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  • Author or Editor: Gregor C. Leckebusch x
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Jens Grieger
,
Gregor C. Leckebusch
, and
Uwe Ulbrich

Abstract

This paper investigates climate change signals of Southern Hemisphere (SH) moisture flux simulated by three members of one CMIP3 coupled atmosphere–ocean general circulation model (AOGCM) and a multimodel ensemble of CMIP5 simulations. Generally, flux changes are dominated by increased atmospheric moisture due to temperature increase in the future climate projections. An approach is presented to distinguish between thermodynamical and dynamical influences on moisture flux. Furthermore, a physical interpretation of the transport changes due to dynamics is investigated by decomposing atmospheric waves into different length scales and temporal variations. Signals of moisture flux are compared with fluctuations of geopotential height fields as well as climate signals of extratropical cyclones. Moisture flux variability in the synoptic length scale with temporal variations shorter than 8 days can be assigned to the SH storm track. Climate change signals of these atmospheric waves show a distinctive poleward shift. This can be attributed to the climate change signal of extratropical cyclones. Furthermore, the climate change signal of atmospheric waves can be better understood if strong cyclones that intensify especially on the Eastern Hemisphere are taken into account. Antarctic net precipitation is calculated by means of the vertically integrated moisture flux. Future projections show increasing signals of net precipitation, whereas the dynamical part of net precipitation decreases. This can be understood by means of the low-variability component of synoptic-scale waves, which show a decreasing signal, especially off the coast of West Antarctica. This is shown to be due to changing variability of the Amundsen–Bellingshausen Seas low.

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Simon Wild
,
Daniel J. Befort
, and
Gregor C. Leckebusch
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Dominik Renggli
,
Gregor C. Leckebusch
,
Uwe Ulbrich
,
Stephanie N. Gleixner
, and
Eberhard Faust

Abstract

The science of seasonal predictions has advanced considerably in the last decade. Today, operational predictions are generated by several institutions, especially for variables such as (sea) surface temperatures and precipitation. In contrast, few studies have been conducted on the seasonal predictability of extreme meteorological events such as European windstorms in winter. In this study, the predictive skill of extratropical wintertime windstorms in the North Atlantic/European region is explored in sets of seasonal hindcast ensembles from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) and the ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) projects.

The observed temporal and spatial climatological distributions of these windstorms are reasonably well reproduced in the hindcast data. Using hindcasts starting on 1 November, significant predictive skill is found for the December–February windstorm frequency in the period 1980–2001, but also for the January–April storm frequency. Specifically, the model suite run at Météo France shows consistently high skill.

Some aspects of the variability of skill are discussed. Predictive skill in the 1980–2001 period is usually higher than for the 1960–2001 period. Furthermore, the level of skill turns out to be related to the storm frequency of a given winter. Generally, winters with high storm frequency are better predicted than winters with medium storm frequency. Physical mechanisms potentially leading to such a variability of skill are discussed.

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Auwal F. Abdussalam
,
Andrew J. Monaghan
,
Daniel F. Steinhoff
,
Vanja M. Dukic
,
Mary H. Hayden
,
Thomas M. Hopson
,
John E. Thornes
, and
Gregor C. Leckebusch

Abstract

Meningitis remains a major health burden throughout Sahelian Africa, especially in heavily populated northwest Nigeria with an annual incidence rate ranging from 18 to 200 per 100 000 people for 2000–11. Several studies have established that cases exhibit sensitivity to intra- and interannual climate variability, peaking during the hot and dry boreal spring months, raising concern that future climate change may increase the incidence of meningitis in the region. The impact of future climate change on meningitis risk in northwest Nigeria is assessed by forcing an empirical model of meningitis with monthly simulations of seven meteorological variables from an ensemble of 13 statistically downscaled global climate model projections from phase 5 of the Coupled Model Intercomparison Experiment (CMIP5) for representative concentration pathway (RCP) 2.6, 6.0, and 8.5 scenarios, with the numbers representing the globally averaged top-of-the-atmosphere radiative imbalance (in W m−2) in 2100. The results suggest future temperature increases due to climate change have the potential to significantly increase meningitis cases in both the early (2020–35) and late (2060–75) twenty-first century, and for the seasonal onset of meningitis to begin about a month earlier on average by late century, in October rather than November. Annual incidence may increase by 47% ± 8%, 64% ± 9%, and 99% ± 12% for the RCP 2.6, 6.0, and 8.5 scenarios, respectively, in 2060–75 with respect to 1990–2005. It is noteworthy that these results represent the climatological potential for increased cases due to climate change, as it is assumed that current prevention and treatment strategies will remain similar in the future.

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Auwal F. Abdussalam
,
Andrew J. Monaghan
,
Vanja M. Dukić
,
Mary H. Hayden
,
Thomas M. Hopson
,
Gregor C. Leckebusch
, and
John E. Thornes

Abstract

Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

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Urs Neu
,
Mirseid G. Akperov
,
Nina Bellenbaum
,
Rasmus Benestad
,
Richard Blender
,
Rodrigo Caballero
,
Angela Cocozza
,
Helen F. Dacre
,
Yang Feng
,
Klaus Fraedrich
,
Jens Grieger
,
Sergey Gulev
,
John Hanley
,
Tim Hewson
,
Masaru Inatsu
,
Kevin Keay
,
Sarah F. Kew
,
Ina Kindem
,
Gregor C. Leckebusch
,
Margarida L. R. Liberato
,
Piero Lionello
,
Igor I. Mokhov
,
Joaquim G. Pinto
,
Christoph C. Raible
,
Marco Reale
,
Irina Rudeva
,
Mareike Schuster
,
Ian Simmonds
,
Mark Sinclair
,
Michael Sprenger
,
Natalia D. Tilinina
,
Isabel F. Trigo
,
Sven Ulbrich
,
Uwe Ulbrich
,
Xiaolan L. Wang
, and
Heini Wernli

The variability of results from different automated methods of detection and tracking of extratropical cyclones is assessed in order to identify uncertainties related to the choice of method. Fifteen international teams applied their own algorithms to the same dataset—the period 1989–2009 of interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERAInterim) data. This experiment is part of the community project Intercomparison of Mid Latitude Storm Diagnostics (IMILAST; see www.proclim.ch/imilast/index.html). The spread of results for cyclone frequency, intensity, life cycle, and track location is presented to illustrate the impact of using different methods. Globally, methods agree well for geographical distribution in large oceanic regions, interannual variability of cyclone numbers, geographical patterns of strong trends, and distribution shape for many life cycle characteristics. In contrast, the largest disparities exist for the total numbers of cyclones, the detection of weak cyclones, and distribution in some densely populated regions. Consistency between methods is better for strong cyclones than for shallow ones. Two case studies of relatively large, intense cyclones reveal that the identification of the most intense part of the life cycle of these events is robust between methods, but considerable differences exist during the development and the dissolution phases.

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