Articles | Volume 5, issue 1
https://doi.org/10.5194/ascmo-5-37-2019
© Author(s) 2019. 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-5-37-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
10 Apr 2019
| 10 Apr 2019
Future climate emulations using quantile regressions on large ensembles
Matz A. Haugen
CORRESPONDING AUTHOR
Department of Statistics, University of Chicago, 5747 S. Ellis Ave, 60637 Chicago, IL, USA
Michael L. Stein
Department of Statistics, University of Chicago, 5747 S. Ellis Ave, 60637 Chicago, IL, USA
Ryan L. Sriver
Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Elisabeth J. Moyer
Department of Statistics, University of Chicago, 5747 S. Ellis Ave, 60637 Chicago, IL, USA
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Paul Konopka, Christian Rolf, Marc von Hobe, Sergey M. Khaykin, Benjamin Clouser, Elisabeth Moyer, Fabrizio Ravegnani, Francesco D'Amato, Silvia Viciani, Nicole Spelten, Armin Afchine, Martina Krämer, Fred Stroh, and Felix Ploeger
Atmos. Chem. Phys., 23, 12935–12947, https://doi.org/10.5194/acp-23-12935-2023, https://doi.org/10.5194/acp-23-12935-2023, 2023
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We studied water vapor in a critical region of the atmosphere, the Asian summer monsoon anticyclone, using rare in situ observations. Our study shows that extremely high water vapor values observed in the stratosphere within the Asian monsoon anticyclone still undergo significant freeze-drying and that water vapor concentrations set by the Lagrangian dry point are a better proxy for the stratospheric water vapor budget than rare observations of enhanced water mixing ratios.
Kara D. Lamb, Jerry Y. Harrington, Benjamin W. Clouser, Elisabeth J. Moyer, Laszlo Sarkozy, Volker Ebert, Ottmar Möhler, and Harald Saathoff
Atmos. Chem. Phys., 23, 6043–6064, https://doi.org/10.5194/acp-23-6043-2023, https://doi.org/10.5194/acp-23-6043-2023, 2023
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This study investigates how ice grows directly from vapor in cirrus clouds by comparing observations of populations of ice crystals growing in a cloud chamber against models developed in the context of single-crystal laboratory studies. We demonstrate that previous discrepancies between different experimental measurements do not necessarily point to different physical interpretations but are rather due to assumptions that were made in terms of how experiments were modeled in previous studies.
Clare E. Singer, Benjamin W. Clouser, Sergey M. Khaykin, Martina Krämer, Francesco Cairo, Thomas Peter, Alexey Lykov, Christian Rolf, Nicole Spelten, Armin Afchine, Simone Brunamonti, and Elisabeth J. Moyer
Atmos. Meas. Tech., 15, 4767–4783, https://doi.org/10.5194/amt-15-4767-2022, https://doi.org/10.5194/amt-15-4767-2022, 2022
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In situ measurements of water vapor in the upper troposphere are necessary to study cloud formation and hydration of the stratosphere but challenging due to cold–dry conditions. We compare measurements from three water vapor instruments from the StratoClim campaign in 2017. In clear sky (clouds), point-by-point differences were <1.5±8 % (<1±8 %). This excellent agreement allows detection of fine-scale structures required to understand the impact of convection on stratospheric water vapor.
Sergey M. Khaykin, Elizabeth Moyer, Martina Krämer, Benjamin Clouser, Silvia Bucci, Bernard Legras, Alexey Lykov, Armin Afchine, Francesco Cairo, Ivan Formanyuk, Valentin Mitev, Renaud Matthey, Christian Rolf, Clare E. Singer, Nicole Spelten, Vasiliy Volkov, Vladimir Yushkov, and Fred Stroh
Atmos. Chem. Phys., 22, 3169–3189, https://doi.org/10.5194/acp-22-3169-2022, https://doi.org/10.5194/acp-22-3169-2022, 2022
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The Asian monsoon anticyclone is the key contributor to the global annual maximum in lower stratospheric water vapour. We investigate the impact of deep convection on the lower stratospheric water using a unique set of observations aboard the high-altitude M55-Geophysica aircraft deployed in Nepal in summer 2017 within the EU StratoClim project. We find that convective plumes of wet air can persist within the Asian anticyclone for weeks, thereby enhancing the occurrence of high-level clouds.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Abigail Snyder, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Karina Williams, Ziwei Wang, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 3995–4018, https://doi.org/10.5194/gmd-13-3995-2020, https://doi.org/10.5194/gmd-13-3995-2020, 2020
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Improving our understanding of the impacts of climate change on crop yields will be critical for global food security in the next century. The models often used to study the how climate change may impact agriculture are complex and costly to run. In this work, we describe a set of global crop model emulators (simplified models) developed under the Agricultural Model Intercomparison Project. Crop model emulators make agricultural simulations more accessible to policy or decision makers.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Juraj Balkovic, Philippe Ciais, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, Munir Hoffmann, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Nikolay Khabarov, Marian Koch, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Xuhui Wang, Karina Williams, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 2315–2336, https://doi.org/10.5194/gmd-13-2315-2020, https://doi.org/10.5194/gmd-13-2315-2020, 2020
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Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Crop models, which represent plant biology, are necessary tools for this purpose since they allow representing future climate, farmer choices, and new agricultural geographies. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, under the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to evaluate and improve crop models.
Benjamin W. Clouser, Kara D. Lamb, Laszlo C. Sarkozy, Jan Habig, Volker Ebert, Harald Saathoff, Ottmar Möhler, and Elisabeth J. Moyer
Atmos. Chem. Phys., 20, 1089–1103, https://doi.org/10.5194/acp-20-1089-2020, https://doi.org/10.5194/acp-20-1089-2020, 2020
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Previous measurements of water vapor in the upper troposphere and lower stratosphere (UT/LS) have shown unexpectedly high concentrations of water vapor in ice clouds, which may be due to an incomplete understanding of the structure of ice and the behavior of ice growth in this part of the atmosphere. Water vapor measurements during the 2013 IsoCloud campaign at the AIDA cloud chamber show no evidence of this
anomalous supersaturationin conditions similar to the real atmosphere.
Adria K. Schwarber, Steven J. Smith, Corinne A. Hartin, Benjamin Aaron Vega-Westhoff, and Ryan Sriver
Earth Syst. Dynam., 10, 729–739, https://doi.org/10.5194/esd-10-729-2019, https://doi.org/10.5194/esd-10-729-2019, 2019
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Simple climate models (SCMs) underlie many important scientific and decision-making endeavors. This illustrates the need for their use to be rooted in a clear understanding of their fundamental responses. In this study, we provide a comprehensive assessment of model performance by evaluating the fundamental responses of several SCMs. We find biases in some responses, which have implications for decision science. We conclude by recommending a standard set of validation tests for any SCM.
Andrew Poppick, Elisabeth J. Moyer, and Michael L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 33–53, https://doi.org/10.5194/ascmo-3-33-2017, https://doi.org/10.5194/ascmo-3-33-2017, 2017
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We show that ostensibly empirical methods of analyzing trends in the global mean temperature record, which appear to de-emphasize assumptions, can nevertheless produce misleading inferences about trends and associated uncertainty. We illustrate how a simple but physically motivated trend model can provide better-fitting and more broadly applicable results, and show the importance of adequately characterizing internal variability for estimating trend uncertainty.
Whitney K. Huang, Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, https://doi.org/10.5194/ascmo-2-79-2016, 2016
Ann M. Fridlind, Rachel Atlas, Bastiaan van Diedenhoven, Junshik Um, Greg M. McFarquhar, Andrew S. Ackerman, Elisabeth J. Moyer, and R. Paul Lawson
Atmos. Chem. Phys., 16, 7251–7283, https://doi.org/10.5194/acp-16-7251-2016, https://doi.org/10.5194/acp-16-7251-2016, 2016
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Images of crystals within mid-latitude cirrus clouds are used to derive consistent ice physical and optical properties for a detailed cloud microphysics model, including size-dependent mass, projected area, and fall speed. Based on habits found, properties are derived for bullet rosettes, their aggregates, and crystals with irregular shapes. Derived bullet rosette fall speeds are substantially greater than reported in past studies, owing to differences in mass, area, or diameter representation.
L. Cheng, J. Zhu, and R. L. Sriver
Ocean Sci., 11, 719–741, https://doi.org/10.5194/os-11-719-2015, https://doi.org/10.5194/os-11-719-2015, 2015
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1. Argo floats were used to examine tropical cyclone (TC) induced ocean thermal changes on the global scale by comparing temperature profiles before and after TC passage.
2. Global average of the vertical structure of the average ocean thermal response for two different categories: tropical storms/depressions (TS/TD) and hurricanes were presented.
3. Significant differences between weak storm (TS/TD) and strong storm (hurricane) were found.
W. B. Leeds, E. J. Moyer, and M. L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 1–14, https://doi.org/10.5194/ascmo-1-1-2015, https://doi.org/10.5194/ascmo-1-1-2015, 2015
L. Cheng, J. Zhu, and R. L. Sriver
Ocean Sci. Discuss., https://doi.org/10.5194/osd-11-2907-2014, https://doi.org/10.5194/osd-11-2907-2014, 2014
Preprint withdrawn
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1. TCs are responsible for 1.87 PW (11.05 W/m2) of heat transfer annually from the global ocean to the atmosphere during storm passage (0-3 days) on a global scale. Of this total, 1.05±0.20 PW (4.80±0.85 W/m2) is caused by TS/TD and 0.82±0.21 PW (6.25±1.5 W/m2) is caused by hurricanes.
2.The net ocean heat uptake caused by all storms is 0.34 PW (4-20 days mean). Hurricanes induce 0.75±0.25 PW (5.98±2.1 W/m2) net heat gain, and TS/TD leads to 0.41±0.21 PW (1.90±0.96 W/m2) net heat loss.
M. Bolot, B. Legras, and E. J. Moyer
Atmos. Chem. Phys., 13, 7903–7935, https://doi.org/10.5194/acp-13-7903-2013, https://doi.org/10.5194/acp-13-7903-2013, 2013
R. L. Sriver, M. Huber, and L. Chafik
Earth Syst. Dynam., 4, 1–10, https://doi.org/10.5194/esd-4-1-2013, https://doi.org/10.5194/esd-4-1-2013, 2013
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Analysis of the evolution of parametric drivers of high-end sea-level hazards
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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
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Comparison and assessment of large-scale surface temperature in climate model simulations
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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
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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.
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
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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
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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
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
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
Boberg, F. and Christensen, J. H.: Overestimation of Mediterranean summer
temperature projections due to model deficiencies, Nat. Clim. Change, 2,
433–436, 2012. a
Bondell, H. D., Reich, B. J., and Wang, H.: Noncrossing quantile regression
curve estimation, Biometrika, 97, 825–838, 2010. a
Brown, S. J., Caesar, J., and Ferro, C. A. T.: Global changes in extreme daily
temperature since 1950, J. Geophys. Res.-Atmos., 113, D05115,
https://doi.org/10.1029/2006JD008091, 2008. 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
Cannon, A. J.: Multivariate bias correction of climate model output: Matching
marginal distributions and intervariable dependence structure, J.
Climate, 29, 7045–7064, 2016. a
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias correction of GCM
precipitation by quantile mapping: How well do methods preserve changes in
quantiles and extremes?, J. Climate, 28, 6938–6959, 2015. a
Castruccio, S. and Stein, M. L.: Global space-time models for climate
ensembles, Ann. Appl. Stat., 7, 1593–1611, https://doi.org/10.1214/13-AOAS656, 2013. a
Daouia, A., Gardes, L., and Girard, S.: On kernel smoothing for extremal
quantile regression, Bernoulli, 19, 2557–2589, 2013. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, A. C. M., van de Berg, B. L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L.S., Healy, B., Hersbach,
H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and
Vitart, F.: The
ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc.,
137, 553–597, 2011. a
Dee, D. P., Balmaseda, M., Balsamo, G., Engelen, R., Simmons, A. J., and
Thépaut, J.-N.: Toward a Consistent Reanalysis of the Climate System,
B. Am. Meteorol. Soc., 95, 1235–1248,
https://doi.org/10.1175/BAMS-D-13-00043.1, 2014. a, b
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
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate
change projections: the role of internal variability, Clim. Dynam., 38,
527–546, 2012. a
Deser, C., Phillips, A. S., Alexander, M. A., and Smoliak, B. V.: Projecting
North American climate over the next 50 years: Uncertainty due to
internal variability, J. Climate, 27, 2271–2296, 2014. a
Diffenbaugh, N. S., Singh, D., Mankin, J. S., Horton, D. E., Swain, D. L.,
Touma, D., Charland, A., Liu, Y., Haugen, M., Tsiang, M., and Rajaratnam, B.: Quantifying
the influence of global warming on unprecedented extreme climate events,
P. Natl. Acad. Sci. USA, 114, 4881–4886, 2017. a
Dixon, K. W., Lanzante, J. R., Nath, M. J., Hayhoe, K., Stoner, A.,
Radhakrishnan, A., Balaji, V., and Gaitán, C. F.: Evaluating the
stationarity assumption in statistically downscaled climate projections: is
past performance an indicator of future results?, Clim. Change, 135,
395–408, https://doi.org/10.1007/s10584-016-1598-0, 2016. a, b
Efron, B.: Nonparametric estimates of standard error: the jackknife, the
bootstrap and other methods, Biometrika, 68, 589–599, 1981. a
Fasiolo, M., Goude, Y., Nedellec, R., and Wood, S. N.: Fast calibrated
additive quantile regression, arXiv preprint arXiv:1707.03307, available at:
https://arxiv.org/abs/1707.03307 (last access: 1 April 2019), 2017. a
Forrester, A. I., Sóbester, A., and Keane, A. J.: Multi-fidelity
optimization via surrogate modelling, P. Roy. Soc.
A, 463, 3251–3269, https://doi.org/10.1098/rspa.2007.1900, 2007. a
Friederichs, P. and Hense, A.: Statistical downscaling of extreme precipitation
events using censored quantile regression, Mon. weather Rev., 135,
2365–2378, 2007. a
Friedman, J., Hastie, T., and Tibshirani, R.: The elements of statistical
learning, 10, Springer series in statistics, New York, NY, USA, 2001. a
Geraci, M. and Bottai, M.: Linear quantile mixed models, Stat.
Comput., 24, 461–479, 2014. a
Graham, L. P., Hagemann, S., Jaun, S., and Beniston, M.: On interpreting
hydrological change from regional climate models, Clim. Change, 81,
97–122, https://doi.org/10.1007/s10584-006-9217-0, 2007. a
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
Hayhoe, K., Cayan, D., Field, C. B., Frumhoff, P. C., Maurer, E. P., Miller,
N. L., Moser, S. C., Schneider, S. H., Cahill, K. N., Cleland, E. E., Dale,
L., Drapek, R., Hanemann, R. M., Kalkstein, L. S., Lenihan, J., Lunch, C. K.,
Neilson, R. P., Sheridan, S. C., and Verville, J. H.: Emissions pathways,
climate change, and impacts on California, P. Natl.
Acad. Sci., 101, 12422–12427, https://doi.org/10.1073/pnas.0404500101, 2004. a
He, X., Tuo, R., and Wu, C. F. J.: Optimization of multi-fidelity computer
experiments via the EQIE criterion, Technometrics, 59, 58–68,
https://doi.org/10.1080/00401706.2016.1142902, 2017. a
Hogan, E. and Sriver, R.: Analyzing the effect of ocean internal variability on
depth-integrated steric sea-level rise trends using a low-resolution CESM
ensemble, Water, 9, 483, https://doi.org/10.3390/w9070483, 2017. a
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) large ensemble project: A
community resource for studying climate change in the presence of internal
climate variability, B. Am. Meteorol. Soc., 96,
1333–1349, 2015. a
Kennedy, M. and O'Hagan, A.: Predicting the output from a complex computer code
when fast approximations are available, Biometrika, 87, 1–13,
https://doi.org/10.1093/biomet/87.1.1, 2000. a, b, c
Koenker, R.: Quantile regression for longitudinal data, J. Multi.
Anal., 91, 74–89, 2004. a
Koenker, R.: Quantile Regression, Econometric Society Monographs, Cambridge
University Press, https://doi.org/10.1017/CBO9780511754098, 2005. a
Koenker, R.: quantreg: Quantile Regression, r package version 5.33, available
at: https://CRAN.R-project.org/package=quantreg (last access:
1 February 2019), 2017. a
Koenker, R. and Bassett Jr., G.: Regression quantiles, Econometrica, 46,
33–50, 1978. a
Kozumi, H. and Kobayashi, G.: Gibbs sampling methods for Bayesian quantile
regression, J. Stat. Comput. Simul., 81, 1565–1578, 2011. a
Le Gratiet, L. and Garnier, J.: Recursive co-kriging model for design of
computer experiments with multiple levels of fidelity, Int. J.
Uncertainty Quant., 4, 365–386, 2014. a
Maraun, D.: Nonstationarities of regional climate model biases in European
seasonal mean temperature and precipitation sums, Geophys. Res.
Lett., 39, L06706, https://doi.org/10.1029/2012GL051210, 2012. a
McGinnis, S., Nychka, D., and Mearns, L. O.: A new distribution mapping
technique for climate model bias correction, in: Machine Learning and Data
Mining Approaches to Climate Science, 91–99, Springer Nature Switzerland AG,
Switzerland, 2015. a
McKinnon, K. A., Rhines, A., Tingley, M. P., and Huybers, P.: The changing
shape of Northern Hemisphere summer temperature distributions, J.
Geophys. Res.-Atmos., 121, 8849–8868, 2016. a
Miao, C., Su, L., Sun, Q., and Duan, Q.: A nonstationary bias-correction
technique to remove bias in GCM simulations, J. Geophys. Res.-Atmos., 121, 5718–5735, 2016. a
Olsson, J., Berggren, K., Olofsson, M., and Viklander, M.: Applying climate
model precipitation scenarios for urban hydrological assessment: A case study
in Kalmar City, Sweden, Atmos. Res., 92, 364–375, 2009. a
Piani, C., Weedon, G., Best, M., Gomes, S., Viterbo, P., Hagemann, S., and
Haerter, J.: Statistical bias correction of global simulated daily
precipitation and temperature for the application of hydrological models,
J. Hydrol., 395, 199–215, 2010. a
Poppick, A., Moyer, E. J., and Stein, M. L.: Estimating trends in the global
mean temperature record, Adv. Stat. Clim. Meteorol. Oceanogr., 3, 33–53,
https://doi.org/10.5194/ascmo-3-33-2017, 2017. a, b, c, d
Räisänen, J. and Räty, O.: Projections of daily mean temperature
variability in the future: cross-validation tests with ENSEMBLES regional
climate simulations, Clim. Dynam., 41, 1553–1568, 2013. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
available at: http://www.R-project.org/ (last access: 23 February 2019), 2013. a
Reich, B. J., Fuentes, M., and Dunson, D. B.: Bayesian spatial quantile
regression, J. Am. Stat. Assoc., 106, 6–20,
2011. a
Reiss, P. T. and Huang, L.: Smoothness selection for penalized quantile
regression splines, Int. J. Biostat., 8, https://doi.org/10.1515/1557-4679.1381, 2012. a
Rhines, A., McKinnon, K. A., Tingley, M. P., and Huybers, P.: Seasonally
resolved distributional trends of North American temperatures show
contraction of winter variability, J. Climate, 30, 1139–1157, 2017. a
Stoner, A. M., Hayhoe, K., Yang, X., and Wuebbles, D. J.: An asynchronous
regional regression model for statistical downscaling of daily climate
variables, Int. J. Climatol., 33, 2473–2494, 2013. a
Swain, D. L., Tsiang, M., Haugen, M., Singh, D., Charland, A., Rajaratnam, B.,
and Diffenbaugh, N. S.: The extraordinary California drought of 2013/2014:
Character, context, and the role of climate change, B. Am.
Meteorol. Soc., 95, S3–S7, 2014. a
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, 12–29, 2012. a
Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., et al.: The
representative concentration pathways: an overview, Clim. Change, 109, https://doi.org/10.1007/s10584-011-0148-z,
2011. a
Vega-Westhoff, B. and Sriver, R. L.: Analysis of ENSO's response to unforced
variability and anthropogenic forcing using CESM, Sci. Rep., 7,
18047, https://doi.org/10.1038/s41598-017-18459-8, 2017. a
Vrac, M., Stein, M., Hayhoe, K., and Liang, X.-Z.: A general method for
validating statistical downscaling methods under future climate change,
Geophys. Res. Lett., 34, L18701, https://doi.org/10.1029/2007GL030295, 2007. a
Watterson, I. G.: Calculation of probability density functions for temperature
and precipitation change under global warming, J. Geophys.
Res.-Atmos., 113, D12106, https://doi.org/10.1029/2007JD009254, 2008. a
Wei, Y., Pere, A., Koenker, R., and He, X.: Quantile regression methods for
reference growth charts, Stat. Med., 25, 1369–1382, 2006. a
Willems, P. and Vrac, M.: Statistical precipitation downscaling for small-scale
hydrological impact investigations of climate change, J. Hydrol.,
402, 193–205, 2011. a
Wood, A. W., Leung, L. R., Sridhar, V., and Lettenmaier, D.: Hydrologic
implications of dynamical and statistical approaches to downscaling climate
model outputs, Clim. Change, 62, 189–216, 2004. a
Yu, K. and Jones, M.: Local linear quantile regression, J. Am.
Stat. Assoc., 93, 228–237, 1998. a
Yu, K. and Moyeed, R. A.: Bayesian quantile regression, Stat.
Prob. Lett., 54, 437–447, 2001. a
Zscheischler, J. and Seneviratne, S. I.: Dependence of drivers affects risks
associated with compound events, Sci. Adv., 3, e1700263, https://doi.org/10.1126/sciadv.1700263, 2017. a
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.
This work uses current temperature observations combined with climate models to project future...
