Advances in the homogenization of daily peak wind gusts: an application to the Australian series

Póster presentado en: EGU General Assembly 2018 celebrada del 8 al 13 de abril en Viena, Austria.Daily Peak Wind Gusts (DPWG) time-series are valuable data for evaluation of wind related hazard risk to the population and different economic sectors. Yet wind time-series are prone to be affected by inhomogeneities temporally and spatially (e.g. through change of instruments at a site compared to surrounding sites) that may mislead the studies of their variability and trends. The aim of this work is to present the advances in the homogenization of DPWG by analyzing 548 sites time-series across Australia covering the 1941-2016 time period. Due to the low correlation coefficients between these series, especially in the first decades when the station density is much lower, the average wind speed data from the NCEP/NCAR reanalysis were tried as reference series. However, their lower correlations with the DPWG data suggests avoiding this approach. We proposed a robust monthly homogenization using the R package Climatol, which detected 353 break-points at the monthly scale. Some of them were supported by the history of the stations, but detailed analysis of the metadata of 35 selected stations did not find a good correspondence since many changes do not necessarily produce inhomogeneities. When NCEP/NCAR reanalysis are used as references, more break-points are detected around 2003, but it is not clear whether they are due to a general change of the DPWG algorithm in the observation network or rather an artifact due to inhomogeneities in the reanalysis series. The monthly dates of the detected break-points were used in a new application of the Climatol package to adjust the series at daily basis, yielding a homogenized and filled DPWG database for assessing the variability of extreme wind events. Resultant trends of the homogenized DPWG series showed the benefits of the homogenization in the form a much lower dispersion of their values.This work has been also supported by the Project “Detection and attribution of changes in extreme wind gusts ove rland” (2017-03780) funded by the Swedish Research Council, and the MULTITEST (Multiple verification of automatic software homogenizing monthly temperatura and precipitation series; CGL2014-52901-P) Project ,funded b ythe Spanish Ministry of Economy and Competitivity

An approach to homogenize daily peak wind gusts: an application to the Australian series

Daily Peak Wind Gust (DPWG) time series are important for the evaluation of wind-related hazard risks to different socioeconomic and environmental sectors. Yet, wind time series analyses can be impacted by several artefacts, both tempo-rally and spatially, which may introduce inhomogeneities that mislead the study of their decadal variability and trends. The aim of this study is to present a strategy in the homogenization of a challenging climate extreme such as the DPWG using 548 time series across Australia for 1941–2016. This automatic homogenization of DPWG is implemented in the recently developed Version 3.1 of the R package Climatol. This approach is an advance in homogenization of climate records as it identifies 353 break points based on monthly data, splits the daily series into homo- geneous subperiods, and homogenizes them without needing the monthly corrections. The major advantages of this homogenization strategy are its ability to: (a) automatically homogenize a large number of DPWG series, including short-term ones and without needing site metadata (e.g., the change in observational equipment in 2010/2011 was correctly identified); (b) use the closest reference series even not sharing a common period with candidate series or presenting missing data; and (c) supply homogenized series, correcting anomalous data (quality control by spatial coherence), and filling in all the missing data. The NCEP/NCAR reanalysis wind speed data were also trialled in aiding homogenization given the station density was very low during the early decades of the record; however, reanalysis data did not improve the homogenization. Application of this approach found a reduced range of DPWG trends based on site data, and an increased negative regional trend of this climate extreme, compared to raw data and homogenized data using NCEP/NCAR. The analysis produced the first homogenized DPWG dataset to assess and attribute long-term variability of extreme winds across Australia.C.A.-M. received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 703733 (STILLING project). This work was also supported by the project “Detection and attribution of changes in extreme wind gusts over land” (2017-03780) funded by the Vetenskapsrådet, and the MULTITEST (Multiple verification of automatic software homogenizing monthly temperature and precipitation series; CGL2014-52901-P) project, funded by the Spanish Ministry of Economy and Competitivity

A new approach to homogenize daily peak wind gusts: an application to the Australian series

Póster presentado en: EMS Annual Meeting - European Conference for Applied Meteorology and Climatology 2018, celebrado en Budapest del 3 al 7 de septiembre de 2018.Daily Peak Wind Gusts (DPWG) time-series are valuable data for the evaluation of wind related hazard risks to different socioeconomic and environmental sectors. Yet wind time-series analyses can be impacted by several artefacts, both temporally and spatially, that may introduce inhomogeneities that mislead the studies of their decadal variability and trends. The aim of this study is to present a new strategy in the homogenization of a challenging climate extreme such as the DPWG using 548 time-series across Australia for 1941-2016. This automatic homogenization of DPWG is implemented in the recently developed Version 3.0 of the R package Climatol. The new approach is an advance in homogenization of climate records as identifies 353 breakpoints based on monthly data, splits the daily series into homogeneous sub-periods, and homogenizes them without needing the monthly corrections. The major advantages of this homogenization strategy are its ability to: (i) automatically homogenize a large number of DPWG series, including short-term ones and without needing site metadata (e.g., the change in observational equipment in 2010/2011 was correctly identified); (ii) use the closest reference series even not sharing a common period with candidate series or presenting missing data; and (iii) supply homogenized series, correcting anomalous data (quality control by spatial coherence), and filling in all the missing data. The NCEP/NCAR reanalysis wind speed data was also trialled in aiding homogenization given the station density was very low during the early decades of the record; however, reanalysis data did not improve the homogenization. Application of the new approach found a reduced range of DPWG trends based on site data, and an increased negative regional trend of this climate extreme, compared to raw data and homogenized data using NCEP/NCAR. The analysis produced the first homogenized DPWG dataset to assess and attribute long-term variability of extreme winds across Australia.This work has been also supported by the Project “Detection and attribution of changes inextreme wind gusts over land ”(2017-03780) funded by the Swedish Research Council, and the MULTITEST (Multiple verification of automatic software homogenizing monthly temperatura and precipitation series; CGL2014-52901-P) project, funded by the Spanish Ministry of Economy and Competitivity

Trends of daily peak wind gusts in Australia, 1948-2016

Póster presentado en: EGU General Assembly 2019 celebrada del 7 al 12 de abril en Viena, Austria.Daily Peak Wind Gust (DPWG) time series are important for the evaluation of wind-related hazard risks to different socioeconomic and environmental sectors. Yet wind time series analyses can be impacted by several artefacts, such as anemometer changes and site location changes, both temporally and spatially, that may introduce inhomogeneities that mislead the study of their decadal variability and trends. A previous study (EGU2018-14546 and Azorin-Molina et al. 2019. Int. J. Climatol. 39(4), 2260-2277) presented a strategy in the homogenization of this challenging climate extreme such as the DPWG. The automatic homogenization of this DPWG dataset was implemented in the recently developed version 3.1 of the R package Climatol which: (i) represents an advance in homogenization of this extreme climate record; and (ii) produced the first homogenized DPWG dataset to assess and attribute long-term variability of extreme winds across Australia. Given the inconsistencies of wind gust trends under the widespread decline in near-surface wind speed (stilling), the aim of this poster presentation is to show DPWG trends in 35 Bureau of Meteorology operated stations for 1948-2016, with particular focus on the spatiotemporal magnitude (wind speed maxima) of DPWG at annual, seasonal and monthly timescales.This work has been supported by the project “Detection and attribution of changes in extreme wind gusts over land” (2017-03780) funded by the Swedish Research Council

A decline of observed daily peak wind gusts with distinct seasonality in Australia, 1941–2016

Wind gusts represent one of the main natural hazards due to their increasing socioeconomic and environmental impacts on, for example, human safety, maritime–terrestrial–aviation activities, engineering and insurance applications, and energy production. However, the existing scientific studies focused on observed wind gusts are relatively few compared to those on mean wind speed. In Australia, previous studies found a slowdown of near-surface mean wind speed, termed ‘‘stilling,’’ but a lack of knowledge on the multidecadal variability and trends in the magnitude (wind speed maxima) and frequency (exceeding the 90th percentile) of wind gusts exists. A new homogenized daily peak wind gusts (DPWG) dataset containing 548 time series across Australia for 1941–2016 is analyzed to determine long-term trends in wind gusts. Here we show that both the magnitude and frequency of DPWG declined across much of the continent, with a distinct seasonality: negative trends in summer–spring–autumn and weak negative or nontrending (even positive) trends in winter. We demonstrate that ocean–atmosphere oscillations such as the Indian Ocean dipole and the southern annular mode partly modulate decadal-scale variations of DPWG. The long-term declining trend of DPWG is consistent with the ‘‘stilling’’ phenomenon, suggesting that global warming may have reduced Australian wind gusts

WMO assessment of weather and climate mortality extremes : lightning, tropical cyclones, tornadoes, and hail

A World Meteorological Organization (WMO) Commission for Climatology international panel was convened to examine and assess the available evidence associated with five weather-related mortality extremes: 1) lightning (indirect), 2) lightning (direct), 3) tropical cyclones, 4) tornadoes, and 5) hail. After recommending for acceptance of only events after 1873 (the formation of the predecessor of the WMO), the committee evaluated and accepted the following mortality extremes: 1) ''highest mortality (indirect strike) associated with lightning'' as the 469 people killed in a lightning-caused oil tank fire in Dronka, Egypt, on 2 November 1994; 2) ''highest mortality directly associated with a single lightning flash'' as the lightning flash that killed 21 people in a hut in Manica Tribal Trust Lands, Zimbabwe (at time of incident, eastern Rhodesia), on 23 December 1975; 3) ''highest mortality associated with a tropical cyclone'' as the Bangladesh (at time of incident, East Pakistan) cyclone of 12-13 November 1970 with an estimated death toll of 300 000 people| 4) ''highest mortality associated with a tornado'' as the 26 April 1989 tornado that destroyed the Manikganj district, Bangladesh, with an estimated death toll of 1300 individuals| and 5) ''highest mortality associated with a hailstorm'' as the storm occurring near Moradabad, India, on 30 April 1888 that killed 246 people. These mortality extremes serve to further atmospheric science by giving baseline mortality values for comparison to future weather-related catastrophes and also allow for adjudication of new meteorological information as it becomes available.https://www.ametsoc.org/ams/index.cfm/publications/journals/weather-climate-and-society2018-01-30hj2017Geography, Geoinformatics and Meteorolog

A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900

A new dataset of integrated and homogenized monthly surface air temperature over global land for the period since 1900 [China Meteorological Administration global Land Surface Air Temperature (CMA-LSAT)] is developed. In total, 14 sources have been collected and integrated into the newly developed dataset, including three global (CRUTEM4, GHCN, and BEST), three regional and eight national sources. Duplicate stations are identified, and those with the higher priority are chosen or spliced. Then, a consistency test and a climate outlier test are conducted to ensure that each station series is quality controlled. Next, two steps are adopted to assure the homogeneity of the station series: (1) homogenized station series in existing national datasets (by National Meteorological Services) are directly integrated into the dataset without any changes (50% of all stations), and (2) the inhomogeneities are detected and adjusted for in the remaining data series using a penalized maximal t test (50% of all stations). Based on the dataset, we re-assess the temperature changes in global and regional areas compared with GHCN-V3 and CRUTEM4, as well as the temperature changes during the three periods of 1900–2014, 1979–2014 and 1998–2014. The best estimates of warming trends and there 95% confidence ranges for 1900–2014 are approximately 0.102 ± 0.006 °C/decade for the whole year, and 0.104 ± 0.009, 0.112 ± 0.007, 0.090 ± 0.006, and 0.092 ± 0.007 °C/decade for the DJF (December, January, February), MAM, JJA, and SON seasons, respectively. MAM saw the most significant warming trend in both 1900–2014 and 1979–2014. For an even shorter and more recent period (1998–2014), MAM, JJA and SON show similar warming trends, while DJF shows opposite trends. The results show that the ability of CMA-LAST for describing the global temperature changes is similar with other existing products, while there are some differences when describing regional temperature changes

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Indicators of Global Climate Change 2023: annual update of key indicators of the state of the climate system and human influence

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC). Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open-data, open-science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.11388387, Smith et al., 2024a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced. For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023. This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53±5.4 Gt CO2e yr−1 over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for some of the indicators presented here.HORIZON EUROPE Framework ProgrammeH2020 European Research CouncilResearch Councils UKEngineering and Physical Sciences Research CouncilPeer Reviewe

Towards a more reliable historical reanalysis: improvements for version 3 of the Twentieth Century Reanalysis system

Historical reanalyses that span more than a century are needed for a wide range of studies, from understanding large‐scale climate trends to diagnosing the impacts of individual historical extreme weather events. The Twentieth Century Reanalysis (20CR) Project is an effort to fill this need. It is supported by the National Oceanic and Atmospheric Administration (NOAA), the Cooperative Institute for Research in Environmental Sciences (CIRES), and the U.S. Department of Energy (DOE), and is facilitated by collaboration with the international Atmospheric Circulation Reconstructions over the Earth initiative. 20CR is the first ensemble of sub‐daily global atmospheric conditions spanning over 100 years. This provides a best estimate of the weather at any given place and time as well as an estimate of its confidence and uncertainty. While extremely useful, version 2c of this dataset (20CRv2c) has several significant issues, including inaccurate estimates of confidence and a global sea level pressure bias in the mid‐19th century. These and other issues can reduce its effectiveness for studies at many spatial and temporal scales. Therefore, the 20CR system underwent a series of developments to generate a significant new version of the reanalysis. The version 3 system (NOAA‐CIRES‐DOE 20CRv3) uses upgraded data assimilation methods including an adaptive inflation algorithm; has a newer, higher‐resolution forecast model that specifies dry air mass; and assimilates a larger set of pressure observations. These changes have improved the ensemble‐based estimates of confidence, removed spin‐up effects in the precipitation fields, and diminished the sea‐level pressure bias. Other improvements include more accurate representations of storm intensity, smaller errors, and large‐scale reductions in model bias. The 20CRv3 system is comprehensively reviewed, focusing on the aspects that have ameliorated issues in 20CRv2c. Despite the many improvements, some challenges remain, including a systematic bias in tropical precipitation and time‐varying biases in southern high‐latitude pressure fields