Climate Forecast Research Articles

Effects of seasonal climate forecasts on livelihood outcomes: a case of smallholder farmers in rural Zambia

In as much as seasonal climate forecasts are an essential tool for supporting livelihoods in rainfed agriculture production, the effects of seasonal climate forecasts on income and yield at the agro ecological zone and wealth quartile levels are not sufficiently researched in Zambia. In order to close this gap, we focused on investigating the impacts of using climate information on productivity and income, while using an endogenous switching regression on a representative subnational and national level data from 7241 agricultural households. Using STATA 17 for analysis, the study found that smallholder farmers who integrated seasonal climate forecasts outperformed those who did not have the information, achieving higher yields (421.54 kg/ha). The localized effects of seasonal forecasting by agro ecological zone indicated that low rainfall areas had significant yield gains than other zones, and wealth results for wealth quartiles showed that farmers in higher quartiles had better yield (126.14 kg/ha) and income (1509.44 kg/ha) gains.. This study therefore recommends that policymakers develop policies and strategies that are region-specific to foster climate awareness to and integration of adaptation techniques by farmers. Additionally, the study also recommends the provision of social safety net to less-endowed farmers as well as investing in better early warning systems to enhance participation in using forecasted information for a sustainable future.

Climate Smart Agriculture Opportunities and Challenges in Afghanistan

Agriculture in dryland regions is directly affected by climate change. In Afghanistan, climate-smart agriculture (CSA) improves productivity resilience and reduces greenhouse gas emissions (GHG). However, climate change, water shortage, population growth, and food insecurity are major challenges in developing countries such as Afghanistan. In the face of climate change, traditional agriculture methods need to be transformed into CSA that are capable of improving food production within the constraints of climate change. A review study was conducted to identify opportunities and challenges for CSA in Afghanistan. Among the many CSA technologies in Afghanistan, the review found, the use of drought resistance varieties, implementation of conservation agriculture, the use of Zai and Half-moon techniques for planting pits, moisture conservation, rainwater harvesting, watershed management, drip irrigation system, soil carbon sequestration, control erosion techniques, and climate forecast services are the best opportunities and as promising options for risk management and adaptation to climate change. Still, unfortunately, most of these options have not yet been implemented by Afghan farmers, just followed by researchers. In addition, CSA in Afghanistan faces several solvable challenges. Limited understanding of the CSA concept and framework, limited investment to develop and implement CSA technologies, inadequate communication between government, policymakers, farmers, climate change impacts, GHG, and traditional agriculture. The review recommended that CSA practices be suitable options for all stakeholders to increase income and ensure food security and sustainable agriculture by adaptation practices against climate change. This review provides new strategies and ideas for strengthening sustainable agriculture, food security, environmental protection, and mitigating the impact of climate change.

Climate resilience and profitability thresholds in Chesapeake Bay oyster aquaculture

Shellfish aquaculture producers in coastal systems are facing uncertain future growing conditions as climate change alters weather patterns and raises sea level. We examined expected mid-century (2059–2068) changes in aquaculture profitability from recent conditions by integrating models of climate change, estuarine hydrodynamics and biogeochemistry, oyster growth, oyster mortality, and economics, using the Chesapeake Bay, USA as a case study. We developed an economic stochastic dynamic programming (SDP) approach that generates optimal grower behavior to maximize profits under uncertainty by dynamically choosing planting density, replanting and mitigation use, in response to changing oyster stock status and water quality conditions. Separate models were developed for bottom culture largely serving the cannery market, and container culture largely serving the half-shell market, to reflect different production costs, market prices, and oyster growth and survival. The coupled hydrodynamic-biogeochemical and oyster ecology models projected high spatial variability in oyster growth and mortality with the most favorable growing conditions in the lower north and upper mid bay, where mortality is lowest, and the upper south bay, where growth is highest. Climate change by late mid-century generated modest water quality changes and virtually no mortality rate changes. Nonetheless, our modeling revealed that even if growers made optimal management choices under uncertainty, the majority of modeled sites would see a decline in profitability under climate change, primarily due to potential reductions in food availability. Bottom culture was more resilient to the future climate at most sites, being less sensitive to small changes in growth than container culture. Information on how aquaculture conditions currently vary in space was more important for profitability than future climate forecasts. Our stochastic dynamic programming approach tailored grower behavior to each site and unfolding annual conditions, including highly targeted and cost-effective mitigation adjustments to boost oyster survival or growth.

Synergizing climate dynamics, species distribution, and structural parameters for sustainable management of Pseudrocedrela kotschyi in Benin (West Africa)

Climate change poses significant threats to biodiversity, profoundly impacting plant species. Pseudocedrela kotschyi, a vital component of tropical ecosystems is experiencing severe population decline due to habitat degradation and regeneration issues. Understanding how climate change exacerbates these problems combined with assessment of its structural parameters is essential for developing conservation strategies and ensuring the survival of this species. Occurrence records and bioclimatic data were utilized for niche modeling. Additionally, forest inventories were conducted across various habitats of the species in Benin, to characterize the structural parameters of its populations. An ecological niche modeling approaches was used to depict niche differentiation among subpopulations of Pseudocedrela kotschyi and assess the climate change impacts on the sepecies in Benin. The Maximum Entropy Algorithm (MaxEnt) was used to simulate the species’ current and future distributions under different shared socio-economic pathway (SSP) climate scenarios. Three distinct populations of the species were identified. The populations exhibited no niche overlap among subpopulations indicating local adaptation. In non-protected areas, P. kotschyi individuals tend to be smaller in size. This trend is particularly pronounced in the Sudano-Guinean zone, where protected habitats benefit from a higher monthly thermal amplitude. In contrast, in the Guineo-Congolian zone, both protected and unprotected habitats are influenced by the minimum temperature of the coldest month and the mean annual temperature, leading to higher densities of regeneration and adults of P. kotschyi. In the Sudanian zone, regardless of the protection status, higher annual rainfall supports larger mean diameters for P. kotschyi and its population. This study underscores the importance of preserving the species' habitats, regulating potentially harmful human activities, and incorporating future climate forecasts into management plans to ensure the sustainable conservation of P. kotschyi.

Dashboard for interpreting future climate files used in the simulation of buildings – An outdoor thermal comfort approach

The Earth’s changing climate significantly affects the built environment, including thermal comfort and energy demand. This study introduces a code pipeline and a dashboard to create and interpret future climate files (EPW format) used in building simulations, focusing on outdoor thermal comfort. The code pipeline uses the Future Weather Generator (FWG) to generate city climate forecasts. It also integrates the climatic data with thermal comfort indices like the Universal Thermal Climate Index (UTCI) and Discomfort Index (DI). Then, a dashboard is used to assess the country’s outputs and provide a comprehensive tool for researchers, policymakers, and practitioners to assess and mitigate climate change impacts on buildings and cities, ensuring accurate inputs for thermal comfort and energy efficiency modelling. A case study was carried out in Brazil, simulating all 578 available EPW files. Predictions indicate an overall increase in dry bulb temperature and changes in the country’s relative humidity, radiation, and wind speed. Given the specific changes in different bioclimatic zones, a tailored tool is essential for understanding local conditions. The key benefit is the creation of up-to-date validated weather files crucial for climate-adaptive planning.

A Reanalysis Precipitation Integration Method Utilizing the Generalized Three-Cornered Hat Approach and High-Resolution, Gauge-Based Datasets

The development of high-precision, long-term, hourly-scale precipitation data is essential for understanding extreme precipitation events. Reanalysis systems are particularly promising for this type of research due to their long-term observations and wide spatial coverage. This study aims to construct a more robust precipitation dataset by integrating three widely-used reanalysis precipitation estimates: Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). A novel integration method based on the generalized three-cornered hat (TCH) approach is employed to quantify uncertainties in these products. To enhance accuracy, the high-density daily precipitation data from the Asian Precipitation-Highly-Resolved Observation Data Integration Towards Evaluation (APHRODITE) dataset is used for correction. Results show that the TCH method effectively identifies seasonal and spatial uncertainties across the products. The TCH-weighted product (TW), calculated using signal-to-noise ratio weighting, outperforms the original reanalysis datasets across various watersheds and seasons. After correction with APHRODITE data, the enhanced integrated product (ATW) significantly improves accuracy, making it more suitable for extreme precipitation event analysis. Quantile mapping was applied to assess the ability of TW and ATW to represent extreme precipitation. Both products showed improved accuracy in regional average precipitation, with ATW demonstrating superior improvement. This integration method provides a robust approach for refining reanalysis precipitation datasets, contributing to more reliable hydrological and climate studies.

Seasonal Upwelling Forecasts in the California Current System

AbstractCoastal upwelling plays a vital role in the support and maintenance of productive marine ecosystems throughout the California Current System (CCS). Here, we evaluate upwelling forecast skill using ∼30 years of seasonal reforecasts from four global climate models contributing to the North American Mulitmodel Ensemble (NMME). The models skillfully predict upwelling intensity throughout much of the CCS in boreal winter, and in the South‐Central CCS in spring/summer. The models also skillfully predict various aspects of upwelling phenology, including the timing of the spring transition, as well as the total vertical transport integrated over the course of the upwelling season. Climatic sources of forecast skill vary with season, with contributions from the El Niño‐Southern Oscillation in winter‐spring, and the North Pacific Oscillation and the North Pacific Meridional Mode in the winter‐summer. Our results highlight the potential of seasonal climate forecasts to inform management of upwelling‐sensitive marine resources.

Tropical Cyclone Wind Shear-Relative Asymmetry in Reanalyses

Abstract While tropical cyclones (TCs) are axisymmetric vortices to the first order, they often exhibit noteworthy structural asymmetries. These often result from environmental vertical wind shear, which tilts the vortex and induces a wavenumber 1 pattern in the circulation and precipitation fields. Reanalyses and climate models have improved in representing the TC structure and climatology, but their relatively coarse resolution and dependence on parameterized physics cast doubt on their ability to capture the asymmetric TC structure. We perform the most comprehensive process-oriented assessment of TC asymmetry to date in reanalyses. Specifically, we analyze the composite shear-relative TC structure in ERA5 and Climate Forecast System Reanalysis (CFSR), which vary in their resolutions, physical parameterization suites, and data assimilation techniques. These structures are compared with aircraft reconnaissance radar observations. In agreement with the observations, the strongest tangential winds are usually found left-of-shear, while inner core rainfall, ascent, vortex tilt, and low-level inflow are favored directly downshear or in the downshear-left quadrant. Outer rainband convection generally peaks in the downshear-right quadrant. Thermodynamic asymmetries are also apparent, with anomalous low-level moisture right-of-shear, midlevel warmth in the upshear-right quadrant (uptilt), and cloud properties suggestive of a realistic precipitation life cycle from growth to fallout. We also decompose rainfall contributions from the convective parameterization and large-scale cloud schemes and highlight the roles of vorticity advection, buoyancy advection, and diabatic processes in driving asymmetric vertical motions in the inner core and outer rainband regions. Our results suggest that process-level studies of TC asymmetry and TC–wind shear interaction under future warming are viable using climate models. Significance Statement Asymmetries are common in tropical cyclones (TCs), influencing their intensity, track, and hazards. Vertical wind shear often plays a leading-order role in causing these asymmetries. It is uncertain how well asymmetric structures and processes are captured in reanalyses and global climate models (GCMs) with grid spacings of 0.25° and coarser. In this study, we first evaluate TC asymmetry in reanalyses, which have the benefit of being forced by observations. This helps to assess whether the resolutions associated with GCMs sufficiently capture asymmetric structures and processes and motivates upcoming work with free-running GCMs to study how TC asymmetry may change in a warming climate.

Application of the Standardized Precipitation Index for Forecasting Seasonal Drought in Vietnam

Drought is a natural disaster that causes significant damages to the economy, society and environment. Forecasting droughts is crucial for disaster risk management and water security. This study introduces a new method for forecasting seasonal droughts in Vietnam using the Standardized Precipitation Index (SPI) which is based on a combination of dynamic modeling and statistical methods. The precipitation forecast products from regional climate models (RegCM, clWRF) with inputs from Climate Forecast System (CFSv2) were used to calculate the SPI index after statistical calibration. Results show that calibrating model precipitation with statistical methods such as Artificial Neural Networks (ANN) and Multivariate Linear Regression (MLR) significantly improves the precipitation forecast accuracy. Evaluation indices indicate that the SPI calculated with the post-calibration model precipitation has a good reproducibility for drought events, particularly mild droughts. However, there remains some limitations on accurate forecast of the intensity and spatial distribution of moderate drought events in certain regions such as Central Vietnam.

Improvement in the Low Temperature Prediction Skill During Cold Winters Over the Mid–High Latitudes of Eurasia in CFSv2

ABSTRACTRegional cold winters have occurred frequently in Eurasia since the beginning of the 21st century, increasing the interannual variability in winter temperatures and increasing the difficulty of prediction. In this study, we evaluate the performance of Climate Forecast version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP) in predicting winter temperature anomalies over the Northern Hemisphere and find that CFSv2 has significantly lower temperature prediction ability for cold winters in the mid–high latitudes of Eurasia since the 21st century. This is mainly due to the stronger response to global warming and the weaker response to sea ice anomalies in the preceding autumn in CFSv2 than the in reanalysis. Accordingly, two targeted correction methods have been developed to improve the prediction ability, with the first method removing the linear temperature trend of CFSv2 predictions and the second method considering the effects of autumn Arctic Sea ice anomalies via a dynamical–statistical correction approach (DSCA). Both methods can effectively improve the prediction ability of winter temperature anomalies in the mid–high latitudes of Eurasia, especially in cold winters. The anomaly correlation coefficient (ACC) increased from −0.03 to 0.13 before and after the modification by the DSCA, and from −0.12 to 0.25 for cold winters. The DSCA significantly reduced the root mean square error (RMSE) of the CFSv2 predictions by approximately 10%.

Abstract 4145826: Atrial Fibrillation Screening During Sinus Rhythm Periods by Interrelated Systems Dynamics Analysis

Background: Previous studies have shown that AF screening in at-risk populations can reduce stroke incidence. However, non-targeted screening approaches often result in high false positive rates, placing an unnecessary burden on the healthcare system. In contrast, artificial intelligence-guided screening has been demonstrated to increase diagnostic yield in large prospective clinical trials. This approach, however, requires recording an ECG and a large-scale dataset for model training. Heart rate variability (HRV) analysis has proven effective in deciphering key heart dynamics. By analyzing HRV as interrelated dynamic systems, it may be possible to facilitate targeted AF screening using wearable devices that measure heart rate. The Koopman operator, used for data-driven modeling of interrelated dynamic systems, has been shown to accurately predict complex phenomena in chaotic systems such as climate forecasting and drug adverse reaction prediction. This is achieved by utilizing common characteristics of the systems for most model parameters, with only a small fraction of the parameters being specific to a certain system. Methods: Long (>10 hour) records from 361 individuals (AFDB, LTAFDB) and healthy individuals' datasets from PhysioNet and THEW were analyzed for inter-beat intervals. The unified dataset was then split into 94 training, 17 validation, and 250 test set patients. Recordings from the training set were used to train both the common and specific parts of the interrelated dynamic systems model for each patient, along with a shared small neural network classifying patients into low and high risk for AF based on the unique (not shared between patients) singular values of the dynamic system model. Patient-specific dynamic system models were then fitted for the validation and test sets to calculate the patient dynamic singular values, which were used to classify patients into low and high risk for AF groups. Results: Atrial fibrillation occurred in 48 of 202 (23%) patients classified as low risk and 35 of 48 (72.9%) patients classified as high risk (odds ratio 8.63, 95% CI 4.23-17.64), yielding 72.9% sensitivity with 76.2% specificity. Conclusion: In this retrospective analysis, classification of the dynamic system model singular values identified patients at high risk for atrial fibrillation from sinus rhythm period.

The curious case of a strong relationship between ENSO and Indian summer monsoon in CFSv2 model

An ensemble of forecasts is necessary to identify the uncertainty in predicting a non-linear system like climate. While ensemble averages are often used to represent the mean state and diagnose physical mechanisms, they can lead to information loss and inaccurate assessment of the model’s characteristics. Here, we highlight an intriguing case in the seasonal hindcasts of the Climate Forecast System version 2 (CFSv2). While all ensemble members often agree on the sign of predicted El Niño Southern Oscillation (ENSO) for a particular season, non-ENSO climate forcings, although present in some of the individual members, are disparate. As a result, an ensemble mean retains ENSO anomalies while diminishing non-ENSO signals. This difference between ENSO and non-ENSO signals significantly influences moisture convergence and Indian summer monsoon rainfall (ISMR). This stronger influence of ENSO on seasonal predictions increases ENSO–ISMR correlation in ensemble mean seasonal hindcasts. Thus, this discrepancy in the ENSO–ISMR relationship is not present in the individual ensemble members, considered individually or together (without averaging) as independent realizations. Therefore, adequate care should be taken while evaluating physical mechanisms of teleconnection in ensemble mean predictions that can often be skewed due to constructive or destructive superposition of different impacts.

A stochastic precipitating quasi-geostrophic model

Efficient and effective modeling of complex systems, incorporating cloud physics and precipitation, is essential for accurate climate modeling and forecasting. However, simulating these systems is computationally demanding since microphysics has crucial contributions to the dynamics of moisture and precipitation. In this paper, appropriate stochastic models are developed for the phase-transition dynamics of water, focusing on the precipitating quasi-geostrophic (PQG) model as a prototype. By treating the moisture, phase transitions, and latent heat release as integral components of the system, the PQG model constitutes a set of partial differential equations (PDEs) that involve Heaviside nonlinearities due to phase changes of water. Despite systematically characterizing the precipitation physics, expensive iterative algorithms are needed to find a PDE inversion at each numerical integration time step. As a crucial step toward building an effective stochastic model, a computationally efficient Markov jump process is designed to randomly simulate transitions between saturated and unsaturated states that avoids using the expensive iterative solver. The transition rates, which are deterministic, are derived from the physical fields, guaranteeing physical and statistical consistency with nature. Furthermore, to maintain the consistent spatial pattern of precipitation, the stochastic model incorporates an adaptive parameterization that automatically adjusts the transitions based on spatial information. Numerical tests show the stochastic model retains critical properties of the original PQG system while significantly reducing computational demands. It accurately captures observed precipitation patterns, including the spatial distribution and temporal variability of rainfall, alongside reproducing essential dynamic features such as potential vorticity fields and zonal mean flows.

Advancing global solar photovoltaic power forecasting with sub-seasonal climate outlooks

Given the high weather dependency of solar photovoltaic energy, accurate weather information ranging from days to weeks in advance are required for stable plant operation and management. This study emphasizes that sub-seasonal climate outlooks can advance global solar power forecasting. We evaluate the capacity factor (CF) for standard test conditions (CFstc), calculated from atmospheric reanalysis of surface solar irradiance and ambient air temperature, by comparing it with actual CF from two solar plants in Korea. Results confirm CFstc as a reliable estimate of actual CF. Application of this methodology to four-week outlooks from two state-of-the-art sub-seasonal climate forecast systems shows a significant anomaly correlation coefficient (ACC) skill for global solar power forecasting at least one week in advance (5–11 forecast days). Regional ACC skills vary at extended lead times, with South Asia, eastern South America and eastern Australia maintaining ACC > 0.6 for up to two weeks (12–18 forecast days). Notably, useful ACC skill persists for up to four weeks (26–32 forecast days) across 70.9 % of global land areas. These findings suggest that sub-seasonal climate outlooks can provide decision-making information in developing more reliable solar energy strategies, highlighting the importance of transdisciplinary collaboration between renewable energy and meteorological sectors.

Comparing Satellite, Reanalysis, Fused and Gridded (In Situ) Precipitation Products Over Türkiye

ABSTRACTPrecipitation is the fundamental source for various research areas, including hydrology, climatology, geomorphology, and ecology, serving essential roles in modelling, distribution, and process analysis. However, the accuracy and precision of spatially distributed precipitation estimates is a critical issue, particularly for daily scale and topographically complex areas. Although many datasets have been developed based on different algorithms and sources are developed for this purpose, determining which of these datasets best reflects actual conditions is quite challenging. This study, hence, aims to compare the 25 global distributed precipitation estimates (gridded, satellite, model, and fused) concerning 221 ground‐based observations based on the ranking of 18 continuous (evaluation statistics), eight categorical (precipitation indices), and two seasonality metric (high and low precipitation). Upon examining the results, gridded and model precipitation data including APHRODITE (Asian Precipitation—Highly‐Resolved Observational Data Integration Towards Evaluation), CPC (Global Unified Gauge‐Based Analysis of Daily Precipitation), ERA5‐Land (ECMWF Reanalysis 5th Generation for Lands), and CFSR (Climate Forecast System Reanalysis) occupy the top four positions in continuous metrics. In contrast, satellite data such as PERSIANN‐PDIR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), CMORPH (Climate Prediction Center morphing method), IMERG (The Integrated Multi‐Satellite Retrievals for GPM), and TRMM‐TMPA (Tropical Rainfall Measuring Mission/Multi‐satellite Precipitation Analysis) dominate in the top four positions in categorical metrics. For seasonality of high and low precipitation, fused, gridded, and reanalyses products such as CPC, MSWEP (Multi‐Source Weighted‐Ensemble Precipitation, version 2), HydroGFD (Hydrological Global Forcing Data), CFSR rank among top four. Based on the first five rankings of all metrics, fused (multiple sourced) and gridded datasets accurately reflect the actual situations compared to other precipitation products. Reanalysis (model) and satellite‐based follow this rank, respectively. The results clearly indicate that fused precipitation derived products from multiple sources offer better accuracy and precision in representing the spatial distribution of precipitation on a daily scale.

Integrated risk analysis for urban flooding under changing climates

Urban flooding poses significant threats to human lives and urban development worldwide, while the impact of climate change on urban flooding remains unclear. To systematically analyze the variabilities of hydrological patterns and urban flooding under changing climates, this study coupled the downscaled general circulation model (GCM) projection with hydrologic-hydraulic modeling, with consideration of both the low-greenhouse gas (GHG) emission scenario of SSP1–2.6 and the high-GHG emission scenario of SSP5–8.5. Results demonstrated that the original GCM projection effectively captured the changing trend of rainfall patterns in the given area, with an overestimation of rainfall peaks. The GCM downscaling through the k-nearest neighbors (kNN)-based analog method significantly improved the model accuracy. Scenario analysis indicated that climate change significantly affected the regional hydrology, with the precipitation, surface runoff, and floods increasing by a maximum value of 17.10%, 12.66%, and 63.26%, respectively. The interannual comparison demonstrated that the temporal variability in precipitation and flood intensified with the increase in GHG emissions during 2025–2100, suggesting the uncertainty of long-term climate forecasts. According to flood risk analysis, long-term and short-term floods exhibit varied changing trends across climate scenarios, despite the strong correlations between precipitation and runoff, implying the complexity of flood generation mechanisms. The methods and findings herein provide valuable insights for policymakers and practitioners, to cope with the increasing urban flood risk within the context of evolving environment.

Combining the uncombinable: corporate memories, ethnobiological observations, oceanographic and ecological data to enhance climatic resilience in small-scale fisheries

The global food production system is increasingly strained by abrupt and unpredictable weather events, which hinder communities' ability to adapt to climate variations. Despite advances in meteorological predictions, many communities lack the academic knowledge or infrastructure to interpret these complex models. This gap highlights the need for solutions that make climate forecasts more accessible and actionable, especially for communities reliant on natural resources. This study explores the potential of enhancing seasonal climate forecasts by integrating local ecological knowledge (LEK) with scientific data. Specifically, we combined ethnobiological information gathered between 2022 and 2024 with existing oceanographic and ecological data to create an ethnobiological calendar for four fishing cooperatives. An ethnographic approach was used to understand the population's ethnobiological knowledge and their perceptions of marine heatwaves and climate change impacts. Coastal monitoring data was collected using moorings that recorded temperature over a 14-year period (2010–2024). To characterize giant kelp dynamics, we used an existing dataset of multispectral Landsat images, which estimates the surface canopy biomass of giant kelp forests. Ecological monitoring was conducted annually every summer from 2006 to 2023 to record the in situ abundance of ecologically and economically important invertebrate and fish species. Combining oceanographic, ecological, and ethnographic data, allowed for alligning fishers' observations with recorded marine heatwave events and ecological shifts. Our findings revealed that these observations closely matched documented marine heatwave data and corresponding ecological changes. The integration of LEK with scientific oceanographic data can significantly improved our understanding of dynamic climate regimes, offering contextually relevant information that enhances the reliability and utility of seasonal climate forecasts. By incorporating yearly data into an ethnobiological calendar, we promote more inclusive, community-based approaches to environmental management, advocating for the integration of LEK in climate adaptation efforts, emphasizing its crucial role in strengthening resilience strategies against climatic shocks.

The added value of using convective-permitting regional climate model simulations to represent cloud band events over South America

Climate science has long explored whether higher resolution regional climate models (RCMs) provide improved simulation of regional climates over global climate models (GCMs). The advent of convective-permitting RCMs (CPRCMs), where sufficiently fine-scale grids allow explicitly resolving rather than parametrising convection, has created a clear distinction between RCM and GCM formulations. This study investigates the simulation of tropical-extratropical (TE) cloud bands in a suite of pan-South America convective-permitting Met Office Unified Model (UM) and Weather Research and Forecasting (WRF) climate simulations. All simulations produce annual cycles in TE cloud band frequency within 10–30% of observed climatology. However, too few cloud band days are simulated during the early summer (Nov–Dec) and too many during the core summer (Jan–Feb). Compared with their parent forcing, CPRCMs simulate more dry days but systematically higher daily rainfall rates, keeping the total rain biases low. During cloud band systems, the CPRCMs correctly reproduced the observed changes in tropical rain rates and their importance to climatology. Circulation analysis suggests that simulated lower subtropical rain rates during cloud bands systems, in contrast to the higher rates in the tropics, are associated with weaker northwesterly moisture flux from the Amazon towards southeast South America, more evident in the CPRCMs. Taken together, the results suggest that CPRCMs tend to be more effective at producing heavy daily rainfall rates than parametrised simulations for a given level of near-surface moist energy. The extent to which this improves or degrades biases present in the parent simulations is strongly region-dependent.

Physics informed data-driven near-wall modelling for lattice Boltzmann simulation of high Reynolds number turbulent flows

Data-driven approaches offer novel opportunities for improving the performance of turbulent flow simulations, which are critical to wide-ranging applications from wind farms and aerodynamic designs to weather and climate forecasting. However, current methods for these simulations often require large amounts of data and computational resources. While data-driven methods have been extensively applied to the continuum Navier-Stokes equations, limited work has been done to integrate these methods with the highly scalable lattice Boltzmann method. Here, we present a physics-informed neural network framework for improving lattice Boltzmann-based simulations of near-wall turbulent flow. Using a small amount of data and integrating physical constraints, our model accurately predicts flow behaviour at a wide range of friction Reynolds numbers up to 1.0 × 106. In contradistinction with other models that use direct numerical simulation datasets, this approach reduces data requirements by three orders of magnitude and allows for sparse grid configurations. Our work broadens the scope of lattice Boltzmann applications, enabling efficient large-scale simulations of turbulent flow in diverse contexts.

Assessing Subseasonal Forecasts of Dry Spells and Heatwaves at the Regional Scale in Brazil

This study evaluates the performance of subseasonal forecasts for dry spells and heatwaves at a regional scale in Brazil. The forecasts’ verification was designed to provide end-users with relevant information about the forecasts’ quality. The U.K. Met Office model was assessed using a significant sample of weekly forecasts: 552 for dry spells and 240 for heatwaves. The analysis reveals that the overall performance of the forecasts is low, with a chance of detecting an event close to 0.2, indicating that only one out of five observed dry spells is accurately predicted on average. The application of quantile mapping corrections demonstrates improvements in predicting shorter dry spells (up to 5 days) and longer lead times, although the timing of these forecasts often remains inaccurate, leading to increased false alarms. A significant improvement in the forecast quality occurs when categorization by duration is disregarded. The detection chances increase to 0.5−0.7 for dry spells and 0.5 for heatwaves. The Brier Score indicates that the probabilistic forecasts issued by the model are equivalent or less skilful than climatological probabilities. Overall, the findings underscore the challenges in forecasting dry spells and heatwaves in Brazil and highlight the need for ongoing improvements in forecasting methodologies to enhance their reliability and utility for regional decision-making. This research contributes to understanding subseasonal climate forecasting and its implications for managing climate-related risks in Brazil.