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  • 1
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    Changes in temperature and heat waves over Africa using observational and reanalysis data sets
    Wiley ; 2022
    In:  International Journal of Climatology Vol. 42, No. 2 ( 2022-02), p. 1165-1180
    Details
    In: International Journal of Climatology, Wiley, Vol. 42, No. 2 ( 2022-02), p. 1165-1180
    Abstract: Providing comprehensive regional‐ and local‐scale information on changes observed in the climate system plays a vital role in planning effective and efficient climate change adaptation options, specifically over resource‐limited regions. Here, we assess changes in temperature and heat waves over different regions of the African continent, with a focus on spatiotemporal trends and the time of emergence of change in hot extremes from natural variability. We analyse absolute and relative threshold indices. Data sets include temperatures from observations (CRUTS4.03 and BEST) and from three representative state‐of‐the‐art reanalyses (ERA5, MERRA2 and JRA‐55) for the common period 1980–2018. Statistically significant warming is observed over all regions of Africa in temperature time series from CRU observations and reanalysis data, although the trend strength varies between data sets. Also, extreme temperatures and heat wave indices from BEST observations and all reanalysis data sets reveal increasing trends over all regions of the African continent. However, there are differences in both trend strength and time evolution of heat wave indices between different reanalysis data sets. Most data sets agree in identifying 2010 as a peak heat year over Northern and Western Africa while Eastern and Southern Africa experienced the highest heat wave occurrence in 2016. Our results clearly reveal that heat wave occurrences have emerged from natural climate variability in Africa. The earliest time of emergence takes place in the Northern Africa region in the early 2000s while in the other African regions emergence over natural variability is found mainly after 2010. This also depends on the respective index metrics, where indices based on more consecutive days show later emergence of heat wave trends. Overall, significant warming and an increase in heat wave occurrence is found in all regions of Africa and has emerged from natural variability in the past one or two decades.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    URL: Article
    DOI: 10.1002/joc.v42.2
    DOI: 10.1002/joc.7295
    RVK:
    RA 1000
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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  • 2
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    Indices for monitoring changes in extremes based on daily temperature and precipitation data
    Wiley ; 2011
    In:  WIREs Climate Change Vol. 2, No. 6 ( 2011-11), p. 851-870
    Details
    In: WIREs Climate Change, Wiley, Vol. 2, No. 6 ( 2011-11), p. 851-870
    Abstract: Indices for climate variability and extremes have been used for a long time, often by assessing days with temperature or precipitation observations above or below specific physically‐based thresholds. While these indices provided insight into local conditions, few physically based thresholds have relevance in all parts of the world. Therefore, indices of extremes evolved over time and now often focus on relative thresholds that describe features in the tails of the distributions of meteorological variables. In order to help understand how extremes are changing globally, a subset of the wide range of possible indices is now being coordinated internationally which allows the results of studies from different parts of the world to fit together seamlessly. This paper reviews these as well as other indices of extremes and documents the obstacles to robustly calculating and analyzing indices and the methods developed to overcome these obstacles. Gridding indices are necessary in order to compare observations with climate model output. However, gridding indices from daily data are not always straightforward because averaging daily information from many stations tends to dampen gridded extremes. The paper describes recent progress in attribution of the changes in gridded indices of extremes that demonstrates human influence on the probability of extremes. The paper also describes model projections of the future and wraps up with a discussion of ongoing efforts to refine indices of extremes as they are being readied to contribute to the IPCC's Fifth Assessment Report. WIREs Clim Change 2011, 2:851–870. doi: 10.1002/wcc.147 This article is categorized under: Paleoclimates and Current Trends 〉 Modern Climate Change
    Type of Medium: Online Resource
    ISSN: 1757-7780 , 1757-7799
    URL: Issue
    URL: Article
    DOI: 10.1002/wcc.v2.6
    DOI: 10.1002/wcc.147
    Language: English
    Publisher: Wiley
    Publication Date: 2011
    detail.hit.zdb_id: 2532966-2
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  • 3
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    The early 20th century warming: Anomalies, causes, and consequences
    Wiley ; 2018
    In:  WIREs Climate Change Vol. 9, No. 4 ( 2018-07)
    Details
    In: WIREs Climate Change, Wiley, Vol. 9, No. 4 ( 2018-07)
    Abstract: The most pronounced warming in the historical global climate record prior to the recent warming occurred over the first half of the 20th century and is known as the Early Twentieth Century Warming (ETCW). Understanding this period and the subsequent slowdown of warming is key to disentangling the relationship between decadal variability and the response to human influences in the present and future climate. This review discusses the observed changes during the ETCW and hypotheses for the underlying causes and mechanisms. Attribution studies estimate that about a half (40–54%; p 〉  .8) of the global warming from 1901 to 1950 was forced by a combination of increasing greenhouse gases and natural forcing, offset to some extent by aerosols. Natural variability also made a large contribution, particularly to regional anomalies like the Arctic warming in the 1920s and 1930s. The ETCW period also encompassed exceptional events, several of which are touched upon: Indian monsoon failures during the turn of the century, the “Dust Bowl” droughts and extreme heat waves in North America in the 1930s, the World War II period drought in Australia between 1937 and 1945; and the European droughts and heat waves of the late 1940s and early 1950s. Understanding the mechanisms involved in these events, and their links to large scale forcing is an important test for our understanding of modern climate change and for predicting impacts of future change. This article is categorized under: Paleoclimates and Current Trends 〉 Modern Climate Change
    Type of Medium: Online Resource
    ISSN: 1757-7780 , 1757-7799
    URL: Issue
    URL: Article
    DOI: 10.1002/wcc.2018.9.issue-4
    DOI: 10.1002/wcc.522
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 2532966-2
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  • 4
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    Detection and attribution of climate change: a regional perspective
    Wiley ; 2010
    In:  WIREs Climate Change Vol. 1, No. 2 ( 2010-03), p. 192-211
    Details
    In: WIREs Climate Change, Wiley, Vol. 1, No. 2 ( 2010-03), p. 192-211
    Abstract: The Intergovernmental Panel on Climate Change fourth assessment report, published in 2007 came to a more confident assessment of the causes of global temperature change than previous reports and concluded that ‘it is likely that there has been significant anthropogenic warming over the past 50 years averaged over each continent except Antarctica.’ Since then, warming over Antarctica has also been attributed to human influence, and further evidence has accumulated attributing a much wider range of climate changes to human activities. Such changes are broadly consistent with theoretical understanding, and climate model simulations, of how the planet is expected to respond. This paper reviews this evidence from a regional perspective to reflect a growing interest in understanding the regional effects of climate change, which can differ markedly across the globe. We set out the methodological basis for detection and attribution and discuss the spatial scales on which it is possible to make robust attribution statements. We review the evidence showing significant human‐induced changes in regional temperatures, and for the effects of external forcings on changes in the hydrological cycle, the cryosphere, circulation changes, oceanic changes, and changes in extremes. We then discuss future challenges for the science of attribution. To better assess the pace of change, and to understand more about the regional changes to which societies need to adapt, we will need to refine our understanding of the effects of external forcing and internal variability. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Paleoclimates and Current Trends 〉 Detection and Attribution
    Type of Medium: Online Resource
    ISSN: 1757-7780 , 1757-7799
    URL: Issue
    URL: Article
    DOI: 10.1002/wcc.v1:2
    DOI: 10.1002/wcc.34
    Language: English
    Publisher: Wiley
    Publication Date: 2010
    detail.hit.zdb_id: 2532966-2
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  • 5
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    Use of models in detection and attribution of climate change
    Wiley ; 2011
    In:  WIREs Climate Change Vol. 2, No. 4 ( 2011-07), p. 570-591
    Details
    In: WIREs Climate Change, Wiley, Vol. 2, No. 4 ( 2011-07), p. 570-591
    Abstract: Most detection and attribution studies use climate models to determine both the expected ‘fingerprint’ of climate change and the uncertainty in the estimated magnitude of this fingerprint in observations, given the climate variability. This review discusses the role of models in detection and attribution, the associated uncertainties, and the robustness of results. Studies that use observations only make substantial assumptions to separate the components of observed changes due to radiative forcing from those due to internal climate variability. Results from observation‐only studies are broadly consistent with those from fingerprint studies. Fingerprint studies evaluate the extent to which patterns of response to external forcing (fingerprints) from climate model simulations explain observed climate change in observations . Fingerprints are based on climate models of various complexities, from energy balance models to full earth system models. Statistical approaches range from simple comparisons of observations with model simulations to multi‐regression methods that estimate the contribution of several forcings to observed change using a noise‐reducing metric. Multi‐model methods can address model uncertainties to some extent and we discuss how remaining uncertainties can be overcome. The increasing focus on detecting and attributing regional climate change and impacts presents both opportunities and challenges. Challenges arise because internal variability is larger on smaller scales, and regionally important forcings, such as from aerosols or land‐use change, are often uncertain. Nevertheless, if regional climate change can be linked to external forcing, the results can be used to provide constraints on regional climate projections. WIREs Clim Change 2011 2 570–591 DOI: 10.1002/wcc.121 This article is categorized under: Climate Models and Modeling 〉 Knowledge Generation with Models
    Type of Medium: Online Resource
    ISSN: 1757-7780 , 1757-7799
    URL: Issue
    URL: Article
    DOI: 10.1002/wcc.v2.4
    DOI: 10.1002/wcc.121
    Language: English
    Publisher: Wiley
    Publication Date: 2011
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  • 6
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    Single‐step attribution of increasing frequencies of very warm regional temperatures to human influence
    Wiley ; 2011
    In:  Atmospheric Science Letters Vol. 12, No. 2 ( 2011-04), p. 220-227
    Details
    In: Atmospheric Science Letters, Wiley, Vol. 12, No. 2 ( 2011-04), p. 220-227
    Abstract: Seasonal near‐surface temperatures have increased in many regions of the World. Previous work has shown that this has led to rapidly increasing frequencies of very warm Northern Hemisphere summer temperatures. Here we show, using a ‘single‐step’ attribution framework, that increases in frequencies of very warm seasonal temperatures, not just in Northern Hemisphere summers but in other regions and seasons, can be directly attributed to human influence. In the June‐August and September‐November seasons, many of the sub‐continental regions of Africa and Asia show robust attributable increase in the frequencies of anomalously warm seasonal temperatures. Copyright © 2011 Royal Meteorological Society, Crown Copyright and Crown in the right of Canada
    Type of Medium: Online Resource
    ISSN: 1530-261X , 1530-261X
    URL: Issue
    URL: Article
    DOI: 10.1002/asl.v12.2
    DOI: 10.1002/asl.315
    Language: English
    Publisher: Wiley
    Publication Date: 2011
    detail.hit.zdb_id: 2025884-7
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  • 7
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    Substantial changes in the probability of future annual temperature extremes
    Wiley ; 2021
    In:  Atmospheric Science Letters Vol. 22, No. 11 ( 2021-11)
    Details
    In: Atmospheric Science Letters, Wiley, Vol. 22, No. 11 ( 2021-11)
    Abstract: Extreme temperature events causing significant environmental and humanitarian impacts are expected to increase in frequency and magnitude due to global warming. The latest generation of climate model projections, Coupled Model Intercomparison Project Phase Six (CMIP6), provides a new and improved database to investigate change in future daily scale extreme temperature events. This study examines the changes in 1, 3, and 5 day averaged annual maximum temperature in four large CMIP6 ensembles. It analyses, using a generalized extreme value (GEV) method, the change in extreme daily mean temperatures at 1.5 and 2°C of global warming, levels highlighted by the 2016 Paris Agreement, and additionally at 3°C. Extremely hot events are characterized using the annual maxima of daily near surface air temperature in the SSP370 scenario. Global changes in the mode of the distributions (location parameter) follow long‐term summer warming and show very similar spatial patterns. Changes in variability (scale parameter) show a clear trend of increases over the tropics and decreases over higher latitudes, while changes to the tails of distributions (shape parameter) show less globally consistent trends but clear signals over the Arctic sea ice, behaviour also seen in variability. Risk ratios (RRs) indicating the change in probability of hot daily extremes that currently have a 10 year return period increase globally with mean temperature change, with greater increases over the tropics. Globally averaged changes in RR over land range from 3.1–3.6 to 7.9–8.3 for 1.5 and 3°C of warming, respectively. For the latter case, this indicates previously rare, once‐in‐a‐decade summer extremes will occur almost annually in the future under high warming.
    Type of Medium: Online Resource
    ISSN: 1530-261X , 1530-261X
    URL: Issue
    URL: Article
    DOI: 10.1002/asl2.v22.11
    DOI: 10.1002/asl.1061
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2025884-7
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  • 8
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    Using early extremes to place the 2022 UK heat waves into historical context
    Wiley ; 2023
    In:  Atmospheric Science Letters Vol. 24, No. 7 ( 2023-07)
    Details
    In: Atmospheric Science Letters, Wiley, Vol. 24, No. 7 ( 2023-07)
    Abstract: As global surface temperatures continue to rise, both the duration and the intensity of heat waves across most land areas are expected to increase. The 2022 European summer broke a number of temperature records where a new record daily maximum temperature of 40.3°C was reached on 19th July making it the hottest July heat wave event in the UK. This paper aims to detect and analyse historical heat wave events, particularly prior to 1927 and compare these with recent events, particularly, 2022, which featured four summer heat wave events in the UK. This allows us to understand how noteworthy historical extremes are in comparison to those in recent decades, to place modern events into historical context, and to extend the sample of extreme events. Summer heat wave events have been detected between 1878 and 2022 from long station data in the UK. Heat wave extent, duration, and intensity have been analysed to compare past heat waves to the recent 2022 heat waves. For each of the summer months at least one of the top 10 most intense events between 1878 and 2022 occurred in the earliest third of the dataset (before 1927) emphasising the value of analysing early heat events. In all detected events, the anomalous UK heat was part of large‐scale European extreme heat when examining 20th‐century reanalysis data, associated with a high‐pressure system. The 2022 July event resembles in pattern of warming and circulation some earlier events, for example, in 1925. While there is a clear trend in the monthly data and the overall frequency of anomalously hot days, heat wave activity on daily scales even in the period 1878 and 1926 is considerable and in some cases comparable to modern heat wave events in the UK. The most intense events detected led to societal impacts based on UK newspaper articles from the period including impacts on the agricultural sector, health impacts, and travel disruptions, broadly comparable to impacts from recent events.
    Type of Medium: Online Resource
    ISSN: 1530-261X , 1530-261X
    URL: Issue
    URL: Article
    DOI: 10.1002/asl2.v24.7
    DOI: 10.1002/asl.1159
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2025884-7
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  • 9
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    The influences of data precision on the calculation of temperature percentile indices
    Wiley ; 2009
    In:  International Journal of Climatology Vol. 29, No. 3 ( 2009-03-15), p. 321-327
    Details
    In: International Journal of Climatology, Wiley, Vol. 29, No. 3 ( 2009-03-15), p. 321-327
    Abstract: Percentile‐based temperature indices are part of the suite of indices developed by the WMO CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices. They have been used to analyse changes in temperature extremes for various parts of the world. We identify a bias in percentile‐based indices which consist of annual counts of threshold exceedance. This bias occurs when there is insufficient precision in temperature data, and affects the estimation of the means and trends of percentile‐based indices. Such imprecision occurs when temperature observations are truncated or rounded prior to being recorded and archived. The impacts on the indices depend upon the type of relation (i.e. temperature greater than or greater than or equal to ) used to determine the exceedance rate. This problem can be solved when the loss of precision is not overly severe by adding a small random number to artificially restore data precision. While these adjustments do not improve the accuracy of individual observations, the exceedance rates that are computed from data adjusted in this way have properties, such as long‐term mean and trend, which are similar to those directly estimated from data that are originally of the same precision as the adjusted data. Copyright © 2008 Royal Meteorological Society
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    URL: Article
    DOI: 10.1002/joc.v29:3
    DOI: 10.1002/joc.1738
    RVK:
    RA 1000
    Language: English
    Publisher: Wiley
    Publication Date: 2009
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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