Native to tropical and subtropical regions of Southeast Asia, Aedes albopictus, commonly known as the tiger mosquito, has been spreading worldwide with the aid of human activity. The geographical distribution and temporal dynamics of this mosquito are of special interest, given its role as a vector for arboviruses such as dengue (DENV) and chikungunya (CHIKV). Climate change, and its consequent increase in ­­both mean surface temperatures and the frequency and intensity of heat waves, has the potential to affect the behavior and seasonal activity of this mosquito, thereby posing a significant risk to human health. Understanding the impact of mean temperature changes and extremes on potential vector-borne disease risk is paramount to forecasting future trends as well as developing meaningful intervention strategies. In this work, we study the dynamics of Ae. albopictus over three decades, spanning 1990-2019, with a particular emphasis on the Italian Peninsula, which has remained a significant hotspot in Europe, since its introduction in the 1990s. We employed and adapted VECTRI, a climate-sensitive dynamical model that was originally designed for malaria. The model has been modified to parameterize Ae. albopictus and successfully calibrated to reproduce the seasonality of the vector using ovitrap data from various locations in Italy. Driving the model using high resolution EOBS gridded observation data, we perform various experiments to isolate the impact of temperature trends and late-spring to summer temperature extremes. Our results show a temperature-driven linear increase in the length of the mosquito season, with larger increases over the southern regions. Overall, temperature extremes tend to increase the bulk egg population across the country, although different spatial trends are highlighted: warm events tend to reduce vector populations in the Po valley and southern regions of Italy,already subject to the highest temperatures, while they tend to increase vector abundance over fringe highland areas. Our results indicate that 10-day temperature forecasts could be utilized to predict mosquito activity and consequently guide vector control intervention strategies such as insecticide spraying in the higher altitude regions identified in this study.

The Next Generation of Earth Modeling Systems (nextGEMS) project aimed to produce multidecadal climate simulations, for the first time, with resolved kilometer-scale (km-scale) processes in the ocean, land, and atmosphere. In only 3 years, nextGEMS achieved this milestone with the two km-scale Earth system models, ICOsahedral Non-hydrostatic model (ICON) and Integrated Forecasting System coupled to the Finite-volumE Sea ice-Ocean Model (IFS-FESOM). nextGEMS was based on three cornerstones: (1) developing km-scale Earth system models with small errors in the energy and water balance, (2) performing km-scale climate simulations with a throughput greater than 1 simulated year per day, and (3) facilitating new workflows for an efficient analysis of the large simulations with common data structures and output variables. These cornerstones shaped the timeline of nextGEMS, divided into four cycles. Each cycle marked the release of a new configuration of ICON and IFS-FESOM, which were evaluated at hackathons. The hackathon participants included experts from climate science, software engineering, and high-performance computing as well as users from the energy and agricultural sectors. The continuous efforts over the four cycles allowed us to produce 30-year simulations with ICON and IFS-FESOM, spanning the period 2020-2049 under the SSP3-7.0 scenario. The throughput was about 500 simulated days per day on the Levante supercomputer of the German Climate Computing Center (DKRZ). The simulations employed a horizontal grid of about 5 km resolution in the ocean and 10 km resolution in the atmosphere and land. Aside from this technical achievement, the simulations allowed us to gain new insights into the realism of ICON and IFS-FESOM. Beyond its time frame, nextGEMS builds the foundation of the Climate Change Adaptation Digital Twin developed in the Destination Earth initiative and paves the way for future European research on climate change.

Low-level winds over Antarctica are overwhelmingly controlled by the local orography. They, in turn, exert a large control on sea ice formation and transport. In Global Circulation Models, the influence of orography on the climate system is modelled via orographic gravity wave drag (OGWD) parameterizations. Models usually partition the drag exerted on the atmosphere by the sub-grid scale orography into two components due to flow blocking and gravity waves. In this work, we investigate the relationship between Antarctic sea ice and the parameterized OGWD in the UK Earth System Model (UKESM). We present results from sensitivity tests performed using the UKESM-CMIP6 historical runs.In these simulations, the partition between the “flow-blocking” component and the “gravity wave” component of the OGWD parameterization was altered to simulate “flow-over” and “flow-blocking” regimes. These experiments show that sea ice strongly responds to changes in the orographic gravity wave drag. The strong sea ice decline simulated by the control run from 1980 to 2015, not matched by the observational record, is halted and is delayed by 15-20 years (across the ensemble members) in our flow-blocking regime simulation. Conversely, in the flow-over regime simulation, sea ice begins declining about 10 years earlier than in the control run. The systematic response of the coupled system suggests the existence of a dynamical relationship between sea ice and OGWD. The pan-Antarctic signal for sea ice decline derives from the Weddell Sea sector. The pathway through which OGWD influences sea ice is via modifications of the flow regime across the Antarctic Peninsula, and thus the surface wind stress across the Weddell Sea sector, which in turn alters the occurrence of oceanic deep convection. This happens because the flow regime across the Antarctic Peninsula is critical in determining the strength and pattern of the surface winds on both the windward side (Bellingshausen and Amundsen Seas sector) and the lee side (Weddell Sea sector) of the mountain ridge.

We examine mesoscale convective organisation in the tropical western Pacific using a multivariate analysis of column humidity, precipitation and sea surface temperature (SST) observations. We demonstrate that in boreal summer and autumn, convection remains spatially random despite radiative-feedbacks acting to aggregate convection, which we attribute to the high density of convective moisture sources and the role of wind shear. Instead, in winter and spring, a weak meridional SST gradient exists and convection is usually clustered over the regions of warmer SSTs, with significant meridional humidity gradients. However, this is sporadically interrupted by episodes of convection migration to the coldest SSTs and limited spatial humidity variance. These episodes are the result of westward propagating equatorial waves, which remove meridional humidity gradients. It appears that the drivers of mesoscale convective clustering and humidity variability in the Pacific warm pool are the SST gradients, shear, and equatorial wave dynamics.

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.

Due to the continuing large number of malaria-related deaths in tropical Africa, the need to develop a robust Malaria Early Warning System (MEWS) for effective action is growing to guide cost-effective implementation of interventions. This study employs a two-stage hierarchical evaluation technique to evaluate the ability of the VECTRI malaria model to simulate malaria dynamics at seasonal time scale (1 - 7 months) over Nigeria and West Africa. Two sets of malaria simulations are considered. The first set is based on VECTRI simulations driven by observed rainfall and temperature datasets (hereafter referred to as control run). The second is based on malaria simulations driven by the European Centre for Medium-Range Weather Forecasting (ECMWF) System5 ensemble seasonal forecasting system (hereafter referred to as Forecast run). Different metrics are employed to assess the skill of the VECTRI malaria model. Results based on the control run indicate that the model can reproduce hyper-endemic zones and the evolution of malaria cases, particularly the observed increase in cases with decreasing population density. Despite having significant biases and low correlation, the model successfully predicts annual anomalies in malaria cases across Nigeria, particularly in the savannah region that experience large malaria burden. Annual correlations between the VECTRI Forecast run and the VECTRI Control run are relatively low at all lead times (LT) and for each start date (SD) across West Africa, although correlation generally increases from the Gulf of Guinea to the Sahel. Despite low correlations, the Rank Probability Skill Score (RPSS) reveals that the model has a statistically significant skill in predicting malaria occurrences across all categories of malaria cases, regardless of start date or lead time. While the Guinea Forest has the strongest RPSS, the increase or decrease in skill from the first to seventh lead time varies significantly across the region. In addition, the VECTRI malaria model has a good ability to discriminate variability in malaria cases across all regions, with an average Area Under the Relative Operating Characteristics (ROC) Curve (AUC) of approximately 0.62. Our findings suggest that the VECTRI malaria model could be used as a reliable Malaria Early Warning System (MEWS), particularly for identifying malaria hyper-endemic zones in West Africa at seasonal time scale.

With the goal of eradication by 2030, Malaria poses a significant health threat, profoundly influenced by meteorological and hydrological conditions. In support of malaria vector control efforts, we present a high‐resolution, coupled physically‐based modeling approach integrating WRF‐Hydro and VECTRI. This model approach accurately captures topographic details at the scale of larvae habitats in the Nouna Health and Demographic Surveillance Systems in Sub‐Saharan Africa. Our study demonstrates the proficiency of the high‐resolution hydrometeorological model, WRF‐Hydro, in replicating observed climate characteristics. Comparisons with in‐situ local weather data reveal root mean square errors between 0.6 and 0.87 mm/day for rainfall and correlations ranging from 0.79 to 0.87 for temperatures. Additionally, WRF‐Hydro's surface hydrology reproduces the seasonal and intraseasonal variability of the ponded water fraction with 96% accuracy, validated against Sentinel‐1 data at a 100‐m resolution. The VECTRI model demonstrates sensitivity to surface hydrology representation, particularly when comparing conceptual and detailed physical process models, for variables such as larvae density, mosquito abundance, and EIR. The model's ability to replicate the seasonality of malaria transmission aligns well with available cohort malaria data suggesting its potential for predicting the impacts of climate change on mosquito abundance and transmission intensity in endemic tropical and subtropical zones. This integrated approach opens avenues for enhanced understanding and proactive management of malaria.

The Asian tiger mosquito, Aedes albopictus and the yellow fever mosquito, Aedes aegypti have been spreading worldwide and are reshaping the distribution of arboviruses. Both Aedes species have recently been observed in densely populated cities of Cyprus, a touristic island that is a historic bridge between Europe and Asia. Given the high public health stakes for Cyprus and the wider East Mediterranean region, the objectives of this study are three-fold. First, we present a novel delimitation strategy using spatially dense networks of ovitraps deployed in 500x500m cells in Limassol and Larnaca following the detection of Aedes species. Second, we use a dynamical vector model to estimate the potential of both species to spread further over Cyprus. Finally, we employ a basic reproduction number (R0) model to assess the potential transmission risk of arboviruses for the wider East Mediterranean region. Our results underline our delimitation strategy's usefulness in delineating Ae. albopictus populations in Limassol and indicate the need for increased surveillance efforts for Ae. aegypti in Larnaca. Our vector model reveals that cities such as Nicosia, Paphos and Ayia Napa are climatically suitable for the establishment of both Ae. aegypti and Ae. albopictus. Finally, the R0 model captures historical hotspots of dengue transmission over the East Mediterranean region, with large R0 values simulated over Cyprus, Greece, Turkey, southern Italy and southern Spain. We recommend stringent vector surveillance at entry points in Greece and a rapid elimination in Cyprus to prevent the return of Ae. aegypti to the European continent. ### Competing Interest Statement The authors have declared no competing interest.

The Asian tiger mosquito, Aedes albopictus, has spread widely throughout Italy since its introduction, with significant public health implications. We examine how decadal temperature trends and sub-monthly heatwave events affect its climate-driven geographical distribution and temporal dynamics using a new regional-scale dynamical Aedes model. The model is calibrated using [Formula: see text] years of ovitrap data for Emilia-Romagna and reproduces the vector seasonality and, to a lesser extent, its inter-annual variability. Simulated vector density hotspots overlap with densely populated areas in Rome, Milan, Naples, Foggia, Catania, Palermo, Lecce, Cagliari, Genoa, Turin and large urban centres in Emilia-Romagna. Lower risk is simulated over the Central Apennine mountains and the Alps. At decadal time scale, we simulate a lengthening of the active mosquito season by 0.5-3 weeks per decade, with the vector becoming homodynamic in southern Italy. Depending on the climatic setting, heatwaves can increase or reduce vector populations and, in some locations, can temporarily decrease mosquito populations. Such decreases can be followed by a population rebound and overshoot. Given the model's skill in reproducing key spatio-temporal Ae. albopictus features, there is potential to develop an early warning system to inform control efforts at a national scale.

We introduce a minimal stochastic lattice model for the column relative humidity (R) in the tropics, which incorporates convective moistening, horizontal transport and subsidence drying. The probability of convection occurring in a location increases with R, based on Tropical Rainfall Measuring Mission observations, providing a positive feedback that could lead to aggregation. We show that the simple model reproduces many aspects of full-physics cloud resolving model experiments. Depending on model parameter settings and domain size and resolution choices, it can produce both random and aggregated equilibrium states. Clustering occurs more readily with larger domains and coarser resolutions, in agreement with full-physics models. Using dimensional arguments and fits from empirical data, we derive a dimensionless parameter which we call the aggregation number, N-ag, that predicts whether a specific model and experiment setup will result in an aggregated or random state. The parameter includes the moistening feedback strength, the horizontal moisture transport efficiency, the subsidence timescale, the domain size and spatial resolution. Using large ensembles of experiments, we show that the transition between random and aggregated states occurs at a critical value of N-ag. We argue that N-ag could help to understand the differences in aggregation states between full-physics, cloud resolving models, which show little consensus about the robustness of self-organized patterns, whose emergence appears to be sensitive to the model setup, physics and parameterizations.

Organized systems of deep convective clouds are often associated with high-impact weather and changes in such systems may have implications for climate sensitivity. This has motivated the derivation of many organization indices that attempt to measure the level of deep convective aggregation in models and observations. Here we conduct a comprehensive review of existing methodologies and highlight some of their drawbacks, such as only measuring organization in a relative sense, being biased toward particular spatial scales, or being very sensitive to the details of the calculation algorithm. One widely used metric, Iorg, uses statistics of nearest-neighbor distances between convective storms to address the first of these concerns, but we show here that it is insensitive to organization beyond the meso-b scale and very contingent on the details of the implementation. We thus introduce a new and complementary metric, Lorg, based on all-pair convective storm distances, which is also an absolute metric that can discern regular, random, and clustered cloud scenes. It is linearly sensitive to spatial scale in most applications and robust to the implementation methodology. We also derive a discrete form suited to gridded data and provide corrections to account for cyclic boundary conditions and finite, open boundary domains of nonequal aspect ratios. We demonstrate the use of the metric with idealized synthetic configurations, as well as model output and satellite rainfall retrievals in the tropics. We claim that this new metric usefully supplements the existing family of indices that can help to understand convective organization across spatial scales.

This study examines the spatiotemporal variations of PM2.5, PM10, SO2, O3, NO, and NO2 concentrations in Northwestern South America (NWSA). We assess the efficacy of existing policies, identify underlying phenomena, and highlight areas for further research. Significant findings have emerged by analyzing reanalysis and in-situ data, employing the WRF-Chem model, and utilizing a new Lagrangian framework designed to overcome some drawbacks common to analysis of pollution Long-Range Transport. Wildfires in the first half of the year and volcanic activity (for SO2) in July-August contribute to over 90 % of the pollutant's advection, leading to high pollution levels in urban areas. SO2 volcanic emissions contribute to secondary PM, explaining the peak in PM concentrations in Cali in July. In the second half of the year, pollutant behavior varies based on factors such as city characteristics, vehicular-volume, air temperature, wind speed, and boundary layer height, and O3 is influenced by solar radiation and the NO/NO2 ratio. Diurnal variations of PM and NOx correlate with vehicular density, SO2 with industrial activity, and O3 depends on solar radiation. Trend analysis reveals decreasing PM10 levels except in three Cundinamarca cities and Cali suggesting the need to implement/evaluate control plans in those locations. Although data is limited, NO and NO2 levels show an increasing trend due to the rising number of vehicles. SO2 levels are decreasing, except in Cali, potentially influenced by the nearby industrial and polluted city of Yumbo. O3 displays a downward trend in most cities, except Bogotá, due to the NO/NO2 ratio favoring O3 increase. These findings provide a starting point for further research to deepen our understanding of NWSA air pollution. Such investigations are essential before modifying existing policies or enacting new ones. Collaborative efforts at the international, regional, and inter-city levels are crucial for effective air quality management.

Global Earth System Models at storm-resolving resolutions (SR-ESM, with horizontal resolutions of ~4km) are being developed as part of the nextGEMS collaborative European EU’s Horizon 2020 programme. Through breakthroughs in simulation realism, these models will eventually allow us to understand and reliably quantify how the climate will change on a global and regional scale, and how the weather, including its extreme events, will look like in the future.As part of the Storms & Ocean theme, we are exploring how resolving convective storms, ocean mesoscale eddies, and air-sea interaction on these scales influences the development of tropical SST anomalies and their influence on the mean climate (ITCZ and circulation biases) and its variability. Existing biases in the SR-ESM simulations in the first two development cycles are interpreted using the vertically integrated atmospheric energy budget to disentangle local and remote influences on tropical precipitation. More specifically, these biases are decomposed in hemispherically symmetric and antisymmetric components, which are linked, respectively, to biases in the atmospheric net energy input near the equator (tropical SST biases, low level clouds, etc) and to the cross-equatorial atmospheric energy flux (driven by inter-hemispheric contrast in net energy input, for instance biases in clouds in the southern ocean). We also explore the role that transient eddies, both of extratropical and tropical origin that are usually neglected in this framework, play in the global energetics and tropical precipitation patterns.

<p>Idealized high-resolution models show spontaneous aggregation of tropical convection on the beta-mesoscale driven by radiative feedbacks, and the resulting drying implies a potentially important impact on climate sensitivity missing in classic convective parameterization schemes. Here, we combine multiple state-of-the-art observations and reanalysis of the tropical atmosphere and ocean in a 1000 x 700 km region in the tropical Western Pacific warm pool region, along with numerical models and machine learning techniques to demonstrate that in boreal summer, while radiative and surface fluxes act to cluster convection, the convection remains in a random configuration as evidenced by very limited spatial variability in total column humidity. Instead, in the winter/spring period, when the warm pool is displaced southwards, the region lies on the warm pool boundary with stronger north-south surface temperature gradients. Convection usually remains strongly organized in these periods but is interspersed with occasional random episodes. This entails a sudden flipping into the random state associated with the southerly flow anomalies that advect convection and humidity over the cooler sea surface temperature (SST) regions. Observations and models suggest that this advection of humidity is the principal driver of organization and disorganization of convection and that diabatic feedbacks instead always act to try and cluster convection. Results also indicate that when convection is organized, the atmosphere is significantly drier than when convection is random and that the Longwave (LW) clear-sky top of atmosphere flux is significantly larger in the organized state, principally due to the moisture differences between both configurations. The LW all-sky flux difference between both states is less significant compared to the LW clear-sky because it is largely driven by the cloud cover, which, although smaller for the organized state, does not differ significantly. These differences between organized and random convective states, and the role of the diabatic processes in providing forcing for aggregation, mostly reproduce the findings of idealized models. However, this study indicates that in the real tropical atmosphere diabatic forcing is inadequate to lead to aggregation on its own over homogeneous SSTs, and instead, spatial SST gradients and large-scale dynamics are key to driving aggregation and determining its breakup over the warm pool region.</p>

A new database of the Entomological Inoculation Rate (EIR) was used to directly link the risk of infectious mosquito bites to climate in Sub‐Saharan Africa. Applying a statistical mixed model framework to high‐quality monthly EIR measurements collected from field campaigns in Sub‐Saharan Africa, we analyzed the impact of rainfall and temperature seasonality on EIR seasonality and determined important climate drivers of malaria seasonality across varied climate settings in the region. We observed that seasonal malaria transmission was within a temperature window of 15°C–40°C and was sustained if average temperature was well above 15°C or below 40°C. Monthly maximum rainfall for seasonal malaria transmission did not exceed 600 in west Central Africa, and 400 mm in the Sahel, Guinea Savannah, and East Africa. Based on a multi‐regression model approach, rainfall and temperature seasonality were found to be significantly associated with malaria seasonality in all parts of Sub‐Saharan Africa except in west Central Africa. Topography was found to have significant influence on which climate variable is an important determinant of malaria seasonality in East Africa. Seasonal malaria transmission onset lags behind rainfall only at markedly seasonal rainfall areas such as Sahel and East Africa; elsewhere, malaria transmission is year‐round. High‐quality EIR measurements can usefully supplement established metrics for seasonal malaria. The study's outcome is important for the improvement and validation of weather‐driven dynamical mathematical malaria models that directly simulate EIR. Our results can contribute to the development of fit‐for‐purpose weather‐driven malaria models to support health decision‐making in the fight to control or eliminate malaria in Sub‐Saharan Africa.

Convective self-aggregation can spontaneously appear in radiative-convective equilibrium (RCE) simulations using idealized experiments with cloud-resolving models and it has been suggested that cold pools could play an important role in the development of organization, by delaying its onset when the cold pools have larger radii. Cold pool radius is determined by the amount of precipitation produced by microphysical schemes (precipitation efficiency) and the strength of the evaporation. We demonstrate this using idealized RCE experiments with the WRF model that convective cold pool characteristics can differ dramatically between 5 of the standard schemes commonly used in the model. We then systematically increase/decrease the cold pool size by changing the evaporation of rain in the 5 microphysics schemes to observe the impact on convective aggregation. One complication in interpreting the results of such experiments is that a change in the evaporation of rain also produces a change in the profile of net convective heating that could also impact organization. To isolate this effect, a second set of experiments is performed by artificially increasing (decreasing) the horizontal wind speed used in the surface flux calculation for all grid points determined to lie within cold pool interiors to produce a faster (slower) cold pool recovery and impact their ultimate radii. The ensembles of the experiments show that the larger the cold pool radii, the larger the spatial variance of the water vapor path is in the equilibrium state and they also demonstrate how the cold pool size impacts the strength and even the sign of the surface latent heat contribution to aggregation. Nonetheless, the strong forcing of aggregation by radiation feedbacks in these experiments means that the cold pool changes do not produce large modifications to the aggregation onset time. Thus the aggregation onset may be more strongly impacted by the microphysical processes that determine the convective anvil size and low-level cloud cover, and thus ultimately the cloud-radiative forcing. This is under investigation in ongoing experiments that modify the ice fall speed and the autoconversion of cloud water to rain in the 5 microphysical schemes, which will also be reported in the presentation.

Convective self-aggregation can spontaneously appear in radiative-convective equilibrium (RCE) simulations using idealized experiments with cloud-resolving models and it has been suggested that cold pools could play an important role in the development of organization, by delaying its onset when the cold pools have larger radii. Cold pool radius is determined by the amount of precipitation produced by microphysical schemes (precipitation efficiency) and the strength of the evaporation. We demonstrate this using idealized RCE experiments with the WRF model that convective cold pool characteristics can differ dramatically between 5 of the standard schemes commonly used in the model. We then systematically increase/decrease the cold pool size by changing the evaporation of rain in the 5 microphysics schemes to observe the impact on convective aggregation. One complication in interpreting the results of such experiments is that a change in the evaporation of rain also produces a change in the profile of net convective heating that could also impact organization. To isolate this effect, a second set of experiments is performed by artificially increasing (decreasing) the horizontal wind speed used in the surface flux calculation for all grid points determined to lie within cold pool interiors to produce a faster (slower) cold pool recovery and impact their ultimate radii. The ensembles of the experiments show that the larger the cold pool radii, the larger the spatial variance of the water vapor path is in the equilibrium state and they also demonstrate how the cold pool size impacts the strength and even the sign of the surface latent heat contribution to aggregation. Nonetheless, the strong forcing of aggregation by radiation feedbacks in these experiments means that the cold pool changes do not produce large modifications to the aggregation onset time. Thus the aggregation onset may be more strongly impacted by the microphysical processes that determine the convective anvil size and low-level cloud cover, and thus ultimately the cloud-radiative forcing. This is under investigation in ongoing experiments that modify the ice fall speed and the autoconversion of cloud water to rain in the 5 microphysical schemes, which will also be reported in the presentation.

Malaria remains a major health problem predominantly in tropical countries and is still being one of the biggest causes of mortality worldwide. It is an ancient vector borne infectious disease caused by parasitic protozoans of the genus Plasmodium and is transmitted by female mosquitos of the Anopheles species. The spatiotemporal distribution of this vector is sensitive to climate conditions and the distribution of hydrometeorological variables, particularly temperature, precipitation, and humidity. We present first results of a joint high resolution hydrometeorological- and subsequent dynamical vector transmission modelling. Our approach uses the couple atmospheric- and terrestrial model system WRF-Hydro, with a 1km grid spacing for the atmospheric part and a 100m grid spacing for the hydrological part. Besides traditional hydrometeorological variables, WRF-Hydro further resolves the surface water, which is potentially a crucial step forward for the grid cell distributed dynamical vector transmission model VECTRI. Our study addresses two Health and Demographic Surveillance Systems (HDSS) site regions at Nouna in Burkina Faso and Kisumu in Kenya. We present an analysis of the performance of the hydrometeorological model system and first results of the VECTRI modeling. Preliminary results of the WRF-Hydro -VECTRI model system capture the Malaria seasonal variations correctly and show reasonable reproduction of the year-to-year variability of HDSS observed total Malaria cases.

Numerical simulations of radiative-convective equilibrium in high-resolution cloud-resolving models (CRMs) revealed the tendency of atmospheric convection to self-aggregate on periods of several weeks when the domain is sufficiently large. Nevertheless, even though CRM simulations manage to identify some of the physical mechanisms driving convective clustering, the occurrence of organization seems to be dependent on the model setup, physics and parameterizations. Robust findings from simpler, idealized models, which may reproduce some of the features of the full-physics systems, are thus beneficial to better understand the differences existing between CRMs.To this end, we have developed a simplified two dimensional stochastic model able to predict the evolution of column total water relative humidity (CRH) in the tropical free troposphere. The model prognostic equation includes a convective moistening term, diffusive lateral transport and subsidence drying, similar to model of Craig and Mack (2013), but one novelty of the new model is that, instead of the convective moistening term as a smooth deterministic function of the background humidity, we treat convection as a point process and account for stochastic variability in the convective moistening process. Therefore the model allows experiments to use domain sizes and grid resolutions similar to those used for the idealized CRM experiments.It is found that, depending on the chosen parameter settings, the simple model can reproduce equilibrium states of strong convective aggregation and also randomly distributed states, analogous to the CRM results. A sensitivity of the occurrence of self-organization to the initial conditions, i.e., a modest hysteresis, is also found, which also agrees with the full physics CRMs. Large ensembles of numerical experiments were performed for different values of the subsidence timescale, the moisture diffusion coefficient and the parameter that determines convective sensitivity to background humidity, as well as for a range of domain sizes and horizontal grid spacings. Using dimensional arguments, combined with empirical fits from numerical data, we define a dimensionless parameter whose value indicates whether a clustered state is likely to emerge for a given set of parameter values and experimental configurations. This quantity contains dependencies on all the model processes, while also explicitly including the domain size and resolution in an attempt to explain these latter sensitivities observed in the full-physics CRM experiments.

The present study focuses on the mechanism that controls the transition of the Euro-Atlantic circulation responses to El Niño–Southern Oscillation (ENSO) from early (December) to late winter (February) for the period 1981–2015. A positive phase of ENSO induces a precipitation dipole with increased precipitation in the western and reduced precipitation in the eastern tropical Indian Ocean; this occurs mainly during early winter (December) and less so in late winter (February). It is shown that these interbasin atmospheric teleconnections dominate the response in the Euro-Atlantic sector in early winter by modifying the subtropical South Asian jet (SAJET) and forcing a wavenumber-3 response that projects spatially onto the positive North Atlantic Oscillation (NAO) pattern. On the contrary, during late winter, the response in the Euro-Atlantic sector is dominated by the well-known ENSO wave train from the tropical Pacific region, involving extratropical anomalies that project spatially on the positive phase of the Pacific–North American (PNA) pattern and the negative phase of the NAO pattern. Numerical experiments with an atmospheric model (an AGCM) forced by an Indian Ocean heating dipole anomaly support the hypothesis that the Indian Ocean modulates the SAJET and enforces the Rossby wave propagation to the Euro-Atlantic region in early winter. These phenomena are also investigated using the ECMWF SEAS5 reforecast dataset. In SEAS5, the ENSO interbasin tropical teleconnections and the response of the Euro-Atlantic circulation anomalies and their change from early to late winter are realistically predicted, although the strength of the early winter signal originated from the Indian Ocean is underestimated.