Insights from Earth system model initial-condition large ensembles and future prospects

Deser, Clara; Lehner, Flavio; Rodgers, Keith B.; Ault, Toby R.; Delworth, T. L.; DiNezio, Pedro; Fiore, Arlene M.; Frankignoul, Claude; Fyfe, John C.; Horton, Daniel E.; Kay, J. E.; Knutti, Reto; Lovenduski, Nicole S.; Marotzke, Jochem; McKinnon, Karen A.; Minobe, Shoshiro; Randerson, James T.; Screen, James A.; Simpson, Isla R.; Ting, Mingfang

doi:10.1038/s41558-020-0731-2

Cited by 590 publications

(631 citation statements)

References 94 publications

Supporting

Mentioning

621

Contrasting

Unclassified

Order By: Relevance

“…To aid future studies on the specific weather events that result in the most extreme impacts, we advocate an interdisciplinary approach for the selection of such events: 'ensemble climate-impact modelling'. We suggest that the large ensembles of climate data commonly used in meteorological research (Deser et al 2020) are used in their entirety as input for impact models, resulting in large ensembles of impact data. From this dataset of societal/natural impacts the most extreme events ('extreme impact events') can be selected (figure 1).…”

Section: Methods: Event Selection Based On Extreme Impactmentioning

confidence: 99%

“…The aim of this essay is two-fold: first, to highlight the nontrivial meteorology-impact relation and the significance of considering actual impacts when investigating the effects of severe weather and climate change on communities and ecosystems, and second, to promote an integrated climate and impact modelling approach that addresses this complicated relationship. We argue that the ensemble modelling practice common in physical climate science (Deser et al 2020) should be extended with an ensemble impact modelling approach to investigate extreme impact events (figure 1). We think that such impactdriven science, which must be built on collaboration between a wide range of academic specialisations as well as stakeholders (Vera 2018), will help gain new insights since societal or ecological vulnerabilities can be more accurately linked to (changing) meteorological conditions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions

Wiel

Selten

Bintanja

et al. 2020

Environ. Res. Lett.

Self Cite

View full text Add to dashboard Cite

The investigation of risk due to weather and climate events is an example of policy relevant science. Risk is the result of complex interactions between the physical environment (geophysical events or conditions, including but not limited to weather and climate events) and societal factors (vulnerability and exposure). The societal impact of two similar meteorological events at different times or different locations may therefore vary widely. Despite the complex relation between meteorological conditions and impacts, most meteorological research is focused on the occurrence or severity of extreme meteorological events, and climate impact research often undersamples climatological natural variability. Here we argue that an approach of ensemble climate-impact modelling is required to adequately investigate the relationship between meteorology and extreme impact events. We demonstrate that extreme weather conditions do not always lead to extreme impacts; in contrast, extreme impacts may result from (coinciding) moderate weather conditions. Explicit modelling of climate impacts, using the complete distribution of weather realisations, is thus necessary to ensure that the most extreme impact events are identified. The approach allows for the investigation of highimpact meteorological conditions and provides higher accuracy for consequent estimates of risk.

show abstract

Section: Methods: Event Selection Based On Extreme Impactmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions

Wiel

Selten

Bintanja

et al. 2020

Environ. Res. Lett.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The historical simulations from CMIP5, CESM‐LE, and MPI‐GE end in 2005. The climate models substantially differ in their representation of internal variability (Hawkins et al, 2016; Olonscheck & Notz, 2017; Deser et al, 2020). We conform all observational data sets and model simulations to a spatial resolution of 1° × 1° by bilinear interpolation and analyze annual means.…”

Section: Models As Known and Observations As Unknown Distributionsmentioning

confidence: 99%

Broad Consistency Between Observed and Simulated Trends in Sea Surface Temperature Patterns

Olonscheck

Rugenstein

Marotzke

2020

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Using seven single-model ensembles and the two multimodel ensembles CMIP5 and CMIP6, we show that observed and simulated trends in sea surface temperature (SST) patterns are globally consistent when accounting for internal variability. Some individual ensemble members simulate trends in large-scale SST patterns that closely resemble the observed ones. Observed regional trends that lie at the outer edge of the models' internal variability range allow two nonexclusive interpretations: (a) Observed trends are unusual realizations of the Earth's possible behavior and/or (b) the models are systematically biased but large internal variability leads to some good matches with the observations. The existing range of multidecadal SST trends is influenced more strongly by large internal variability than by differences in the model formulation or the observational data sets.Plain Language Summary Climate model simulations agree well with the observed evolution of the global mean sea surface temperature, but their ability to realistically represent changes in the patterns of sea surface temperatures has been questioned. We show with an unprecedented number of simulations from different models and different initial conditions that the observed and simulated changes in SST patterns are consistent in most regions of the ocean. For each model, a few individual simulations recreate the observed patterns. In some regions, the observed changes may be an extreme realization of the Earth's possible behavior. Alternatively, structural model errors may be hidden by the large range of possible realizations. Key Points:• Observed and simulated SST trends are consistent in at least 90% of the global ocean area since the midtwentieth century • This consistency is obtained over larger areas in CMIP6 models than in CMIP5 models • Observed regional trends that lie at the edge of the simulated distribution of internal variability are hard to interpret Supporting Information:• Supporting Information S1 • Text S1

show abstract

“…For knowledge to be actionable in the current context means that the predictions are accurate, precise and reproducible, and are made on time scales that are sufficiently rapid to be used in a decision-making context. The most familiar example of actionable predictions arises in weather forecasting, as well as climate science, where ensemble-based methods play a central role [15,16]. People wish to know tomorrow's weather today, not tomorrow let alone in three months' time.…”

Section: Introductionmentioning

confidence: 99%

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

et al. 2020

View full text Add to dashboard Cite

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

show abstract

Insights from Earth system model initial-condition large ensembles and future prospects

Cited by 590 publications

References 94 publications

Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions

Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions

Broad Consistency Between Observed and Simulated Trends in Sea Surface Temperature Patterns

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

Contact Info

Product

Resources

About