Articles | Volume 6, issue 2
17 Sep 2020
17 Sep 2020
A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series
Donald P. Cummins et al.
No articles found.
Ewan Woodley, Stewart Barr, Peter Stott, Pierrette Thomet, Sally Flint, Fiona Lovell, Evelyn O'Malley, Dan Plews, Chris Rapley, Celia Robbins, Rebecca Pearce, and Rebecca Sandover
Geosci. Commun. Discuss.,
Revised manuscript accepted for GCShort summary
This paper reports on research insights from a collaboration between UK climate scientists and artist researchers to advocate for a more creative and emotionally attentive approach to climate science engagement and advocacy. The paper highlights innovative ways in which climate change communication can be re-imagined through different art forms to enable complex concepts to become knowable, accessible and engaging to wider publics.
Laura C. Dawkins and David B. Stephenson
Nat. Hazards Earth Syst. Sci., 18, 2933–2949,Short summary
Natural hazard losses are sensitive to the dependency between extreme values of the hazard variable at different spatial locations. It is therefore important to correctly identify and quantify dependency to accurately model the hazard and its resulting losses. Through application to a large data set of windstorm hazard footprints, this study demonstrates how extreme-value methods can be used to explore extremal dependency and hazard losses in very high dimensional natural hazard data sets.
Keith J. Beven, Susana Almeida, Willy P. Aspinall, Paul D. Bates, Sarka Blazkova, Edoardo Borgomeo, Jim Freer, Katsuichiro Goda, Jim W. Hall, Jeremy C. Phillips, Michael Simpson, Paul J. Smith, David B. Stephenson, Thorsten Wagener, Matt Watson, and Kate L. Wilkins
Nat. Hazards Earth Syst. Sci., 18, 2741–2768,Short summary
This paper discusses how uncertainties resulting from lack of knowledge are considered in a number of different natural hazard areas including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. As every analysis is necessarily conditional on the assumptions made about the nature of sources of such uncertainties it is also important to follow the guidelines for good practice suggested in Part 2.
Laura C. Dawkins, David B. Stephenson, Julia F. Lockwood, and Paul E. Maisey
Nat. Hazards Earth Syst. Sci., 16, 1999–2007,Short summary
A decline in damaging European windstorms has led to a reduction in insured losses in the 21st century. This decline is explored through understanding how and why a damaging windstorm characteristic has changed in recent years. For individual windstorm events, the area of damaging winds is shown to have reduced due to a significant decrease in extreme winds in north-western Europe. This decline is largely related to changes in a large-scale atmospheric circulation pattern in the North Atlantic.
K. J. Beven, S. Almeida, W. P. Aspinall, P. D. Bates, S. Blazkova, E. Borgomeo, K. Goda, J. C. Phillips, M. Simpson, P. J. Smith, D. B. Stephenson, T. Wagener, M. Watson, and K. L. Wilkins
Nat. Hazards Earth Syst. Sci. Discuss.,
Preprint withdrawnShort summary
Uncertainties in natural hazard risk assessment are generally dominated by the sources arising from lack of knowledge or understanding of the processes involved. This is Part 2 of 2 papers reviewing these epistemic uncertainties and covers different areas of natural hazards including landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. It is based on the work of the UK CREDIBLE research consortium.
J. F. Roberts, A. J. Champion, L. C. Dawkins, K. I. Hodges, L. C. Shaffrey, D. B. Stephenson, M. A. Stringer, H. E. Thornton, and B. D. Youngman
Nat. Hazards Earth Syst. Sci., 14, 2487–2501,
Related subject area
Climate researchA multi-method framework for global real-time climate attributionAnalysis of the evolution of parametric drivers of high-end sea-level hazardsComparing climate time series – Part 3: Discriminant analysisSpatial heterogeneity in rain-bearing winds, seasonality and rainfall variability in southern Africa's winter rainfall zoneSpatial heterogeneity of 2015–2017 drought intensity in South Africa's winter rainfall zoneA statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea levelA machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5A protocol for probabilistic extreme event attribution analysesThe effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate modelsRobust regional clustering and modeling of nonstationary summer temperature extremes across GermanyPossible impacts of climate change on fog in the Arctic and subpolar North AtlanticApproaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation modelsComparison and assessment of large-scale surface temperature in climate model simulationsFuture climate emulations using quantile regressions on large ensemblesDownscaling probability of long heatwaves based on seasonal mean daily maximum temperaturesEstimates of climate system properties incorporating recent climate changeThe joint influence of break and noise variance on the break detection capability in time series homogenizationA space–time statistical climate model for hurricane intensification in the North Atlantic basinBuilding a traceable climate model hierarchy with multi-level emulators
Daniel M. Gilford, Andrew Pershing, Benjamin H. Strauss, Karsten Haustein, and Friederike E. L. Otto
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 135–154,Short summary
We developed a framework to produce global real-time estimates of how human-caused climate change affects the likelihood of daily weather events. A multi-method approach provides ensemble attribution estimates accompanied by confidence intervals, creating new opportunities for climate change communication. Methodological efficiency permits daily analysis using forecasts or observations. Applications with daily maximum temperature highlight the framework's capacity on daily and global scales.
Alana Hough and Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 117–134,Short summary
We use machine learning to assess how different geophysical uncertainties relate to the severity of future sea-level rise. We show how the contributions to coastal hazard from different sea-level processes evolve over time and find that near-term sea-level hazards are driven by thermal expansion and the melting of glaciers and ice caps, while long-term hazards are driven by ice loss from the major ice sheets.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 97–115,Short summary
A common problem in climate studies is to decide whether a climate model is realistic. Such decisions are not straightforward because the time series are serially correlated and multivariate. Part II derived a test for deciding wether a simulation is statistically distinguishable from observations. However, the test itself provides no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between stochastic processes.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 31–62,Short summary
Cape Town is situated in a small but ecologically and climatically highly diverse and vulnerable pocket of South Africa uniquely receiving its rain mostly in winter. We show complex structures in the spatial patterns of rainfall seasonality and year-to-year changes in rainfall within this domain, tied to spatial differences in the rain-bearing winds. This allows us to develop a new spatial subdivision of the region to help future studies distinguish spatially distinct climate change responses.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 63–81,Short summary
Day Zerowater crisis affecting Cape Town after the severe 2015–2017 drought motivated renewed research interest into causes and projections of rainfall variability and change in this water-stressed region. Unusually few wet months and very wet days characterised the Day Zero Drought. Its extent expanded as it shifted gradually north-eastward, concurrent with changes in the weather systems driving drought. Our results emphasise the need to consider the interplay between drought drivers.
Erica L. Ashe, Nicole S. Khan, Lauren T. Toth, Andrea Dutton, and Robert E. Kopp
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 1–29,Short summary
We develop a new technique to integrate realistic uncertainties in probabilistic models of past sea-level change. The new framework performs better than past methods (in precision, accuracy, bias, and model fit) because it enables the incorporation of previously unused data and exploits correlations in the data. This method has the potential to assess the validity of past estimates of extreme sea-level rise and highstands providing better context in which to place current sea-level change.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244,Short summary
Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203,Short summary
Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
Mark D. Risser and Michael F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 115–139,Short summary
Evaluation of modern high-resolution global climate models often does not account for the geographic location of the underlying weather station data. In this paper, we quantify the impact of geographic sampling on the relative performance of climate model representations of precipitation extremes over the United States. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance.
Meagan Carney and Holger Kantz
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 61–77,Short summary
Extremes in weather can have lasting effects on human health and resource consumption. Studying the recurrence of these events on a regional scale can improve response times and provide insight into a changing climate. We introduce a set of clustering tools that allow for regional clustering of weather recordings from stations across Germany. We use these clusters to form regional models of summer temperature extremes and find an increase in the mean from 1960 to 2018.
Richard E. Danielson, Minghong Zhang, and William A. Perrie
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 31–43,Short summary
Visibility is estimated for the 21st century using global and regional climate model output. A baseline decrease in visibility in the Arctic (10 %) is more notable than in the North Atlantic (< 5 %). We develop an adjustment that yields greater consistency among models and explore the justification of our ad hoc adjustment toward ship observations during the historical period. Baseline estimates are found to be sensitive to the representation of temperature and humidity.
Sophie C. Lewis, Sarah E. Perkins-Kirkpatrick, and Andrew D. King
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 133–146,Short summary
Extreme temperature and precipitation events in Australia have caused significant socio-economic and environmental impacts. Determining the factors contributing to these extremes is an active area of research. This paper describes a set of studies that have examined the causes of extreme climate events in recent years in Australia. Ideally, this review will be useful for the application of these extreme event attribution approaches to climate and weather extremes occurring elsewhere.
Raquel Barata, Raquel Prado, and Bruno Sansó
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 67–85,
Matz A. Haugen, Michael L. Stein, Ryan L. Sriver, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 37–55,Short summary
This work uses current temperature observations combined with climate models to project future temperature estimates, e.g., 100 years into the future. We accomplish this by modeling temperature as a smooth function of time both in the seasonal variation as well as in the annual trend. These smooth functions are estimated at multiple quantiles that are all projected into the future. We hope that this work can be used as a template for how other climate variables can be projected into the future.
Rasmus E. Benestad, Bob van Oort, Flavio Justino, Frode Stordal, Kajsa M. Parding, Abdelkader Mezghani, Helene B. Erlandsen, Jana Sillmann, and Milton E. Pereira-Flores
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 37–52,Short summary
A new study indicates that heatwaves in India will become more frequent and last longer with global warming. Its results were derived from a large number of global climate models, and the calculations differed from previous studies in the way they included advanced statistical theory. The projected changes in the Indian heatwaves will have a negative consequence for wheat crops in India.
Alex G. Libardoni, Chris E. Forest, Andrei P. Sokolov, and Erwan Monier
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 19–36,Short summary
We present new probabilistic estimates of model parameters in the MIT Earth System Model using more recent data and an updated method. Model output is compared to observed climate change to determine which sets of model parameters best simulate the past. In response to increasing surface temperatures and accelerated heat storage in the ocean, our estimates of climate sensitivity and ocean diffusivity are higher. Using a new interpolation algorithm results in smoother probability distributions.
Ralf Lindau and Victor Karel Christiaan Venema
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 1–18,Short summary
Climate data contain spurious breaks, e.g., by relocation of stations, which makes it difficult to infer the secular temperature trend. Homogenization algorithms use the difference time series of neighboring stations to detect and eliminate this spurious break signal. For low signal-to-noise ratios, i.e., large distances between stations, the correct break positions may not only remain undetected, but segmentations explaining mainly the noise can be erroneously assessed as significant and true.
Erik Fraza, James B. Elsner, and Thomas H. Jagger
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 105–114,Short summary
Climate influences on hurricane intensification are investigated by averaging hourly intensification rates over the period 1975–2014 in 8° by 8° latitude–longitude grid cells. The statistical effects of hurricane intensity, sea-surface temperature (SST), El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Madden–Julian Oscillation (MJO) are quantified. Intensity, SST, and NAO had a positive effect on intensification rates. The NAO effect should be further studied.
Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 17–37,Short summary
In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
Adler, A.: Pade: Padé Approximant Coefficients, available at: https://CRAN.R-project.org/package=Pade (last access: 9 August 2019), R package version 0.1-4, 2015. a
Aldrin, M., Holden, M., Guttorp, P., Skeie, R. B., Myhre, G., and Berntsen, T. K.: Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content, Environmetrics, 23, 253–271, https://doi.org/10.1002/env.2140, 2012. a
Álvarez, M., Luengo, D., and Lawrence, N. D.: Latent Force Models, in: Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, edited by: van Dyk, D. and Welling, M., vol. 5 of Proceedings of Machine Learning Research, pp. 9–16, PMLR, Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA, available at: http://proceedings.mlr.press/v5/alvarez09a.html (last access: 10 May 2020), 2009. a
Andrews, T. and Forster, P. M.: Energy budget constraints on historical radiative forcing, Nat. Clim. Change, 10, 313–316, 2020. a
Andrews, T., Andrews, M. B., Bodas-Salcedo, A., Jones, G. S., Kulhbrodt, T., Manners, J., Menary, M. B., Ridley, J., Ringer, M. A., Sellar, A. A., Senior, C. A., and Tang, Y.: Forcings, Feedbacks and Climate Sensitivity in HadGEM3-GC3.1 and UKESM1, J. Adv. Model. Earth Syst., https://doi.org/10.1029/2019MS001866, 2019 (data available at: https://github.com/timothyandrews/HadGEM3-ERF-Timeseries, last access: 10 December 2019). a, b, c, d, e
Cowtan, K. and Way, R. G.: Coverage Bias in the HadCRUT4 Temperature Series and its Impact on Recent Temperature Trends, Q. J. Roy. Meteor. Soc., 140, 1935–1944, https://doi.org/10.1002/qj.2297, 2014 (data available at: https://www-users.york.ac.uk/~kdc3/papers/coverage2013/had4_krig_annual_v2_0_0.txt, last access: 29 November 2019). a, b
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.: Evaluation of Climate Models, in: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013. a
Forster, P. M., Richardson, T., Maycock, A. C., Smith, C. J., Samset, B. H., Myhre, G., Andrews, T., Pincus, R., and Schulz, M.: Recommendations for Diagnosing Effective Radiative Forcing from Climate Models for CMIP6, J. Geophys. Res.-Atmos., 121, 12460–12475, https://doi.org/10.1002/2016JD025320, 2016. a, b
Geoffroy, O., Saint-Martin, D., Olivie, D. J. L., Voldoire, A., Bellon, G., and Tyteca, S.: Transient Climate Response in a Two-Layer Energy-Balance Model. Part I: Analytical Solution and Parameter Calibration Using CMIP5 AOGCM Experiments, J. Climate, 26, 1841–1857, https://doi.org/10.1175/jcli-d-12-00195.1, 2013. a, b
Hasselmann, K.: Stochastic Climate Models Part I. Theory, Tellus, 28, 473–485, 1976. a
Johansson, D. J., O’Neill, B. C., Tebaldi, C., and Häggström, O.: Equilibrium climate sensitivity in light of observations over the warming hiatus, Nat. Clim. Change, 5, 449–453, 2015. a
Kalman, R. E.: A New Approach to Linear Filtering and Prediction Problems, J. Basic. Eng.-T. ASME, 82, 35–45, 1960. a
Kaufmann, B.: Fitting a Sum of Exponentials to Numerical Data, ArXiv Physics e-prints, available at: https://arxiv.org/abs/physics/0305019 (last access: 21 March 2019), 2003. a
Ljungqvist, G. J. E.: Decomposing global warming using Bayesian statistics, PhD thesis, Chalmers University of Technology, Gothenburg, Sweden, 2015. a
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and Natural Radiative Forc-ing. In: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013. a, b
Richardson, M., Cowtan, K., Hawkins, E., and Stolpe, M. B.: Reconciled climate response estimates from climate models and the energy budget of Earth, Nat. Clim. Change, 6, 931–935, 2016. a
Särkkä, S., Álvarez, M. A., and Lawrence, N. D.: Gaussian process latent force models for learning and stochastic control of physical systems, IEEE T. Automat. Contr., 64, 2953–2960, 2018. a
signal developers: signal: Signal processing, available at: http://r-forge.r-project.org/projects/signal/ (last access: 9 August 2019), 2014. a
Smith, C. J., Kramer, R. J., Myhre, G., Forster, P. M., Soden, B. J., Andrews, T., Boucher, O., Faluvegi, G., Fläschner, D., Hodnebrog, Ø., Kasoar, M., Kharin, V., Kirkevåg, A., Lamarque, J.‐F., Mülmenstädt, J., Olivié, D., Richardson, T., Samset, B. H., Shindell, D., Stier, P., Takemura, T., Voulgarakis, A., and Watson‐Parris, D.: Understanding rapid adjustments to diverse forcing agents, Geophys. Res. Lett., 45, 12023–12031, https://doi.org/10.1029/2018GL079826, 2018. a
Stern, D. I.: A Three-Layer Atmosphere-Ocean Time Series Model of Global Climate Change, Rensselaer Working Papers in Economics 0510, Rensselaer Polytechnic Institute, Department of Economics, available at: https://www.researchgate.net/publication/24125133_A_Three-Layer_Atmosphere-Ocean_Time_Series_Model_of_Global_Climate_Change (last access: 14 September 2020), 2005. a, b
Urban, N. M. and Keller, K.: Probabilistic hindcasts and projections of the coupled climate, carbon cycle and Atlantic meridional overturning circulation system: a Bayesian fusion of century-scale observations with a simple model, Tellus A, 62, 737–750, https://doi.org/10.1111/j.1600-0870.2010.00471.x, 2010. a
Vial, J., Dufresne, J.-L., and Bony, S.: On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates, Clim. Dynam., 41, 3339–3362, 2013. a
Yu, D. and Chakravorty, S.: An autoregressive (AR) model based stochastic unknown input realization and filtering technique, in: 2015 American Control Conference (ACC), pp. 1499–1504, IEEE, 2015. a
We have developed a novel and fast statistical method for diagnosing effective radiative forcing (ERF), a measure of the net effect of greenhouse gas emissions on Earth's energy budget. Our method works by inverting a recursive digital filter energy balance representation of global climate models and has been successfully validated using simulated data from UK Met Office climate models. We have estimated time series of historical ERF by applying our method to the global temperature record.
We have developed a novel and fast statistical method for diagnosing effective radiative forcing...