Climate model forecast biases assessed with a perturbed physics ensemble

Mulholland, David P.; Haines, Keith; Sparrow, Sarah; Wallom, David

doi:10.1007/s00382-016-3407-x

Cited by 14 publications

(11 citation statements)

References 35 publications

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“…By design, such statistical approaches inherently assume that the key drivers are meteorological and neglect feedback with, for example, the biosphere, that can be included in more specialized ESMs; for example, Coupled Chemistry Models. The power of LEs-even without additional complexity-as tools to investigate mean state biases 77 and extreme events, as well as their impacts on ecosystems, food security and public health, remains largely unexplored.…”

Section: Emerging Earth System Applicationsmentioning

confidence: 99%

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

et al. 2020

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Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed.

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Section: Emerging Earth System Applicationsmentioning

confidence: 99%

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

et al. 2020

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“…For the emulator, a two-layer feed-forward artificial neural network (ANN; Knutti et al, 2003;Sanderson et al, 2008a;Mulholland et al, 2017) was used. Although other machinelearning algorithms could be suitable (Rougier et al, 2009;Neelin et al, 2010;Bellprat et al, 2012aBellprat et al, , b, 2016, we chose an ANN because it permits multiple simultaneous emulator targets (i.e., TOA SW and LW at the same time).…”

Section: Discussionmentioning

confidence: 99%

“…The studies span a range of topics, from the earlier studies focusing on climate sensitivity (e.g., Murphy et al, 2004;Stainforth et al, 2005;Sanderson et al, 2008aSanderson et al, , b, 2010Sanderson, 2011), to later ones attempting to generate plausible representations of the climate without flux adjustments (e.g., Irvine et al, 2013;Yamazaki et al, 2013) and using history matching to reduce parameter space uncertainty . More recently, Mulholland et al (2017) demonstrated the potential of using PPEs to improve the skill of initialized climate model forecasts with 1-month lead time, and Sparrow et al (2016) showed that large PPEs can be used to identify subgrid-scale parameter settings that are capable of best simulating the ocean state over the recent past . However, very little (Bellprat et al, 2012b(Bellprat et al, , 2016 has been published on using PPEs for parameter refinement with the aim of improving regional climate models (RCMs).…”

Section: Introductionmentioning

confidence: 99%

Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation

Rupp

Hawkins

et al. 2019

Geosci. Model Dev.

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Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental midlatitudes. This work, with the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multiphased parameter refinement approach, particularly over the northwestern United States (NWUS). Each phase consists of (1) creating a perturbed parameter ensemble with the coupled global-regional atmospheric model, (2) building statistical emulators that estimate climate metrics as functions of parameter values, (3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74 % of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, as well as land surface parameters that affect plant photosynthesis, transpiration, and evaporation, but also suggest that this iterative approach to perturbed parameters has an important role to play in the evolution of physical parameterizations.

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“…The second is simulation bias reduction methods through perturbed parameter studies (e.g. Hawkins et al, 2019;Li et al, 2019;Mulholland et al, 2017). The third category is extreme weather event attribution studies where quantitative assessments are made of the change in likelihood of extreme weather events occurring between past, present and possible future climates (e.g.…”

Section: Introductionmentioning

confidence: 99%

OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting

Sparrow¹,

Bowery²,

Carver³

et al. 2020

Preprint

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Abstract. Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organisations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales, by running these models in high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climateprediction.net and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of tropical cyclone Karl from September 2016, studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet-streak near Scotland and heavy rainfall over Norway. For the validation we use a two thousand member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF’s forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts as well as discussing the use of large ensembles in the context of forecasting extreme events.

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Climate model forecast biases assessed with a perturbed physics ensemble

Cited by 14 publications

References 35 publications

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

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

Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation

OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting

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