levels (GWL) over the two time periods, T1(2021-2050) and T2(2071-2100). Using bivariate copulas, we analyse the changes in return estimates for temperature extreme considering 10, 20, 50, and 100-year return periods over 2, 5, and 10-day durations. Amongst various distributions, the lognormal and extreme value distribution appeared as the most suitable distributions to represent duration and temperature intensity, respectively. As far as copula analysis is concerned, the Gumbel-Hougaard copula was found to be most suitable to illustrate the joint behaviour. With respect to base period, more than 60%, 64% and 80% of urban agglomerations report an increase in the extreme temperature return values under 1.5°C, 2°C, and 3°C GWL respectively. By the end of the century, more than 83% of urban agglomerations will experience an increase in the extreme temperature return values. A significant regional variation has been observed in the percentage change of the return estimates. Cities such as Mysore, Bangalore, Pune, Dharwad, Coimbatore report 9-18% decrease in the extreme temperature return values. Whereas, cities such as Amritsar, Jalandhar, Delhi, Shimla, and Kanpur report 23%-28%, increase in return values. Furthermore, these cities are projected to experience an increase of 30% by the end of the century. The findings highlight the urgent necessity for the implementation of climate change mitigation strategies that are more closely aligned with the objectives outlined in the Paris Agreement. By implementing strategies aimed at limiting global warming, we can effectively alleviate the detrimental impacts and increasing hazards linked to extreme heat events.

How to cite: Joshi, N., Maurya, H., Suryavanshi, S., and Dubey, A.: Appraisal and Prognosis: Towards Projecting the Future Changes in Urban Extreme Temperature Events over India under Climate Change Scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1395, https://doi.org/10.5194/egusphere-egu24-1395, 2024.

Attribution of different hazards and impacts of climate change to specific radiative forcings, including greenhouse gasses, is emerging as a critical field for evidence-based decisions used in, e.g. legal settings, and for Loss and Damage. A recent report published by Wellcome shows that there are 13 climate-health attribution publications to date, mainly using methods that are adapted from the core attribution community, including the good practice and IPCC recommendations. Most of these studies have cut corners from what many in the attribution community would call ‘the gold standard’, but for good reason, the health signal is more complex than a purely climate signal. Here I discuss a number of new approaches that can be used to attribute human health outcomes from climate change. I give an example using forecast-based attribution, which allows for a low-bias, high-spatial resolution assessment to be made. I concentrate on the Pacific NorthWest heatwave, and couple the results to all-cause, age-specific mortality. I show how this can be used for a variety of different health outcomes, including cause-specific mortality, and morbidity, e.g. mental health related, or vector-borne diseases. I discuss how different attribution techniques can be used to complement each other in the context of health. 

How to cite: Mitchell, D., Shapland, C. Y., Lo, E., Tilling, K., and Leach, N.: Novel methods needed to attribute human health impacts of climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4029, https://doi.org/10.5194/egusphere-egu24-4029, 2024.

Heatwaves are increasing in frequency, intensity, and duration, and represent the category of extreme event that is most easily attributable to anthropogenic warming. Yet how the spatiotemporal patterns of attribution outcomes link to population dynamics and demographic patterns is still poorly understood. Here we investigate whether children and young people are already being affected by a disproportionately greater number of attributable heat extremes, especially in the Global South. Using observations, reanalysis, and simulations of temperature changes available through the ISIMIP3b and CMIP6 projects, in combination with demographic data, we will investigate whether temperature extremes emerge more clearly and consistently from the noise across low-income countries in lower latitudes, which have some of the youngest populations. Our anticipated findings could have implications for children and young people seeking redress from climate harms, for example through climate lawsuits.

How to cite: Pietroiusti, R., Fischer, E., Stuart-Smith, R., Harrington, L., Grant, L., Savaresi, A., Adelman, S., and Thiery, W.: Children disproportionally exposed to attributable heatwaves at low-latitude low-income countries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12807, https://doi.org/10.5194/egusphere-egu24-12807, 2024.

Climate change significantly threatens food security, particularly in low-income countries heavily reliant on subsistence and rainfed agriculture. Most existing empirical literature has examined the impacts of climate variability and extremes on undernutrition and agricultural outputs independently. There is a lack of studies exploring the causal pathway from climate change to agricultural shocks and their consequent nutritional and health impacts.

In this study, we investigate the extent to which the current and historical burden of child stunting in Burkina Faso can be attributed to climate change-induced agricultural deficits. First, we combine individual anthropometric data from five rounds of the Demographic Health Survey (DHS) and provincial-level crop yield data to assess the association between child stunting and exposure to agricultural deficits at birth. We define agricultural deficits as annual deviations in crop yields from their long-term average for three major food crops in the region: maize, millet, and sorghum. Second, we employ observationally-derived climate reanalysis data as well as counterfactual and factual climate data from ATTRICI (four pairs of datasets based on different reanalysis data), part of ISIMIP3a. These are analysed with a statistical crop yield modelling approach to estimate crop yields with and without climate change, respectively.

The epidemiological analysis reveals a non-linear health risk function, with risk of child stunting increasing rapidly when crop yields at birth are lower than the period average (<100%). The crop yield modelling shows a clear climate signal in annual variation in crop yields. The comparison between the factual and counterfactual climate data show a signal, especially in temperature. The outputs of the two models and the counterfactual/factual datasets are combined in an attribution framework in order to estimate the number of stunted children at the province level that can be attributed to climate change-induced agricultural deficits for the period 1984-2022. Repeating the analysis with factual and counterfactual CMIP6-DAMIP data to attribute explicitly the anthropogenic climate change is also considered. The study thus complements the climate impact attribution literature by a regional case study of so-far not attributed health aspects of crucial societal and economic importance.

How to cite: Dimitrova, A., Laudien, R., Dimitrova, A., Undorf, S., and Waid, J.: Attributing child undernutrition from agricultural shocks to climate change in Burkina Faso, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19145, https://doi.org/10.5194/egusphere-egu24-19145, 2024.

Sub-Saharan Africa is projected to be exposed to substantial climate change hazards, especially in its agricultural sector, so adaptation will be necessary to safeguard food security. However, tropical and subtropical maize production regions approach critical temperature thresholds in the growing season already in today’s climate, and climate change might already be contributing to this. Projecting future, and attributing already observed, yield impacts due to anthropogenic climate change under adaptation assumptions can thus provide valuable context to future adaptation needs. No adaptation impact studies currently exist for heat-tolerance of maize in West Africa, let alone one that combines projections and counterfactual historical simulations to this effect.

Here, we report on a study in which we focus on maize in Cameroon to model the effect and potential of crop-varietal heat-tolerance adaptation. We use climate reanalysis data (mainly W5E5), historical and counterfactual bias-corrected and downscaled CMIP6-DECK and -DAMIP simulations along with ISIMIP3a data, and future projections from CMIP6/ISIMIP3b. The two climate change scenarios SSP1-2.6 and SSP3-7.0 were analysed for 2020-2100 and historical simulations for 1984-2014.

The process-based crop model APSIM was run in a spatially disaggregated, grid-based approach as to represent Cameroon’s diverse agro-ecological zones. The impact of heat tolerance adaptation in maize was assessed by parametrising one unadapted baseline variety and one synthetic heat-tolerant variety in APSIM and comparing yield outcomes.

Yields are substantially higher for the heat-tolerant variety. Either variety experience similar losses in the projected future compared to now, increasing with climate change scenario and time. Impacts on maize yield are dominantly attributed to heat stress. Already observed climate change impacts compared to counterfactual climate further indicate that adaptation to present-day climate can be considered climate change adaptation beyond development.

How to cite: Jansen, L., Undorf, S., and Gornott, C.: Attributed and projected climate change impacts on maize yield in Cameroon as mediated by heat-tolerance adaptation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1910, https://doi.org/10.5194/egusphere-egu24-1910, 2024.

Climate models can produce accurate representations of the most important modes of climate variability, but they cannot be expected to follow their observed time evolution. This makes direct comparison of simulated and observed variability difficult, and creates uncertainty in estimates of forced change. Here we discuss the use of a particle filter data-assimilation technique in a global climate model, that sub-selects members among an ensemble of simulations, to follow the observed Northern Atlantic Oscillation, El Niño Southern Oscillation and Southern Annular Mode, without the use of nudging terms. We investigate the role of these three modes of climate variability, as pacemakers of climate variability since 1781, evaluating where their evolution masks or enhances forced climate trends. Since the climate model also contains external forcings, these simulations, in combination with model experiments with identical forcing but no assimilation, can be used to compare the forced response to the effect of the three modes assimilated and evaluate the extent to which these are confounded with the forced response. The assimilated model is significantly closer than the “forcing only” simulations to annual temperature and precipitation observations over many regions, in particular the tropics, the North Atlantic and Europe. We will show that the NAO variability leads to large multi-decadal trends in temperature, and sea-ice concentration, and that constraining the El Niño–Southern Oscillation reconciles simulated global cooling with that observed after volcanic eruptions.

How to cite: Schurer, A., Hegerl, G., Goosse, H., Bollasina, M., England, M., Mineter, M., Smith, D., and Tett, S.: Quantifying the contribution of forcing and three prominent modes of variability to historical climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3444, https://doi.org/10.5194/egusphere-egu24-3444, 2024.

Regional climate change projections over the course of the 21st century require the accurate simulation of both anthropogenic and natural variability. The spatial patterns of natural variability are relatively well constrained on sub-decadal timescales based on instrumental data evidence, and climate models can simulate them. For longer (supra-decadal) timescales, however, the spatial patterns of natural (temperature) variability are poorly constrained because of the shortness of the instrumental record and the overlap with anthropogenic influences. Insights gained from paleoclimate data over land in mid-latitudes suggest that oceanic influence was the main driver of increased low-frequency natural variability, in contrast to its stabilizing role on sub-decadal timescales. Here, by studying the spatial imprint of multi-decadal climate variability, we show that the instrumental data is consistent with this hypothesis. While the pattern is also observed in climate models, it is much weaker and seems to rely solely on forced variability. Therefore, while climate models can simulate anthropogenic warming, our evidence indicates, particularly over the northern land mid-latitudes, that they are not simulating supra-decadal natural variability (forced and internal) consistent with instrumental observations in terms of the spatial pattern and its amplitude.  

How to cite: Hébert, R., Skiba, V., and Laepple, T.: The Emergence of Low-Frequency Variability: Comparison of Historical Data and Simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13745, https://doi.org/10.5194/egusphere-egu24-13745, 2024.

The Pacific Walker circulation (PWC) and Hadley circulation (HC) are the most prominent circulations of the Earth, which can exert far-reaching impacts on global and regional hydrological cycles. Both of these two large-scale circulations have experienced significant changes under global warming. Specifically, the PWC is reported to strengthen since the 1980s while the HC is proposed to widen. The causes behind these observed changes have been the subject of climate research, with divergent views on the influence of external forcing versus internal variability. Here, based on initial-condition large ensemble simulations, we quantify the relative contributions of internal variability and external forcing in modulating recent changes in tropical large-scale atmospheric circulations. We find that the recent PWC strengthening and HC widening is robust consequences of internal variability rather than external forcing. We further reveal that Interdecadal Pacific Oscillation (IPO) is the dominant internal mode, with its phase evolution contributing about 63% of the observed PWC strengthening and at least 73% of the HC widening in the Northern Hemisphere.

How to cite: Wu, M.: Role of Interdecadal Pacific Oscillation (IPO) in modulating recent changes in tropical large-scale atmospheric circulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15720, https://doi.org/10.5194/egusphere-egu24-15720, 2024.

Global mean surface air temperature stands is a critical indicator for gauging climate change, both on contemporary and over centennial scales. Previous studies on surface air temperature (SAT) variations tend to emphasize the uncertainties in model-simulated global warming projections, instead of differentiating the observed SAT trend patterns. Our study aims to partition observed SAT trends into forced and unforced components on decadal to multidecadal scales. Utilizing historical simulations from the ensemble mean of six large ensemble models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we develop a regression model specifically designed to robustly detect and attribute trends in the observed SAT. We evaluate the models' capability to replicate the detected forced SAT trends. Our findings indicate that external forcings are a significant driver of  SAT trend patterns on multidecadal scales, with pronounced warmingtrends over the Eurasian and North American continents. Conversely, on decadal scales, the forced SAT trends are not as evident within the observational data. Our results also underscore the limitations of current state-of-the-art climate models in capturing decadal trend variability. Interestingly, when comparing high- to low-sensitivity climates—those with high (ECS > 4.5K) versus low (ECS < 4.5K) equilibrium climate sensitivity—we find the high-sensitivity models to underrepresent the unforced signals of observed SAT trends. By leveraging significant observational data that captures the forced trend patterns on multidecadal timescales, we could enhance and constrain the future projection of SAT trends and variability more effectively.

How to cite: Aru, H., Li, C., and Olonscheck, D.: Disentangling the forced and unforced components of observed surface air temperature trends, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18953, https://doi.org/10.5194/egusphere-egu24-18953, 2024.

According to state-of-the-art climate simulations, the future evolution of the wintertime North Atlantic jet stream is highly uncertain compared to other ocean basins. This has important consequences on the projected daily weather variability and the occurrence of extreme events over Europe. In this context, disentangling the forced trends in the North Atlantic jet caused by an increase in greenhouse gases from its natural variability is a challenging but extremely relevant task.

In this study, we use a deep learning-based method, the Latent Linear Adjustment Autoencoder (LLAE), to separate forced trends from natural variability in an ensemble of fully-coupled Community Earth System Model simulations. The LLAE consists of a variational autoencoder and an additional linear component. The model predicts the component of the wind associated with natural variability from upper-level detrended temperature and geopotential. The residual between this prediction and the original wind field can be interpreted as the component of the wind related to the external forcing. Despite the large variability of the original trends, especially in the historical period, the LLAE is effective in extracting the forced component of the trend, which is similar for all ensemble members. The main characteristics of the forced trend are an increase in the wind speed along a southwest-northeast oriented band and an extension of the jet over Europe. These features are common for different periods and have similarities to the full North Atlantic jet trend in the ERA5 reanalysis.

How to cite: Hermoso, A. and Schemm, S.: Disentangling forced trends in the North Atlantic jet in CESM2 using deep learning , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3785, https://doi.org/10.5194/egusphere-egu24-3785, 2024.

How to cite: Satpathy, S. S. and Franzke, C. L. E.: Detection and Attribution of the Weakening of Global Angular Momentum, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7224, https://doi.org/10.5194/egusphere-egu24-7224, 2024.