A Comparison Between Station Observations and Reanalysis Data in the Identification of Extreme Temperature Events

Sheridan, Scott C.; Lee, Cameron C.; Smith, Erik T.

doi:10.1029/2020gl088120

Cited by 60 publications

(43 citation statements)

References 46 publications

(63 reference statements)

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“…Climatological reference values were computed for the 1990-2019 reference period, and all anomalies were calculated in respect to that period. The ERA5 reanalysis has been shown to provide very good matching with station observational data, in particular during extreme temperature episodes (Sheridan et al 2020).…”

Section: Meteorological Datamentioning

confidence: 92%

Heat-related mortality amplified during the COVID-19 pandemic

et al. 2022

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Excess mortality not directly related to the virus has been shown to have increased during the COVID-19 pandemic. However, changes in heat-related mortality during the pandemic have not been addressed in detail. Here, we performed an observational study crossing daily mortality data collected in Portugal (SICO/DGS) with high-resolution temperature series (ERA5/ECMWF), characterizing their relation in the pre-pandemic, and how it aggravated during 2020. The combined result of COVID-19 and extreme temperatures caused the largest annual mortality burden in recent decades (~ 12 000 excess deaths [~ 11% above baseline]). COVID-19 caused the largest fraction of excess mortality during March to May (62%) and from October onwards (85%). During summer, its direct impact was residual, and deaths not reported as COVID-19 dominated excess mortality (553 versus 3 968). A prolonged hot spell led mortality to the upper tertile, reaching its peak in mid-July (+ 45% deaths/day). The lethality ratio (+ 14 deaths per cumulated ºC) was higher than that observed in recent heatwaves. We used a statistical model to estimate expected deaths due to cold/heat, indicating an amplification of at least 50% in heat-related deaths during 2020 compared to pre-pandemic years. Our findings suggest mortality during 2020 has been indirectly amplified by the COVID-19 pandemic, due to the disruption of healthcare systems and fear of population in attending healthcare facilities (expressed in emergency room admissions decreases). While lockdown measures and healthcare systems reorganization prevented deaths directly related to the virus, a significant burden due to other causes represents a strong secondary impact. This was particularly relevant during summer hot spells, when the lethality ratio reached magnitudes not experienced since the 2003 heatwaves. This severe amplification of heat-related mortality during 2020 stresses the need to resume normal healthcare services and public health awareness. Supplementary Information The online version contains supplementary material available at 10.1007/s00484-021-02192-z.

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Section: Meteorological Datamentioning

confidence: 92%

Heat-related mortality amplified during the COVID-19 pandemic

et al. 2022

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“…A fair comparison between station points and gridded products is often a challenge, and there is no specific procedure recommended for it (Maraun et al ., 2010). In this work, all data sets were resampled into the stations network irregular grid (considered as the reference data set) by considering the closest grid cell of each data set in its native spatial resolution similar to the procedure adopted by other studies (e.g., Acharya et al ., 2019; Krauskopf and Huth, 2020; Sheridan et al ., 2020). However, a sensibility study on the comparison procedure was initially performed by resampling all data sets into a regular grid with different methods (bilinear interpolation and nearest neighbour).…”

Section: Methodsmentioning

confidence: 99%

“…Reanalyses performance in reproducing surface variables has been widely evaluated in several studies (e.g., Donat et al ., 2014; Lindsay et al ., 2014; Isotta et al ., 2015; Yang and Zhang, 2018; Wang et al ., 2019; Krauskopf and Huth, 2020; Sheridan et al ., 2020). Jones et al .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of multiple reanalyses in reproducing the spatio‐temporal variability of temperature and precipitation indices over southern South America

Balmaceda-Huarte

Olmo

Bettolli

et al. 2021

Intl Journal of Climatology

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Several temperature and precipitation indices, with special focus on extremes, were analysed in different sub‐regions of southern South America during 1979–2017 using multiple reanalyses, the CPC gridded data set and the most extended network of meteorological stations employed in regional climate studies up to date. Reanalyses generally well represented the spatial patterns of the indices, although they showed some differences in extreme indices over large portions of southern South America and tended to overestimate precipitation maximums, especially in southern Chile. Furthermore, ERA‐Interim presented clear difficulties in reproducing precipitation near the Andes Mountains, exhibiting the largest overestimations. This seemed to be improved in the new generation of ERA reanalyses (ERA5). When evaluating the long‐term changes, most of the data sets agreed in general warming conditions, stronger and more homogeneous for the maximum temperature. NCEP1 and NCEP2 reanalyses showed contrary temporal changes in almost all the temperature indices. Precipitation indices exhibited less consistent changes among reanalyses, although significant upward trends were detected for precipitation extremes in southeastern South America and downward trends were detected in southern Chile in the observational data sets. In addition, most of the data sets agreed in drier conditions in the arid diagonal region of Argentina as reflected by significant positive trends for dry spells and negative trends for the total annual precipitation. In terms of the inter‐annual correspondence, reanalyses usually presented good correlations to the stations reference in the regional averaged series, mainly for temperature indices and more variable for precipitation indices. Overall, no reanalysis was found to perform best. The use of reanalyses data to perform regional climate studies should consider the existent differences among them and with observational data. Moreover, using multiple sources of information is strongly recommended to account for observational uncertainty, especially in regions like southern South America, where data availability and its resolution are often limited.

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“…Climate change has resulted in the strengthening of extreme weather and climate events in every region worldwide. Many scholars have observed significant changes in extreme cold and warm weather at global or regional scales (Zhang et al, 2019;Sheridan et al, 2020;Lei et al, 2021a). Extreme high-temperature events can cause heatwaves and droughts, which directly affect human survival, health, and crop growth (Alexander et al, 2006;Donat et al, 2014;Jiang et al, 2019;Zhan et al, 2020;Watts et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

Lei²,

Chen³

et al. 2022

Front. Environ. Sci.

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The fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts (ERA5) is the latest reanalysis product. However, the reliability of ERA5 to capture extreme temperatures is still unclear over China. Hence, based on conventional meteorological station data, a new criterion (DISO) was used to validate the ERA5 capturing extreme temperature indices derived from the Expert Team on Climate Change Detection and Indices (ETCCDI) across the six subregions of China on different timescales. The conclusions are as follows: the original daily temperatures (mean temperature, maximum temperature, and minimum temperature) can be well reproduced by ERA5 reanalysis over China. ERA5 tends to exhibit more misdetection for the duration of extreme temperature events than extreme temperature intensity and frequency. In addition, ERA5 performed best in the summer and worst in the winter, respectively. The trend of absolute indices (e.g., TXx and TNx), percentile-based indices (e.g., TX90p, TX10p, TN90p, and TN10p), and duration indices (e.g., WSDI, CSDI, and GSL) can be captured by ERA5, but ERA5 failed to capture the tendency of the diurnal temperature range (DTR) over China. Spatially, ERA5 performs well in southeastern China. However, it remains challenging to accurately recreate the extreme temperature events in the Tibetan Plateau. The elevation difference between the station and ERA5 grid point contributes to the main bias of reanalysis temperatures. The accuracy of ERA5 decreases with the increase in elevation discrepancy.

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A Comparison Between Station Observations and Reanalysis Data in the Identification of Extreme Temperature Events

Cited by 60 publications

References 46 publications

Heat-related mortality amplified during the COVID-19 pandemic

Heat-related mortality amplified during the COVID-19 pandemic

Evaluation of multiple reanalyses in reproducing the spatio‐temporal variability of temperature and precipitation indices over southern South America

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

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