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The recent advancements in climate modeling partially build on the improvement of horizontal resolution in different components of the simulating system. A higher resolution is expected to provide a better representation of the climate variability, and in this work we are particularly interested in the potential improvements in representing extreme events of high temperature and precipitation. The two versions of the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC-CM2) model used here adopt the highest horizontal resolutions available within the last family of the global coupled climate models developed at CMCC to participate in the Coupled Model Intercomparison Projects, Phase 6 (CMIP6) effort. The main aim of this study is to document the ability of the CMCC-CM2 models to represent the spatial distribution of extreme events of temperature and precipitation, under the historical period, comparing model results to observations, the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), multi-source weighted-ensemble precipitation (MSWEP) and Climate Hazards Group infrared precipitation with station data (CHIRPS) observations. For a more detailed evaluation we use both 6-hourly and daily time series, to compute indices representative of intense and extreme conditions. In terms of mean climate, the two models are able to realistically reproduce the main patterns of temperature and precipitation. The high resolution version (1/4∘ horizontal resolution) of the atmospheric model provides better results than the standard resolution one (1°), not only in terms of means but also in terms of intense and extreme events of temperature defined at daily and 6-hourly frequencies. This is also the case of average and intense precipitation. On the other hand the extreme precipitation is not improved by the adoption of a higher horizontal resolution.

Rapid intensification/weakening (RI/RW) refers to a significant increase/decrease in tropical cyclone (TC) intensity over a short period of time. A TC can also undergo multiple RI/RW events during its lifetime, and these events pose a significant challenge for forecasting TC activity. In fact, RW is one major source of large intensity forecasting errors as well as RI. These processes can be associated to particular large-scale conditions, both in terms of atmospheric drivers - such as vertical wind shear or dry air intrusion - and oceanic drivers - such as sea surface temperature (SST) gradient.In this work we aim to verify the ability of the new CMCC-CM3 model (a preliminary version of the General Circulation Model that will take part to the 7th Coupled Model Intercomparison Project - CMIP7 effort) in representing Tropical Cyclone activity with a particular focus on RI and RW. The simulations used in this work have been provided within the EU project BlueAdapt at a 25km horizontal resolution in atmosphere and ocean components, ensuring the representation of realistic TCs both in terms of spatial variability and intensity. Less agreement is found in representing RI/RW timing and duration, but better results are obtained, compared to the previous version of the model CMCC-CM2. The role of the ocean in determining RI and RW is also investigated.

Extreme climate events such as heat waves cause enormous stress on human health and ecosystems and economic losses in agriculture, energy, or water management activities. In particular, the combined effect of above-normal nighttime temperatures and high humidity poses a high risk to human health. This is related to the thermal discomfort which prevents the human body’s recovery from daytime high-heat exposure. Seasonal forecasts of the nighttime heat waves might be used as a tool to anticipate these risks and to better manage their social and economic impacts. However, the ability of the seasonal forecast systems to predict these extreme events has not been explored so far. This work provides insight into the potential of four seasonal forecasting systems (CMCC Seasonal Prediction System 3.5, DWD System 2.1, ECMWF SEAS5, and Météo-France System 7) to provide skillful and reliable predictions of the nighttime heat waves in Europe during the boreal summer season. Different potential proxies for the assessment of nighttime heat waves have been considered: nighttime apparent temperature computed from temperature and humidity at night, the temperature at night, or daily minimum temperature. There are different indices that can be used to investigate extreme temperatures, but the one chosen in this study is very suitable for seasonal forecast analysis because it is invariant to the mean biases and provides an integrated view of the nighttime heat waves for the entire season with information on their duration, frequency, and intensity. The forecast quality assessment has revealed that state-of-the-art seasonal forecast systems are able to provide useful information on the nighttime heat waves in Southern Europe, which is a particularly vulnerable region where timely climate information can benefit the decision-making processes.

Yangtze River Valley (YRV) locates in Southeast China, is home to about a third of the population in China. Summer extreme precipitation in Yangtze River can lead to extensive social problems and loss of lives. Understanding the characteristics of extreme precipitation and identifying the possible driving factors can increase our ability to plan for, manage and respond to related extreme events over the YRV. This study applies ERA5 data during the period of 1950~2021 to examine the possible influence of ENSO and the sea surface temperature (SST) variability over the Indian Ocean domain on the interannual variability of the extreme precipitation over the YRV. The related physical processes that link the summer Yangtze River extreme precipitation, ENSO and Indian Ocean Dipole (IOD) are investigated.Using composites analysis and Pearson correlation method, we found that both ENSO and IOD have delayed effects on summer extreme precipitation over the YRV, warm ENSO events and positive IOD phases are in favor of increased extreme precipitation in the subsequent summers, and vice versa. The anomalous anticyclone over the western Pacific Ocean (WNPAC) is the key factor in altering the inter-annual variability of extreme precipitation over the YRV. By comparing the extreme precipitation composites with different ENSO-IOD coupling events, we found that the signals of enhanced extreme precipitation are significant when El Niño occurs with a positive phase of IOD in the previous winter. The results based on the large circulation patterns also support that IOD plays an essential role in modulating the WNPAC. Our research highlights the need for a fundamental exploration into air-sea interactions over the tropical Pacific associate to ENSO-IOD coupling modes, our understanding in learning the impacts of these modes of variability on precipitation extremes over the YRV will contribute to improve the predictability of extreme events over this region.Keywords:Yangtze River Valley, extreme precipitation, ENSO, IOD, western North Pacific anomalous anticyclone

Tropical Cyclones (TCs) have the potential to cause extreme rainfall and storm surge, which in turn can lead to riverine and coastal flooding with huge damage to property and loss of lives.The use of precipitation forecasts in the context of decision-making and anticipatory action is currently hampered by the limited skill of numerical weather prediction models in forecasting the characteristics of such extreme rainfall events (especially their severity and location) with a sufficiently long lead time.In this study, we present a post-processing scheme for precipitation forecasts based on a popular deep-learning algorithm (U-Net). We design our Machine Learning (ML) model to reduce the local biases of precipitation forecasts from TCs and adjust the spatial distribution of extreme rainfall. For this, we use a composite loss function to train the model, based on the combination of the Mean Absolute Error (MAE) and the Fractions Skill Score (FSS). We first demonstrate the potential of our ML-based approach working on ERA5 reanalysis data and subsequently apply it to the ensemble mean of ECMWF sub-seasonal forecasts with a lead time up to 10-days. As for the ensemble spread, we investigate possible post-processing adjustments based on the improvement of the spread-error relationship and of action-relevant scores of interest for humanitarian agencies, namely False Alarm Ratios (FAR) and Hit Rates (HR). We train and validate the model on a historical dataset of global TC precipitation events, using ECMWF re-forecasts over 20 years and a multi-source observational dataset (MSWEP) as reference. The results are evaluated with a multi-criteria approach including MAE, FSS, FAR, and HR, to assess the capacity of improving the predicted severity and spatial patterns of TC precipitation, as well as their potential for triggering anticipatory actions. Finally, we discuss how the outputs of our model can be used and further improved to support humanitarian actions aimed at saving lives in vulnerable communities in Mozambique.

Extra-Tropical Cyclones are major systems ruling and influencing the atmospheric structure at mid-latitudes. They are characterised by strong winds and heavy precipitation, and can cause considerable storm surges potentially devastating for coastal regions. The availability of historical observations of the extreme events caused by intense ETCs are rather limited, hampering risk evaluation. Increasing the amount of significant data available would substantially help several fields of analysis influenced by these events, such as coastal management, agricultural production, energy distribution, air and maritime transportation, and risk assessment and management.Here, we address the possibility of generating synthetic ETC atmospheric fields of mean sea level pressure, wind speed, and precipitation in the North Atlantic by training a Generative Adversarial Network (GAN). The purpose of GANs is to learn the distribution of a training set based on a game theoretic scenario where two networks compete against each other, the generator and the discriminator. The former is trained to generate synthetic examples that are plausible and resemble the real ones. The input of the generator is a vector of random Gaussian values, whose domain is known as the “latent space”. The discriminator learns to distinguish whether an example comes from the dataset distribution. The competition set by the game-theoretic approach improves the network until the counterfeits are indistinguishable from the originals.To train the GAN, we use atmospheric fields extracted from the ERA5 reanalysis dataset in the geographic domain with boundaries 0°- 90°N, 70°W - 20°E and for the period 1st January 1979 - 1st January 2020. We analyse the generated samples’ histograms, the samples’ average fields, the Wasserstein distance and the Kullback-Leibler divergence between the generated samples and the test set distributions. Results show that the generative model has learned the distribution of the values of the atmospheric fields and the general spatial trends of the atmosphere in the domain. To evaluate better the atmospheric structure learned by the network, we perform linear and spherical interpolations in the latent space. Specifically, we consider four cyclones and compare the frames of their tracks to those of the synthetic tracks generated by interpolation. The interpolated tracks show interesting features consistent with the original tracks. These findings suggest that GANs can learn meaningful representations of the ETCs’ fields, encouraging further investigations to model the tracks’ temporal evolution.

Typical end-to-end Earth System Modelling (ESM) workflows rely on different steps including data pre-processing, numerical simulation, output post-processing, as well as data analytics and visualization. The approaches currently available for implementing scientific workflows in the climate context do not properly integrate the entire set of components into a single workflow and in a transparent manner. The increasing usage of High Performance Data Analytics (HPDA) and Machine Learning (ML) in climate applications further exacerbate the issues. A more integrated approach would allow to support next-generation ESM and improve the workflow in terms of execution and energy consumption.Moreover, a seamless integration of components for HPDA and ML into the ESM workflow will open the floor to novel applications and support larger scale pre- and post-processing. However, these components typically have different deployment requirements spanning from HPC (for ESM simulation) to Cloud computing (for HPDA and ML). It is paramount to provide scientists with solutions capable of hiding the technical details of the underlying infrastructure and improving workflow portability.In the context of the eFlows4HPC project, we are exploring the use of innovative workflow solutions integrating approaches from HPC, HPDA and ML for supporting end-to-end ESM simulations and post-processing, with a focus on extreme events analysis (e.g., heat waves and tropical cyclones). In particular, the envisioned solution exploits PyCOMPSs for the management of parallel pipelines, task orchestration and synchronization, as well as PyOphidia for climate data analytics and ML frameworks (i.e., TensorFlow) for data-driven event detection models. This contribution presents the approaches being explored in the frame of the project to address the convergence of HPC, Big Data and ML into a single end-to-end ESM workflows.

Recent studies exhibit a considerable reduction of summer season precipitation frequency in the new generation of convection-permitting (CP) climate simulations. This seems to over-correct non-CP wet biases with a knock-on effect on summer temperatures via soil moisture-atmosphere feedbacks. However, it is difficult to elucidate which part of the warmth/dryness in CP simulations can be ascribed to land-atmosphere coupling and/or "atmosphere-only" processes. Another layer of uncertainty belongs to the still crude representation of land surface/sub-surface processes that become especially relevant when approaching such high resolution.In this study we explore the modulation of land-atmosphere coupling when moving from a non-CP to a CP-scale climate modeling, considering increasingly sophisticated land surface model configurations. We perform a two-step dynamical downscaling at ~15 km (convection-parameterized) and ~ 3km (convection-permitting) resolutions with the WRF-4.2.1 regional climate model driven by ECMWF-ERA5 reanalysis. The greater alpine region and the extended summer season (May to September) of 2003 are the spatial and temporal domains of interest. A mini multi-physics ensemble is generated with four Noah-MP land-surface model configurations to examine if, and how, including crucial land processes (e.g., vertical soil water transport) modifies hot-temperature forcing mechanisms in the two resolutions. Moreover, each ensemble member is run according to three different initial soil moisture levels, defining reference, anomalously dry- and wet-initialization experiments.Preliminary results show an improved representation of precipitation statistics (seasonal cumulative, frequency, and 99th percentile) from CP simulations, particularly over complex orography. Generally, maximum temperature reproduction benefits from the CP scale. However, localized warm biases persist over flat terrains regardless of the land surface model configuration. Finally, the two resolutions show a substantially different decay of the initial soil moisture state. At CP scale all three runs converge to similar soil moisture at the end of the integration. Conversely, non-CP runs preserve large soil moisture differences until the end of the summer season, signaling longer soil moisture memory and different soil moisture-precipitation feedback.These factors might significantly affect the reproduction and predictability of environmental and societal relevant hydroclimatic extremes on a wide-ranging temporal scale, from seasonal climate predictions to long-term climate projections.

Tropical cyclones (TCs) are one of the costliest and deadliest natural disasters due to the combination of their strong winds and induced storm surges and heavy precipitation, which can cause devastating floods. Unfortunately, due to its high spatio-temporal variability, complex underlying physical process, and lack of high-quality observations, precipitation is still one of the most challenging aspects of a TC to model. However, as precipitation is a key forcing variable for hydrological processes acting across multiple space-time scales, accurate precipitation input is crucial for reliable hydrological simulations and forecasts.A popular source of precipitation data is the ERA5 reanalysis dataset, frequently used as input to hydrological models when studying floods. However, ERA5 systematically underestimates TC-induced precipitation compared to MSWEP, a multi-source observational dataset fusing gauge, satellite, and reanalysis-based data, currently one of the most accurate precipitation datasets. Moreover, the spatial distribution of TC-rainfall in ERA5 has large room for improvement.Here, we present a precipitation correction scheme based on U-Net, a popular deep-learning architecture. Rather than only adjusting the per-pixel precipitation values at each timestep of a given TC, we explicitly design our model to also adjust the spatial distribution of the precipitation; to the best of our knowledge, we are the first to do so. The key novelty of our model is a custom-made loss function, based on the combination of the fractions skill score (FSS) and mean absolute error (MAE) metrics. We train and validate the model on 100k time steps (with an 80:20 train:test split) from global TC precipitation events. We show how a U-Net trained with our loss function can reduce the per-pixel MAE of ERA5 precipitation by nearly as much as other state-of-the-art methods, while surpassing them significantly in terms of improved spatial patterns of precipitation. Finally, we discuss how the outputs of our model can be used for future research.

This paper presents an analysis of future precipitation patterns over the Lake Victoria Basin using bias-corrected CMIP6 model projections. A mean increase of about 5% in mean annual (ANN) and seasonal [March-May (MAM), June-August (JJA), and October-December (OND)] precipitation climatology is expected over the domain by mid-century (2040-2069). The changes intensify towards the end of the century (2070-2099) with an increase in mean precipitation of about 16% (ANN), 10% (MAM), and 18% (OND) expected, relative to the 1985-2014 baseline period. Additionally, the mean daily precipitation intensity (SDII), the maximum 5-day precipitation values (RX5Day), and the heavy precipitation events, represented by the width of the right tail distribution of precipitation (99p-90p) show an increase of 16%, 29%, and 47%, respectively, by the end of the century. The projected changes have a substantial implication for the region - which is already experiencing conflicts over water and water-related resources.

Many cyclone detection and tracking methods (CDTMs) have been developed in the past to study the climatology of extratropical cyclones. However, all CDTMs have different approaches in defining and tracking cyclone centers. This naturally leads to cyclone track climatologies of inconsistent physical characteristics. More than that, it is typical for CDTMs to produce a non-negligible amount of bogus tracks which can be perceived as “false positives”, or more generally as CDTM artifacts, i.e. tracks of weak atmospheric features that do not correspond to large or mesoscale vortices. Lack of consensus in CDTM outputs and the inclusion of significant amounts of bogus tracks therein, has long prohibited the production of a commonly accepted reference dataset of extratropical cyclone tracks. Such a dataset could allow comparable results on the analysis of storm track climatologies and could also contribute to the evaluation and improvement of CDTMs. To cover this gap, we present a new methodological approach that combines overlapping tracks from different CDTMs and produces composite tracks that concentrate the agreement of more than one CDTM. In this study we apply this methodology to the outputs of 10 well-established CDTMs which were originally applied to ERA5 reanalysis in the 42-year period of 1979–2020. We tested the sensitivity of our results to the spatio-temporal criteria that identify overlapping cyclone tracks, and for benchmarking reasons, we produced five reference datasets of subjectively tracked cyclones. Results show that climatological numbers of composite tracks are substantially lower than the ones of individual CDTM, while benchmarking scores remain high (i.e. counting the number of subjectively tracked cyclones captured by the composite tracks). This suggests that our method is able to filter out a large portion of bogus tracks. Indeed, our results show that composite tracks tend to describe more intense and longer-lasting cyclones with more distinguished early, mature and decay stages than the cyclone tracks produced by individual CDTMs. Ranking the composite tracks according to their confidence level (defined by the number of contributing CDTMs), it is shown that the higher the confidence level, the more intense and long-lasting cyclones are produced. Given the advantage of our methodology in producing cyclone tracks with physically meaningful, distinctive life stages and including a minimum number of bogus tracks, we propose composite tracks as reference datasets for climatological research in the Mediterranean. The supplementary material provides the composite Mediterranean tracks for all confidence levels and in the conclusion we discuss their adequate use for scientific research and applications.

This paper presents an innovative design and fabrication workflow for a lightweight composite slab prototype that combines mineral foam 3D printing (F3DP) and concrete casting. Non-standardized concrete elements that are geometrically optimized for resource efficiency often result in complex shapes that are difficult to manufacture. This paper extends the research in earlier studies, showing that F3DP can address this challenge. F3DP is used to construct 24 stay-in-place formwork elements for a lightweight, resource-efficient ribbed concrete element with a 2 × 1.3 m footprint. This advancement highlights the improved robotic F3DP setup, computational design techniques for geometry and print path generation, and strategies to achieve near-net-shape fabrication. The resulting prototype shows how complex geometries that were previously cost-prohibitive can be produced efficiently. Discussing the findings, challenges, and future improvements offers useful perspectives and supports the development of this resourceful and sustainable construction technique., International Journal of Architectural Computing, ISSN:1478-0771, ISSN:2048-3988

Here we explore the relationship between the global climatological characteristics of tropical cyclones (TCs) in climate models and the modeled large-scale environment across a large number of models. We consider the climatology of TCs in 30 climate models with a wide range of horizontal resolutions. We examine if there is a systematic relationship between the climatological diagnostics for the TC activity [number of tropical cyclones (NTC) and accumulated cyclone energy (ACE)] by hemisphere in the models and the environmental fields usually associated with TC activity, when examined across a large number of models. For low-resolution models, there is no association between a conducive environment and TC activity, when integrated over space (tropical hemisphere) and time (all years of the simulation). As the model resolution increases, for a couple of variables, in particular vertical wind shear, there is a statistically significant relationship in between the models’ TC characteristics and the environmental characteristics, but in most cases the relationship is either nonexistent or the opposite of what is expected based on observations. It is important to stress that these results do not imply that there is no relationship between individual models’ environmental fields and their TC activity by basin with respect to intraseasonal or interannual variability or due to climate change. However, it is clear that when examined across many models, the models’ mean state does not have a consistent relationship with the models’ mean TC activity. Therefore, other processes associated with the model physics, dynamical core, and resolution determine the climatological TC activity in climate models.

Significance Much work has been done to quantify tropical cyclone (TC)-induced precipitation and its role in determining flood events. We identify the role of tropical cyclones in reducing precipitation over particular areas of the tropical domain. The significant reduction in the precipitation over the Maritime Continent when the TC season is particularly active is in line with the net reduction in westward water flow into the Maritime Continent atmosphere, induced by TC-associated circulation over the region., The Maritime Continent plays a role in the global circulation pattern, due to the energy released by convective condensation over the region which influences the global atmospheric circulation. We demonstrate that tropical cyclones contribute to drying the Maritime Continent atmosphere, influencing the definition of the onset of the dry season. The process was investigated using observational data and reanalysis. Our findings were confirmed by numerical experiments using low- and high-resolution versions of the CMCC-CM2 General Circulation Model contributing to the HighResMIP CMIP6 effort.

Tropical cyclones (TCs) are one of the most devastating natural disasters worldwide, in terms of both economic damage ($1100 billion in the last 20 years) and fatalities (210,000 in the last 20 years). The accuracy of TC track and intensity forecasts has been increasing steadily since the 1980s, but it remains difficult to predict how many TCs will form each year and where, as the physical processes that lead to the formation of TCs are still poorly understood. Several Genesis Potential Indexes (GPIs) have been proposed that predict the likelihood of formation and distribution of TC genesis given large-scale factors such as sea surface temperature and air humidity. These indices are constructed as the product of a number (typically 3-5) of dynamic and thermodynamic variables, each of which is assigned a coefficient and an exponent. Such indices serve not only to improve our understanding of TC formation by isolating the variables most linked to it, but also as a guideline for insurance companies and governments of how severe a TC season will be, and to predict how climate change will affect the frequency and severity of TCs in climate model simulations. Nevertheless, current GPIs have large margins of error, especially at the local scale and in terms of interannual variability.In this work, we explore the search space for this type of index, intended as the space of variables are relative coefficients and exponents that can be used to structure a GPI. We begin by optimizing the coefficients and exponents of the well-known GPI developed by Emanuel and Nolan (ENGPI), and show that a simple genetic algorithm can lead to substantial improvements in spatial correlation between the index and observed data. However, we also show that interannual and spatial correlation may be conflicting objectives which cannot be optimized simultaneously. We then modify the structure of the ENGPI by introducing new variables used in other similar indices, some of which seem to lead to improvements. We then repeat the above experiments using different genetic algorithms, and find that different algorithms converge to different solutions with similar performance, indicating that there are many valid ways to structure a GPI; we offer an interpretation of this finding, which we believe to be relevant for future research. Finally, we show that thermodynamic variables tend to be discarded when optimizing interannual correlation, but not when optimizing spatial correlation; we offer a possible explanation of why this may be.

Hail is a meteorological phenomenon with adverse impacts affecting multiple socio-economic sectors such as agriculture, renewable energy and insurance (e.g. Púčik et al., 2019; Martius et al., 2018; Macdonald et al., 2016). The mitigation of the hail-related risk in particularly sensitive regions such as Italy has fostered hail research, aiming at a deeper understanding of the favorable environmental conditions for hail formation and the improvement of hail forecasting skills (Mohr and Kunz, 2013). Nevertheless, one of the major limitations for the study of long-term hail variability is the inherent difficulty in measuring all the hail occurrences and the consequent scarce temporal and spatial coverage of hail observations (Mohr et al., 2015). Therefore, in this study, the Probability Density Functions (PDFs) of several large-scale meteorological variables and convective indices from the ERA5 reanalysis are considered instead, with the aim of describing a conditioned hail probability, following the statistical method by Prein and Holland (2018). Then, the best set of variables to be used as predictors in the hail model are selected with a machine learning approach, based on a genetic algorithm. The model output is an estimation of the hail probability over Italy in the 1979-2020 period, on a 30x30 km grid. The model is validated over the Friuli-Venezia-Giulia region through an independent dataset based on hail pads. The estimated hail probability has been used to characterize the seasonality, long-term variability and trends of the hail frequency and to investigate the potential large-scale drivers of hailstorms over Italy. REFERENCES:Púčik, T., Castellano, C., Groenemeijer, P., Kühne, T., Rädler, A. T., Antonescu, B., & Faust, E. (2019). Large hail incidence and its economic and societal impacts across Europe. Monthly Weather Review, 147(11), 3901-3916. doi: 10.1175/MWR-D-19-0204.1.Martius, O., Hering, A., Kunz, M., Manzato, A., Mohr, S., Nisi, L., & Trefalt, S. (2018). Challenges and recent advances in hail research. Bulletin of the American Meteorological Society, 99(3), ES51-ES54. doi: 10.1175/BAMS-D-17-0207.1.Macdonald, H., Infield, D., Nash, D. H., & Stack, M. M. (2016). Mapping hail meteorological observations for prediction of erosion in wind turbines. Wind Energy, 19(4), 777-784. doi: 10.1002/we.1854.Mohr, S., Kunz, M., & Geyer, B. (2015). Hail potential in Europe based on a regional climate model hindcast. Geophysical Research Letters, 42(24), 10-904. doi:10.1002/2015GL067118.Prein, A. F., & Holland, G. J. (2018). Global estimates of damaging hail hazard. Weather and Climate Extremes, 22, 10-23. doi: 10.1016/j.wace.2018.10.004.

Global temperatures have shown a warming trend over the last century, mainly as a result of anthropogenic activities. Rising temperatures are a potential cause for increase of extreme climate events, such as heat waves, both in severity and frequency. Under an increasing extreme event scenario, the world population of mid- and low-latitude countries is more vulnerable to heat related mortality and morbidity. In India, the events occurred in recent years have made this vulnerability clear, since the numbers of heat related deaths are on a rise.Over India, the heat waves occur during the months of April to June and can impact various sectors including health, agriculture, ecosystems and the national economy. In May 2015, a severe heat wave due to the delayed onset of southwest monsoon affected parts of south-eastern India, which claimed more than 2500 lives. Preliminary results show the prevalence of Heat events in six different regions of India during the pre-monsoon (March, April, May) and transitional (May, June, July) months.We consider daily maximum temperatures and NOAA’s Heat Index (HI), a combination of temperature and relative humidity (also known as apparent temperature) which gives an insight into the discomfort because of increment in humidity. It is important to take HI along with temperature anomalies, since humidity also plays a role in transitional period.Heatwaves over India are known to be linked with mortality and have indirect impacts on human health. To evaluate the heat related risk of mortality on Indian population, indicators and clusters of heat events were computed by taking into account population weighted temperature exposure.

The dominant modes of variability at the sub-seasonal to seasonal (S2S) time scales in the tropical atmosphere, with particular emphasis on the Madden-Julian oscillation (MJO), is investigated using the observed NOAA outgoing longwave radiation (OLR). This study focuses on the boreal winter season (November to April) of the period 1993-2016. The multi-channel singular spectral analysis (MSSA) method is introduced and used to isolate the dominant modes associated with the tropical boreal winter daily OLR anomalies. The results show that the dominant MSSA mode consists of an intraseasonal oscillation with a period of around 35-day and two seasonally persistent modes. The 35-day oscillation is related to the intraseasonal convective activity of the tropics with little contribution to the seasonal mean value, and the phase composites and propagation characteristics of the 35-day mode are almost identical to the MJO (referred to as the MJO mode). The seasonally persistent modes are characterized by large-scale patterns that prevail over most of the tropics with the same sign anomalies throughout the boreal winter season, which represent inter-annual variations and are related to the El Niño-Southern Oscillation (ENSO) pattern. In this work, the MSSA based analysis is applied to the fully coupled (atmosphere-ocean-land-cryosphere) Euro-Mediterranean Center on Climate Change (CMCC) seasonal prediction system to evaluate the model's performance in simulating observed dominant modes of the boreal winter tropical variability.

Tropical cyclones (TCs) are regularly listed among the costliest natural disasters, due to the associated strong wind, heavy precipitation, and risk of storm surges. Therefore, being able to understand and predict TC activity at different time scales would lead to clear societal and economic benefits.Several genesis potential indices (GPIs) have been introduced in the literature, linking TC activity to favourable conditions in a number of large-scale meteo-climatic variables. The advantage of using such indices lies in the ability to study TC occurrence in climate model simulations, which do not usually reproduce individual TC accurately due to the limited horizontal resolution.Existing GPIs generally have good skill in reproducing the spatial pattern and seasonal cycle of historical TC activity. On the other hand, they commonly fail to reproduce TC interannual variability across different ocean basins. A further issue is found for climate projections where, for those climate models with high-enough resolution to allow for TC tracking, the trends of GPI and directly detected cyclones are often in disagreement.Here we revisit the issue of TC genesis potential in reproducing TC activity by exploiting the last generation of climate model simulations, obtained from the CMIP6 model intercomparison project. Using data obtained from both the ScenarioMIP and HighResMIP simulations, we investigate the effect of horizontal resolution and other model features on the modelled GPI’s skill in reproducing TC interannual variability and trends.

Here, we analyze future projections of cyclone activity in the Mediterranean region at the end of the twenty-first century based on an ensemble of state-of-the-art fully-coupled Regional Climate System Models (RCSMs) from the Med-CORDEX initiative under the Representative Concentration Pathway (RCP) 8.5. Despite some noticeable biases, all the RCSMs capture spatial patterns and cyclone activity key characteristics in the region and thus all of them can be considered as plausible representations of the future evolution of Mediterranean cyclones. In general, the RCSMs show at the end of the twenty-first century a decrease in the number and an overall weakening of cyclones moving across the Mediterranean. Five out of seven RCSMs simulate also a decrease of the mean size of the systems. Moreover, in agreement with what already observed in CMIP5 projections for the area, the models suggest an increase in the Central part of the Mediterranean region and a decrease in the South-eastern part of the region in the cyclone-related wind speed and precipitation rate. These rather two opposite tendencies observed in the precipitation should compensate and amplify, respectively, the effect of the overall reduction of the frequency of cyclones on the water budget over the Central and South-eastern part of the region. A pronounced inter-model spread among the RCSMs emerges for the projected changes in the cyclone adjusted deepening rate, seasonal cycle occurrence and associated precipitation and wind patterns over some areas of the basin such as Ionian Sea and Iberian Peninsula. The differences observed appear to be determined by the driving Global Circulation Model (GCM) and influenced by the RCSM physics and internal variability. These results point to the importance of (1) better characterizing the range of plausible futures by relying on ensembles of models that explore well the existing diversity of GCMs and RCSMs as well as the climate natural variability and (2) better understanding the driving mechanisms of the future evolution of Mediterranean cyclones properties.

The recent advancements in climate modelling partially build on the improvement of horizontal resolution in different components of the simulating system. A higher resolution is expected to provide a better representation of the climate variability, and in this work we are particularly interested in the potential improvements in representing extreme events of high temperature and precipitation. The two versions of the CMCC-CM2 model used here, adopt the highest horizontal resolutions available within the last family of the global coupled climate models de¬veloped at CMCC to participate in the CMIP6 effort. The main aim of this study is to document the ability of the CMCC-CM2 models in representing the spatial distribution of extreme events of temperature and precipitation, under the historical period, comparing model results to observations (ERA5 Reanalysis and CHIRPS observations). For a more detailed evaluation we investigate both 6 hourly and daily time series for the definition of the extreme conditions. In terms of mean climate, the two models are able to realistically reproduce the main patterns of temperature and precipitation. The very-high resolution version (¼ degree horizontal resolution) of the atmospheric model provides better results than the high resolution one (one degree), not only in terms of means but also in terms of extreme events of temperature defined at daily and 6-hourly frequency. This is also the case of average precipitation. On the other hand the extreme precipitation is not improved by the adoption of a higher horizontal resolution.

The North Pacific Ocean is the most active region on our planet in terms of tropical cyclone (TC) activity. These storms are responsible for numerous fatalities and economic damages, affecting the livelihood of those living in the impacted areas. Historically the examination of TCs in the North Pacific Ocean has been performed separately for its two main sub-basins: the West North Pacific and the East North Pacific. Here, we consider the TC activity in the North Pacific as a single basin and examine the climate processes responsible for its number of TC days. We show that the Pacific Decadal Oscillation modulates the number of TC days in the North Pacific Ocean through its connection to the sea surface temperature. The insights from this work will advance the understanding of the climate processes responsible for these storms, and will provide valuable information toward our preparation and adaptation efforts on long timescales.

This study investigated the influence of land-use and precipitation change and variability on Lake Victoria’s water-level fluctuations. Extreme precipitation events, corresponding to extreme water-levels, over the lake and its catchment area were identified and their return periods estimated by fitting them into a generalized extreme value (GEV) distribution. Using general circulation models from the 6th phase of the Coupled Model Intercomparison Project (CMIP6)’s Detection & Attribution Model Intercomparison Project (DAMIP), an assessment of the potential contribution of human-induced climate change on the observed precipitation patterns over the study area was done. The greatest precipitation anomalies for the period 1900-2020 were recorded in 1961’s October-December (OND) season and 2019’s June-August (JJA) and OND seasons, corresponding to the period when the highest water-levels were recorded in Lake Victoria. While land-use change in the study domain was observed, extended and unusually heavy June to December 2019 precipitation bore the greatest responsibility for the 2019/2020 high water-levels in Lake Victoria. The OND precipitation event of 2019 was a 1-in-52-year event compared to the 1961’s 1-in-693 years. Differences in return periods at various parts of the lake imply a high spatial climate variability within the lake itself. An analysis of the fraction of attributable risk (FAR) showed natural variability to have a greater influence on the JJA and OND precipitation patterns over Lake Victoria than human-induced climate change. However, variability over the land area of the study domain was mainly driven by human-induced climate change rather than natural variability, implying a unique climate system over Lake Victoria. Findings from the current study enhance the understanding of Lake Victoria’s water budget and motivate for further research to inform effective strategies on the planning and use of Lake Victoria’s water resources in a changing climate.

In late/early 2019/2020, unprecedented high-water-levels were observed in Lake Victoria causing massive flooding in the low-lying lake-adjacent areas and disrupting human and natural systems in the Lake Victoria Basin (LVB). The high lake water-level coincided with unusually heavy and prolonged 2019 June to December precipitation in the LVB. The current study estimates future precipitation patterns over the LVB using HighResMIP and ScenarioMIP general circulation model (GCM) simulations from the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Results show that HighResMIP and ScenarioMIP simulations can adequately reproduce LVB’s precipitation patterns – albeit with location-specific biases. Generally, the GCM simulations tend to over-estimate precipitation patterns over Lake Victoria while under-estimating precipitation patterns over the lake-adjacent areas. Projections show significant future precipitation changes over the LVB relative to the 1970-1999 baseline, with more pronounced changes over the lake than in lake-adjacent areas. Overall, mean annual precipitation is projected to increase by about 18% and 31% by the end of the century, under SSP2-4.5 and SSP5-8.5 scenarios, respectively. Additionally, mean daily precipitation intensity (SDII) is projected to increase by up-to 14% while the maximum 5-day precipitation values (RX5Day) increase by up-to 71% under the SSP5-8.5 scenario. Heavy precipitation events, represented by the width of the right tail distribution of precipitation (99p-90p), are projected to increase by 50% and 94% under SSP2-4.5 and SSP5-8.5, respectively. Given that direct precipitation accounts for about 80% of Lake Victoria’s water budget, the lake’s future water-level fluctuations are likely to be more rampant and unpredictable under the changing climate. Hence, enhanced production and use of climate services is recommended to minimize the risk posed by potentially high water-level fluctuations in Lake Victoria and, ultimately, enhance the socio-economic safety of communities in the LVB.