bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing heat stress with a mesoscale model. An application of WRF-comfort to Madrid
Authors: Martilli Alberto , Nazarian Negin , Krayenhoff Scott , Lachapelle Jacob , Lu Jiachen , Rivas Esther , Rodriguez-Sanchez Alejandro , Sanchez Beatriz , Santiago Jose Luis
Published: July 2024

Heat stress depends on a set of metereological variables, namely, air temperature, wind speed, air humidity and mean radiant temperature. In urban areas, wind speed and mean radiant temperature are strongly spatially hetereogeneous, at scales of few meteres, much smaller than the typical resolution of mesoscale models, which is of the order of one kilometer or several hundreds of meters. This is the main obstacle to produce reliable estimates of heat stress at city scale. In this contribution, we present a methodology, built over a set of microscale simulations, to represent subgrid scale variability of wind speed and mean radiant temperature, and as a consequence heat stress. The scheme is implemented in the multilayer urban canopy parameterization BEP-BEM embedded in the mesoscale model WRF (therefore called WRF-comfort), and it opens the way to the city scale evaluation of the impact of different adaptation/mitigation strategies on heat stress, something that is essential to plan liveable future cities in the context of a changing climate. This is illustrated with a series of simulations for a summertime period over the city of Madrid (Spain). Then main outcome of the study is that the time evolution and spatial variability of UTCI (the Universal Thermal Climate Index, one of the most used heat stress indexes) are strongly affected by the urban morphology, and that the spatial pattern of UTCI at city scale is only partially similar to the one of air temperature, and dissimilar to the one of Land Surface Temperature, as it can be seen from satellite, a variable often used to assess urban overheating.
Effective aerosol scavenging scheme for dispersion modelling
Authors: Uppstu Andreas , Kouznetsov Rostislav , Sofiev Mikhail
Published: July 2024

The global budget of aerosols in the atmosphere depends heavily on their deposition. For global scale transport models this presents great challenges, as especially the wet deposition of aerosols is a nonlinear sub-gridcell process. With a uniform precipitation rate in space and time, the aerosol concentration decreases exponentially. However, the precipitation is generally not spread evenly across a model gridcell, and in case the model relies on offline meteorological data, it is not spread evenly over the time step either. These inhomogeneities will easily lead to overscavenging, and effective parametrized methods are thus required to reduce it. We have developed simple limits for the scavenging rate based on the convective available potential energy and on the horizontal wind speed, assuming that these quantities limit the mixing rate of aerosols between the precipitating and non-precipitating parts of the gridcell. We have implemented the method within the global transport and chemistry model SILAM (silam.fmi.fi), and present an evaluation of the method against measurements of aerosol optical depth (AOD), surface particulate matter concentrations and depositions of various aerosol species. Specifically, we focus on the long-range transport of Saharan dust. AOD measurements over Sahara are overwhelmingly sensitive to dust emission, largely independent of scavenging or other aerosol species, which means that the modeled emission may be adjusted independently of the modeled deposition. On the other hand, the long-range transport of dust plumes across the Atlantic and the Mediterranean depends heavily on the scavenging model, with incorrect scavenging leading to greatly reduced skill scores against AOD and in situ measurements. Application of the method has led to model skill scores that are competitive with online models that assimilate satellite-retrieved AOD. However, finding out how the effective scavenging rate scales properly with the grid cell dimensions and the length of the model time step remains a challenge.
What was the contribution of the ocean to the 2016 Antarctic sea ice extent drop and was this event unprecedented?
Authors: Mezzina Bianca , Goosse Hugues , Huot Pierre-Vincent , Marchi Sylvain , Van Lipzig Nicole , Dalaiden Quentin , Francis Feba , Fogt Ryan
Published: July 2024

The 2016 Antarctic sea ice extent drop was a rapid decline that led to persistent low sea ice conditions. The drop followed a period of stability and even slight sea ice extent increase: this sudden change from extreme high values to an absolute minimum, which occurred in less than two years, is distinctive of this event, together with the lack of recovery that ensued.Even though new record lows have been recently established, there is still a lot to uncover on the dynamics of the 2016 drop. While the event was triggered by atmospheric forcing, the potential preconditioning role of the ocean is unclear. To shed light on it, we perform sensitivity experiments with a fully-coupled regional climate model, which is re-initialized in January 2016 using different ocean and sea ice conditions but keeping boundary forcings in the atmosphere and ocean unchanged. We show that the state of the Southern Ocean in early 2016 does not determine whether the drop occurs or not, but indeed has an impact on its amplitude, regional characteristics and subsequent sea ice recovery.This dramatic drop is unprecedented in our current observational record, but it is possible that other similar events have occurred before the beginning of the satellite era. We explore this possibility in a new spatial reconstruction of atmospheric and sea ice variables covering 1958-2023, built using a data assimilation method that combines station-based observations and results from large ensembles of simulations. Additionally, we force a sea ice-ocean model with the atmospheric reconstruction to estimate the oceanic variations. Our results indicate that a drop similar to the 2016 one occurred at the end of the 1970s. We then compare the spatial patterns and changes during the two events and investigate the links between the sea ice, ocean and atmosphere.
Impact of Euro-Atlantic blocking on the occurrence of heat waves and cold spells in Poland
Authors: Bednorz Ewa , Tomczyk Arkadiusz M.
Published: July 2024

The spatiotemporal variability of atmospheric blockings over the Euro-Atlantic region and their influence on the occurrence of the persisting extreme temperature conditions in Poland namely heat waves and cold spells during the period 1978-2023 was analysed. Blockings were identified at 500 hPa geopotential level, using the meridional geopotential gradient method, supplemented with the quantile filter and persistence filter. Heat waves (HWs) and cold spells (CSs) were defined as sequences of at least 3 days with the maximum air temperature above 30°C or below -10°C, respectively.The climatology of Euro-Atlantic blocking occurrence in the zonal belt between 45 and 75 degrees in the northern hemisphere exhibits high spatiotemporal variability. Blocking structures are most frequent in the spring (MAM) occurring with a 10% frequency in the arched belt extending from the west of the British Islands through the North Sea, south Scandinavia, to the east of the Baltic Sea. In summer (JJA) they are most frequent in the area spanning from south Finland to west Russia, as well as over Greenland. During winter (DJF) blockings are most commonly located over the southeast Atlantic (west of France) and extending northeastward up to southern Scandinavia with their occurrence being least frequent in autumn (SON).The mean annual number of HW days ranges from one day in the southern mountainous regions and in northern Poland to more than four days in the central and western parts. However, during the extremely hot summer of 2015, the number of HW days exceeded 20 in central and southern Poland. CSs have become rarer, with the average seasonal number of CS days amounting to 1-2 days in recent decades, and only in singular seasons such as 1986/1987, more than 10 days fulfilled the criteria of CS in northeastern Poland. The occurrence of HWs in Poland is constantly accompanied by blocking situations, most often located northeast of Poland, while the winter CSs are associated with the blockings located over the North Atlantic and northern Scandinavia.
Scaling up Artificial Intelligence to fight mis- and disinformation on weather and climate
Authors: Perrin Brigitte
Published: July 2024

Weather and climate professionals are pioneers in artificial intelligence. Over fourty years ago, the first intelligent forecasting models were built, which gives our community a strong legitimacy in the use of data to produce information. Today, artificial intelligence and machine learning are used extensively to look into weather patterns, and to analyze the evolution of climate over time. But in parallel of this growing success, National Meteorological and Hydrological Services, journalists, scientists, and climatologists around the world are experiencing more and more attacks and threats from climate denialists. How can artificial intelligence be scaled up to communicate about weather and climate, and make the authoritative information more accessible to those who refute the science?
Tropical Atlantic rainfall drives bias in extratropicalseasonal forecasts
Authors: Collier Tamara , Kettleborough Jamie , Scaife Adam , Hermanson Leon , Davis Philip
Published: July 2024

We investigate the impact of Tropical Atlantic seasonal forecast biases on the North Atlantic. The analysis focuses on the hindcasts of the Met Office Global Seasonal forecast system (GloSea).  A tripolar bias occurs in Tropical Atlantic rainfall, where the seasonal forecast system is too dry on the equator and too wet to the north and south of the equator, showing a “double ITCZ” pattern. Our analysis uses a novel ensemble-based method to estimate the impact of this tropical rainfall bias on forecasts of the Extratropical North Atlantic. Prior to completing the analysis, ENSO is regressed out of the fields to ensure this common teleconnection is not the source of this bias. The inter-ensemble spread of the forecast model is used to estimate the impact of the bias in Tropical Atlantic rainfall on the North Atlantic by selecting model members that happen to produce forecast anomalies that most closely resemble the tropical rainfall bias and using these as a proxy for the model error. The Tropical Atlantic rainfall bias impacts Rossby wave sources over the Subtropical Atlantic and there is a clear Rossby wave pattern originating from this area which is comparable to the mean bias in hindcasts. The bias impacts the PMSL over the North Atlantic and projects onto the NAO pattern. We show that the bias is not limited to the GloSea System and is also seen in other seasonal forecast systems. Our results suggest that Tropical Atlantic rainfall errors explain a significant amount of the mean bias in seasonal forecasts over the Extratropical North Atlantic.
Improved description of temperature profiles over short vegetation: rethinking the roughness height
Authors: Boekee Judith , van der Linden Steven , ten Veldhuis Marie-Claire , Verouden Iris , Nollen Paul , Dai Yi , Jongen Harro , van de Wiel Bas
Published: July 2024

Accurate estimations of the surface temperature and momentum profiles are crucial to determine the exchange of energy and moisture between the surface and the atmosphere. Yet, getting a good estimate of the temperature profile over the widespread and frequently studied grass surface remains challenging. In this study, we present an extension to the Monin--Obukov similarity theory (MOST) for the roughness sublayer (RSL) over short vegetation. We test our theory using temperature measurements from fiber optic cables in an array-shaped set-up. This provides a high vertical measurement resolution that enables us to measure the sharp temperature gradients near the surface.It is well-known that MOST is invalid in the RSL as the flow is distorted by roughness elements. However, to derive the surface temperature, it is common practice to extrapolate the logarithmic profiles down to the surface through the RSL. Instead of logarithmic behaviour defined by MOST near the surface, our observations show near-linear temperature profiles. This log-to-linear transition is described over an aerodynamically smooth surface by the Van Driest equation in classical turbulence literature. Here we propose that the Van Driest equation can also be used to describe this transition over a rough surface, by replacing the viscous length scale with a surface length scale Ls that represents the size of the smallest eddies near the grass structures. We show that Ls scales with the geometry of the vegetation and that the model shows the potential to be scaled up to tall canopies. The adapted Van Driest model outperforms the roughness length concept in describing the temperature profiles near the surface and predicting the surface temperature.
Wind Influence on Rainfall Microphysics through High-resolution Observations
Authors: Testik Firat Y.
Published: July 2024

Rainfall microphysics is a critical component for numerous applications including remote sensing and quantitative precipitation estimations, meteorological and hydrological modeling, soil erosion, and telecommunication signal propagation in the atmosphere. This study delves into the effects of wind on rainfall microphysics by utilizing high-resolution commercially available and in-house developed meteorological instruments. Specifically, here we present our findings on the effects of wind on the evolution of raindrop size distribution (DSD) and the governing microphysical processes (i.e. raindrop fall and collisions). This is an observation-based study utilizing high-resolution field measurements. The effect of wind on DSD observations was uncovered using a large dataset available from a multi-institutional field campaign that was conducted in central Oklahoma. The fall and collisions of raindrops were investigated using a large dataset collected at our outdoor rainfall laboratory located on the West campus of the University of Texas at San Antonio, Texas, USA during a 3-year-long field campaign. The dataset includes observations from a unique optical-type disdrometer, called High-speed Optical Disdrometer (HOD), that we developed. HOD’s innovative technology enables capturing high-resolution sequential images of the same hydrometeor multiple times as it passes through the measurement volume to provide high-accuracy measurements of hydrometeor characteristics and visual observations of the processes, including first-time field observations of raindrop collisions.  We will provide an overview of the DSD evolution through the air column and discuss the underlying physical processes in light of the wind effects. This material is based upon work supported by the National Science Foundation under Grants No. AGS-1741250.
There is a link between the global surface area receiving daily precipitation, global precipitation and theglobal mean temperature
Authors: Benestad Rasmus , Lussana Cristian , Dobler Andreas
Published: July 2024

We show that the fraction of Earth’s surface area receiving daily precipitation is closely connected to the global statistics of local wet-day frequency and mean precipitation intensity. Our analysis, based on the ERA5 reanalysis, revealed a close match between the global mean surface temperature and both the total mass of 24-h precipitation falling on Earth’s surface as well as surface area receiving 24-h precipitation in the ERA5 data, highlighting the dependency between the greenhouse effect and the global hydrological cycle. Moreover, the total planetary precipitation and the global daily precipitation area represent links between the global warming and extreme precipitation amounts that traditionally have not been included in sets of essential climate indicators. Hence, both the total amount of precipitation falling on Earth’s surface and the fraction of the surface area on which it falls represent two key global climate indicators for Earth’s global hydrological cycle. Furthermore, the global surface area fraction of daily precipitation is connected with the global statistics of local wet-day frequencies in addition to the mean precipitation intensity. Previous work suggest that these two parameters can be used to get approximate estimates of probability for heavy daily precipitation amounts. Based on these results, we argue that the present set of global climate indicators should be extended to include the total global daily precipitation and the fraction of Earth's surface area receiving daily precipitation. It's also useful to compute the fractional global surface area with daily precipitation exceeding thresholds such as 30 mm/day and 50 mm/day. 
“People don’t understand probabilities” – or do they? 
Authors: Mylne Ken , Roberts Helen
Published: July 2024

Weather forecasting science has long been developing ensemble forecasts as a way to improve forecast capability and provide better information to support users’ decisions. The science is well proven and the Met Office will soon move to an ensemble-only NWP (Numerical Weather Prediction) system. Ensemble forecasts can be used in a number of ways, but fundamentally they provide a probabilistic picture of the weather forecast which includes a most likely outcome but also information on the confidence, uncertainty or risks associated with forecast outcomes. In order to pull through the full benefits of this information it is important to communicate this information effectively to as many users as possible so that they can make appropriate risk-based decisions. There is a widely-held belief that people will find probabilistic information hard to understand or make use of – “People don’t understand probabilities” – which provides a significant obstacle to communicating it.This challenge for ensemble forecasts has long been recognised and there has been extensive research conducted into effective communication and people’s understanding of such forecasts. This paper offers a review of that research to help guide future communications of forecasts. The overwhelming and consistent conclusion found in the literature is that people do understand the probabilistic information and make better decisions when presented with it, provided that the information is presented appropriately.The literature review provides strong support for communicating probabilistic information to forecast users, including the general public. It does not support the idea that people’s understanding should be a barrier to communicating such information. While not every single person will understand or take full advantage of the additional information, most people will benefit and make better decisions as a result. 
Ensemble Classes of Use Cases
Authors: Mylne Ken , Walters David , Roberts Nigel , Barciela Rosa , Gray Mike , Petch Jon , Wells Oak , Sachon Patrick , Skea Alasdair , Davies Paul , Willington Steve , Titley Helen
Published: July 2024

Ensemble Numerical Weather Prediction (NWP) systems have now been used operationally for over 30 years, mainly as a supplement to single realisation “deterministic” forecasts based on a higher resolution NWP model. Ensembles sample a range of possible outcomes and allow the estimation of probabilities and uncertainties in the forecast.  Recognising the greater forecast skill of ensemble forecasts, the Met Office is moving towards a completely ensemble-based NWP system with no separate higher resolution deterministic forecasts, so users and developers will need to consider how these ensemble-based forecasts can best be used to cover the wide variety of applications and use cases. Ensembles offer a wealth of information which can be used in many different ways, from picking preferred or representative members to basing forecasts on a full probability distribution; forecasts may also be presented in many ways, either deterministically or taking account of the forecast uncertainty, depending on the use case and the decisions to be supported. In order to help users get the most benefit from ensembles, we present a framework of “classes of use cases” that classifies different user requirements of an NWP system and describe how these can be met. Which class is most appropriate for a particular user and application will depend on the nature of the decision(s) to be taken, the vulnerability of the user to weather impacts and the risk appetite of the user. The use cases are not fundamentally new, but by classifying them we aim to help users and service providers to identify the best approaches to address the needs of different users, and to identify synergies between the needs of different users.   By classifying the use cases in this way we also highlight the implications for how we should be developing and processing ensemble data to deliver to these use cases, to ensure that the maximum benefits from ensemble NWP forecasts are pulled through for the whole of society.
Application of a weighted ensembleforecasting method based on online learningin subseasonal forecast in the South China
Authors: Xin Fei
Published: July 2024

Under the proposal of “seamless forecasting”, it has become a key problem for meteorologists to improve the skillsof subseasonal forecasts. Since the launch of the subseasonal-to-seasonal (S2S) plan by WMO, the precision of modelpredictions has been further developed. However, when we are focusing on the practical applications of modelsin the South China (SC) in recent years, we found that large disagreements appear between forecast members. Someof the members predicted well in this area, while others are not satisfactory. To improve the accuracy of subseasonalforecast in the SC, new methods making full use of different forecast models must be proposed. In this passage, weintroduced a weighted ensemble forecasting method based on online learning (OL) to overcome this difficulty. Asthe state-of-the-art forecast models in the world, three models from China Meteorological Administration, EuropeanCentre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction provided by the S2Sprediction dataset are used as ensemble members, and an ensemble weight is trained through the aforementionedOL model for the predictions of temperature and precipitation in subseasonal timescale in the SC. The results showthat the forecast results produced under the OL method are better than the original model predictions. Comparedwith the three model ensemble results, the weighted ensemble model has a good ability in depicting the temperatureand precipitation in the SC. Furthermore, we also compared this strategy against the climatology predictionsand found out that the weighted ensemble model is superior in 10–30 days. Thus, the weighted ensemble methodtrained thorough OL may shed light on improving the skill of subseasonal forecasts.
How concerning is Lucifer? Insights from an experimental study of public responses to heat event naming in England and Italy
Authors: Summers Barbara , Taylor Andrea , Bellomo Pietro , Dessai Suraje
Published: July 2024

In 2023 areas in southern Europe experienced record-breaking high temperatures. With these events being dubbed Cerberus and Charon, debate has arisen in both the forecasting community and the media as to a) whether severe heat events should be officially named by meteorological services; and b) whether threatening/mythological names should be used. This study was undertaken as a step towards addressing the lack of empirical evidence on whether heat event naming affects public responses to heat protection messaging, and whether the nature of the name (mythological vs non-mythological) matters. We undertook online experiments with regionally representative samples in England (n=2152), where official storm naming exists but heat events are not currently named, and Italy (n=1984), where weather events are not officially named but where there is a tradition of unofficially naming anticyclones after figures from mythology or classical history. Participants were recruited through the market research company Cint and randomly allocated to view a hypothetical heat warning that referred to a forecast for an unnamed event (‘a heatwave’), a mythologically named event (UK=Heatwave Lucifer, Italy=A heatwave caused by anticyclone Lucifero) or a non-mythologically named event (UK=Heatwave Arnold, Italy=A heatwave caused by anticyclone Arnold).  Participants were asked to rate how severe they would anticipate the event to be, how concerning it would be, how much they would trust the message and their anticipated behavioural response. Multivariate Analysis of Variance tests indicated that English participants in the mythological name condition reported slightly greater anticipated severity and concern than in the no-name condition. However, while statistically significant the size of this effect was very small, accounting for less than 1% of variance in concern, with factors such as perceptions of the pleasantness of hot weather and concern about heat events increasing in the future having a much stronger effect. Amongst the Italian sample no statistically significant differences were found between those in the unnamed and mythologically named event conditions. However, the mythologically named event elicited slightly greater perceived severity, concern and trust than the non-mythologically named event. As with the English sample, size of the effect was, while statistically significant, very small. No effect on behavioural intention was found in either of the countries. Together, our findings suggests that naming in isolation only has a very small effect on anticipated response to heat messaging, and that the nature of this effect is dependent on cultural context.
ENSO predictability in the CESM model from 1880 to 2017
Authors: Liu Ting
Published: July 2024

The El Niño–Southern Oscillation (ENSO) is a prominent interannual signal in the global climate system with widespread climatic influence. Our current understanding of ENSO predictability is based mainly on long-term retrospective forecasts obtained from intermediate complexity and hybrid coupled models. Compared with those models, complicated coupled general circulation models (CGCMs) include more realistic physical processes and have the potential to reproduce the ENSO complexity. However, hindcast studies based on CGCMs have only focused on the last 20–60 yrs. In this study, we conducted an ensemble retrospective prediction from 1881 to 2017 using the Community Earth System Model to evaluate El Niño–Southern Oscillation (ENSO) predictability and its variability on different timescales. To our knowledge, this is the first assessment of ENSO predictability using a long-term ensemble hindcast with a complicated coupled general circulation model (CGCM). Our results indicate that both the dispersion component (DC) and signal component (SC) contribute to the interannual variation of ENSO predictability (measured by relative entropy, RE). In detail, the SC is more important for ENSO events, whereas the DC is of comparable important for short lead times and in weak ENSO signal years. The SC dominates the seasonal variation of ENSO predictability, and an abrupt decrease in signal intensity results in the spring predictability barrier feature of ENSO. At the interdecadal scale, the SC controls the variability of ENSO predictability, while the magnitude of ENSO predictability is determined by the DC. The seasonal and interdecadal variations of ENSO predictability in the CGCM are generally consistent with results based on intermediate complexity and hybrid coupled models. However, the DC has a greater contribution in the CGCM than that in the intermediate complexity and hybrid coupled models. 
Climate conditions of the coastal part of Labrador in the late 18th century
Authors: Singh Garima , Przybylak Rajmund , Araźny Andrzej , Wyszyński Przemysław , Chmist Konrad
Published: July 2024

This study focuses on the analysis of air temperature, pressure and wind conditions of Labrador's coastal region in the period 1771-1787.To reconstruct the climatic conditions of this historical era, we draw upon invaluable instrumental meteorological observations made by the Moravian missionaries. All data were taken from three primary archival collections: the Moravian Archives in Herrnhut (Germany), the Moravian Archives at Muswell Hill in London, and the Archives of the Royal Society in London (Great Britain).The eighteenth-century Moravian missionary observations offer a unique perspective on the climate of the Labrador coast, providing essential data on air temperature, atmospheric pressure, wind force and direction, and short descriptions of the weather, observed two, three, or four times a day. By examining records in a detailed and systematic manner, we aim to better understand the region's climate variability and trends during the early instrumental period. For this study, we utilized sub-daily air temperature, atmospheric pressure, and wind force and direction measurements from three sites: Nain (1771-1786), Okak (1776-1787), and Hoffenthal (1782-1786). All available historical data were quality controlled and converted to present units (°C, hPa, ms˗1 ). The necessary corrections to all studied variables were introduced. For example, the original daily air temperature means (calculated from different measurement times) were corrected to real means calculated from 24 hourly measurements. Corrections for each month were calculated based on contemporary data (1991–2010). The corrected data have been used to calculate monthly, seasonal and yearly means and other statistics. For example, we calculated a number of different categories of cold/warm days, growing degree days sum (GDD), air thawing index degree-days sums (ATI), positive degree-days sum (PDD) and air freezing index degree-days sum (AFI). The last four indices were calculated using definitions proposed by Nordli et al. (2020). The climate conditions of the study period in the coastal part of Labrador are compared to present-day ones.The work was supported by the National Science Centre, Poland project No. 2020/39/B/ST10/00653.References:Nordli, Ø., Wyszyński, P., Gjelten, H. M., Isaksen, K., Łupikasza, E., Niedźwiedź, T., and Przybylak, R., 2020, Revisiting the extended Svalbard Airport monthly temperature series, and the compiled corresponding daily series 1898–2018, Polar Res., 39, 3614, http://dx.doi.org/10.33265/polar.v39.3614.
Relating synoptic air masses to human mortality
Authors: Lee Cameron , Silva Alindomar , Ibebuchi Chibuike , Sheridan Scott
Published: July 2024

Prior research has studied the utility of various metrics of thermal comfort in an effort to elucidate the nuanced relationship between temperature and human mortality. Synoptic-scale air masses (AMs) – sometimes referred to as surface weather types – are one of these metrics. Within the last 5 years, a new global-scale AM classification dataset was published – the GWTC2 – which has not yet been evaluated in terms of its relationship to human mortality. The GWTC2 is a system of multivariate surface AM classification, identifying spatiotemporally-relative AMs, and frontal passages, for every day (from 1979-present) at every location (at 0.5° x 0.5° spatial resolution) across the globe. Thus, this research leverages the GWTC2 to examine the lagged relationship between AMs and anomalous mortality for 61 cities across various climates zones of the continental United States. Results show that AMs are significantly related to human mortality, at most locations and in all seasons of the year. The Humid-Warm (HW) and Warm (W) AMs yielded significant and immediate (at 0-1 day lag times) increases in mortality in summer, though the immediate increases after HW occurred were not followed by decreases in mortality – indicating a lack of the “mortality displacement” effect highlighted in some prior research. Meanwhile, the Dry-Cool (DC) and Cool (C) AMs both resulted in a delayed, but extended period (from 1 to 19 days of lag) of excess mortality in all seasons outside of summer, generally peaking 2 to 4 days after the occurrence of the DC or C air mass. Of note, warm front passages (increased mortality) and cold front passages (decreased mortality) yielded significant anomalous mortality responses one day after their occurrence in every season of the year – the most seasonally-consistent AM-mortality relationship discovered. Furthermore, many of these results were consistent across the majority of the 61 cities examined. Finally, artificial neural network (ANN)-based modeling revealed that a meta-model (i.e., a model developed on the combined data of all 61 locations) was more skillful in predicting AM-mortality relationships for many cities than developing 61 individual ANN models for each city. Future research should explore whether the consistency of these results holds up when exploring the impact of GWTC2 air masses in other parts of the globe.
First results and future plans for ecRad radiation in Météo-France models
Authors: Schäfer Sophia , Hogan Robin , Rodier Quentin , Libois Quentin , Seity Yann , Roehrig Romain , Ukkonen Peter
Published: July 2024

Radiation in the atmosphere provides the energy that drives atmospheric dynamics and physics on all scales, from cloud particle growth to global weather and climate. Radiation schemes in global weather and climate models have to simplify the complex interaction of radiation with the Earth system. Capturing the interactions of gases and clouds with radiation is particularly challenging, since gas effects are extremely wavelength-dependent, while clouds vary strongly on small spatial and temporal scales, and they both interact strongly with radiation. Uncertainties in the radiation scheme and the cloud, aerosol and gas and inputs lead to uncertainties in weather and climate processes, such as energy balance, cloud development and dynamics.The radiation scheme ecRad (Hogan & Bozzo 2018) has been operational in the IFS model at ECMWF since 2017 and in ICON at Deutscher Wetterdienst (DWD) since 2021 and will be the next radiation scheme in the operational numerical weather prediction models AROME and ARPEGE, the climate model ARPEGE-Climat and the regional research model Méso-NH at Météo-France. As a modular scheme, ecRad provides the opportunity to vary parametrisations and assumptions individually. Several options are available for the radiation solver, cloud vertical overlap and horizontal inhomogeneity treatment and cloud hydrometeor optical property parametrisations. The solver SPARTACUS is the only radiation solver in a global model that can treat 3D radiative effects. The new gas optics model ecCKD can improve both precision and cost of the gas optics calculation, as can recent code optimisations.We will present the status of and future plans for implementation in the Météo-France models, and show first evaluation results for radiation, energy balance and clouds on various scales scales. We will also investigate the impact of cloud and aerosol input and search for the best settings for radiation balance, model energy and physics and forecast performance. Finally, we will present future plans for radiation work in the Météo-France models. Reference:Hogan, R. J., & Bozzo, A. (2018), A flexible and efficient radiation scheme for the ECMWF model. Journal of Advances in Modeling Earth Systems, 10, 1990-2008. https://doi.org/10.1029/2018MS001364
Investigating the large uncertainties in future changes in runoff over western and central Europe
Authors: Deman Juliette , Boé Julien
Published: July 2024

There are large uncertainties in the future evolution of water resource availability over western and central Europe. This availability, characterized by total runoff, is the result of two driving variables, precipitation and evapotranspiration. The signal-to-noise ratio for changes in precipitation is low. Changes in evapotranspiration are strongly influenced by uncertainties associated with the modelling of land-atmosphere interactions.Here we highlight the diverging hydrological behavior of models from the 6th phase of the Coupled Model Intercomparison Project over western and central Europe. Some models project a marked decrease in runoff in the late 21st century while others simulate no change or a slight increase. The decrease in runoff is driven either by a decrease in precipitation and evapotranspiration or by an increase in precipitation and an even larger increase in evapotranspiration. The mechanisms responsible for the inter-model spread are investigated with experiments from the Model Intercomparison Project on Land Surface, Snow and Soil Moisture (LS3MIP, van den Hurk et al. 2016) and on the coupled Climate-Carbon Cycle (C4MIP, Jones et al. 2016). The change in large-scale atmospheric circulation is shown to be an important driver of the inter-model spread in precipitation changes in winter. In summer, the physiological effect of CO2 has a direct influence on evapotranspiration and precipitation changes. Models with a strong biogeochemical effect of CO2 show a strong decrease in evapotranspiration and precipitation in summer.The potential relationship between the mean present-day state and trends, and the inter-model spread in the future changes in the hydrological cycle projected by the CMIP6 models is also examined. The models that project a large decrease in runoff are often associated with a large climatological value of evapotranspiration. Among them, some models also show strong positive trends in precipitation and evapotranspiration that are consistent with their future response at the end of the 21st century. However, it remains difficult to judge the realism of models on the basis of this assessment alone, in particular due to large observational uncertainties. van den Hurk et al.: LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project – aims, setup and expected outcome, Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, 2016.Jones, C. D et al.: C4MIP – The Coupled Climate–Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6, Geosci. Model Dev., 9, 2853–2880, https://doi.org/10.5194/gmd-9-2853-2016, 2016.
Early Detection of Extreme Events through NWP Plugins
Authors: Bonanni Antonino , Hawkes James , Sarmany Domokos , Quintino Tiago
Published: July 2024

Early detection of extreme meteorological events in Numerical Weather Predictions (NWP) brings an intrinsically high societal and economical value by providing precious lead time for putting effective countermeasures in place. For this reason, an increasing number of initiatives are arising to develop the underlying technological solutions. One prominent drive in Europe is the EU flagship initiative Destination Earth (DestinE) to develop highly accurate digital models of the Earth at global scale (Digital Twins).Under the DestinE initiative, the European Centre for Medium-Range Weather Forecasts (ECMWF) is developing a mechanism that allows extending data processing functionalities of a NWP model "on-the-fly" (while the model data still resides in memory). This mechanism is based on a software called Plume that allows the development of model functionalities as plugins. Plume plugins are separate software libraries that are loaded dynamically at the beginning of the NWP simulation, access the model data in memory and perform data processing tasks like extreme event detection. This very early access of data in memory through plugins has many advantages, including reducing the detection lead time, avoiding writing output data to disk unnecessarily and promoting collaborative development of plugins to be run with a NWP model.  The object of this presentation is the development of a Plume plugin that performs Tropical Cyclone detection and tracking. The underlying detection algorithm is based on a Machine Learning model that analyses NWP model data in memory and performs the Tropical Cyclone detection at each iteration of the model. In addition, the plugin notifies an external notification system (Aviso) whenever an extreme event is detected (according to pre-defined criteria, for example a tropical cyclone with intensity above a threshold). This whole mechanism could be used for notifying and triggering appropriate downstream workflows in response of detected events. The Tropical Cyclone detection plugin prototype is demonstrated within the ECMWF Integrated Forecasting System (IFS).  In summary, the Plume plugin system can add on-the-fly data processing capabilities to a NWP model through plugins and can be used to implement various data processing tasks including early extreme event detection. Furthermore, a plugin architecture preserves a modular structure of additional NWP data processing functionalities  and guarantees a collaborative development environment.
Ensemble-based regional reanalysis system for Central Europe: Assimilation experiments with ERA5 observations, data conversion, results and outlook
Authors: Kelbch Alexander , Valmassoi Arianna , Spangehl Thomas , Borsche Michael
Published: July 2024

The development of regional reanalyses aims at the provision of high-resolution data sets that are suitable for climate applications and climate services. As the desired high-resolution information can barely be provided by either synoptic or remote sensing observation data, a growing interest in high-quality regional reanalyses is recognisable. Particular demand arises from the renewable energy sector. Further quality gains are expected by using an ensemble approach, e.g. by making available the desired uncertainty information when moving towards higher resolution. Within the framework of the Innovation Programme for applied Researches and Developments (IAFE) at Germany's national meteorological service (DWD) our project aims to develop and evaluate an operational ensemble-based regional reanalysis system incorporating the current NWP model of DWD (ICON). One final goal of the project is to provide a basic framework for user-oriented verification.  For our new reanalysis system only observation data of the ECMWF ERA5 reanalysis will be used. To provide these data to the operational DWD assimilation scheme a converter has been developed. Test experiments have been performed to evaluate whether the current system performance can be improved by using ERA5 observation datasets instead of the operationally used observations from DWDs data base. For testing we use the Basic Cycling Environment (BACY) of DWD being characterized by different modules for each cycling step (e.g boundary data, ICON run, assimilation scheme). BACY enables users to perform cycling over multiple time steps (automatization) as well as repeated tests of a single module for debugging or binary checks. Here we present the BACY specifications chosen for the reanalysis production as well as results of the assimilation experiments.
Quantifying the impact of extreme heat and adaptation strategies on urban air conditioning use and energy consumption in Nicosia, Cyprus. 
Authors: Vurro Giandomenico , Martilli Alberto , Hadjinicolaou Panos , Carlucci Salvatore
Published: July 2024

Human-induced climate change is expected to affect the entire Mediterranean area during the 21st century; notably, the Eastern Mediterranean and Middle East (EMME) region has been recognized as a climate-change hotspot. That manifests with increased temperatures and more frequent heatwaves, posing a significant challenge for urban areas in warm periods. Furthermore, as the urban population continues to grow and urbanization expands in this area, the potential escalation of heatwaves in the already sensitive EMME environment is expected to have direct adverse effects on human health, agriculture, and the water-energy nexus. Therefore, analyzing the impact of exacerbated environmental conditions is crucial for understanding the vulnerability of cities and developing effective mitigation and adaptation strategies.
Our aim is to quantify the impact of extreme temperatures on the building energy use for space cooling, rejected heat to the ambient, and outdoor thermal comfort in Nicosia, Cyprus, over a heat wave event within the period from the 24th of July until the 10th of August 2021. To achieve this, the Weather Research and Forecasting (WRF) model is coupled with the Multilayer BEP/BEM scheme to study different adaptation and mitigation strategies evaluated against two baseline scenarios, the first without considering the air conditioning and the second where the air conditioning is on to maintain indoor thermal comfort. The adaptation/mitigation scenarios are (i) partial coverage of roofs with photovoltaic panels to increase the generation of energy from renewable energy sources, (ii) adoption of cool roofs to minimize heat absorption, and (iii) plantation of trees and expansion of green areas within the city to reduce air temperature and improve outdoor thermal comfort.
By comparing these three strategies against themselves and the baseline scenario, we can identify their contribution to reducing buildings’ energy consumption and rejected heat and increasing outdoor thermal comfort. Therefore, the outcomes of this study can provide valuable insights to policymakers and urban planners in addressing climate change impacts in city regeneration projects by increasing urban resilience against extreme heat.
The application of AI tools in weather and climate science
Authors: Krzyżewska Agnieszka
Published: July 2024

Recent years have witnessed significant advancements in the development of Artificial Intelligence (AI) tools, notably Large Language Models (LLMs), with prominent systems including ChatGPT by OpenAI, Gemini by Google, and Copilot by Microsoft. Despite inherent limitations, the diversity of these tools' applications across various fields of life, including scientific research, has expanded significantly.This study evaluates the utility of various AI tools within the fields of meteorology and climatology, ensuring their applications follow ethical standards in scientific publication. The tools assessed include ChatGPT versions 3.5 and 4.0, Gemini (Google), Copilot (Microsoft), Perplexity, and GPT-based systems such as DataAnalyst, Consensus, ScholarGPT, and Academic Assistant Pro, among others. Each tool was subjected to identical inputs (prompts, data, photographs) and their responses were evaluated on a 0-10 scale for accuracy and relevance. The scoring was based on the percentage of verifiable content in the responses to ensure objectivity. The research spanned from May 2023 to April 2024.The AI systems were tasked with responding to queries on climate change in Poland, identifying key research papers on humid heat waves, classifying cloud types, creating a climate map from provided data, and comparing two climate maps.The outcomes varied significantly across tasks. ChatGPT 3.5 demonstrated an answer accuracy of 30-40% (topic: climate change in Poland). The Consensus system excelled in identifying and summarizing key papers on humid heat waves research. ChatGPT 4.0 emerged as the most effective tool for cloud classification, with Copilot also delivering commendable results; however, Gemini (Advanced) struggled with cloud recognition tasks. DataAnalyst proved capable of generating basic climate maps, but with some inaccuracies such as station misplacements. When comparing two climate maps, all systems performed adequately, with the most precise descriptions provided by Bard (Google).