This research proposes for the first time a new multiscalar indicator to evaluate wind speed conditions using a standardized index: The Standardized Wind Speed Index (SWSI). The SWSI has the advantage that allows spatial and temporal comparability and facilitates the interpretation of windier phases but also prolonged periods of low wind speeds or wind droughts, which are the main focus of this research. Typically, wind droughts have been evaluated and defined in terms of their impact on the wind industry, a crucial element in the transition to greener energy sources. The SWSI fills a gap left by operational definitions, enabling wind droughts to be characterized not only as short-term energy risks but also as long-term climatological phenomena with broader environmental implications. The study used observed global monthly near-surface wind speed data from the HadISD database for 1973-2023. A parametric approach has been followed, testing 10 different statistical distributions. After applying various tests, the Generalized Logistic distribution has been selected for the definition of the SWSI. This index enables the evaluation of wind speed conditions regionally and globally on different spatio-temporal scales (1-, 3- and 12-month); yet any desired time-scale can be used. Here we also show the robustness of the SWSI in detecting two extreme wind drought events occurred in USA (2015) and UK (2021) with strong decreases in primary energy production.

Despite continued expansion of installed capacity, observed U.S. wind energy generation fell by 9.1 TWh in 2023—the first such decline in history, disrupting decades of growth and challenging energy decarbonization. The causes remain underexplored. We analyzed 1,316 wind farms across the contiguous U.S. from 2000 to 2023, focusing on 885 sites operational since 2010. Using spatial analysis, numerical modeling, and station-based observational wind speed data, we assessed wind speed variability, siting decisions, and technological factors. Reduced wind speeds caused an estimated 14.0 TWh loss relative to 2022, outpacing the 6.2 GW of added capacity. Although technological improvements historically mitigated wind speed fluctuations, these were insufficient in 2023. We found that large-scale climate patterns, particularly the Tropical North Atlantic Index (TNA), a measure of Atlantic sea surface temperature variability, explain 77% of decadal wind speed trends, driving the 2023 decline. As wind energy production depends on installed capacity and wind speed, growing capacity amplifies the impact of wind speed variability. The 2023 decline marks a turning point, signaling that multi-annual variability in wind speed may increasingly affect energy supply, underscoring the urgent need to integrate climate-driven wind speed projections into sustainable energy generation planning.

One of the goals of climate monitoring is to ensure that the ongoing changes in the Earth's climate system are placed in context against longer-term changes and are clearly and widely communicated. There are a number of reports produced on an annual basis which collate and synthesise the outputs from climate monitoring products for a range of essential climate variables (ECVs) and extreme climate events. Among the most highly regarded peer-reviewed global-scale publications, a provisional version of the World Meteorological Organisation (WMO) State of the Global Climate report feeds into the United Nations Framework Convention on Climate Change Conference of the Parties (UNFCCC COP) process each year, directly informing policy makers and stakeholders about the current state of several key climate metrics. The more comprehensive BAMS State of the Climate report is an almanac of the major events of each year and an assessment of more than three-dozen ECVs that encompass Earth's land, oceans, cryosphere, and atmosphere, which provides a useful reference document for a wide range of stakeholders, from the general public to educators to private and public decision makers. In this presentation we outline the process behind each report, the wide range of information they contain, and give pointers on how to get more involved.

Extreme events have widespread impacts across human health, our infrastructure, and the natural environment. So far there has not been a global product which presents climate indices relevant for different sectors of our society, including health, agriculture, and water resources. Here we present an extension to HadEX3, an existing dataset of extremes indices based on in situ observations, by including indices recommended by the World Meteorological Organisation (WMO) which were developed with sector specific applications in mind. We have used the approach and methodology of HadEX3, and where possible the same underlying daily temperature and rainfall observations, to produce quasi-global land fields over 1901-2018. We will demonstrate the key features of this extension, with temperature indices showing changes consistent with global scale warming, as indicated by heat wave characteristics showing increases in the number, duration, and intensity of these extreme events in most places.

A consistent approach to evaluate the annual wettest day (Rx1day) across global and regional gridded observational datasets is presented using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) to define climatological regions. Global daily 1 degrees 3 1 degrees latitude/longitude gridded products available from the Frequent Rainfall Observations on Grids (FROGS) database are compared with regional high-resolution (-1-25 km) daily gridded rainfall datasets using several interpolation methods and order of operation. Climatologies are calculated for each global product and region using the overlapping period 2001-16, with global datasets then organized into in situ, satellite, and reanalysis groupings and compared with each other and the regional reference. Our findings show that reanalyses (especially CFSR and MERRA-2) tend to be among the wetter products for precipitation extremes in most regions and that reanalysis groupings also have the largest spread. Perhaps surprisingly, regional datasets are often among the drier, if not the driest products in many regions (especially Southeast Asia, Eurasia, and the Middle East), and are almost always drier than reanalyses except in a few cases. Rx1day is significantly positively correlated in most regions and products except in a handful of cases where data issues are likely to affect correlations. Rx1day timing deviates substantially between products, but agreement is highest among in situ products (40%-70% of the time) especially in data-dense regions with least agreement among reanalyses (10%-40% of the time). Despite uncertainties, the mean relative long-term trend estimates in Rx1day averaged across global land areas, with respect to increases in global mean temperature, are close to 7% degrees C21. SIGNIFICANCE STATEMENT: While there have been numerous global and regional assessments of trends and variability of historical rainfall extremes, there has been little coordination or limited ability to compare across studies or to use regional products consistently when evaluating global products. Our results show for the first time that despite large uncertainty in the wettest day of the year [annual wettest day (Rx1day)] estimates, regional (continental scale) gridded precipitation products are consistently drier than their global counterparts. However, the trend in Rx1day averaged across all products is broadly consistent with the global increase of-7% degrees C21 found in other studies. Given the huge spread in observations of daily precipitation extremes, our findings have implications for their efficacy in informing global monitoring, event attribution, and model evaluation efforts.

The year 2023 represents a significant milestone in climate history: it was indeed confirmed by the Copernicus Climate Change Service (C3S) as the warmest calendar year in global temperature data records since 1850. With a deviation of 1.48ºC from the 1850-1900 pre-industrial level, 2023 largely surpasses 2016, 2019, 2020, previously identified as the warmest years on record. As expected, this sustained warmth leads to an increase in frequency and intensity of Extreme Events (EE) with dramatic environmental and societal consequences.To assess the evolution of these EE and establish adaptation and mitigation strategies, it is crucial to evaluate the trends of extreme indices (EI). However, the observational climate data that are commonly used for the calculation of these indices frequently contains missing values, resulting in partial and inaccurate EI. As we delve deeper into the past, this issue becomes more pronounced due to the scarcity of historical measurements.To circumvent the lack of information, we are using a deep learning technique based on a U-Net made of partial convolutional layers [1]. Models are trained with Earth system model data from CMIP6 and has the capability to reconstruct large and irregular regions of missing data using minimal computational resources. This approach has shown its ability to outperform traditional statistical methods such as Kriging by learning intricate patterns in climate data [2].In this study, we have applied our technique to the reconstruction of gridded land surface EI from an intermediate product of the HadEX3 dataset [3]. This intermediate product is obtained by combining station measurements without interpolation, resulting in numerous missing values that varies in both space and time. These missing values affect significantly the calculation of the long-term linear trend (1901-2018), especially if we consider solely the grid boxes containing values for the whole time period. The trend calculated for the TX90p index that measures the monthly (or annual) frequency of warm days (defined as a percentage of days where daily maximum temperature is above the 90th percentile) is presented for the European continent on the left panel of the figure. It illustrates the resulting amount of missing values indicated by the gray pixels. With our AI method, we have been able to reconstruct the TX90p values for all the time steps and calculate the long-term trend shown on the right panel of the figure. The reconstructed dataset is being prepared for the community in the framework of the H2020 CLINT project [4] for further detection and attribution studies.[1] Liu G. et al., Lecture Notes in Computer Science, 11215, 19-35 (2018)[2] Kadow C. et al., Nat. Geosci., 13, 408-413 (2020)[3] Dunn R. J. H. et al., J. Geophys. Res. Atmos., 125, 1 (2020)[4] https://climateintelligence.eu/

The Canadian Fire Weather Index (FWI), widely used to assess wildfire danger, typically relies on noon-specific meteorological data. However, climate models often provide only daily aggregated values, posing a challenge for accurate FWI calculations. We evaluated daily approximations for FWI95d—the annual count of extreme fire-weather days—against the standard noon-based method (1980–2023). Our findings reveal that noon-based FWI95d show a global increase of 65

The Canadian Fire Weather Index (FWI) is widely used to assess wildfire danger and relies on meteorological data at local noon. However, climate models often provide only daily aggregated data, which poses a challenge for accurate FWI calculations in climate change studies. Here, we examine how using daily approximations for FWI95d -- the annual count of extreme fire weather days exceeding the 95th percentile of local daily FWI values -- compares to the standard noon-based approach for the period 1980--2023. Our findings reveal that FWI95d calculated with noon-specific data increased globally by approximately 65\%, corresponding to 11.66 additional extreme fire weather days over 44 years. In contrast, daily approximations tend to overestimate these trends by 5--10\%, with combinations involving minimum relative humidity showing the largest divergences. Globally, up to 15 million km$^2$, particularly in the western United States, southern Africa, and parts of Asia, exhibit significant overestimations. Among our daily approximation methods, the least biased proxy is the one that uses daily mean data for all variables. We recommend (i) prioritizing the inclusion of sub-daily meteorological data in future climate model intercomparison projects to enhance FWI accuracy, and (ii) adopting daily mean approximations as the least-biased alternative if noon-specific data are unavailable.

Understanding past climate conditions is essential for addressing future climate challenges. However, observational climate datasets often contain missing values, especially in older records, leading to incomplete and inaccurate analyses. Interpolation methods like kriging are commonly employed to address this issue by filling data gaps. Nevertheless, these approaches often fail to effectively reconstruct complex climatic patterns [1, 2].This study leverages the transformative power of deep learning to accurately reconstruct two observational datasets. The first dataset is an intermediate product of HadEX3 [3], which contains gridded extreme indices over land regions, such as the TX90p index, corresponding to the percentage of days where daily maximum temperature is above the 90th percentile. The second dataset is the Full data GPCC product [4], containing global precipitation fields at monthly frequency. To reconstruct these two datasets with high accuracy, we employ and compare three deep learning approaches: a U-Net with partial convolutional layers, a diffusion model and a graph neural network. In all cases, models are trained on CMIP6 climate model data, evaluated on unseen CMIP6 and ERA5 data and compared to Kriging. The best-performing models are then applied to the observational datasets, providing new insights into historical climate conditions to inform more effective climate adaptation strategies. The reconstructed datasets are being prepared for the community in the framework of the H2020 CLINT project [5] and the Horizon Europe EXPECT project [6].[1] Kadow C. et al., Nat. Geosci., 13, 408-413 (2020)[2] Plésiat É. et al., Nat. Commun., 15, 9191 (2024)[3] Dunn R.J.H. et al., J. Geophys. Res. Atmos., 125, 1 (2020)[4] Schneider, U. et al., DOI: 10.5676/DWD_GPCC/FD_M_V2022_100 (2022)[5] https://climateintelligence.eu/[6] https://expect-project.eu/

2023 was the warmest year on record with a global mean temperature of 1.45 +/- 0.12 degC above pre-industrial levels, surpassing the previous record (from 2016) by 0.17 degC. The onset of El Ni & ntilde;o in early July was accompanied by record-breaking land and sea-surface temperatures, with June to September all exceeding previous monthly temperature records, and July and August being the hottest months on record. Sea-surface temperatures have attained record highs for all months since March 2023 and Antarctic sea ice reached record lows throughout the year. 2023 was the warmest year on record with a global mean temperature of 1.45 +/- 0.12 degC above pre-industrial levels, surpassing the previous record (from 2016) by 0.17 degC. The onset of El Ni & ntilde;o in early July was accompanied by record-breaking land and sea-surface temperatures, with June to September all exceeding previous monthly temperature records, and July and August being the hottest months on record. Sea-surface temperatures have attained record highs for all months since March 2023 and Antarctic sea ice reached record lows throughout the year.image

Extreme stratospheric polar vortex (SPV) events can influence winter tropospheric circulation for up to 60 d. Their impacts on air temperature have been extensively studied recently. However, there is a research gap in their effects on wind speeds and its extremes. This study aims to evaluate, for the first time, the impacts of such extreme SPV events on observed and modelled near-surface wind gusts across Europe. We have analysed wind gust data coming from: station-based observations (for the Iberian Peninsula and Scandinavia), the ERA5 reanalysis and the SEAS5 and GloSea6 seasonal forecasting systems. We assess their similarities in reproducing 4 parameters of their corresponding distributions: median, standard deviation, skewness and kurtosis. For all these datasets, the results indicate that extreme positive SPV events are followed by negative wind gust anomalies in Southern Europe and positive in Northern Europe. Whereas, negative SPV events (such as Sudden Stratospheric Warmings) have positive gust anomalies in Southern Europe and negative in the north. A central region shows negligible anomalies in both cases. This highlights the ability of SPV as a predictor for short-medium-term forecasting of extreme wind events, which would have direct applications to many socioeconomic and environmental issues such as the estimation of wind-power generation.

AbstractGlobal gridded data sets of observed extremes indices underpin assessments of changes in climate extremes. However, similar efforts to enable the assessment of indices relevant to different sectors of society have been missing. Here we present a data set of sector‐specific indices, based on daily station data, that extends the HadEX3 data set of climate extremes indices. These additional indices, which can be used singly or in combinations, have been recommended by the World Meteorological Organization and are intended to empower decision makers in different sectors with accurate historical information about how sector‐relevant measures of the climate are changing, especially in regions where in situ daily temperature and rainfall data are hard to come by. The annual and/or monthly indices have been interpolated on to a 1.875° × 1.25° longitude‐latitude grid for 1901–2018. We show changes in globally‐averaged time series of these indices in comparison with reanalysis products. Changes in temperature‐based indices are consistent with global scale warming, with days with Tmax > 30°C (TXge30) increasing virtually everywhere with potential impacts on crop fertility. At the other end of the scale, the number of days with Tmin < −2°C (TNltm2) are reducing, decreasing potential damage from frosts. Changes in heat wave characteristics show increases in the number, duration and intensity of these extreme events in most places. The gridded netCDF files and, where possible, the underlying station indices are available from https://www.metoffice.gov.uk/hadobs/hadex3 and https://www.climdex.org.

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies. The authors use artificial intelligence to accurately reconstruct past climate extremes from sparse observational data, providing quantitative evidence of hot and cold extremes in the early 20th century and shedding light on their evolution.

Concentrations of greenhouse gases reached record levels in 2022 and energy continued to accumulate in the climate system. Despite the cooling influence of ongoing La Niña conditions, globally 2022 was the fifth or sixth warmest year on record. We give a summary of the global climate with a focus on the United Kingdom and Europe. Notably, the United Kingdom had its warmest year on record, including a brief record‐breaking heatwave in which multiple stations exceeded 40°C for the first time. The previous maximum temperature record of the United Kingdom was surpassed by the considerable margin of 1.6 degC on 19 July.

&lt;p&gt;Evaluating the trends of extreme indices (EI) is crucial to detect and attribute extreme events (EE) and establish adaptation and mitigation strategies to the current and future climate conditions. However, the observational climate data used for the calculation of these indices often contains many missing values and leads to incomplete and inaccurate EI. This problem is even greater as we go back in time due to the scarcity of the older measurements.&lt;/p&gt; &lt;p&gt;To tackle this problem, interpolation techniques such as the kriging method are often used to fill in the gaps. However, it has been shown that such techniques are inadequate to reconstruct specific climatic patterns [1]. Deep-learning based technologies give the possibility to surpass standard statistical methods by learning complex patterns and features in climate data.&lt;/p&gt; &lt;p&gt;In this work, we are using an inpainting technique based on a U-Net neural network made of partial convolutional layers and a loss function designed to produce semantically meaningful predictions [1]. Models are trained using vast amounts of climate model data and can be used to reconstruct large and irregular regions of missing data with few computational resources.&lt;/p&gt; &lt;p&gt;The efficiency of the method is well demonstrated through its application to the HadEX3 dataset [2]. This dataset contains gridded land surface EI, among which the TX90p index that measures the monthly (or annual) frequency of warm days (defined as a percentage of days where daily maximum temperature is above the 90th percentile). As for other EI, there is a lack of TX90p values in many regions of the world, even in recent years. It is particularly true when looking at an intermediate product of HadEX3 where the station-based indices have been combined without interpolation. This is illustrated by the left map of the figure where the gray pixels correspond to missing values. By training our model using data from the CMIP6 archive, we have been able to reconstruct the missing TX90p values for all the time steps of HadEX3 (see right map in the figure) and detect EE that were not included in the original dataset. The reconstructed dataset is being prepared for the community in the framework of the H2020 CLINT project [3] for further detection and attribution studies.&lt;/p&gt; &lt;p&gt;&lt;img src=&quot;&quot; alt=&quot;&quot; /&gt;&lt;/p&gt; &lt;p&gt;[1] Kadow C. et al., Nat. Geosci., 13, 408-413 (2020)&lt;br /&gt;[2] Dunn R.J.H. et al., J. Geophys. Res. Atmos., 125, 1 (2020)&lt;br /&gt;[3] https://climateintelligence.eu/&lt;/p&gt;

Abstract The 2021 IPCC report found that most studies show declining trends for the global diurnal temperature range (DTR) since the 1950s, decreasing mainly during 1960–1980. This issue is revisited here using an up‐to‐date in‐situ data set, Hadley Center Integrated Surface Database, constrained by rigorous station selection conditions. The global observed DTR trend was found to reverse during 1980–2021, increasing significantly at a rate of 0.091 ± 0.008°C decade−1. The trend was dominated by a faster rate of increasing daily maximum air temperature. This increasing observed trend in the past four decades was not fully captured in raw CMIP6 models, as models only partially capture the spatial patterns. With global CMIP6 outputs and regionally‐available observations, the global land DTR was then estimated, through emergent constraints, to be 0.063 ± 0.012°C decade−1. The study raises concern for risks of increasing DTR globally and provides new insights into global DTR assessment.

Wind energy, an important component of clean energy, is highly dictated by the disposable wind speed within the working regime of wind turbines (typically between 3 and 25 m s ^−1 at the hub height). Following a continuous reduction (‘stilling’) of global annual mean surface wind speed (SWS) since the 1960s, recently, researchers have reported a ‘reversal’ since 2011. However, little attention has been paid to the evolution of the effective wind speed for wind turbines. Since wind speed at hub height increases with SWS through power law, we focus on the wind speed frequency variations at various ranges of SWS through hourly in-situ observations and quantify their contributions to the average SWS changes over 1981–2021. We found that during the stilling period (here 1981–2010), the strong SWS (⩾ 5.0 m s ^−1 , the 80th of global SWS) with decreasing frequency contributed 220.37% to the continuous weakening of mean SWS. During the reversal period of SWS (here 2011–2021), slight wind (0 m s ^−1 < SWS < 2.9 m s ^−1 ) contributed 64.07% to a strengthening of SWS. The strengthened strong wind (⩾ 5.0 m s ^−1 ) contributed 73.38% to the trend change of SWS from decrease to increase in 2010. Based on the synthetic capacity factor series calculated by considering commercial wind turbines (General Electric GE 2.5-120 model with rated power 2.5 MW) at the locations of the meteorological stations, the frequency changes resulted in a reduction of wind power energy (−10.02 TWh yr ^−1 , p < 0.001) from 1981 to 2010 and relatively weak recovery (2.67 TWh yr ^−1 , p < 0.05) during 2011–2021.

Editors note: For easy download the posted pdf of the State of the Climate in 2022 is a low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.