This paper shows the impact on global cooling demand of moving from a 1.5ºC to 2.0ºC temperature ... more This paper shows the impact on global cooling demand of moving from a 1.5ºC to 2.0ºC temperature increase. African countries have the highest increase in cooling requirements. The United Kingdom, Switzerland, and Norway (traditionally unprepared for extreme heat) will suffer the largest relative cooling demand surges.
A new climate modelling project has been developed for regional climate simulation and the attrib... more A new climate modelling project has been developed for regional climate simulation and the attribution of weather and climate extremes over Australia and New Zealand. The project, known as weather@home Australia-New Zealand, uses public volunteers' home computers to run a moderate-resolution global atmospheric model with a nested regional model over the Australasian region. By harnessing the aggregated computing power of home computers, weather@home is able to generate an unprecedented number of simulations of possible weather under various climate scenarios. This combination of large ensemble sizes with high spatial resolution allows extreme events to be examined with well-constrained estimates of sampling uncertainty. This paper provides an overview of the weather@home Australia-New Zealand project, including initial evaluation of the regional model performance. The model is seen to be capable of resolving many climate features that are important for the Australian and New Zealand regions, including the influence of El Niño-Southern Oscillation on driving natural climate variability. To date, 75 model simulations of the historical climate have been successfully integrated over the period 1985-2014 in a time-slice manner. In addition, multithousand member ensembles have also been generated for the years 2013, 2014 and 2015 under climate scenarios with and without the effect of human influences. All data generated by the project are freely available to the broader research community.
Extremely wet winters resulting from one or multiple precipitation events can contribute to flood... more Extremely wet winters resulting from one or multiple precipitation events can contribute to flooding leading to severe societal, natural, and economic impacts. When such extremely wet conditions occur simultaneously at multiple locations within the same region, their impacts may be enhanced and lead to extreme cumulative losses (Leonard et al., 2014). In fact, widespread extreme events can affect the ability of governments and international (re-)insurance companies to respond to the emergency, given that resources and funds need to be provided at multiple locations and industrial sectors simultaneously (Enríquez et al., 2020; Kemter et al., 2020; Zscheischler et al., 2020). Recent extreme events showed that, as a result of persistent atmospheric conditions, extremely wet winters and associated flooding can affect multiple countries simultaneously and put high pressure on railway/road networks and transnational risk-reduction mechanisms (
Climate resilience and sustainability, Aug 23, 2021
The climate modeling techniques of event attribution enable systematic assessments of the extent ... more The climate modeling techniques of event attribution enable systematic assessments of the extent that anthropogenic climate change may be altering the probability or magnitude of extreme events. In the consecutive years of 2018, 2019, and 2020, rainfalls caused repeated flooding impacts in the lower Parnaíba River in Northeastern Brazil. We studied the effect that alterations in precipitation resulting from human influences on the climate had on the likelihood of flooding using two ensembles of the HadGEM3-GA6 atmospheric model: one driven by both natural and anthropogenic forcings; and the other driven only by natural atmospheric forcings, with anthropogenic changes removed from sea surface temperatures and sea ice patterns. We performed hydrological modeling to base our assessments on the peak annual streamflow. The change in the likelihood of flooding was expressed in terms of the ratio between probabilities of threshold exceedance estimated for each model ensemble. With uncertainty estimates at the 90% confidence level, the median (5% 95%) probability ratio at the threshold for flooding impacts in the historical period (1982-2013) was 1.12 (0.97 1.26), pointing to a marginal contribution of anthropogenic emissions by about 12%. For the 2018, 2019, and 2020 events, the median (5% 95%) probability ratios at the threshold for flooding impacts were higher at 1.25 (1.07 1.46), 1.27 (1.12 1.445), and 1.37 (1.19 1.59), respectively; indicating that precipitation change driven by anthropogenic emissions has contributed to the increase of likelihood of these events by about 30%. However, there are other intricate hydrometeorological and anthropogenic processes undergoing long-term changes that affect the flood hazard in the lower Parnaíba River. Trend and flood frequency analyses performed on observations showed a nonsignificant long-term reduction of annual peak flow, likely due to decreasing precipitation from natural climate variability and increasing evapotranspiration and flow regulation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Understanding extreme events with multi-thousand member high-resolution global atmospheric simulations
<p>Multi-thousand member climate model simulations are highly valuable for ... more <p>Multi-thousand member climate model simulations are highly valuable for showing characteristics of extreme weather events in historical and future climates. However, until now, studies using such a physically-based approach have been limited to using models with a resolution much coarser than the most modern systems. We have developed a global atmospheric model with ~60km resolution that can be run in the climateprediction.net distributed computing system to produce such large datasets. This resolution is finer than that of many current global climate models and sufficient for good simulation of extratropical synoptic features such as storms. It also allows many extratropical extreme weather events to be simulated without requiring regional downscaling. We will show that this model's simulation of extratropical winter weather is competitive with that in other state-of-the-art models. We will also present the first results generated by this system. One application has been the production of ~2000 member simulations based on sea surface temperatures in severe future winters produced in the UK Climate Projections 2018 dataset, generating large numbers of examples of plausible extreme wet and warm UK seasons. Another is showing the increasing spatial extent of precipitation extremes in the Northern Hemisphere extratropics. </p>
Multi-thousand-year simulations of December-February precipitation and zonal upper-level wind
This dataset contains multi-thousand-year ensemble simulations of wintertime (December-February) ... more This dataset contains multi-thousand-year ensemble simulations of wintertime (December-February) precipitation total and average zonal winds at 250 hPa and 850 hPa. It includes data in a present-day scenario (2006-2015) and two future scenarios within which the world would be 1.5°C and 2.0°C warmer than pre-industrial conditions in 1850-1900. The simulations were run through the global model of the atmosphere and land surface HadAM4 (Williams et al., 2003) with a horizontal resolution of 5/6°x5/9° (approximately 60km in middle latitudes) and 38 vertical levels and a large ensemble. Following the HAPPI experiment design described by Mitchell et al. (2017), simulations were driven by prescribed fields of sea ice concentration, sea surface temperature, and atmospheric gas concentrations. The prescribed fields are observations for the present-day scenario. For future simulations, the prescribed fields were modified based on changes derived from CMIP5 multi-model means. The different rea...
Identifying local‐scale meteorological conditions favorable to large fires in Brazil
Climate Resilience and Sustainability, 2021
This study aims to investigate local-scale meteorological conditions associated with large fires ... more This study aims to investigate local-scale meteorological conditions associated with large fires in Brazil during recent decades. We assess whether there are large fire types with preceding predictors. Our results show that large fires, defined with a threshold of a daily burned area >95th percentile of the historical record, mainly occur in August and September in Brazil, and Amazônia and Cerrado experience much higher numbers of large fires than the other biomes. There are two large fire types that have robust meteorological signatures: (1) a wind driven type, characterized by peak wind speed on the day of the fire, and anomalously high wind speed a few (∼3) days before and after the fire; and (2) a Hot-Drought driven type, characterized by anomalously high temperature, low relative humidity, and consistent drought conditions indicated by anomalously high fuel aridity starting as far back as 5 months prior to the fires. A third one is characterized by no anomalous meteorological conditions. The wind driven type most frequently occurs in southern and southeastern Amazônia, Pantanal, and western and northern-to-central Cerrado, with some occurrences over the western Caatinga region bordering Cerrado, southern Cerrado, and southern Mata Atlântica; whereas the Hot-Drought driven type most frequently occurs in southern and southeastern Amazônia, Pantanal and western and northern-to-central Cerrado, with some occurrences over the western Caatinga region bordering Cerrado, southern Cerrado, central-to-southern Mata Atlântica, and a few occurrences over Northern Brazil where the Amazônia meets Roraima. Southern and southeastern Amazônia, Pantanal and western and northern-to-central Cerrado are the major large fire prone regions. Our results highlight that understanding the temporal and spatial variability of the meteorological conditions associated with large fires is essential for developing spatially explicit forecasting, and future projections of large fire hazards under climate change in Brazil, in particular the Hot-Drought driven type
The Heavy Precipitation Event of May-June 2013 in the Upper Danube and Elbe Basins
Bulletin of the American Meteorological Society, 2014
SEPTEMBER 2014 AMERICAN METEOROLOGICAL SOCIETY | precipitation amount is only the 18th highest of... more SEPTEMBER 2014 AMERICAN METEOROLOGICAL SOCIETY | precipitation amount is only the 18th highest of the analogue time series, showing that even if the atmospheric conditions were favorable to wet conditions over Southern Europe, they do not fully explain the exceptional character of the precipitation anomaly. We conjecture that a potential amplifying cause could be that the oceanic air masses carried by regimes of westerly winds were moister than usual due to warmer SSTs in the Northeast Atlantic (between 0.5 and 1.5 K above normal). We performed an additional analysis by searching circulation analogues among the years of warm Northeast Atlantic SST (i.e., above the 1971–2000 average). The mean monthly European precipitation amounts reconstructed from such “filtered” analogues exceed those of “regular” analogues, picked over 1948–2012 (not shown). Although this is not a definite proof, this pleads in favor of this mechanism. Conclusions. Our analysis suggests that the high precipitati...
Summer 2010 saw two simultaneous extremes linked by an atmospheric wave train: a record-breaking ... more Summer 2010 saw two simultaneous extremes linked by an atmospheric wave train: a record-breaking heatwave in Russia and severe floods in Pakistan. Here, we study this wave event using a large ensemble climate model experiment. First, we show that the circulation in 2010 reflected a recurrent wave train connecting the heatwave and flooding events. Second, we show that the occurrence of the wave train is favored by three drivers: (1) 2010 sea surface temperature anomalies increase the probability of this wave train by a factor 2-to-4 relative to the model’s climatology, (2) early-summer soil moisture deficit in Russia not only increases the probability of local heatwaves, but also enhances rainfall extremes over Pakistan by forcing an atmospheric wave response, and (3) high-latitude land warming favors wave-train occurrence and therefore rainfall and heat extremes. These findings highlight the complexity and synergistic interactions between different drivers, reconciling some seemingl...
Extreme precipitation can have catastrophic effects in China by triggering floods, landslides, an... more Extreme precipitation can have catastrophic effects in China by triggering floods, landslides, and other natural disasters. We measure extreme precipitation over eastern China by the seasonal maximum of total precipitation over 5 consecutive days (Rx5day) in June, July, and August (JJA), which contributes more than 20% of the climate mean of JJA regional total precipitation. Based on the empirical orthogonal teleconnection (EOT) method, this work identifies four dominant regions of observed Rx5day interannual variability in eastern China: north-eastern China (EOT1), the southern lower reaches of the Yangtze valley (EOT2), southern China (EOT3) and the northern lower reaches of the Yangtze valley (EOT4). EOT1 extreme precipitation is related to a strong East Asian Summer Monsoon (EASM), a weak monsoon front and a northward displaced upper-tropospheric westerly jet. EOT2 and EOT4 extreme precipitation are located to the south and north of the lower reaches of the Yangtze valley, respe...
This study investigates the potential influences of anthropogenic forcings and natural variabilit... more This study investigates the potential influences of anthropogenic forcings and natural variability on the risk of summer extreme temperatures over China. We use three multi-thousand-member ensemble simulations with different forcings (with or without anthropogenic greenhouse gases and aerosol emissions) to evaluate the human impact, and with sea surface temperature patterns from three different years around the El Niño-Southern Oscillation (ENSO) 2015/16 event (years 2014, 2015 and 2016) to evaluate the impact of natural variability. A generalized extreme value (GEV) distribution is used to fit the ensemble results. Based on these model results, we find that, during the peak of ENSO (2015), daytime extreme temperatures are smaller over the central China region compared to a normal year (2014). During 2016, the risk of nighttime extreme temperatures is largely increased over the eastern coastal region. Both anomalies are of the same magnitude as the anthropogenic influence. Thus, ENSO can amplify or counterbalance (at a regional and annual scale) anthropogenic effects on extreme summer temperatures over China. Changes are mainly due to changes in the GEV location parameter. Thus, anomalies are due to a shift in the distributions and not to a change in temperature variability.
The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC ... more The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 • C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 • C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate
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Papers by Sarah Sparrow