Sonia I. Seneviratne

Last Name

Seneviratne

First Name

Sonia I.

ORCID

0000-0001-9528-2917

Organisational unit

03778 - Seneviratne, Sonia / Seneviratne, Sonia

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Publications 1 - 10 of 367

Statistical Analyses of Land-Atmosphere Feedbacks and Their Possible Pitfalls
Orlowsky, Boris; Seneviratne, Sonia I. (2010)
Journal of Climate
New diagnostic estimates of variations in terrestrial water storage based on ERA-Interim data
Mueller, Brigitte; Hirschi, Martin; Seneviratne, Sonia I. (2011)
Hydrological Processes
Climate research must sharpen its view
Marotzke, Jochem; Jakob, Christian; Bony, Sandrine; et al. (2017)
Nature Climate Change
Extending MESMER-X: a spatially resolved Earth system model emulator for fire weather and soil moisture
Quilcaille, Yann; Gudmundsson, Lukas; Seneviratne, Sonia I. (2023)
Earth System Dynamics
Climate emulators are models calibrated on Earth system models (ESMs) to replicate their behavior. Thanks to their low computational cost, these tools are becoming increasingly important to accelerate the exploration of emission scenarios and the coupling of climate information to other models. However, the emulation of regional climate extremes and water cycle variables has remained challenging. The MESMER emulator was recently expanded to represent regional temperature extremes in the new "MESMER-X"version, which is targeted at impact-related variables, including extremes. This paper presents a further expansion of MESMER-X to represent indices related to fire weather and soil moisture. Given a trajectory of global mean temperature, the extended emulator generates spatially resolved realizations for the seasonal average of the Canadian Fire Weather Index (FWI), the number of days with extreme fire weather, the annual average of the soil moisture, and the annual minimum of the monthly average soil moisture. For each ESM, the emulations mimic the statistical distributions and the spatial patterns of these indicators. For each of the four variables considered, we evaluate the performances of the emulations by calculating how much their quantiles deviate from those of the ESMs. Given how it performs over a large range of annual indicators, we argue that this framework can be expanded to further variables. Overall, the now expanded MESMER-X emulator can emulate several climate variables, including climate extremes and soil moisture availability, and is a useful tool for the exploration of regional climate changes and their impacts.
Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation
Lejeune, Quentin; Davin, Edouard Léopold; Guillod, Benoît; et al. (2015)
Climate Dynamics
The extent of the Amazon rainforest is projected to drastically decrease in future decades because of land-use changes. Previous climate modelling studies have found that the biogeophysical effects of future Amazonian deforestation will likely increase surface temperatures and reduce precipitation locally. However, the magnitude of these changes and the potential existence of tipping points in the underlying relationships is still highly uncertain. Using a regional climate model at a resolution of about 50 km over the South American continent, we perform four ERA-interim-driven simulations with prescribed land cover maps corresponding to present-day vegetation, two deforestation scenarios for the twenty-first century, and a totally-deforested Amazon case. In response to projected land cover changes for 2100, we find an annual mean surface temperature increase of 0.5 °C over the Amazonian region and an annual mean decrease in rainfall of 0.17 mm/day compared to present-day conditions. These estimates reach 0.8 °C and 0.22 mm/day in the total-deforestation case. We also compare our results to those from 28 previous (regional and global) climate modelling experiments. We show that the historical development of climate models did not modify the median estimate of the Amazonian climate sensitivity to deforestation, but led to a reduction of its uncertainty. Our results suggest that the biogeophysical effects of deforestation alone are unlikely to lead to a tipping point in the evolution of the regional climate under present-day climate conditions. However, the conducted synthesis of the literature reveals that this behaviour may be model-dependent, and the greenhouse gas-induced climate forcing and biogeochemical feedbacks should also be taken into account to fully assess the future climate of this region.
Summary of a workshop on extreme weather events in a warming world organized by the Royal Swedish Academy of Sciences
Chen, Deliang; Rodhe, Hennings; Emanuel, Kerry; et al. (2020)
Tellus B: Chemical and Physical Meteorology
Climate change is not only about changes in means of climatic variables such as temperature, precipitation and wind, but also their extreme values which are of critical importance to human society and ecosystems. To inspire the Swedish climate research community and to promote assessments of international research on past and future changes in extreme weather events against the global climate change background, the Earth Science Class of the Royal Swedish Academy of Sciences organized a workshop entitled ‘Extreme weather events in a warming world’ in 2019. This article summarizes and synthesizes the key points from the presentations and discussions of the workshop on changes in floods, droughts, heat waves, as well as on tropical cyclones and extratropical storms. In addition to reviewing past achievements in these research fields and identifying research gaps with a focus on Sweden, future challenges and opportunities for the Swedish climate research community are highlighted.
Long-term predictability of soil moisture dynamics at the global scale: Persistence versus large-scale drivers
Nicolai-Shaw, Nadine; Gudmundsson, Lukas; Hirschi, Martin; et al. (2016)
Geophysical Research Letters
Here we investigate factors that influence the long lead time predictability of soil moisture variability using standard statistical methods. As predictors we first consider soil moisture persistence only, using two independent global soil moisture data sets. In a second step we include three teleconnection indices indicative of the main northern, tropical, and southern atmospheric modes, i.e., the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI), and the Antarctic Oscillation (AAO). For many regions results show significant skill in predicting soil moisture variability with lead times up to 5 months. Soil moisture persistence plays a key role at monthly to subseasonal time scales. With increasing lead times large-scale atmospheric drivers become more important, and areas influenced by teleconnection indices show higher predictability. This long lead time predictability of soil moisture may help to improve early warning systems for important natural hazards, such as heat waves, droughts, wildfires, and floods.
Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100
Quilcaille, Yann; Batibeniz, Foulden; Ribeiro, Andreia F.S.; et al. (2023)
Earth System Science Data
Human-induced climate change is increasing the incidence of fire events and associated impacts on livelihood, biodiversity, and nature across the world. Understanding current and projected fire activity together with its impacts on ecosystems is crucial for evaluating future risks and taking actions to prevent such devastating events. Here we focus on fire weather as a key driver of fire activity. Fire weather products that have a global homogenous distribution in time and space provide many advantages to advance fire science and evaluate future risks. Therefore, in this study we calculate and provide for the first time the Canadian Fire Weather Index (FWI) with all available simulations of the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Furthermore, we expand its regional applicability by combining improvements to the original algorithm for the FWI from several packages. A sensitivity analysis of the default version versus our improved version shows significant differences in the final FWI. With the improved version, we calculate the FWI using average relative humidity in one case and minimum relative humidity in another case. We provide the data for both cases while recommending the one with minimum relative humidity for studies focused on actual FWI values and the one with average relative humidity for studies requiring larger ensembles. The following four annual indicators, (i) maximum value of the FWI (fwixx), (ii) number of days with extreme fire weather (fwixd), (iii) length of the fire season (fwils), and (iv) seasonal average of the FWI (fwisa), are made available and are illustrated here. We find that, at a global warming level of 3 degrees C, the mean fire weather would increase on average by at least 66% in duration and frequency, while associated 1-in-10-year events would approximately triple in duration and increase by at least 31% in intensity. Ultimately, this new fire weather dataset provides a large ensemble of simulations to understand the potential impacts of climate change spanning a range of shared socioeconomic narratives with their radiative forcing trajectories over 1850-2100 at annual and 2.5 degrees x 2.5 degrees resolutions. The produced full global dataset is a freely available resource at https://doi.org/10.3929/ethz-b-000583391 (Quilcaille and Batibeniz, 2022) for fire danger studies and beyond, which highlights the need to reduce greenhouse gas emissions for reducing fire impacts.
Quantifying soil moisture impacts on light use efficiency across biomes
Stocker, Benjamin; Zscheischler, Jakob; Keenan, Trevor F.; et al. (2018)
New Phytologist
Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for soil moisture. However, soil moisture limitation is known to strongly affect plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to soil moisture (fLUE), separated from VPD and greenness changes, using artificial neural networks trained on eddy covariance data, multiple soil moisture datasets and remotely sensed greenness. This reveals substantial impacts of soil moisture alone that reduce GPP by up to 40% at sites located in sub‐humid, semi‐arid or arid regions. For sites in relatively moist climates, we find, paradoxically, a muted fLUE response to drying soil, but reduced fLUE under wet conditions. fLUE identifies substantial drought impacts that are not captured when relying solely on VPD and greenness changes and, when seasonally recurring, are missed by traditional, anomaly‐based drought indices. Counter to common assumptions, fLUE reductions are largest in drought‐deciduous vegetation, including grasslands. Our results highlight the necessity to account for soil moisture limitation in terrestrial primary productivity data products, especially for drought‐related assessments.
Importance of Framing for Extreme Event Attribution: The Role of Spatial and Temporal Scales
Kirchmeier-Young, Megan; Wan, Hui; Zhang, Xuebin; et al. (2019)
Earth's Future
Event attribution, which determines how anthropogenic climate change has affected the likelihood of certain types of extreme events, is of broad interest to industries, governments, and the public. Attribution results can be highly dependent on the definition of the event and the characteristics assessed, which are part of framing the attribution question. Despite a widely acknowledged sensitivity to framing, little work has been done to document the impacts on attribution and the resulting implications. Here, we use a perfect‐model approach and large ensembles of coupled climate‐model simulations to demonstrate how event attribution depends on the spatial and temporal scales used to define the event. In general, stronger attribution is found for events defined over longer time scales and larger spatial scales due to enhanced signal‐to‐noise ratios. With strong warming trends, most regions see large changes in the likelihood of temperature extremes at all scales, even at low levels of global mean temperature increase. For precipitation extremes, spatial scale plays a strong role. It may be possible to attribute changes in likelihood for extreme precipitation events defined over larger scales, but greater levels of global warming are often required before it is possible to attribute changes in the likelihood of smaller‐scale precipitation events. Care must be taken to understand the scales used in event attribution, in order to properly understand the results.

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Sonia I. Seneviratne

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