Expert Team On Climate Change Detection And Indices Research Articles

Performance of CHIRPS for estimating precipitation extremes in the Wala Basin, Jordan

Abstract The evaluation of open-source precipitation data is crucial to enable the selection of the most appropriate product for a specific research. This study aims to evaluate the capability of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) with a spatial resolution of 0.05° for estimating monthly and annual precipitation in the Wala basin, Jordan, from 1987 to 2017 using a point-to-pixel comparison approach. Eleven precipitation extreme indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), were used in this study. The findings show that CHIRPS correlated moderately with stations in monthly precipitation estimation, with the Pearson correlation coefficient values ranging from 0.50 to 0.73. However, CHIRPS had low correlations with stations in most of the extreme indices, except PRCPTOT, R10mm, and R20mm. The CHIRPS, particularly in the extreme years, overestimated low precipitation amounts and underestimated high ones. Moreover, CHIRPS underestimated the calculation of consecutive dry days, consecutive wet days, R10mm, R20mm, and R30mm, while an overestimation was found for the R95p, R99p, and Rx1day. The trend analysis and Wilcox text showed a lack of resemblance between the CHIRPS and gauges, showing a bias correction is needed before applying an extreme analysis in this region.

Impacts of improved horizontal resolutions in the simulations of mean and extreme precipitation using CMIP6 HighResMIP models over West Africa.

We conducted an analysis of 16 historical simulations from the High-Resolution Model Intercomparison Project (HighResMIP) as part of the Coupled Model Intercomparison Project (CMIP) phase 6 (CMIP6). These simulations encompass both high- and low-resolution models and aim to investigate the impact of improved horizontal resolution on mean and extreme precipitation in West Africa between 1985 and 2014. Six Expert Team on Climate Change Detection and Indices (ETCCDI) were used to charactererize extreme indices. Bias adjustment was used to detect and adjust the biases in the models. Our observations indicate that the southeastern and southwestern regions of West Africa experience the most significant precipitation, which aligns with the simulations from HighResMIP. The enhanced horizontal resolution notably influences the simulation of orographically induced rainfall in elevated areas and intensifies precipitation in various aspects. When examining the highest 1-day precipitation, our observations reveal that most of the Guinea Coast region had 1-day rainfall exceeding 100mm. However, this was overestimated and in some simulations underestimated by HighResMIP simulations. Furthermore, an increase in horizontal resolution appears to enhance the ability of high-resolution models to replicate the observed patterns of heavy precipitation (R10mm) and very heavy rainfall (R20mm) days. Spatial and temporal analysis suggests that uncertainty exists in the simulation of extreme precipitation in both high- and low-resolution simulations over West Africa. Also, bias adjustment shows a significant bias in the simulations. To address this issue, we employed a bias adjustment approach.

Effect of climate change to characteristic of extreme rainfall over Batanghari watershed

Climate change will cause extreme rainfall, both wet and dry. Extreme rainfall will cause catastrophic floods and droughts. Therefore, it is important to analyse extreme rainfall and flood events in the future. Disasters and environmental issues in the Batanghari watershed include flooding, drought, forest and land fires, sedimentation, and water quality degradation. Therefore, the Batanghari watershed is selected as a study area in this research. Research on climate change in the Batanghari watershed is crucial to understanding the trend of extreme conditions now and in the future. Characteristics of extreme rainfall will be analysed using the climate index that was developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Results show that climate change in the Batanghari watershed is indicated by increasing 5 of 6 extreme rainfall indices: R95p, R99p, Rx1day, Rx5day, and SDII. An increase in extreme rainfall is correlated with an increase in flood events in the Batanghari watershed in similar periods. The extreme rainfall index that most influences flooding in the Batanghari watershed is PRCPTOT and R99p. Flooding is expected to occur more frequently in the future than it does now. The Batanghari Hilir Sub-watershed is more vulnerable to extreme flooding in the future than other sub-watershed.

Assessment of mean precipitation and precipitation extremes in Iran as simulated by dynamically downscaled RegCM4

This paper aims to assess the mean precipitation and precipitation extremes over Iran as simulated by the Regional Climate Model (RegCM4). A simulation spanning 20 years (1991-2010) at a horizontal resolution of 20 Km is driven by the NCEP / NCAR reanalysis. We evaluated the model by comparing simulated precipitation with observations using Bias, Root-Mean-Square Error, and Index of Agreement metrics. We examined the extreme precipitations based on a set of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) in three categories of intensity (Rx1day and SDII), duration (CDD and CWD), and frequency (R10mm and R20mm). The linear trends are calculated using the Theil–Sen estimator method, and the statistical significance (95% confidence level) is determined by using a modified Mann-Kendall (MMK) trend test. The RegCM4 model satisfactory captured the spatial distribution of precipitation and precipitation extremes, although high bias remained in small parts of Iran, including the northwest and southeast. The northwest bias is due to spring convectional precipitation and the southeast bias could be caused by precipitation from the Asian summer monsoon system, which both of them may not be well simulated by the applied Grell convective scheme. Results indicate that the model reasonably captures Rx1day, SDII, CWD, R10mm, and R20mm Indices over Iran. In good agreement with precipitation observations, the southern coast of the Caspian Sea represents the second-highest extreme precipitation, except for SDII, which is probably due to the high frequency of rainy days in this region. The highest CDD of more than 200 days is found in the arid and semi-arid regions of the southeast. In general, precipitation decreased in most regions of Iran, especially the western, southern, and interior regions. In addition, the results reveal that heavy (R10mm) and very heavy (R20mm) precipitation events have also decreased in the same regions. Results also emphasize an increase in consecutive dry days (CDD) in most parts, especially in the southeast, which deserves more attention in future research. The decreasing trend of precipitation and the increasing trend of CDD show that Iran has become drier in the 2000s compared to the 1990s.

Winter warm spells over Italy: Spatial–temporal variation and large‐scale atmospheric circulation

AbstractThis article analyses the winter warm spells (WWS) that occurred in central Mediterranean over the period 1993–2022, examining the daily maximum temperatures collected at eight airport sites located in the Italian Peninsula, belonging to different climate zones. According to the definition proposed in 1999 by the Expert Team on Climate Change Detection and Indices (ETCCDI), a WWS is a sequence of at least six consecutive days when daily maximum air temperature exceeds the calendar day 90th percentile centred on a 5‐day window for a base period. WWS occurring over the entire Italian territory or only over northern/central/southern Italy have been identified and related to the peculiar synoptic conditions. It was found that December is the month most prone to WWS and, on average, WWS last 9.4 days in northern Italy, 6.6 days in central Italy, and 8.5 days in southern Italy. Over the period under investigation, the Italian Peninsula experienced only one common event characterized by persistent high‐pressure systems associated with air subsidence over western Mediterranean and, therefore, with exceptional warming. Finally, it has been proven that the definition of WWS proposed by ETCCDI allows to capture synoptic scale events but, in orographically complex areas such as Italy, underestimates moderate spells, which generally might have a duration of at least 3 days. Consequently, it is important to consider the possibility of reducing the period length threshold used for the detection of WWS when orographically heterogeneous regions are studied.

Assessing exposure to climate extremes over the Arabian Peninsula using ERA5 reanalysis data: Spatial distribution and temporal trends

Understanding climate extremes and indices is crucial for addressing climate change impacts, including the increased variability in local weather patterns and extreme events. Previous studies on the Arabian Peninsula (AP) have been limited by sparse station data and restricted spatial coverage. To overcome this limitation, this paper aims to assess the spatial distribution and temporal trends of temperature and precipitation indices suggested by the Expert Team on Climate Change Detection and Indices (ETCCDI) using ERA5 reanalysis data from 1951 to 2020 for the AP. Additionally, this study investigated the changes relative to the reference period (1951–1980). The modified Mann-Kendall test and Sen's Slope estimator are utilized to detect significant trends and estimate their magnitude. Results revealed that ERA5 was consistent with previous studies that utilized in-situ station data, which offers a more detailed picture of the affected areas. The findings indicated a significant warming trend in temperature indices, with an increase of over 1 °C per decade observed across several areas, including the northern AP, and certain regions experiencing a discrepant change of 3 °C increase compared to the reference period. Changes in rainfall indices indicate a shift in rainfall patterns from the AP's fertile southwest regions towards more intense patterns in specific eastern regions, including Oman, Kuwait, Saudi Arabia, and Yemen. However, the precipitation temporal trends are weak in magnitude and variable spatially, with dominant decreases in both intensity (-10 mm per decade) and frequency indices (-5 days per decade). Some locations are subject to the combined effects of most heatwave indicators simultaneously, while flood indicators and yet others by drought indicators influence others. All these locations have been accurately identified in this study. The findings provide valuable information regarding the region's climate change vulnerability and adaptation needs.

Large-scale atmospheric teleconnections and spatiotemporal variability of extreme rainfall indices across India

Identifying trends in hydrometeorological time series during extreme weather events and their interactions with large-scale atmospheric teleconnections is crucial for climate change research. This study evaluates 14 precipitation-based indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) across seven climatic zones of India using gridded daily rainfall data from the India Meteorological Department (IMD) for 120 years (1902–2021) utilised. Trend analysis was carried out using the Mann-Kendall (MK) test, Theil-slope Sen's estimator, Innovative Trend Analysis (ITA), and other statistical tools. Change point detection is established using the Pettitte test and Cumulative Sum algorithm. The relationships between large-scale atmospheric teleconnections and ETCCDI indices are also found, and Multiple Linear Regression (MLR) models are developed between them.The results show significant increasing trends in extreme rainfall indices in India's Ladakh region, located in the arid desert-cold climatic zone. The annual, Southwest Monsoon (SW-Monsoon), Northeast Monsoon (NE-Monsoon), and summer rainfall trends were positive, while winter rainfall had a negative trend across most climatic zones. Significant associations between large-scale atmospheric teleconnections, including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Global Temperature Anomaly (GTA), Southern Oscillation Index (SOI), SST of Niño 3.4 region, Oceanic Niño Index (ONI), and Dipole Mode Index (IOD) and ETCCDI indices were established across multiple climatic zones. Using MLR analysis, this study attempts to establish, for the first time, the relationship between teleconnections and ETCCDI indices across India. Extreme rainfall indices are influenced by climate change during the SW-Monsoon across most of the climatic zones of India. During the previous El Niño event (2014–2016), average annual rainfall decreased by 19.5%, SW-Monsoon rainfall decreased by 25.2%, and NE-Monsoon rainfall decreased by 64.1% in India. The findings may provide valuable insights into mitigation strategies to sustain the adverse effects of extreme weather conditions and enhance climate resilience.

PROYEKSI TREN SUHU UDARA DI JAWA TIMUR BERDASARKAN SKENARIO RCP 4.5

Changes in the pattern and intensity of climate parameters, including air temperature, can be used to detect climate changes. It is very important to study air temperature trends related to climate change in East Java because this region is one of the provinces that contributes the largest rice production in Indonesia. Food security is negatively impacted by climate change. The research aims to determine changes temperature trend in detail on a regional scale. Climate change detection is carried out by analyzing the air temperature index issued by the Expert Team on Climate Change Detection and Indices (ETCCDI). Apart from looking at current conditions, this study examines projected future temperature trends using the RCP 4.5 scenario. RCP 4.5 is the scenario that most closely describes current climate conditions. Based on the research results, MPI-ESM-LR is the model that has the best accuracy compared to other methods. The air temperature index for the baseline period (1991-2020) shows an increasing trend of several significant intensity indices (p<0.05) at the maximum and minimum temperatures, which causes the air temperature during the day and at night to increase. The frequency index (TN10p TN90p, TX10p, and TX90p) shows a warming trend and several regions experience a significant upward trend. The projection period (2021-2050) shows an increasing trend in most of East Java and several indices such as TNn, TXn and TX90p show an increasing trend of warming which is higher than the baseline period. 

Climate Risk and Vulnerability Assessment of Georgian Hydrology under Future Climate Change Scenarios

The Climate Risk and Vulnerability Assessment (CRVA) is a systematic process used to identify gaps in regional climate adaptation strategies. The CRVA method assesses regional vulnerability, adaptation capacity, exposure, and sensitivity to climate change to support improved adaptation policies. This CRVA study assesses Georgia’s climate exposure, geographic sensitivity, and socio-economic sensitivity by focusing on the impacts of climate change on regional hydrology. The projected change in climate extreme indices, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), is assessed against the 1961–1990 baseline under future Representative Concentration Pathway (RCP) scenarios. These indices encompass various climate factors such as the maximum daily temperature, warmth duration, total precipitation, heavy and extreme precipitation, maximum 5-day precipitation, and consecutive drought duration. This evaluation helps us understand the potential climate exposure impacts on Georgia. The climate-induced geographic sensitivity is examined based on water stress, drought risk, and changes in soil productivity using the Normalized Difference Vegetation Index (NDVI). The climate-induced socio-economic sensitivity is determined using the Gross Domestic Product per capita (GDP), Human Development Index, Education Index, and population density. The highest vulnerability to climate change was found in the Kakheti and Kvemo Kartli regions, with the vulnerability index values ranging from 6 to 15, followed by Mtskheta-Mtianeti, Samtskhe–Javakheti, and Shida Kartli with vulnerability index values ranging from 2 to 8. The location of these regions upstream of the Alazani-Iori, Khrami-Debeda, and Mktvari river basins indicates that the country’s water resources are vulnerable to climate change impacts in the future under the RCP 4.5 and 8.5 scenarios.

Analysis of Extreme Precipitation Variation Characteristics and the Influencing Factors in the Yunnan-Guizhou Plateau Region, China

The increase of extreme precipitation (EP) frequency and the aggravation of disasters have seriously disrupted the normal economic and social development of human beings. The complex topography of the Yunnan-Guizhou Plateau region (YGPR) and the fact that moisture originates from two different directions, the Pacific Ocean and the Indian Ocean, make the mechanism of EP more complicated. Exploring the variation characteristics and influencing factors of EP in YGPR is of great significance for regional disaster prevention and mitigation and water resources management. In this study, 11 extreme precipitation indices (EPIs) defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated based on daily precipitation data of 1960–2020 from 83 national meteorological stations in the study area. The Mann–Kendall test and Wavelet analysis were used to analyze the variation characteristics of EP and explore the influence mechanisms of geographical factors and atmospheric circulation on EP in the spatial and temporal perspective. The conclusions are as follows: (1) The EP of the study area has an overall increasing trend in the research period, with the increase of persistent dry days, the precipitation concentration, intensity, and extreme heavy precipitation (EHP); (2) It shows the obvious spatial difference in the study area, with the high-value areas of extreme drought (ED) in the northwestern region and the total annual precipitation, EHP, and intensity in the southeastern region. In addition, ED and EHP tends to increase in the western region of the study area as well as in the middle east and southeast; (3) EHP is significantly positively correlated with longitude and highly negatively with latitude. Meanwhile, EHP shows a correlation with altitude (negative at low altitude and positive at high altitude); (4) The degree of drought change is greatly affected by North Atlantic Oscillation/El Niño-Southern Oscillation (ENSO) events. The variation of extreme heavy precipitation is greatly influenced by the summer monsoon of South Asian, East Asian, and South China Sea; (5) All the EPIs show persistence. The study results can contribute to the understanding of EP variation in the study area and provide some scientific references for regional water resource management, meteorological warning, and agricultural production safeguard.

Future extreme precipitation changes in the South Asian Summer Monsoon Dominance Region

This study aims to investigate the future changes in precipitation extremes across the South Asian summer monsoon domain using the latest CMIP6 global climate model (GCM) outputs under four SSP-RCPs for the near future (2031–2065) and the far future (2066–2100). The models were bias-corrected using nonparametric quantile transformation based on the smoothing spline technique. Future analysis was done in a more robust and systematic way by using seasonal-based as well as regional-based approaches. Two seasons, pre-monsoon from March to May (MAM) and monsoon from June to September (JJAS) were considered for seasonal assessment. The study area was divided into subzones based on Köppen-Geiger's climate structure for regional assessment. Five extreme precipitation indices, which describe extreme precipitation events, R95pTOT, Rx1day, Rx5day, PRCPTOT and SDII from Expert Team on Climate Change Detection and Indices (ETCCDI), were applied. The evaluation results reveal that the performance of the bias correction method differs based on the extreme precipitation index, climate zone, and season. Based on the model agreement, a negative shift in the precipitation extremes in the future will be dominant during the pre-monsoon season, particularly in the subzones of Tropical rainforest (southern regions of Sri Lanka), Tropical monsoon (Western Ghats of India, coastal regions of Myanmar, regions of Bangladesh), Tropical savannah (regions of Myanmar), and Arid steppe hot (southern regions of India, central parts of Myanmar). During monsoon season, both negative and positive shifts will prevail in the subzones, and the precipitation extremes during the far future will be more intensified than near future in most of the subzones. Additionally, the shifts will tend to decline from SSP1–2.6 to SSP5–8.5 in the near future and increment from SSP1–2.6 to SSP5–8.5 in the far future.

Spatio-Temporal Evolution of Rainfall over the Period 1981–2020 and Management of Surface Water Resources in the Nakanbe–Wayen Watershed in Burkina Faso

Spatio-temporal analysis of rainfall trends in a watershed is an effective tool for sustainable water resources management, as it allows for an understanding of the impacts of these changes at the watershed scale. The objective of the present study is to analyze the impacts of climate change on the availability of surface water resources in the Nakanbe–Wayen watershed over the period from 1981 to 2020. The analysis was conducted on in situ rainfall data collected from 14 meteorological stations distributed throughout the watershed and completed with CHIRPS data. Ten precipitation indices, recommended by the ETCCDI (Expert Team on Climate Change Detection and Indices), were calculated using the RClimDex package. The results show changes in the distribution of annual precipitation and an increasing trend in annual precipitation. At the same time, a trend towards an increase in the occurrence and intensity of extreme events was also observed over the last 4 decades. In light of these analyses, it should be emphasized that the increase in precipitation observed in the Nakanbe–Wayen watershed is induced by the increase in the occurrence and intensity of events, as a trend towards an increase in persistent drought periods (CDD) is observed. This indicates that the watershed is suffering from water scarcity. Water stress and water-related hazards have a major impact on communities and ecosystems. In these conditions of vulnerability, the development of risk-management strategies related to water resources is necessary, especially at the local scale. This should be formulated in light of observed and projected climate extremes in order to propose an appropriate and anticipated management strategy for climate risks related to water resources at the watershed scale.

Evaluation of Changes in Temperature Extremes in Vamsadhara River Basin, Odisha, India

With an emphasis on extreme events, several climate change indices that were derived from daily temperature data were computed and studied. Odisha is a coastal state of India which is severely prone to extreme events. With climate change, extreme events are also increasing in the world especially in coastal regions. Vamsadhara river basin which is situated at Odisha is selected for the study. The objective of this study was to compute and analyze the Expert Team on Climate Change Detection and Indices (ETCCDI) extreme indices using CLIMPACT software. 14 temperature indices were used to analyze the signals of climate change in the study area. The IMD data for the period 1961-2022 has been used for the study. The trend detection of indices was done through Mann-Kendall test and magnitude of trend was calculated using Sen’s Slope estimator. The result showed that warm days (TX90), Diurnal temperature range (DTR) and Warm Spell Duration Index (WSDI) are increasing with 1%, 10% and 1% level of significance respectively. This indicates that indices associated with warming are increasing in river basin. The cold night (TX10) indices found to be decreasing with 10% level of significance and TNx showed a negative trend at 10% level of significance.

Analysis of climate extremes indices in tropical South America through the RegCM4.7

AbstractIn recent decades, the frequency and intensity of extreme weather events induced by global warming have increased significantly, exerting serious impacts on the development of society and ecosystems. Based on low (RCP2.6) and high (RCP8.5) greenhouse gas emissions scenarios from the Intergovernmental Panel on Climate Change, we use the general circulation model HadGEM2‐ES that is part of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to drive the regional climate model RegCM4.7. This study evaluated the ability of both models to simulate (1986–2005) and projection (2080–2099) spatio‐temporal features of extreme indices over tropical South America (TSA). Indices based on precipitation and daily maximum and minimum temperature follow the definitions of the Expert Team on Climate Change Detection and Indices (ETCCDI). In general, the RegCM4.7 reproduces accordingly the spatial distribution of the ETCCDI indices derived from precipitation and temperature on the TSA, in addition to added value to HadGEM2‐ES, especially over regions of complex topography. HadGEM2‐ES performs poorly along the west coast, due to the topography and altitude of the Andes. On the other hand, it demonstrates better performance in the Amazon Basin compared to RegCM4.7. The projection results showed that climate extreme indices based on precipitation and temperature indicate that drought events may advance to the end of the century, showing a spatial pattern that precipitation will be gradually reduced in the Amazon Basin and Northeast region of Brazil. Despite some discrepancies between the models, studies like this one help us to understand how climate change can affect regional planning and development.

Spatiotemporal variability of extreme temperature indices and their implications over the heterogeneous river basin, India.

The current study on spatiotemporal variability of temperature presents a holistic approach for quantifying the joint space-time variability of extreme temperature indices over the physio-climatically heterogeneous Tapi River basin (TRB) using two unsupervised machine learning algorithms, i.e., principal component analysis (PCA) and cluster analysis. The long-term variability in extreme temperature indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), was evaluated for 1951-2016. The magnitude and statistical significance of the temporal trend in extreme temperature indices were estimated using non-parametric Sen's slope estimator and modified Mann Kendall (MMK) tests, respectively. The multivariate assessment of temporal trends using PCA resulted in four principal components (PCs) encapsulating more than 90% variability. The cluster analysis of corresponding PCs resulted in two spatial clusters exhibiting homogeneous spatiotemporal variability. Cluster 1 is characterized by significantly increasing hottest, very hot, and extremely hot days with rising average maximum temperature and intraday temperature variability. On the other hand, cluster 2 showed significantly rising coldest nights, mean minimum, mean temperature, and Tx37 with significantly decreasing intraday and interannual temperature variability, very cold, and extremely cold nights with reducing cold spell durations. The summertime heat stress computation revealed that the Purna sub-catchment of the Tapi basin is more vulnerable to various health issues and decreased work performance (> 10%) for more than 45days per year. The current study dealing with the associated effects of rising temperature variability on crop yield, human health, and work performance would help policymakers formulate better planning and management strategies to safeguard society and the environment.

Projected changes of precipitation extremes in North America using CMIP6 multi-climate model ensembles

In this study, we estimated a comprehensive set of extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) over North America (NA) using 18 selected higher-resolution GCMs of the sixth Coupled Model Intercomparison Project (CMIP6). The GCMs are evaluated in their historical simulations (1981–2010) against those estimated from in-situ (Daymet) and reanalysis (NARR) dataasets, followed by the projected changes in precipitation extremes of NA in response to climate warming (SSP1-2.6, SSP2-4.5 and SSP5-8.5) scenarios of CMIP6 in terms of temporal variations, spatial distributions, seasonal patterns and model agreement over the 21st century relative to 1981–2010. Results show that the CMIP6 ensemble median (CMIP6-EnM) can generally capture the observed extreme precipitation patterns of NA, and it out-performs all 18 individual GCMs with negative RMSE′XY, higher R statistics (R > 0.8) and the signal-to-noise ratio (SNR) > 1 in most sub-regions of NA. In general, extreme precipitation events are projected to occur more frequently and severely in the future, particularly in latitudes above 55°N of NA and in coastal areas. In contrast, dry conditions in southern NA (e.g., NCA and SCA) are projected to intensify over the 21st century. Seasonal changes are projected to be more significant in winter than in summer for northern NA (e.g., GIC, NWN and NEN), while in Central America (e.g., WNA, CAN, ENA), precipitation extremes will likely be less severe in both summer and winter. However, unlike strong model agreements in high (100%) and mid-latitudes (80%), there is often a wide disparity among the signs of projected changes between GCMs in southern NA. Unless greenhouse gas emissions are drastically reduced immediately, NA will likely be vulnerable to the impact of worsening extreme precipitation events especially in northern and coastal regions.

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent different degrees of wet bias in the southern Xinjiang (SX). Most GCMs are generally able to capture extreme precipitation trends, but to reproduce the performance of interannual variability for heavy precipitation days (R10mm), SDII and CWD need to be improved. The four multi-model ensemble methods can reduce the internal system bias and variability within individual models and outperform individual models in capturing the spatial and temporal variability of extreme precipitation. However, significant uncertainties remain in the simulation of extreme precipitation indices in SX and Tianshan Mountain (TM). Comparatively, IWM simulations of extreme precipitation in the ACA and its sub-regions are more reliable. The results of this study can provide a reference for the application of GCMs in ACA and sub-regions and can also reduce the uncertainty and increase the reliability of future climate change projections through the optimal multi-model ensemble method.

On the Use of Reanalysis Data to Reconstruct Missing Observed Daily Temperatures in Europe over a Lengthy Period of Time

In this study, a methodology that can reconstruct missing daily values of maximum and minimum temperatures over a long time period under the assumption of a sparse network of meteorological stations is described. To achieve this, a well-established software used for quality control, homogenization and the infilling of missing climatological series data, Climatol, is used to combine a mosaic of data, including daily observations from 15 European stations and daily data from two high-resolution reanalysis datasets, ERA5-Land and MESCAN-SURFEX; this is in order reconstruct daily values over the 2000–2018 period. By comparing frequently used indices, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in studies of climate change assessment and goodness-of-fit measures, the reconstructed time series are evaluated against the observed ones. The analysis reveals that the ERA5-Land reconstructions outperform the MESCAN-SURFEX ones when compared to the observations in terms of biases, the various indices evaluated, and in terms of the goodness of fit for both the daily maximum and minimum temperatures. In addition, the magnitude and significance of the observed long-term temporal trends maintained in the reconstructions, in the majority of the stations examined, for both the daily maximum and daily minimum temperatures, is an issue of the greatest relevance in many climatic studies.

Evaluation of ERA5-Land reanalysis datasets for extreme temperatures in the Qilian Mountains of China

An increase in extreme temperature events could have a significant impact on terrestrial ecosystems. Reanalysis temperature data are an important data set for extreme temperature estimation in mountainous areas with few meteorological stations. The ability of ERA5-Land reanalysis data to capture the extreme temperature index published by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated by using the observational data from 17 meteorological stations in the Qilian Mountains (QLM) during 1979–2017. The results show that the ERA5-Land reanalysis temperature data can capture well for the daily maximum temperature, two warm extremes (TXx and TX90p) and one cold extreme (FD0) in the QLM. ERA5-Land’s ability to capture temperature extremes is best in summer and worst in spring and winter. In addition, ERA5-Land can capture trends in all extreme temperature indices except the daily temperature range (DTR). The main bias of ERA5-Land is due to the difference in elevation between the ground observation station and the ERA5-Land grid point. The simulation accuracy of ERA5-Land increases with the decrease of elevation difference. The results can provide a reference for the study of local extreme temperature by using reanalysis data.

Early 21st Century Trends of Temperature Extremes over the Northwest Himalayas

The rising intensity and frequency of extreme temperature events are caused due to climate change and are likely to affect the entire world. In this context, the Himalayas are reported to be very sensitive to changes in temperature extremes. In this study, we investigate the variability of temperature extremes over the Northwest Himalayas in the early 21st century (2000–2018). Here, we used 14 temperature indices recommended by ETCCDI (Expert Team on Climate Change Detection and Indices). The present study reveals the trends of extreme temperature indices on the spatial scale for the western part of the Northwest Himalayas. The 14 temperature indices were used to assess the behavior of extreme temperature trends with their significance. This study reports that the northwestern region of the study area has a cooling effect due to an increase in the trends of cold spells, cold days/nights, and frost days, while the southwestern region significantly shows the warming effects due to the increasing trends in warm spells, warm days/nights, and summer days. On the other hand, the eastern region of the study area shows mixed behavior, i.e., some places show warm effects while some reveal cold effects in the early 21st century. Overall, this study implies the northwestern parts have cooling trends while the southwestern and southeastern parts have warming trends during the early 21st century.