Shraddha Gupta

Assessing Earth system responses arising from carbon dioxide removal (CDR) requires developing and simulating pairs of scenarios – a mitigation scenario with deployment of CDR and a corresponding no-CDR baseline. The latter describes a world where no CDR is deployed, such that net carbon emissions are higher and a given temperature target may be missed. While over the past years a rich literature on mitigation scenarios with CDR has been emerging, no-CDR baselines have mostly been explored in stylized Earth system model (ESM) experiments. In such simulations, a no-CDR baseline simply assumes that CDR is “switched off”, while socio-economic constraints are not considered. However, the deployment of CDR in mitigation scenarios, created by integrated assessment models (IAMs), is embedded in a consistent socio-economic description of plausible futures, and disallowing CDR may affect climate drivers due to changes in the energy system and in land-use dynamics. Particularly, when moving towards an activity-driven representation of CDR in emission-driven ESMs, where the activity that draws down CO ₂ from the atmosphere is explicitly modelled, the creation of no-CDR baselines comes with challenges and trade-offs. Here, we conceptualize a framework for emission-driven ESM simulations of IAM scenarios that allows us to determine carbon-cycle feedbacks and biogeophysical effects of CDR deployment using no-CDR baselines. We show that different options exist for the creation of no-CDR baselines, which offer different insights and have their specific advantages and limitations. We also demonstrate that internal variability of the climate system inherently limits our ability to detect the small signals related to CDR deployment and its feedbacks. Hence, unless a sufficiently large initial conditions ensemble is employed, stylized modelling approaches may remain preferable for some applications, e.g., the quantification of regional biogeophysical effects of CDR deployment. Both, the efficiency of CDR (defined as CO ₂ removed per unit of resources employed) as well as related carbon-cycle feedbacks and biogeophysical effects are expected to be scenario- and model-dependent. Our simulation design of concentration- and emission-driven no-CDR baselines, together with an improved representation of CDR in the IAM – ESM modelling chain, opens an avenue towards estimating CDR efficiencies and their uncertainties under various future scenarios in upcoming model intercomparison activities.

Assessing Earth system responses arising from carbon dioxide removal (CDR) requires developing and simulating pairs of scenarios – a mitigation scenario with deployment of CDR and a corresponding no-CDR baseline. The latter describes a world where no CDR is deployed, such that net carbon emissions are higher and a given temperature target may be missed. While over the past years a rich literature on deep mitigation scenarios with CDR has been emerging, no-CDR baselines have mostly been explored in stylized Earth system model (ESM) experiments. In such simulations, a no-CDR baseline simply assumes that CDR is “switched off”, while socio-economic constraints are not considered. However, the deployment of CDR in deep mitigation scenarios, created by integrated assessment models (IAMs), is embedded in a consistent socio-economic description of plausible futures, and disallowing CDR may affect climate drivers due to changes in the energy system and in land-use dynamics. Particularly, when moving towards an activity-driven representation of CDR in emission-driven ESMs, where the activity that draws down CO₂ from the atmosphere is explicitly modelled, the creation of no-CDR baselines comes with challenges and trade-offs. Here, we conceptualize a framework for emission-driven ESM simulations of IAM scenarios that allows us to determine carbon-cycle and biogeophysical feedbacks of CDR deployment using no-CDR baselines. We show that different options exist for the creation of no-CDR baselines, which offer different insights and have their specific advantages and limitations. We also demonstrate that internal variability of the climate system inherently limits our ability to detect the small signals related to CDR deployment and its feedbacks. Hence, unless a sufficiently large initial conditions ensemble is employed, stylized modelling approaches may remain preferable for some applications, e.g., the quantification of regional biogeophysical effects of CDR deployment.

Assessing Earth system responses arising from carbon dioxide removal (CDR) requires developing and simulating pairs of scenarios – a mitigation scenario with deployment of CDR and a corresponding no-CDR baseline. The latter describes a world where no CDR is deployed, such that net carbon emissions are higher and a given temperature target may be missed. While over the past years a rich literature on deep mitigation scenarios with CDR has been emerging, no-CDR baselines have mostly been explored in stylized Earth system model (ESM) experiments. In such simulations, a no-CDR baseline simply assumes that CDR is “switched off”, while socio-economic constraints are not considered. However, the deployment of CDR in deep mitigation scenarios, created by integrated assessment models (IAMs), is embedded in a consistent socio-economic description of plausible futures, and disallowing CDR may affect climate drivers due to changes in the energy system and in land-use dynamics. Particularly, when moving towards an activity-driven representation of CDR in emission-driven ESMs, where the activity that draws down CO₂ from the atmosphere is explicitly modelled, the creation of no-CDR baselines comes with challenges and trade-offs. Here, we conceptualize a framework for emission-driven ESM simulations of IAM scenarios that allows us to determine carbon-cycle and biogeophysical feedbacks of CDR deployment using no-CDR baselines. We show that different options exist for the creation of no-CDR baselines, which offer different insights and have their specific advantages and limitations. We also demonstrate that internal variability of the climate system inherently limits our ability to detect the small signals related to CDR deployment and its feedbacks. Hence, unless a sufficiently large initial conditions ensemble is employed, stylized modelling approaches may remain preferable for some applications, e.g., the quantification of regional biogeophysical effects of CDR deployment.

Terrestrial carbon dioxide removal (CDR), including afforestation and reforestation (A/R) and other land-based approaches, is a key element of climate mitigation pathways consistent with the Paris Agreement. While the mitigation potential and Earth system responses to terrestrial CDR deployment have been increasingly explored, its influence on land–atmosphere coupling and temperature extremes remains underexplored, particularly at regional scales. Understanding these processes is essential for evaluating both synergies and trade-offs associated with land-based mitigation strategies, including potential implications for biogeophysical co-benefits, resilience, and permanence.Here, we present a spatio-temporal explicit analysis of how terrestrial CDR pathways modify land–atmosphere coupling and associated hot extremes across regions and seasons. The analysis is based on emission-driven simulations from the fully coupled MPI Earth System Model and considers a range of future scenarios that include both stylized large-scale terrestrial CDR deployment and more realistic mitigation pathways developed within CDRSynTra, LAMACLIMA, and RESCUE projects. This scenario diversity allows us to explore the robustness, plausibility, and potential non-linearities of land–atmosphere responses to terrestrial CDR. The scenarios considered include large-scale A/R aligned with national pledges, transformation pathways characterized by global sustainability and global inequality, and climate stabilization pathways with and without temperature overshoot that rely on portfolios of multiple CDR approaches. We apply various land–atmosphere coupling diagnostics, such as measures of soil-moisture control on latent and sensible heat fluxes, and relate these to hot-day and heatwave metrics over land to assess the processes linking surface fluxes, moisture availability, and temperature extremes. By explicitly focusing on regional responses, the analysis captures spatial heterogeneity in land–atmosphere feedbacks that is not apparent in global-mean assessments. Seasonal variability (e.g., during spring and summer) and different future time horizons (near-, mid-, and late-century; before and after overshoot), are considered to assess the sensitivity of land–atmosphere coupling processes to the timing and magnitude of the application of terrestrial CDR. Identifying regions where terrestrial CDR strongly modifies land–atmosphere coupling and heat extremes can help highlight hotspots for targeted monitoring and evaluation by indicating where observations and diagnostics are most relevant for tracking biophysical responses and emerging risks. Analyses indicate that regions such as Scandinavia, West Asia, and Northeast China exhibit contrasting responses, where changes in heat extremes coincide with shifts in soil-moisture control and evaporative cooling, and where observational coverage of surface fluxes remains limited. Such regional insights can also inform the assessment of where terrestrial CDR deployment may be associated with co-benefits, and where land–atmosphere feedbacks could pose challenges or limitations, including adaptation-relevant impacts on heat stress and labor productivity. Overall, this work helps fill a key gap in current assessments by explicitly linking terrestrial CDR deployment to land–atmosphere coupling and heat extremes at regional scales, and by providing a process-based assessment framework that can support risk-aware evaluation of land-based CDR strategies and be extended to other terrestrial CDR approaches.

Assessing Earth system responses arising from carbon dioxide removal (CDR) requires developing and simulating pairs of scenarios – a mitigation scenario with deployment of CDR and a corresponding no-CDR baseline. The latter describes a world where no CDR is deployed, such that net carbon emissions are higher and a given temperature target may be missed. While over the past years a rich literature on deep mitigation scenarios with CDR has been emerging, no-CDR baselines have mostly been explored in stylized Earth system model (ESM) experiments. In such simulations, a no-CDR baseline simply assumes that CDR is “switched off”, while socio-economic constraints are not considered. However, the deployment of CDR in deep mitigation scenarios, created by integrated assessment models (IAMs), is embedded in a consistent socio-economic description of plausible futures, and disallowing CDR may affect climate drivers due to changes in the energy system and in land-use dynamics. Particularly, when moving towards an activity-driven representation of CDR in emission-driven ESMs, where the activity that draws down CO₂ from the atmosphere is explicitly modelled, the creation of no-CDR baselines comes with challenges and trade-offs. Here, we conceptualize a framework for emission-driven ESM simulations of IAM scenarios that allows us to determine carbon-cycle and biogeophysical feedbacks of CDR deployment using no-CDR baselines. We show that different options exist for the creation of no-CDR baselines, which offer different insights and have their specific advantages and limitations. We also demonstrate that internal variability of the climate system inherently limits our ability to detect the small signals related to CDR deployment and its feedbacks. Hence, unless a sufficiently large initial conditions ensemble is employed, stylized modelling approaches may remain preferable for some applications, e.g., the quantification of regional biogeophysical effects of CDR deployment.

In 2018 and 2019, Kerala, the southernmost state in India, experienced extreme precipitation, leading to appallingly devastating floods that damaged life and property. Kerala is vulnerable to flooding due to its topography, geographical location, and meteorology. Several phenomena have been attributed to these extreme precipitations; however, no single explanation suffices to explain such complex climate phenomena. We view the occurrence of extreme precipitation that leads to floods, such as an emerging phenomenon through the lens of complex system theory. We analyze the patterns of synchrony of extreme fluctuations in precipitation, outgoing longwave radiation, and water vapor transport. We construct time-varying functional climate networks, in which the statistical similarity between the time series of extreme precipitation at different spatial locations is estimated using event synchronization. The network topology reveals that excessive precipitation during the Kerala floods was associated with a coherent pattern of synchronized extreme rainfall. In the coherent phenomena discovered, the extreme rainfall was synchronized across a wide range of length scales spanning 100–1000 km. Furthermore, it traverses a synoptic scale path. After originating in the equatorial Indian Ocean, the coherent pattern moves eastward across the Bay of Bengal. The pattern stops over the Maritime Continent and changes its direction. It moves westward toward the Indian peninsula and accumulates over southwest India. We find that the extreme precipitation was driven by enhanced convective activity, leading to cloudiness and high-vapor transport in the atmosphere. Our findings improve the understanding of intraseasonal variability in the Indian monsoon and extreme precipitation events.

The objective of the Paris Agreement is to obtain commitments from signing parties to reduce greenhouse gas (GHG) emissions to levels consistent with a global warming well below 2 °C above pre-industrial levels and to pursue further efforts to limit it to 1.5 °C. To achieve such ambitious goals, countries should collectively aim to reach a global peak in GHG emissions as soon as possible and to steadily decrease their emissions after that, to ideally accomplish a climate neutral world by the middle of this century. However, it soon became clear that the efforts being committed by signing parties were not enough to achieve such ambitious goals. As a result, the world is currently on track to warming levels that will either temporarily or permanently exceed the agreed temperature targets. Carbon Dioxide Removal (CDR) can complement the phase-out of fossil fuels, supporting net emission reduction towards the achievement of net-zero and, in case of temperature overshoot, net-negative targets. To reach such goals, while limiting the severity of side-effects, a broad set of CDR options - a CDR portfolio - will be necessary.The RESCUE project aims to develop realistic scenarios that incorporate a mix of large-scale implementations of technology- and ecosystem-based CDR. These scenarios are based on the current state-of-the-art knowledge regarding various aspects, including the limits and constraints of CDR on both land and ocean. The actual effectiveness of these CDR portfolios depends on a series of mechanisms and feedback loops linked to a changing climate.Currently, the only tools that allow us to understand, assess, and quantify the processes that might affect the effectiveness of large-scale CDR interventions are Earth System Models (ESMs). However, even state-of-the-art ESMs are not yet equipped to represent the variety of CDR approaches being proposed. The RESCUE project is developing these representations in five European ESMs. The rationale behind these developments is to advance beyond the approach used in CMIP6, where negative emissions were prescribed from socioeconomic models as part of a given scenario. Inline with the CMIP7 emissions-driven experimental design focus, CDR interventions in RESCUE are provided to the ESMs as activities (e.g., area and carbon capture and storage efficiency of bioenergy plantations or alkalinity additions to the ocean) with the potential to alter net land and ocean CO2 fluxes. However, the ESM itself quantifies the actual effectiveness, taking into account known climate interactions and feedbacks. Here, we describe the overall design of these CDR representations, necessary assumptions and future directions for their improvement.

Assessing Earth system feedbacks arising from carbon dioxide removal (CDR) requires developing and simulating pairs of scenarios - a mitigation scenario with deployment of CDR and a corresponding no-CDR baseline. Both scenarios respect a specific long-term constraint on a carbon emission budget (i.e. emission reductions are pursued at the same level of ambition), but the latter describes a world where no CDR is deployed, such that net carbon emissions are larger and a given temperature threshold is missed. While over the past years a rich literature on deep mitigation scenarios with CDR has been emerging, the need for such no-CDR baselines has never been articulated explicitly. In idealized Earth system model (ESM) simulations of CDR, a no-CDR baseline is easy to imagine and implement, since socio-economic constraints are typically not taken into account. However, the deployment of CDR in deep mitigation scenarios, created by integrated assessment models (IAMs), is embedded in a consistent socio-economic description of plausible futures, and disallowing CDR may change many aspects of such scenarios, for example, the energy-system and land-use. Particularly, when moving towards an “activity-driven” representation of CDR in ESMs, where the activity that leads to a drawdown of CO2 is explicitly modelled (rather than prescribed by using removal fluxes from the IAM simulation), the creation of no-CDR baselines comes with challenges. Here, we conceptualize how carbon cycle and biophysical feedbacks of CDR deployment can be determined from scenario simulations and corresponding no-CDR baselines. We show that different options exist for the creation of no-CDR baselines, which offer different insights and have their specific advantages and limitations. We argue that for certain applications (e.g., the determination of regional biophysical feedbacks) the use of idealized no-CDR baselines is unavoidable to some extent, implying that we have to accept some degree of socio-economic inconsistency in no-CDR baselines.

The quality of weather forecasts has improved considerably in recent decades as models can better represent the complexity of the Earth’s climate system, benefitting from assimilation of comprehensive Earth observation data and increased computational resources. Analysis of errors is an integral part of numerical weather prediction to produce better quality forecasts. The Earth’s climate, being a highly complex interacting system, often gives rise to significant statistical relationships between the states of the climate at distant geographical locations. Likewise, correlated errors in forecasting the state of the system can arise from predictable relationships between forecast errors at various regions resulting from an underlying systematic or random process. Estimation of error correlations is very important for producing quality forecasts and is a key issue for data assimilation. However, the size of the corresponding correlation matrix is larger than what is possible to represent on geographical maps in order to diagnose its full spatial variation.In this work, we propose an approach based on complex network theory to quantitatively study the spatiotemporal coherent structures of medium-range forecast errors of different climate variables. We demonstrate that the spatial variation of the network measures computed from the error correlation matrix can provide insights into the origin of forecast errors in a climate variable by identifying spatially coherent patterns of regions having common sources of error. Notably, the network topology of forecast errors of a climate variable is significantly different from those of random networks corresponding to a deterministic phenomenon which the model fails to simulate adequately. This is especially important to reveal the spatial heterogeneity of the errors – for example, the forecast errors of outgoing long-wave radiation in tropical regions can be correlated across very long distances, indicating an underlying climate mechanism as the source of the error. Additionally, we highlight that these structures of forecast errors may not always be directly derivable from the spatiotemporal co-variability pattern of the corresponding climate variable, contrary to the expectations that the patterns should resemble each other. We further employ other common statistical tools such as, empirical orthogonal functions, to support these findings. Our results underline the potential of complex networks as a very promising diagnostic tool to gain better understanding of the spatial variation, origin, and propagation of forecast errors.