Climate Research Unit Time Series Research Articles

Global-scale ERA5 product precipitation and temperature evaluation

ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis Fifth Generation) is a significant dataset in climate reanalysis. Conducting a global-scale accuracy assessment and spatiotemporal distribution characteristic study of its temperature and precipitation data is crucial for gaining in-depth understanding of extreme climate changes. This study, based on CRU TS v4.04 (Climatic Research Unit Time-Series version 4.04) gridded data, employs multiple statistical indicators and detection metrics to assess the accuracy of the ERA5 temperature and precipitation dataset at a monthly scale over the past 70 years. Additionally, it utilizes Empirical Orthogonal Functions (EOF) to explore its spatiotemporal distribution characteristics. The results indicate that: (1) ERA5 demonstrates good consistency in most regions globally; however, the influence of uneven station density distribution results in weaker correlation in areas with sparse station density. (2) The accuracy of ERA5 data exhibits certain differences between the two periods (1950–1979, 1979–2019); with the overall performance of ERA5 temperature data near the Earth’s surface being better than that of precipitation data. (3) ERA5 precipitation data in the mid and low latitudes of the Southern and Northern Hemispheres exhibit characteristics of positive–negative distribution, while temperature data show an opposite distribution in the Northern and Southern Hemispheres (with a predominant pattern of warmer temperatures in the north and colder temperatures in the south). Additionally, the typical patterns for both variables occur in January and July each year. The above study will provide reference and insights for future climate change prediction and adaptation efforts.

Evaluation of Grid-Based Aridity Indices in Classifying Aridity Zones in Iraq

In this study, the aridity index (AI) based on gridded climate data was validated for defining aridity and classifying aridity zones in Iraq through comparison with the results obtained by the station-based aridity index. Gauge-based gridded climate data taken from Climatic Research Unit Timeseries (CRU TS) were used to determine the annual value of four aridity indices (Lang, De Martonne, Ernic and UNEP AI) over the period 1998-2011. The results showed that the aridity distribution maps derived using grid-based aridity indices were reasonably close to those found using station-based ones. The four aridity indices properly identified similar aridity (dryness) classifications in both the station-based and grid-based aridity maps. The area percentage of each aridity class predicted by grid-based AIs was also compared with that obtained by the station-based AIs. The results showed that the variances between the area percentages predicted by grid-based AIs and those estimated using station-based AIs are fairly slight. The Lang AI exhibited the least variance (0.4%) while the De Martonne AI had the biggest variance (-4.8%). Despite these minor variances, it is however possible to conclude that the grid-based aridity index classified the aridity zones of Iraq as properly as the station-based aridity index did.

Compound dry and hot events over major river basins of the world from 1921 to 2020

Compound dry and hot events (CDHEs) are among the most destructive compound extremes. Under global warming, changes in precipitation, temperature and their dependence make profound contributions to CDHEs. In this paper, the contributions of these three factors are explicitly quantified based on a novel mathematical method. Specifically, time series of precipitation and temperature are employed to identify CDHEs and then changes of CDHEs are attributed by using the partial derivatives-based sensitivity analysis. Based on the Climatic Research Unit Time-Series (CRU TS), a case study of CDHEs is devised for the major river basins (MRBs) of the world. The results highlight that from the period 1921–1970 to the period 1971–2020, CDHEs did occur more frequently across most MRBs. The temperature tended to make the largest contribution, followed by precipitation and the dependence between precipitation and temperature. In Africa, South America and Western Europe, the rising temperature is generally the dominant factor for increases of heatwaves that contribute to CDHEs. In Asia, increases of droughts along with increases of heatwaves raise the risk of CDHEs. For MRBs with moderate increases in temperature, increasing precipitation is shown to mitigate or even offset the risks of CDHEs. In the meantime, the increasing dependence is observed to reduce the frequency of CDHEs in the Huai He and the Mississippi even though temperature is increasing. Overall, the attributing results of CDHEs from 1921 to 2020 can serve as a reference for the preparation and mitigation of CDHEs for MRBs across the world.

Landslide-Hazard-Avoiding Highway Alignment Selection in Mountainous Regions Based on SAR Images and High-Spatial-Resolution Precipitation Datasets: A Case Study in Southwestern China

Landslides recurrently cause severe damage and, in some cases, the full disruption of many highways in mountainous areas, which can last from a few days to even months. Thus, there is a high demand for monitoring tools and precipitation data to support highway alignment selections before construction. In this study, we proposed a new system highway alignment selection method based on coherent scatter InSAR (CSI) and ~1 km high-spatial-resolution precipitation (HSRP) analysis. Prior to the CSI, we calculated and analyzed the feasibility of Sentinel-1A ascending and descending data. To illustrate the performance of the CSI, CSI and SBAS–InSAR were both utilized to monitor 80 slow-moving landslides, which were identified by optical remote-sensing interpretation and field investigation, along the Barkam–Kangting Highway Corridor (BKHC) in southwestern China, relying on 56 Sentinel-1A descending images from September 2019 to September 2021. The results reveal that CSI has clearer deformation signals and more measurement points (MPs) than SBAS-InSAR. And the maximum cumulative displacements and rates of the landslides reach −75 mm and −64 mm/year within the monitoring period (CSI results), respectively. Furthermore, the rates of the landslides near the Jinchuan River are higher than those of the landslides far from the river. Subsequently, to optimize the highway alignment selection, we analyzed the spatiotemporal evolution characteristics of feature points on a typical landslide by combining the −1 km HSRP, which was calculated from the 30′ Climatic Research Unit (CRU) time-series datasets, with the climatology datasets of WorldClim using delta spatial downscaling. The analysis shows that the sliding rates of landslides augment from the back edge to the tongue because of fluvial erosion and that accelerated sliding is highly related to the intense precipitation between April and September each year (ASP). Consequently, three solution types were established in our method by setting thresholds for the deformation rates and ASPs of every landslide. Afterward, the risk-optimal alignment selection of the BKHC was finalized according to the solution types and consideration of the construction’s possible impacts. Ultimately, the major problems and challenges for our method were discussed, and conclusions were given.

Standardized precipitation index analysis and drought frequency tendencies in lower eastern counties of Kenya

The standardized precipitation index (SPI) is a fundamental indicator of meteorological, hydrological, and agricultural droughts in the world. This study aims to evaluate different timescales, 3 months (SPI-3), 6 months (SPI-6), 9 months (SPI-9), and 12 months (SPI-12) indices from meteorological data in quantifying drought characterization in lower eastern counties of Kenya from 1990 to 2018 for observed data and from 1986 to 2018 for Climatic Research Unit Time Series (CRU) data. Precipitation in-situ data (annual) and high-resolution (0.5 × 0.5 degree grid) monthly-scale precipitation data were sought from Kenya Meteorological Department and CRU TS, respectively. Z-Score (SPI) was computed for each year (in-situ data) and month (CRU TS data) using the SPI algorithm, expressed as the departure from the mean in standard deviation units. Quality control of CRU TS data was done by checking outlier values and comparing the data with precipitation data obtained from the meteorological department as well as ERA5 reanalysis data. Results showed that extreme to mild drought was experienced across the Kenyan counties for both annual in-situ and monthly gridded data. Machakos county experienced a year of extreme drought, while Makueni and Taita-Taveta have had 2 and 4 years of severe droughts, respectively. The monthly SPI indices of 3, 6, 9, and 12 months showed a remarkably consistent behavioral pattern detecting extreme droughts across the counties. Considering the uncertainties, unpredictability, and shifting of the long and short rainy seasons in Kenya, results were obtained related to dry and wet episodes and to their relationship with agricultural production as well as water availability and environmental management.

Evaluation and bias correction of CRU TS4.05 potential evapotranspiration across vast environments with limited data

Long-term and reliable gridded estimates of potential evapotranspiration (PET) are often dearth. Being the longest available dataset with no validation, this work makes an effort to answer two questions: First, how well does the Climatic Research Unit (CRU) Time-Series (TS) Version 4.05 PET (CRU-PET) dataset capture FAO Penman-Monteith grass reference evapotranspiration (ETo) across a range of environments and, second, can a simple adjustment approach be devised to curb the error, if any? The CRU-PET dataset is evaluated against ETo in two data-scarce countries, namely Sudan and South Sudan. Monthly station data measured at 12 stations spread over hyper-arid, arid, semi-arid and dry sub-humid zones are used. It is shown generally that CRU-PET tends to perform better in dry than wet conditions, and in arid than in humid locations. In this regard, CRU-PET has a tendency to overestimate (underestimate) the stations' values in the arid (humid) zone, with best performance characteristics achieved north of latitude 15° N. Rainfall plays a key role in determining the bias and has a non-linear effect. This bias is, however, unclear in rainless months when both augmented overestimates and underestimates are observed. Incorporating geographical coordinates (latitude, longitude and altitude) as co-variables with monthly rainfall in a multiple linear regression explained 40.7% of the variations in the bias. In this data-scarce study area (>2.5 million km2), this simple adjustment significantly improves the stations' CRU-PET as indicated by eight performance metrics. Furthermore, validation analysis showed a reduction of the overall mean bias error based on the 12 stations from −0.255 to 0.086 mm/day. To apply this correction method on a spatial domain, gridded precipitation data are needed. We used the Global Precipitation Climatology Centre (GPCC 8) dataset, with which 41.9% of the CRU-PET bias is explained. In conclusion, this study cautions the use of CRU-PET dataset without prior evaluation in areas with similar geographical boundaries, climatic conditions and limited availability of data.

Paleogene Ficus leaves from India and their implications for fig evolution and diversification.

Ficus is a scientifically and economically important genus with abundant fossil records from the Paleocene to Pleistocene, but with an intriguing early evolutionary history that remains unresolved. Here, the foliage of three well-preserved figs is described from the early Paleogene succession of the Gurha mine, Rajasthan, India. These fossils provide new morphological data that strengthens our understanding of the past occurrences of Ficus and, alongside all validly published records of fossil figs, helps to trace the evolutionary history of figs. Fossils were identified and described by comparison with their closest modern analogs using the Nearest Living Relative (NLR) technique. Validated fig records are listed and categorized into six geological time frames. Modern precipitation data for the current distributions of NLRs were downloaded from the Climatic Research Unit Timeseries. Fossil leaves assigned to three new species Ficus paleodicranostyla, F. paleovariegata, and F. paleoauriculata closely resemble their modern analogs based on leaf morphology. Reliable fossil records were used to hypothesize historical fig distributions and paleodispersal pathways. Precipitation data suggest higher precipitations at the fossil locality during the early Paleogene than at present. The fossils described herein supplement fig fossil records known from other regions indicating that figs were widely diverse across low latitudes by the early Paleogene. These data support a Eurasian origin for figs, highlight a pivotal role for the Indian subcontinent during the early phase of fig diversification, and depict a perhumid-to-humid climate with high rainfall concordant with paleoclimate evidence from the Gurha mine.

Assessing improvement in the fifth‐generation ECMWF atmospheric reanalysis precipitation over East Africa

AbstractThis study aims to assess the performance of European Center for Medium‐Range Weather Forecasting (ECMWF) Reanalysis fifth generation (ERA5) in terms of the progress made over its predecessor, ERA‐Interim, over East Africa (EA) in reproducing observed rainfall. The observed rainfall used as reference rainfall are Global Precipitation Climatology Center (GPCC) and Climate Research Unit Time Series (CRU‐TS). The performance of ERA5 to its predecessor is evaluated using root‐mean‐square error (RMSE), correlation, and bias. The reductions of wet bias from ERA‐Interim to ERA5 are 16.81–6.94% from June to September (JJAS), 31.99–19.33% from September to December (SOND), and 29.24–17.69% from December to February (DJF) over most of EA relative to CRU‐TS. Similar reductions in the wet bias relative to GPCC are also noted. Spatially, notable reductions are observed over western Ethiopia, Sudan Republic, South Sudan, and Uganda. The decreasing trends revealed in the bias of seasonal ERA5 rainfall relative to CRU‐TS and GPCC are attributed to an increase in the number and quality of assimilated observations in ERA5 over its predecessors. RMSE of ERA5 rainfall relative to the two reference rainfall and correlations with the two reference rainfall are consistent with spatial and temporal features captured in the bias. Specifically, the correlation of the monthly time series of ERA5 ranges from 0.90 with CRU‐TS over equatorial EA (EEA) to 0.98 with GPCC over southern EA (SEA) in contrast to that of ERA‐Interim which ranges from 0.84 with CRU‐TS over EEA to 0.93 with CRU‐TS over SEA. However, the wet bias in ERA5 along the Great Rift Valley remained the same as in ERA‐Interim whereas spatial correlation of ERA5 with the two reference rainfall is relatively weaker than that of ERA‐Interim.

Choice of climate data affects the performance and interpretation of species distribution models.

Climate is a core aspect of a species’ niche that is frequently incorporated into species distribution models (SDMs). The proliferation of readily accessible climate data is stimulating the increasing use of climate-based species distribution models (CSDMs) in conservation planning and management. However, it is uncertain how sensitive CSDMs are to the choice of climate data used. We compared the performance of maximum entropy-based CSDMs calibrated with seven different climate data sets (WorldClim, Chelsa, TerraClimate, Climate Research Unit Time-Series [CRU-TS], PRISM, StreamCat, and EarthEnv). For each species (four amphibians and two reptiles in California), we used a standardized, objective procedure to select climate predictors from each data set. We then assessed performance with standard metrics, calculated variable importance scores, and compared predicted distributions to historical range maps of the target species. The climate data set used affected CSDM performance, with models based on some data sets overpredicting and others underpredicting distributions compared with historical range maps of each of the species. Critically, for the six species we tested, no one climate data set always performed worst or best. These results indicate that the effects of different climate data sets should be incorporated into model evaluation when developing, applying, and interpreting climate-based SDMs.

Long‐term meteorological drought characterization in the São Francisco watershed, Brazil: A climatic water balance approach

AbstractPartially located in the Brazilian Semiarid region, the São Francisco watershed (SFW) is a strategic hydrological system for power generation, agricultural development and the integration of the national territory. Droughts are recurrent in the Brazilian Semiarid, and climate change projected scenarios indicate their intensification in the SFW, which may compromise the access to its resources. In this context, the objective of this study was to characterize long‐term spatial and temporal meteorological drought patterns over the SFW while accounting for its different climates. We used gridded Climate Research Unit Time Series rainfall and potential evapotranspiration (PET) data for the 1942–2019 (78 years) period to retrieve a monthly climatic water balance‐based drought index. The climatological aspects of drought incidence in the SFW were assessed through linear trend analysis, the estimation of the relative effects of precipitation and PET trends in drought occurrence, and the identification of the main interannual variability modes controlling the triggering of meteorological drought through wavelet functions. Results showed that water deficit periods are becoming more frequent and intense, affecting larger areas in the middle (tropical climate) and lower (semiarid climate) portions of the SFW. Months with water surplus in these regions are also becoming less frequent. In the upper (humid subtropical climate) SFW, evidences show that water deficit periods are becoming more frequent, with an expansion of areas affected by droughts. In this subregion, increasing PET trends played a more important role than precipitation trends in the propagation of drought. Finally, low‐frequency teleconnection mechanisms such as the El Niño–Southern Oscillation were found to drive the occurrence of drought in most of the SFW, while higher frequency oscillations (such as the Atlantic Meridional Mode) play a role in the lower latitudes of the basin. In the upper SFW, results indicated that interannual variability might be driven by oscillations with stronger intraseasonal components.

Climatic Change Characteristics towards the “Warming–Wetting” Trend in the Pan-Central-Asia Arid Region

In recent years, the warming–wetting trend in the arid region of Northwest China has attracted widespread attention. To reveal whether this phenomenon exists in the whole Pan-Central-Asia arid region, this paper adopts the latest monthly gridded dataset of the Climate Research Unit Time Series version 4.05 (CRU TS v4.05) and the multi-model ensemble data of the Coupled Model Intercomparison Project Phase 6 (CIMP6) for discussion from multiple perspectives. The results show that the Pan-Central-Asia arid region has been getting warmer and wetter in the last 80 years. Since the turn of the 21st century, there has been an apparent slowdown in the regional wetting trend despite the acceleration of precipitation increase, mainly caused by the growth in evapotranspiration potential. The interannual scale dominates the precipitation change, including significant quasi-three-year and quasi-six-year cycles. The interannual variability in precipitation is mainly affected by the change in the phases of Pacific decadal oscillation (PDO), while long-term variation dominates the temperature change, which is significantly related to the variations in the Arctic oscillation (AO). Thus, future research and predictions of regional precipitation should focus on the PDO variations, followed by the Southern Oscillation Index (SOI), whereas for research on, and predictions of, temperature, the effect of AO variations should be emphasized. Except for a few regions in Central-Eastern Mongolia and Central Kazakhstan, precipitation levels in most parts of the Pan-Central-Asia region have been increasing. The regional temperature exhibits a distribution pattern which decreases from northwest to southeast. The increase in precipitation in the Pan-Central-Asia arid region alleviates the drought in most regions, including most of Northwest China. However, the arid and semi-arid climate patterns in this region have not changed. The warming–wetting trend will significantly accelerate in medium-emissions scenarios in the next 80 years. Although the increase in precipitation may be a positive aspect of this trend, the rise in potential evapotranspiration caused by sharp warming may cause greater challenges to the regional climate and ecological environment.

Temperature and precipitation changes over the glaciated parts of Indian Himalayan Region during 1901-2016.

The existing knowledge on long-term climate trends over glaciated parts of Indian Himalayan Region (IHR) is limited. The present study aims at assessing the long-term (1901-2016) as well as the recent (1990-2016) temperature and precipitation trends over the glaciated parts of western (WH), central (CH) and eastern Himalaya (EH) within the IHR using Climate Research Unit Time Series version 4.01 (CRU TS4.01) data. Mann-Kendall and Sen's slope estimator tests were employed to determine the monotonic trend direction and magnitude of change over time on annual and seasonal basis. The temperature and precipitation trends were quantitatively assessed here in terms of percent change over mean as well as in absolute terms. Results show that annual average temperature remains > 0°C in WH (2.26°C) and CH (3.24°C) but < 0°C in EH (-0.97°C). Long-term analysis (1901-2016) reveals the maximum warming in EH (74.67% or 0.93°C) followed by WH (52.56% or 0.64°C) and minimum in CH (44.31% or 0.73°C). The winter warming is notably higher (WH: 1.11°C, CH: 1.19°C and EH: 1.41°C) than the summer (WH: 0.31°C, CH: 0.26°C and EH: 0.54°C). Annual precipitation gradually increases from WH (535.57mm) to CH (749.91mm) to EH (1249.49mm), of which 68%, 76%, and 90% respectively, are summer-induced. Nevertheless, precipitation showed no clear trend in WH (slight increase of 4.53%) and EH (slight decrease of -5.30%), but a clear reduction in CH (-19.25%). Seasonally, precipitation decreased in winter (-4.53%) but increased in summer (10.65%) in WH, clearly decreased in both winter (-24.69%) and summer (-17.01%) in CH, and slightly increased in winter (2.21%) but decreased in summer (-6.80%) in EH. In recent decades (1990-2016), warming trend further accelerated in WH (0.95°C) and CH (1.01°C) but decreased in EH (0.60°C). The overall precipitation trends also changed during 1990-2016 as WH experienced an overall reduction (-5%), CH maintained a declining trend (-13.10%), and EH showed slight increase (1.01%). The study concludes that the climate of glaciated parts has changed significantly, but the trend and magnitude is highly heterogeneous over different regions which likely influenced the glaciated environment.

气候变化下中国不同植被区总初级生产力对干旱的响应

PDF HTML阅读 XML下载 导出引用 引用提醒 气候变化下中国不同植被区总初级生产力对干旱的响应 DOI: 10.5846/stxb202010232707 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划资助（2019YFAO606900）；国家自然科学基金面上基金项目（42077436） Response of gross primary production to drought under climate change in different vegetation regions of China Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:干旱变化具有明显的空间分异，不同植被类型对干旱的响应亦有差别。开展气候变化下不同植被覆盖类型对干旱响应的差异分析，厘清温升干旱化进程对植被的影响，对了解植被发展动态及预测未来格局有着非常重要的意义。基于1982-2017年的总初级生产力（GPP）数据和同时期东安格利亚大学气候研究中心（CRU）时间序列（TS）气候数据，分析了中国8个植被区GPP和干旱的变化趋势，通过对比标准化降水指数（SPI）和标准化降水蒸散指数（SPEI）的趋势差异识别了典型的温升干旱化区域，在此基础上研究气温上升如何影响GPP对干旱的响应，进一步讨论了不同植被类型对干旱的敏感性差异。结果表明：（1）36年来8个植被区除青藏高原高寒植被区呈湿润化，其他植被区均呈现变干趋势；（2）气温上升大面积加剧了温带荒漠区和温带草原区的变干趋势；（3）亚热带常绿阔叶林区和热带季风雨林、雨林区的GPP受温度和干旱影响相当，青藏高原高寒植被区和针叶、落叶林混交林区的GPP受温度主导，其他植被区GPP均受干旱主导。 Abstract:Variations of drought have obviously spatial differentiation, and different vegetation types have different responses to drought. However, few studies have been conducted to distinguish the contribution of temperature increase to the variations of drought and to investigate the differences in the responses of different vegetation types to warming aridification on a large scale. Analyzing differences in the response of different vegetation cover types to drought under climate change and clarifying the impact of warming aridification on vegetation in China are of great importance for understanding the development dynamics of vegetation and predicting future patterns. In this study, based on climatic data from the University of East Anglia Climatic Research Unit (CRU) Time-series (TS) version 4.04 dataset, the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) were calculated in 1982-2017. Using Theil-Sen median trend analysis and the Mann-Kendall test, we analyzed trends in gross primary production (GPP) and drought in the eight vegetation regions of China. By comparing the trend differences between SPI and SPEI, typical warming aridification areas were identified under three scenarios ([SPI not significantly drier+SPEI significantly drier],[SPI wetter+ SPEI not significantly drier], and[SPI wetter+SPEI significantly drier]). Finally, after detrending GPP and temperature, the correlation analysis and multiple regression were used to investigate how temperature increase affected the response of GPP to drought and further discuss the differences in sensitivity of different vegetation regions to drought. The results showed that all eight vegetation regions showed a drying trend over past 36 years, except for Qinghai-Tibet plateau high cold vegetation region, which was getting wet. The GPP of each vegetation region mainly increased, among which the Qinghai-Tibet plateau high cold vegetation region showed the most significant increase in GPP, which might be related to the warming and wetting trend in this region. The temperature increase had the strongest exacerbating effect on drying in temperate desert region and temperate steppe region, and the weakest exacerbating effect on drying in temperate needleleaf and broadleaf forest region and cold temperate needleleaf forest region, while it significantly increased the sensitivity of GPP to drought in the latter. There was variability in the response of GPP to temperature and drought conditions in different vegetation regions:the GPP of the subtropical evergreen broad-leaved forest region and the tropical monsoon forest and rain forest region was comparably affected by both temperature and drought, the GPP of the Qinghai-Tibet plateau high cold vegetation region and temperate needleleaf and broadleaf forest region was dominantly affected by temperature, and the GPP in all other vegetation regions was mainly affected by drought. 参考文献 相似文献 引证文献

Evaluation of the CRU TS3.1, APHRODITE_V1101, and CFSR Datasets in Assessing Water Balance Components in the Upper Vakhsh River Basin in Central Asia

In this study, the applicability of three gridded datasets was evaluated (Climatic Research Unit (CRU) Time Series (TS) 3.1, “Asian Precipitation—Highly Resolved Observational Data Integration Toward the Evaluation of Water Resources” (APHRODITE)_V1101, and the climate forecast system reanalysis dataset (CFSR)) in different combinations against observational data for predicting the hydrology of the Upper Vakhsh River Basin (UVRB) in Central Asia. Water balance components were computed, the results calibrated with the SUFI-2 approach using the calibration of soil and water assessment tool models (SWAT–CUP) program, and the performance of the model was evaluated. Streamflow simulation using the SWAT model in the UVRB was more sensitive to five parameters (ALPHA_BF, SOL_BD, CN2, CH_K2, and RCHRG_DP). The simulation for calibration, validation, and overall scales showed an acceptable correlation between the observed and simulated monthly streamflow for all combination datasets. The coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) showed “excellent” and “good” values for all datasets. Based on the R2 and NSE from the “excellent” down to “good” datasets, the values were 0.91 and 0.92 using the observational datasets, CRU TS3.1 (0.90 and 0.90), APHRODITE_V1101+CRU TS3.1 (0.74 and 0.76), APHRODITE_V1101+CFSR (0.72 and 0.78), and CFSR (0.67 and 0.74) for the overall scale (1982–2006). The mean annual evapotranspiration values from the UVRB were about 9.93% (APHRODITE_V1101+CFSR), 25.52% (APHRODITE_V1101+CRU TS3.1), 2.9% (CFSR), 21.08% (CRU TS3.1), and 27.28% (observational datasets) of annual precipitation (186.3 mm, 315.7 mm, 72.1 mm, 256.4 mm, and 299.7 mm, out of 1875.9 mm, 1236.9 mm, 2479 mm, 1215.9 mm, and 1098.5 mm). The contributions of the snowmelt to annual runoff were about 81.06% (APHRODITE_V1101+CFSR), 63.12% (APHRODITE_V1101+CRU TS3.1), 82.79% (CFSR), 81.66% (CRU TS3.1), and 67.67% (observational datasets), and the contributions of rain to the annual flow were about 18.94%, 36.88%, 17.21%, 18.34%, and 32.33%, respectively, for the overall scale. We found that gridded climate datasets can be used as an alternative source for hydrological modeling in the Upper Vakhsh River Basin in Central Asia, especially in scarce-observation regions. Water balance components, simulated by the SWAT model, provided a baseline understanding of the hydrological processes through which water management issues can be dealt with in the basin.

Drought responsiveness in two Mexican conifer species forming young stands at high elevations

Aim of study: To determine the response of high-altitudinal forests to seasonal drought.Area of study: Monte Tláloc, Estado de México and Rancho Joyas del Durazno, Municipality of Río Verde, San Luis Potosí, México.Materials and methods: In this study, we evaluate the response to drought and hydroclimate in two young Mexican conifers sampled at high elevation, correlating records of tree-ring growth and the Normalized Difference Vegetation Index (NDVI).Main results: The results show that Pinus teocote and Abies religiosa are vulnerable to the precipitation regime and warm conditions of winter-spring. The physiological response mechanisms seem to be differentiated between the species, according to the effects of drought stress. The NDVI demonstrated the different temporal responses of the species according to their inherent physiological mechanisms in response to hydroclimatic limitations. This differentiation can be attributed to the spatial variation present in the particular physical and geographic conditions of each area. The dry and warm seasonal climates reveal P. teocote and A. religiosa to be species that are vulnerable to drought conditions. However, further evaluation of the resistance and resilience of these species is necessary, as well as disentanglement of the effects of associated mechanisms that can influence the predicted processes of extinction or migration.Research highlights: Pinus teocote and Abies religiosa are vulnerable to the seasonal drought conditions. These results are of particular importance given the climatic scenarios predicted for elevated ecotones. Tree-ring widths and NDVI improved the response of radial growth to the climate, enhancing our understanding of forest growth dynamics. The response to climatic variability depends on the particular species.Keywords: High elevation; tree-ring; ENSO; NDVI; climate-growth relationship. Abbreviations used: Normalized Difference Vegetation Index (NDVI); Tree-Ring Width (TRw); precipitation (PP); maximum temperature (Tmax); minimum temperature (Tmin); El Niño-Southern Oscillation (ENSO); Climatic Research Unit Time-series data version 4.04 data (CRU TS v. 4.04); Standardized Precipitation-Evapotranspiration Index (SPEI); Climatic Research Unit Time-series data version 4.03 data (CRU TS v. 4.03); first-order autocorrelation (AC); mean sensitivity (MS); mean correlation between trees (Rbt); expressed population signal (EPS); Ring Width Index (RWI).

Atmospheric circulation and mortality by unintentional drowning in Spain: from 1999 to 2018.

Drowning deaths are a leading cause of unintentional deaths worldwide. Few studies have analysed the role of meteorology in drowning, and with inconclusive results. The aim of this work is to analyse the temporal and geographical distribution of deaths by accidental drowning and submersion in Spain over 20 years, and to assess the relationship between accidental drowning and main atmospheric circulation patterns. An ecological study was performed, in which drowning and submersion mortality data from 1999 to 2018, considering demographic variables, were analysed. To study the association with atmospheric circulation we used an ERA5 reanalysis product over the whole European continent and the Climatic Research Unit Time Series (CRU TS) data set. The annual average rate of deaths by accidental drownings was 11.86 deaths per million of habitants in Spain. The incidence in males was four times higher than in females, and when comparing age groups, the rate in the eldest group was the highest. Unintentional drowning deaths were not equally distributed around the country; the provinces with the highest registered standardized drowning death rates were touristic waterfront provinces either in Eastern Spain or in one of the archipelagos. There was a significant relationship between accidental drowning and meteorological variables during summer months, and drowning deaths were spatially correlated with sea-level pressure over the Mediterranean basin. Although the mortality rate registered a statistically significant decreasing tendency over the studied period, our results must be taken into consideration to improve the prevention strategies in the country since most of these deaths are avoidable.

Construction of homogenized daily surface air temperature for the city of Tianjin during 1887–2019

Abstract. Century-long continuous daily observations from some stations are important for the study of long-term trends and extreme climate events in the past. In this paper, three daily data sources – (1) the Department of Industry Agency of the British Concession in Tianjin covering 1 September 1890–31 December 1931, (2) the Water Conservancy Commission of North China covering 1 January 1932–31 December 1950 and (3) monthly journal sheets for Tianjin surface meteorological observation records covering 1 January 1951–31 December 2019 – have been collected from the Tianjin Meteorological Archive. The completed daily maximum and minimum temperature series for Tianjin from 1 January 1887 (1 September 1890 for minimum) to 31 December 2019 has been constructed and assessed for quality control with an early extension from 1890 back to 1887. Several significant breakpoints are detected by the penalized maximal T test (PMT) for the daily maximum and minimum time series using multiple reference series around Tianjin from monthly Berkeley Earth (BE), Climatic Research Unit Time-Series version 4.03 (CRU TS4.03) and Global Historical Climatology Network (GHCN) v3 data. Using neighboring daily series the record has been homogenized with quantile matching (QM) adjustments. Based on the homogenized dataset, the warming trend in annual mean temperature in Tianjin averaged from the newly constructed daily maximum and minimum temperature is evaluated as 0.154 ± 0.013 ∘C per decade during the last 130 years. Trends of temperature extremes in Tianjin are all significant at the 5 % level and have much more coincident change than those from the raw data, with amplitudes of −1.454, 1.196, −0.140 and 0.975 d per decade for cold nights (TN10p), warm nights (TN90p), cold days (TX10p) and warm days (TX90p) at the annual scale. The adjusted daily maximum, minimum and mean surface air temperature dataset for Tianjin city presented here is publicly available at https://doi.org/10.1594/PANGAEA.924561 (Si and Li, 2020).

Spatiotemporal variability of rainfall trends and influencing factors in Rwanda

Rainfall is the most important meteorological variable that influences the economic development of Rwanda. Changes in rainfall trends and variability over recent past years have become a great concern to policymakers and scientists. This study aims at examining the spatiotemporal variability of rainfall over Rwanda and the teleconnections of rainfall with different large-scale ocean-atmospheric variables at different timescales. The study used rainfall data of Climate Hazards Category Infrared Precipitation with Stations (CHIRPS) and Climate Research Unit Time Series Version 4 (CRU) for the period 1981–2017. Several statistical methods, including standardized anomaly, Empirical Orthogonal Functions (EOF), Pearson Correlation, Mann-Kendall (MK), and Sen's gradient estimator, were used to assess the variability, trends, and teleconnections of rainfall with various driving factors. Results revealed a bimodal rainfall pattern in its annual cycle. The spatial distribution of annual and seasonal rainfall showed a southwest to northwest rainfall gradient. The MK test revealed a decreasing trend in annual rainfall in the southwest part of the country. Overall, March to May (MAM) rainy seasons showed a decreasing and September to December (SOND) rainy season an increasing trend over Rwanda. The EOF analysis revealed that the leading mode of variability for MAM rainfall parades a unimodal scheme with negative loadings that can explain 59.3% of the total rainfall variance. The dominant mode of variability of SOND rainfall revealed the same pattern but with positive loadings that can explain 58.1% of the total variance. Spatial correlation showed that the MAM (SOND) rainfall has a weak (strong) relationship with the Indian Ocean sea surface temperature (SST), which means a negative (positive) Indian Ocean Dipole can lead to anomalously wet (dry) conditions over Rwanda. A stronger influence of El-Nino Southern Oscillation (ENSO) on SOND rainfall than MAM rain was noticed. The results of this study are crucial in developing appropriate mitigation measures to curb the impacts of climate change on the agriculture and water resources of Rwanda.

Evaluation of six precipitation products in the Mekong River Basin

Continuous and reliable precipitation records are essential for hydrological and meteorological studies. In the Mekong River Basin (MRB), there are many satellite-based and gauge-based precipitation products. It is necessary to evaluate the reliabilities of these precipitation products. In this study, we evaluated six widely used precipitation products in the MRB on a monthly scale, namely Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources v1101 (APHRODITE), Global Precipitation Climatology Center v2018 (GPCC), Climatic Research Unit Time-Series v4.03 (CRU), Climate Hazards group InfraRed Precipitation with Stations v2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation v1.0 (MSWEP), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). Two evaluation methods, namely the reference gauge method and the hydrological simulation method, were used. For the reference gauge method, APHRODITE had the best consistency with precipitation observations at 25 reference gauges among the six precipitation products. For the hydrological simulation method, hydrological model using APHRODITE as the input obtained the most accurate runoff simulations among the six precipitation products. Thus, APHRODITE could be a reliable precipitation product in the MRB. The evaluation results provide a useful reference for the hydrological and meteorological study in the MRB.

Assessment of Gridded CRU TS Data for Long-Term Climatic Water Balance Monitoring over the São Francisco Watershed, Brazil

Understanding the long-term behavior of rainfall and potential evapotranspiration (PET) over watersheds is crucial for the monitoring of hydrometeorological processes and climate change at the regional scale. The São Francisco watershed (SFW) in Brazil is an important hydrological system that transports water from humid regions throughout the Brazilian semiarid region. However, long-term, gapless meteorological data with good spatial coverage in the region are not available. Thus, gridded datasets, such as the Climate Research Unit TimeSeries (CRU TS), can be used as alternative sources of information, if carefully validated beforehand. The objective of this study was to assess CRU TS (v4.02) rainfall and PET data over the SFW, and to evaluate their long-term (1942–2016) climatological aspects. Point-based measurements retrieved from rain gauges and meteorological stations of national agencies were used for validation. Overall, rainfall and PET gridded data correlated well with point-based observations (r = 0.87 and r = 0.89), with a poorer performance in the lower (semiarid) portion of the SFW (r ranging from 0.50 to 0.70 in individual stations). Increasing PET trends throughout the entire SFW and decreasing rainfall trends in areas surrounding the semiarid SFW were detected in both gridded (smoother slopes) and observational (steeper slopes) datasets. This study provides users with prior information on the accuracy of long-term CRU TS rainfall and PET estimates over the SFW.