Recommendations for Future Research Priorities for Climate Modeling and Climate Services

Hewitt, Chris; Guglielmo, Francesca; Joussaume, Sylvie; Bessembinder, Janette; Christel, Isadora; Doblas‐Reyes, Francisco J.; Djurdjević, Vladimir; Garrett, Natalie; Kjellström, Erik; Kržič, Aleksandra; Costa, María Máñez; Clair, Asun Lera St.

doi:10.1175/bams-d-20-0103.1

Cited by 30 publications

(16 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to an increasing need from decision makers and other community stakeholders for near‐term climate predictions, there has been a coordinated effort to increase the availability of decadal outlooks from initialized climate models and other operational forecast systems (e.g., Boer et al., 2016; Graham et al., 2011; Hewitt et al., 2021; Kushnir et al., 2019; Meehl, Goddard, et al., 2013; Meehl et al., 2021; Merryfield et al., 2020; Smith et al., 2019). However, these simulations can be computationally expensive to run.…”

Section: Discussionmentioning

confidence: 99%

Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks

Labe

Barnes

2022

Geophysical Research Letters

View full text Add to dashboard Cite

The global mean surface temperature (GMST) record exhibits both interannual to multidecadal variability and a long‐term warming trend due to external climate forcing. To explore the predictability of temporary slowdowns in decadal warming, we apply an artificial neural network (ANN) to climate model data from the Community Earth System Model Version 2 Large Ensemble. Here, an ANN is tasked with whether or not there will be a slowdown in the rate of the GMST trend by using maps of ocean heat content (OHC) at the onset. Through a machine learning explainability method, we find the ANN is learning off‐equatorial patterns of anomalous OHC that resemble transitions in the phase of the Interdecadal Pacific Oscillation in order to make slowdown predictions. Finally, we test our ANN on observed historical data, which further reveals how explainable neural networks are useful tools for understanding decadal variability in both climate models and observations.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks

Labe

Barnes

2022

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…For CPRCM outputs to become more increasingly used and lead to more actionable information (Senior et al, 2021; Orr et al, 2021; Hewitt et al, 2021), climate services will have to learn to deal with massive datasets including sub‐daily data and targeting more local adaptation issues such as UHI and flash flood studies where CPRCMs can bring new information (Termonia et al, 2018). New bias correction techniques would be needed to adjust finer temporal and spatial scale simulations toward weather station data (Argüeso et al, 2013; Haerter et al, 2015).…”

Section: Conclusion: Next Steps For Exploiting Cprcm Benefitsmentioning

confidence: 99%

Convection‐permitting modeling with regional climate models: Latest developments and next steps

Lucas‐Picher

Argüeso

Brisson

et al. 2021

WIREs Climate Change

123

View full text Add to dashboard Cite

Approximately 10 years ago, convection-permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1-4 km) decadal-long climate simulations with explicitly resolved deep convection. This explicit representation is expected to reduce climate projection uncertainty related to deep convection parameterizations found in most climate models. A recent surge in CPRCM decadal simulations over larger domains, sometimes covering continents, has led to important insights into CPRCM advantages and limitations. Furthermore, new observational gridded datasets with fine spatial and temporal ($1 km; $1 h) resolutions have leveraged additional knowledge through evaluations of the added value of CPRCMs. With an improved coordination in the frame of ongoing international initiatives, the production of ensembles of CPRCM simulations is expected to provide more robust climate projections and a better identification of their associated uncertainties. This review paper presents an overview of the methodology to produce CPRCM simulations and the latest research on the related added value in current and future climates. Impact studies that are already taking advantage of these new CPRCM simulations are highlighted. This review paper ends by proposing next steps that could be accomplished to continue exploiting the full potential of CPRCMs.

show abstract

“…Knowing -predicting or projecting -the future climate in all its ne-scale details, and associated uncertainties, can be considered as the holy grail in climate change adaptation (Giorgi 2019;Hewitt et al 2021). The quest for this underlies large international efforts to produce high resolution climate information such as CORDEX (Giorgi et al 2009;Jacob et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A Perfect Model Study on the Reliability of the Added Small-Scale Information in Regional Climate Change Projections

Lenderink

Vries

Meijgaard

et al. 2022

Preprint

View full text Add to dashboard Cite

The issue of the added value (AV) of high resolution regional climate models is complex and still strongly debated. Here, we approach AV in a perfect model framework within a 16-member single model initial condition large ensemble with the regional climate model RACMO2 embedded in the global climate model EC-Earth2.3. In addition, we also used an ensemble produced by a pseudo global warming (PGW) approach. Results for winter temperature and precipitation are investigated from two different perspectives: i) a signal-to-noise perspective analysing the systematic response to changing emission forcings versus internal variability, and ii) a prediction perspective aimed at predicting a 30-year future climate state. Systematic changes in winter temperature and precipitation contain fine-scale response patterns, but in particular for precipitation these patterns are small compared to internal variability and single members of the ensemble therefore only provide limited information on these systematic patterns. However, they can be estimated more reliably from PGW members because of the stronger constraints on internal variability. From the prediction perspective, we analysed AV of fine-scale information by comparing three set of prediction pairs. This analysis shows that there is added value in the fine-scale information for temperature, yet for precipitation adding fine-scale changes generally deteriorates the predictions. Using only the large-scale change (without fine scales) from a single ensemble member as a delta change on top of the present-day climate state, already provides a robust estimate of the future climate state and therefore can be used as a simple benchmark to measure added value.

show abstract

Recommendations for Future Research Priorities for Climate Modeling and Climate Services

Abstract: Capsule Recommendations for key future research topics for climate modelling and climate services are presented, as compiled by a group of experts across Europe.

Cited by 30 publications

References 24 publications

Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks

Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks

Convection‐permitting modeling with regional climate models: Latest developments and next steps

A Perfect Model Study on the Reliability of the Added Small-Scale Information in Regional Climate Change Projections

Contact Info

Product

Resources

About