Articles | Volume 9, issue 1
https://doi.org/10.5194/ascmo-9-29-2023
© Author(s) 2023. 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-9-29-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
24 Apr 2023
| 24 Apr 2023
Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios
Institute of Meteorology and Climatology, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Gregor Mendel Straße 33, 1180 Vienna, Austria
Imran Nadeem
Institute of Meteorology and Climatology, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Gregor Mendel Straße 33, 1180 Vienna, Austria
Herbert Formayer
Institute of Meteorology and Climatology, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Gregor Mendel Straße 33, 1180 Vienna, Austria
Related authors
Vinzent Klaus, Johannes Laimighofer, and Fabian Lehner
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-224, https://doi.org/10.5194/nhess-2024-224, 2025
Preprint under review for NHESS
Short summary
Short summary
On 17 August 2024, a thunderstorm in Vienna led to a record-breaking rainfall of 110 mm in two hours. An analysis of the exceptionally long hourly rain gauge time series (since 1941) estimates this event's return period at 662 years. The extremity of the event is further confirmed by neighbouring weather stations and rain-radar data.
Philipp Maier, Fabian Lehner, Tatiana Klisho, and Herbert Formayer
EGUsphere, https://doi.org/10.5194/egusphere-2024-670, https://doi.org/10.5194/egusphere-2024-670, 2024
Preprint withdrawn
Short summary
Short summary
In this article, we analyse the ability of climate models to produce south foehn in the valleys of western Austria using a machine learning technique and assess, how well they capture different aspects of annual foehn occurrence. The performance of these models is largely influenced by the underlying global climate model. After weighting the models based on their performance, we found that foehn occurrence slightly decreases with global warming, but events tend to become more widespread.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-498, https://doi.org/10.5194/hess-2021-498, 2021
Manuscript not accepted for further review
Short summary
Short summary
Climate model output usually has systematic errors which can be reduced with statistical methods. We review existing bias adjustment methods for climate data and discuss their shortcomings. We define three demands for the method and combine two existing methods for our own approach. We then test it in comparison to two other methods using real and artificially created daily temperature and precipitation data for Austria. The results show that our method is able to meet all three demands.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-515, https://doi.org/10.5194/hess-2020-515, 2020
Manuscript not accepted for further review
Short summary
Short summary
We present an improved statistical bias correction method for climate models and put a focus on precipitation. Our method corrects model data so that it matches the observations in the historical period in a climatological sense better than other state-of-the-art methods while at the same time keeping long term trends in the future period unmodified. The corrected data can serve as a more accurate input for climate impact studies e.g. in hydrology or forestry.
Vinzent Klaus, Johannes Laimighofer, and Fabian Lehner
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-224, https://doi.org/10.5194/nhess-2024-224, 2025
Preprint under review for NHESS
Short summary
Short summary
On 17 August 2024, a thunderstorm in Vienna led to a record-breaking rainfall of 110 mm in two hours. An analysis of the exceptionally long hourly rain gauge time series (since 1941) estimates this event's return period at 662 years. The extremity of the event is further confirmed by neighbouring weather stations and rain-radar data.
Philipp Maier, Fabian Lehner, Tatiana Klisho, and Herbert Formayer
EGUsphere, https://doi.org/10.5194/egusphere-2024-670, https://doi.org/10.5194/egusphere-2024-670, 2024
Preprint withdrawn
Short summary
Short summary
In this article, we analyse the ability of climate models to produce south foehn in the valleys of western Austria using a machine learning technique and assess, how well they capture different aspects of annual foehn occurrence. The performance of these models is largely influenced by the underlying global climate model. After weighting the models based on their performance, we found that foehn occurrence slightly decreases with global warming, but events tend to become more widespread.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-498, https://doi.org/10.5194/hess-2021-498, 2021
Manuscript not accepted for further review
Short summary
Short summary
Climate model output usually has systematic errors which can be reduced with statistical methods. We review existing bias adjustment methods for climate data and discuss their shortcomings. We define three demands for the method and combine two existing methods for our own approach. We then test it in comparison to two other methods using real and artificially created daily temperature and precipitation data for Austria. The results show that our method is able to meet all three demands.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-515, https://doi.org/10.5194/hess-2020-515, 2020
Manuscript not accepted for further review
Short summary
Short summary
We present an improved statistical bias correction method for climate models and put a focus on precipitation. Our method corrects model data so that it matches the observations in the historical period in a climatological sense better than other state-of-the-art methods while at the same time keeping long term trends in the future period unmodified. The corrected data can serve as a more accurate input for climate impact studies e.g. in hydrology or forestry.
Heidelinde Trimmel, Philipp Weihs, David Leidinger, Herbert Formayer, Gerda Kalny, and Andreas Melcher
Hydrol. Earth Syst. Sci., 22, 437–461, https://doi.org/10.5194/hess-22-437-2018, https://doi.org/10.5194/hess-22-437-2018, 2018
Short summary
Short summary
In eastern Austria, where air temperature rise is double that recorded globally, stream temperatures of a human-impacted river were simulated during heat waves, as calculated by regional climate models until 2100. An increase of up to 3 °C was predicted – thus exceeding thresholds of resident cold-adapted species. Vegetation management scenarios showed that adding vegetation can reduce both absolute temperatures and its rate of increase but is not able to fully mitigate the expected rise.
Thomas Zieher, Martin Rutzinger, Barbara Schneider-Muntau, Frank Perzl, David Leidinger, Herbert Formayer, and Clemens Geitner
Nat. Hazards Earth Syst. Sci., 17, 971–992, https://doi.org/10.5194/nhess-17-971-2017, https://doi.org/10.5194/nhess-17-971-2017, 2017
Short summary
Short summary
At catchment scale, it is challenging to provide the required input parameters for physically based slope stability models. In the present study, the parameterization of such a model is optimized against observed shallow landslides during two triggering rainfall events. With the resulting set of parameters the model reproduces the location and the triggering timing of most observed landslides. Based on that, potential effects of increasing precipitation intensity on slope stability are assessed.
Related subject area
Climate research
Analysis of meteorological drought using satellite-based rainfall products over southern Ethiopia
Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales
Formally combining different lines of evidence in extreme-event attribution
Environmental sensitivity of the Caribbean economic growth rate
Spatial patterns and indices for heat waves and droughts over Europe using a decomposition of extremal dependency
Changes in the distribution of annual maximum temperatures in Europe
Comparing climate time series – Part 4: Annual cycles
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
Comparing climate time series – Part 3: Discriminant analysis
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
Tesfay Mekonnen Weldegerima and Tewelde Berihu Gebresilassie
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 59–71, https://doi.org/10.5194/ascmo-11-59-2025, https://doi.org/10.5194/ascmo-11-59-2025, 2025
Short summary
Short summary
Drought frequently affects southern Ethiopia, and inadequate rainfall data complicate monitoring. Satellite data from three sources were evaluated for accuracy, showing the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) to be the most reliable. We assessed drought severity using the standardized precipitation index, aligning results with historical drought patterns. The study emphasizes that the CHIRPS enhances drought monitoring and early-warning systems in the region.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 29–49, https://doi.org/10.5194/ascmo-10-29-2024, https://doi.org/10.5194/ascmo-10-29-2024, 2024
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
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, https://doi.org/10.5194/ascmo-8-117-2022, https://doi.org/10.5194/ascmo-8-117-2022, 2022
Short summary
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, https://doi.org/10.5194/ascmo-8-97-2022, https://doi.org/10.5194/ascmo-8-97-2022, 2022
Short summary
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, 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
Bao, Y. and Wen, X.: Projection of China’s near- and long-term climate in a
new high-resolution daily downscaled dataset NEX-GDDP, J.
Meteorol. Res., 31, 236–249, https://doi.org/10.1007/s13351-017-6106-6, 2017. a
Boberg, F. and Christensen, J.: Overestimation of Mediterranean summer
temperature projections due to model deficiencies, Nat. Clim. Change, 2,
433–436, https://doi.org/10.1038/nclimate1454, 2012. a
Boé, J., Terray, L., Habets, F., and Martin, E.: Statistical and dynamical
downscaling of the Seine basin climate for hydro-meteorological studies,
Int. J. Climatol., 27, 1643–1655, https://doi.org/10.1002/joc.1602,
cited By 264, 2007. a
Bürger, G., Schulla, J., and Werner, A.: Estimates of future flow, including
extremes, of the Columbia River headwaters, Water Resour. Res., 47, W10520,
https://doi.org/10.1029/2010WR009716, 2011. a, b
Bürger, G., Sobie, S., Cannon, A., Werner, A., and Murdock, T.: Downscaling
extremes: An intercomparison of multiple methods for future climate, J. Climate, 26, 3429–3449, https://doi.org/10.1175/JCLI-D-12-00249.1, 2013. a
Cannon, A.: Multivariate quantile mapping bias correction: an N-dimensional
probability density function transform for climate model simulations of
multiple variables, Clim. Dynam., 50, 31–49,
https://doi.org/10.1007/s00382-017-3580-6, 2018. a, b, c, d
Casanueva, A., Herrera, S., Iturbide, M., Lange, S., Jury, M., Dosio, A.,
Maraun, D., and Gutiérrez, J.: Testing bias adjustment methods for regional
climate change applications under observational uncertainty and resolution
mismatch, Atmos. Sci. Lett., 21, e978, https://doi.org/10.1002/asl.978, 2020. a, b, c
Charles, S. P., Chiew, F. H. S., Potter, N. J., Zheng, H., Fu, G., and Zhang, L.: Impact of downscaled rainfall biases on projected runoff changes, Hydrol. Earth Syst. Sci., 24, 2981–2997, https://doi.org/10.5194/hess-24-2981-2020, 2020. a, b, c, d
Déqué, M.: Frequency of precipitation and temperature extremes over France in
an anthropogenic scenario: Model results and statistical correction according
to observed values, Global Planet. Change, 57, 16–26,
https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007. a, b
Di Luca, A., de Elía, R., Bador, M., and Argüeso, D.: Contribution of mean
climate to hot temperature extremes for present and future climates, Weather
and Climate Extremes, 28, https://doi.org/10.1016/j.wace.2020.100255,
2020a. a
Di Luca, A., Pitman, A., and de Elía, R.: Decomposing Temperature Extremes
Errors in CMIP5 and CMIP6 Models, Geophys. Res. Lett., 47, e2020GL088031,
https://doi.org/10.1029/2020GL088031, 2020b. a
Doblas-Reyes, F. J., Sörensson, A. A., Almazroui, M., Dosio, A., Gutowski,
W. J., Haarsma, R., Hamdi, R., Hewitson, B., Kwon, W.-T., Lamptey, B. L.,
Maraun, D., Stephenson, T. S., Takayabu, I., Terray, L., Turner, A., and Zuo,
Z.: Chapter 10: Linking Global to Regional Climate Change, in: Climate Change
2021: The Physical Science Basis. Contribution of Working Group I to the
Sixth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan,
C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M.,
Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T.,
Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, 2021. a
Gobiet, A., Suklitsch, M., and Heinrich, G.: The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal, Hydrol. Earth Syst. Sci., 19, 4055–4066, https://doi.org/10.5194/hess-19-4055-2015, 2015. a
Grillakis, M., Koutroulis, A., and Tsanis, I.: Multisegment statistical bias
correction of daily GCM precipitation output, J. Geophys. Res.-Atmos., 118, 3150–3162, https://doi.org/10.1002/jgrd.50323, 2013. a, b
Grillakis, M. G., Koutroulis, A. G., Daliakopoulos, I. N., and Tsanis, I. K.: A method to preserve trends in quantile mapping bias correction of climate modeled temperature, Earth Syst. Dynam., 8, 889–900, https://doi.org/10.5194/esd-8-889-2017, 2017. a, b
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012. a, b, c
Gutiérrez, J., Maraun, D., Widmann, M., Huth, R., Hertig, E., Benestad, R.,
Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S., San Martín, D., Herrera,
S., Bedia, J., Casanueva, A., Manzanas, R., Iturbide, M., Vrac, M.,
Dubrovsky, M., Ribalaygua, J., Pórtoles, J., Räty, O., Räisänen, J.,
Hingray, B., Raynaud, D., Casado, M., Ramos, P., Zerenner, T., Turco, M.,
Bosshard, T., Štěpánek, P., Bartholy, J., Pongracz, R., Keller, D.,
Fischer, A., Cardoso, R., Soares, P., Czernecki, B., and Pagé, C.: An
intercomparison of a large ensemble of statistical downscaling methods over
Europe: Results from the VALUE perfect predictor cross-validation experiment,
Int. J. Climatol., 39, 3750–3785, https://doi.org/10.1002/joc.5462,
2019. a, b
Hagemann, S., Chen, C., Haerter, J., Heinke, J., Gerten, D., and Piani, C.:
Impact of a statistical bias correction on the projected hydrological changes
obtained from three GCMs and two hydrology models, J. Hydrometeorol., 12, 556–578, https://doi.org/10.1175/2011JHM1336.1, 2011. a
Hatchett, B., Koračin, D., Mejía, J., and Boyle, D.: Assimilating urban heat
island effects into climate projections, J. Arid Environ., 128,
59–64, https://doi.org/10.1016/j.jaridenv.2016.01.007, 2016. a
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013. a, b, c
Hiebl, J. and Frei, C.: Daily temperature grids for Austria since
1961 – concept, creation and applicability, Theor. Appl.
Climatol., 124, 161–178, https://doi.org/10.1007/s00704-015-1411-4, 2016. a
Hiebl, J. and Frei, C.: Daily precipitation grids for Austria since
1961 – development and evaluation of a spatial dataset for hydroclimatic
monitoring and modelling, Theor. Appl. Climatol., 132, 327–345,
https://doi.org/10.1007/s00704-017-2093-x, 2018. a
Hofstätter, M., Jacobeit, J., Homann, M., Lexer, A., Chimani, B., Philipp,
A., Beck, C., and Ganekind, M.: Wetrax: Auswirkungen des Klimawandels auf
großflächige Starkniederschläge in Mitteleuropa, Analyse der
Veränderungen von Zugbahnen und Großwetterlagen, Abschlussbericht
WEather Patterns, CycloneTRAcks and related precipitation EXtremes,
Geographica Augustana, ISSN 1862-8680, 2015. a
Horton, R., De Mel, M., Peters, D., Lesk, C., Bartlett, R., Helsingen, H.,
Bader, D., Capizzi, P., Martin, S., and Rosenzweig, C.: Assessing Climate
Risk in Myanmar: Technical Report, New York, NY, USA: Center for Climate
Systems Research at Columbia University, WWF-US and WWF-Myanmar, 2017. a
Jandl, R., Ledermann, T., Kindermann, G., Freudenschuss, A., Gschwantner, T.,
and Weiss, P.: Strategies for climate-smart forest management in Austria,
Forests, 9, 592, https://doi.org/10.3390/f9100592, 2018. a
Li, H., Sheffield, J., and Wood, E.: Bias correction of monthly precipitation
and temperature fields from Intergovernmental Panel on Climate Change AR4
models using equidistant quantile matching, J. Geophys. Res.-Atmos., 115, D10101, https://doi.org/10.1029/2009JD012882, 2010. a, b, c, d
Maraun, D.: Bias correction, quantile mapping, and downscaling: Revisiting the
inflation issue, J. Climate, 26, 2137–2143,
https://doi.org/10.1175/JCLI-D-12-00821.1, 2013. a
Maraun, D.: Bias Correcting Climate Change Simulations – a Critical Review,
Current Climate Change Reports, 2, 211–220, https://doi.org/10.1007/s40641-016-0050-x,
2016. a, b, c, d
Maraun, D., Shepherd, T., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J.,
Hagemann, S., Richter, I., Soares, P., Hall, A., and Mearns, L.: Towards
process-informed bias correction of climate change simulations, Nat.
Clim. Change, 7, 764–773, https://doi.org/10.1038/nclimate3418, 2017. a
Maraun, D., Huth, R., Gutiérrez, J., Martín, D., Dubrovsky, M., Fischer, A.,
Hertig, E., Soares, P., Bartholy, J., Pongrácz, R., Widmann, M., Casado, M.,
Ramos, P., and Bedia, J.: The VALUE perfect predictor experiment: Evaluation
of temporal variability, Int. J. Climatol., 39,
3786–3818, https://doi.org/10.1002/joc.5222, 2019. a, b, c, d
Maraun, D., Truhetz, H., and Schaffer, A.: Regional Climate Model Biases, Their
Dependence on Synoptic Circulation Biases and the Potential for Bias
Adjustment: A Process-Oriented Evaluation of the Austrian Regional Climate
Projections, J. Geophys. Res.-Atmos., 126, e2020JD032824,
https://doi.org/10.1029/2020JD032824, 2021. a, b
Maurer, E. P. and Pierce, D. W.: Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean, Hydrol. Earth Syst. Sci., 18, 915–925, https://doi.org/10.5194/hess-18-915-2014, 2014. a
Mearns, L., Bukovsky, M., Leung, R., Qian, Y., Arritt, R., Gutowowski, W.,
Takle, E., Biner, S., Caya, D., Correia Jr., J., Jones, R., Sloloan, L., and
Snyder, M.: Reply to “Comments on `the North American regional climate change
assessment program: overview of phase I results”', B. Am.
Meteorol. Soc., 94, 1077–1078, https://doi.org/10.1175/BAMS-D-13-00013.1,
2013. a
Mehrotra, R. and Sharma, A.: A multivariate quantile-matching bias correction
approach with auto- and cross-dependence across multiple time scales:
implications for downscaling, J. Climate, 29, 3519–3539,
https://doi.org/10.1175/JCLI-D-15-0356.1, 2016. a, b, c
Mehrotra, R. and Sharma, A.: A Resampling Approach for Correcting Systematic
Spatiotemporal Biases for Multiple Variables in a Changing Climate, Water Resour. Res., 55, 754–770, https://doi.org/10.1029/2018WR023270, 2019. a, b
Mehrotra, R., Johnson, F., and Sharma, A.: A software toolkit for correcting
systematic biases in climate model simulations, Environ. Model.
Softw., 104, 130–152, https://doi.org/10.1016/j.envsoft.2018.02.010, 2018. a, b, c
Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A., and
Ramirez-Villegas, J.: High-resolution and bias-corrected CMIP5 projections
for climate change impact assessments, Sci. Data, 7, 7,
https://doi.org/10.1038/s41597-019-0343-8, 2020. a
Nguyen, H., Mehrotra, R., and Sharma, A.: Correcting for systematic biases in
GCM simulations in the frequency domain, J. Hydrol., 538, 117–126,
https://doi.org/10.1016/j.jhydrol.2016.04.018, 2016. a, b
Nguyen, H., Mehrotra, R., and Sharma, A.: Can the variability in precipitation
simulations across GCMs be reduced through sensible bias correction?, Clim.
Dynam., 49, 3257–3275, https://doi.org/10.1007/s00382-016-3510-z, 2017. a, b
Pastén-Zapata, E., Jones, J., Moggridge, H., and Widmann, M.: Evaluation of
the performance of Euro-CORDEX Regional Climate Models for assessing
hydrological climate change impacts in Great Britain: A comparison of
different spatial resolutions and quantile mapping bias correction methods,
J. Hydrol., 584, 124653, https://doi.org/10.1016/j.jhydrol.2020.124653, 2020. a, b
Piani, C., Haerter, J., and Coppola, E.: Statistical bias correction for daily
precipitation in regional climate models over Europe, Theor. Appl.
Climatol., 99, 187–192, https://doi.org/10.1007/s00704-009-0134-9, 2010. a, b, c, d
Potter, N. J., Chiew, F. H. S., Charles, S. P., Fu, G., Zheng, H., and Zhang, L.: Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections, Hydrol. Earth Syst. Sci., 24, 2963–2979, https://doi.org/10.5194/hess-24-2963-2020, 2020. a, b, c
Seaby, L., Refsgaard, J., Sonnenborg, T., Stisen, S., Christensen, J., and
Jensen, K.: Assessment of robustness and significance of climate change
signals for an ensemble of distribution-based scaled climate projections,
J. Hydrol., 486, 479–493, https://doi.org/10.1016/j.jhydrol.2013.02.015,
2013. a
Seidl, R., Albrich, K., Erb, K., Formayer, H., Leidinger, D., Leitinger, G.,
Tappeiner, U., Tasser, E., and Rammer, W.: What drives the future supply of
regulating ecosystem services in a mountain forest landscape?, Forest Ecol. Manage., 445, 37–47, https://doi.org/10.1016/j.foreco.2019.03.047, 2019. a
Sillmann, J., Kharin, V., Zhang, X., Zwiers, F., and Bronaugh, D.: Climate
extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation
in the present climate, J. Geophys. Res.-Atmos., 118,
1716–1733, https://doi.org/10.1002/jgrd.50203, 2013. a
Smith, A., Freer, J., Bates, P., and Sampson, C.: Comparing ensemble
projections of flooding against flood estimation by continuous simulation,
J. Hydrol., 511, 205–219, https://doi.org/10.1016/j.jhydrol.2014.01.045,
2014. a
Stauffer, R., Mayr, G., Messner, J., Umlauf, N., and Zeileis, A.:
Spatio-temporal precipitation climatology over complex terrain using a
censored additive regression model, Int. J. Climatol., 37,
3264–3275, https://doi.org/10.1002/joc.4913, 2017. a
Switanek, M. B., Troch, P. A., Castro, C. L., Leuprecht, A., Chang, H.-I., Mukherjee, R., and Demaria, E. M. C.: Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes, Hydrol. Earth Syst. Sci., 21, 2649–2666, https://doi.org/10.5194/hess-21-2649-2017, 2017. a, b, c, d, e, f, g, h, i, j, k
Teng, J., Potter, N. J., Chiew, F. H. S., Zhang, L., Wang, B., Vaze, J., and Evans, J. P.: How does bias correction of regional climate model precipitation affect modelled runoff?, Hydrol. Earth Syst. Sci., 19, 711–728, https://doi.org/10.5194/hess-19-711-2015, 2015. a, b
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model
simulations for hydrological climate-change impact studies: Review and
evaluation of different methods, J. Hydrol., 456-457, 12–29,
https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012. a
Themeßl, M., Gobiet, A., and Heinrich, G.: Empirical-statistical downscaling
and error correction of regional climate models and its impact on the climate
change signal, Clim. Change, 112, 449–468,
https://doi.org/10.1007/s10584-011-0224-4, 2012. a, b
Thom, H. C.: A note on the gamma distribution, Mon. Weather Rev., 86,
117–122, 1958. a
Thrasher, B., Maurer, E. P., McKellar, C., and Duffy, P. B.: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping, Hydrol. Earth Syst. Sci., 16, 3309–3314, https://doi.org/10.5194/hess-16-3309-2012, 2012. a
Unterberger, C., Brunner, L., Nabernegg, S., Steininger, K., Steiner, A.,
Stabentheiner, E., Monschein, S., and Truhetz, H.: Spring frost risk for
regional apple production under a warmer climate, PLoS ONE, 13, 1–18,
https://doi.org/10.1371/journal.pone.0200201, 2018. a
Vlček, O. and Huth, R.: Is daily precipitation Gamma-distributed?. Adverse
effects of an incorrect use of the Kolmogorov-Smirnov test, Atmos.
Res., 93, 759–766, https://doi.org/10.1016/j.atmosres.2009.03.005, 2009.
a, b
Volosciuk, C., Maraun, D., Vrac, M., and Widmann, M.: A combined statistical bias correction and stochastic downscaling method for precipitation, Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, 2017. a, b
Vrac, M., Noël, T., and Vautard, R.: Bias correction of precipitation through
singularity stochastic removal: Because occurrences matter, J.
Geophys. Res., 121, 5237–5258, https://doi.org/10.1002/2015JD024511, 2016. a, b
Widmann, M., Bretherton, C., and Salathé Jr., E.: Statistical precipitation
downscaling over the northwestern united states using numerically simulated
precipitation as a predictor, J. Climate, 16, 799–816,
https://doi.org/10.1175/1520-0442(2003)016<0799:SPDOTN>2.0.CO;2, 2003. a
Widmann, M., Bedia, J., Gutiérrez, J., Bosshard, T., Hertig, E., Maraun, D.,
Casado, M., Ramos, P., Cardoso, R., Soares, P., Ribalaygua, J., Pagé, C.,
Fischer, A., Herrera, S., and Huth, R.: Validation of spatial variability in
downscaling results from the VALUE perfect predictor experiment,
Int. J. Climatol., 39, 3819–3845, https://doi.org/10.1002/joc.6024,
2019. a, b, c, d
Wiens, D. P., Cheng, J., and Beaulieu, N. C.: A class of method of moments
estimators for the two-parameter gamma family, Pak. J.
Stat., 19, 129–141, 2003. a
Yang, W., Andréasson, J., Graham, L., Olsson, J., Rosberg, J., and Wetterhall,
F.: Distribution-based scaling to improve usability of regional climate model
projections for hydrological climate change impacts studies, Hydrol.
Res., 41, 211–229, https://doi.org/10.2166/nh.2010.004, 2010. a
Short summary
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.
Climate model output has systematic errors which can be reduced with statistical methods. We...
