International Journal of Climatology最新文献

{"title":"Monthly High-Resolution Historical Climate Data for North America Since 1901","authors":"Tongli Wang,&nbsp;Andreas Hamann,&nbsp;Zihaohan Sang","doi":"10.1002/joc.8726","DOIUrl":"https://doi.org/10.1002/joc.8726","url":null,"abstract":"<p>Interpolated grids of historical climate variables are widely used in climate change impact and adaptation research. Here, we contribute monthly historical time series grids since 1901 for our data product <i>ClimateNA</i>, which integrates historical data and future projections to generate high-resolution gridded data and point estimates for North America. The historical climate grids in this study are based on interpolations of monthly anomalies (change factors) with thin-plate splines, but a novel aspect is that we rely on high-quality 1961–1990 normal estimates from <i>ClimateNA</i> to serve as reference for the change factor calculations instead of the reference being derived from station data itself. This allowed us to utilise records from 66,282 climate stations for interpolations, regardless of their temporal coverage. Another aspect that deviates from standard practice is that we reduce overfitting by optimising thin-plate splines at a 0.5° grid level instead of fitting weather station observations directly. The high-resolution grids generated with this approach compared favourably with other time series products, such as Daymet and advanced multi-source products, such as MSWEP, in statistical and mapped visual comparisons, and provide additional historical coverage since the beginning of the 20th century.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 3","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8726","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143530681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rainfall Erosivity Projection in South-East Australia Using the Improved Regional Climate Simulations","authors":"Qinggaozi Zhu,&nbsp;Xihua Yang,&nbsp;Fei Ji,&nbsp;Zheyuan Du","doi":"10.1002/joc.8702","DOIUrl":"https://doi.org/10.1002/joc.8702","url":null,"abstract":"<div>\u0000 \u0000 <p>Rainfall erosivity is one of the most dynamic factors in the soil erosion process. The increase in soil erosion caused by high rainfall erosivity, and the subsequent loss of soil nutrients, can lead to reduced food production and ecosystem services. This research program under the New South Wales (NSW) Climate Change Adaptation Strategy, assesses rainfall pattern change, rainfall erosivity and erosion risk across NSW under future climate conditions. Daily rainfall erosivity and erosion risk were modelled by Revised Soil Loss Universal Equation (RUSLE) approach and compared with that driven by observed rainfall data. Future rainfall erosivity and soil erosion risk change were investigated from daily precipitation projection of the updated NSW and Australian Regional Climate Modelling (NARCliM1.5) for two future scenarios, RCP4.5 and RCP8.5, from the historical (1986–2005) to far future (2060–2079) periods. The annual average rainfall erosivity is projected to increase about 8% under RCP 4.5 and further decrease 5% under RCP 8.5 in NSW due to the predicted temperature rises. More frequent heavy rainfall events are projected to occur during summer (December–January–February), and the rainfall from these extreme rainfall events is expected to account for 51% of the total annual rainfall in the far future. NARCliM-derived results underestimate annual rainfall erosivity compared with observation-derived erosivity. There are greater instability (root mean squared error [RMSE]: 803.2) and erosivity uncertainty (Bias: 16%~48%) in high rainfall zones. At a monthly scale, dry months (June–July–August) are becoming drier, while wet months (December–January–February) are becoming wetter and more erosive. 67% of NSW is predicted to experience increased rainfall erosivity under RCP4.5, whereas most of NSW will shift to drought and its consequent effects under the high-end emission scenario (RCP 8.5). To address the dual challenges of excessive wetness in coastal and north-east NSW and increasing aridity in Western NSW, it is necessary to develop climate change adaptation management strategies based on high-risk areas and monthly or seasonal conditions. With the emerging launch of NARCliM2.0, we anticipate further improvements of these predictions will be achieved by more accurate models and data at higher spatial and temporal resolutions.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 2","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Methods Based on Limited Meteorological Data to Simulate Potential Evapotranspiration: A Case Study of Source Region of Yellow River Basin","authors":"Yinan Pei,&nbsp;Shengqi Jian,&nbsp;Guodong Zhang","doi":"10.1002/joc.8717","DOIUrl":"https://doi.org/10.1002/joc.8717","url":null,"abstract":"<div>\u0000 \u0000 <p>Simulation of potential evapotranspiration (<i>PET</i>) is an important part of drought warning and water resource planning. However, the commonly used empirical models need to input a large number of meteorological elements. Therefore, to improve the efficiency and accuracy of <i>PET</i> simulation in areas lacking meteorological data, this study evaluated the performance of Extreme learning machine (ELM), multi-layer perceptron (MLP), and Random Forest (RF) three machine learning methods to simulate daily <i>PET</i> using limited meteorological data in the source region of the Yellow River (SYRB). Two bionic optimization algorithms, Grey Wolf Optimizer (GWO) and Sparrow Search Algorithm (SSA), were used to optimise the hyperparameters of the model to improve the accuracy of the model. In addition, the effect of months on daily <i>PET</i> simulations was evaluated. The <b>results</b> showed that the daily maximum temperature (<i>T</i>\u0000 _max) was the most important factor affecting the <i>PET</i> simulation, and the daily average relative humidity (<i>RH</i>) and wind speed (<i>U</i>\u0000 ₁₀) were the secondary factors. It is recommended to use <i>T</i>\u0000 _max, <i>RH</i>, <i>U</i>\u0000 ₁₀, and sunshine duration as the optimum combination of input (<i>R</i>\u0000 ² &gt; 0.95). In the case of limited meteorological data, the input combination of <i>T</i>\u0000 _max, RH, <i>U</i>\u0000 ₁₀, or <i>T</i>\u0000 _max, <i>RH</i> (<i>R</i>\u0000 ² &gt; 0.75) was considered. Considering the accuracy and the time and space overhead of the model, the ELM-GWO model is recommended. When month information was used as an input factor, model performance improved in all scenarios, and June to July was the most accurate month for the model to simulate daily <i>PET</i>. This research resultwill allow researchers to choose the appropriate meteorological factor when simulating the <i>PET</i> to provide the reference.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 3","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143530791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction of 2-m Air Temperature From ERA5 Reanalysis at Dome A, Antarctica","authors":"Yifan Wu,&nbsp;Yu Jiang,&nbsp;Yi Zhang,&nbsp;Yichen Li,&nbsp;Xin Chen,&nbsp;Wenqian Zhang,&nbsp;Xi Zhao","doi":"10.1002/joc.8722","DOIUrl":"https://doi.org/10.1002/joc.8722","url":null,"abstract":"<div>\u0000 \u0000 <p>In this study, we jointly used in situ air temperature from AWS and reanalysis data from ERA5 to make the first-ever reconstruction of a 42-year (1978–2020) air temperature time series for Dome A, Antarctica. By analysing the impact of environmental variables, we found that the 10-m u-component of wind was the predominant one for air temperature bias between ERA5 and AWS, followed by total cloud cover. Air temperature deviations between ERA5 and AWS during the period of 2005–2020 were successfully reduced by applying a random forest (RF) model, decreasing the bias by 0.52°C, the RMSE by 3.16°C and the MAE by 2.77°C. We next applied the RF model to predict the 2-m air temperature difference which was added back to correct ERA5 from 1978 to 2004. This yielded an accurate time series of air temperature from 1978 to 2020. Using the innovative trend analysis method to analyse the temperature trend of the corrected data, we found that Dome A has experienced a gradual warming of 0.10°C dec⁻¹ over the 42-year period. Among the seasonal temperature changes, spring showed a significant warming trend of 0.57°C dec⁻¹, autumn and winter showed no significant warming, while summer showed a slightly cooling trend. Also, over the 42-year analysis period, a stable oscillation period of ~28 year was observed. This cycle emerged as the dominant pattern, influencing the overall temperature evolution. The method proposed in this research, which combines machine learning with AWS to correct ERA5 air temperature data, holds the potential to address spurious changes of reanalysis data in long-time series studies, thus improving the reliability of trend analyses.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 3","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143530656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MPI-ESM Grand Ensemble-Simulated Influence of the Mount Pinatubo Volcanic Eruption on Winter Climate Over the Mid-to High-Latitude Northern Hemisphere Continents","authors":"Zongjin Qin,&nbsp;Tao Wang,&nbsp;Ya Gao,&nbsp;Yuanhai Fu","doi":"10.1002/joc.8719","DOIUrl":"https://doi.org/10.1002/joc.8719","url":null,"abstract":"<div>\u0000 \u0000 <p>In this study, the effects of the Mount Pinatubo eruption on surface air temperature (SAT) over mid- to high-latitude Northern Hemisphere (NH) continents in December–January–February (DJF) 1991/92 were investigated using MPI-ESM Grand Ensemble simulations, observations and reanalysis data. The results indicated that the 1991 Mount Pinatubo eruption was not the primary cause of the SAT warming anomaly over the mid- to high-latitude NH continents in DJF 1991/92. In the observations, a positive Arctic Oscillation (AO) or North Atlantic Oscillation (NAO)-like pattern dominated the warming of Eurasia, while a Pacific North American (PNA)-like pattern dominated the warming of North America. However, the model ensemble mean (MEM) simulated SAT and sea level pressure anomalies were much weaker over high-latitude continents. Furthermore, by categorising the 100 MPI-ESM Grand Ensemble simulations into four categories, we found that the probability of warm and cold temperature anomalies occurring over Eurasia and North America was nearly equal. Only about 22% of the MPI-ESM Grand Ensemble members simulated winter warming over the mid- to high-latitude NH continents that matched observations. Our study suggested that this winter warming was mostly caused by the internal variability of the climate system, which was consistent with previous studies. A more detailed analysis indicated that, following the Mount Pinatubo eruption, the intrinsic phase shifts in the AO and PNA remained key factors driving the SAT variations in Eurasia and North America, respectively.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 3","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143530377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Widespread Decline of the Warm Season Snow Depth Over Arctic Sea Ice Revealed by Satellite Passive Microwave Measurements","authors":"Haili Li,&nbsp;Chang-Qing Ke,&nbsp;Qinghui Zhu,&nbsp;Xiaoyi Shen,&nbsp;Yu Cai","doi":"10.1002/joc.8716","DOIUrl":"https://doi.org/10.1002/joc.8716","url":null,"abstract":"<div>\u0000 \u0000 <p>Summer snow plays an essential role in Arctic hydrology and in maintaining mass and energy balance of sea ice. However, there are great challenges in retrieving long-term summer snow depths over Arctic sea ice. Here, we proposed a combined novel five-variable long short-term memory (hereafter CN5VLSTM) model based on brightness temperature data to yield warm-season snow depth estimates. Then, year-round snow depth estimates were obtained for the first time. The CN5VLSTM model and five additional snow depth methods were assessed during the warm season based on the ice mass balance buoy (IMB), Alfred Wegener Institute (AWI) snow buoy (AWI-SB) and Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) snow buoy (MOSAiC-SB). According to the three buoy products, the accuracy of the CN5VLSTM-derived snow depth was highest among the five snow depth estimates with RMSEs of 10.2, 16.4, and 10.1 cm, respectively. Except for in May, the Arctic snow depth showed mainly a downward trend in warm months, and a significant downward trend was found in the Central Arctic. Excluding the Barents Sea, Kara Sea and Canadian Archipelago, the average year-round snow depth decreased in the other subregions, and a significant negative trend was observed in the East Siberian and Chukchi Seas. Snowfall was an important factor that was related to the changes in snow depth in the East Siberian and Chukchi Seas. This study can provide new insights into the evolution characteristics of summer snow depth.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 2","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Annual and Monthly Precipitation Anomalies in Ecuador Bioregions Using WorldClim CMIP6 GCM Ensemble Projections and Dynamic Time Warping","authors":"Fabián Santos,&nbsp;José Jara,&nbsp;Nicole Acosta,&nbsp;Raúl Galeas,&nbsp;Bert de Bièvre","doi":"10.1002/joc.8685","DOIUrl":"https://doi.org/10.1002/joc.8685","url":null,"abstract":"<div>\u0000 \u0000 <p>The Coupled Model Inter-comparison Project phase 6 (CMIP6) provides a suite of general circulation models (GCMs) and Socioeconomic Shared Pathways (SSPs) primarily for continental-scale climate assessments. However, adapting these models for sub-national assessments, particularly in countries with varied geography like Ecuador, and for complex variables such as precipitation, introduces challenges, including uncertainties in selecting appropriate GCMs and SSPs. To address these issues, we adopt a biogeographical approach that integrates regional climatic variations. Our analysis explores 26 GCMs, four SSP scenarios and four 20-year time frames from WorldClim to evaluate discrepancies between the GCM precipitation projections, historical data and national climate projections across five Ecuadorian bioregions. This approach enabled us to sort the GCMs by annual precipitation medians, classify their monthly precipitation using Dynamic Time Warping (DTW) clustering, and develop ensembles highlighting both the largest and average precipitation anomalies within and beyond the bioregions. Among the 26 models examined, 16 projected an increase in annual precipitation in Ecuador, especially during the wet seasons, with the BCC-CSM2-MR model showing peak values, notably in the Choco region and eastern Amazon basin. Conversely, 10 models, with CMCC-ESM2 showing the largest decreases, projected reduced precipitation across almost all Ecuadorian territories, except the Choco region. The largest reductions were in the Amazon basin, raising concerns about reduced precipitation. Discrepancies, primarily in the Andes and Galapagos bioregions, reveal the challenges posed by their complex topography and insular environments. While the GCMs captured spatial patterns of ENSO, our research was constrained to 20-year averages, making direct comparison with historical records infeasible, highlighting the need for further research with shorter time frames and finer spatial resolutions. The variability in precipitation was linked to geographical factors, GCM configurations and unexpected SSP outcomes. Therefore, selecting GCMs and climatic indices tailored to specific bioregions is recommended for effective climate change impact assessments.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 2","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why Super Typhoon Occurrence Over the Western North Pacific Ocean Tends to be More in Autumn Than Summer","authors":"Zhiming Feng,&nbsp;Xiuping Yao,&nbsp;Chenfei Liao,&nbsp;Jiali Ma,&nbsp;Ning Pan,&nbsp;Yanluan Lin","doi":"10.1002/joc.8694","DOIUrl":"https://doi.org/10.1002/joc.8694","url":null,"abstract":"<div>\u0000 \u0000 <p>Based on the tropical cyclone (TC) best-track datasets from the China Meteorological Administration during 1949–2020 and the fifth generation ECMWF atmospheric reanalysis (ERA5) datasets, we investigate the characteristics of super typhoons (SuperTYs) over the western North Pacific (WNP) and associated mechanism in this study. The results show that SuperTYs are prone to occur in autumn over the WNP, nearly 30% of the autumn TCs develop into SuperTYs, and autumn SuperTYs account for more than half of the annual total. This is due to both favourable oceanic and atmospheric conditions. In southeastern WNP, the sea surface temperature (SST) in autumn is higher than that in summer, inducing zonal circulation anomalies and enhancing low-level westerlies. Consequently, the monsoon trough strengthens and extends eastward, favouring enhanced autumn typhoon occurrence in the southeastern WNP. This southeastward shift facilitates TCs to remain over the warm ocean for a longer period and makes them more prone to develop into SuperTYs. Furthermore, TCs tend to take westward-moving tracks in autumn due to stronger easterly steering flows compared with summer, resulting in more TCs passing over the South China Sea (SCS) to the east of the Philippines where the vertical wind shear (VWS) is relatively weaker and prone to develop into superTYs.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 2","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indian Summer Monsoon Rainfall Characteristics Derived From Multiple Gridded Precipitation Datasets: A Comparative Assessment","authors":"Sandipan Paul,&nbsp;Priyank J. Sharma,&nbsp;Ramesh S. V. Teegavarapu","doi":"10.1002/joc.8708","DOIUrl":"https://doi.org/10.1002/joc.8708","url":null,"abstract":"<div>\u0000 \u0000 <p>Precipitation, a crucial component of the Earth system processes, regulates the spatiotemporal cyclicity of water, energy, and carbon fluxes. Accurate precipitation datasets leverage the understanding of precipitation dynamics and are vital for hydro-climatological studies. South Asian monsoon is a complex, multi-scale interacting, synoptic, and ocean–land–atmosphere coupled system, contributing to significant spatial and temporal variability in summer monsoonal rainfall across India. This study evaluates four types of gridded (observational, satellite, reanalysis, and hybrid) precipitation products in their ability to replicate Indian Summer Monsoonal Rainfall (ISMR) characteristics using the India Meteorological Department (IMD) 0.25° gridded data as the baseline. A comparative assessment is performed in this study that uses several continuous and interval-based performance measures to evaluate the overall rainfall magnitude detectability and time-matched capturing of rainfall events. A new metric, rank score, is developed by aggregating multiple measures to find the best product. The analyses based on several performance measures indicate that MSWEP is the best dataset (rank one) that closely approximates the occurrence and magnitude of IMD-based rainfall events, while APHRODITE, CHIRPS, and IMDAA are ranked as the next best set of products. PGF is ranked the lowest among all products evaluated and is not recommended for applications. Nonetheless, APHRODITE suffers from strong negative biases, while the reanalysis (IMDAA, ERA5-Land, PGF) datasets show significant positive biases. Among the products evaluated, APHRODITE, ERA5-Land, and IMDAA have shown a limited ability to detect excess, normal, and deficit monsoon years, respectively. In general, the performance of satellite-based data products is superior to that of reanalysis datasets in accurately characterising the monsoon years. ERA5-Land is noted to be the best-performing dataset among the reanalysis products. The comprehensive comparative assessment carried out in this study benefits the selection and use of appropriate gridded precipitation products for hydroclimatic modelling, climate variability, and change studies.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 2","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Trends in Extreme Temperature Events Across the Contiguous United States","authors":"Chibuike Chiedozie Ibebuchi,&nbsp;Cameron C. Lee,&nbsp;Scott C. Sheridan","doi":"10.1002/joc.8693","DOIUrl":"https://doi.org/10.1002/joc.8693","url":null,"abstract":"<p>Extreme heat events (EHEs) are becoming prevalent across the globe and are a major factor in terms of temperature-related mortality in the United States (US). In this study, we compare trends in extreme temperature events (ETEs) across the Contiguous US, from 3 reanalysis products, namely: European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5), Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA2) and North American Regional Reanalysis (NARR). We focused on the trends (1980–2022) in absolute extreme heat and cold events (ECE) as well as seasonally relative extreme heat and cold events (REHE and RECE). ETEs are defined based on a duration-intensity metric calculated from excess apparent temperature factors, based on the exceedance of apparent temperature beyond local percentile thresholds while incorporating an acclimatisation factor. Our results show that the reanalysis data sets generally produced consistent climatology of ETEs, though with some inconsistencies in their number and spatial distribution. ETE trends in the study region are spatially heterogeneous and were more consistent between MERRA2 and ERA5. Nonetheless, all data sets agree that the frequency of EHEs is significantly increasing in the western parts of the US, whereas REHEs are significantly increasing in the southern parts. The highest increase in the frequency of EHEs occurs in southern California and Nevada, while REHE trends are maximal in Florida. RECEs are significantly decreasing more in spatial scale and magnitude than ECEs, especially towards the coastal regions. The highest decrease in RECEs is in Florida peninsula, southern California and Nevada. The data sets show inconsistency in ECE trends. Trends in excess temperature factors further indicated that extreme cold conditions are decreasing faster compared to the increasing trends of extreme heat conditions. Our findings highlight the need for improving the monitoring of ETEs across the US and for policies that mitigate the impact of ETEs on biological systems.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 2","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8693","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}