ERA-40 Reanalysis Research Articles

Climatology of Wet-Bulb Globe Temperature and Associated Heat Waves in the U.S. Great Plains

Abstract Extreme heat such as that seen in the United States and Europe in summer 2022 can have significant impacts on human health and infrastructure. The Occupational Safety and Health Administration (OSHA) and the U.S. Army use wet-bulb globe temperature (WBGT) to quantify the impact of heat on workers and inform decisions on workload. WBGT is a weighted average of air temperature, natural wet-bulb temperature, and black globe temperature. A local hourly, daily, and monthly WBGT climatology will allow those planning outdoor work to minimize the likelihood of heat-related disruptions. In this study, WBGT is calculated from the ERA5 reanalysis and is validated by the Oklahoma Mesonet and found to be adequate. Two common methods of calculating WBGT from meteorological observations are compared. The Liljegren method has a larger diurnal cycle than the Dimiceli method, with a peak WBGT about 1°F higher. The high- and extreme-risk categories in the southern U.S. Great Plains (USGP) have increased from 5 days per year to 15 days from 1960 to 2020. Additionally, the largest increases in WBGT are occurring during DJF, potentially lengthening the warm season in the future. Heat wave definitions based on maximum, minimum, and mean WBGT are used to calculate heat wave characteristics and trends with the largest number of heat waves occurring in the southern USGP. Further, the number of heat waves is generally increasing across the domain. This study shows that heat wave days based on minimum WBGT have increased significantly which could have important impacts on human heat stress recovery.

Drought risk in Moldova under global warming and possible crop adaptation strategies.

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence. Observed station data (1950-2020 and 1850-2020), ERA5 reanalysis data (1950-2020), and climate model simulations (period 1970-2100) are used. Crop yield data (maize, sunflower, grape), data from experimental plots (wheat), and the Enhanced Vegetation Index from Moderate Resolution Imaging Spectroradiometer satellites were also used. Results show that although the severity of meteorological droughts has decreased in the last 170 years, the impact of precipitation deficits on different crop yields has increased, concurrent with a sharp increase in temperature, which negatively affected crop yields. Annual crops are now more vulnerable to natural rainfall variability and, in years characterized by rainfall deficits, the possibility of reductions in crop yield increases due to sharp increases in temperature. Projections reveal a pessimistic outlook in the absence of adaptation, highlighting the urgency of developing new agricultural management strategies.

Comparative analysis of heavy rainfall area between landfalling typhoon LUPIT (2109) and typhoon LISA (9610)

Based on the ERA5 reanalysis data and the surface observations from automatic weather stations, a comparative analysis has been conducted to investigate the differences in heavy rainfall distributions caused by two landfalling tropical cyclones (TCs): LUPIT (2109) and LISA (9610). The two TCs have similar tracks, intensity and landing points, but show different asymmetric features in their rainstorm location relative to their tracks. The results indicate that the TC rainfall differences are mainly caused by different rainstorm formation mechanisms. The wind shear contributes most to the rainstorm of LISA, while land-sea contrast and topographical effect are the main factors of LUPIT rainstorm. Under the influence of strong environmental vertical wind shear and the weak cold air invasion from the west, the circulation center of LISA tilts westward with height, which cooperates with the low-level water vapor convergence and vertical ascending movement on the western side of the TC center to jointly cause the heavy rainstorm to the west of LISA center. In contrast, LUPIT has weak environmental vertical wind shear and no obvious structure tilting with height. Topographic effect plays a crucial role in causing the heavy rainstorm on the north of TC center. The southeasterly jet is blocked by the Taimu Mountain in the northeastern Fujian Province, and the strong ascending motion caused by the terrain-induced convergence appears to the north of LUPIT center. In addition, the moisture convergence is more pronounced in the north and weaker in the south. The intrusion of weak cold air from the east to the coastal areas of central-northern Fujian, and the moisture-convergence distribution, jointly cause the heavy rainstorm to the north of LUPIT.

Mean flow and eddy summer moisture transport over East Asia in reanalysis data and a regional climate simulation

Understanding the impact of atmospheric variability on climatological mean moisture transport is crucial because moisture transport determines continental water availability as well as convective organization and resulting precipitation. Here, we analyze the mean flow and eddy components of summer moisture transport in the downwind of the Tibetan Plateau (TP), a region that is characterized by interactions between monsoon systems, extratropical circulation, and mountainous weather systems. Using 40 years of ERA5 reanalysis data and a regional WRF simulation, we determine the absolute and relative contributions of mean flow and eddy moisture transport from multi-daily to sub-daily scales. We also link these components to large-scale circulation indices, precipitation, evaporation, and mesoscale convective systems (MCSs). The results show that the largest contributions of eddies to the climatological mean moisture transport are found in the immediate downwind region of the TP. Half of the total eddy transport downwind of the TP is due to multi-daily eddy transport and the other half is due to daily to sub-daily eddy transport. Regional precipitation anomalies are dominated by the mean flow component of southerly moisture influxes which in turn are positively correlated with different South Asian summer monsoon indices and negatively correlated with the West Northern Pacific monsoon index. The eddy transport from the south is positively correlated with a lower jet latitude but does not show any significant correlations with precipitation or MCS activity, likely due to the dominant role of the mean flow moisture transport. While the relative contributions of eddies to the climatological mean moisture transport are similar in ERA5 and WRF, the correlations between moisture transport components and large-scale circulation indices are generally weaker in WRF. This suggests that the dynamical downscaling does not significantly change the role of eddy moisture transport averaged for the region, but it resolves processes that decouple the moisture transport from its large-scale forcing.

A machine learning model that outperforms conventional global subseasonal forecast models

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF’s state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research.

Temporal and spatial changes in the environmental lapse rate distribution over the Arctic

Abstract The Environmental Lapse Rate (ELR) depicts how the temperature near the surface varies with altitude and can be used for temperature downscaling coarse resolution data and for understanding boundary layer processes. We calculated the ELR using ERA5 reanalysis data, examined its temporal and larger-scale spatial variability, and found a prevalent seasonal ELR cycle over the Arctic. There are extensive positive ELR values resulting from pervasive inversions over most of the Arctic in winter; hence, we also explored the possible factors that lead to inversions in polar regions. Our results can serve as a reference for future research on the inversions in different morphological regions at different pressure levels. By improving the characterization of the ELR, we obtain a more explicit representation of the vertical temperature variation across the Arctic region and examine potential trends in ELR over time. Our results challenge the commonly assumed fixed ELR values that are typically used in the Arctic region in, for example, correcting ice-core temperature reconstructions or estimating higher-resolution runoff from land ice.

Atmospheric Circulation in the Furnas Reservoir Region, MG: Sensitivity Experiments with RegCM5 in Convection-Permitting Mode

In the 1950s, the Furnas reservoir was created, an artificial lake surrounded by hills, for the operation of the Furnas Hydroelectric Power Plant in southeastern Brazil. Along the shores of the Furnas reservoir, there are no meteorological stations, and only three are found at a distance greater than 10 km from the reservoir, which makes it challenging to assess the lake's influence on the local climate. To address this issue, the Regional Climate Model version 5 (RegCM5) was run in convection-permitting (CP) mode for two months, with a horizontal resolution of 1 km, to investigate the near-surface atmospheric circulation patterns around the reservoir. Three numerical experiments were conducted using RegCM5-CP nested in ERA5 reanalysis: a control simulation (CTRL), an experiment where the topography was assumed to be flat throughout the domain (expTOPO), and an experiment where the water body was replaced by vegetation (expVEG). The first month of simulation in each experiment was excluded from the analysis as it was considered a spin-up period. RegCM5-CP has good performance in simulating the diurnal cycle of the 2-m air temperature and reasonable performance for the 10-m wind intensity compared to observed data from three meteorological stations in the domain of simulation (but distant from the reservoir shore). Despite the geographic complexity of the Furnas reservoir, the experiments revealed coupling between lake and valley breezes during the daytime and land and mountain breezes during nighttime in different segments of the reservoir, such as in Guapé (near the dam). However, mountain-valley breezes are more dominant.

Role of cyclone activity in summer precipitation over the northern margin of Eurasia

In recent decades, the atmospheric moisture capacity has increased globally in concert with global warming, with a particularly notable warming trend in Arctic regions. However, due to limited observational data, the variation and causes of polar precipitation, especially large-scale precipitation events associated with Arctic cyclones, remain unclear. In this paper, GPM satellite data are compared with ERA5 reanalysis data to explore the characteristics of summer precipitation at the northern margin of the Eurasian region (NMER) and the influence of cyclone activity on precipitation. It is revealed that high precipitation values in the Arctic region, as indicated by the GPM and ERA5 data, are mainly concentrated at the NMER. However, the GPM data show an overall larger precipitation amount, while the station observations more closely agree with the ERA5 precipitation changes at the NMER. The cyclone identification results indicate that summer cyclones at the NMER are mainly distributed in the Barents, Kara and Laptev Seas, and the precipitation contribution rate of ERA5-derived cyclones is 37.35%, which is significantly higher than that of GPM-derived cyclones (29.47%). Furthermore, high cyclone activity results in more intense precipitation, with the top 5% of the strongest cyclones contributing 60% (GPM) and 40% (ERA5) to the total cyclonic precipitation.

Evaluating the spatiotemporal dynamics of driving factors for multiple drought types in different climate regions of China

Extreme weather events and natural hazards are increasingly prevalent with global warming, making it imperative to focus on the driving factors and dynamic changes associated with drought—a disaster with widespread impacts and significant losses. This study used a longtime-series of ERA5 reanalysis data to elucidate the dynamic change characteristics of direct and indirect driving factors across China. Ground observation data spanning an extended period were used to estimate the performance of the reanalysis data. The selected driving factors for drought included precipitation (PRE), potential evapotranspiration, soil moisture (SM), bare soil evapotranspiration (BSET), vegetation canopy evapotranspiration (VCET), subsurface runoff (SUBRO), surface runoff (SURRO), temperature (TEM), specific humidity, and near-surface wind speed (WS). Meteorological drought, hydrological drought, and agricultural drought were delineated using the Standardized Precipitation Evapotranspiration Index, Standardized Runoff Index, and Standardized Soil Moisture Index. The Random Forest method was used to ascertain the relative importance of driving factors and their main contributions to different types of droughts. The findings revealed that ERA5 exhibited great simulation performance compared to observational data. However, the correlation coefficient of SM might be relatively lower than that of other driving factors across China. Notably, BSET and PRE exhibited higher relative importance degrees (RID) for meteorological drought in subregions 1, 2, and 3. At the same time, SUBRO demonstrated a more statistically significant influence in subregions 3, 4, and 5. SURRO played a statistically significant role in subregion 1. Moreover, BSET and PRE were pivotal for meteorological drought in northern China, whereas VCET assumes importance for hydrological drought in southern China and the North China Plain. Across different types of droughts, PRE, SM, and TEM generally exhibited relatively large RID values in China. The dynamic changes in RID for both direct and indirect driving factors varied for different drought types across different subregions of China. Notably, the RID displayed contrasting dynamic changes between PRE and BSET for meteorological drought in most subregions. Additionally, the degree of relative importance of near-surface WS tended to be lower for hydrological drought across various subregions. This study contributed to a deeper understanding of drought evolution and enhanced monitoring efforts.

Disentangling Forced Trends in the North Atlantic Jet From Natural Variability Using Deep Learning

AbstractRegional weather variability and extremes over Europe are strongly linked to variations in the North Atlantic jet stream, especially during the winter season. Projections of the evolution of the North Atlantic jet are essential for estimating the regional impacts of climate change. Therefore, separating forced trends in the North Atlantic jet from its natural variability is an extremely relevant task. Here, a deep learning based method, the Latent Linear Adjustment Autoencoder (LLAE), is used for this purpose on an ensemble of fully‐coupled climate simulations. The LLAE is based on a variational autoencoder and an additional linear component. The model uses detrended temperature and geopotential to predict the component of the zonal wind associated with natural variability. The residual between this prediction and the original wind field is interpreted as the forced component of the jet. The method is first tested for the geostrophic wind for which the forced trend can be obtained analytically from the difference between geostrophic wind computed from detrended and full geopotential. Despite the large variability of the total trends, the LLAE is shown to be effective in extracting the forced component of the trend for each individual ensemble member in both geostrophic and full wind fields. The LLAE‐derived forced trend shows an increase in the upper‐level zonal wind speed along a southwest–northeast oriented band over the ocean and a jet extension toward Europe. These are common characteristics over different periods and show some similarities to the upper‐level zonal wind speed trend obtained from the ERA5 reanalysis.

Long-term trend analysis of surface temperature over North-East India and adjoining regions based on CRU and ERA5 reanalysis

Long-term trend analysis of surface temperature over North-East India and adjoining regions based on CRU and ERA5 reanalysis

Contrasting seasonal responses to wind in migrating songbirds on a globally important flyway.

During spring migration, nocturnal migrants attempt to minimize their travel time to reach their breeding grounds early. However, how they behave and respond to unfavourable conditions during their springtime travels is much less understood. In this study, we reveal the effects of atmospheric factors on nocturnal bird migration under adverse conditions during spring and autumn, based on one of the most detailed bird migration studies globally, using radar data from 13 deployments over a period of seven years (2014-2020) in the Levant region. Using ERA5 reanalysis data, we found that migratory birds maintain similar ground speeds in both autumn and spring migrations, but during spring, when encountering unfavourable winds, they put more effort into maintaining their travel speed by increasing self-powered airspeed by 18%. Moreover, we report for the first time that spring migrants showed less selectivity to wind conditions and migrated even under unfavourable headwind and crosswind conditions. Interestingly, we discovered that temperature was the most important weather parameter, such that warm weather substantially increased migration intensities in both seasons. Our results enhance our understanding of bird migration over the Levant region, one of the world's largest and most important migration flyways, and the factors controlling it. This information is essential for predicting bird migration, which-especially under the ongoing anthropogenic changes-is of high importance.

Determination of occurrence of atmospheric blockings over the Caspian Sea region in winters of 1959–2022, and influence of them on the ice regime of the Northern Caspian Sea

The ice regime of the Caspian Sea has pronounced influence on the heat and moisture exchange of the reservoir with the atmosphere, the state of the ecosystem, as well as human marine activities, including shipping, fishing, construction of hydraulic structures, etc. Consequently, the development of existing ideas about the causes of changes in the characteristics of the ice regime of such water bodies is actual and socially significant problem of limnology, hydrometeorology, ecology, and navigation. This study was aimed at determination the frequency of occurrence of atmospheric blockings over the Caspian region with standings longer 5 days in winter period of 1959–2022, and investigation of influence of them on the ice regime in the Northern area of the Sea. The following information and data were used: changes in hourly mean values of atmospheric pressure at the sea level, geopotential of isobaric surfaces 850, 500 and 300 hPa, presented in the ERA5 reanalysis; and observational data on air temperature and ice cover from hydrometeorological stations located in the Caspian region of Kazakhstan and Russia. It has been established that in the winter during a long standing of atmospheric blockings the mean daily air temperatures noticeably drop. The values of all the studied characteristics of every atmospheric blocking which occurred in 1959–2022 were estimated as well the influence of them on the ice regime in the North of the Caspian Sea was analyzed. The relationship between a decrease in the frequency of atmospheric blockings (AB), sums of negative air temperatures on its coasts, and the ice cover thickness in the corresponding areas of coastal waters has been revealed in the region. The longer the total AB duration, the lower is the air temperature, and the ice thickness in February is larger.

Meteorological regime of the Elbrus high-mountain zone during the accumulation period

Unique automated meteorological observations were carried out on the southern slope of Elbrus, near Pastukhov Rocks, at 4700 m a.s.l., during the 2021–2022 accumulation season. Data were obtained on air temperature, humidity, wind speed and direction, snowdrift and radiation fluxes with a temporal resolution of 1 minute or less. Analysis of the data series showed that the representative winter air temperature at this altitude on the southern slope of Elbrus is –10 °С, and the minimum is –36.4 °С; the partial pressure of water vapor does not exceed 3.5 hPa. At the same time, the average daily maximum of wind speed amounted 13.1 m s–1 with the absolute maximum of 54.1 m/s. Snowstorms with a snow transport intensity of more 0.1 kg/m2s–1 are quite common phenomenon in winter, while the maximum average value of the transport reaches 0.87 kg/m2s–1. An empirical relationship was established between the average hourly wind speed and the maximum gust speed for the same period, and it was shown that for these conditions the wind gust exceeds the average hourly wind speed by 1.8 times, while the representative value of the standard deviation of wind speed is 5.8 m s–1. This information may be useful not only for the glaciologic problems and modeling, but also for construction and engineering surveys, which are relevant in view of the present-day active development of the mountain ski infrastructure on the southern macro-slope of the Elbrus. In addition, the obtained series of instrumental observations were used to assess the quality of reanalysis data for high mountain regions taking as an example the ERA5. The ERA5 reanalysis was demonstrated to reproduce rather successfully the air temperature, wind speed and humidity in high mountain conditions, but extreme values for all these parameters are underestimated. Thus, the minimum temperature in winter turned out to be overestimated by 2 °C, and the maximum was underestimated by 4 °C, while the wind speed, according to the ERA5 reanalysis, never exceeded 40 m/s during the above observation period. It is also shown that the FlowCapt4 acoustic blizzard gauge (driftometer) can be used to estimate average wind speeds since it is less sensitive to severe high-altitude conditions compared to acoustic and cup anemometers.

Different trends in the dynamics of Arctic lakes in North Siberia under climate change in 1985–2021

The methods and results of a comparative analysis of the effects of climate changes on the dynamics of the areas of thermokarst lakes over the past 36 years in the Arctic regions on the Yamal, Gydan, and Taimyr peninsulas are considered, the areas of which are 114, 175 and 426 thousand km2, respectively. All three regions are located within the permafrost zone of the Siberian Arctic. Using images of the Landsat 4, 5, 7, and 8 satellites, time series of data on average values of lake areas for the indicated regions were obtained on the basis on averaging areas of lakes over 23 test (key) areas. The total area of the test sites is about 800 km2. Using the ERA5 reanalysis system, time series of data on the mean annual air temperature in these territories have been generated, which show a rise of the temperature over the studied period 1985–2021. A comparison of trends in changes in regional mean areas of lakes and mean annual air temperature shows that with approximately the same rate of the temperature rise on these peninsulas, different trends in the dynamics of the lake areas are observed, which are manifested, on the one hand, in a noticeable reduction in the areas of lakes in the territories of Yamal and Gydan and, on the other hand, in their growth in Taimyr. Air temperature averaged over the period 1985–2021 and coefficients of the linear trend of changes in the lake areas in each of the above regions were compared. The results show that on the territories of Yamal and Gydan, where the lake areas decrease, the mean air temperature for the same period is equal to –8.1±0.9 and –8.9±0.9 °С, respectively. On the Taimyr territory, where the lake areas increase, the mean air temperature is significantly lower: –12.8±0.94 °С. Thus, this makes possible to make a conclusion that these considered regions differ significantly from each other by values of mean air temperature, and respectively, they are characterized by different trends in changes in areas of the thermokarst lakes.

The impacts of global warming on climate zones over China based on CMIP6 models

AbstractClimate zones are expected to shift in response to climate change, which significantly influences vegetation distribution and provides essential guidance for human activities including production, lifestyle and economic development. Quantifying the shifts in climate zones due to global warming is therefore crucial. The primary metric for categorizing climate zones is the number of days with a daily mean temperature above 10°C (DT10). Utilizing DT10 and the ERA5 reanalysis data, it is observed that climate zones in China have gradually shifted northward over the past 65 years. Notably, the interdecadal changes in the climate zones differ between the eastern and western regions divided by the 110°E longitude. The western regions show minimal shifts, whereas the eastern regions, particularly the central and southern parts of Northeast China, exhibit obvious northward shifts. Consequently, the simulation capabilities of 41 CMIP6 models for Chinese climate zones were assessed, and it was found that 11 models demonstrated robust performance. These models were further used to analyse interdecadal variations and project future shifts in climate zones in China. The results show that the spatial pattern of climate zones in China can be well captured by the CMIP6 models, except for ACCESS‐CM2, FGOALS‐g3 and GFDL‐CM4. Each CMIP6 model seems to be more suitable for specific climatic zones concerning trends and decadal variations in China. By 2100, a northward shift is projected for all climate zones in the east of 110°E under SSP2‐4.5 and SSP5‐8.5 emission scenarios, particularly in northern China. It should be noted that the potential disappearance of the northern subtropical belt, likely to be replaced by the middle subtropical belt in the future.

Modeling pan-Arctic seasonal and interannual landfast sea ice thickness and snow depth between 1979 and 2021

ABSTRACT Landfast sea ice (LFSI) is sensitive to local climate change, making it an important component of the cryosphere system. In this study, the LFSI around the pan-Arctic domain was simulated from 1979 to 2021 using a well-validated snow and ice thermodynamic model (HIGHTSI) under the framework of the Fast Ice Prediction System (FIPS), forced by the ERA5 reanalysis. The simulation results agree well with the in-situ observations in the Canadian Arctic, with a mean error of −0.06 ± 0.29 m for ice thickness and −0.04 ± 0.12 m for snow depth. A decrease of −2.8 ± 0.4 cm/10a in thickness and −16.2 ± 1.5 km3/a in volume for the Arctic LFSI was modeled during this period. There was significant spatial variability among the different domains, with the fastest decline found in the Vilkitsky Strait. The modeled snow depth shows large interannual and spatial variations, which was confirmed by other modeling results. The spatiotemporal variations in both air temperature and precipitation are the driving factors for the multi-decadal variations in LFSI thickness. The decreasing air temperature during the 2010s aligned with a slower thickness decrease and a slight volume increase for LFSI, which agreed with the pan-Arctic sea ice pattern.

Mesoscale weather influenced by auroral gravity waves contributing to conditional symmetric instability release?

Abstract. We consider possible influence on severe weather occurrence in the context of solar wind coupling to the magnetosphere–ionosphere–atmosphere system, mediated by aurorally excited atmospheric gravity waves. Solar wind high-speed streams from coronal holes cause intensifications of ionospheric currents at high latitudes launching gravity waves propagating in the upper and lower atmosphere. While these gravity waves reach the troposphere with much attenuated amplitudes, they can contribute to conditional symmetric instability release and intensification of storms. Severe weather events, including winter storms and heavy rainfall causing floods and flash floods, show a tendency to follow arrivals of solar wind high-speed streams from coronal holes. The ERA5 re-analysis is used to evaluate slantwise convective available potential energy and vertically integrated extent of realizable symmetric instability to assess the likelihood of slantwise convection in frontal zones of extratropical cyclones during severe snowstorms and flash floods. The observed low-level southerly winds and high wind shears in these regions are favorable conditions for over-reflection of down-going aurorally excited gravity waves potentially contributing to conditional symmetric instability release leading to slantwise convection and high-rate precipitation.

Heatwave Location Changes in Relation to Rossby Wave Phase Speed

AbstractSurface anticyclones connected to the ridge of an upper‐tropospheric Rossby wave are the main dynamical drivers of mid‐latitude summer heatwaves. It is, however, unclear to what extent an anomalously low zonal phase speed of the wave in the upper troposphere is necessary for persistent temperature extremes at the surface. Here, we use spectral decomposition to separate fast and slow synoptic‐scale waves. A composite analysis of ERA5 reanalysis data reveals that, while in some regions heatwaves become more frequent during episodes of weak or no phase propagation, temperature extremes in other regions are commonly associated with more rapidly eastward propagating Rossby waves. Reflected in the mean heatwave duration as well, this relationship is possibly linked to a longitudinal phase preference of slow and fast waves or a meridional storm track shift. These findings open up new questions about the influence of mid‐latitude dynamics on temperature extremes.

Increases of Offshore Wind Potential in a Warming World

AbstractOffshore wind farms, a rapidly expanding sector within wind energy, are playing a significant role in achieving global carbon neutrality, and this trend is to continue. Here, we utilize ERA5 reanalysis to correct offshore wind speed trends predicted by CMIP6 models. This approach led to enhanced projections for changes in offshore Wind Power Density (WPD) under four Shared Socioeconomic Pathways (SSPs) scenarios. Throughout the 21st century, global offshore WPD is projected to follow an upward trend across all SSP scenarios. Notably, Europe stands out with the most substantial increase in offshore WPD among regions with higher current installations, projected to reach up to 26% under 4°C global warming. Our study uncovers a notable increase of global offshore WPD in a warmer climate, which offers valuable insights for the strategic planning of future global wind energy.