A simple model is used to analyse the relation between the phenomenon of convective aggregation at small scales and  larger scale variability, including MJO-like behaviour, that results from coupling between dynamics and moisture in the tropical atmosphere. The model is based on the  single-layer dynamical equations coupled to a moisture equation to represent the dynamical effects of latent heating and radiative heating. The moisture variable q evolves through the effect of horizontal convergence, nonlinear horizontal advection and diffusion. Following previous work, the coupling between moisture and dynamics is included in such a way that a horizontally homogenous state may be unstable to inhomogeneous disturbances and, as a result, localised regions evolve towards either dry or moist states, with respectively divergence or convergence in the horizontal flow. The behaviour of the model system is investigated using a combination of theory and numerical simulation. The spatial organisation of the moist and dry regions demonstrates a spatial coarsening that, if moist regions and dry regions are interpreted respectively as convecting and non-convecting, represents a form of convective aggregation. When the weak temperature gradient (WTG) approximation (i.e. a local balance between heating and convergence) applies and horizontal advection is neglected the system reduces to a nonlinear reaction-diffusion equation for q and the coarsening is a well-know aspect of such systems. When nonlinear advection of moisture is included the large-scale flow that arises from the spatial pattern of divergence and convergence leads to a distinctly different coarsening process. When  thermal and frictional damping and f-plane rotation are included in the dynamics, there is dynamical length scale Ldyn that sets an upper limit for the spatial coarsening of the moist and dry regions. The f-plane results provide a basis for interpreting the behaviour of the system on an equatorial β-plane, where the dynamics implies a displacement in the zonal direction of the divergence relative to q and hence to coherent equatorially confined zonally propagating disturbances, comprising separate moist and dry regions. In many cases the propagation speed and direction depend on the equatorial wave response to the moist heating, with the relative strength of the Rossby wave response to the Kelvin wave response determining whether the propagation is eastward or westward. The key overall properties of the propagating disturbances, the spatial scale and the phase speed, depend on nonlinearity in the coupling between moisture and dynamics and any linear theory for such disturbances therefore has limited usefulness. The model described here, in which the moisture and dynamical fields vary in two spatial dimensions and important aspects of nonlinearity are captured, provides an intermediate model between theoretical models based on linearisation and one spatial dimension and three-dimensional GCMs or convection-resolving models.

How to cite: Haynes, P. and Davison, M.: A simple dynamical model linking radiative-convective instability, convective aggregation and large-scale dynamics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12620, https://doi.org/10.5194/egusphere-egu24-12620, 2024.

Deep convection gives rise to large upper level clouds that strongly interact with radiation and are important to the climate energy budget. From an object-oriented perspective, these individual deep cloud systems are characterized by a well depicted cloud shield life cycle, starting with small cloud extents that grow at varying rates before decaying and vanishing. A simple formulation of the growth rate of the cloud shield has been proposed that links together the growth rate on the convective part of the cloud, the mass flux of both the convective and stratiform parts of the cluster and a simple removal sink term (Elsaesser et al., 2022). In this presentation we first show using a suite of satellite observations (infrared from geostationary satellites, GPM radar, etc.) that the functional form of the proposed equation is well suited to quantify the shield growth rate. We then focus on RCE simulations, with deep cloud system objects post processed, to explore the relative role of each term of the growth rate budget. Three different models are used in the same RCEMIP-like configurations. The results show that the budget equation works very well for each model, although the time constants require model-dependent adjustments. We will further show in Vienna the commonalities and the specificities of each model.

How to cite: Roca, R., Fiolleau, T., and Elsaesser, G.: Growth rate of deep convective system cloud shields: satellite observations and km-scale radiative convective equilibrium simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7675, https://doi.org/10.5194/egusphere-egu24-7675, 2024.

The highly nonlinear moisture-precipitation relationship P(r) is a simple statistical model for tropical precipitation P that takes column relative humidity r as its only input variable. Its simplicity and robustness make the relationship useful for conceptual models and as a tool for model diagnostics. We use 10 years of daily ERA5 Reanalysis data to test if P(r) can reproduce the tropical land-ocean contrast of precipitation which we quantify by the tropical precipitation ratio χ(t), defined as the ratio between the spatiotemporal mean rain rate over land and ocean. We find that P(r) can adequately reproduce both magnitude and phase of the average seasonal cycle of χ(t) as long as we take into account that P(r) gets modified by the presence of land and that the relationship varies seasonally over both land and ocean. Since the values of χ(t) indicate that precipitation is enhanced over tropical land, we investigate in a second step whether this enhancement is explained by the distinct P(r) relationships over land and ocean, i.e. whether it rains more over land than over ocean for a given value of r, or by distinct land and ocean humidity distributions, or both. Our results show that the influence of the land surface on P(r) has the effect of disfavoring precipitation over land rather than enhancing it. Precipitation enhancement over land, thus, stems from the distinct humidity distributions over land and ocean with the land distribution exhibiting a more pronounced tail towards high r values compared to the ocean distribution.

How to cite: Schmidt, L. and Hohenegger, C.: Tropical land-to-ocean precipitation differences explained in the framework of the moisture-precipitation relationship, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10815, https://doi.org/10.5194/egusphere-egu24-10815, 2024.

We investigate the small scale dynamical controls on the Intertropical convergence zone (ITCZ) in the Atlantic and East Pacific basins using 5 year, global km-scale coupled atmosphere-ocean-land simulations. To this end, using an ITCZ-based coordinate system, we develop a composited view of the zonal mean statistics of potential vorticity (PV) during Boreal Summer (JJA) at geopotential heights of 0.3, 1.5 and 4 km. The ITCZ-based coordinate system is defined locally at each longitude, such that the ITCZ latitude — identified as the latitude where the column water vapor is a maximum — constitutes the origin. The zonal, 5-year JJA mean latitudes of the Atlantic and East Pacific ITCZ determined based on this definition are 7.8°N and 10.8°N, respectively. The thus obtained composited PV profiles are robust with low inter-annual variability. The PV profiles exhibit a similar structure in both the basins of interest: a gradual increase in the PV values with latitude, followed by a sharp increase in the PV values in the ITCZ due to latent heating. The necessary conditions for the instability of the zonal flow are met, as the sign of the meridional gradient of PV is reversed at the ITCZ. The magnitudes of PV statistics in and around the Atlantic ITCZ are slightly smaller than those of the East Pacific ITCZ. The differences in PV values in the basins can be explained using one of the processes governing the vertical vorticity in the ITCZ, i.e., vortex stretching due to convergence. The vortex stretching term is proportional to the Coriolis parameter, i.e., for a given convergence rate in the ITCZ, more northern ITCZ latitudes could experience greater jumps in vertical vorticities. We also find that at geopotential heights of 0.3 and 1.5 km, the monthly mean relative vertical vorticity in the ITCZ increases as the ITCZ moves to the North. 

How to cite: Praturi, D. S. and Stevens, B.: On the vorticity statistics in the Atlantic and East Pacific ITCZ, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4564, https://doi.org/10.5194/egusphere-egu24-4564, 2024.

We identify for the first time an Indian Easterly Jet (IEJ) in the mid-troposphere during the pre-monsoon season, using ERA5 reanalysis data.  The IEJ is weaker and smaller in zonal extent than the African Easterly Jet over West Africa, with a climatological location of 10°N, 60–90°E, 700 hPa, and strength 6–7 m s⁻¹ during March–May. The IEJ is a thermal wind associated with low-level meridional gradients in temperature (positive) and moisture (negative), resulting from equatorward moist convection in the ITCZ and poleward dry convection arising due to surface heating of northern India and surrounding inland desert regions. The IEJ is associated with a negative meridional potential vorticity gradient, therefore satisfying the Charney-Stern necessary condition for instability.  However, no wave activity is detected in various metrics, suggesting that the potential for combined barotropic-baroclinic instability is not often realized. This is likely related to the small zonal extent of the jet, with insufficient time for wave growth, or the lack of upstream orography. The IEJ is found to be linked with the meteorology of pre-monsoon India. IEJ strong years feature increased near-surface temperatures and drier conditions over India, while the opposite is found in IEJ weak years. Initial investigations did not indicate strong relationships between the IEJ state and the El Nino-Southern Oscillation or the Madden Julian Oscillation, although more detailed investigations are needed to clarify this.  This study provides an introduction to the IEJ’s role in pre-monsoon dynamics, and a platform for further research. This includes identifying any links with pre-monsoon meteorological hazards (heatwaves and thunderstorms) and potential impacts on the subsequent monsoon, and understanding large-scale conditions that drive changes in the IEJ.

How to cite: Croad, H., Shonk, J., Chevuturi, A., Turner, A., and Hodges, K.: The Indian Easterly Jet During the Pre-Monsoon Season in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8141, https://doi.org/10.5194/egusphere-egu24-8141, 2024.

How to cite: Bartana, H., Garfinkel, C., Schwartz, C., Shamir, O., and Rao, J.: Influence of Subtropical Jets on the Equatorial Spectrum: implications for future changes in Kelvin waves and MJO variance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15819, https://doi.org/10.5194/egusphere-egu24-15819, 2024.

The manifestation of the diurnal cycle (DC) over the Maritime Continent is influenced by the large-scale environment, including the state of the Madden–Julian Oscillation (MJO). It is widely recognised in existing literature that the amplitude of the DC of precipitation is greatest at around the same time as, or perhaps slightly ahead of, greatest mean precipitation. However, there is weaker consensus on the impact of the MJO (if any) on the phase of the DC.

Here, the boreal winter (DJF) composite DC of precipitation is calculated for each of the 8 MJO phases (P1–8) defined by Wheeler and Hendon (2004), using 20 years of IMERG — the state-of-the-art gridded satellite-derived precipitation data product with 30-minute resolution. With such high temporal resolution, subtle changes in the phase of the DC can be, and are, identified. The western sides of Sumatra, Borneo and Java typically experience an earlier than usual diurnal precipitation maximum around P5–6, while the eastern sides of these islands experience an earlier than usual diurnal precipitation maximum around P2–3. An analogous west–east divide in DC phase regime is also observed over certain water bodies, such as the Makassar Strait. The magnitude of this phase fluctuation is greatest across eastern Sumatra, parts of eastern Borneo, the western Java Sea and the eastern Makassar Strait, where a range of DC phase of over four hours is frequently observed. The opposing nature of the western and eastern local regimes is a consequence of changes to the direction of phase propagation; westward phase propagation is favoured over the Makassar Strait and the eastern sides of Sumatra and Borneo during P2–3, while eastward phase propagation dominates across entire islands and water bodies during P5–6.

If, by extension, the DC of cloud cover shows strong regional fluctuations in timing, these results will have significant implications for the influence of the MJO on regional radiation budgets.

How to cite: Mustafa, J., Matthews, A., Hall, R., and Heywood, K.: Modulation of the Maritime Continent diurnal cycle of precipitation by the Madden-Julian Oscillation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12050, https://doi.org/10.5194/egusphere-egu24-12050, 2024.

The Madden–Julian oscillation (MJO) is the most predominant tropical intraseasonal variability and is a source of modulating global weather patterns. An accurate simulation of the MJO still remains a challenge for general circulation models (GCMs), and in fact, climate simulations with GCMs often struggle with the mean state-variability tradeoff. For this issue, a global storm-resolving climate simulation, which a recent increase in computing power makes possible, is expected to be a useful way because of its merit of direct coupling between moist processes and dynamics.

The present study examines the MJO representation in an AMIP-type ~10-year simulation with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at 3.5-km horizontal resolution, in comparison with that in one of conventional GCMs (MIROC6). This NICAM simulation successfully reproduces the realistic initiation frequency, propagation, and hierarchical structure of MJO convection, as well as realistic mean states (e.g., mean tropical precipitation), whereas MIROC6 overly underestimates the number of robust MJO events, and the activity of westward-propagating synoptic-scale waves embedded within MJO convective envelopes. As specific processes, the enhanced mixed Rossby-gravity wave-like systems seem to be a precursor and building blocks of the MJO simulated with NICAM at least over the Indian Ocean, consistent with several observational studies. In addition, NICAM-MJO propagation into the western Pacific is helped by high-frequency intermittent advective moistening that can be triggered by the upper-tropospheric PV intrusion from the extratropics. This feature is also found in some of observed MJO events.

Our results suggest that good MJO simulations can attribute to representing the feedback from a complex of synoptic-scale waves onto MJO convective envelopes appropriately, and that the extratropics sometimes plays an active role in MJO dynamics. A success in simulating and scrutinizing these cross-scale interactions about the MJO is one of clear merits of kilometer-scale climate simulations without the assumption of a priori scale separation and quasi-equilibrium characteristics.

How to cite: Takasuka, D.: MJO initiation and propagation simulated with a global kilometer-scale climate simulation: Implication for the cross-scale interaction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14122, https://doi.org/10.5194/egusphere-egu24-14122, 2024.

We simulate the Madden-Julian oscillation (MJO) over an aquaplanet with uniform surface temperature using the multiscale modeling framework (MMF) configuration of the Energy Exascale Earth System Model (E3SM-MMF). The simulated MJOs have similar spatial structures and propagation behavior to observations. To explore the processes involved in the propagation and maintenance of the MJO, we perform a vertically resolved moist static energy (MSE) analysis for the MJO (Yao, Yang, and Tan 2022; Yao and Yang 2023). Unlike the column-integrated MSE analysis, our method quantifies how individual physical processes amplify and propagate the MJO’s characteristic vertical structure. We find that radiation, convection, and boundary layer processes all contribute to maintaining the MJO, balanced by the large-scale MSE transport. Furthermore, large-scale dynamics, convection, and boundary layer processes all contribute to the eastward propagation of the MJO, while radiation slows the propagation. We further show that the MJO can still self-emerge when radiative heating rate is horizontally homogenized, highlighting that convective and boundary-layer MSE transports are sufficient to sustain the MJO. These transport processes might be overlooked in the column-integrated MSE analysis.

How to cite: Yang, D., Hannah, W., and Yao, L.: Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13921, https://doi.org/10.5194/egusphere-egu24-13921, 2024.

Idealized simulations of radiative-convective equilibrium (RCE) with cloud resolving models have been used as a numerical laboratory to understand how diabatic processes can drive convective clustering, which in turn leads to significant drying of the free troposphere and increase in spatial humidity variability.  These processes, such as feedbacks between radiation, clouds and water vapor have been found to have relevance for numerous large-scale modes of convective organization, such as the width of the upward branch of the Hadley cell, ENSO and the Madden Julian Oscillation.  However, the controls of water vapor associated with convective variability on the sub-1000km mesoscale are less well known.  We adopt a simple multivariate analysis technique previously used to assess convective organization in RCE, and apply it to analyze convective organization and its impact on column integrated humidity (precipitable water, PW) variability for order 10⁶ km² mesoscale-size boxes in the tropical western Pacific warm pool region lying on or to the north of the equator.  We find that during the boreal summer/autumn periods, when sea surface temperature (SST) gradients are very limited in the target regions, convection remains mostly random and the horizontal PW gradients are small on these scales, this despite the action of diabatic feedbacks such as LW-cloud feedbacks and surface latent heat fluxes that are acting to  force clustering of convection. In stark contrast, during the other months of the year, when the zones are subject to a weak meridional SST gradient of SST (> 10^-3 K km^-1), convection is mostly aggregated over the warmer SSTs, with much larger PW gradients associated with an increase of clear sky OLR exceeding 10 W m^-2. However, this situation is regularly disturbed by intermittent, multi-day episodes of more homogeneous convection distribution and limited spatial PW gradients. During these periods the SST-PW relationship flips, and the convecting regions are found over the coldest SSTs.  By using an index based on the SST-PW covariance, we construct a composite of 44 such events over a 4 year period which shows that they are associated with a westward-propagating, convectively-coupled Rossby wave like mode that is symmetric about the equator.  An independent multivariate (SST-PW) rotated EOF analysis confirms this, indicating the robustness of the result. We hypothesize that the longer-term variations in an convective organization index which was directly related to the tropics-wide energy budget (Bony et al. 2020) may be driven by the frequency of occurrence of these westward propagating modes, that seem to act as a primary control on mesoscale water vapor variability in the warm pool region in the boreal winter and spring months.

How to cite: Tompkins, A., Casallas, A., and Vianca De Vera, M.: Dynamical controls of mesoscale water vapor variability in the tropical western Pacific, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3598, https://doi.org/10.5194/egusphere-egu24-3598, 2024.

During the 2021-2023 Atlantic hurricane seasons, 24 Saildrone uncrewed surface vehicles (USVs) were deployed in the western Atlantic Ocean, Caribbean Sea, and Gulf of Mexico to collect ocean-atmosphere data within hurricane eyewalls. Sixteen different USVs intercepted tropical storms and hurricanes a total of 26 times, all with sustained wind measurements of at least tropical storm force (34 kt). Four USVs measured sustained hurricane-force winds (64+ kt) in the eyewalls of Hurricanes Sam (2021), Fiona (2022), Idalia (2023), and Lee (2023). An important advantage of the USVs compared to other observing platforms is that they can be actively steered into the paths of hurricanes and record data continuously during eyewall transects, enabling new insights into air-sea interaction processes in extreme conditions. This presentation gives an overview of the key observations and scientific results from the 2021-2023 missions. Direct measurements of the air-sea momentum flux and drag coefficient (Cd) from the USVs’ 20-Hz wind data show a distinct peak in Cd at wind speeds of around 40-50 kt and then a decrease and leveling off as winds approach 80 kt. These results extend findings from previous studies with direct covariance flux measurements, which were limited to winds of less than 50 kt. Measurements from the USVs also show diminished surface ocean cooling under the cores of Hurricanes Sam and Idalia due to strong upper-ocean salinity stratification, emphasizing the importance of salinity observations in the western Atlantic and Gulf of Mexico for potential improvements in hurricane intensity prediction.

How to cite: Foltz, G., Zhang, C., Chiodi, A., Zhang, D., Mazza, E., Cokelet, E., Looney, L., Schulz, H., Chi, N.-H., Zhang, J., Savarin, A., Kim, H.-S., Burger, E., Bringas, F., and Edwards, C.: Ocean-Atmosphere Observations and Key Results from the 2021-2023 Atlantic Hurricane Saildrone Missions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6417, https://doi.org/10.5194/egusphere-egu24-6417, 2024.

    This study investigates interagency discrepancies among best-track estimates of tropical cyclone (TC) intensity in the western North Pacific, provided by the Joint Typhoon Warning Center (JTWC), the China Meteorological Administration (CMA), and the Regional Specialized Meteorological Center (RSMC) Tokyo during 2013–2019. The results reveal evident differences in maximum wind speed (MSW) estimates, where linear systematic differences are significant. However, the Dvorak parameter (CI) numbers derived from the MSWs reported by the three agencies are internally consistent. Further analysis suggests that the remained CI discrepancies are related to differences in the estimation of intensity trends, initial intensities, and TC positions among these datasets. In addition, the CI estimates provided by the JTWC for TCs over the open ocean are generally higher than those reported by the CMA and RSMC. However, the CMA (RSMC) tends to estimate stronger intensity for TCs in the China (Japan) mainland and coastal zone than those in the open ocean with the same intensity in JTWC dataset. This pattern potentially reflects the extensive use of surface observations by these two agencies for landfalling and offshore TCs. These results may help the research community to get more accurate details about the TCs in WNP from the best track datasets of different agencies.

How to cite: Bai, L., Xu, Y., Tang, J., and Guo, R.: Interagency discrepancies in tropical cyclone intensity estimates over the western North Pacific in recent years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2780, https://doi.org/10.5194/egusphere-egu24-2780, 2024.

Quickly intensifying tropical cyclones (TCs) are exceptionally hazardous for Atlantic coastlines.  An analysis of observed maximum changes in wind speed for Atlantic TCs from 1971-2020 indicates that TC intensification rates have already changed as anthropogenic greenhouse gas emissions have warmed the planet and oceans.  Mean maximum TC intensification rates are up to 28.7% greater in a modern era (2001-2020) compared to a historical era (1971-1990).  In the modern era, it is about as likely for TCs to intensify by at least 50 kts in 24 hours, and more likely for TCs to intensify by at least 20 kts within 24 hours than it was for TCs to intensify by these amounts in 36 hours in the historical era.  Finally, the number of TCs that intensify from a Category 1 hurricane (or weaker) into a major hurricane within 36 hours has more than doubled in the modern era relative to the historical era.  Significance tests suggest that it would have been statistically impossible to observe the number of TCs that intensified in this way during the modern era if rates of intensification had not changed from the historical era.    

How to cite: Garner, A.: Observed Increases in North Atlantic Tropical Cyclone Peak Intensification Rates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4089, https://doi.org/10.5194/egusphere-egu24-4089, 2024.

Tropical cyclones (TCs) are among the most severe and destructive natural events, and they have a major detrimental impact on both the economy and society. This study used Geo-KOMPSAT-2A (GK2A) satellite data to estimate TC intensity in the western North Pacific based on a convolutional neural network (CNN) model. Given that the GK2A data cover only the period since 2019, we applied transfer learning to the model using information learned from previous Communication, Ocean, and Meteorological Satellite (COMS) data, which cover a considerably longer period (2011–2019). Transfer learning is a powerful technique that can improve the performance of a model even if the target task is based on a small amount of data. Experiments with various transfer learning methods using the GK2A and COMS data showed that the frozen–fine-tuning method had the best performance due to the high similarity between the two datasets. The test results for 2021 showed that employing transfer learning led to a 20% reduction in the root mean square error (RMSE) compared to models using only GK2A data. For the operational model, which additionally used TC images and intensities from six hours earlier, transfer learning reduced the RMSE by 5.5%. Because continuous long-term data are not always available for TC intensity estimation based on geostationary satellite images, these results imply that transfer learning may constitute a new advance in this area.

How to cite: Jung, H., Moon, I.-J., and Kim, D.-H.: Tropical cyclone intensity estimation using two geostationary satellite data and deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13617, https://doi.org/10.5194/egusphere-egu24-13617, 2024.

Challenges persist in accurately predicting sharp changes in tropical cyclone (TC) motion over a short period of time, even with the employment of state-of-art forecasting technologies. The precise connection between these sudden changes and specific TC structure remains unclear. Here, we delve into the relationship between TC asymmetry (TCA) of outer-core size and anomalous motion, using best-track data spanning the period from 2001 to 2022. Results indicate that TCs characterized by lower TCA tend to display more pronounced deflections than their normal motion. Furthermore, fast-moving TCs exhibit heightened asymmetry and a propensity to accelerate, whereas slow-moving ones lean towards greater symmetry. In addition, TCs demonstrating substantial angular deviations are more prevalent at lower speeds, while fast-moving ones rarely generate anomalous deflections. These findings provide valuable insights into the potential impact of TCA on anomalous TC motion, which can ultimately be used to enhance the accuracy of TC track forecasting.

How to cite: Tang, X., Li, H., and Fang, J.: Relationship between Tropical Cyclone Size Asymmetry and Anomalous Motion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7240, https://doi.org/10.5194/egusphere-egu24-7240, 2024.

How to cite: Ciardullo, G., Hwong, Y.-L., Primavera, L., and Muller, C.: Understanding the role of rain evaporation during tropical cyclogenesis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13227, https://doi.org/10.5194/egusphere-egu24-13227, 2024.

In recent decades, the Atlantic region has seen more frequent and intense hurricanes, with the 2023 season ranking as the fourth-most active on record. Despite progress in understanding hurricane dynamics and identifying precursors, challenges persist in seasonal predictions.

Conventional correlation measures often fall short in capturing causal relationships. Therefore, in this study, we employed the causal AI discovery tool PCMCI+ — a combination of the PC (Peter and Clark) algorithm and the Momentary Conditional test. PCMCI+ excels at uncovering causal relationships in time series data by addressing issues like autocorrelation, indirect links and common drivers.

PCMCI+ was applied to nearly 150 lagged atmospheric and oceanic monthly ERA-5 time series from January to May between 1980 and 2022. Precursor regions, identified based on their causal links to hurricane numbers, were determined. Linear regression models and random forests were then used to predict hurricane numbers for each season.

Results indicate that adopting PCMCI+ to select causal precursor regions significantly improved the accuracy of seasonal hurricane predictions, achieving a correlation of over 0.9 between observed and predicted numbers. While this study contributes to improved forecast precision, its primary focus is on exploring and discussing identified causes. The selected precursor regions are explained in the context of atmosphere-ocean interactions, providing valuable insights into their role in hurricane formation.

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant (RS-2023-00237121).

How to cite: Latos, B., Moon, I.-J., and Kim, D.-H.: Advancing seasonal hurricane predictions using causal AI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8728, https://doi.org/10.5194/egusphere-egu24-8728, 2024.

The North Atlantic hurricane season is officially recognized to start on June 1^st and end on November 30^th. The awareness of this time window is crucial both for federal, state and local agencies as well as the general public to put in place preparation and mitigation efforts to mitigate the impacts of this natural hazard. However, there is an underlying assumption of stationarity in the seasonality of these storms, implying a lack of narrowing or expanding of the hurricane season over the years. Here, we consider the days when tropical cyclones happen (TC_days) to model their seasonality using circular statistics, which is the appropriate modeling framework due to the nature of this quantity. By using mixtures of distributions to model the inter-annual variability of the TC_days, we find an expansion of the tails of the distributions over the period 1966 – 2020 for the North Atlantic basin, leading to a more prolonged hurricane season over the recent decades. These results will be explained in terms of physical drivers, providing insights into the mechanisms that could be responsible for the observed lengthening of the hurricane season.

How to cite: Treppiedi, D., Villarini, G., Noto, L. V., Scoccimarro, E., Yang, W., and Vecchi, G. A.: Changes in the Seasonality of North Atlantic Tropical Cyclones, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15011, https://doi.org/10.5194/egusphere-egu24-15011, 2024.