Warm Spell Duration Index Research Articles

Impact of climate change on rice growth and yield in China: Analysis based on climate year type

Climate change threatens China’s rice production, making it crucial to assess the impact of climate change and climate year type (CYT) on rice production across regions to safeguard food security. The impact of climate change under nine CYTs with different combinations of temperature and precipitation on two rice cropping systems, including single rice and double rice (early and late rice) was evaluated. The results indicate that: (1) the Northeast region was expected to undergo the greatest warming of 2.03–2.48 °C, and future climate conditions would be dominated by Warm-Humid, Warm-Normal, and Warm-Dry CYTs across all regions. (2) Climate change would significantly shorten anthesis days after sowing and maturity days after sowing of single rice by 6–12 days and 9–24 days, with little change observed for late rice (< 1 day). Late rice yield suffered more from climate change compared to single and early rice yield, declining by 8.8 %–16.13 %. (3) Different CYTs exhibited varying impacts on rice yields. Yields were projected to decrease by approximately 4.765 % to 18.645 % in Warm-Humid, Warm-Normal, and Warm-Dry CYTs. Conversely, the Northeast region was anticipated to experience an increase in yield. (4) Relationships between rice yield and meteorological factors varied by region, variety, and CYT. Among the nine CYTs, high killing degree days, mean daily temperature, mean solar radiation and warm spell duration index were the main factors influencing changes in rice yield, explaining nearly 80 % of yield change. Our results would help to develop adaptation strategies in different regions and rice cropping systems.

The Dynamics of Vegetation Evapotranspiration and Its Response to Surface Meteorological Factors in the Altay Mountains, Northwest China

The Altay Mountains’ forests are vital to Xinjiang’s terrestrial ecosystem, especially water regulation and conservation. This study evaluates vegetation evapotranspiration (ET) from 2000 to 2017 using temperature, precipitation, and ET data from the China Meteorological Data Sharing Service. The dataset underwent quality control and was interpolated using the inverse distance weighted (IDW) method. Correlation analysis and climate trend methodologies were applied to assess the impacts of temperature, precipitation, drought, and extreme weather events on ET. The results indicate that air temperature had a minimal effect on ET, with 68.34% of the region showing weak correlations (coefficients between −0.2 and 0.2). Conversely, precipitation exhibited a strong positive correlation with ET across 98.91% of the area. Drought analysis, using the standardized precipitation evapotranspiration index (SPEI) and the Temperature Vegetation Dryness Index (TVDI), showed that ET was significantly correlated with the SPEI in 96.47% of the region, while the TVDI displayed both positive and negative correlations. Extreme weather events also significantly influenced ET, with reductions in the Simple Daily Intensity Index (SDII), heavy precipitation days (R95p, R10), and increases in indicators like growing season length (GSL) and warm spell duration index (WSDI) leading to variations in ET. Based on the correlation coefficients and their significance, it was confirmed that the SII (precipitation intensity) and R95p (heavy precipitation) are the main factors causing vegetation ET increases. These findings offer crucial insights into the interactions between meteorological variables and ET, essential information for sustainable forest management, by highlighting the importance of optimizing water regulation strategies, such as adjusting species composition and forest density to enhance resilience against drought and extreme weather, thereby ensuring long-term forest health and productivity in response to climate change.

Analysis of Spatial and Temporal Distribution and Changes in Extreme Climate Events in Northwest China from 1960 to 2021: A Case Study of Xinjiang

Xinjiang, as a climate-sensitive region in Northwest China, holds significant importance in studying extreme climate events for agricultural production and socioeconomic development. Using data spanning from 1960 to 2021 from 52 meteorological stations across Xinjiang, encompassing 23 indices of extreme climate events, the 5-year moving average, linear trend fitting, and inverse distance weighting (IDW) are used to analyze the distribution patterns and temporal changes in extreme climatic phenomena within the region. The results indicate that, over the period from 1960 to 2021, the Amplitude Temperature Index, Heat Index, and Warm Spell Duration Index in Xinjiang exhibited a marked increasing trend, whereas the Cold Index and Cold Spell Duration Index displayed a significant decreasing trend. The range of changes in the extreme temperature indices from 1990 to 2021 is higher than that of 1960 to 1989. The areas with high values of amplitude temperature extreme indices are primarily concentrated in the southern part, while the areas with high values of cold indices are mainly distributed in the northern part. The upward/downward trends all account for over 80.00% of the entire region. The precipitation scale indices, precipitation day indices, intense precipitation index, and extreme precipitation index all showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indices from 1990 to 2021 was lower than that from 1960 to 1989. Furthermore, areas with high precipitation values and regions with high trend values of climate tendency are predominantly concentrated in the northern and western parts of Xinjiang, with over 71.00% of the entire region experiencing an upward trend. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

On the persistence of near-surface temperature dynamics in a warming world.

We consider issues related to the effect of climate change on the persistence of (trend-corrected) temperatures using global gridded data for both land and oceans. We first discuss how the presence of trends and additive noise affects inference about persistence. Ignoring a trend induces an upward bias, while not accounting for noise induces a downward bias. We show that the increase in persistence in the commonly used Warm Spell Duration Index is simply an artifact of increasing temperatures. To purge the impact of both trends and noise, we adopt a simple state-space model. Of separate interest, we document a much larger noise component for land than for oceans. The estimates of the persistence are much larger for oceans than for land. Inspection of the estimates across various subsamples and the application of tests for structural changes suggest the same pattern of persistence for both land and oceans across time, with few minor exceptions. Hence, our results show that surface temperature persistence has remained constant during the observed period.

Temporal and Spatial Variations of Extreme Climate Events in Northwestern China from 1960 to 2020

In the context of global warming, the frequency and intensity of extreme weather and climate events have been increasing. Characterized by scarce water resources and fragile ecosystems, Northwest China has experienced a climate shift since the 1980s. In this study, spatial and temporal patterns of changes in the indices of climate extremes, based on daily maximum and minimum temperature and precipitation at 172 meteorological stations in Northwest China, were analyzed for the period 1960–2020. A total of 26 indices divided into two categories, 16 extreme temperature indices and 10 extreme precipitation indices, were used. Analysis of these indices revealed a general warming trend in the region, which consistent with global warming. The regional occurrence of summer days, tropical nights, growing season length, warm nights, warm days, and warm spell duration index increased by 0.22, 0.14, 0.29, 0.73, 0.46, and 0.11 days/decade, respectively. Over the same period, the occurrence of frost days, icing days, cool nights, cool days, and cold spell duration index decreased by −0.38, −0.21, −0.93, −0.44, and −0.13 days/decade, respectively. The decreasing trends in cold extremes were greater than the increasing trends in warm extremes. Additionally, many regions have experienced increasing trends in several precipitation indices. The annual total wet-day precipitation increased by 5.3 mm/decade. Increasing trends were also evident in simple daily intensity index, heavy precipitation days, very heavy precipitation days, very wet days, and extremely wet days. Consecutive dry days decreased by −1.5 days/decade, while no significant change was observed in consecutive wet days. In contrast to the remarkable spatial consistency of temperature extremes, precipitation extremes exhibited large and expected spatial variability. Most precipitation indices showed increasing trends in the western region of Northwest China and decreasing trends in the eastern part of Northwest China. These results indicate a transition from cold–dry to warm–wet in Northwestern China. Our findings suggest that Northwest China is experiencing more extreme climate events, which could consequently impact hydrological processes, ecological processes, and human health. These observations increase our understanding of the interactions between climate change and regional climate variability, which is conducive to improving disaster prevention.

Variations of Extreme Temperature Event Indices in Six Temperature Zones in China from 1961 to 2020

In this study, eight extreme temperature event indices were calculated based on daily maximum, minimum, and mean temperature data recorded at 699 National Reference Stations in China during 1961–2020. The yearly change of mean temperature and the magnitude, frequency, and duration of extreme temperature events in six temperature zones were evaluated. All temperature zones had a trend of an increase in mean temperature (rate: 2.1–3.3 °C per 10 years), and the warming was more significant in the warm temperate zone and the Qinghai–Tibet Plateau zone (QPZ). For extreme temperature events, the extreme maximum and minimum temperatures in most temperature zones showed significant trends of increase, and the rates of increase were greater in the northern zones and QPZ. The rate of increase in extreme minimum temperature was substantially (up to three times) higher than the rate of increase in extreme maximum temperature in the same temperature zone; however, the finding was the opposite for the cold temperate zone (CTZ), which is the northernmost region of China. The rate of increase in extreme maximum temperatures was the greatest (0.35 °C per 10 years), whereas the rate of increase in extreme minimum temperatures was the smallest (0.17 °C per 10 years). The number of warm days/nights and the warm spell duration index also showed significant trends of increase that were most obvious in the southern zones and QPZ. In the tropical zone (TZ), which is the southernmost part of mainland China, the number of warm nights was only 15.3 days in 1961–1970, whereas it increased to 61.9 days in 2011–2020 (an increase of 303.9%). The rate of increase in warm nights in TZ (8.8 days per 10 years) was four times that in CTZ (2.2 days per 10 years). The number of cold days/nights and the cold spell duration index showed significant trends of decrease, with the greatest rates of reduction in QPZ and TZ. In evaluating the frequency of extreme temperature events, the amplitude of warming of the night index was found to be greater than that of the day index. In evaluating the duration of extreme temperature events, the variation of the cold index was found to be greater than that of the warm index. The notable asymmetries found in the variations of the minimum/maximum temperatures, day/night indices, and cold/warm spell durations in China are direct manifestations of global warming.

Impact of Temperature Extremes on Carbon Emissions from Crop Production in Hebei Province, China

The study investigated the impact of temperature extremes on carbon emissions (CE) from crop production. (1) Background: Many scholars have studied climate extremes. However, the research on the relationship between temperature extremes and CE is not extensive, which deserves attention. (2) Methods: The study adopted a fixed-effect model to analyze the impact of temperature extremes on CE from crop production, and the moderating effect was tested using total factor productivity (TFP) in agriculture. (3) Results: Temperature extremes in Hebei Province were mainly reflected in a decline in the cold day index (TX10p) and a rise in the warm spell duration index (WSDI) and the number of summer days (SU25). Additionally, TX10p was positively correlated with CE. For every 1% reduction in TX10p, CE dropped by 0.237%. There was no significant correlation between WSDI and CE. Finally, the agricultural TFP had a significant moderating effect on CE, with each 1% increase resulting in a corresponding 0.081% decrease in CE. (4) Conclusions: The results indicated a warming trend in Hebei Province, which resulted in a decrease in the number of winter days, and reduced CE from crop production. The improvement of input efficiency in agricultural production factors helped moderate the CE.

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.

Regional climate projections for the State of São Paulo, Brazil, in the 2020 - 2050 period

The objective of this article is to present climate projections for the 2020–2050 period for several climate variables for the State of São Paulo, considering two scenarios RCP 4.5 and RCP 8.5. These projections were based on four climate models (HadGEM2-ES, MIROC5, CanESM2, and BESM) with downscaling of 20 km improved by the CPTEC/INPE Eta Regional Climate Model. Nine variables related to air temperature and rainfall were selected, and the deviation between the observed period (1961–1990) and the projected period (2020–2050) was calculated for the two RCP scenarios for each model and selected variable. Then, the highest and lowest values of the deviations were selected, which represent the maximum and minimum deviations projected for each cell. With this, we obtained the widest range between the possible trends of the four models compared for each variable analyzed in the state of São Paulo. The models used for the two RCP scenarios show the warming of the atmosphere, which tends to be less intense on the shoreline due to the control exerted by the ocean, and greater in the northwest region of the state, furthest from the Atlantic. A reduction in the Cold Spell Duration Index (CSDI) and an increase in the Warm Spell Duration Index (WSDI) is projected, with these trends increasing from south to north of the state. Considering the precipitation, no consensus between the trends of the models used in this study was found, with projections of both increase and decrease in annual total variables and in related extreme indices. Among the different projected trends, the reduction in precipitation is the worst scenario to be faced, given the essential aspect of water. The indicators related to intense precipitation (R95p and RX5day) showed a wide range between the maximum and minimum values projected in the analyzed scenarios, and the increase of these indicators deserves particular attention, since the increase in intense precipitation is a potential trigger of disasters, such as landslides, floods, and accelerated erosion. The results, given their relevance, were applied to the Ecological-Economic Zoning (ZEE) of the State of São Paulo, a technical and political planning instrument that establishes guidelines for land use and management, considering the environmental characteristics and socioeconomic dynamics of different regions of the state.

Historical trends in climate indices relevant to surface air temperature and precipitation in Japan for recent 120 years

AbstractGeographical distributions of trends in climate indices with statistical significance in Japan are investigated at each of the 51 observation stations for the recent 120 years. We employed a comprehensive set of the 70 climate indices on an annual timescale to detect climate changes and to facilitate climate information for a variety of sectors, which are developed by expert team on sector‐specific indices in the World Meteorological Organization. The rising trends have regional differences such as the annual mean of daily maximum and minimum surface air temperatures in the Kanto‐Koushin region. The statistically significant increasing trends in three daily heavy precipitation‐relevant climate indices, R95p, R99p, and RX1day, are seen in Hokuriku, Shikoku, the western part of Northern and Southern Kyusyu regions, and isolated stations in the other regions. Rotational empirical orthogonal function analysis of R99p revealed different features of statistically significant modes suggesting the trends are attributed to changes in different atmospheric phenomena as well as those of warm spell duration index. The number of the stations with statistically significant trends varies with an index. Indies relevant to daily minimum surface air temperatures have statistically significant trends at most of the 51 stations. Consecutive dry days are the most sensitive to climate changes for the 120 years among the indices relevant to precipitation in this analysis. The indices relevant to heavy precipitation are not always been most sensitive to climate change detections in Japan. The geographical distributions of the percentage of the number of indices with statistically significant changes to the total number of the stations provide information about stations sensitive to climate changes. The highest sensitivity is seen at Kumamoto. These results directly provide detection of climate changes and show the potentials of which index is sensitive to ongoing climate changes from the viewpoint of geographical distributions.

Long-term trends in daily extreme air temperature indices in Ireland from 1885 to 2018

Long-term instrumental series are crucial to analyse extreme air temperature indices and to examine modern climate warming within a historical context. This research provides the first assessment in Ireland of the frequency, duration, intensity and geographical distribution of the daily extreme air temperature indices recommended by the ETCCDI (Expert Team on Climate Change Detection and Indices) and based on long-term rescued, quality-controlled and homogenised data. Prior analysis of air temperature indices in Ireland had focused on the period since the second half of the 20th century, where digital data were readily available. Earlier estimations of long-term air temperature trends in Ireland focused on non-homogenised data from a small network of stations. For this research, 11 long-term (1885–2018) maximum and minimum air temperature series were analysed to assess seasonal and annual trends and construct extreme air temperature indices in Ireland. The non-parametric Mann-Kendall test was employed to test the statistical significance of the maximum and minimum air temperatures and indices trends (p < 0.05). Significant increasing trends were assessed in the seasonal and annual maximum and minimum air temperature series in Ireland, with greater increases in the spring and autumn seasons. Significant increasing trends were determined in the warm days (+6.8 days), warm nights (+7.5 nights), warm spell duration index (+3.9 days), growing season length (+22 days), coldest night (+2.7 °C) and coldest day (+1.5 °C) in the period 1885–2018 in Ireland. In the same period, significant decreasing trends were identified in the cold days (−9.3 days), cold nights (−7 nights), frost days (−13.7 days), cold spell duration index (−6.9 days) and diurnal air temperature range (−0.1 °C) in Ireland. The results follow global patterns presented in the Six Assessment Report of the Intergovernmental Panel on Climate Change. The findings of this research will be crucial to assist stakeholders and policymakers in defining climate action, adaptation and mitigation plans in response to shifting trends in air temperature extremes.

High-resolution Projection Dataset of Agroclimatic Indicators over Central Asia

To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia (CA), six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and their changes in the near-term future (2031–50) are assessed relative to the reference period (1986–2005). The quantile mapping (QM) method is applied to correct the model data before calculating the indicators. Results show the QM method largely reduces the biases in all the indicators. Growing season length (GSL, day), summer days (SU, day), warm spell duration index (WSDI, day), and tropical nights (TR, day) are projected to significantly increase over CA, and frost days (FD, day) are projected to decrease. However, changes in biologically effective degree days (BEDD, °C) are spatially heterogeneous. The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.

Projection of Extreme Temperature Events over the Mediterranean and Sahara Using Bias-Corrected CMIP6 Models

Climate change continues to increase the intensity, frequency and impacts of weather and climate extremes. This work uses bias-adjusted Coupled Model Intercomparison Project Phase six (CMIP6) model datasets to investigate the future changes in temperature extremes over Mediterranean (MED) and Sahara (SAH) regions. The mid- (2041–2070) and far-future (2071–2100) are studied under two Shared Socioeconomic Pathways: SSP2-4.5 and SSP5-8.5 scenarios. Quantile mapping function greatly improved the performance of CMIP6 by reducing the notable biases to match the distribution of observation data, the Climate Prediction Center (CPC). Results show persistent significant warming throughout the 21st century, increasing with the increase in radiative forcing. The MED will record a higher increase in temperature extremes as compared to SAH. The warming is supported by the projected reduction in cold days (TX10p) and cold nights (TN10p), with the reduction in the number of cold nights exceeding cold days. Notably, warm spell duration index (WSDI) and summer days (SU) have a positive trend in both timelines over the entire study area. There is a need to simulate how climate sensitive sectors, such as water and agriculture, are likely to be affected by projected changes under different scenarios for informed decision making in the choice and implementation of adaptation and mitigation effective measures.

Climate projections at a convection-permitting scale of extreme temperature indices for an archipelago with a complex microclimate structure

In island systems with complex orography (e.g. Canary Islands), obtaining projections of climate extremes throughout the 21st century is necessary to evaluate the possible adverse effects of climate change. In this work, a dynamic downscaling methodology was applied to obtain the projections of temperature extremes indices. The WRF modeling system was properly configured with a spatial resolution of 3 km, for the periods: 2030–2059 (MID) and 2070–2099 (END), and for the RCPs 4.5 and 8.5 scenarios. This spatial-temporal resolution allows better modeling of the land-surface coupling processes (e.g., latent and sensible heat fluxes), which are one of the main sources of uncertainties in temperature extremes modeling. The initial and boundary conditions were defined by three CMIP5 Earth Systems Models: GFDL-ESM2M, MIROC-ESM, and IPSL-CM5. The future changes were calculated against the modeled reference period was 1980–2009 (HIS). The selected extremes indices were those defined by the Team of Experts on Climate Change Detection and Indices (ETCCDI) and were: monthly absolute maximum and minimum temperature respectively (TX and TN), monthly maximum of the diurnal temperature range (DTR), tropical nights (TR), warm days (TX90P), cold nights (TN10P), warm-spell duration index (WSDI) and cold-spell duration index (CSDI). Also, the return levels and return periods for annual maximum temperature were analyzed using the Generalized Extreme Value distribution (GEV).The modeled indices were compared with those obtained from observations at nine ground-based stations for the HIS period. Despite the high spatial and temporal resolution of the models a bias is still observed between the modeled and observational values for the absolute indices, even when the simulations are driven by reanalysis data. However, the comparison of these indices around their previously unbiased means yields values on average of 0.85 with a standard deviation of 0.06 on a Perkins-based skill score. Regarding the 20-year return levels for maximum temperature, differences between the average of the models and observations are below 2 °C for all sites, except for the highest stations IZO and TFN, which reach 2.9 and 4.2 °C, respectively.The analyses of the results indicate that the future projections of the indices obtained using any of the models remain constant from the mid-century to the end of the century for the RCP45 whereas they continue to increase if the RCP85 is considered. This finding shows that all models closely follow the variation in the CO2-equivalent concentrations used as input. Thus, TX and TN are expected to increase, with an average change for the END period and RCP8.5 of 4.0 ± 0.5 °C and 4.4 ± 0.4 °C for TN. TX90p increases considerably (30 percentage points), and the TN10p index will be close to zero. The increase in temperatures is mainly due to, in addition to the modification of the synoptic patterns, a decrease in total cloud cover and soil moisture.This decrease in soil moisture has a direct effect on the decrease in latent heat flux and an increase in sensible heat flux, associated with a projected increase of DTR. Also, the 20-year return levels for maximum temperature obtained for the HIS period will correspond to return periods between 1 and 6 years at the END period and RCP8.5.

The Future Extreme Temperature under RCP8.5 Reduces the Yields of Major Crops in Northern Peninsular of Southeast Asia.

This study explores the impact of rising near-future temperatures on crop yields, particularly rice and maize, in northern Thailand between 2020 and 2029. The potential for high temperatures in northern Thailand between 2020 and 2029 under the Representative Concentration Pathways 8.5 (RCP8.5) scenario indicates that Thailand experienced hot trends between 2020 and 2029 as measured by the annual maximum value of daily maximum temp (TXx), annual minimum value of daily maximum temp (TXn), annual minimum value of daily minimum temp (TNn), and annual maximum value of daily minimum temp (TNx). Northern Thailand had the most dramatic changes in TXn and TNn. Furthermore, TXn levels were found to be significantly higher in northern Thailand. The number of days when TX < 10th percentile (TX10p) intensity decreased, while the number of days with TN < 10th percentile (TN10p) intensity was increasing. The number of days when TN > 90th percentile (TX90p) has become increasingly rare in northern Thailand. The TN90p was dropping in northern Thailand, whereas the Warm Spell Duration Index (WSDI) was growing. Additionally, the cold spell duration index (CSDI) continues to decline. This indicates that the heat persistence index is increasing in northern Thailand. Temperature rises are the most likely to have a detrimental impact on agricultural production, and climate models can predict regional temperature changes with more precision than precipitation. Throughout the planting season (June-December), average yearly temperatures in rice and maize growing areas have climbed by 0.5–0.6°C. The impact estimates for maize and rice are generally negative, that is, −10 ± 4.6% per °C and −8 ± 3.5% per °C, respectively.

Spatiotemporal variation in temperature extremes and their association with large scale circulation patterns in the Central Karakorum during 1982–2019

Understanding dynamical changes in climate extremes are vital in predicting extreme climate events and lowering their associated impacts on the environment and society. The objectives of this study is to analyze the spatiotemporal variations in temperature extremes and their association with selected atmospheric circulation indices. We use daily maximum temperature (TX) and minimum daily temperature (TN) data for five glaciers in the Hunza district of central Karakoram for the period 1982–2019. We validate the European Reanalysis (ERA5) data with available observed and in-situ data before further analysis. A total of eight indices based on maximum and minimum temperatures are chosen. They include maximum of TX, minimum of TN, three warm indices, and three cold indices. The Mann-Kendall method and linear trend analysis were used to examine the trends, and Pearson correlation analysis was used to analyze the correlation between the temperature extremes and different atmospheric circulation patterns. The warm indices, including the number of warm nights, warm days, and Warm Spell Duration Index (WSDI), and TN and TX, showed increasing trends in the central Karakoram from 1982 to 2019. On the other hand, cold indices, including the number of cold nights, cold days, and Cold Spell Duration Indicator (CSDI), showed decreasing trends. Warm indices and TX showed significant positive correlations with atmospheric circulations. The Arctic Oscillation index (AO) displayed significant correlations with the warm indices (p < 0.01) and negative correlations with the cold indices. The results also showed that warm indices and extreme indices had no correlations with the ENSO index. These findings will provide useful insights in predicting extreme climate events and taking measures to reduce their associated impacts on the water resources in the Karakoram.

Historical variability and future changes in seasonal extreme temperature over Iran

The extreme temperature indices (ETI) are an essential indicator of climate change. The detection of their changes over the next years can play an essential role in the climate action plan (CAP). In this study, four temperature indices (mean of daily minimum temperature (TN), mean of daily maximum temperature (TX), cold-spell duration index (CSDI), and warm-spell duration index (WSDI)) were defined by ETCCDI and two new indices,the maximum number of consecutive frost days (CFD) and the maximum number of consecutive summer days (CSU), were used to examine ETIs in Iran under climate change conditions. We used minimum and maximum daily temperatures of five general circulation models (GCMs), including HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, MIROC-ESM-CHEM, and NorESM1-M, from the set of CMIP5 bias-correction models. We investigated two representative concentration pathway (RCP) scenarios of RCP4.5 and RCP8.5 during the historical (1965–2005) and future (2021–2060 and 2061–2100) periods. The performance of each model evaluated using the Taylor diagram on a seasonal scale. Among models, GFDL-ESM2M and HadGEM2-ES showed the highest, and NorESM1-M and IPSL-CM5A-LR showed the lowest performance in Iran. Then, an ensemble model was generated using independence weighted mean (IWM) method. The results of multi-model ensembles (MME) showed a higher performance compared to individual CMIP5 models in all seasons. Also, the uncertainty value significantly reduced, and the correlation value of the MME model reached 0.95 in all seasons. Additionally, it is found that WSDI and CSU indices showed positive anomalies in future periods, and CSDI and CFD showed negative anomalies throughout Iran. Also, at the end of the twenty-first century, no cold spells are projected in almost every part of Iran. The CSU index showed that summer days are increasing sharply; according to the results of the RCP8.5 scenario in spring (MAM) and autumn (SON), the CSU will increase by 18.79 and 20.51 days, respectively, at the end of the twenty-first century. It projected that in the future, the spring and autumn seasons will be shorter and summers will be much longer than before.

TIN‐Copula bias‐correction method for model‐derived maximum temperature in the MENA region

AbstractGlobal and regional climate models' prediction accuracy is limited, often with systematic biases between the model output and observed conditions. The present research aims to minimize the biases between the maximum temperature simulated by a global (HadGEM3) and a regional (WRF) climate model for the period of 2006–2014, based on the ERA5 reanalysis data, used as a proxy for observations (calibration period 1981–2000). For the bias correction, a new approach with the TIN‐Copula method (combination of Triangular Irregular Networks‐TINs and Copulas), suitable for gridded datasets is used. The methodology applies TINs to define the grids to which the corrections are made, and copulas for analysing the dependence between the reanalysis and simulated data during the calibration period. The dependence structure is then used for the bias adjustment. Our methodology is applied to the Middle East and North Africa MENA‐CORDEX domain, where extreme heat conditions prevail, which are projected to accelerate during the 21st century. Besides to the entire area, a detailed analysis was performed for six subregions. Our results indicate that the maximum temperature output of both models diverges from the ERA5 reanalysis, especially during summer. In several subregions and seasons, discrepancies are highest for the HadGEM3 model, which has a much coarser resolution than WRF. The effectiveness of the TIN‐Copula method was tested spatially, temporally and based on two relevant climate indices [daily maximum temperature (TXx) and warm spell duration index (WSDI]), showing that it minimizes model biases.

Evaluation of the CMIP5 general circulation models for simulating the precipitation and temperature of the Koshi River Basin in Nepal

AbstractThis study evaluates the performance of 12 different general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to simulate precipitation and temperature in the Koshi River Basin, Nepal. Four statistical performance indicators: correlation coefficient, normalised root-mean-square deviation (NMRSD), absolute NMRSD, and average absolute relative deviation are considered to evaluate the GCMs using historical observations. Seven different climate indices: consecutive dry days, consecutive wet days, cold spell duration index, warm spell duration index, frost days, very wet days, and simple daily intensity index are considered to identify the most suitable models for the basin and future climate impact assessment studies. Weights for each performance indicator are determined using the entropy method, with compromise programming applied to rank the GCMs based on the Euclidian distant technique. The results suggest that CanESM2 and CSIRO-MK3.6.0 are the most suitable for predicting extreme precipitation events, and BCC-CSM 1.1, CanESM2, NorESM1-M, and CNRM-CM5 for extreme temperature events in Himalayan river basins. Overall, IPSL-CM5A-MR, CanESM2, CNRM-CM5, BCC-CSM 1.1, NorESM1-M, and CSIRO-Mk3.6.0 are deemed suitable models for predicting precipitation and temperature in the Koshi River Basin, Nepal.

Do Extreme Climate Events Cause the Degradation of Malus sieversii Forests in China?

Frequent extreme climate events have attracted considerable attention around the world. Malus sieversii in Xinjiang is the ancestor of cultivated apple, and it is mainly distributed in the Ili river valley at end of the Tianshan Mountains. Wild fruit forests have been degraded, but the cause remains unclear. In order to identify whether extreme climate events caused this degradation reanalysis data and atmospheric circulation indices were used to determine the trends and the reasons for extreme climate changes. Subsequently, we further investigated the effect of extreme climate events on wild fruit forest using characteristics of extreme climate indices and tree-ring chronology. We found increasing trends in both extreme precipitation and warm indices, and decreasing trends in cool indices. Extreme climate events were mainly associated with the Atlantic Multidecadal Oscillation (AMO). Analysis of data of wind and geopotential height field at 500 hPa showed that strengthening wind, increasing geopotential height, cyclone and anti-cyclone circulation drivers contributed to extreme climate events. In the non-degraded region, there were significant positive correlations between tree-ring chronology and both extreme precipitation and extreme warm indices (except for warm spell duration indicator). The other extreme indices (except for heavy rain days) had a large correlation range with tree-rings in a 4–8-year period. These results indicated that extreme precipitation and extreme warm indices intensified M. sieversii growth of the non-degraded region on multi-time scales. In contrast, the degraded region showed insignificant negative relationship between tree-ring chronology and both extreme precipitation and extreme warm indices [except for warm spell duration index (WSDI)], and significant negative correlations in a 4–8-year period were detected between tree-ring chronology and most of the extreme precipitation indices, including heavy rain days, very wet days, cold spell duration indicator, simple precipitation intensity index (SDII), and annual total precipitation. Under the long disturbance of inappropriate anthropic activities, extreme climate has caused the outbreak of pests and diseases resulting in the degeneration of wild fruit forest. Our study provides scientific guidance for the ecosystem conservation in wild fruit forest in China, and also across the region.