Land surface models are widely used to study climate change and its impacts, but uncertainties in input parameter settings and model errors hamper their use. We use Uncertainty Quantification (UQ) techniques to constrain the input parameters of JULES-ES-1.0, the land surface component of the UK Earth system model UKESM1.0. We use an ensemble of historical simulations of the land surface model to rule out ensemble members and corresponding input parameter settings that do not match modern observations of the land surface and carbon cycle. Using a Gaussian Process emulator trained on the ensemble to predict the model output, we can repeat this process for parts of parameter space where the model is not yet tested. We use history matching - an iterated approach to constraining JULES-ES-1.0 - running an initial ensemble and training the emulator, before choosing a second wave of ensemble members consistent with historical land surface and carbon cycle observations. We rule out 88% of the initial input parameter space as being statistically inconsistent with observed land surface behaviour. We use the emulator to perform 3 types of sensitivity analysis to identify the most (and least) important input parameters for controlling the global output of JULES-ES-1.0, and provide information on how parameters might be varied to improve the performance of the model, eliminate model biases, and make better carbon cycle projections.

How to cite: McNeall, D., Robertson, E., and Wiltshire, A.: Constraining the carbon cycle in JULES-ES-1.0, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12656, https://doi.org/10.5194/egusphere-egu23-12656, 2023.

Emulators, or reduced complexity climate models, are surrogate Earth system models that produce projections of key climate quantities with minimal computational resources. Using time-series modeling or more advanced machine learning techniques, statistically-driven emulators have emerged as a promising venue, producing spatially resolved climate responses that are visually indistinguishable from state-of-the-art Earth system models. Yet, their lack of physical interpretability limits their wider adoption. In fact, the exploration of future emission scenarios still relies on one-dimensional energy balance models which can lack the flexibility to capture certain climate responses.

Here, we propose a statistically-driven emulator that hinges on an energy balance model. Using Gaussian processes, we formulate an emulator of temperature response that exactly satisfies the physical thermal response equations of an energy balance model. The result is an emulator that (1) enjoys the flexibility of statistical machine learning models and can be fitted against observations to produce spatially-resolved predictions (2) has physically-interpretable parameters that can be used to make inference about the climate system. This model shows skilfull prediction of annual mean global distribution of temperature, even over scenarios outside the range of observed emissions of greenhouse gases and aerosols during training — improvement in RMSE of 27% against the energy balance model and of 60% against a physics-free machine learning model. In addition, the Bayesian nature of our formulation provides a principled way to perform uncertainty quantification on the predictions.

We outline how the probabilistic nature of this emulator makes it a natural candidate for detection and attribution studies and discuss extension to a precipitation emulator also incorporating physical constraints. We hope that by combining the ability of machine learning techniques to capture complex patterns with the interpretability of energy balance models, our work will produce a reliable tool for efficiently and accurately exploring future climates.

How to cite: Bouabid, S., Sejdinovic, D., and Watson-Parris, D.: Probabilistic climate emulation with physics-constrained Gaussian processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15660, https://doi.org/10.5194/egusphere-egu23-15660, 2023.

Climate extremes are among the most impactful consequences of climate change. Moreover, droughts and fires may also hinder envisioned solutions to mitigate climate change, by reducing the efficiency of bio-energies with carbon capture storage and afforestation. Though, investigating such issues would benefit from a tool allowing fast computation of spatial climate extremes, for coupling to other models and exploration of scenarios. Here, we present an approach for emulating such extremes that are based on extensions of the spatially-resolved climate model emulator MESMER-X. In particular, we consider four annual indicators of the Canadian Fire Weather Index and the annual mean soil moisture derived from the Climate Model Intercomparison Project phase 6 for training and emulation. To emulate these indicators, we consider extensions to the framework that include the Gaussian and the Poisson distributions, non-linear evolutions of the parameters of the distribution and lagged effects. We show that the emulator reproduces the trajectories and the statistics of these annual indicators for fire weather and droughts accurately. By doing so, we show that the theoretical framework of MESMER-X can be applied for a large number of annual indicators of climate extremes.

How to cite: Quilcaille, Y., Gudmundsson, L., and Seneviratne, S.: Spatially-resolved emulations of droughts and fire weather using MESMER-X, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14884, https://doi.org/10.5194/egusphere-egu23-14884, 2023.

Emulators of Earth System Models (ESM) are runtime efficient models that mimic the behavior of an ESM using simple statistical methods. Because of their low complexity, emulators allow to quickly generate thousands of realizations of high-resolution data. Thus, they have proven to be valuable tools for exploring the emission space, quantifying different sources of uncertainty, and investigating extreme events. In this contribution, we introduce an extension to the Modular Earth System Model Emulator (MESMER) for generating monthly precipitation fields. Precipitation is emulated based off monthly temperature such that also the joint precipitation-temperature characteristics match the distribution of the underlying climate model. The emulation consists of two steps. First, the logarithm of precipitation at each location is assumed to depend linearly on temperatures at selected other locations nearby. The selected locations and the linear coefficients are optimized using a Lasso Regression. This step thus yields a deterministic precipitation response that encodes spatiotemporal relationships between precipitation at a given location and temperature at surrounding locations. Second, the residual variability is assumed to be independent from temperature and is modelled as a multi-dimensional noise process containing spatial correlations. The emulator is trained and tested on CMIP6 data. We show that the emulation set-up performs well in simulating the annual cycle, long-term trends in monthly precipitation as well as spatial patterns and natural variability of the underlying climate model. This offers a promising avenue for, as a next step, extending the MESMER emulation framework to other variables.

How to cite: Schöngart, S., Lejeune, Q., Gudmundsson, L., Nath, S., Seneviratne, S., and Schleußner, C.-F.: Extending MESMER-M to jointly emulate Earth System Model temperature and precipitation realizations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16600, https://doi.org/10.5194/egusphere-egu23-16600, 2023.

El Niño-Southern Oscillation (ENSO) is the most consequential climate phenomenon affecting global extreme weathers often with devastating socioeconomic impact. However, quantifying the impact on global economy has been a challenge and has so far focused on tangible loss such as reduced agriculture outputs and infrastructure damage. Elusive are issues to what extent the impact affects the macroeconomy, how long the impact lasts, and how the impact may change in a warming climate. Using a smooth nonlinear climate-economy model fitted with historical data, here we find a damaging impact from El Niño in which acceleration of impact lasts for three years after an initial shock, with a total effect an order of magnitude greater than previous estimates; impact from La Niña is not symmetric and far weaker. We attribute a loss of US$2.1T and US$4.0T in global economy to the 1997-98 and 2015-16 El Niño events, but a gain of only US$0.08T from the 1998-99 La Niña event. In a warming climate, economic loss grows exponentially with increased ENSO variability. Under a high-emission scenario, changes in ENSO variability cause an additional median loss of US$33T to global economy at a 3% discount rate aggregated over the last 80 years of the 21st century, but possibly as large as US$375T, highlighting an exacerbated economic damage from future ENSO under global warming. Further, the additional loss lessens with lower emissions and achieving the Paris Agreement reduces the additional loss by half, pointing to a strong incentive for mitigation.

How to cite: Liu, Y., Cai, W., Lin, X., Li, Z., and Zhang, Y.: Nonlinear El Niño impact on global economy in a warming climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1022, https://doi.org/10.5194/egusphere-egu23-1022, 2023.

Since many new generation earth system models (ESMs) have been suggested to be ‘hot models’ that overestimate future global warming, the IPCC AR6 used the constrained range of global warming instead of that in the raw ensemble. However, it is not clear how this advance in climate science can contribute to reducing climate-related uncertainties in impact assessments. Here, we show that the climate-related uncertainty of the economic impact of climate change in the world can be observationally constrained. By applying an impact emulator of Takakura et al. (2021, https://doi.org/10.5194/gmd-14-3121-2021), we estimate the economic impacts in nine sectors based on 67 ESMs’ future climate change projections. The impacts in eight sectors are closely related to the recent past trend of global mean temperature. Observational constraints lower the upper bound of the aggregate economic impact simulated by the single emulator from 2.9% to 2.5% of the world gross domestic product and reduce 31% of variance under the RCP4.5 or SSP2-4.5 scenarios. Please see Shiogama et al. (2022, https://doi.org/10.1088/1748-9326/aca68d) for more details.

How to cite: Shiogama, H., Takakura, J., and Takahashi, K.: Observational constraints on economic impact assessments of climate change simulated by an impact emulator, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3013, https://doi.org/10.5194/egusphere-egu23-3013, 2023.

Climate model emulation has long been and is increasingly applied to the results of integrated assessment modelling (IAM)models (IAMs), to determine the climate outcomes, primarily global mean surface temperature (GMST), of emissions pathways. Originally provided at the global level, more recently approaches have been developed to reproduce a growing number of climate variables, also with spatial, even gridded, resolution. Here we build on these approaches to demonstrate a workflow and post-processing package, that takes the GMST trajectory, e.g. from a simple climate model (SCM) and calculates a range of climate impacts and exposure indicators based on the GMST trajectory. To do this, we built a database of post-processed climate impacts from global climate [WM1] CMIP6 & ISIMIP-3 GCMs and impacts [WM2] models, and also calculated population and land area exposure to the indicators through time and for spatial units, e.g. countries. Indicators include temperature and precipitation extremes, heatwaves, degree days, drought intensity, water stress, and indicators of hydrological variability. Using a high-resolution temperature time timeslice approach, GMST trajectories are then mapped to the impact and exposure indicators to produce gridded maps of climate impacts through time, and trajectories of climate exposure by spatial unit. Using this approach we demonstrate the rapid post-processing of SCM [WM3] results such that ensembles of global IAM [WM4] mitigation pathways, such as those from AR6, can be accompanied by a new suite of climate impacts and risk information – and discuss related uncertainties and avenues for further research to incorporate vulnerabilities.

How to cite: Byers, E., Werning, M., Krey, V., and Riahi, K.: Closing the loop between integrated assessment and climate risk research – rapid climate risk emulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14279, https://doi.org/10.5194/egusphere-egu23-14279, 2023.

Steep emissions reductions are needed in the coming decades to limit warming to 2°C or lower, followed by multiple gigatons of annual carbon dioxide removal (CDR) in the second half of the 21st century. In this presentation we make a first assessment of the “CDR gap” and ask whether countries are preparing for the CDR scale-up challenge. We find that most countries pledge only a small expansion of CDR by 2030 in their nationally determined contributions (NDCs), while only a subset have proposed CDR in their long-term mitigation strategies. There is a significant gap between these proposed CDR levels and levels in scenarios that limit warming to 2°C or lower. While some scenarios have low CDR requirements, these require even steeper emissions reductions that we are not on track to achieve. Most countries prioritize conventional CDR on land (i.e. the management of forest sinks) which has low permanence and may raise land use conflicts. Conventional CDR on land will be extremely difficult just to maintain, let alone expand to meet net zero emissions targets. By contrast, countries focus far less in their NDCs and long-term strategies on novel CDR methods such as bioenergy with carbon capture and storage, direct air capture, or blue carbon. For these technologies to make a meaningful contribution to long-term climate mitigation, urgent support is required in the formative phases of their development. Above all, rapid emissions reductions are needed to reduce our dependence on CDR and prevent a widening CDR gap by 2050.

How to cite: Lamb, W. F., Gasser, T., Grassi, G., Minx, J., Gidden, M., Powis, C., Geden, O., Nemet, G., Pratama, Y., Riahi, K., Smith, S., and Steinhauser, J.: The carbon dioxide removal gap: current removals and country proposals versus future requirements for limiting warming to 2°C or lower, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6660, https://doi.org/10.5194/egusphere-egu23-6660, 2023.

Ambitious climate change mitigation scenarios typically include substantial amounts of carbon dioxide removal. Such negative emissions are projected by integrated assessment models (IAMs) to be partly provided by afforestation and reforestation. At present, only few IAMs incorporate climate information or natural forest disturbances of any kind on forest dynamics. In this study we show how exposed to fire weather the afforestation areas in the IAM projections are. We illustrate that IAM mitigation scenarios lack climate information to arrive at more realistic projections of carbon stocks in forests, more reliable land use distributions and more realistic forestation costs. In this work we combine forest fractional cover from IAM land use projections and fire weather index (FWI) from multi-model climate projections, based on the latest simulations assessed in the 6th assessment report of the IPCC. With this metric we show how forests and afforestation areas are and will be affected by fire weather. We find a strong upward trend of forest mean fire weather under a 2 °C warming scenario (SSP1-2.6 for both land use and climate, roughly compatible with 2.6 W/m² climate forcing) driven by afforestation more than by fire weather intensification, increasing exposure by 27 % by the end of the century. We argue that climate information, especially climate forcing of forest disturbances (fires, hot droughts, etc.) needs to be included in the modelling framework of IAMs. Such developments would enhance the consistency between emission and climate projections. While such efforts are underway, IAM scenarios currently available likely underestimate management cost of large scale afforestation or overestimate the effectiveness and hence the remaining carbon budget for positive emissions.

How to cite: Jäger, F., Quilcaille, Y., Schwaab, J., Windisch, M., Humpenöder, F., Popp, A., Doelman, J., van Vuuren, D., Frank, S., Lessa Derci Augustynczik, A., Havlik, P., Narayan, K. B., and Seneviratne, S. I.: Fire Weather Compromises Large Scale Afforestation Scenarios, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12337, https://doi.org/10.5194/egusphere-egu23-12337, 2023.

The objective of this study is to analyze how inequality affects the demand for emission reduction policies. It is generally recognized that a more equal income distribution can improve environmental quality (IPCC, 2022) influencing several mechanisms, such as the value placed on environmental public goods, the influence of social norms or the cost-benefit distribution of environmental protection. However, the focus so far has been on outputs (i.e., pollution concentration), disregarding the fact that a major component in determining the impact on the environment is the demand for – and implementation of – policies, which are the tools to actually define emission caps or incentivize green technologies.

To fill this gap, we explicitly focus on the relationship between inequality and environmental policies. Our leading research question is: how does the distribution of income affect the demand of emission reduction policies?

Our analysis covers national mitigation-related policies implemented in G20 countries between 1997 and 2021. We use the Climate Policy Database (Nascimento et al., 2022) to create indicators of policy adoption. In line with the policy density approach, we use the count of mitigation policies adopted annually by each country as dependent variable, and consider it as an approximation of climate policy demand.

To capture different aspects of income distribution, we adopt different inequality measures (WID, 2022). We consider the national income shares of specific parts of the population (Top 10%, Bottom 10%, Bottom 40%) as well as commonly used inequality indices (Gini index and Palma ratio). We also construct a composite index, which combines the Gini with the ratio of the income shares held by the top and bottom 10% (Sitthiyot & Holasut 2022). We interact our inequality indicators with GDP per capita (PPP), as we assume that the impact of inequality may differ according to the national income level.

Given the count data nature of our dependent variable, our empirical strategy is based on a fixed-effects Poisson regression model. We control for several institutional and policy-relevant variables.

Our results show that the impact of inequality on climate policy implementation depends on the country's average income level. While in wealthy countries a reduction of inequality leads to a lower number of mitigation policies, in poorer countries an increase in inequality may drive the adoption of new policies. At the same time, the effect of economic growth is also not straightforward: an increase in average income has a positive impact on policy adoption in low-inequality societies. Conversely, an average income increase has a negative impact on climate mitigation adoption in highly unequal societies.

Our findings confirm that inequality plays a key role in the adoption of national mitigation policies. These results, which are robust across multiple specifications of inequality indicators, highlight the importance of advancing knowledge on how equity and environmental challenges interact in order to get full support and progress with the climate agenda. Our results aim to inform the current policy debate on potential trade-offs between climate and equity by presenting new evidence on the interconnections between social and environmental goals.

How to cite: Bellanca, M., Davide, M., and De Cian, E.: Inequality and the adoption of climate mitigation policies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13183, https://doi.org/10.5194/egusphere-egu23-13183, 2023.

We developed an emulator for Integrated Assessment Models (emIAM) based on a marginal abatement cost (MAC) curve approach (Xiong et al., 2022a,b). Using the output of IAMs in the ENGAGE Scenario Explorer and the GET model, we derived a large set of MAC curves: ten IAMs; global and eleven regions; three gases CO2, CH4, and N2O; eight portfolios of available mitigation technologies; and two emission sources. We tested the performance of emIAM by coupling it with a simple climate model ACC2 (Tanaka et al., 2021). We found that the optimizing climate-economy model emIAM-ACC2 adequately reproduced a majority of original IAM emission outcomes under similar conditions, allowing systematic explorations of IAMs with small computational resources. emIAM can expand the capability of simple climate models as a tool to calculate cost-effective pathways linked directly to a temperature target.

References
1. Tanaka, K., O. Boucher, P. Ciais, D. J. A. Johansson, J. Morfeldt (2021) Cost-effective implementation of the Paris Agreement using flexible greenhouse gas metrics. Science Advances, 7, eabf9020. doi:10.1126/sciadv.abf9020. 
2. Xiong, W., K. Tanaka, P. Ciais, D. J. A. Johansson, M. Lehtveer (2022a) emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves. arXiv: 2212.12060 on 23 December 2022 (version 1). https://arxiv.org/abs/2212.12060
3. Xiong, W., K. Tanaka, P. Ciais, D. Johansson, M. Lehtveer (2022b) Data for "emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves" [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7478234

How to cite: Tanaka, K., Xiong, W., Ciais, P., Johansson, D., and Lehtveer, M.: emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11431, https://doi.org/10.5194/egusphere-egu23-11431, 2023.

Economic forecasts of the effects of climate policy are frequently based on static economic theory and are not regularly updated. Moreover, their source code is often not public, making replication and critical evaluation difficult. The predominant models used for climate policy narratives, so called Integrated Assessment Models, are rarely estimated using empirical data and are hence highly affected by the modeller’s assumptions. To improve the estimation of likely effects of climate policy, we present the “Aggregate Model”, a data-based model for dozens of countries that flexibly estimates and forecasts economic time series and allows for the simulation of different climate policy options. Data-based models need to incorporate long-term trends and account for both structural breaks and outliers that otherwise distort the model estimates and may lead to systematic forecast error. Our model uses various techniques from the time series literature, such as indicator saturation, model selection, and testing for co-integration. These techniques are automated to a high degree, simplifying the model estimation procedure.  The Aggregate Model is distributed as an open-source R package, allowing for simple replication and modification by users through its modular structure.

How to cite: Schwarz, M., Kurle, J., Pretis, F., and Martinez, A.: Automatic and Open-Source Model with Forecasts for Climate Policy and Economics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14067, https://doi.org/10.5194/egusphere-egu23-14067, 2023.

Evaluating the impacts of climate change, be that on health, critical infrastructure, biodiversity, agriculture, economic impacts or any other sectors, requires a multi-sector, multi-scenario analysis.  Such analysis is only possible though integrated impact assessment models within a framework architecture that can handle the associated complexities of generating harmonized results from a heterogenous set of models and drivers. Addressing these challenges, the OpenCLIM framework has been developed to collate existing models from across sectors to form an open, extensible framework, which explores the impacts of climate change in a consistent, -scenario and -scale approach, using Great Britain as a case study. 

Domain-based, siloed approaches are no longer suitable for assessing climate change impacts and integrated assessment platforms where compound cross-sector risks can be assessed are now integral and expected. To this end, the OpenCLIM project has developed a novel, open framework, enabling the hosting of models for assessing climate hazard risks and potential impacts. The nature of the framework enables the coupling of disparate models to explore challenges considering trade-offs between possible interventions, such as the change in risk from increased temperatures when flood management infrastructure policy is applied to reduce flood risk, or the density of new buildings is changed. 

Implemented on the DAFNI (Data and Analytics Facility for National Infrastructure) platform, the framework uses the concepts of workflows in which models can be coupled and run with data and parameterisation passed between models while datasets are available within a shared data archive. A set of tools, or adaptors, to support coupling enable the ease of use and integration of new models into existing workflows, new workflows, or coupling of existing workflows. This flexible framework creates a powerful integrated impact assessment model, and when coupled with accessible data resources, such as climate scenarios and socio-economic datasets, offers a platform for assessing the impacts of climate change across domains. 

An example of the OpenCLIM platform is the assessment of the impact of flooding using the City Catchment Analysis Tool (CityCAT). Climate projections suggest a global increase in extreme rainfall events and the subsequent impact of flooding. Considering socio-economic changes to the urban environment, the OpenCLIM workflows couple future narratives for the urban landscape with flooding events of varying durations and intensities. Cost damage curves are then applied to assess the indicative economic cost of damages, facilitating comparisons between population density changes, climate extremes and the effectiveness of mitigation strategies/adaptation options. 

The OpenCLIM framework exemplifies an open, extendable, flexible integrated assessment model for climate impacts enabling cross-sector and compound risks to be assessed from human, nature, and economic aspects.  The concepts and tools explored and resolved within the framework, although initially with a GB focus, are applicable beyond these bounds where models and data exist.  

How to cite: Butters, O., Robson, C., and Smith, B.: OpenCLIM: A national scale framework for evaluating the effects of climate change for socio-economic scenarios and adaptation policies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14835, https://doi.org/10.5194/egusphere-egu23-14835, 2023.

Unprecedented climate change not only affects our health, but also poses a significant risk to the economic and financial systems. Limiting the global temperature rise at below 1.5 °C, as suggested by the Paris Agreement, has a significant effect on financial economics. Physical climate change, such as global warming and sea level rise, may directly reduce a firm's productivity. Climate change may also indirectly affect a firm's costs due to governmental sanctions and regulations, such as the emission trading scheme. Simultaneously, some firms strategically use climate change issues as opportunities. As climate change risks do not unidirectionally affect firms, it is important to understand how firms and managers perceive climate change effects. In this manner, we examine the effects of manager's perspectives on climate change on stock price crash risk. The analysis confirms that manager's climate change perspective is negatively associated with future stock price crash risk likelihood. Various channel tests show that investor attention and analyst coverage are potential channels through which a firm's climate change perspective improves financial stability and ultimately reduces crash risk. Our results are also robust to alternative climate change perspective measures.

How to cite: Jung, H. and Song, C.-K.: Managerial perspectives on climate change and stock price crash risk, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1964, https://doi.org/10.5194/egusphere-egu23-1964, 2023.

Innovation and technological progress are the main drivers of sustainable development and economic growth, and they both play a role in addressing climate change and related actions. These drivers rely on Research and Development (R&D), i.e., the systematic creative work aiming to increase the stock of knowledge and devoted to the creation and development of new products and procedures. Against the existing literature that differently addresses the economic implications of the R&D sector, in this work, we introduce a novel quantification of the R&D content embedded in countries’ export baskets. Considering the current need to understand the dynamics of CO₂ emissions and their nexus with the economic aspects, the R&D content in nations’ export baskets is related to the country-specific terrestrial carbon emissions. To this aim, we refer the CO₂ emissions embedded in nations' export baskets to the dollars the country at hand exports, defining a country-specific CO₂ export intensity; in this way, we can compare economies of different sizes (for example, Germany and Paraguay). Our results show that as countries export products with an increasing R&D content, their CO₂ export intensity decreases. Germany, Japan, and the United States are examples of countries exporting high R&D products and having low CO₂ export intensity. China stands as an example of elevated CO₂ intensity despite having a high R&D content embedded in its export basket. Fuel exporting economies (such as the Russian Federation) and the majority of developing countries have low R&D-oriented export baskets, with high CO₂ export intensities. Our work provides a novel perspective of the R&D-CO₂ emissions nexus, highlighting the R&D centrality in the green transition and decarbonization process.

How to cite: Costantini, L., Laio, F., Ridolfi, L., and Sciarra, C.: Investigating the R&D-CO2 nexus in the international trade, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11904, https://doi.org/10.5194/egusphere-egu23-11904, 2023.

In a time of ever-intensifying climate change, it is crucial to understand the timescales needed to overcome adaptation constraints, namely what makes adaptation challenging. Currently, evidence on constraints focusses on the local level and present-day dynamics. Here, we combine qualitative and case study data with national macro indicators and use the Shared Socioeconomic Pathways to look at the pace of various scenarios of future socio-economic development. We find that regardless of the scenario, long timescales will be required to overcome constraints, challenging adaptation for decades to come, in particular in countries on the frontline of climate change. The persistence of adaptation constraints calls for stringent mitigation, improved adaptation along with dedicated finance and increasing efforts to address loss and damage. Our novel approach allows to ground truth existing indicators that can be further used in climate modelling efforts (including economic models), improving the representation of adaptation and its risk reduction potential.

How to cite: Theokritoff, E., van Maanen, N., Andrijevic, M., Thomas, A., Lissner, T., and Schleussner, C.-F.: Adaptation constraints in scenarios of socio-economic development, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12124, https://doi.org/10.5194/egusphere-egu23-12124, 2023.