We describe the coordination of ORCESTRA, a field campaign designed to advance understanding of how mesoscale convective processes influence Earth's climate. ORCESTRA harmonized the execution of eight sub-campaigns that spanned the tropical North Atlantic from 10 August to 30 September 2024. The ORCESTRA sub-campaigns performed measurements from three research aircraft, a research vessel, two ground stations, and EarthCARE, a new Earth Explorer Satellite. These platforms performed thousands of atmospheric soundings, supported a rich constellation of active and passive remote sensing, conducted a wide variety of in situ measurements, and deployed autonomous sensors in the air and sea. Together with its measurements, ORCESTRA is being accompanied by extensive numerical experimentation to advance the development of a new generation of more physical climate models.
Despite the urgency to mitigate climate change, global greenhouse gas concentrations are still increasing, and even global emissions are still growing. Furthermore, the global sinks-both natural and technical-are not improving rapidly enough. Therefore, discussion and investigations to utilize solar radiation modification (SRM) have resurfaced. Stratospheric Aerosol Injection (SAI) is one of the most studied methods, particularly using sulfate aerosols. However, sulfate SAI includes several risks, such as deposition of acidic aerosol particles on the Earth's surface, increasing acidification and ozone depletion, and changing weather patterns. Here we propose an alternative approach, namely Organic Stratospheric Aerosol Injection (OSAI). The OSAI is initiated by an injection of isoprene or monoterpenes, which, according to our conceptual investigations and global model simulations, seem to be plausible candidates for this purpose. The organic SAI is likely to avoid some of the problems inherent to sulfate SAI, even though further research is needed in terms of its potential influences on stratospheric heating and the ozone layer. While SRM is still preferably avoided, our results suggest that organic aerosols may be an alternative to sulfate for further consideration if it is deemed a viable and necessary option.
Radiance observations from microwave sounders have become one of the most valuable sources of information that is used in Numerical Weather Prediction (NWP). However, uncertainties in the land (and sea-ice) surface modelization do not permit optimal use of channels that receive a mixed contribution from the surface and the troposphere (i.e., low-peaking channels). In the context of the preparation of the Arctic Weather Satellite (AWS) mission, the HARMONIE-AROME model was run to evaluate the impact of assimilating the low-peaking channels of existing instruments using the so-called “dynamic emissivity method.” The aim of this work was also to build the best possible version of HARMONIE-AROME data assimilation system (3D-Var and 4D-Var) before the launch of the AWS instrument that happened in August 2024 and to be ready to assimilate the data as soon as the instrument was declared operational in June 2025.
Storylines are physically plausible scenarios of future climate change, statistically derived from an ensemble of climate model projections and organized according to the magnitude of projected changes in two or more remote drivers that strongly influence the spatial pattern of the climate response. Here, we provide novel insights into the Arctic storylines identified by Levine et al. (2024), where Barents-Kara Sea warming and lower-tropospheric Arctic warming during the extended summer season (May-October) were remote drivers, as we identify a set of models from the Coupled Model Intercomparison Project phase 6 to represent the storylines. We do this by first identifying models that are similar to these storylines in terms of each remote driver response and quantifying this similarity. Second, we evaluate the model's performance in terms of a simple performance score based on the mean normalized root-mean-square error for multiple climate variables of importance for the storylines. The normalized values vary between 0 and 1 for all variables, allowing them to exert a comparable influence on the score. The advantage of the score is that it provides an easily implementable and interpretable way of identifying models that are characterized by large errors relative to the rest of the ensemble. Finally, we combine the similarity estimate and the score to select models to represent the storylines. We focus on the Arctic during the extended summer season for which the storylines were designed, but also consider other seasons and regions. Through this exercise, we also document the methodology, benefits, and limitations of the score.
Modern data assimilation schemes typically use the same discrete dynamical model not only to evolve the state estimate in time but also to approximate the evolution, or propagation, of the estimation error covariance. Ensemble-based methods, such as the ensemble Kalman filter, approximate the evolution of the covariance through the propagation of individual ensemble members. Thus, it is tacitly assumed that if the discrete state propagation and resulting mean state estimates are accurate, then the ensemble-based discrete covariance propagation will be accurate as well, apart from sampling errors due to limited ensemble size. Through a series of numerical experiments supported by analytical results, we demonstrate that this assumption is false when correlation length scales approach grid resolution. We show that for states satisfying advective dynamics, although the discrete state propagation and ensemble mean state estimates are accurate, the corresponding ensemble covariances can be remarkably inaccurate. The inaccuracy of the ensemble covariances is well beyond that expected of typical sampling errors or numerical discretization errors. The underlying problem is a fundamental discrepancy between discrete covariance propagation and the continuum covariance dynamics, which we can identify because the exact continuum covariance dynamics are known. Errors in the ensemble covariances, which can be at least one order of magnitude larger than those of the mean state when correlation lengths begin to approach grid scale, cannot be rectified by covariance inflation and localization. This work brings to light a fundamental problem in data assimilation schemes that propagate covariances using the same discrete dynamical model used to propagate the state.
Tellus, originally founded in the late 1940s by Carl-Gustaf Rossby, is relaunched as a community-driven Earth system science journal managed by research-active scientists. The new Tellus builds on its historical legacy while responding to the growing need for an integrated understanding of the processes governing Earth’s atmosphere, ocean, land, cryosphere, and biosphere. As an independent, not-for-profit, diamond open-access journal, Tellus aims to promote scientific integrity, transparency, and scholarly exchange by prioritizing quality and community oversight in the publishing process. The journal welcomes original research articles, perspectives, and synthesis papers that advance interdisciplinary understanding of Earth system processes and climate dynamics. By fostering meaningful interaction between authors, editors, and reviewers, Tellus seeks to stimulate curiosity, encourage cross-disciplinary dialogue, and support the dissemination of influential research for the global Earth system science community.
Water is one of the most essential components for sustaining human and other forms of life; thus, it must be supplied to meet life's necessities. Understanding the consequences of global warming and the trends in climate change is essential for the effective operation and strategic planning of water resource projects. Consequently, accurately and efficiently characterizing drought at the regional level constitutes a more complicated and challenging obstacle. The primary goal of this study is to predict future drought characteristics in Turkey's Konya Closed Basin (KCB) region by employing the Quality Boosted Regional Drought Index (QBRDI) framework. This study uses precipitation time series data from nine sites spanning 57 years to examine drought features in Turkey's KCB. The validity of the weighting procedure in QBRDI is compared with the findings of the simple model average (SMA) using statistical measures: root mean square error (RMSE) and relative absolute error (RAE). It is observed that the RMSE value (6.282) for the weighting procedure in QBRDI is lower than the RMSE value (15.158) computed within SMA. Similarly, the value of RAE across weighting procedure in QBRDI is 0.08525, while that for SMA is 1.409. The lower values of both RMSE and RAE indicate that the weighting procedure utilized in QBRDI is more reliable than that used in the SMA. Consequently, these measurements suggest that the weighting procedure utilized in QBRDI possesses higher reliability and validity for drought assessment than that in the SMA. For application, the long-term phenomenon of drought is determined using the steady-state probability of Markov chain. The findings from steady-state probabilities show that extreme drought tends to have a higher probability than extreme wet across most timescales. This pattern suggests that drought events are marginally more probable than extreme wet events, reinforcing the need for drought-related risk evaluation and mitigation measures. In addition, the Mann-Kendall test is employed to assess the presence of a monotonic trend, while Sen's slope estimator is used to quantify the magnitude of that trend, as it serves as a complement to the Mann-Kendall test by providing a robust estimate of the trend slope.
The global ocean’s overturning circulation plays an important role in climate and climate variability through its transport of heat, freshwater and nutrients. As part of this three-dimensional overturning circulation, dense waters sink in narrow regions at high latitudes in the North Atlantic and along the Antarctic coast. To close this circulation, it is generally assumed that either intense interior mixing by winds and internal tides, or wind-driven upwelling is required to bring these water masses back to the surface. Nevertheless, more recent work questions this requirement for winds and tides, arguing that surface buoyancy forcing alone can drive such a circulation through a process known as rotating horizontal convection. In particular, it has been shown that the presence of a re-entrant channel, such as the Southern Ocean, is required for rotating horizontal convection to generate many features of the global ocean’s overturning circulation. Building on previous work in which rotating horizontal convection was forced by only thermal forcing, here we demonstrate, using an idealised eddying ocean model with both thermal and haline surface forcing, that rotating horizontal convection can produce many of the observed features of the global ocean’s overturning circulation. These results therefore suggest that a global “thermohaline circulation” can exist in the ocean in the absence of winds and in the limit of small vertical diffusion.
The transport of the Southern Ocean's Antarctic Circumpolar Current, closely linked to the global stratification to the north and in turn the inter-hemispheric overturning circulation, is a key metric for quantifying ocean circulation. Understanding the sensitivity of transport to changes in forcing is important in understanding the role of the Southern Ocean in past, present and future climates. Here, we report on an investigation of a negative sensitivity regime, whereby the circumpolar transport decreases with increasing wind forcing, a phenomenon previously reported in ocean modelling investigations where the residual overturning circulation is oriented opposite to the present-day configuration. The present study finds that this negative sensitivity is a subtle effect resulting from both eddy saturation and a negative residual overturning circulation, the latter referring to a poleward mass flux in the warm surface layers. The work provides an examination and rationalisation of the sensitivities relating to the Southern Ocean circumpolar transport, and additionally touches on a numerical methodology that is particularly adept for the study of equilibrium sensitivities, with implications for analogous explorations in the paleoclimate context.
The Ensemble Kalman Smoother (EnKS), an extension of the Ensemble Kalman Filter, can improve the accuracy of the state estimate by assimilating ‘future’ observations. We propose to use the EnKS algorithm to enhance the accuracy and reliability of preexisting reanalyses produced with a fully coupled Earth system model. The offline EnKS is applied to two reanalyses of the Norwegian Climate Prediction Model (NorCPM) to update sea surface height, mixed layer depth, and temperature and salinity for all depth levels of the reanalyses. In an idealized framework, we tune temporal localization parameters and reveal that the optimal temporal localization parameter is 0.1, corresponding to a time delay of about 13 days. In a real framework, we find that observation error variance has to be inflated by a factor of four to account for the autocorrelation of the gridded observational product and avoid overfitting. In both frameworks, the offline EnKS improves the accuracy for the top 300 m temperature, sea surface height, and mixed layer depth, but yields limited improvements in the top 300 m salinity and the water properties below 300 m. Also, it enhances the reliability of the reanalysis. The improvement is notably lower in a real framework than in an idealized framework; this is mostly due to the lack of high quality and independent datasets for proper validation. Overall, this study demonstrates that the offline EnKS has the potential for efficiently improving pre-existing reanalyses.
The air-sea heat exchange has a substantial impact on the heat budget of the surface ocean. Due to the heat loss across the air-sea interface, the uppermost millimeter of the ocean is generally cooler than the underlying water. This cool-skin layer can be naturally disrupted by processes such as wave breaking or wind stress, as well as by anthropogenic factors like ships or offshore wind turbines. Understanding recovery times after complete disturbance is hindered by observational challenges. Previous studies, which used stationary observations with thermal imagery, have focused on natural disruptions. This study presents in situ high-resolution temperature measurements of the skin and near-surface layer during the complete disturbance of the ocean’s surface layer using the autonomous surface vehicle Halobates. The vehicle drifted within artificially disturbed water masses, enabling observation and analysis of thermal recovery. The average temperature difference between skin and near-surface layer was –0.240 ± 0.037°C. The thickness of the skin layer was computed as 1.06 ± 0.16 mm approaching 0.2 mm during disturbances. Recovery times of the cool-skin layer ranged from 62 to 157 seconds, with rates between –0.047 and –0.140°C min–1. The main findings demonstrate that the complete recovery of the cool-skin layer can take up to three minutes following intense surface disruption, such as that caused by vessel-induced turbulence. These results have important implications for modeling air-sea interactions in areas with frequent human activity. Combined with prior research on rapid recovery from natural disturbances, these results enhance our understanding of surface layer dynamics.
How much rain can we expect in Toulouse on Wednesday next week? It is impossible to provide a precise and definitive answer to this question due to the limited predictability of the atmosphere. Ideally, a forecast would be probabilistic, for example expressed in the form of a probability of, say, having at least some rain. However, for some forecast users and applications, an answer expressed in millimeter of rain per 24 h would be needed. A so-called point-forecast can be the output of a single deterministic model. But with ensemble forecasts at hand, how to summarize optimally the ensemble information into a single outcome? The ensemble mean or quantile forecasts are commonly used and proved useful in certain circumstances. In this study, we suggest a new type of point-forecasts, the crossing-point quantile, and argue that it could be better suited for precipitation forecasting than existing approaches, at least for some users. More precisely, for a well-calibrated predictive distribution, the crossing-point quantile is the optimal forecast in terms of Peirce skill score (and equivalently in terms of area under the relative operating characteristic curve) for any event of interest. Along a theoretical proof, we present an application to daily precipitation forecasting over France and discuss the necessary conditions for optimality.
The influence of subpolar North Atlantic hydrographic conditions on the North Sea is well recognized, yet the precise pathways taken by Atlantic Water to reach its gateway remain uncertain. Using satellite-derived velocity fields, we map the open-ocean routes leading to the North Sea. Our Lagrangian analysis shows that the Rockall Trough serves as the primary route in a time-mean sense, with its dominance becoming particularly evident under anomalously cold subpolar conditions. During anomalously warm periods, however, Atlantic Water is preferentially routed through the Iceland Basin. Empirical orthogonal function analysis of the Lagrangian trajectories reveals a dipole mode of variability between the Rockall Trough and the Iceland Basin, with its first principal component explaining 74% (R = 0.86) of the variance in multi-year ocean heat content variability. These trajectories further demonstrate that this variability is closely linked to the north-south shifts of the North Atlantic Current. Such spatial shifts are likely driven by variations in northward ocean heat transport at the intergyre boundary, with the strength of the subpolar overturning circulation in preceding years potentially playing a critical role. This connection suggests that the conditions in the North Sea as well as the pathways Atlantic Water is advected along to reach it could be predictable several years in advance.
Shortly after World War II, the first two models of baroclinic instability were independently published by Charney (1947) and Eady (1949). Both authors describe the origin and initial development of midlatitude weather disturbances from infinitesimal perturbations of a certain initial state of the atmosphere. In this paper we focus on the latter model (Eady, 1949). Using the normal mode method, we derive analytical formulas for the structure of perturbations in the field of geopotential, temperature, isobaric divergence, and vertical velocity. We work in the p coordinate system, in contrast to the original Eady study and its modifications. This allows us to not limit ourselves to the troposphere but to introduce a natural boundary condition at the upper boundary of the atmosphere. The derived analytical relationships accurately well describe some aspects of synoptic disturbances in the midlatitudes. We focus particularly on the wavelength of disturbances, their vertical structure, the position of the minimum divergence level, and the growth rate of emerging synoptic disturbances.
Information contained in a 2-hour recorded interview with Chester Newton in 1990 revealed his early experiences and education at the Institute of Meteorology, University of Chicago (1946-1951), and at the International Meteorological Institute (IMI) at Stockholm University (1951-1953). The first part of this history paper is a narrative based on Newton's remembrances of the education, not only for himself, but for the collection of multi-talented graduate students in the two institutes. He pays special attention to Rossby's philosophy of teaching and research. The second part of the paper focuses on Rossby's continued guidance of Newton between 1953 and the year of Rossby's death, 1957. The discussion of this guidance is based on a set of documents sent by Newton to the author in the early-mid 1990s. Newton's masterful way of describing the milieu where he budded as a young scientist gives the listener a vicarious sense of being a student under Rossby and the coterie of likeminded professors and visitors at "Rossby's institutes".
Extreme sea levels are a major global concern due to their potential to cause fatalities and significant economic losses in coastal areas. Consequently, accurate projections of these extremes for the coming century are crucial for effective coastal planning. While it is well established that relative sea level rise driven by ongoing climate change is a key factor influencing future extreme sea levels, changes in storm surges resulting from shifts in storm climatology may also play a critical role. In this study, we project future daily maximum storm tides (the combination of storm surge and tides) using a random forest machine learning approach for 59 stations around the Baltic Sea, based on atmospheric variables such as surface pressure, wind speed, and wind direction derived from climate datasets. The results suggest both positive and negative changes, with sub-regional variations, in 50-year storm tide return levels across the Baltic Sea when comparing the period of 2070–2099 to 1850–1879. Localized increases of up to 10 cm are projected along the west coast of Sweden and the northern Baltic Sea, while decreases of up to 6 cm are anticipated along the south coast of Sweden, the Gulf of Riga, and the mouth of the Gulf of Finland. Negligible levels of change are expected in other parts of the Baltic Sea. The variability in atmospheric drivers across the four climate models contributes to a high degree of uncertainty in future climate projections.
High-resolution integrated oceanographic observations of the Maltese Ocean Front from unique legacy data of aerial remote sensing, ship, and anchored current meter array and thermistor chains are revisited in an integrated case study. It is shown that significant upwelling and downwelling occurred along the slope of the sharp vertical interface of 10-15 meters of the front during calm wind conditions. The surface of the frontal boundary meandered with a horizontal wavelength varying between 15-30 km and a width of 20 km, spinning off eddies with typical scale between 15-35 km. The frontal wave propagated southward 2.1 km/day or 2.4 cm/s. The location of the front at 15 degrees 20' E longitude was well correlated with the shelf-slope east of Malta. The front was caused by the low salinity Atlantic water inflow between Sicily and Malta converging with the Ionian Sea higher salinity water along the shelf edge slope. The response of the front to a sudden strong wind event with peak values of 14-21 m/s, nearly down along the front from the northwest, caused the mixed layer to increase from about 10 to 20-25 m, smearing out the outcrop of the frontal boundary, ceasing the upwelling but increasing the downwelling below the surface layer. The previous sharp interface below the mixed surface layer was broken up, causing more internal variability. Some local instabilities were observed in the density along the frontal interface, which is important for ocean mixing.
This study suggests potential benefits in forecast performance by assimilating clear-sky satellite radiances from geostationary based SEVIRI instrument into the limited area model of HARMONIE-AROME in the far North of Europe. The current geographical limitations of assimilating geostationary have been breached and a series of data assimilation experiments were conducted over a winter period to assess the impact of including SEVIRI water vapor channels at large satellite zenith angles. The results suggest improved background departures and improved analyses for humidity and temperature fields and subsequent enhancements in forecast skill in surface meteorological parameters like pressure, temperature, humidity and clouds. The findings highlight opportunities for using geostationary satellite data beyond current limitations and identify areas for further improvements.
Windstorms are one of the most important natural hazards affecting Europe. This article investigates the potential impacts of climate change on windstorm losses in Europe employing the Loss Index (LI) method. A large EURO-CORDEX multi-model ensemble at 12 km resolution with 20 different general circulation model to regional climate model (GCM-RCM) chains following the historical plus RCP8.5 scenario is considered. A comparison between the simulated historical 10 m wind gusts and ERA5 reanalysis reveals substantial model biases. An Empirical Quantile Mapping method is employed to bias-correct the daily wind gust speeds, leading to the effective reduction of these biases. Considering different global warming levels (GWLs), our results show an increase in windstorm intensity for Western, Central and Eastern Europe in a warming world, and a general decrease in windstorm frequency for large parts of Europe. While the ensemble mean changes are mostly moderate for +2°C world, signals are more pronounced for +3°C. The projected changes in windstorm losses are small and mostly non-robust, with negative trends for Central Europe and positive trends for Eastern Europe. For the most extreme loss events, the EURO-CORDEX ensemble projects shorter return periods for Eastern Europe independent of the GWL, while no clear trends for Core Europe emerge. Our results show a large spread between the individual ensemble members, without a clear dominance of a single GCM or RCM. In summary, the projected changes in windstorm losses are subtle, but important particularly for Central and Eastern Europe, which should be considered in the mid- and long-term planning of the insurance industry.
