The Arctic is experiencing unprecedented moistening, which is expected to have far-reaching impact on global climate and weather patterns. However, it remains unclear whether this newly-sourced moisture originates locally from ice-free ocean regions or is advected from lower latitudes. In this study, we use water vapour isotope measurements in combination with trajectory-based diagnostics and an isotope-enabled AGCM, to assess seasonal shifts in moisture sources and transport pathways in the Arctic. Continuous measurements of near-surface vapour, δ18O, and δD were performed onboard RV Polarstern during the MOSAiC expedition from October 2019 to September 2020. Combining this isotope dataset with meteorological observations reveals that the spatiotemporal evolution of δ18O mimics changes in local temperature and humidity at synoptic to seasonal time scales, while corresponding d-excess changes suggest a seasonal shift in the origin of moisture. Simulation results from the particle dispersion model FLEXPART support these findings, indicating that summer moisture originates from nearby open ocean, while winter moisture comes from more remote sources with longer residence time over sea-ice. Results from a nudged ECHAM6-wiso simulation also indicate that evaporative processes from the ocean surface reproduce summer sotope values, but are insufficient to explain measured winter isotope values. Our study provides the first isotopic characterization of Central Arctic moisture over the course of an entire year, helping to differentiate the influence of local processes versus large-scale vapour transport on Arctic moistening. Future process-based investigations should focus on assessing the non-equilibrium isotopic fractionation during airmass transformation over sea-ice.

A document by Sushel Unninayar. Click on the document to view its contents.

The paper presents a new method for the decomposition of the horizontal wind divergence among the linear wave solutions on the sphere: inertia-gravity (IG), mixed Rossby-gravity (MRG), Kelvin and Rossby waves. The work is motivated by the need to quantify the vertical velocity and momentum fluxes in the tropics where the distinction between the Rossby and gravity regime, present in the extratropics, becomes obliterated. The new method decomposes divergence and its power spectra as a function of latitude and pressure level. Its application on ERA5 data in August 2018 reveals that the Kelvin and MRG waves made about 6% of the total divergence power in the upper troposphere within 10S-10N, that is about 25% of divergence. Their contribution at individual zonal wavenumbers k can be much larger; for example, Kelvin waves made up to 24% of divergence power at synoptic k in August 2018. The relatively small roles of the Kelvin and MRG waves in tropical divergence power are explained by decomposing their kinetic energies into rotational and divergent parts. The Rossby wave divergence power is 0.3-0.4% at most, implying up to 6% of global divergence due to the beta effect. The remaining divergence is about equipartitioned between the eastward- and westward-propagating IG modes in the upper troposphere, whereas the stratospheric partitioning depends on the background zonal flow. This work is a step towards a unified decomposition of the momentum fluxes that supports the coexistence of different wave species in the tropics in the same frequency and wavenumber bands. 

Canada’s first and only F5/EF5 tornado associated with a supercell touched down near Elie, Manitoba in the late afternoon of 22 June 2007. An observational and numerical simulation analysis with the Weather Research and Forecasting (WRF) model was undertaken to characterize the pre-storm environment and processes leading to storm initiation. WRF sufficiently reproduced the synoptic and mesoscale features, including a supercell-like storm in the region of interest, and supplemented available observations. Synthesis of observational and simulation data suggests that the environment near Elie immediately before storm initiation was primed for tornadic supercells, with large most-unstable and mixed-layer convective available potential energy (4000 J kg^-1) and sufficient vertical shear (effective bulk wind shear 40 kt; effective storm-relative helicity >200 m2 s^-2). Despite enhancement owing to a cold pool left behind by passing early-afternoon convection, shear remained weaker than those typically found in other North American significant tornadic supercell events. The interaction between a surface trough and convective boundary-layer thermals was the primary triggering mechanism of the Elie supercell. The former appeared to be associated with a low pressure arising from the juxtaposition of lower-troposphere cyclonic differential vorticity advection and lee troughing over the western Red River Valley. More observational analysis and numerical sensitivity experiments are required to better diagnose Manitoba terrain’s contribution to the Elie supercell initiation.

Comprehending the intricate interplay between atmospheric aerosols and water vapour in subsaturated regions is vital for accurate modelling of aerosol–cloud–radiation–climate dynamics. But the microphysical mechanisms governing these interactions with ambient aerosols remain inadequately understood. Here we report results from high-altitude, relatively pristine site in Western-Ghats of India during monsoon, serving as a baseline for climate processes in one of the world’s most polluted regions. Utilizing a novel quartz crystal microbalance (QCM) approach, we conducted size-resolved sampling to analyse humidity-dependent growth factors, hygroscopicity, deliquescence behaviour, and aerosol liquid water content (ALWC). Fine-mode aerosols (≤2.5 μm) exhibited size-dependent interactions with water vapour, contributing significantly to ALWC. Deliquescence was observed in larger aerosols (>180 nm), influenced by organic species, with deliquescence relative humidity (DRH) lower than that of pure inorganic salts. This research highlights the significance of understanding ambient aerosol-water interactions and hygroscopicity for refining climate models in subsaturated conditions.

Sap flow observations provide a basis for estimating transpiration and understanding forest water use dynamics and plant-climate interactions. This study developed a continental modeling approach using Long Short-Term Memory networks (LSTMs) to predict hourly tree-level sap flow across Europe based on the SAPFLUXNET database. We developed models with varying levels of training sets to evaluate performance in unseen conditions. The average Kling-Gupta Efficiency was 0.77 for gauged models trained on 50 % of time series across all forest stands and was 0.52 for ungauged models trained on 50 % of the forest stands. Continental models matched or exceeded performance of specialized and baseline models for all genera and forest stands, demonstrating the potential of LSTMs to generalize hourly sap flow across tree, climate, and forest types. This work highlights hence the potential of deep learning models to generalize sap flow for enhancing tree to continental ecohydrological investigations.

The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO₂ fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO₂ fertilization effect from other long-term drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO₂ fertilization effect on GPP and daily maximum net ecosystem production (NEPmax) using eddy covariance records across 38 extratropical forest sites. We find the median stimulation rate of GPP and NEPmax to be 16.4 ± 4% and 17.2 ± 4% per 100 ppm increase in atmospheric CO₂ across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process-based land surface models. We acknowledge that additional factors, including nitrogen deposition and land management, may impact plant productivity, potentially confounding the attribution to the CO₂ fertilization effect. Assuming these site-specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO₂ fertilization effect. However, disentanglement of these long-term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long-term CO₂ fertilization across multiple eddy covariance sites. Such observation-based quantification is key to constraining the long-standing uncertainties in the land carbon cycle under rising CO₂ concentrations.

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic-scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with a long-wave baroclinic Rossby wave. This result demonstrates that eastern and midwestern ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations.

[ Predicting the spatiotemporal distribution of rainfall remains a key challenge in Tropical Meteorology, partly due to an incomplete understanding of the effects of different environmental factors on atmospheric convection. In this work, we use numerical simulations of tropical ocean domains to study how rainfall responds to imposed localized thermal and mechanical forcings to the atmosphere. We use the Normalized Gross Moist Stability—NGMS—to quantify the net precipitation response associated with a given net atmospheric heating. We find that NGMS values differ considerably for different forcings, but show that the relationship between precipitation and column relative humidity collapses along a universal curve across all of them. We also show that the vertical component of the Gross Moist Stability only approximates the NGMS well at scales larger than a couple hundred kilometers, indicating that general horizontal mixing processes are not negligible at smaller scales. ]

This study demonstrates the advantages of scale- and variable-dependent localization (SDL and VDL) on three-dimensional ensemble variational data assimilation of the hourly-updated high-resolution regional forecast system, the Rapid Refresh Forecast System (RRFS). SDL and VDL apply different localization radii for each spatial scale and variable, respectively, by extended control vectors. Single-observation assimilation tests and cycling experiments with RRFS indicated that SDL can enlarge the localization radius without increasing the sampling error caused by the small ensemble size and decreased associated imbalance of the analysis field, which was effective at decreasing the bias of temperature and humidity forecasts. Moreover, simultaneous assimilation of conventional and radar reflectivity data with VDL, where a smaller localization radius was applied only for hydrometeors and vertical wind, improved precipitation forecasts without introducing noisy analysis increments. Statistical verification showed that these impacts contributed to forecast error reduction, especially for low-level temperature and heavy precipitation.

In past global dust storms, no long lasting anomalies in the pressure cycle had been observed. The Global Dust Storm of Mars Year 34 (MY34), however, left behind an average surface pressure lower than what was expected based on the the values recorded on previous years by the Rover Environmental Monitoring Station (REMS) on Curiosity. The main signal contribution to the daily average surface pressure is the CO2 cycle, which is controlled by the Polar ice sublimation and freezing cycles. We used REMS and Mars Climate Sounder (MCS) data to search for correlations between the REMS anomaly and anomalies in the circulation compared to MCS observations from previous years. The findings include an early start of the retreat season for the Northern Polar cap, followed by the longest period of growth for the Southern Polar (SP) cap ice expansion since Curiosity had landed and then, during the dust storm, the longest retreat season of the Southern Polar cap. We also find a larger Northern Polar Cap extension after the storm, suggestive of a larger deposition of CO2 ice. The changes in length of the SP growth and retreat seasons might be consequence of the response of the zonal mean circulation to the dust storm. Changes in the structure of the zonal mean circulation compared to previous years are found in MCS data and presented. The combination of these anomalies constraint what physical processes may have caused this response in surface pressure after the dust storm.

Ocean–atmosphere interactions largely control the variabilities of the climate system on Earth. However, how much the atmospheric internal signals contribute to climate variabilities is not yet known. Here, we develop an interactive ensemble coupled model (called Hydra-SINTEX) to investigate the influences of atmospheric internal variations (AIV) on the mean-states and variability of the climate system. The results show that, while the climatological mean-states are little affected, the AIV can largely influence the variabilities of the climate over the globe. We pay particular attention to two regions, i.e., the tropical eastern Indian Ocean, which is the key area of the Indian Ocean Dipole (IOD), and the subtropical North Pacific. We found that the variabilities of sea surface temperature (SST) in these two regions are much reduced in the absence of the AIV but with distinct mechanisms. Without the AIV, the intensity of the IOD is largely reduced in association with weakened air–sea coupling in the tropics. This indicates the importance of atmospheric noise forcing on the development of the IOD. In contrast, the reduction of the SST variability in the subtropical North Pacific, where local air–sea interaction itself is weak, is caused by the absence of the AIV that is generated by both the mid-latitude atmospheric processes and the weakened remote influence of the tropical SST in accordance with the reduced SST signals there.

In the face of a changing climate, the understanding, predictions and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, a reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support the next generation of climate services.

Severe Typhoon Koinu passed south of Hong Kong on 8 and 9 October 2023, triggering the issurance of the Increasing Gale or Storm Signal No. 9, the second highest tropical cyclone warning signal in Hong Kong. Koinu was a difficult case for TC warning service due to its compact size and rather erratic movement over coastal waters of Guangdong. To monitor Koinu's movement and wind structure, the Hong Kong Observatory utilized various observational platforms, including meteorological aircraft, ocean radar, and synthetic aperture radar on polar orbiting satellites. The paper presents major observations derived from these measurements. The aircraft probe and drosonde data suggested boundary layer inflow, warm core structure, eyewall updraft and downdraft, and high turbulence in the eyewall of the typhoon. The weather radar observations indicated occurrence of a waterspout in the vicinity of the typhoon. Additionally, the study evaluates the forecasting performance of the AI-based Pangu-Weather model, and the results highlight its better performance than the global numerical weather prediction models in forecasting tropical cyclones in the region. The documentation of these observations aims to provide valuable references for weather forecasters and stimulate further research on forecasting this type of tropical cyclones.

We investigate the representation of individual supercells and intriguing tornado-like vortices in a simplified, locally-refined global atmosphere model. The model, featuring grid stretching, can locally enhance the model resolution and economically reach the cloud-resolving scale. Given an unstable sheared environment, the model can simulate supercells realistically, with a near-ground vortex and funnel cloud at the center of a rotating updraft reminiscent of a tornado. An analysis of the vorticity budget suggests that the updraft core of the supercell tilts environmental horizontal vorticity into the tornado-like vortex. The updraft also acts to amplify the vortex through vertical stretching. Results suggest that the simulated vortex is dynamically similar to observed tornadoes and modeling studies at much higher horizontal resolution.

Satellite microwave radiance observations are strongly sensitive to sea ice, but physical descriptions of the radiative transfer of sea ice and snow are incomplete. Further, the radiative transfer is controlled by poorly-known microstructural properties that vary strongly in time and space. A consequence is that surface-sensitive microwave observations are not assimilated over sea ice areas, and sea ice retrievals use heuristic rather than physical methods. An empirical model for sea ice radiative transfer would be helpful but it cannot be trained using standard machine learning techniques because the inputs are mostly unknown. The solution is to simultaneously train the empirical model and a set of empirical inputs: an “empirical state” method, which draws on both generative machine learning and physical data assimilation methodology. A hybrid physical-empirical network describes the known and unknown physics of sea ice and atmospheric radiative transfer. The network is then trained to fit a year of radiance observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), using the atmospheric profiles, skin temperature and ocean water emissivity taken from a weather forecasting system. This process estimates maps of the daily sea ice concentration while also learning an empirical model for the sea ice emissivity. The model learns to define its own empirical input space along with daily maps of these empirical inputs. These maps represent the otherwise unknown microstructural properties of the sea ice and snow that affect the radiative transfer. This “empirical state” approach could be used to solve many other problems of earth system data assimilation.

Surface Air-pressure is one of the most important parameters used in Numerical Weather Prediction (NWP) models. Although it has been measured using weather stations on the ground for many decades, the numbers of measurements are sparse and concentrated on land. Global measurements can only be achieved by using remote sensing from Space, which is challenging; however, a novel design using Differential Absorption Radar (DAR) can provide a potential solution. The technique relies on two facts: firstly the electromagnetic fields are absorbed mainly by two atmospheric components the oxygen and water vapour, and secondly that oxygen is well mixed in the atmosphere. In this work we discuss a space-borne concept, which aims at providing near global, consistent, and regular observations for determining surface air pressure from space by a design of a multi-tone radar operating on the upper wing of the O2 absorption band with tones from 64 to 70 GHz. Simulations of radar vertical profiles based on the output of a state of-the-art microphysical retrievals applied to the A-Train suite of sensors are exploited to establish the performance of such a system for surface pressure determination. In particular the identification and quantification of errors introduced by the presence of water vapour, cloud liquid water and rain water and the potential of a correction via the three-tone method is discussed. Results show that accuracies of the order of few hPa are at reach.

Low-level jets (LLJs), wind speed maxima in the lower troposphere, impact several environmental and societal phenomena. In this study we take advantage of the spatially and temporally complete meteorological dataset from ERA5 to present a global climatology of LLJs taking into consideration their formation mechanisms, characteristics and trends during the period of 1992-2021. The global mean frequency of occurrence was of 21% with values of 32% and 15% for land and ocean. We classified the LLJs into three regions: non-polar land (LLLJ), polar land (PLLJ) and coastal (CLLJ). Over LLLJ regions, the average frequency of occurrence was of 20%, with 75% of them associated with a near-surface temperature inversion i.e. associated with inertial oscillation at night. Over PLLJ regions the LLJs were also associated with a temperature inversion, but were much more frequent (59%), suggesting other driving mechanisms than the nocturnal inversion. They were also the lowest and the strongest LLJs. CLLJs were very frequent in some hotspots, specially on the west coast of the continents, with neutral to unstable stratification close to the surfaces, that became more stably stratified with increasing height. We found distinct regional trends in both the frequency and intensity of LLJs, potentially leading to changes in the emission and transport of dust aerosols, polar ice and moisture over the world. However, it is currently unclear the evolution of the trends with global warming and what the implications are for climate and weather extremes. Future studies will investigate long-term trends for LLJs and the associated implications.

The Horn of Africa region has frequently been affected by severe droughts and food crises over the last several decades, and this will increase under projected global-warming and socio-economic pathways. Therefore, exploring novel methods of increasing early warning capabilities is of vital importance to reducing food-insecurity risk. In this study, we present the XGBoost machine-learning model to predict food-security crises up to 12 months in advance. We used >20 datasets and the FEWS IPC current-situation estimates to train the machine-learning model. Food-security dynamics were captured effectively by the model up to three months in advance (R2 > 0.6). Specifically, we predicted 20% of crisis onsets in pastoral regions (n = 84) and 40% of crisis onsets in agro-pastoral regions (n = 23) with a 3-month lead time. We also compared our 8-month model predictions to the 8-month food-security outlooks produced by FEWS NET. Over a relatively short test period (2020–2022), results suggest the performance of our predictions is similar to FEWS NET for agro-pastoral and pastoral regions. However, our model is clearly less skilled in predicting food security for crop-farming regions than FEWS NET. With the well-established FEWS NET outlooks as a basis, this study highlights the potential for integrating machine-learning methods into operational systems like FEWS NET.

Clear-Air Turbulence (CAT) is associated with wind shear in the vicinity of jet streams in upper atmospheric levels. This turbulence occurs in cloudless regions and causes most weather-related aircraft accidents. Recent studies have shown that in response to climate change, CAT could significantly increase over certain regions as a consequence of strengthening of jet streams. In this study we use several atmospheric reanalyses and coupled model experiments database to evaluate CAT recent and future changes in the Northern Hemisphere. Several CAT diagnostics are computed to assess the sensitivity of results to different turbulence representations. A significant positive trend in CAT frequency is found in the reanalyses in different regions in the Northern Hemisphere over the period 1980-2021. The signal-to-noise analysis shows that over North Africa, East Asia and Middle East the increase of CAT occurrence in the last decades is likely attributed to global warming. In contrast, over the North Atlantic and North Pacific the internal climate variability is too strong to detect a response to anthropogenic forcing in the observed trends. Future climate projections show that over several regions in the Northern Hemisphere, CAT is projected to increase with a high model agreement and independently of the CAT diagnostic used. The largest increase in CAT is projected to occur over East Asia. In the North Atlantic, large uncertainty remains due to lack of model agreement and differences among the various CAT diagnostics.

Mesoscale convective systems (MCSs) are clusters of thunderstorms that are important in Earth’s water and energy cycle. Additionally, they are responsible for extreme events such as large hail, strong winds, and extreme precipitation. Automated object-based analyses that track MCSs have become popular since they allow us to identify and follow MCSs over their entire life cycle in a Lagrangian framework. This rise in popularity was accompanied by an increasing number of MCS tracking algorithms, however, little is known about how sensitive analyses are concerning the MCS tracker formulation. Here, we assess differences between six MCS tracking algorithms on South American MCS characteristics and evaluating MCSs in kilometer-scale simulations with observational-based MCSs over three years. All trackers are run with a common set of MCS classification criteria to isolate tracker formulation differences. The tracker formulation substantially impacts MCS characteristics such as frequency, size, duration, and contribution to total precipitation. The evaluation of simulated MCS characteristics is less sensitive to the tracker formulation and all trackers agree that the model can capture MCS characteristics well across different South American climate zones. Dominant sources of uncertainty are the segmentation of cloud systems and the treatment of splitting and merging of storms in MCS trackers. Our results highlight that comparing MCS analyses that use different tracking algorithms is challenging. We provide general guidelines on how MCS characteristics compare between trackers to facilitate a more robust assessment of MCS statistics in future studies.

Large satellite discrepancies and model biases in representing precipitation over the Southern Ocean (SO) are related directly to the region’s limited surface observations of precipitation. To help address this knowledge gap, the study investigated the precipitation characteristics and rain rate retrievals over the remote SO using ship-borne data of the Ocean Rainfall And Ice-phase precipitation measurement Network disdrometer (OceanRAIN) and dual-polarimetric C-band radar (OceanPOL) aboard the Research Vessel (RV) Investigator in the Austral warm seasons of 2016 to 2018. Seven distinct synoptic types over the SO were analyzed based on their radar polarimetric signatures, surface precipitation phase, and rain microphysical properties. OceanRAIN observations revealed that the SO precipitation was dominated by drizzle and light rain, with small-sized raindrops (diameter < 1 mm) constituting up to 47 % of total accumulation. Precipitation occurred most frequently over the warm sector of extratropical cyclones, while concentrations of large-sized raindrops (diameter > 3 mm) were prominent over synoptic types with colder and more convectively unstable environments. OceanPOL observations complement and extend the surface precipitation properties sampled by OceanRAIN, providing unique information to help characterize the variety of potential precipitation types and associated mechanisms under different synoptic conditions. Raindrop size distributions (DSD) measured with OceanRAIN over the SO were better characterized by analytical DSD forms with two-shape parameters than single-shape parameters currently implemented in satellite retrieval algorithms. This study also revised a rainfall retrieval algorithm for C-band radars to reflect the large amount of small drops and provide improved radar rainfall estimates over the SO.

A document by Shaokun Deng. Click on the document to view its contents.

Convective rolls contribute largely to the exchange of momentum, sensible heat and moisture in the boundary layer. They have been shown to reinforce air-sea interaction under strong wind conditions. This raises the question of how surface turbulent fluxes can, in turn, affect the rolls. Representing the air-sea exchanges during extreme wind conditions is a major challenge in weather prediction and can lead to large uncertainties in surface wind speed. The sensitivity of rolls to different representations of surface fluxes is investigated using Large Eddy Simulations. The study focuses on the Mediterranean windstorm Adrian, where convective rolls resulting from thermal and dynamical instabilities are responsible for the transport of strong winds to the surface. Considering sea spray in the parameterization of surface fluxes significantly influences roll morphology. Sea spray increases heat fluxes and favors convection. With this more pronounced thermal instability, the rolls are 30\% narrower and extend over a greater height, and the downward transport of momentum is intensified by 40\%, resulting in higher wind speeds at the surface. Convective rolls vanish within a few minutes in the absence of momentum fluxes, which maintain the wind shear necessary for their organization. They also quickly weaken without sensible heat fluxes, which feed the thermal instability required for their development, while latent heat fluxes play minor role. These findings emphasize the necessity of precisely representing the processes occurring at the air-sea interface, as they not only affect the thermodynamic surface conditions but also the vertical transport of momentum within the windstorm.