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.

A document by Martin Velez Pardo. Click on the document to view its contents.

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.

A document by Bidyut Bikash Goswami. Click on the document to view its contents.

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.

A document by Shannon L Jones. Click on the document to view its contents.

Hurricane Nicholas was classified as a Category 1 tropical cyclone (TC) at 0000 UTC on 14 September 2021 and made landfall along the upper Texas Gulf Coast at 0530 UTC. The sustained maximum wind speed increased from a low-end estimate of 13 m s-1 (0000 UTC 13 September) to 33 m s-1 (0000 UTC 14 September) indicating rapid intensification. Lightning activity, monitored by the Houston Lightning Mapping Array (HLMA), developed in the rainband at 1700 UTC on 13 September, diminished by 2030 UTC, and re-intensified after 2200 UTC. At 2004 UTC (13 September), a curved megaflash (~220 km) was observed in the outer rainband’s stratiform precipitation region. Convection developed and intensified in the eastern eyewall region by 0130 UTC on 14 September. Several transient luminous events (TLEs) were observed in the western eyewall region between 0230-0300 UTC with VHF source points exceeding 40 km during a decline in lightning activity. The TLEs occurred during a period of strong cloud top divergence resulting from complex interactions between southwesterly low-level and westerly deep layer wind shear. Charge analysis of Nicholas revealed an overall normal dipole structure, while the megaflash and TLE cases exhibited inverted charge structures. The upper-level screening and primary charge layer heights of the TLEs heavily influenced the VHF source altitudes. Interestingly, a surface wind gust of 42 m2 s-2 was observed near the time of the first TLE, suggesting a second period of brief intensification. Future investigations of TC evolution and behavior may benefit from charge analyses.

Global warming has led to significant glacier retreat around the Tarim River Basin. This has resulted in a rise in water resources in southern Xinjiang. Meanwhile, the development of human society has driven a substantial increase in water consumption. This has disrupted the regional water supply-demand balance, making the risk of water resource stress more prominent. Given the characteristics of water resources utilization in arid areas and taking into account the changing trends in precipitation, glacial meltwater, and runoff, along with population and economic development, we employed the water stress index method to assess the current situation and potential future changes in water stress in the three regions of southern Xinjiang. The results indicated the following: The synergistic effects of precipitation and glacial meltwater have significantly increased river runoff, resulting in increased available water. The total water demand in the Aksu and Kashgar regions has shown a substantial increase, while the Hotan region has experienced a decrease. The Aksu and Kashgar regions have exhibited an upword trend in water stress, while the Hotan region has seen some relief. Nevertheless, all the three regions still face high water stress levels. In comparison to the historical period (2000-2020), the available water and total water demand are projected to increase during the next four periods (2030s, 2050s, 2070s and 2090s) under the SSP2-4.5 and SSP5-8.5 scenarios of the CMIP6 model. Notably, the Aksu region is expected to face increasing water stress, indicating a significant risk of water scarcity and insecurity in the future.

A document by Dale Richard Durran. Click on the document to view its contents.

Numerical simulations of weather and climate models are conventionally carried out using double-precision floating-point numbers throughout the vast majority of the code. At the same time, the urgent need of high-resolution forecasts given limited computational resources encourages development of much more efficient numerical codes. A number of recent studies has suggested the use of reduced numerical precision, including half-precision floating-point numbers increasingly supported by hardware, as a promising avenue. In this paper, the possibility of using half-precision calculations in the radiation scheme ecRad operationally used in the ECMWF's Integrated Forecasting System (IFS). By deliberately mixing half-, single- and double-precision variables, we develop a mixed-precision version of the Tripleclouds solver, the most computationally demanding part of the radiation scheme, where reduced-precision calculations are emulated by a Fortran software rpe. By employing two tools that estimate the dynamic range of model parameters and identify problematic areas of the model code using ensemble statistics, the code variables were assigned particular precision levels.It is demonstrated that heating rates computed by the mixed-precision code are reasonably close to those produced by the double-precision code. Moreover, it is shown that using the mixed-precision ecRad in OpenIFS has a very limited impact on the accuracy of a medium-range forecast in comparison to the original double-precision configuration. These results imply that mixed-precision arithmetic could successfully be used to accelerate the radiation scheme ecRad and, possibly, other parametrization schemes used in weather and climate models without harming the forecast accuracy.