Articles | Volume 4, issue 1/2
https://doi.org/10.5194/ascmo-4-37-2018
© Author(s) 2018. 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-4-37-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
05 Dec 2018
| 05 Dec 2018
Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures
Rasmus E. Benestad
CORRESPONDING AUTHOR
The Norwegian Meteorological institute, Henrik Mohns plass 1, 0313 Oslo, Norway
Bob van Oort
CICERO Center for International Climate Research, Gaustadalléen 21, 0349 Oslo, Norway
Flavio Justino
Universidade Federal de Viçosa, Department of Agricultural Engineering, Viçosa, MG, Brazil
Frode Stordal
Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, 0316 Oslo, Norway
Kajsa M. Parding
The Norwegian Meteorological institute, Henrik Mohns plass 1, 0313 Oslo, Norway
Abdelkader Mezghani
The Norwegian Meteorological institute, Henrik Mohns plass 1, 0313 Oslo, Norway
Helene B. Erlandsen
The Norwegian Meteorological institute, Henrik Mohns plass 1, 0313 Oslo, Norway
Jana Sillmann
CICERO Center for International Climate Research, Gaustadalléen 21, 0349 Oslo, Norway
Milton E. Pereira-Flores
Universidade Federal de Viçosa, Department of Agricultural Engineering, Viçosa, MG, Brazil
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Abdelkader Mezghani, Andreas Dobler, Jan Erik Haugen, Rasmus E. Benestad, Kajsa M. Parding, Mikołaj Piniewski, Ignacy Kardel, and Zbigniew W. Kundzewicz
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Johanne H. Rydsaa, Frode Stordal, Anders Bryn, and Lena M. Tallaksen
Biogeosciences, 14, 4209–4227, https://doi.org/10.5194/bg-14-4209-2017, https://doi.org/10.5194/bg-14-4209-2017, 2017
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Flavio Justino, Douglas Lindemann, Fred Kucharski, Aaron Wilson, David Bromwich, and Frode Stordal
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These modeling results have enormous implications for paleoreconstructions of the MIS31 climate that assume overall ice-free conditions in the vicinity of the Antarctic continent. Since these reconstructions may depict dominant signals in a particular time interval and locale, they cannot be assumed to geographically represent large-scale domains, and their ability to reproduce long-term environmental conditions should be considered with care.
Rasmus E. Benestad, Kajsa M. Parding, Abdelkader Mezghani, and Anita V. Dyrrdal
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Rasmus E. Benestad, Retish Senan, and Yvan Orsolini
Earth Syst. Dynam., 7, 851–861, https://doi.org/10.5194/esd-7-851-2016, https://doi.org/10.5194/esd-7-851-2016, 2016
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Christine Smith-Johnsen, Yvan Orsolini, Frode Stordal, Varavut Limpasuvan, and Kristell Pérot
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-758, https://doi.org/10.5194/acp-2016-758, 2016
Preprint withdrawn
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Mesospheric ozone enhancements during sudden stratospheric warmings in the northern hemisphere have been reported in the literature. In the southern hemisphere, only one warming event has occurred, and this paper is the first to explain the mesospheric ozone enhancement during this event in 2002, using both a whole atmosphere chemistry climate model and satellite observations from GOMOS.
Tomasz Berezowski, Mateusz Szcześniak, Ignacy Kardel, Robert Michałowski, Tomasz Okruszko, Abdelkader Mezghani, and Mikołaj Piniewski
Earth Syst. Sci. Data, 8, 127–139, https://doi.org/10.5194/essd-8-127-2016, https://doi.org/10.5194/essd-8-127-2016, 2016
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Three meteorological variables (precipitation, minimum temperature, and maximum temperature) are interpolated on a 5 km grid, available at three temporal aggregations (daily, monthly and annual), and prepared for the period 1951–2013 in two numerical formats: Geotiff and NetCDF3. The spatial extent includes the union of Poland and the Vistula and Oder basins.
B. Kravitz, A. Robock, S. Tilmes, O. Boucher, J. M. English, P. J. Irvine, A. Jones, M. G. Lawrence, M. MacCracken, H. Muri, J. C. Moore, U. Niemeier, S. J. Phipps, J. Sillmann, T. Storelvmo, H. Wang, and S. Watanabe
Geosci. Model Dev., 8, 3379–3392, https://doi.org/10.5194/gmd-8-3379-2015, https://doi.org/10.5194/gmd-8-3379-2015, 2015
A. D. Ávila, Y. E. Carvajal, and F. Justino
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-3-4095-2015, https://doi.org/10.5194/nhessd-3-4095-2015, 2015
Preprint withdrawn
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Tropical regions, such as the South America Andes and mountainous countries, are highly susceptible to floods and flash floods due to environmental and socio-economic characteristics. In this study, critical thresholds of daily accumulated rainfall for the prediction of flash floods in the Cali River basin are proposed. These findings can shed some light on hydrologic behavior, and provide decision-making criteria for water resource planners to aid in the prevention and mitigation strategies.
J. H. Rydsaa, F. Stordal, and L. M. Tallaksen
Biogeosciences, 12, 3071–3087, https://doi.org/10.5194/bg-12-3071-2015, https://doi.org/10.5194/bg-12-3071-2015, 2015
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MODIS land surface data with WRF V3.5.1 and Noah LSM is used to investigate the sensitivity of the atmosphere to changes in structural vegetation in the boreal ecosystem. Results show that high north evergreen forest expansion leads to larger latent heat fluxes, while increased summer precipitation and reduced wind speed lead to lower sensible heat flux. Replacement of evergreen forest with mixed forest have largely opposite effects, moderating the regional effects on the atmosphere.
M. von Hobe, S. Bekki, S. Borrmann, F. Cairo, F. D'Amato, G. Di Donfrancesco, A. Dörnbrack, A. Ebersoldt, M. Ebert, C. Emde, I. Engel, M. Ern, W. Frey, S. Genco, S. Griessbach, J.-U. Grooß, T. Gulde, G. Günther, E. Hösen, L. Hoffmann, V. Homonnai, C. R. Hoyle, I. S. A. Isaksen, D. R. Jackson, I. M. Jánosi, R. L. Jones, K. Kandler, C. Kalicinsky, A. Keil, S. M. Khaykin, F. Khosrawi, R. Kivi, J. Kuttippurath, J. C. Laube, F. Lefèvre, R. Lehmann, S. Ludmann, B. P. Luo, M. Marchand, J. Meyer, V. Mitev, S. Molleker, R. Müller, H. Oelhaf, F. Olschewski, Y. Orsolini, T. Peter, K. Pfeilsticker, C. Piesch, M. C. Pitts, L. R. Poole, F. D. Pope, F. Ravegnani, M. Rex, M. Riese, T. Röckmann, B. Rognerud, A. Roiger, C. Rolf, M. L. Santee, M. Scheibe, C. Schiller, H. Schlager, M. Siciliani de Cumis, N. Sitnikov, O. A. Søvde, R. Spang, N. Spelten, F. Stordal, O. Sumińska-Ebersoldt, A. Ulanovski, J. Ungermann, S. Viciani, C. M. Volk, M. vom Scheidt, P. von der Gathen, K. Walker, T. Wegner, R. Weigel, S. Weinbruch, G. Wetzel, F. G. Wienhold, I. Wohltmann, W. Woiwode, I. A. K. Young, V. Yushkov, B. Zobrist, and F. Stroh
Atmos. Chem. Phys., 13, 9233–9268, https://doi.org/10.5194/acp-13-9233-2013, https://doi.org/10.5194/acp-13-9233-2013, 2013
R. E. Benestad
Pattern Recogn. Phys., 1, 91–92, https://doi.org/10.5194/prp-1-91-2013, https://doi.org/10.5194/prp-1-91-2013, 2013
R. E. Benestad, H. O. Hygen, R. van Dorland, J. Cook, and D. Nuccitelli
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esdd-4-451-2013, https://doi.org/10.5194/esdd-4-451-2013, 2013
Revised manuscript not accepted
A. Hilboll, A. Richter, A. Rozanov, Ø. Hodnebrog, A. Heckel, S. Solberg, F. Stordal, and J. P. Burrows
Atmos. Meas. Tech., 6, 565–584, https://doi.org/10.5194/amt-6-565-2013, https://doi.org/10.5194/amt-6-565-2013, 2013
Ø. Hodnebrog, T. K. Berntsen, O. Dessens, M. Gauss, V. Grewe, I. S. A. Isaksen, B. Koffi, G. Myhre, D. Olivié, M. J. Prather, F. Stordal, S. Szopa, Q. Tang, P. van Velthoven, and J. E. Williams
Atmos. Chem. Phys., 12, 12211–12225, https://doi.org/10.5194/acp-12-12211-2012, https://doi.org/10.5194/acp-12-12211-2012, 2012
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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
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
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
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
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.
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
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.
A new study indicates that heatwaves in India will become more frequent and last longer with...
