63 research outputs found

    Australian climate warming: observed change from 1850 and global temperature targets

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    Mean annual temperature is often used as a benchmark for monitoring climate change and as an indicator of its potential impacts. The Paris Agreement of 2015 aims to keep the global average temperature well below 2°C above pre-industrial levels, with a preferred limit of 1.5°C. Therefore, there is interest in understanding and examining regional temperature change using this framework of ‘global warming levels’, as well as through emissions pathways and time horizons. To apply the global warming level framework regionally, we need to quantify regional warming from the late 19th century to today, and to future periods where the warming levels are reached. Here we supplement reliable observations from 1910 with early historical datasets currently available back to 1860 and the latest set of global climate model simulations from CMIP5/CMIP6 to examine the past and future warming of Australia from the 1850–1900 baseline commonly used as a proxy for pre-industrial conditions. We find that Australia warmed by ~1.6°C between 1850–1900 and 2011–2020 (with uncertainty unlikely to substantially exceed ±0.3°C). This warming is a ratio of ~1.4 times the ~1.1°C global warming over that time, and in line with observed global land average warming. Projections for global warming levels are also quantified and suggest future warming of slightly less than the observed ratio to date, at ~1.0–1.3 for all future global warming levels. We also find that to reliably examine regional warming under the emissions pathway framework using the latest climate models from CMIP6, appropriate weights to the ensemble members are required. Once these weights are applied, results are similar to CMIP5

    IPCC, 2023: Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland.

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    This Synthesis Report (SYR) of the IPCC Sixth Assessment Report (AR6) summarises the state of knowledge of climate change, its widespread impacts and risks, and climate change mitigation and adaptation. It integrates the main findings of the Sixth Assessment Report (AR6) based on contributions from the three Working Groups1 , and the three Special Reports. The summary for Policymakers (SPM) is structured in three parts: SPM.A Current Status and Trends, SPM.B Future Climate Change, Risks, and Long-Term Responses, and SPM.C Responses in the Near Term.This report recognizes the interdependence of climate, ecosystems and biodiversity, and human societies; the value of diverse forms of knowledge; and the close linkages between climate change adaptation, mitigation, ecosystem health, human well-being and sustainable development, and reflects the increasing diversity of actors involved in climate action. Based on scientific understanding, key findings can be formulated as statements of fact or associated with an assessed level of confidence using the IPCC calibrated language

    Indicators of Global Climate Change 2022: annual update of large-scale indicators of the state of the climate system and human influence

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    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), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. 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, surface temperature changes, the Earth's energy imbalance, 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.8000192, Smith et al., 2023a). 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 human-induced warming reached 1.14 [0.9 to 1.4] ∘C averaged over the 2013–2022 decade and 1.26 [1.0 to 1.6] ∘C in 2022. Over the 2013–2022 period, human-induced warming has been increasing at an unprecedented rate of over 0.2 ∘C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 54 ± 5.3 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that increases in greenhouse gas emissions have slowed, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for human influence on climate.ISSN:1866-3516ISSN:1866-350

    Indicators of Global Climate Change 2022: annual update of large-scale indicators of the state of the climate system and human influence

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    Abstract. 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), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. 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, surface temperature changes, the Earth's energy imbalance, 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.8000192, Smith et al., 2023a). 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 human-induced warming reached 1.14 [0.9 to 1.4] ∘C averaged over the 2013–2022 decade and 1.26 [1.0 to 1.6] ∘C in 2022. Over the 2013–2022 period, human-induced warming has been increasing at an unprecedented rate of over 0.2 ∘C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 54 ± 5.3 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that increases in greenhouse gas emissions have slowed, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for human influence on climate

    Headline Indicators for Global Climate Monitoring

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    International audienceThe World Meteorological Organization has developed a set of headline indicators for global climate monitoring. These seven indicators are a subset of the existing set of essential climate variables (ECVs) established by the Global Climate Observing System and are intended to provide the most essential parameters representing the state of the climate system. These indicators include global mean surface temperature, global ocean heat content, state of ocean acidification, glacier mass balance, Arctic and Antarctic sea ice extent, global CO2 mole fraction, and global mean sea level. This paper describes how well each of these indicators are currently monitored, including the number and quality of the underlying datasets; the health of those datasets; observation systems used to estimate each indicator; the timeliness of information; and how well recent values can be linked to preindustrial conditions. These aspects vary widely between indicators. While global mean surface temperature is available in close to real time and changes from preindustrial levels can be determined with relatively low uncertainty, this is not the case for many other indicators. Some indicators (e.g., sea ice extent) are largely dependent on satellite data only available in the last 40 years, while some (e.g., ocean acidification) have limited underlying observational bases, and others (e.g., glacial mass balance) with data only available a year or more in arrears

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

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    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

    Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Technical Summary

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    The Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) assess the physical science basis of climate change. As part of that contribution, this Technical Summary (TS) is designed to bridge between the comprehensive assessment of the WGI Chapters and its Summary for Policymakers (SPM). It is primarily built from the Executive Summaries of the individual chapters and atlas and provides a synthesis of key findings based on multiple lines of evidence (e.g., analyses of observations, models, paleoclimate information and understanding of physical, chemical and biological processes and components of the climate system). All the findings and figures here are supported by and traceable to the underlying chapters, with relevant chapter sections indicated in curly brackets

    An updated long‐term homogenized daily temperature data set for Australia

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    Abstract A new version of the long‐term Australian temperature data set, known as ACORN‐SAT (Australian Climate Observations Reference Network—Surface Air Temperature), has been developed. ACORN‐SAT includes homogenized daily maximum and minimum temperature data from 112 locations across Australia, encompassing the period from 1910 to the present, with 60 of the locations having data for the full 1910–2018 period. Homogenization is achieved using a percentile‐matching methodology with a number of improvements beyond practices used in previous versions, including more effective detection and removal of potentially inhomogeneous reference stations and an enhanced breakpoint detection methodology. Explicit corrections have also been introduced for a change in instrument screen size, whilst an assessment has found that the transition from manual to automatic instruments and changes in effective response time of automatic instruments have had a negligible impact on the data. Adjustments associated with documented site moves from in‐town to out‐of‐town locations are predominantly negative, particularly for minimum temperature, with other adjustments showing no strong bias towards either positive or negative values. The new data set shows slightly stronger warming (0.12°C per decade in mean temperature over the 1910–2016 period) than either the previous ACORN‐SAT version (0.10°C) or the unhomogenized gridded data (0.08°C), primarily due to more effective treatment of systematic moves of sites out of towns and the removal of a rounding bias in the version 1 methodology

    Climatology and Variability of the Evaporative Stress Index and its suitability as a tool to monitor Australian drought

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    The seasonal cycle of the Evaporative Stress Index (ESI) over Australia, and its relationship to observed rainfall and temperature, is examined. The ESI is defined as the standardized anomaly of the ratio of actual evapotranspiration to potential evapotranspiration, and as such, is a measure of vegetation moisture stress associated with agricultural or ecological drought. The ESI is computed using the daily output of version 6 of the Bureau of Meteorology's landscape water balance model (AWRA-L v6) on a 5 km horizontal grid over a 45-year period (1975-2019). Here we show that the ESI exhibits marked spatial and seasonal variability and can be used to accurately monitor drought across Australia. Values less than negative one indicate drought. While the ESI is highly correlated with rainfall as expected, its relationship with temperature only becomes significant during the warmer seasons, suggesting a threshold above which temperature may affect vegetation stress. Our analysis also shows that the ESI tends to be strongly negative (i.e. indicating drought) during El Niño and positive phases of the Indian Ocean Dipole (IOD), when conditions tend to be anomalously hot and dry. A negative phase of the Southern Annular Mode also tends to drive negative ESI values during austral spring with a one-month delay

    Development of an Updated Global Land In Situ‐Based Data Set of Temperature and Precipitation Extremes: HadEX3

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    We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version
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