Weather Climate Research Articles

Improving the Use of Weather and Climate Information for Farm Decision Making in Victoria, Oriental Mindoro, Philippines

Weather and climate are crucial factors that greatly influence the agricultural sector, especially in the decision-making process of farmers. A recent study conducted in the municipality of Victoria, Oriental Mindoro, Philippines aimed to evaluate the utilization of climate and weather forecasts in farm operations. The study collected primary data through a household survey using a structured questionnaire, with a total of 324 randomly selected respondents based on Cochran's formula. The findings of the study revealed that a significant majority of farmers, approximately 85%, are aware of climate change. This awareness is heightened by their perception of long-term changes in climate, particularly in rainfall patterns. Farmers recognize and acknowledge the impact of climate change on their agricultural practices. When it comes to obtaining information about weather and climate, the study found that television is the primary source for most farmers. This highlights the importance of broadcasting accurate and timely weather forecasts to ensure farmers stay informed and prepared. Furthermore, the study discovered that weather and seasonal climate forecasts, along with long-term climate projections, significantly influence various farm operations. These operations include the selection of rice varieties, timing of seed sowing, application of fertilizers and pesticides, water management, harvesting and storage, and even selling decisions. The reliance on science-based information is crucial for farmers as traditional knowledge alone is no longer reliable in the face of changing climate conditions. Based on the study's results, it is evident that farmers in Victoria, Oriental Mindoro, require access to science-based knowledge, particularly accurate weather and climate forecasts. The study emphasizes the need for reliable information and other agricultural-related climate services provided by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). Such services can significantly contribute to improving decision-making processes at the farm level.

Deep learning for quality control of surface physiographic fields using satellite Earth observations

Abstract. A purposely built deep learning algorithm for the Verification of Earth System ParametERization (VESPER) is used to assess recent upgrades to the global physiographic datasets underpinning the quality of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF), which is used in both numerical weather prediction and climate reanalyses. A neural network regression model is trained to learn the mapping between the surface physiographic dataset, plus the main meteorologic fields from ERA5, and the MODIS satellite skin temperature observations. Once trained, this tool is applied to rapidly assess the quality of upgrades to the physiographic fields used by land surface schemes. Upgrades which improve the prediction accuracy of the machine learning tool indicate a reduction in the errors in the surface fields used as input to the surface parameterization schemes. Conversely, incorrect specifications of the surface fields decrease the accuracy with which VESPER can make predictions. We apply VESPER to assess the accuracy of recent upgrades to the permanent lake and glacier covers, as well as of planned upgrades to represent seasonally varying water bodies (i.e. ephemeral lakes). We show that, for grid cells where the lake fields have been updated, the prediction accuracy of VESPER in the land surface temperature (as quantified by the mean absolute error) improves by 0.37 K on average, whilst for the subset of points where the lakes have been completely removed and replaced with bare ground, the improvement is 0.83 K. We also show that updates to the glacier cover improve the prediction accuracy by 0.22 K. We highlight how neural networks such as VESPER can assist the research and development of surface parameterizations and their input physiography to better represent Earth’s surface coupled processes in weather and climate models.

L’Œil du cyclone

This article probes the convergence of meteorological disaster and memory in L’Île et une nuit by Daniel Maximin and La Case du commandeur by Édouard Glissant, reading fragments of these novels through reflections lifted from spiritual autobiographies by M. Jacqui Alexander and Mimerose Beaubrun. While anglophone Black studies scholar Christina Sharpe employs weather systems and climate as figures to apprehend the pervasiveness of antiblackness in today’s world, hurricane disaster takes another form as a device used by francophone Caribbean authors to observe and disrupt the ontological negation of Black people. Tracing the multiple resonances between ‘the wake’ (Sharpe, 2016) as a prismatic theoretical tool and meanings of water, passages, and wakefulness generated by hurricanes in Glissant and Maximin, this article explores the relation between this meteorological disaster event and an expansive consciousness of the human disasters of deportation and rupture. It pays attention to homonymic language tethering the eye of the hurricane and other manifestations of suspended wind and saltwater to the ‘vision’ or modes of sensorial knowing this phenomenon confers, addressing the ways in which African diaspora authors and spiritual practitioners understand the disaster of the Middle Passage through the disaster of the hurricane as temporally amplified, immanent, and beyond human.

Beyond a deterministic representation of the temperature dependence of soil respiration

Soil CO2 efflux represents a complex interplay of biological and physical processes that result in the production and transfer of CO2 from soils to the atmosphere. Temperature has been widely recognized as a critical factor regulating soil CO2 efflux and is commonly utilized in deterministic empirical models to predict this important flux for the carbon cycle. This study introduces the Bernstein copula-based cosimulation (BCC) as a data-driven probabilistic approach to model the temperature-soil CO2 efflux relationship. The BCC accounts for the joint probability distribution and temporal dependence of soil CO2 efflux, which are often overlooked in deterministic models. The BCC was implemented as a proof of concept using two years of data on soil CO2 efflux conditioned by soil temperature in a temperate forest. The BBC accurately reproduced the original probability distribution, temporal dependency, and temperature-soil CO2 efflux relationship. Our findings show that a deterministic method, such as the commonly employed exponential relationship between soil CO2 efflux and temperature, is limited for comprehensively capturing the intricate nature of the temperature-soil CO2 efflux relationship. This is due to the confounding and interacting effects of environmental drivers beyond temperature, which are not fully accounted for in such a deterministic approach. Furthermore, the BCC revealed that the probability density between the joint cumulative probability of temperature and soil CO2 efflux is not constant, which raises the concern that deterministic approaches introduce incorrect assumptions for estimating temperature-soil CO2 relationship. In conclusion, we propose that probabilistic approaches hold promise for effectively depicting dependency relationships for soil CO2 efflux modeling, and for improving predictions of the effects of weather variability and climate change.

Tracking Mesoscale Convective Systems in IMERG and Regional Variability of Their Properties in the Tropics

AbstractMesoscale convective systems (MCSs) constitute only a fraction of convective systems in the tropics but significantly impact tropical weather and global climate. Early studies used satellite infrared (IR) data to track MCSs and study their properties but only for a short period due to computing limitations. Though valuable, the IR brightness temperature (Tb)‐derived precipitation properties have biases. The recent availability of Integrated Multi‐satEllite Retrieval for Global (IMERG) precipitation mission rainfall data of lesser bias than IR Tb‐based rainfall and access to high‐performance computers motivated us to track MCSs over global tropics for 10 years, using the Forward in Time (FiT) algorithm. Though IMERG is advantageous, it poses challenges to tracking MCSs due to convective systems connected by light rain areas and resolution differences between contributing passive microwave sensors. The precipitation field is smoothed and normalized to overcome these problems; then, the FiT algorithm identifies MCSs and tracks them. Our results show that MCSs contribute ∼70% of annual precipitation, though they are only ∼7% of all tracked systems. MCSs occur more often over the Amazon basin and Maritime Continent than in central Africa, known for high thunderstorm frequency, highlighting the contrasting convective regimes. The large, long‐lived, and intensely precipitating MCSs occur more often over the ocean than land, except for the Amazon basin. Fast‐moving MCSs often occur over West Africa, the Amazon basin, and the western Pacific, whereas slow‐moving MCSs are common over Colombia and the Maritime Continent.

Effect of different generated weather climate data file on building energy consumption in different climates of Iran

Accurate estimation of building energy consumption is critical, and it can usually be calculated using simulation software like Energy Plus. The input weather data for software is typically a typical or reference year as a standard weather file, such as TMY2. To best estimate building energy consumption, TMY2 data must be generated intelligently. In the present study, first, several TMY2 data files have been generated from different periods (5–21 years) of available measured weather data (9 separate files). Available measured weather data is for 1998 to 2019. Also, a TMY2 file has been generated from the long-term average for the total period (1998–2019), and two TMY2 files have been generated using the CCWeatherGen software for 2020 and 2050. In the next step, the calculated building energy consumption from those files for an educational building will be compared using Energy Plus software. The primary purpose of this study is to investigate the impact of different generated TMY2 (from different weather data periods) on energy consumption and determine the best period to generate TMY2 files from the available period of 5 to 21 years for the capital of Iran (Tehran), which has a semi-arid climate, and Tabriz, which has a cold and mountainous climate. These cities have different climates. Results show that for Tehran, building heating energy consumption changes by about 10% from other generated TMY2 files (one file generated from 2009 to 2019 and another generated from the 2007 to 2019 period). The result shows that generating TMY2 files from different weather data periods affects building energy consumption. So, a suitable strategy for generating TMY2 files is necessary. As a new strategy, the present study proposes that TMY2 files should be generated based on building cooling and heating energy from recent years. Also, because climate change is an important parameter, a TMY2 file should be generated from recent years’ weather data to accurately predict building energy consumption.

Evaluation of the vulnerability of wild bird populations during heatwave events: implications for biodiversity conservation

Heatwaves, defined as prolonged periods of excessive heat, are becoming more frequent and severe due to global climate change. These extreme heat events can have significant impacts on ecosystems and wildlife, particularly on avian fauna. Birds, as vital components of ecosystems, are highly susceptible to the adverse effects of heatwaves. Impacts directly attributable to adverse weather and changing climate regimes include a higher risk of mortality, reduced breeding success, compromised body condition and immunocompetence, declining populations, range changes, and potentially maladaptive behavioral adjustments in foraging, parental care, and migration. This vulnerability is exacerbated by the loss and degradation of their natural habitats, which hinders their ability to adapt to constantly changing climatic conditions. Understanding the vulnerability of bird populations during heatwaves is essential for devising effective conservation strategies. Conservation efforts should focus on preserving and restoring critical bird habitats, providing access to freshwater sources, and creating artificial shelters. Additionally, public education plays a significant role in raising awareness about the importance of bird conservation during heatwave events and in the broader context of climate change. In analyzing the subject, the study aims to provide more precise information to support the public decisions to be made, as well as to guide awareness for the implementation of conservation actions.

A Machine-Learning-Based Study on All-Day Cloud Classification Using Himawari-8 Infrared Data

Clouds are diverse and complex, making accurate cloud type identification vital in improving the accuracy of weather forecasting and the effectiveness of climate monitoring. However, current cloud classification research has largely focused on daytime data. The lack of visible light data at night presents challenges in characterizing nocturnal cloud attributes, leading to difficulties in achieving continuous all-day cloud classification results. This study proposed an all-day infrared cloud classification model (AInfraredCCM) based on XGBoost. Initially, the latitude/longitude, 10 infrared channels, and 5 brightness temperature differences of the Himawari-8 satellite were selected as input features. Then, 1,314,275 samples were collected from the Himawari-8 full-disk data and cloud classification was conducted using the CPR/CALIOP merged cloud type product as training data. The key cloud types included cirrus, deep convective, altostratus, altocumulus, nimbostratus, stratocumulus, stratus, and cumulus. The cloud classification model achieved an overall accuracy of 86.22%, along with precision, recall, and F1-score values of 0.88, 0.84, and 0.86, respectively. The practicality of this model was validated across all-day temporal, daytime/nighttime, and seasonal scenarios. The results showed that the AInfraredCCM consistently performed well across various time periods and seasons, confirming its temporal applicability. In conclusion, this study presents an all-day cloud classification approach to obtain comprehensive cloud information for continuous weather monitoring, ultimately enhancing weather prediction accuracy and climate monitoring.

Building loss assessment using deep learning algorithm from typhoon Rusa

Climate crises such as extreme weather events, natural disasters and climate change caused by climate transformations are causing much damage worldwide enough to be called a climate catastrophe. The private sector and the government across industries are making every effort to prevent and limit the increasing damage, but the results have yet to meet market demand. Therefore, this study proposes a method that uses a deep learning algorithm to predict the damage caused by typhoons. Model development is based on a Deep Neural Network (DNN) algorithm, and learning data is obtained by fine-tuning the network structure and hyperparameters; the amount of damage caused by Typhoon Rusa was known as training data. The constructed DNN model underwent evaluation and validation by computation of mean absolute error (MAE) and root mean square error (RMSE). Furthermore, a comparative analysis was conducted to confirm the applicability of the proposed framework against a traditional multi-regression model to ensure the model's accuracy and resilience. Finally, this study offers a novel approach to predicting typhoon damage using advanced deep-learning techniques. Subsequently, government disaster management officials, facility managers, and insurance companies can utilize this method to accurately predict the extent of damage caused by typhoons. Preventive actions such as improved risk assessment, expanded insurance companies, and enhanced disaster responses plans can be implemented using these outcomes. Ultimately, the proposed model will help to reduce typhoon damage and strengthen general resilience to climate crises.

Abstract A114: Assessing the impact of extreme climate weather events on cancer patients in Puerto Rico: A cross-sectional study

Abstract BACKGROUND: Island communities such as Puerto Rico (PR) are profoundly impacted by climate extremes. Patients with chronic disease, particularly cancer, have unique needs and challenges in the aftermath of extreme weather events. Understanding cancer patients’ barriers, knowledge, risks, and vulnerabilities are essential to develop equitable adaptation strategies. This research aims to investigate the perceptions and experiences of cancer patients associated with extreme weather events over the past 10 years in Puerto Rico (PR). METHODS: We conducted a cross-sectional study via survey questionnaires (April 22, 2023-June 8, 2023) among adults aged ≥21 years from Puerto Rico who are cancer patients/survivors (n=207). A total of 23 questions listed on the survey were used to collect information on variables of interest which included demographic characteristics, information on extreme weather event experiences, and attitude towards climate change. Using the data collected, descriptive statistics were used to describe the study population and multivariable logistic regression models were used to evaluate the associations of interest (IRB approval # 2023-04-101). RESULTS: The average age of individuals recruited is 56.3 years ±13.5 SD, 79.7% are female, 65.2% reported having additional chronic diseases, and 85.0% have more than a high-school education. Regarding extreme weather events, 99% of cancer patients and survivors reported floods, including coastal, fluvial, and urban floods, impacted their communities, 76% reported tropical cyclones impacted their residences and communities, and 72% reported extreme heat impacted both their residences and communities in the last 10 years. Additionally, the most common problems encountered in the aftermath of these extreme weather events were water (88.4%) and electricity service interruption (91.4%), as well as water (88.9%) and electricity service interruption (77.1%). Most participants reported feeling very and extremely worried about their health in the face of climate change (75.0%) and feeling concerned about climate change (85.0%). After adjusting for age, sex, and education, logistic regression models showed that participants with more than one cancer type were more likely to be worried about their health in the face of climate change (OR=2.40,95% CI=1.06-5.35). CONCLUSION: Study findings highlight the burden of extreme weather events and the problems encountered in the aftermath of such events on cancer patients in Puerto Rico. This population expresses concern and worry about climate change and their health, respectively. This information is important for cancer control and emphasizes the need for targeted interventions and management strategies to remove detrimental and avoidable impacts on cancer populations. Citation Format: Jimena Perez, Pablo A. Méndez-Lázaro, Fabiola A. Rivera-Gastón, Ana P. Ortiz. Assessing the impact of extreme climate weather events on cancer patients in Puerto Rico: A cross-sectional study [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A114.

Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison

Accurate estimation of terrestrial evapotranspiration (ET) is crucial to understand the water cycle and partitioning of turbulent energy fluxes at the land surface. However, large-scale ET estimation is always difficult to achieve due to the complexity of controlling factors and the heterogeneity of landscapes. A recent model, referred to as the surface flux equilibrium (SFE) model, proposes that evaporative fraction (EF, the ratio of ET to available energy at the surface) can be estimated accurately from near-surface specific humidity and air temperature, which makes it applicable to common weather stations and climate reanalysis datasets. However, previous validation only focused on the evaluation of this model at site and basin scale for ET estimation, making the real performance of the SFE model for EF estimation heretofore unknown. There also exists a gap in the comparison between the SFE model and other existing products in the representation of global ET patterns and variation trend. Against this background, a comprehensive validation was performed in this study to evaluate the accuracy of the SFE model for both EF and ET estimation. The validation against 136 flux towers worldwide shows that the ground-based SFE model achieved EF estimation with a root-mean-square-error (RMSE) of 0.22 and 0.16 at daily and monthly scale, respectively. The corresponding RMSE of ET estimation was 0.94 mm/day and 16.37 mm/month. The application of the SFE model on a global scale was achieved by using two reanalysis products (ERA5-Land and GLDAS-CLSM) as inputs respectively. The results indicate that the SFE model did hold potential to reduce the RMSE of these two products for ET estimation, but the cost was the decrease in correlation coefficient (r). The comparison with five existing global ET products (BESS, GLEAM, AVHRR, MOD16, and SSEBop) shows that the SFE model performed on the basis of ERA5-Land product has achieved moderate accuracy among these products. Judged from r, mean absolute error, RMSE, and bias, its accuracy was ranked as fourth, fourth, second, and second, respectively. However, a further evaluation over different land cover types suggests that the ERA-based SFE model has significant superiority over cropland, relative superiority over forest and savannas, and significant inferiority over shrubland and grassland. This analysis is the first global validation of the SFE model for EF estimation as well as its comparison with existing global ET products. The validation analysis proves that this simple model has generally achieved comparable accuracy to existing complex models in the representation of global ET patterns and variation trend.

Why are severe weather and anomalous climate events often associated with the orthogonal convergence of airflows?

Severe weather often occurs in the areas where different cold-warm and dry-wet air masses converge orthogonally. The orthogonal convergence of two-adjacent air parcels can be observed from planetary-scale to synoptic-scale and meso-scale circulations. This study pointed out that an orthogonal convergence of two-adjacent horizontal air parcels can produce a significant non-zero shear stress with its modulus as anomalous energy density and form vertical motions of new airflows, while tailgating and head-on horizontal convergences cannot. Therefore, the orthogonal convergence can induce strong vertical motions and result in severe weather. Continuous convergence of airflows can lead to unusual climatic events. Storm cases of single tornado, tornado swarms, a strong tropical cyclone, and extratropical cyclones associated with heavy rainfall or heavy airborne dust show that the shear stress modulus and total kinetic energy of anomalous winds can better explain extreme weather events than other commonly used dynamical parameters such as divergence and vorticity. Therefore, the shear stress modulus can be used in the forecasting of extreme weather events in operation.

Characterizing the tropospheric water vapor spatial variation and trend using 2007–2018 COSMIC radio occultation and ECMWF reanalysis data

Abstract. Atmospheric water vapor plays a crucial role in the global energy balance, hydrological cycle, and climate system. High-quality and consistent water vapor data from different sources are vital for weather prediction and climate research. This study assesses the consistency between the Formosa Satellite Mission 3–Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT-3/COSMIC) radio occultation (RO) and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Model 5 (ERA5) water vapor datasets. Comparisons are made across different atmospheric pressure levels (300, 500, and 850 hPa) from 2007 to 2018. Generally, the two datasets show good spatial and temporal agreement. COSMIC's global water vapor retrieval is slightly lower than ERA5's at 500 and 850 hPa, with distinct latitudinal differences between hemispheres. COSMIC exhibits global water vapor increasing trends of 3.47 ± 1.77 % per decade, 3.25 ± 1.25 % per decade, and 2.03 ± 0.65 % per decade at 300, 500, and 850 hPa, respectively. Significant regional variability in water vapor trends, encompassing notable increasing and decreasing patterns, is observable in tropical and subtropical regions. At 500 and 850 hPa, strong water vapor increasing trends are noted in the equatorial Pacific Ocean and the Laccadive Sea, while decreasing trends are evident in the Indo-Pacific Ocean region and the Arabian Sea. Over land, substantial increasing trends at 850 hPa are observed in the southern United States, contrasting with decreasing trends in southern Africa and Australia. The differences between the water vapor trends of COSMIC and ERA5 are primarily negative in the tropical regions at 850 hPa. However, the water vapor increasing trends at 850 hPa estimated from COSMIC are significantly higher than the ones derived from ERA5 data for two low-height stratocumulus-cloud-rich ocean regions west of Africa and South America. These regions with notable water vapor trend differences are located in the Intertropical Convergence Zone (ITCZ) area with frequent occurrences of convection, such as deep clouds. The difference in characterizing water vapor distribution between RO and ERA5 in deep cloud regions may cause such trend differences. The assessment of spatiotemporal variability in RO-derived water vapor and reanalysis of atmospheric water vapor data helps ensure the quality of these datasets for climate studies.

Perhitungan Cooling Load Temperature Difference (CLTD) Secara Manual Dan Menggunakan Aplikasi Daikin Pada Gedung Multimedia Politeknik Tanjungbalai

Air conditioning for residential comfort is important for human life for the early days of the changing weather climate that affects the temperature of the air temperature in residential dwellings. The air conditioning system is one of the business actions in the form of maintaining the temperature level in the room from the low level of the ambient temperature around it by absorbing heat from the material or room. The air conditioning load is also carried out to determine the AC capacity needed. Research conducted by the Glamour Mansion Tanjung Morawa Housing Complex using the method used is to calculate cooling capacity based on GA (General Arranggement) which includes several parameters, namely the number of people, number of windows, room volume, equipment, and heat from lights. So that the calculation of Heat Calculation, Air Capacity, and Cooling Capacity in a complex is obtained. From the results of the study it is known that the AC cooling load for the Glamour Mansion Tanjung Morawa Complex is the living room 1482 BTU / h, Room I 1953 BTU / h, Room II 800 BTU / h. 1 AC with a capacity of ½ PK in the room. 1 AC with a capacity of ½ PK in each room.

Variation in the Impact of ENSO on the Western Pacific Pattern Influenced by ENSO Amplitude in CMIP6 Simulations

AbstractThe Western Pacific pattern (WP) is one of the most consequential atmospheric teleconnections during boreal winter, as it profoundly influences weather extremes and climate variability across Eurasia and North America. The El Niño‐Southern Oscillation (ENSO) is suggested to be a crucial external forcing of WP variability. In this study, we meticulously evaluated the capability of 56 coupled climate models from the Coupled Model Intercomparison Project 6th phase (CMIP6) in replicating the winter ENSO‐WP connection. Our results reveal notable diversities in the portrayal of the winter ENSO‐WP relation across CMIP6 models. This model diversity in the ENSO‐WP connections primarily stems from the spread of the models' simulated ENSO amplitude. Specifically, models with enhanced ENSO amplitudes exhibit intensified sea surface temperature (SST) anomalies in the equatorial central‐eastern Pacific. This enhancement of SST anomalies faciliates stronger atmospheric convective anomalies, especially in the extratropical eastern Pacific and the tropical western North Pacific. Of particular note is the intensified atmospheric convective anomalies in the tropical western North Pacific, which exert a pivotal role in shaping the WP through initiating a distinct atmospheric teleconnection pattern.

Deep convective cloud system size and structure across the global tropics and subtropics

Abstract. A new database is constructed comprising millions of deep convective clouds that spans the global tropics and subtropics for the entire record of the MODIS instruments on the Terra and Aqua satellites. The database is a collection of individual cloud objects ranging from isolated convective cells to mesoscale convective cloud systems spanning hundreds of thousands of square kilometers in cloud area. By matching clouds in the database with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis dataset and microwave imager brightness temperatures from the Advanced Microwave Scanning Radiometer – EOS (AMSR-E) instrument, the database is designed to explore the relationships among the horizontal scale of cloud systems, the thermodynamic environment within which the cloud resides, the amount of aerosol in the environment, and indicators of the microphysical structure of the clouds. We find that the maximum values of convective available potential energy and vertical shear of the horizontal wind associated with a cloud impose a strong constraint on the size attained by a convective cloud system, although the relationship varies geographically. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems. The observed relationships between cloud system size and structure from this database can be compared with similar relationships derived from simulated clouds in atmospheric models to evaluate the representation of clouds and convection in weather forecast and climate projection simulations, including whether models exhibit the same relationships between the atmospheric environment and cloud system size and structure. Furthermore, the dataset is designed to probe the impacts of aerosols on the size and structure of deep convective cloud systems.

Climate influence on compound solar and wind droughts in Australia

Solar photovoltaic and wind power are central to Australia’s renewable energy future, implying an energy sector vulnerable to weather and climate variability. Alignment of weather systems and the influence of large-scale climate modes of variability risks widespread reductions in solar and wind resources, and could induce grid-wide impacts. We therefore systematically analyse the relationship between compound solar radiation and wind speed droughts with weather systems and climate modes of variability over multiple time scales. We find that compound solar and wind droughts occur most frequently in winter, affecting at least five significant energy-producing regions simultaneously on 10% of days. The associated weather systems vary by season and by drought type, although widespread cloud cover and anticyclonic circulation patterns are common features. Indices of major climate modes are not strong predictors of grid-wide droughts, and are typically within one standard deviation of the mean during seasons with the most widespread events. However, the spatial imprints of the teleconnections display strong regional variations, with drought frequencies varying by more than ten days per season between positive and negative phases of climate modes in some regions. The spatial variability of these teleconnection patterns suggests that droughts in one region may be offset by increased resource in another. Our work highlights the opportunity for minimising the impact of energy production variability by utilising weather and climate intelligence. Exploiting the spatial variability associated with daily weather systems and the seasonal influence of climate modes could help build a more climate-resilient renewables-dominated energy system.

The Insp-AIR-ation (Art + Science Project)

For many emerging into the realisation that the climate crisis is here and present and that we will all be affected, there is a feeling of being overwhelmed and the sense that we are standing on the edge of a precipice. In these times of uncertainty and fear, artist interventions have a capacity to engage with these embodied experiences and bring a sense of hope to the conversation through creative reflective engagement. The act of making can reduce anxiety and is a way for people to express themselves as they enter this phase of climate adaptation. As UK activist group, Culture Declares Emergency, puts it, ‘creativity is the antidote of despair’ (2019). Through the Insp-AIR-ation ArtScience community project, artist facilitators focused on perceptions of air quality. The ways in which we organise our collective lives are very influential on weather cycles and climate rhythms. Through the kaupapa (guiding process) in this emergent project, we sought to give voice to the concerns of community groups, their hopes, and aspirations. This arts project provided people with a space and platform to shape their feelings and express values. While science has been pivotal in highlighting the precariousness of our current way of living, the arts have never been more important than now in expressing who we are and shaping a positive response towards a liveable and just future for all. Commenting on the Climate Crisis, former US advisor, Gus Speth has called for a social response and culture as a necessary agent for bringing about transformational change. Basarab Nicolescu, in La transdisciplinarité: Manifeste (1996), talks about building bridges between science and our ways of being through symbolic language which is enriched by the originating values of the community. This project is one such response in building bridges.

Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method

Abstract. A particle-based cloud model was developed for meter- to submeter-scale-resolution simulations of warm clouds. Simplified cloud microphysics schemes have already made meter-scale-resolution simulations feasible; however, such schemes are based on empirical assumptions, and hence they contain huge uncertainties. The super-droplet method (SDM) is a promising candidate for cloud microphysical process modeling and is a particle-based approach, making fewer assumptions for the droplet size distributions. However, meter-scale-resolution simulations using the SDM are not feasible even on existing high-end supercomputers because of high computational cost. In the present study, we overcame challenges to realize such simulations. The contributions of our work are as follows: (1) the uniform sampling method is not suitable when dealing with a large number of super-droplets (SDs). Hence, we developed a new initialization method for sampling SDs from a real droplet population. These SDs can be used for simulating spatial resolutions between meter and submeter scales. (2) We optimized the SDM algorithm to achieve high performance by reducing data movement and simplifying loop bodies using the concept of effective resolution. The optimized algorithms can be applied to a Fujitsu A64FX processor, and most of them are also effective on other many-core CPUs and possibly graphics processing units (GPUs). Warm-bubble experiments revealed that the throughput of particle calculations per second for the improved algorithms is 61.3 times faster than those for the original SDM. In the case of shallow cumulous, the simulation time when using the new SDM with 32–64 SDs per cell is shorter than that of a bin method with 32 bins and comparable to that of a two-moment bulk method. (3) Using the supercomputer Fugaku, we demonstrated that a numerical experiment with 2 m resolution and 128 SDs per cell covering 13 8242×3072 m3 domain is possible. The number of grid points and SDs are 104 and 442 times, respectively, those of the highest-resolution simulation performed so far. Our numerical model exhibited 98 % weak scaling for 36 864 nodes, accounting for 23 % of the total system. The simulation achieves 7.97 PFLOPS, 7.04 % of the peak ratio for overall performance, and a simulation time for SDM of 2.86×1013 particle ⋅ steps per second. Several challenges, such as incorporating mixed-phase processes, inclusion of terrain, and long-time integrations, remain, and our study will also contribute to solving them. The developed model enables us to study turbulence and microphysics processes over a wide range of scales using combinations of direct numerical simulation (DNS), laboratory experiments, and field studies. We believe that our approach advances the scientific understanding of clouds and contributes to reducing the uncertainties of weather simulation and climate projection.

Spatiotemporal Variations in Precipitation Forecasting Skill of Three Global Subseasonal Prediction Products over China

Abstract Subseasonal to seasonal (S2S) predictions, which bridge the gap between weather forecasts and climate outlooks, have the great societal benefits of improving water resource management and food security. However, there are tremendous disparities in the forecasting skills of subseasonal precipitation prediction products. This study investigates the spatiotemporal variations in the precipitation forecasting skill of three subseasonal prediction products from the CMA, ECMWF, and NCEP over China. Daily precipitation predictions with lead times ranging from 1 to 30 days and cumulative precipitation predictions over 1–30 days were evaluated in nine major river basins. The daily prediction skill rapidly declines with lead time. In contrast, the correlation coefficient between the cumulative precipitation predictions and corresponding observations increases at first and peaks at 0.7–0.8 after 3–5 days, then gradually decreases and settles at approximately 0.2–0.6. Among the three evaluated models, the ECMWF model demonstrates the best skill, maintaining a correlation coefficient of approximately 0.5 for 2-week cumulative precipitation. Moreover, the correlation coefficient of the model’s prediction is 0.2–0.5 higher than that of the climatological prediction over a large domain for the 30-day cumulative precipitation during the rainy summer. Similarly, the equitable threat score for forecasting below- and above-normal precipitation events presents good results in eastern China but is affected by biases of raw predictions. The variations in the subseasonal prediction skill at different time scales reveal the potential values of cumulative precipitation predictions. The findings of this study can provide practical information for applications that prioritize the long-term aggregation of hydrometeorological variables. Significance Statement The daily and cumulative precipitation prediction skills of three subseasonal prediction products were evaluated over China in this study. Our results reveal the spatiotemporal variations in prediction skill, especially with respect to time scale. Compared to daily precipitation predictions, cumulative precipitation predictions are more skillful, with correlation coefficients peaking at 0.7–0.8 after 3–5 days. These results can provide valuable information for water resource managers who are more concerned with the general conditions over a period than with hydrometeorological events occurring on a particular day. This study can guide end users in applying appropriate time scales to fully exploit numerical weather prediction information and satisfy their specific needs.