Drivers of midlatitude extreme heat waves revealed by analogues and machine learning

2021 ◽  
Author(s):  
George Miloshevich ◽  
Dario Lucente ◽  
Corentin Herbert ◽  
Freddy Bouchet
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Climate Model ◽  
Heat Waves ◽  
Return Time ◽  
Human Society ◽  
Teleconnection Patterns ◽  
Long Term Effect ◽  
Westerly Flow ◽  
Composite Maps

<p>One of the big challenges today is to appropriately describe heat waves, which are relevant due to their impact on human society. Common characteristics in mid-latitudes involve meanders of the westerly flow and concomitant large anticyclonic anomalies of the geopotential field. These anomalies form the so-called teleconnection patterns, and thus it is natural to ask how robust such structures are in various models and how much data we require to make statistically significant inferences. In addition, it is natural to ask what are the precursor phenomena that would improve forecasting capabilities of the heat waves. In particular, what kind of long term effect does the soil moisture have and how it compares to the respective quantitative contribution to the predictability of the teleconnection patterns.</p><p> </p><p>In order to answer these questions we perform various types of regression on a climate model. We construct the composite maps of the geopotential height at 500 hPa and estimate return times of heatwaves of different severity. Of particular interest to us is a committor function, which is essentially a probability a heat wave occurs<span> given the current state of the system. Committor functions can be efficiently computed using the analogue method, which involves learning a Markov chain that produces synthetic trajectories from the real trajectories. Alternatively they can be estimated using machine learning approach. Finally we compare the composite maps in real dynamics to the ones generated by the Markov chain and observe how well the rare events are sampled, for instance to allow extending the return time plots. </span></p>

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

Prediction of heat waves using meteorological variables in diverse regions of Iran with advanced machine learning models

2021 ◽  
Author(s):  
Seyed Babak Haji Seyed Asadollah ◽  
Najeebullah Khan ◽  
Ahmad Sharafati ◽  
Shamsuddin Shahid ◽  
Eun-Sung Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heat Waves ◽  
Meteorological Variables ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Relationships of surface air temperature anomalies over Europe to persistence of atmospheric circulation patterns conducive to heat waves

2008 ◽  
Vol 14 ◽  
pp. 243-249 ◽  
Author(s):  
J. Kyselý ◽  
R. Huth
Keyword(s):  
Atmospheric Circulation ◽  
Air Temperature ◽  
Heat Waves ◽  
Temperature Extremes ◽  
Human Society ◽  
Positive Temperature ◽  
Temperature Anomalies ◽  
Circulation Types ◽  
Circulation Patterns ◽  
Air Temperature Anomalies

Abstract. Heat waves are among natural hazards with the most severe consequences for human society, including pronounced mortality impacts in mid-latitudes. Recent studies have hypothesized that the enhanced persistence of atmospheric circulation may affect surface climatic extremes, mainly the frequency and severity of heat waves. In this paper we examine relationships between the persistence of the Hess-Brezowsky circulation types conducive to summer heat waves and air temperature anomalies at stations over most of the European continent. We also evaluate differences between temperature anomalies during late and early stages of warm circulation types in all seasons. Results show that more persistent circulation patterns tend to enhance the severity of heat waves and support more pronounced temperature anomalies. Recent sharply rising trends in positive temperature extremes over Europe may be related to the greater persistence of the circulation types, and if similar changes towards enhanced persistence affect other mid-latitudinal regions, analogous consequences and implications for temperature extremes may be expected.

scholarly journals Extremal Dependence between Temperature and Ozone over the Continental U.S.

2017 ◽  
Author(s):  
Pakawat Phalitnonkiat ◽  
Wenxiu Sun ◽  
Mircea D. Grigoriu ◽  
Peter G. M. Hess ◽  
Gennady Samorodnitsky ◽  
...  
Keyword(s):  
Climate Model ◽  
Heat Waves ◽  
Strong Dependence ◽  
Measurement Data ◽  
The United States ◽  
Value Theory ◽  
Temperature Extremes ◽  
Measured Temperature ◽  
Extremal Dependence ◽  
Year 2000

Abstract. The co-occurrence of heat waves and pollution events and the resulting high mortality rates emphasizes the importance of the co-occurrence of pollution and temperature extremes. Through the use of extreme value theory and other statistical methods ozone and temperature extremes and their joint occurrence are analyzed over the United States during the summer months (JJA) using Clean Air Status and Trends Network (CASTNET) measurement data and simulations of the present and future climate and chemistry in the Community Earth System Model (CESM1) CAM4-chem. Three simulations using CAM4-chem were analyzed: the Chemistry Climate Model Initiative (CCMI) reference experiment using specified dynamics (REFC1SD) between 1992–2010, a 25-year present-day simulation branched off the CCMI REFC2 simulation in the year 2000 and a 25-year future simulation branched off the CCMI REFC2 simulation in 2100. The latter two simulations differed in their concentration of carbon dioxide (representative of the years 2000 and 2100) but were otherwise identical. A new metric is developed to measure the joint extremal dependence of ozone and temperature by evaluating the spectral dependence of their extremes. Two regions of the U.S. give the strongest measured extreme dependence of ozone and temperature: the northeast and the southeast. The simulations do not capture the relationship between temperature and ozone over the northeast but do simulate a strong dependence of ozone on extreme temperatures over the southeast. In general, the simulations of ozone and temperature do not capture the width of the measured temperature and ozone distributions. While on average the future increase in the 90th percentile temperature and the 90th percentile ozone slightly exceed the mean increase over the continental U.S., in many regions the width of the temperature and ozone distributions decrease. The location of future increases in the tails of the ozone distribution are weakly related to those of temperature with a correlation of 0.3.

scholarly journals Deep Learning for Climate Model Output Statistics

2021 ◽  
Author(s):  
Michael Steininger ◽  
Daniel Abel ◽  
Katrin Ziegler ◽  
Anna Krause ◽  
Heiko Paeth ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Deep Learning ◽  
Climate Models ◽  
Climate Model ◽  
Model Output ◽  
Climate Change Effects ◽  
Climate Model Output ◽  
Improved Performance ◽  
Model Output Statistics

<p>Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.</p>

scholarly journals Machine Learning Predictions of a Multiresolution Climate Model Ensemble

2018 ◽  
Vol 45 (9) ◽  
pp. 4273-4280 ◽  
Author(s):  
Gemma J. Anderson ◽  
Donald D. Lucas
Keyword(s):  
Machine Learning ◽  
Climate Model ◽  
Model Ensemble

scholarly journals Integrated Assessment of Natural Hazards, Including Climate Change’s Influences, for Cultural Heritage Sites: The Case of the Historic Centre of Rethymno in Greece

2019 ◽  
Vol 10 (3) ◽  
pp. 343-361 ◽  
Author(s):  
Mohammad Ravankhah ◽  
Rosmarie de Wit ◽  
Athanasios V. Argyriou ◽  
Angelos Chliaoutakis ◽  
Maria João Revez ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Natural Hazards ◽  
Hazard Assessment ◽  
Climate Model ◽  
Heat Waves ◽  
Hazard Analysis ◽  
Sudden Onset ◽  
Assessment Procedure ◽  
Northern Coast ◽  
Historic Centre

Abstract Within the framework of disaster risk management, this article proposes an interdisciplinary method for the analysis of multiple natural hazards, including climate change’s influences, in the context of cultural heritage. A taxonomy of natural hazards applicable to cultural heritage was developed based on the existing theoretical and conceptual frameworks. Sudden-onset hazards, such as earthquakes and floods, and slow-onset hazards, such as wetting–drying cycles and biological contamination, were incorporated into the hazard assessment procedure. Future alteration of conditions due to climate change, such as change in heat waves’ duration, was also taken into account. The proposed hazard assessment framework was applied to the case of the Historic Centre of Rethymno, a city on the northern coast of the island of Crete in Greece, to identify, analyze, and prioritize the hazards that have the potential to cause damage to the center’s historic structures. The assessment procedure includes climate model projections, GIS spatial modeling and mapping, and finally a hazard analysis matrix to enable the sharing of a better understanding of multiple hazards with the stakeholders. The results can facilitate decision making by providing the vulnerability and risk analysis with the nature and spatial distribution of the significant hazards within the study area and its setting.

A consistent multi-method global extreme event attribution framework

2020 ◽  
Author(s):  
Karsten Haustein ◽  
Benjamin Strauss ◽  
Sihan Li ◽  
Friederike Otto
Keyword(s):  
Climate Model ◽  
Extreme Event ◽  
Global Climate ◽  
Global Climate Model ◽  
Return Time ◽  
Event Attribution ◽  
Temperature Thresholds ◽  
Scaling Methods ◽  
Future Warming ◽  
Cold Extremes

<div> <div> <div> <p>In order to streamline observational and global climate model based extreme event attribution techniques, we propose a multi-method framework which drastically increases the robustness of rapid attribution studies, hence further facilitating the communication of extreme weather related risks across the globe.</p> <p>We use advanced observational datasets for temperature (Berkeley Earth) and rainfall (CPC), together with CMIP5 simulations and the large HadRM3P ensemble from the weather@home project (W@H) Recent (Climatology) and current/future warming scenarios (1°C, 1.5°C, 2°C, 3°C and 4°C) are juxtaposed to pre-industrial (Natural) baseline conditions.</p> <p>Two scaling approaches are applied to the observational data to estimate the statistics of future warming scenarios. One in which percentiles of the metric of interest (Tmax, Tmin, Precip) are scaled with Global Mean Surface Temperature (GMST) and another in which the mean is scaled against GMST. Model subsetting (similar to the HAPPI experiment) as function of GMST is applied to the CMIP5 data in order to assign the warming thresholds. W@H scenarios are prescribed to achieve the desired warming threshold. We analyse the results in terms of classes of events, using percentiles, absolute and return-time based thresholds. Before the subsetting, model biases are removed means of quantile-mapping (both for CMIP5 and W@H).</p> <p>The results between both scaling methods and model subsetting are mostly consistent across many regions and virtually for all temperature thresholds under consideration. The percentile-based scaling method does, however, reveal that the tail of the distributions (highest Tmax, lowest Tmin) has potentially widened with warming. Overall, we find that historically rare extreme events become increasingly common in the future as far as Tmax and Precip is concerned. In contrast, cold extremes become increasingly rare.</p> </div> </div> </div>

scholarly journals CO2 Sensitivity of Extreme Climate Events in the Western United States

2006 ◽  
Vol 10 (15) ◽  
pp. 1-17 ◽  
Author(s):  
Jason L. Bell ◽  
Lisa C. Sloan
Keyword(s):  
United States ◽  
Extreme Events ◽  
Climate Model ◽  
Heat Waves ◽  
Regional Climate ◽  
Western United States ◽  
Climate Extreme ◽  
Climate Model Simulations ◽  
Atmospheric Co2 Concentrations ◽  
High Elevations

Abstract Based upon trends in observed climate, extreme events are thought to be increasing in frequency and/or magnitude. This change in extreme events is attributed to enhancement of the hydrologic cycle caused by increased greenhouse gas concentrations. Results are presented of relatively long (50 yr) regional climate model simulations of the western United States examining the sensitivity of climate and extreme events to a doubling of preindustrial atmospheric CO2 concentrations. These results indicate a shift in the temperature distribution, resulting in fewer cold days and more hot days; the largest changes occur at high elevations. The rainfall distribution is also affected; total rain increases as a result of increases in rainfall during the spring season and at higher elevations. The risk of flooding is generally increased, as is the severity of droughts and heat waves. These results, combined with results of decreased snowpack and increased evaporation, could further stress the water supply of the western United States.

scholarly journals On the use of composite analyses to form physical hypotheses: An example from heat wave – SST associations

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Ghyslaine Boschat ◽  
Ian Simmonds ◽  
Ariaan Purich ◽  
Tim Cowan ◽  
Alexandre Bernardes Pezza
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Heat Waves ◽  
South Indian Ocean ◽  
South Indian ◽  
Sensitivity Tests ◽  
Pattern Correlation ◽  
Southwest Australia ◽  
Climate Model Simulations

Abstract This paper highlights some caveats in using composite analyses to form physical hypotheses on the associations between environmental variables. This is illustrated using a specific example, namely the apparent links between heat waves (HWs) and sea surface temperatures (SSTs). In this case study, a composite analysis is performed to show the large-scale and regional SST conditions observed during summer HWs in Perth, southwest Australia. Composite results initially point to the importance of the subtropical South Indian Ocean, where physically coherent SST dipole anomalies appear to form a necessary condition for HWs to develop across southwest Australia. However, sensitivity tests based on pattern correlation analyses indicate that the vast majority of days when the identified SST pattern appears are overwhelmingly not associated with observed HWs, which suggests that this is definitely not a sufficient condition for HW development. Very similar findings are obtained from the analyses of 15 coupled climate model simulations. The results presented here have pertinent implications and applications for other climate case studies, and highlight the importance of applying comprehensive statistical approaches before making physical inferences on apparent climate associations.

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