Articles | Volume 8, issue 1
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 97–115, 2022
https://doi.org/10.5194/ascmo-8-97-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 97–115, 2022
https://doi.org/10.5194/ascmo-8-97-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
16 May 2022
16 May 2022
Comparing climate time series – Part 3: Discriminant analysis
Timothy DelSole and Michael K. Tippett
Related authors
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 73–85, https://doi.org/10.5194/ascmo-7-73-2021, https://doi.org/10.5194/ascmo-7-73-2021, 2021
Short summary
Short summary
After a new climate model is constructed, a natural question is whether it generates realistic simulations. Here,
realisticdoes not mean that the detailed patterns on a particular day are correct, but rather that the statistics over many years are realistic. Past approaches to answering this question often neglect correlations in space and time. This paper proposes a method for answering this question that accounts for correlations in space and time.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175, https://doi.org/10.5194/ascmo-6-159-2020, https://doi.org/10.5194/ascmo-6-159-2020, 2020
Short summary
Short summary
Scientists often are confronted with the question of whether two time series are statistically distinguishable. This paper proposes a test for answering this question. The basic idea is to fit each time series to a time series model and then test whether the parameters in that model are equal. If a difference is detected, then new ways of visualizing those differences are proposed, including a clustering technique and a method based on optimal initial conditions.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 73–85, https://doi.org/10.5194/ascmo-7-73-2021, https://doi.org/10.5194/ascmo-7-73-2021, 2021
Short summary
Short summary
After a new climate model is constructed, a natural question is whether it generates realistic simulations. Here,
realisticdoes not mean that the detailed patterns on a particular day are correct, but rather that the statistics over many years are realistic. Past approaches to answering this question often neglect correlations in space and time. This paper proposes a method for answering this question that accounts for correlations in space and time.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175, https://doi.org/10.5194/ascmo-6-159-2020, https://doi.org/10.5194/ascmo-6-159-2020, 2020
Short summary
Short summary
Scientists often are confronted with the question of whether two time series are statistically distinguishable. This paper proposes a test for answering this question. The basic idea is to fit each time series to a time series model and then test whether the parameters in that model are equal. If a difference is detected, then new ways of visualizing those differences are proposed, including a clustering technique and a method based on optimal initial conditions.
Related subject area
Climate research
Spatial heterogeneity in rain-bearing winds, seasonality and rainfall variability in southern Africa's winter rainfall zone
Spatial heterogeneity of 2015–2017 drought intensity in South Africa's winter rainfall zone
A statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea level
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
A protocol for probabilistic extreme event attribution analyses
The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models
A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series
Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany
Possible impacts of climate change on fog in the Arctic and subpolar North Atlantic
Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models
Comparison and assessment of large-scale surface temperature in climate model simulations
Future climate emulations using quantile regressions on large ensembles
Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures
Estimates of climate system properties incorporating recent climate change
The joint influence of break and noise variance on the break detection capability in time series homogenization
A space–time statistical climate model for hurricane intensification in the North Atlantic basin
Building a traceable climate model hierarchy with multi-level emulators
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 31–62, https://doi.org/10.5194/ascmo-8-31-2022, https://doi.org/10.5194/ascmo-8-31-2022, 2022
Short summary
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, https://doi.org/10.5194/ascmo-8-63-2022, https://doi.org/10.5194/ascmo-8-63-2022, 2022
Short summary
Short summary
The
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, https://doi.org/10.5194/ascmo-8-1-2022, https://doi.org/10.5194/ascmo-8-1-2022, 2022
Short summary
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, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
Short summary
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, https://doi.org/10.5194/ascmo-6-177-2020, https://doi.org/10.5194/ascmo-6-177-2020, 2020
Short summary
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, https://doi.org/10.5194/ascmo-6-115-2020, https://doi.org/10.5194/ascmo-6-115-2020, 2020
Short summary
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.
Donald P. Cummins, David B. Stephenson, and Peter A. Stott
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 91–102, https://doi.org/10.5194/ascmo-6-91-2020, https://doi.org/10.5194/ascmo-6-91-2020, 2020
Short summary
Short summary
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.
Meagan Carney and Holger Kantz
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 61–77, https://doi.org/10.5194/ascmo-6-61-2020, https://doi.org/10.5194/ascmo-6-61-2020, 2020
Short summary
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, https://doi.org/10.5194/ascmo-6-31-2020, https://doi.org/10.5194/ascmo-6-31-2020, 2020
Short summary
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, https://doi.org/10.5194/ascmo-5-133-2019, https://doi.org/10.5194/ascmo-5-133-2019, 2019
Short summary
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, https://doi.org/10.5194/ascmo-5-67-2019, https://doi.org/10.5194/ascmo-5-67-2019, 2019
Matz A. Haugen, Michael L. Stein, Ryan L. Sriver, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 37–55, https://doi.org/10.5194/ascmo-5-37-2019, https://doi.org/10.5194/ascmo-5-37-2019, 2019
Short summary
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, https://doi.org/10.5194/ascmo-4-37-2018, https://doi.org/10.5194/ascmo-4-37-2018, 2018
Short summary
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, https://doi.org/10.5194/ascmo-4-19-2018, https://doi.org/10.5194/ascmo-4-19-2018, 2018
Short summary
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, https://doi.org/10.5194/ascmo-4-1-2018, https://doi.org/10.5194/ascmo-4-1-2018, 2018
Short summary
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, https://doi.org/10.5194/ascmo-2-105-2016, https://doi.org/10.5194/ascmo-2-105-2016, 2016
Short summary
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, https://doi.org/10.5194/ascmo-2-17-2016, https://doi.org/10.5194/ascmo-2-17-2016, 2016
Short summary
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.
Cited articles
Abdi, H. and Williams, L. J.: Principal component analysis, WIREs Comput.
Stat., 2, 433–459, https://doi.org/10.1002/wics.101, 2010. a, b
Alexander, M. A., Matrosova, L., Penland, C., Scott, J. D., and Chang, P.:
Forecasting Pacific SSTs: Linear Inverse Model Predictions of the
PDO, J. Clim., 21, 385–402, https://doi.org/10.1175/2007JCLI1849.1,
2008. a
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C.: Time Series Analysis:
Forecasting and Control, Wiley-Interscience, 4th Edn., 746 pp., 2008. a
CMIP5: CLIVAR Exchanges – Special Issue: WCRP Coupled
Model Intercomparison Project – Phase 5 – CMIP5, Project Report 56, CMIP5 [data set], https://eprints.soton.ac.uk/194679/ (last access: 7 May 2020), 2011. a
DelSole, T.: Stochastic models of quasigeostrophic turbulence, Surv.
Geophys., 25, 107–149, 2004. a
DelSole, T.: diff.var.test.R, GitHub [code], https://github.com/tdelsole/Comparing-Time-Series, last access: 17 January 2022. a
DelSole, T. and Tippett, M. K.: Comparing climate time series – Part 1:
Univariate test, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175,
https://doi.org/10.5194/ascmo-6-159-2020, 2020. a
DelSole, T. and Tippett, M.: Statistical Methods for Climate Scientists,
Cambridge University Press, Cambridge, 526 pp., 2022. a
DelSole, T. and Tippett, M. K.: Comparing climate time series – Part 2:
Multivariate test, Adv. Stat. Clim. Meteorol. Oceanogr., 7, 73–85,
https://doi.org/10.5194/ascmo-7-73-2021, 2021a. a
DelSole, T. and Tippett, M. K.: A mutual information criterion with
applications to canonical correlation analysis and graphical models, Stat,
10, e385, https://doi.org/10.1002/sta4.385, 2021b. a
Dias, D. F., Subramanian, A., Zanna, L., and Miller, A. J.: Remote and local
influences in forecasting Pacific SST: a linear inverse model and a
multimodel ensemble study, Clim. Dynam., 52, 1–19, 2018. a
Farrell, B. F. and Ioannou, P. J.: Stochastic dynamics of the midlatitude
atmospheric jet, J. Atmos. Sci., 52, 1642–1656, 1995. a
Flury, B. K.: Proportionality of k covariance matrices, Stat.
Prob. Lett., 4, 29–33,
https://doi.org/10.1016/0167-7152(86)90035-0, 1986. a
Fujikoshi, Y., Ulyanov, V. V., and Shimizu, R.: Multivariate Statistics:
High-dimensional and Large-Sample Approximations, John Wiley & Sons, 533 pp.,
2010. a
Gottwald, G. A., Crommelin, D. T., and Franzke, C. L. E.: Stochastic Climate
Theory, in: Nonlinear and Stochastic Climate Dynamics, edited by: Franzke, C.
L. E. and O'Kane, T. J., 209–240, Cambridge University Press,
https://doi.org/10.1017/9781316339251.009, 2017. a
Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore,
J. H., Menne, M. J., Smith, T. M., Vose, R. S., and Zhang, H.-M.: Extended
Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades,
Validations, and Intercomparisons, J. Clim., 30, 8179–8205,
https://doi.org/10.1175/JCLI-D-16-0836.1, 2017. a
Huddart, B., Subramanian, A., Zanna, L., and Palmer, T.: Seasonal and decadal
forecasts of Atlantic Sea surface temperatures using a linear inverse
model, Clim. Dynam., 49, 1833–1845, https://doi.org/10.1007/s00382-016-3375-1, 2016. a
Kim, S.-H. and Cohen, A. S.: On the Behrens-Fisher Problem: A Review, J. Educat. Behav. Stat., 23, 356–377,
https://doi.org/10.3102/10769986023004356, 1998. a
Majda, A., Timofeyev, J., and Vanden-Eijnden, E.: Stochastic models for
selected slow variables in large deterministic systems, Nonlinearity, 19,
769–794, 2006. a
Mardia, K. V., Kent, J. T., and Bibby, J. M.: Multivariate Analysis, Academic
Press, 418 pp., 1979. a
Newman, M.: An Empirical benchmark for decadal forecasts of global surface
temperature anomalies, J. Clim., 26, 5260–5269, 2013. a
Newman, M. and Sardeshmukh, P. D.: Are we near the predictability limit of
tropical Indo-Pacific sea surface temperatures?, Geophys. Res.
Lett., 44, 8520–8529, https://doi.org/10.1002/2017GL074088, 2017. a
Noble, B. and Daniel, J. W.: Applied Linear Algebra, Prentice-Hall, 3rd Edn., 521 pp.,
1988. a
Penland, C.: Random forcing and forecasting using principal oscillation pattern
analysis, Mon. Weather Rev., 117, 2165–2185, 1989. a
Scheffé, H.: Practical Solutions of the Behrens-Fisher Problem, J. Am. Stat. Assoc., 65, 1501–1508,
https://doi.org/10.1080/01621459.1970.10481179, 1970. a
Shin, S.-I. and Newman, M.: Seasonal predictability of global and North American coastal sea surface temperature and height anomalies, Geophys. Res. Lett., 48, e2020GL091886, https://doi.org/10.1029/2020GL091886, 2021. a, b
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experimental Design, Bull. Am. Meteorol. Soc., 93, 485–498, 2012. a
Vimont, D. J.: Analysis of the Atlantic Meridional Mode Using Linear Inverse
Modeling: Seasonality and Regional Influences, J. Clim., 25, 1194–1212, https://doi.org/10.1175/JCLI-D-11-00012.1, 2012. a
Wills, R. C., Schneider, T., Wallace, J. M., Battisti, D. S., and Hartmann,
D. L.: Disentangling Global Warming, Multidecadal Variability, and El
Niño in Pacific Temperatures, Geophys. Res. Lett., 45, 2487–2496,
https://doi.org/10.1002/2017GL076327, 2018. a, b
Zanna, L.: Forecast skill and predictability of observed North Atlantic sea
surface temperatures, J. Clim., 25, 5047–5056, 2012. a
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.
A common problem in climate studies is to decide whether a climate model is realistic. Such...
