Articles | Volume 6, issue 2
https://doi.org/10.5194/ascmo-6-91-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-6-91-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
17 Sep 2020
| 17 Sep 2020
A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series
Department of Mathematics, University of Exeter, Exeter, UK
David B. Stephenson
Department of Mathematics, University of Exeter, Exeter, UK
Peter A. Stott
Department of Mathematics, University of Exeter, Exeter, UK
Met Office Hadley Centre, Exeter, UK
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Matthew D. K. Priestley, David B. Stephenson, Adam A. Scaife, Daniel Bannister, Christopher J. T. Allen, and David Wilkie
Nat. Hazards Earth Syst. Sci., 23, 3845–3861, https://doi.org/10.5194/nhess-23-3845-2023, https://doi.org/10.5194/nhess-23-3845-2023, 2023
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This research presents a model for estimating extreme gusts associated with European windstorms. Using observed storm footprints we are able to calculate the return level of events at the 200-year return period. The largest gusts are found across NW Europe, and these are larger when the North Atlantic Oscillation is positive. Using theoretical future climate states we find that return levels are likely to increase across NW Europe to levels that are unprecedented compared to historical storms.
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., 5, 339–354, https://doi.org/10.5194/gc-5-339-2022, https://doi.org/10.5194/gc-5-339-2022, 2022
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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 reimagined through different art forms to enable complex concepts to become knowable, accessible, and engaging to the wider public.
Laura C. Dawkins and David B. Stephenson
Nat. Hazards Earth Syst. Sci., 18, 2933–2949, https://doi.org/10.5194/nhess-18-2933-2018, https://doi.org/10.5194/nhess-18-2933-2018, 2018
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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, https://doi.org/10.5194/nhess-18-2741-2018, https://doi.org/10.5194/nhess-18-2741-2018, 2018
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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, https://doi.org/10.5194/nhess-16-1999-2016, https://doi.org/10.5194/nhess-16-1999-2016, 2016
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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., https://doi.org/10.5194/nhess-2015-295, https://doi.org/10.5194/nhess-2015-295, 2016
Preprint withdrawn
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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, https://doi.org/10.5194/nhess-14-2487-2014, https://doi.org/10.5194/nhess-14-2487-2014, 2014
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Environmental sensitivity of the Caribbean economic growth rate
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Changes in the distribution of annual maximum temperatures in Europe
Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios
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Statistical reconstruction of European winter snowfall in reanalysis and climate models based on air temperature and total precipitation
A multi-method framework for global real-time climate attribution
Analysis of the evolution of parametric drivers of high-end sea-level hazards
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Spatial heterogeneity in rain-bearing winds, seasonality and rainfall variability in southern Africa's winter rainfall zone
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Possible impacts of climate change on fog in the Arctic and subpolar North Atlantic
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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
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Building a traceable climate model hierarchy with multi-level emulators
Chalachew Muluken Liyew, Elvira Di Nardo, Rosa Meo, and Stefano Ferraris
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 173–194, https://doi.org/10.5194/ascmo-10-173-2024, https://doi.org/10.5194/ascmo-10-173-2024, 2024
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Global warming is a big issue: it is necessary to know more details to make a forecast model and plan adaptation measures. Warming varies in space and time and models often average it over large areas. However, it shows great variations between months of the year. It also varies between regions of the world and between lowland and highland regions. This paper uses statistical and machine learning techniques to quantify such differences between Italy and the UK at different altitudes.
Friederike E. L. Otto, Clair Barnes, Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, and Robert Vautard
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 159–171, https://doi.org/10.5194/ascmo-10-159-2024, https://doi.org/10.5194/ascmo-10-159-2024, 2024
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To assess the role of climate change in individual weather events, different lines of evidence need to be combined in order to draw robust conclusions about whether observed changes can be attributed to anthropogenic climate change. Here we present a transparent method, developed over 8 years, to combine such lines of evidence in a single framework and draw conclusions about the overarching role of human-induced climate change in individual weather events.
Mark R. Jury
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 95–104, https://doi.org/10.5194/ascmo-10-95-2024, https://doi.org/10.5194/ascmo-10-95-2024, 2024
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A unique link is found between the Caribbean GDP growth rate and the tropical climate system. Although the Pacific El Niño–Southern Oscillation governs some aspects of this link, the Walker circulation and associated humidity over the equatorial Atlantic emerge as leading predictors of economic prosperity in the central Antilles islands.
Svenja Szemkus and Petra Friederichs
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This paper uses the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019), which we extend to the description of common extremes in two variables. We develop an extreme pattern index (EPI), a pattern-based aggregation to describe spatially extended weather extremes. Our results show that the EPI is suitable for describing heat waves. We extend the EPI to describe extremes in two variables and obtain an index to describe compound precipitation deficits and heat waves.
Graeme Auld, Gabriele C. Hegerl, and Ioannis Papastathopoulos
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 45–66, https://doi.org/10.5194/ascmo-9-45-2023, https://doi.org/10.5194/ascmo-9-45-2023, 2023
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In this paper we consider the problem of detecting changes in the distribution of the annual maximum temperature, during the years 1950–2018, across Europe.
We find that, on average, the temperature that would be expected to be exceeded
approximately once every 100 years in the 1950 climate is expected to be exceeded once every 6 years in the 2018 climate. This is of concern due to the devastating effects on humans and natural systems that are caused by extreme temperatures.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 29–44, https://doi.org/10.5194/ascmo-9-29-2023, https://doi.org/10.5194/ascmo-9-29-2023, 2023
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Climate model output has systematic errors which can be reduced with statistical methods. We review existing bias-adjustment methods for climate data and discuss their skills and issues. We define three demands for the method and then evaluate them using real and artificially created daily temperature and precipitation data for Austria to show how biases can also be introduced with bias-adjustment methods themselves.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 187–203, https://doi.org/10.5194/ascmo-8-187-2022, https://doi.org/10.5194/ascmo-8-187-2022, 2022
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Most climate time series contain annual and diurnal cycles. However, an objective criterion for deciding whether two time series have statistically equivalent annual and diurnal cycles is lacking, particularly if the residual variability is serially correlated. Such a criterion would be helpful in deciding whether a new version of a climate model better simulates such cycles. This paper derives an objective rule for such decisions based on a rigorous statistical framework.
Flavio Maria Emanuele Pons and Davide Faranda
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 155–186, https://doi.org/10.5194/ascmo-8-155-2022, https://doi.org/10.5194/ascmo-8-155-2022, 2022
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The objective motivating this study is the assessment of the impacts of winter climate extremes, which requires accurate simulation of snowfall. However, climate simulation models contain physical approximations, which result in biases that must be corrected using past data as a reference. We show how to exploit simulated temperature and precipitation to estimate snowfall from already bias-corrected variables, without requiring the elaboration of complex, multivariate bias adjustment techniques.
Daniel M. Gilford, Andrew Pershing, Benjamin H. Strauss, Karsten Haustein, and Friederike E. L. Otto
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 135–154, https://doi.org/10.5194/ascmo-8-135-2022, https://doi.org/10.5194/ascmo-8-135-2022, 2022
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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, https://doi.org/10.5194/ascmo-8-117-2022, https://doi.org/10.5194/ascmo-8-117-2022, 2022
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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, https://doi.org/10.5194/ascmo-8-97-2022, https://doi.org/10.5194/ascmo-8-97-2022, 2022
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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, https://doi.org/10.5194/ascmo-8-31-2022, https://doi.org/10.5194/ascmo-8-31-2022, 2022
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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
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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
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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
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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
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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
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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, https://doi.org/10.5194/ascmo-6-61-2020, https://doi.org/10.5194/ascmo-6-61-2020, 2020
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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.
We have developed a novel and fast statistical method for diagnosing effective radiative forcing...
