Lukas Gudmundsson

Last Name

Gudmundsson

First Name

Lukas

ORCID

0000-0003-3539-8621

Organisational unit

03778 - Seneviratne, Sonia / Seneviratne, Sonia

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Publications 1 - 10 of 68

Global‐Scale Prediction of Flood Timing Using Atmospheric Reanalysis
Do, Hong Xuan; Westra, Seth; Leonard, Michael; et al. (2020)
Water Resources Research
From emission scenarios to spatially resolved projections with a chain of computationally efficient emulators: coupling of MAGICC (v7.5.1) and MESMER (v0.8.3)
Beusch, Lea; Nicholls, Zebedee; Gudmundsson, Lukas; et al. (2022)
Geoscientific Model Development
Producing targeted climate information at the local scale, including major sources of climate change projection uncertainty for diverse emissions scenarios, is essential to support climate change mitigation and adaptation efforts. Here, we present the first chain of computationally efficient Earth system model (ESM) emulators that allow for the translation of any greenhouse gas emission pathway into spatially resolved annual mean temperature anomaly field time series, accounting for both forced climate response and natural variability uncertainty at the local scale. By combining the global mean, emissions-driven emulator MAGICC with the spatially resolved emulator MESMER, ESM-specific and constrained probabilistic emulated ensembles can be derived. This emulator chain can hence build on and extend large multi-ESM ensembles such as the ones produced within the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The main extensions are threefold. (i) A more thorough sampling of the forced climate response and the natural variability uncertainty is possible, with millions of emulated realizations being readily created. (ii) The same uncertainty space can be sampled for any emission pathway, which is not the case in CMIP6, where only a limited number of scenarios have been explored and some of the most societally relevant strong mitigation scenarios have been run by only a small number of ESMs. (iii) Other lines of evidence to constrain future projections, including observational constraints, can be introduced, which helps to refine projected ranges beyond the multi-ESM ensembles' estimates. In addition to presenting results from the coupled MAGICC-MESMER emulator chain, we carry out an extensive validation of MESMER, which is trained on and applied to multiple emission pathways for the first time in this study. By coupling MAGICC and MESMER, we pave the way for rapid assessments of any emission pathway's regional climate change consequences and the associated uncertainties.
Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land
Beusch, Lea; Gudmundsson, Lukas; Seneviratne, Sonia I. (2020)
Earth System Dynamics
Earth system models (ESMs) are invaluable tools to study the climate system's response to specific greenhouse gas emission pathways. Large single-model initial-condition and multi-model ensembles are used to investigate the range of possible responses and serve as input to climate impact and integrated assessment models. Thereby, climate signal uncertainty is propagated along the uncertainty chain and its effect on interactions between humans and the Earth system can be quantified. However, generating both single-model initial-condition and multi-model ensembles is computationally expensive. In this study, we assess the feasibility of geographically explicit climate model emulation, i.e., of statistically producing large ensembles of land temperature field time series that closely resemble ESM runs at a negligible computational cost. For this purpose, we develop a modular emulation framework which consists of (i) a global mean temperature module, (ii) a local temperature response module, and (iii) a local residual temperature variability module. Based on this framework, MESMER, a Modular Earth System Model Emulator with spatially Resolved output, is built. We first show that to successfully mimic single-model initial-condition ensembles of yearly temperature from 1870 to 2100 on grid-point to regional scales with MESMER, it is sufficient to train on a single ESM run, but separate emulators need to be calibrated for individual ESMs given fundamental inter-model differences. We then emulate 40 climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to create a “superensemble”, i.e., a large ensemble which closely resembles a multi-model initial-condition ensemble. The thereby emerging ESM-specific emulator parameters provide essential insights on inter-model differences across a broad range of scales and characterize core properties of each ESM. Our results highlight that, for temperature at the spatiotemporal scales considered here, it is likely more advantageous to invest computational resources into generating multi-model ensembles rather than large single-model initial-condition ensembles. Such multi-model ensembles can be extended to superensembles with emulators like the one presented here.
Anthropogenic climate change has influenced global river flow seasonality
Wang, Hong; Liu, Junguo; Klaar, Megan; et al. (2024)
Science
Riverine ecosystems have adapted to natural discharge variations across seasons. However, evidence suggesting that climate change has already impacted magnitudes of river flow seasonality is limited to local studies, mainly focusing on changes of mean or extreme flows. This study introduces the use of apportionment entropy as a robust measure to assess flow-volume nonuniformity across seasons, enabling a global analysis. We found that ~21% of long-term river gauging stations exhibit significant alterations in seasonal flow distributions, but two-thirds of these are unrelated to trends in annual mean discharge. By combining a data-driven runoff reconstruction with state-of-the-art hydrological simulations, we identified a discernible weakening of river flow seasonality in northern high latitudes (above 50°N), a phenomenon directly linked to anthropogenic climate forcing.
Showcasing MESMER-X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models
Quilcaille, Yann; Gudmundsson, Lukas; Beusch, Lea; et al. (2022)
Geophysical Research Letters
Emulators of Earth System Models (ESMs) are complementary to ESMs by providing climate information at lower computational costs. Thus far, the emulation of spatially resolved climate extremes has only received limited attention, even though extreme events are one of the most impactful aspects of climate change. Here, we propose a method for the emulation of local annual maximum temperatures, with a focus on reproducing essential statistical properties such as correlations in space and time. We test different emulator configurations and find that driving the emulations with global mean surface temperature offers an optimal compromise between model complexity and performance. We show that the emulations can mimic the temporal evolution and spatial patterns of the underlying climate model simulations and are able to reproduce their natural variability. The general design and the good performance for annual maximum temperatures suggest that the proposed methodology can be applied to other climate extremes.
Representing natural climate variability in an event attribution context: Indo-Pakistani heatwave of 2022
Nath, Shruti; Hauser, Mathias; Schumacher, Dominik L.; et al. (2024)
Weather and Climate Extremes
Attribution of extreme climate events to global climate change as a result of anthropogenic greenhouse gas emissions has become increasingly important. Extreme climate events arise at the intersection of natural climate variability and a forced response of the Earth system to anthropogenic greenhouse gas emissions, which may alter the frequency and severity of such events. Accounting for the effects of both natural climate variability and the forced response to anthropogenic climate change is thus central for the attribution. Here, we investigate the reproducibility of probabilistic extreme event attribution results under more explicit representations of natural climate variability. We employ well-established methodologies deployed in statistical Earth System Model emulators to represent natural climate variability as informed from its spatio-temporal covariance structures. Two approaches towards representing natural climate variability are investigated: (1) where natural climate variability is treated as a single component; and (2) where natural climate variability is disentangled into its annual and seasonal components. We showcase our approaches by attributing the 2022 Indo-Pakistani heatwave to human-induced climate change. We find that explicit representation of annual and seasonal natural climate variability increases the overall uncertainty in attribution results considerably compared to established approaches such as the World Weather Attribution Initiative. The increase in likelihood of such an event occurring as a result of global warming differs slightly between the approaches, mainly due to different assessments of the pre-industrial return periods. Our approach that explicitly resolves annual and seasonal natural climate variability indicates a median increase in likelihood by a factor of 41 (95% range: 6-603). We find a robust signal of increased likelihood and intensification of the event with increasing global warming levels across all approaches. Compared to its present likelihood, under 1.5 °C (2 °C) of global near-surface air temperature increase relative to pre-industrial temperatures, the likelihood of the event would be between 2.2 to 2.5 times (8 to 9 times) higher. We note that regardless of the different statistical approaches to represent natural variability, the outcomes on the conducted event attribution are similar, with minor differences mainly in the uncertainty ranges. Possible reasons for differences are evaluated, including limitations of the proposed approach for this type of application, as well as the specific aspects in which it can provide complementary information to established approaches.
Responsibility of major emitters for country-level warming and extreme hot years
Beusch, Lea; Nauels, Alexander; Gudmundsson, Lukas; et al. (2022)
Communications Earth & Environment
The contributions of single greenhouse gas emitters to country-level climate change are generally not disentangled, despite their relevance for climate policy and litigation. Here, we quantify the contributions of the five largest emitters (China, US, EU-27, India, and Russia) to projected 2030 country-level warming and extreme hot years with respect to pre-industrial climate using an innovative suite of Earth System Model emulators. We find that under current pledges, their cumulated 1991-2030 emissions are expected to result in extreme hot years every second year by 2030 in twice as many countries (92%) as without their influence (46%). If all world nations shared the same fossil CO2 per capita emissions as projected for the US from 2016-2030, global warming in 2030 would be 0.4 °C higher than under actual current pledges, and 75% of all countries would exceed 2 °C of regional warming instead of 11%. Our results highlight the responsibility of individual emitters in driving regional climate change and provide additional angles for the climate policy discourse.
Attributing observed permafrost warming in the northern hemisphere to anthropogenic climate change
Gudmundsson, Lukas; Kirchner, Josefine; Gädeke, Anne; et al. (2022)
Environmental Research Letters
Permafrost temperatures are increasing globally with the potential of adverse environmental and socio-economic impacts. Nonetheless, the attribution of observed permafrost warming to anthropogenic climate change has relied mostly on qualitative evidence. Here, we compare long permafrost temperature records from 15 boreholes in the northern hemisphere to simulated ground temperatures from Earth system models contributing to CMIP6 using a climate change detection and attribution approach. We show that neither pre-industrial climate variability nor natural drivers of climate change suffice to explain the observed warming in permafrost temperature averaged over all boreholes. However, simulations are consistent with observations if the effects of human emissions on the global climate system are considered. Moreover, our analysis reveals that the effect of anthropogenic climate change on permafrost temperature is detectable at some of the boreholes. Thus, the presented evidence supports the conclusion that anthropogenic climate change is the key driver of northern hemisphere permafrost warming.
Emulating Earth System Model Temperatures
Beusch, Lea; Gudmundsson, Lukas; Seneviratne, Sonia I. (2018)
NCAR Techical Notes ~ Proceedings of the 8th International Workshop on Climate Informatics (CI 2018)
Towards more predictive and interdisciplinary climate change ecosystem experiments
Rineau, Francois; Malina, Robert; Beenaerts, Natalie; et al. (2019)
Nature Climate Change

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Lukas Gudmundsson

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