Ashok, Karumuri

Barreiro, Marcelo

Collins, Matthew

Frölicher, Thomas L.

Kang, Sarah M.

Roxy, Mathew Koll

Tedeschi, Renata Goncalves

Wang, Guojian

English

Bern Open Repository and Information System (BORIS)

EDITORIALpublished: 07 December 2021doi: 10.3389/fclim.2021.811205Frontiers in Climate | www.frontiersin.org 1 December 2021 | Volume 3 | Article 811205Edited and reviewed by:Xander Wang,University of Prince EdwardIsland, Canada*Correspondence:Matthew CollinsM.Collins@exeter.ac.ukSpecialty section:This article was submitted toPredictions and Projections,a section of the journalFrontiers in ClimateReceived: 08 November 2021Accepted: 17 November 2021Published: 07 December 2021Citation:Collins M, Ashok K, Barreiro M,Roxy MK, Kang SM, Frölicher TL,Wang G and Tedeschi RG (2021)Editorial: New Techniques forImproving Climate Models,Predictions and Projections.Front. Clim. 3:811205.doi: 10.3389/fclim.2021.811205Editorial: New Techniques forImproving Climate Models,Predictions and ProjectionsMatthew Collins 1*, Karumuri Ashok 2, Marcelo Barreiro 3, Mathew Koll Roxy 4,Sarah M. Kang 5, Thomas L. Frölicher 6,7, Guojian Wang 8 and Renata Goncalves Tedeschi 91College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom, 2University ofHyderabad, Hyderabad, India, 3Department of Atmospheric Sciences, School of Sciences, Universidad de la República,Montevideo, Uruguay, 4Centre for Climate Change Research, Indian Institute of Tropical Meteorology (IITM), Pune, India,5Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, SouthKorea, 6Climate and Environmental Physics, University of Bern, Bern, Switzerland, 7Oeschger Center for Climate ChangeResearch, University of Bern, Bern, Switzerland, 8Commonwealth Scientific and Industrial Research Organisation (CSIRO),Canberra, ACT, Australia, 9 Instituto Tecnológico Vale (ITV), Desenvolvimento Sustentável, Belém, BrazilKeywords: climate, predictions, projections, machine learning, data assimilation, uncertaintiesEditorial on the Research TopicNew Techniques for Improving Climate Models, Predictions and ProjectionsINTRODUCTIONComplex climate models are the main tools used to make climate predictions and projections.Despite decades of development, models are still imperfect and generations of models have shownpersistent mean-state biases such as the “double intertropical convergence zone (ITCZ).” Modelimperfections lead to drift and errors in near-term initialized climate prediction systems anduncertainties in long-term future projections. Techniques such as bias correction and drift removalhave been developed to reduce the impact of model imperfection in the case of predictions.Techniques such as emergent constraints and model selection have been used in projectionstudies. Are these techniques adequate, could they be improved upon, or should the communitybe investing their efforts into significantly improving the performance of climate models? Willhigher resolution bring greater accuracy? Are there new techniques which can significantly improveclimate predictions and projections?The goal of this Research Topic was to explore new techniques for improving climate models,climate predictions, and climate projections. The 11 articles that are appearing in this special issueof Frontiers in Climate Predictions and Projections have together shown new avenues in improvingthe forecasts and projections, and introduce us to new science and new forecast products.DATA AND DATA ASSIMILATIONAmajor topic in data assimilation is the reduction of the drift of the model away from the estimatedobserved state of the system. Volpi et al. introduce an innovative initialization technique to reducethe initial drift through a quantile matching between the observed state at the initialization time,and the model state distribution. The adjusted initial state pertains to the model attractor. Volpi etal. find added value of the quantile matching initialization in theNorth Atlantic subpolar region andover the North Pacific surface temperature as well as for the ocean heat content up to 5 years, andimproved predictive skill of the Atlantic Meridional Overturning Circulation and the barotropicstream function in the Labrador Sea throughout the 5 forecast years.Collins et al. Editorial: New Techniques in ClimateIn satellite-based wind retrievals, accuracy is impaired due tonoise, while the maximal observable resolution is bounded bythe often sparse distribution of sensors. Schweri et al. applyinga neural network from numerical simulations on synthetic datashow that data recovery at high resolution and high quality canbe learned from simulation of physically realizable fluid flows.The study indicates that the learning-based reconstruction isespecially powerful in handling large areas of missing or occludeddata, relative to traditional models for data recovery. The authorsdemonstrate the usefulness of the method a real-world flow dataset retrieved from satellite imagery of stratocumulus clouds onGuadalupe Island.ARTIFICIAL INTELLIGENCE AND MACHINELEARNING (AI/ML) APPROACHESSerifi et al. explore the capabilities of neural networks toreconstruct high-resolution data from low-resolution weathersimulations and observations datasets. They investigatesupervised machine learning using multiple deep convolutionalneural network architectures to test the limits of datareconstruction for various spatial and temporal resolutions, low-frequent and high-frequent input data, and the generalizationto numerical and observed data. Climate data produced by theCOSMO regional climate model at 2.2 km during 2 months of2008, and observations from Switzerland in 2004 at 1 km areused. While slowly-changing information, such as temperaturecan be adequately predicted through an error-predictingnetwork, these networks are far less suitable than deconvolutionalneural networks for the analysis of high-frequent fields likeprecipitation due to poor learning performance.AI/ML techniques also feature in Mitra who uses aprobabilistic graphical model, capable of a binary representation,to identify the spatial distribution in the simulated daily rainfallover the Indian landmass during monsoon in several CoupledModel Intercomparison Project Phase 6 (CMIP6) models, andcompares the patterns with those from the observed rainfallfor the 2000–2014 period. The study suggests that some of theCMIP6 models simulate the spatial distribution of monsoonrainfall to a reasonable degree, but most models underestimateextreme rainfall events, and are unable to reproduce thehomogeneity in rainfall across various regions of the landmass,as observed.CLIMATE PREDICTABILITY ANDPREDICTIONSLooking at the predictability of the climate system, Ikuyajoluet al. employ a computationally fast method to look at thepotential predictability of sea surface temperatures in the tropicalPacific and Indian Oceans during boreal fall. The predictabilityof the basins is controlled by two regularly varying non-linearoscillations, the El Niño-Southern Oscillation (ENSO) and theIndian Ocean Dipole (IOD). Using historical and RCP8.5 outputsfrom several CMIP5 models and reanalysis data, the authors donot find robust changes in predictability in future projections,despite the discrepancies between the models and the reanalysis.A brief investigation of the discrepancy in predictability in thebasin points to a poor representation of the ocean mean state andinter-basin connectivity at the Indonesian Throughflow.Also focussing on the predictability of climate variability,Sahai et al. present the newest version of the IndiaMeteorologicalDepartment multi-physics multi-model ensemble extendedrange prediction system.While this system includes older optionsof coupled climate forecast system version 2 and atmosphericglobal forecast system forced with real-time bias-corrected sea-surface temperature from NCEP coupled forecast system modelversion 2, unlike the predecessor, the horizontal resolutionis “seamless,” i.e., the model forecasts are generated at T574resolution till 15 days, after which, a coarser T382 resolutionis selected. In the newer version, model integrations areperformed six times in a month for real-time prediction. Analysisof the 15 year-hindcast generated demonstrate appreciableimprovements over its predecessor in predicting the large-scalelow variability signal and weekly mean rainfall up to 3 weekslead, and importantly, better performance at subdivisional scales,especially in the northwest and central parts of India.On longer time scales, Feba et al. highlight considerableadvancement in the multi-year prediction and show, for thefirst time, that decadal predictions from two general circulationmodels have significant prediction skills for the IOD for atleast 2 years and up to 8–10 years after initialization. Thisskill is present despite ENSO having a lead prediction skill ofonly 1 year. The source of this multi-year predictability liesin sub-surface signals that propagate from the Southern Oceaninto the Indian Ocean. Prediction skill for a prominent climatedriver like the IOD has wide-ranging benefits for climate scienceand society.Using the technique of event coincidence analysis,Weidermann et al. decipher differential imprints of the EastPacific (EP) and Central Pacific (CP)/Modoki flavors of ENSOson very low and very high seasonal precipitation patterns overdistinct regions across the globe. The authors find that EP periodsexhibit statistically significant event coincidence rates withhydrometeorological anomalies at larger spatial scales, whereassparser patterns emerge along with CP periods. The studydocuments, for the first time, distinct impacts such as increasedrainfall over Central Asia during CP periods. The authors arguefor a thorough distinction of El Niño and La Niña into their tworespective flavors for understanding the emergence of strongregional hydrometeorological anomalies and anticipating theirassociated ecological and socioeconomic impacts.CLIMATE PROJECTIONS ANDUNCERTAINTIESClouds are important for feedbacks in the climate system, yettheir representation leads to considerable uncertainty in modelprojections of climate change. Pathak et al. introduce an efficientuncertainty quantification and Bayesian inference for cloudparameters in order to assess the sensitivity of the outputsof the NCAR Single Column Atmosphere Model to variousFrontiers in Climate | www.frontiersin.org 2 December 2021 | Volume 3 | Article 811205Collins et al. Editorial: New Techniques in Climateparameterization schemes. The method involves using twosurrogate models that propagate uncertainty in test parametersto model outputs. Their exercise, for example, shows that ∼40–80% of the total variance of the climate variables can be attributedto auto-conversion size threshold for ice to snow, ∼15–30% tothe fall speed parameter for stratiform cloud ice, and so on. Thestudy is valuable in quantifying the source of uncertainties in themodel physics.Feedbacks between climate and the carbon cycle also representa major uncertainty in projections. Using a simple carbon cyclemodel, and driving emulators of the temperature responsesof 41 Coupled CMIP6 emulators with 127 different emissionscenarios for the 21st century, Rypdal et al. find almost aperfect linear relationship between maximum global surfaceair temperature and cumulative carbon emissions, allowingunambiguous estimates of Remaining Carbon Budget (RCB)for each CMIP6 model. The range of these estimates acrossthe model ensemble provides a measure of the uncertainty inthe RCB arising from the range in climate sensitivity over thisensemble. Rypdal et al. suggest that observational constraintsimposed on the transient climate response in the model ensemblecan reduce uncertainty in RCB estimate. They also show thatmain uncertainty of the transient climate response to cumulativecarbon emissions and the associated RCB is not due to the spreadof the emission scenarios, but rather the spread of sensitivitiesover the CMIP6 model ensemble.Uncertaintiesmay be reduced by the application of constraintson projections. The paper by Hegerl et al. discusses thechallenges in understanding the role of observations in skillestimates and constraints, and using them consistently inpredicting and projecting changes in European climate. Itdiscusses constraints across prediction and projection methods,their interpretation, and the metrics that drive them. These areillustrated in this paper by examples of state-of-the-art methodsfor predicting and projecting changes in European climate. Theauthors study how the skill estimates may vary over time forinitialized predictions with different phases of climate variabilityand climatic conditions, and are influenced by the presenceof external forcing. This, the authors state, complicates thesystematic use of observational constraints. They also suggestthat sub-selecting simulations from large ensembles based onreproduction of the observed evolution of climate variations isa good testbed for combining projections and predictions.Improving climate predictions and projections is such a bigsubject area within climate science that this Research Topiccould not cover all potential ways in which advances can bemade. Nevertheless, some interesting themes emerge. Blendingmodels with observations on climate time scales, and the useof Artificial Intelligence and Machine Learning techniques, areproposed routes for the creation of so-called “digital twins” ofthe climate system. Such tools are intended to be more powerfulthan our current models and can be better focussed on policyquestions. However, there is still much to be learnt about whatis predictable in the near term and how we can quantify andreduce uncertainties in projections of long-term climate change,these efforts are required even if efforts to limit the size of climatechange succeed, as there is already a requirement for societiesto adapt.AUTHOR CONTRIBUTIONSAll authors listed have made a substantial, direct, and intellectualcontribution to the work and approved it for publication.FUNDINGTLF was supported by the Swiss National Science Foundation(PP00P2_198897), GW was supported by the Centre forSouthern Hemisphere Oceans Research and the Climate SystemsHub of the Australian Government’s National EnvironmentalScience Program, and MC supported by NE/S004645/1.Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.Publisher’s Note: All claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliated organizations, or those ofthe publisher, the editors and the reviewers. Any product that may be evaluated inthis article, or claim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.Copyright © 2021 Collins, Ashok, Barreiro, Roxy, Kang, Frölicher, Wang andTedeschi. This is an open-access article distributed under the terms of the CreativeCommons Attribution License (CC BY). The use, distribution or reproduction inother forums is permitted, provided the original author(s) and the copyright owner(s)are credited and that the original publication in this journal is cited, in accordancewith accepted academic practice. No use, distribution or reproduction is permittedwhich does not comply with these terms.Frontiers in Climate | www.frontiersin.org 3 December 2021 | Volume 3 | Article 811205

Editorial: New Techniques for Improving Climate Models, Predictions and Projections

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Editorial: New Techniques for Improving Climate Models, Predictions and Projections

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