Tropical Atlantic Influence on European Heat Waves

2005 ◽  
Vol 18 (15) ◽  
pp. 2805-2811 ◽  
Author(s):  
Christophe Cassou ◽  
Laurent Terray ◽  
Adam S. Phillips
Keyword(s):  
Atmospheric Circulation ◽  
Long Range ◽  
Heat Waves ◽  
Extreme Weather ◽  
Temperature Data ◽  
Tropical Atlantic ◽  
Caribbean Basin ◽  
Climate Anomalies ◽  
Warm Summer ◽  
The Caribbean

Abstract Diagnostics combining atmospheric reanalysis and station-based temperature data for 1950–2003 indicate that European heat waves can be associated with the occurrence of two specific summertime atmospheric circulation regimes. Evidence is presented that during the record warm summer of 2003, the excitation of these two regimes was significantly favored by the anomalous tropical Atlantic heating related to wetter-than-average conditions in both the Caribbean basin and the Sahel. Given the persistence of tropical Atlantic climate anomalies, their seasonality, and their associated predictability, the suggested tropical–extratropical Atlantic connection is encouraging for the prospects of long-range forecasting of extreme weather in Europe.

scholarly journals A record-breaking trans-Atlantic African dust plume associated with atmospheric circulation extremes in June 2020

2021 ◽  
pp. 1-41
Author(s):  
Bing Pu ◽  
Qinjian Jin
Keyword(s):  
Air Quality ◽  
Atmospheric Circulation ◽  
Long Range ◽  
North Africa ◽  
North Atlantic ◽  
Caribbean Basin ◽  
Dust Emissions ◽  
African Dust ◽  
The Caribbean ◽  
The U.S

AbstractHigh concentrations of dust can affect climate and human health, yet our understanding of extreme dust events is still limited. A record-breaking trans-Atlantic African dust plume occurred during June 14–28, 2020, greatly degrading air quality over large areas of the Caribbean Basin and U.S. Daily PM2.5 concentrations exceeded 50 μg m−3 in several Gulf States, while the air quality index reached unhealthy levels for sensitive groups in more than 11 States. The magnitude and duration of aerosol optical depth over the tropical North Atlantic Ocean were the greatest ever observed during summer over the past 18 years based on satellite retrievals. This extreme trans-Atlantic dust event is associated with both enhanced dust emissions over western North Africa and atmospheric circulation extremes that favor long-range dust transport. An exceptionally strong African easterly jet and associated wave activities export African dust across the Atlantic toward the Caribbean in the middle to lower troposphere, while a westward extension of the North Atlantic subtropical high and a greatly intensified Caribbean low-level jet further transport the descended, shallower dust plume from the Caribbean Basin into the U.S. Over western North Africa, increased dust emissions are associated with strongly enhanced surface winds over dust source regions and reduced vegetation coverage in the western Sahel. While there are large uncertainties associated with assessing future trends in African dust emissions, model-projected atmospheric circulation changes in a warmer future generally favor increased long-range transport of African dust to the Caribbean Basin and the U.S.

scholarly journals Heat Exposure and Youth Migration in Central America and the Caribbean

2017 ◽  
Vol 107 (5) ◽  
pp. 446-450 ◽  
Author(s):  
Javier Baez ◽  
German Caruso ◽  
Valerie Mueller ◽  
Chiyu Niu
Keyword(s):  
Central America ◽  
Heat Waves ◽  
Heat Exposure ◽  
Temperature Data ◽  
Urban Centers ◽  
Standard Deviation Increase ◽  
Heat Effects ◽  
Triple Difference ◽  
The Caribbean ◽  
Youth Migration

We employ a triple difference-in-difference approach, using censuses and georeferenced temperature data, to quantify heat effects on internal migration in Central America and the Caribbean. A 1-standard deviation increase in heat would affect the lives of 7,314 and 1,578 unskilled young women and men. The effect is smaller than observed in response to droughts and hurricanes but could increase with climate change. Interestingly, youth facing heat waves are more likely to move to urban centers than when exposed to disasters endemic to the region. Research identifying the implications of these choices and interventions available to minimize distress migration is warranted.

scholarly journals Extremal dependence as given by the tail pairwise dependence matrix inprecipitation and temperature data

2021 ◽  
Author(s):  
Svenja Szemkus ◽  
Petra Friederichs
Keyword(s):  
Principal Component Analysis ◽  
Spatial Patterns ◽  
Heat Waves ◽  
Principal Component ◽  
Component Analysis ◽  
Extreme Weather ◽  
Temperature Data ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Pairwise Dependence

<p>A better understanding of the dynamics and impacts of extreme weather events and their changes due to climate change is the subject of the ClimXtreme project (climxtreme.net) funded by the German Federal Ministry of Education and Research. <br>The CoDEx project is investigating how data compression techniques can contribute to a better description and understanding of extremes. Various unsupervised learning approaches, such as clustering or principal component analysis, focusing on extremes have been developed recently and will be investigated and compared within the project. <br>We use principal component analysis to study the spatial (co-)occurrence during extreme weather events such as heavy precipitation, heat waves or droughts. The focus on extreme events is done by using the tail pairwise dependence matrix (TPDM), proposed by Cooley and Thibaud (2019) as an analogue to the covariance matrix for extremes. Since the simultaneous occurrence of precipitation deficits and high temperature played an important role, especially in heat waves, we explore how Cooley and Thibaud's concept can be used in this regard. We propose an estimation of the TPDM based on pairwise dependencies of two variables. A singular value decomposition gives us insight into the spatial co-occurrence of extreme spatial patterns, which contributes to the understanding of so-called compound events. <br>We use daily precipitation and temperature data, including observational stations and regional reanalyses in Germany and Europe. Using this method, we extract spatial patterns over Germany and Europe based on extreme dependencies. In addition, we identify historical events, and examine them in more detail in this context.</p>

scholarly journals Countrywide climate features during recorded climate-related disasters

2019 ◽  
Vol 158 (3-4) ◽  
pp. 593-609 ◽  
Author(s):  
Elisabeth Tschumi ◽  
Jakob Zscheischler
Keyword(s):  
Heat Waves ◽  
Developed Countries ◽  
Extreme Weather ◽  
Economic Damage ◽  
Cold Wave ◽  
Positive Temperature ◽  
Climate Data ◽  
Superposed Epoch Analysis ◽  
Climate Anomalies ◽  
Precipitation Decrease

AbstractClimate-related disasters cause substantial disruptions to human societies. With climate change, many extreme weather and climate events are expected to become more severe and more frequent. The International Disaster Database (EM-DAT) records climate-related disasters associated with observed impacts such as affected people and economic damage on a country basis. Although disasters are classified into different meteorological categories, they are usually not linked to observed climate anomalies. Here, we investigate countrywide climate features associated with disasters that have occurred between 1950 and 2015 and have been classified as droughts, floods, heat waves, and cold waves using superposed epoch analysis. We find that disasters classified as heat waves are associated with significant countrywide increases in annual mean temperature of on average 0.13 ∘C and a significant decrease in annual precipitation of 3.2%. Drought disasters show positive temperature anomalies of 0.08 ∘C and a 4.8 % precipitation decrease. Disasters classified as droughts and heat waves are thus associated with significant annual countrywide anomalies in both temperature and precipitation. During years of flood disasters, precipitation is increased by 2.8 %. Cold wave disasters show no significant signal for either temperature or precipitation. We further find that climate anomalies tend to be larger in smaller countries, an expected behavior when computing countrywide averages. In addition, our results suggest that extreme weather disasters in developed countries are typically associated with larger climate anomalies compared to developing countries. This effect could be due to different levels of vulnerability, as a climate anomaly needs to be larger in a developed country to cause a societal disruption. Our analysis provides a first link between recorded climate-related disasters and observed climate data, which is an important step towards linking climate and impact communities and ultimately better constraining future disaster risk.

The Caribbean Basin: A Climatological Study

1989 ◽  
Author(s):  
Kenneth R. Walters ◽  
Andrew G. Korik ◽  
Michael J. Vojtesak
Keyword(s):  
Caribbean Basin ◽  
The Caribbean

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.

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