Showing papers in "International Journal of Climatology in 2023"

Simulation and projection of climate extremes in China by multiple Coupled Model Intercomparison Project Phase 6 models

Abstract: This study evaluates the ability of 23 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating extreme climate events over China. The multimodel ensemble (MME) performs better than most individual models in reproducing the climatological mean distribution of all extreme indices. The MME can reproduce well the climatological mean distributions of five extreme climate indices over China, including annual total precipitation (PTOT), maximum consecutive 5‐day precipitation (RX5), simple daily intensity (SDII), maximum daily maximum temperature (TXX), and minimum daily minimum temperature (TNN), with Taylor skill scores exceeding 0.7. SDII and TXX are the most skilful precipitation and temperature extreme indices simulated by the MME, respectively. The MME has relatively lower skill in simulating the climatological mean distribution of warm days (TX90P) and cold nights (TN10P) over China. Future projections of these extreme climate indices by the end of the 21st century are explored with the MME under the SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 scenarios. The PTOT and RX5 in northwestern China are all projected to increase by more than 30% under SSP5‐8.5. R20 is projected to increase by 4–5 days over southeastern China under SSP5‐8.5. There are fewer (more) consecutive dry days over north China (south China), with a change of 5 days under SSP5‐8.5. The extreme temperature indices, including TX90P, TXX, and TNN, all increase with time and higher SSP scenarios. The three indices increase by 40–55%, 4–6°C, and 4–7°C under SSP5‐8.5 over east China, respectively. The TN10P decreases by more than 6% over east China. The changes in these extreme indices under SSP1‐2.6 and SSP2‐4.5 are similar to those under SSP5‐8.5 but with a smaller magnitude. Large uncertainties still exist in the future projections, especially under the high SSP scenarios.

Future changes in the transitions of monthly‐to‐seasonal precipitation extremes over the Midwest in Coupled Model Intercomparison Project Phase 6 models

Abstract: Precipitation extremes present significant risks to Midwest agriculture, water resources, and natural ecosystems. Recently, there is growing attention to the transitions of precipitation extremes, or shifts between heavy precipitation and drought, due to their profound environmental and socio‐economic impacts. Changes in Midwest precipitation extremes and transitions between extremes over the past few decades have been documented; however, their future changes are still unknown. In this study, we estimate the projected changes in transitions of precipitation extremes in the Midwest based on 17 CMIP6 models. Two Standardized Precipitation Index (SPI) based metrics, intra‐annual variability and transitions, are used to quantify the magnitude, duration, and frequency of variability and transitions between wet and dry extremes. Compared with the observation‐based precipitation datasets, the multimodel ensemble median of CMIP6 can reasonably represent the spatial patterns of SPI extremes and transitions. Climate projections show significantly intensified wet extremes across the Midwest by the end of the century, with a greater increase over the northern Midwest and the Great Lakes region. The short‐term SPI also shows intensified dry extremes over the western half of the Midwest. Consequently, there is a significant increase in the magnitude of intra‐annual variability in most areas. Projections also suggest more frequent and rapid transitions between the wet and dry extremes, especially over the Great Lakes region and northern Midwest. Seasonally, more frequent transitions from a wet spring to a dry summer (or from a dry fall to a wet winter/spring) are projected to occur; and generally, the wet and dry conditions between the transitions are projected to be more intense compared to the historical period. Furthermore, the intensified precipitation extremes and accelerated transitions are greatly alleviated under a lower emission scenario, implying that future changes in hydroclimate extremes, and impacts thereof, in the Midwest are sensitive to climate change mitigation.

Evaluation of Coupled Model Intercomparison Project Phase 6 model‐simulated extreme precipitation over Indonesia

Abstract: The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME‐mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME‐means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS‐g3, IPSL‐CM6A‐LR, and IPSL‐CM6A‐LR‐INCA, and one model in consecutive dry day (CDD) simulation, MPI‐ESM‐1‐2‐HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action.

Severity of winters in the Czech Republic during the 1961–2021 period and related environmental impacts and responses

Abstract: This paper analyses temperature and snow patterns of winters (December–February) averaged for the territory of the Czech Republic during the 1961–2021 period and their broad environmental impacts and responses. Series of mean, maximum, minimum, absolute maximum and absolute minimum temperatures show significant increasing linear trends, while decreasing trends were detected in numbers of frost, ice and extremely cold days, duration of cold waves, snowfall days, sums of heights of new snow, days with snow depths ≥1 cm, mean and maximum snow depths. The winter severity, derived from five temperature and five snow variables and expressed by temperature/snow scores, indicates decreasing severity of winters for 1961–2021, in which temperature severity is more pronounced than that of snow. Decreasing winter severity is in line with decreasing frequency of cyclonic and directional circulation types according to objective classification, while the trend in anticyclonic types was opposite. Types with maritime airflow from the Atlantic Ocean and the Mediterranean contribute to milder and types with continental airflow from the east to colder winters. The coldest winters, 1962/1963 and 1984/1985, and the mildest winters, 2006/2007 and 2019/2020, were analysed in greater detail. Concerning of different analysed environmental impacts, they are influenced not only by severe winter weather, but also by political, socioeconomic and general environmental changes in the country. In line with decreasing winter severity were only statistically significant decreasing trends in proportions of traffic accidents connected with snow and glaze ice on the roads and volumes of damaged wood due to high weights of snow and ice deposit, expressed as salvage felling. Series of other environmental impacts (e.g., fatalities attributed to weather, impacts on the economy and society) reflect rather severity of individual winters and express high interannual variability without any representative trends.

Attribution of observed extreme marine wind speeds and associated hazards to midlatitude cyclone conveyor belt jets near the British Isles

Abstract: Extreme wind speeds, gusts, and wind wave heights associated with midlatitude cyclones pose a hazard to shipping lanes and offshore infrastructure operating in the North Atlantic Ocean seas surrounding the British Isles. Several studies have assessed the variability of wind and waves in this region using reanalyses, but few have used surface observations of extreme wind speeds and wave heights. Here, we use a network of marine surface stations to derive the 2012–2020 climatology of daily maximum wind speed events. An algorithm is used to attribute the extreme wind events, characterized as exceeding the 20 and 25 m·s−1 thresholds, to the cyclone warm conveyor belt (WCB), and early (CCBa) and returning (CCBb) cold conveyor belt jets; cyclones are matched with up to 90% of extreme wind events. The CCBb is most frequently associated with the strong wind speeds, accounting for 46 and 59% of the events exceeding the two thresholds, respectively. The CCBb also leads to the largest number of compound wind and wave hazard events (37 out of 87). Although the WCB is associated with the second largest number of extreme wind events, the CCBa accounts for the second largest number of compound extreme wind and wave events (24). The ERA5 reanalysis underestimates the observed extreme wind speeds, and associated gusts and wind‐wave heights, during extreme wind events for all the conveyor belt jets. The wind speeds and associated gusts are most underestimated, by median values of 4.5 and 5.5 m·s−1, respectively, and similar percentage error ( ≈25% ), when associated with the CCBb; however, the wind‐wave heights are most underestimated, by a median of 3.4 m, when associated with the CCBa. Hence, while the marine CCBb jet, found in mature cyclones, is both most hazardous and underestimated in the ERA5 near the British Isles, the CCBa jet can be nearly as hazardous when considering compound wind‐wave events.

A potential vorticity budget view of the atmospheric circulation climatology over the Tibetan Plateau

Abstract: To aid the understanding of atmospheric circulation climatology over the Tibetan Plateau (TP), this study investigated the comprehensive potential vorticity (PV) budget over the TP using model‐level reanalysis data. Our findings demonstrate the effects of diabatic heating, friction, gravity wave drag, advection, and convection in determining circulation over the TP, from the PV budget perspective. In summer, the diabatic heating‐generated positive (negative) PV tendency facilitates cyclonic (anticyclonic) circulation near the surface (in the middle troposphere). Low PV in the upper troposphere, pertaining to the Asian monsoon anticyclone and monsoonal overturning circulation is induced by the upward transportation of diabatic heating‐generated low PV in the middle troposphere. Horizontal advection further spreads the low PV—that is vertically inputted from the middle troposphere—to a wide area around the TP in the upper troposphere. In winter, the diabatic heating‐generated positive PV near the surface is balanced by the sinking motion‐carried low PV. In addition, gravity wave drag‐generated PV is prominent in the upper troposphere in winter and can affect the downstream climate through advection. We further revealed the crucial role of the diurnal cycle in shaping the near‐surface cyclonic circulation in summer by regulating the vertical structure of diabatic heating.

Spatiotemporal characteristics of human thermal comfort across southern Africa: An analysis of the Universal Thermal Climate Index for 1971–2021

Abstract: The 6th Assessment of the Intergovernmental Panel on Climate Change projects increasing thermal‐associated morbidity and mortality under anthropogenically induced warming. Over 100 indices exist to quantify thermal stress, and among these, the Universal Thermal Climate Index (UTCI) was developed for regional investigations of thermal stress influences on human health. Although by definition a universal index, current applications are mainly limited to Europe. For regions such as Africa, use of the UTCI has been hampered by a lack of available requisite input variables from ground‐based meteorological stations. To overcome this, a gridded dataset, derived from ERA5 reanalysis, of UTCI equivalent temperatures was developed by the European Centre for Medium‐Range Weather Forecasts. Using this dataset for daily average, minimum and maximum UTCI values, we explore spatiotemporal patterns and changes thereof over annual, seasonal, and monthly scales across southern Africa from 1979 to 2021. Across these scales, 9 of 10 UTCI thermal stress categories were observed, ranging from very strong cold stress to extreme heat stress. Spatially, no thermal stress was most widespread for daily mean values, whereas for daily maximum (minimum) values there was a wider heat (cold) stress incidence, with frequent occurrences of moderate and strong heat stress (slight and moderate cold stress). Interannually, a clear El Niño–Southern Oscillation influence on thermal stress was evident during summer, with El Niño (La Niña) phases extending (reducing) heat stress incidences by up to 13.8% (2.9%). Over the study period, heat stress increased at statistically significant rates in many instances, with the strongest, most widespread increases, for the daily average and maximum (minimum), during spring (summer), averaging 0.28 and 0.29°C·decade−1 (0.23°C·decade−1); few regions experienced statistically significant decreasing trends. Overall, the trend results highlight regions vulnerable to significant thermal climate changes, and thus should be considered in decision‐making regarding outdoor activities.

Extreme hourly precipitation characteristics of Mainland China from 1980 to 2019

Abstract: Instantaneous precipitation can often cause devastating disasters on the Earth's surface. Continuous increases in extreme precipitation around the world have caused widespread concern, and it is necessary to study the extreme hourly precipitation over a large scale and long time series. Using specific numbers of unique hourly precipitation point data from 1980 to 2019, our research found that there are different spatial patterns regarding the frequency and intensity of extreme hourly precipitation in Mainland China, but the clear spatial pattern of being weak in the west and strong in the east of China. Most extreme hourly precipitation events occurred from June to August in the north of China and from April to October in the south of China. Afternoon and midnight are the peak periods of extreme precipitation events in southern China. The frequency of extreme hourly precipitation in the whole of China had increased by 0.7 hr/10a and increased significantly in the northwest and southeast of China. The average intensity of extreme hourly precipitation has decreased by 0.1 mm·hr−1/10a in the whole of China. While the maximum intensity has increased in local areas, the trend of changes is not significant in the whole of China. We have discovered that rapid urbanization is likely to be responsible for the frequency of extreme hourly precipitation in China. It is urgent to enact flood protection measures because this change is expected to worsen in the future with intensified urbanization in China.

A comparison of seasonal rainfall forecasts over Central America using dynamic and hybrid approaches from Copernicus Climate Change Service seasonal forecasting system and the North American Multimodel Ensemble

Abstract: Seasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting.

Influence of Indian Ocean warming on rainfall of Sri Lanka

Abstract: Recent studies have pointed out an increased warming over the tropical Indian Ocean (IO) and how such warming could alter the convection and rainfall in the region. In this study, using observational/reanalysis datasets from 1981 to 2020, we show that the early Maha rainfall of Sri Lanka (SL) has been increasing, and it is considerably influenced by the North-western IO sea surface temperature (SST) increase in the second inter-monsoon (SIM). A significant warming in the North-western IO alters the zonal SST gradient and strengthens the low-level circulation, which induces large-scale convergence over the western IO during SIM, contributing to the enhancement of rainfall over SL. However, despite the substantial impact of IO warming, the moderate correlation suggests the importance of examining other factors, which may influence the rainfall variability of SL.

A locally weighted linear ridge regression framework for spatial interpolation of monthly precipitation over an orographically complex area

Abstract: This article aimed to investigate the potential of using three large‐scale precipitation products (PPs), including TRMM, ERA5‐Land, and MSWEP, and two land surface characteristics, including NDVI and Land Surface Temperature (LST), to improve the accuracy of an elevation‐based spatial non‐stationary, Locally Weighted Linear Regression (LWLR) method. A two‐step approach consisting of downscaling and merging was proposed and assessed across an orographically complex region in Iran to construct monthly precipitation maps for the 2001–2015 period. In the first step, three large‐scale PPs were downscaled to 1 km spatial resolution via the Area To Point Kriging (ATPK) method to address the spatial resolution discrepancy between the coarse pixels of PPs and pointwise gauge data. In the second step, five regression models with different combinations of predictors were developed and compared with the benchmark precipitation‐elevation regression model. The L2 regularization method was adopted to overcome the potential multicollinearity issue. The proposed framework could successfully diagnose the multicollinearity issues and dynamically estimate the regularization parameter in space and time. We utilized the holdout method to validate and compare the developed models, in which 50% of gauges were used for fitting the regression functions, and the remaining gauges were assigned to the validation dataset. The validation results showed that the overall monthly accuracy of the benchmark univariate elevation‐based model was improved where the MAE metric was decreased by 16.5%, and CC and KGE were increased by 3.4 and 5.8%, respectively. Also, the rBias was enhanced from −5.72 to 1.24%. Mean monthly precipitation maps of the 2001–2015 period generated by the model with the best combination of predictors and the benchmark model displayed the same spatial pattern consistent with the topography. Further analysis under different validation scenarios revealed that the contribution of PPs and auxiliary variables is the greatest when the training gauge network is sparse. Also, the developed multivariate model has a higher extrapolation ability and is more robust than the benchmark model.

Reconstruction of daily global solar radiation under all‐sky and cloud‐free conditions in Badajoz (Spain) since 1929

Abstract: This work analyses the long‐term temporal variability of the annual and seasonal series of reconstructed global solar radiation for both all‐sky and cloud‐free conditions in Badajoz (Spain) over the 1929–2015 period. Specifically, daily values of global horizontal irradiation (GHI) for all‐sky cases are derived from a semiempirical method based on the relationship between the cloud modification factor and sunshine duration records. Additionally, cloud‐free situations are selected using cloud cover (CC) information recorded by surface observations. Regarding GHI linear trends for all‐sky conditions, three periods are clearly identified: during the 1929–1950 period, there is a positive and statistically significant trend of +4.18 W·m−2·decade−1. It is followed by a significant dimming with a trend of −3.72 W·m−2·decade−1 between 1951 and 1984. GHI levels increase again from 1985 to 2015 with a statistically significant trend of +2.04 W·m−2·decade−1. The seasonal trends are found to be statistically significant only in summer for all the three subperiods. With the goal to find out the possible causes of the reconstructed GHI trends, the temporal variability of the CC was also analysed. It was observed that CC has a statistically significant negative trend between 1985 and 2015 which may partially explain the GHI increase shown for this period. In contrast, not statistically significant trends were found in the annual and seasonal CC series before 1985. The long‐term evolution of the GHI under cloud‐free conditions exhibits the same pattern as all‐sky conditions: an increase during 1929–1950, followed by a decrease in 1951–1984 and then a new increase from 1985 to 2015. Therefore, the positive (negative) linear trends in GHI reported in this study could be partially related to a decrease (increase) in the aerosol load during the analysed three subperiods.

Spatiotemporal analysis of consecutive extreme wet days in China from 1980 to 2020

Abstract: Consecutive extreme wet days (CEWDs) typically affect the likelihood of flooding and landslides and have negative effects on both natural and artificial ecosystems; however, the spatiotemporal changes in their features are still unclear at the national scale. Investigating changes in the frequency and intensity of such events is essential for climate risk management. Here, this study thoroughly analysed CEWD features in China from 1980 to 2020 using the daily observation dataset. We also evaluated and compared the ability of two widely used precipitation products, the European Centre for Medium‐Range Weather Forecasts Reanalysis 5th Generation (ERA5) and the Multi‐Source Weighted‐Ensemble Precipitation version 2 (MSWEP), to detect CEWD features. According to the observation data, the frequency and intensity of CEWD events are related to local climate and present “high eastern and low western” spatial patterns across China. Since 1980, more than half of China's mainland has experienced increases in both the frequency and intensity of CEWDs. Some stations detected decreasing trends in CEWD frequency and amounts, which are primarily located in the eastern coastal regions and southwest of warm temperate humid/subhumid regions. The ERA5 precipitation product generally outperforms MSWEP data in detecting CEWD events, and the latter significantly underestimates the annual frequency and amounts of CEWDs. These findings provide basic and valuable information regarding CEWD features across China and where such studies can be conducted based on precipitation datasets. Furthermore, these findings will also aid policymakers in managing extreme precipitation‐related natural hazards.

Evaluation and future projection of the extreme precipitation over India and its homogeneous regions: A regional earth system model perspective

Abstract: A regional earth system model (ROM) was used to examine the projected change in the precipitation extremes (PEs) and associated dynamical and thermodynamical processes over India during the Indian summer monsoon (ISM). In this regard, PEs are computed for India, its six homogeneous regions and subregions the Western Ghats (WG) and central India (CI). The changes are computed for mid‐future (2040–2069: MDF) and far‐future (2070–2099: FRF) with respect to the historical period (1969–2000). ROM showed better resemblance with observation in simulating PEs over other regional climate models (RCMs) that participated in the CORDEX‐CORE simulation. The intense rainfall (95th percentile: R95) is expected to be enhanced over most of the region during MDF that further intensifies in FRF except CNE (central northeast) and NEI (northeast India), where the increase in R95 was restricted only up to MDF. Interestingly, very intense rainfall (99.9th percentile: R99) showed robust increases in both MDF and FRF for all regions. Additionally, long wet (dry) events were shortened (lengthen). Moreover, the short wet and dry spell frequency has increased, while the duration of the wet (dry) spell has decreased (increased) in both time slices over India with noticeable regional variation. This is attributed to the strong cyclonic circulation, reduced vertical wind shear and enhanced moisture transport during the ISM in both time slices. It is very important to highlight the substantial changes in the precipitation extremes to have better planning and strategies that will be helpful to minimize the incurred losses.

Heat waves in São Paulo State, Brazil: Intensity, duration, spatial scope, and atmospheric characteristics

Abstract: Heat waves (HWs) are atmospheric events of synoptic and global scale that negatively impact productive sectors and the population. This study aims to investigate HWs in the São Paulo (SP) State, Brazil, and describe their intensity, duration, spatial coverage and atmospheric characteristics. HWs were identified using observed data from 65 weather stations for the period 2000–2020. The NCEP‐DOE Reanalysis was used to determine synoptic and global scale circulation characteristics. The results showed that SP experiences different effects of thermal stress on maximum temperatures (Tmax), with the north and northwest sectors reaching the highest average values (38°C). The average temperature of HWs in the state was 34.9°C, with an average duration of 5.3 days and 92% of events taking place between 2010 and 2020. A greater number of events occurred in 2015, 2016 and 2019, mainly in the austral summer and spring. The following atmospheric characteristics were found to be associated with HWs in SP: an anomalous semistationary anticyclonic circulation at 500 hPa over the hinterland regions of Brazil, a thermal low at 1000 hPa over Paraguay, a Rossby wave train originating in the South Pacific Ocean and spreading to centre‐south Brazil, a positive sea surface temperature anomaly in the South Atlantic Ocean near the coast of Brazil and another over the South Pacific, and a negative pattern circling Antarctica. Apparently, there is a certain degree of coupling between sea surface temperature anomalies and the Rossby train, the former being the trigger of the event.

Dynamic downscaling of climate simulations and projected changes in tropical South America using RegCM4.7

Abstract: High‐resolution (dx = 25 km) simulations of the regional climate model RegCM4.7 coupled with the Community Land Model version 4.5 (CLM4.5) under low (RCP2.6) and high (RCP8.5) emissions scenarios, which interact with the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (AR5‐IPCC), were carried out over tropical South America (TSA). These simulations were produced through boundary conditions from simulations driven by the general circulation model HadGEM2‐ES. With the goal of verifying the added value (AV) of RegCM4.7, reproducing in an adequate and coherent way the regional aspects of the historical period (1986–2005), as well as evaluating the regional aspects simulated by the model in the scope of the change projections for the far‐future (2080–2099). For this study, the climate in the TSA was characterized based on the variables of precipitation and near‐surface air temperature. For the evaluation of the spatial–temporal representation, frequency and distribution of the regional and global simulation, the high‐resolution observational dataset of the Climate Research Unit version ts4.02 (CRU) was used. Although some differences and biases still persist, RegCM4.7 presents AV in the spatial representation of precipitation and temperature over the northeast region of Brazil and part of the Andes, especially in winter. However, it does not adequately represent precipitation over the Amazon Basin, especially in summer. Results for future projections indicate that the more refined simulation of RegCM4.7 improves the projected change patterns of the coarser resolution simulation of HadGEM2‐ES and even modifies the precipitation signal in some cases. Both models project increase temperature with greater magnitude for RCP8.5. RegCM4.7 presents a much more refined and realistic spatial distribution. HadGEM2‐ES simulates the major aspects of climate enough to consider forcing RegCM4.7 to generate simulations with better performance and more realistic projections.

Influence of different configurations of western North Pacific anticyclone and Siberian high on spring climate over China

Abstract: This study analyses the spring climate anomalies under different configurations of western North Pacific anticyclone (WNPA) and Siberian high (SH). Compared to the WNPA alone with increased precipitation over southern China, the out‐of‐phase configuration (strong‐WNPA–weak‐SH) can enhance the WNPA‐related southwesterlies over eastern China, favouring increased precipitation with larger amplitude over southern China and southern Northeast China. Differently, the in‐phase configuration (strong‐WNPA–strong‐SH) decreases and confines WNPA‐related southwesterlies over southern China, causing decreased precipitation over eastern Southwest China; additionally, the WNPA‐related southwesterlies converge with SH‐related northwesterlies over the lower reaches of the Yangtze River, causing increased precipitation there. For surface air temperature, the WNPA alone induces warming over western Southwest China through changing heat flux, while the SH alone induces cooling over northern China through changing temperature advection, and over Northwest and southern China through changing heat flux. In the out‐of‐phase configuration, warming expands into Northern China due to the cooperation of weakening SH. In the in‐phase configuration, SH‐related cooling mainly occurs to the north of the Yangtze River because of the obstruction of WNPA. Further analysis indicates the eastern Pacific (EP) and central Pacific (CP) El Niño‐Southern Oscillation (ENSO), European low and Ural high contribute to the different configurations of WNPA and SH. The EP ENSO‐related zonal overturning circulation intensifies the WNPA and European low‐induced warm advection weakens the SH, resulting in the out‐of‐phase configuration. The CP ENSO‐related zonal overturning circulation intensifies the WNPA with a northeast–southwest tilt and Ural high‐induced cold advection intensifies SH, resulting in the in‐phase configuration.

Storylines for future changes of the North Atlantic jet and associated impacts on the UK

Abstract: Climate projections for the UK exhibit substantial uncertainty, and this uncertainty is a hindrance to robust and timely decision making on both adaptation and mitigation policy issues. A large part of the uncertainty is associated with dynamical changes of the regional atmospheric circulation rather than thermodynamic changes which are better constrained by model simulations. Of particular importance for the UK is the extent to which the North Atlantic jet will change over coming decades and the impact this will have on weather and climate in the region. In this article, we propose the use of jet-based storylines for assessing and communicating uncertainty in climate projections for the UK, wherein changes in each impact are explicitly conditioned on changes in the North Atlantic jet. This approach provides a framework for evaluating the impacts associated with a range of plausible future climate outcomes for the UK, including outcomes that may not be well represented in the current generation of climate models, and for communicating these potential outcomes. We construct a simple yet useful set of future jet storylines for both summer and winter and for 2°C and 4°C global warming levels and illustrate the utility of the approach by evaluating the impact of each jet storyline on future changes in UK precipitation. In doing so, we demonstrate that the relationships between the jet and UK precipitation are consistent between observed interannual variability and projected changes. This finding increases our confidence in projecting changes in UK precipitation associated with each storyline.

Observed aerosol‐layer depth at Station Nord in the high Arctic

Abstract: The depth of the aerosol layer at the Villum Research Station at Station Nord in the high Arctic is analysed based on 8 years of observations from a ceilometer and one full year from a wind lidar. The layer is of particular interest for aerosol process modelling and atmospheric chemistry studies. The depth of the aerosol layer is assigned to the inflection point in the attenuated backscatter profile by two methods; one is based on polynomial approximation of the profile and the other is direct numerical differentiation. The analysis is based on two types of hourly profiles; one consists of averaging the attenuated backscatter observations and the other by computing the median. Due to sporadic occurrence of outliers in the ranges around 50 m in the ceilometer observations, this part of the profile is not used in this study. Restricting the observations to heights above 100 m, the depths of the aerosol layer are found to be typically ≈230 m. It varies little between winter and summer, but the spread in the depth is larger during the winter as compared to summer. To extend the study of the aerosol‐layer depth below 100 m, a method is applied that combines the ceilometer measurements with the carrier‐to‐noise ratio from the wind lidar. The results are available for 2018 only, and they show aerosol‐layer depths below ≈80 m as well as depths around 230 m and they show few observations between ≈80 and ≈230 m. Near the ground, the observed backscatter exhibits a pronounced seasonal variation, having low values during the summer and high values during the winter. The strength of the seasonal variability decreases with height, especially above the aerosol‐layer depth, and is virtually absent at 1 km.

Diverging snowfall trends across months and elevation in the northeastern Italian Alps

Abstract: Snowfall and snow accumulation play a crucial role in shaping ecosystems and human activities in the Alpine region. This resource is under threat as a consequence of the visible effects of global warming, and, therefore, it appears urgent to understand how snowfall trends have changed in time and space. In this context, we recovered data from over a hundred snowfall (HN) time series covering the period 1980–2020 over the mountain region of Trentino‐South Tyrol in the northeastern Italian Alps and analysed them to understand snowfall climatology in the region, recent trends and their dependence on elevation and timing of the season. Negative, although not always statistically significant, trends were found in the lowest elevation range (0–1,000 m a.s.l.) over the whole winter season, while some positive and even significant trends were found from January to March above 2,000 m a.s.l. The intermediate elevation range (1,000–2,000 m a.s.l.) exhibits a strong variability with no clear trend. Negative and statistically significant trends were found in April for all elevations. An attribution analysis was performed using precipitation (P), mean air temperature (TMEAN), and large‐scale synoptic descriptors, such as the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) indices. The analysis shows that, overall, P is the driver that best explains the snowfall trends, but, for low elevations, especially during mid‐winter, TMEAN is more relevant. Low elevations are facing a clear decrease in HN due to a significant increase in mean temperatures, while high elevations during mid‐winter display a slight increase in HN, associated with a general increase in precipitation. NAO and AO indices exhibit no significant correlations with HN, except at the lowest elevations and at the beginning of the season.

Performance evaluation of Standardized Copula‐based Drought Index with Reconnaissance Drought Index and Standardized Precipitation Temperature Index using severity–duration frequency curves over Balochistan, Pakistan

Abstract: Drought and aridity are good measures to study climate change that are measured using several indices. In this study, the performance of Standardized Copula‐based Drought Index (SCDI) is compared with Standardized Precipitation Temperature Index (SPTI) and Reconnaissance Drought Index (RDI) for measuring drought in Balochistan province of Pakistan. These standardized drought indices combine the outcomes of De‐Martone Aridity Index (DAI) and UNEP Aridity Index (UAI). SCDI is calculated using probability distributions and copula models that combine the outcomes of UAI and DAI utilizing data of precipitation, potential evapotranspiration (PE), and temperature. Climate data from nine including arid and humid metrological stations are used to study drought conditions in Balochistan province. SCDI outcomes are graphically and numerically compared with SPTI and RDI. Selected drought indices are compared based on frequency of drought and wet events occurred in moderate, severe, and extreme drought categories, which indicate that SCDI has maximum number of events in these categories. It gives better results due to the utilization of more climate data as compared to RDI and SPTI. Severity–duration frequency curves are used to find future projections which shows that drought severity and drought duration have increasing trend with the passage of time. The indices have more differences in results of arid stations. The results indicate that there are no significant chances of drought at lower time scales, but the chances increase with the increase in time scale. The SCDI may also be more useful for agricultural and hydrological droughts due to temperature and PE variables. This study's results will be helpful for climate planning in Balochistan province.

Characteristics of drought, low temperature, and concurrent events of maize in Songliao Plain

Abstract: Maize is susceptible to drought and low temperatures. In recent years, drought, low temperatures, and their composite events have often occurred during the growth period of maize, causing a huge impact. In this study, maize in the Songliao Plain was used as the research object. Based on the crop water surplus deficit index (CWSDI) and heat index (F(T)), combined with the Theil–Sen median and Mann–Kendall tests (Sen–MK) and empirical orthogonal function (EOF), the temporal trends and spatial patterns of drought and low temperatures in different growth periods of maize were obtained. Copula modelling was used to analyse the joint probability distribution and return period of drought and low temperatures. The results showed that the temporal variation trend of the CWSDI was significantly different at different growth periods of maize, and there were different trends and spatial modal distributions. However, the temporal variation trend of F(T) was similar, showing a downward trend over the entire region. There was no obvious asymmetry or skew‐dependent structure in the distribution of concurrent events of drought and low temperature; however, the joint probability contours of different growth periods were biased to the upper left. The recurrence period of drought and low‐temperature concurrent events was within 2 years, and the recurrence period was basically between 2 and 5 years, with a reduction in drought or low temperatures.

Changing seasonality of daily and monthly precipitation extremes for the contiguous USA and possible connections with large‐scale climate patterns

Abstract: Temporal changes in the seasonality of extreme precipitation, and possible teleconnections between the seasonality of extreme precipitation and large‐scale climate patterns are not well understood. In this study, we investigated temporal changes in seasonality of annual daily maximum (ADM) and monthly maximum (MM) precipitation indices over the period 1951–2014 for 1,108 stations across the contiguous USA. We also examined seasonality of extreme precipitation during negative and positive phases of three major oscillations: the El Niño–Southern Oscillation, the Northern Atlantic Oscillation, and the Pacific Decadal Oscillation. Our results show that many climate regions within the contiguous USA display distinct seasonality for both ADM and MM. Comparison of seasonality between two historical records of equal length, that is, before and after 1981, shows great spatial variability across the contiguous USA. While a spatial coherence of change in the mean date of occurrence of extreme precipitation across a large area is not visible, a cluster of stations showing decrease in strength of seasonality for the recent period is concentrated in the eastern Gulf Coast and coastal sites of Northeast and Northwest regions. Extreme precipitation seasonality during negative and positive phases of three climate indices revealed that large‐scale climate variabilities have a strong influence on the mean date of occurrence of extreme precipitation but generally weak influence on the strength of seasonality in the contiguous USA. Results from our study might be helpful for sustainable water resource management, flood risk mitigation, and prediction of future precipitation seasonality.

Multidimensional identification of single and regional meteorological drought considering flash‐season synthesis employed daily standardized effective precipitation and drought index in Guangxi, China

Abstract: The frequent occurrence of extreme drought events in Guangxi has caused huge losses to human beings and economy in the region for many years. Therefore, this study adopted run theory and the objective identification method of regional extreme events (OITREE) and then carried out the comprehensive feature identification of multidimensional elements such as intensity, duration and area of meteorological drought events based on the daily standardized effective precipitation and drought index (SWAP) sequence of Guangxi from 1979 to 2018a. By comparing the evolutionary characteristics of drought elements identified by grid SWAP statistical analysis and OITREE, an integrated optimization scheme combining the accuracy of grid analysis and area identification is formed in this study. It was confirmed that the above two analysis methods had good correlation and consistency in revealing the evolution process of multidimensional elements of drought events. In terms of the frequency of drought events over a long period of time and the comprehensive characteristics of each element, the identification results of the OITREE method can better reveal the comprehensive characteristics of drought in Guangxi. Furthermore, the study found that flash droughts and seasonal droughts occurred alternately and were superimposed concurrently in Guangxi, and there were significant differences in the multidimensional spatial and temporal characteristics of these two types of droughts. Specifically, the frequency of flash drought was 1.60–4.00 times·(a)−1, the duration varied from 20 to 60 days·(a)−1 and the concentration point of drought had a region‐wide dispersion; while the frequency of seasonal drought was 0.82–1.65 times·(a)−1, the duration varied from 40–105 days·(a)−1 and the concentration point of drought had a local concentration. The research results can provide effective scientific support for operational drought refined forecast and early warning, and smart regulation of drought disaster risk.

Space–time variation of large hail‐producing mesoscale convective systems over a complex terrain of the Indian subcontinent as revealed by the integrated Tropical Rainfall Measuring Mission and Global Precipitation Measurement observations

Abstract: Space–time variation of large hail‐producing mesoscale convective systems (MCSs) is investigated over the eastern and northeastern parts of the Indian subcontinent during the premonsoon (March–April–May) season by using long‐term (1998–2020) integrated observations of Precipitation Radar and Microwave Imager onboard the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites. A threshold of 37.0‐GHz polarization‐corrected temperature (≤176 K) is utilized as a hail proxy. Qualitatively, the space–time variation of satellite‐detected hailstorms is reasonably in good agreement with the available hail reports at the ground. The large hail‐producing MCSs are most frequently observed over the plains, whereas such MCSs are not found over mountains. The MCSs in April have the highest probability to contain hail though the hail‐producing MCSs are more common in May. The average morphological and microphysical properties are distinctly different for the MCSs with and without hail. Compared to the active convective cores of MCSs without hail, the hail‐producing convective cores of MCSs (a) are extended higher vertically and wider horizontally, (b) are associated with much larger area (~1,000 vs. 100 km2) occupied by radar reflectivity larger than 40 dBZ in the mixed‐phase region, and (c) are associated with larger values of cloud ice water content (CIWC; 395 vs. 153 mg·m−3) in the mixed‐phase region. The results from the high‐resolution ERA5 reanalysis data show that the hail‐producing MCSs are more sensitive to synoptic forcing than the MCSs without hail. Very strong mean sea‐level pressure anomalies over the whole northern part of India along the Himalayan foothills to the Bay of Bengal occur for the MCSs with hail days. The findings of this study will help the forecasting of these hailstorms and mitigation of their damage within this less explored region.

Dominant modes of precipitation over Africa, and their associated atmospheric circulations from observations

Abstract: This study uses the empirical orthogonal function (EOF) analysis to identify and describe continental-scale seasonal (MAM, JJA, SON and DJF) precipitation modes over Africa based on observation (Global Precipitation Climatology Centre; GPCC) and reanalysis (ERA5) data from 1982 to 2014. Using composite analysis, we attempt to identify the atmospheric circulations (wind and relative humidity) associated with each precipitation mode. Precipitation from ERA5 has a good agreement with that of GPCC, as observed in the good spatial congruence in the EOF analysis and precipitation composites. The EOF results for each season show that the loading patterns of Mode 1 (variance >20% for all seasons) match the long-term mean precipitation distribution. Atmospheric conditions across the continent are primarily driven by the four main high-pressure systems (Azores, St. Helena, Arabian and Mascarene High) that influence moisture distribution and subsequently modulate the seasonal rain belt distribution. Modes 2 (variance >11% for all seasons) and 3 (variance >10% for all seasons) are deviations of the leading mode associated with smaller-scale atmospheric systems. However, large-scale factors still dominate the overall precipitation pattern by modulating the seasonal energy transfer between the hemispheres. The results provide a comprehensive understanding of the continental-wide African seasonal precipitation modes and the associated atmospheric circulation from observations. The findings can be used as a reference for future work using model data for historical and projection studies.

Projection of gust wind over China under RCP8.5 scenario within CORDEX‐EA‐II project

Abstract: A wind gust parameterization scheme combined with the Weather Research and Forecasting (WRF) model used as the regional climate model (RCM) is employed to simulate and project the gust wind speed at 10‐m height above ground level for the whole China in the historical and future period 1981–2005 and 2036–2060. The gust wind speed is compared between the historical and future period to investigate its long‐term change under the background of warming climate within the CORDEX‐EA‐II project under the RCP8.5 scenario. First, the gust wind speed simulated by the gust parameterization inputted with the RCM simulations driven by the ERA‐Interim reanalysis data is compared with the ERA5 reanalysis data and their discrepancy is discussed. Then the historical/future gust wind speed is simulated/projected with the RCM simulations driven by four global climate models' results. The comparisons between the historical simulations and future projections show the gust wind speeds overall change slightly with little response to the future warming climate as the whole China is concerned, and the maximal increment and decrement of averaged annual maximal gust wind speed are, respectively, 2.25 and −2.57 m·s−1. The increases greater than 1 m·s−1 are mostly located in the eastern and southeastern coast and northwestern inland while the decreases less than −1 m·s−1 are mostly located in part of the Tibet Plateau and northwestern inland.

An assessment of CMIP6 climate signals and biases in temperature, precipitation and soil moisture over Europe

Abstract: The CMIP6 projections constitute the basis of our latest understanding of the climate response to anthropogenic forcing. However, there is still considerable uncertainty in the projections, especially at the regional scale. One way to constrain the uncertainty is by comparing the models historical climate change signals against observations and investigate the physical reasons for divergences. Here, we assess the signal-to-noise ratio (S/N) of surface air temperature (SAT), precipitation (PREC) and soil moisture (SM) over Europe for a set of CMIP6 historical simulations and compare them against the E-OBS observational product and the ERA5 reanalysis. We found considerable divergences between the CMIP6 ensemble mean S/N and that of E-OBS and ERA5, as well as between ERA5 and E-OBS. The latter indicates that the S/N is affected by data coverage. We show that the differences among model signals are associated with different atmospheric circulation responses. We also investigate the potential relationships between the models' signals and climatological biases, and we found evidence that the models with a warm climatological bias in southern Europe tend to have smaller SAT signals (warm less). Finally, we found no apparent relationship between SM biases and the warming signal, suggesting that the mechanism by which SM–atmosphere interactions affect climate variability does not explain the mean changes. However, there is a tendency for models with higher SM to dry faster than models with lower SM.

Heat health during future summers in eastern Asia: The combined roles of circulation, temperature and humidity

Abstract: Excessive relative humidity (RH) in combination with high temperature can lead to heat stress, often measured by the Wet Bulb Globe Temperature (WBGT). The Clausius-Clapeyron (CC) relationship implies that warming reduces RH if no extra moisture is added. Over coastal regions like eastern Asia, however, the predominant summer monsoon favours increased moisture transport from surrounding oceans as a result of enhanced evaporation driven by surface temperature increase. This would lessen the RH reduction by potentially two-thirds. Based on two ensembles of climate model simulations, this paper examines the competing effects of near-surface temperature, humidity and circulation patterns in this region and the consequential health risks. Under a high emissions scenario (RCP8.5/SSP5-8.5), surface temperature could increase by 4 – 7°C with WBGT increases of several degrees by the end of the 21st century. Devastating extreme heat health events could therefore become a frequent occurrence as a result. Overall, our results show how humidity can be just as important as temperature when considering the risks to society of excessive heat.

A machine learning approach for prediction of seasonal lightning density in different lightning regions of India

Abstract: Lightning is one of the most severe weather events causing significant loss of human lives and resources. Increasing number of lightning fatalities due to recent climatic changes is emerging out to be a serious concern for India during last few years. Proper characterization and parameterization of the same, therefore, is extremely crucial. However, lightning is an extremely dynamic phenomenon having enormous spatio‐temporal inhomogeneity especially over such a vast country like India with varied topographic and climatological features. Therefore, proper parameterization of lightning activity over India needs consideration of different lightning climatologies. This study has attempted to resolve the issue by regionalizing Indian subcontinent in different lightning climatologies based on lightning density and associated atmospheric variables that is, CAPE, specific humidity at different pressure levels, temperature, k index and cloud particle size and identified seven distinct lightning climatologies over India. A regression model is proposed for estimating the annual and seasonal (monsoon and pre‐monsoon) lightning activities over the seven resulting lightning zones based on the said atmospheric variables using machine learning techniques. Four machine learning models have been tested among which Random forest has shown the best accuracy. The regression model has shown an R‐squared score of 0.81 during monsoon season and 0.71 during the pre‐monsoon. The atmospheric features based on their influences on the lightning activity in these seven climatologies has been ranked which presented the evidences of largely varied interplay between different atmospheric variables and lightning over different parts of the country and during different seasons.