Climate Forecast Research Articles

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

AbstractClimate 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.

Simulating lake ice phenology using a coupled atmosphere–lake model at Nam Co, a typical deep alpine lake on the Tibetan Plateau

Abstract. Simulating the ice phenology of deep alpine lakes is important and challenging in coupled atmosphere–lake models. In this study, the Weather Research and Forecasting (WRF) model, coupled with two lake models, the freshwater lake (WRF–FLake) model and the default lake (WRF–CLake) model, was applied to Nam Co, a typical deep alpine lake located in the centre of the Tibetan Plateau, to simulate its lake ice phenology. Due to the large errors in simulating lake ice phenology, related key parameters and parameterizations were improved in the coupled model based on observations and physics-based schemes. By improving the momentum, hydraulic, and thermal roughness length parameterizations, both the WRF–FLake model and the WRF–CLake model reasonably simulated the lake freeze-up date. By improving the key parameters associated with shortwave radiation transfer processes when lake ice exists, both models generally simulated the lake break-up date well. Compared with WRF–CLake without improvements, the coupled model with both revised lake models significantly improved the simulation of lake ice phenology. However, there were still considerable errors in simulating the spatial patterns of freeze-up and break-up dates, implying that significant challenges in simulating the lake ice phenology still exist in representing some important model physics, including lake physics such as grid-scale water circulation and atmospheric processes such as snowfall and surface snow dynamics. Therefore, this work can provide valuable new implications for advancing lake ice phenology simulations in coupled models, and the improved model also has practical application prospects in weather and climate forecasts.

Microphysical Evolution in Mixed-Phase Midlatitude Marine Cold-Air Outbreaks

Abstract Five cold-air outbreaks are investigated with aircraft offshore of continental northeast America. Flight paths aligned with the cloud-layer flow from January through March span cloud-top temperatures from −5° to −12°C, in situ liquid water paths of up to 500 g m−2, while in situ cloud droplet number concentrations exceeding 500 cm−3 maintain effective radii below 10 μm. Rimed ice is detected in the four colder cases within the first cloud pass. After further fetch, ice particle number concentrations reaching 2.5 L−1 support an interpretation that secondary ice production is occurring. Rime splintering is clearly evident, with dendritic growth increasing ice water contents within deeper clouds with colder cloud-top temperatures. Buoyancy fluxes reach 400–600 W m−2 near the Gulf Stream’s western edge, with 1-s updrafts reaching 5 m s−1 supporting closely spaced convective cells. Near-surface rainfall rates of the three more intense cold-air outbreaks are a maximum near the Gulf Stream’s eastern edge, just before the clouds transition to more open-celled structures. The milder two cold-air outbreaks transition to lower-albedo cumulus with little or no precipitation. The clouds thin through cloud-top entrainment. Significance Statement Cold-air outbreaks off of the eastern U.S. seaboard are visually spectacular in satellite imagery, with overcast, high-albedo clouds transitioning to more broken cloud fields. We use data from the recent NASA Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) aircraft campaign to examine the microphysics and environmental context of five such outbreaks. We find the clouds are not ice-deprived, but updrafts still supply significant liquid water. Cloud transitions are encouraged through near-surface rain for the deeper clouds, and otherwise, clouds thin and break through mixing in drier air from above. These observations support understanding and further modeling examining how mixed-phase cloud microphysics affect cloud reflectivity and surface rainfall rates, important for both weather and climate forecasting.

Orthopaedic patients' emergency department attendance behavior in relation to weather conditions: temperature, rain, day and time, and regional thursday effect.

Great variation was noticed in emergency department (ED) afflux in relation to daily weather conditions, including difficulties in arranging the correct number of orthopaedic on-calls on the floor. This study aimed to quantify the association between the number of patients referred to the orthopaedic team from the ED and weather conditions, particularly daily temperature and rain. Data were obtained from a large state research hospital's ED attendance. Weather information for the local area for the year was obtained from the regional meteorological office. Data were evaluated using descriptive statistics, with one-way analysis of variance for the relationship between the day of the week and patient numbers, the Pearson test for correlations between daily ED patient flow and daily temperature and the Spearman correlation test for correlations between daily patient flow and daily rain status. A positive and meaningful correlation existed between daily patient influx and daily temperature, along with a negative correlation between daily patient flow and daily rain. Throughout the year, significantly more patients were referred to the orthopaedic team on Thursdays than on any other day of the week. Patients tended to visit the ED more on warm days and days without rain. These results may help in organising daily, monthly or seasonal on-call orthopaedic team requirements despite the challenges. Incorporating climate forecasts into planning the on-call service might help alleviate the stress on the team.

Unveiling Indian farmers’ adoption of climate information services for informed decision-making: a path to agricultural resilience

ABSTRACT Amidst the intensifying ramifications of climate change on economic conditions, there exists an imperative for farmers to adopt climate forecast information in agricultural decision-making proactively. The research employed an extended theory of planned behaviour to examine the factors influencing farmers’ intentions to incorporate climate information in their decision-making process. Data from 343 male farmers in India were collected using a multi-stage sampling technique and analysed through Partial Least Squares Structural Equation Modelling (PLS-SEM). The results confirm that attitude, subjective norms, perceived behavioural control, perceived benefits and epistemic values significantly and positively affect the farmers’ intention to use climate forecasts in making informed decisions in farming. The findings carry significant importance for advancing adaptation strategies within the agricultural sector, calling for essential measures like fostering the development of farmers’ associations and promoting fruitful collaboration in climate prediction initiatives. Such actions are instrumental in equipping farmers with the necessary tools and knowledge to tackle climate-related challenges and ensure sustainable agricultural practices effectively.

Enhancing climate forecasting with AI: Current state and future prospect

Background The escalating impact of climate change underscores the critical need for advanced and sustainable climate forecasting techniques. This review examines the current state and future prospects of leveraging Artificial Intelligence (AI) for climate forecasting, focusing on enhancing accuracy and identifying complex patterns in large datasets. Methods A systematic bibliometric methodology was employed, analyzing peer-reviewed literature from the past two decades. The study screened 455 articles from Scopus and Web of Science databases using specific keywords related to AI and weather forecasting. After removing duplicates and irrelevant studies, 218 articles were selected for detailed analysis. Bibliometric analysis was conducted using RStudio software to examine publication trends, co-word co-occurrence, and thematic evolution. Results The findings indicate significant growth in AI applications for climate forecasting, particularly from 2014 to 2023. AI techniques such as machine learning, artificial neural networks, and deep learning have shown promise in improving the accuracy of weather forecasts and early warning systems. The thematic analysis identified key themes like numerical weather prediction, feature selection, and neural networks as fundamental areas of research. Additionally, AI-based early warning systems for extreme weather events were highlighted as a crucial application. Below Figure 1. shows the graphical abstract of research Conclusions AI has the potential to significantly enhance climate forecasting by analyzing vast amounts of data and identifying complex patterns. Future research should focus on developing universal AI models, increasing model accuracy with explainable AI techniques, and integrating region-specific forecasts to aid decision-making in various sectors. Addressing ethical concerns and ensuring sustainable AI applications are essential for the responsible deployment of AI in climate forecasting.

In-season maize yield prediction in Northeast China: The phase-dependent benefits of assimilating climate forecast and satellite observations

Various yield forecasting methods have been reported in literature, but the benefits of assimilating seasonal climate forecasts and satellite observations for in-season yield forecasting during different growth stages have rarely been examined using machine learning. By synthesizing census yields, seasonal climate forecasts (SCF) and satellite-based gross primary production (GPP), this study develops a machine learning (i.e., Random Forest)–based in-season maize yield forecasting model for Northeast China, which produces over 40 % of China's total maize production. Based on the dynamically trained model, 11 numerical experiments are conducted to investigate the benefits of assimilating SCF and GPP relative to the experiments without using SCF and GPP for yield prediction in four forecast phases (planting–three leaves, three leaves–jointing, jointing–tasseling, tasseling–milk). Generally, our yield forecasting model exhibits a promising skill with a low bias of 4.11 %−4.97 %, when the observed climate and GPP are used as inputs. When climate forecasts are assimilated into the model by sampling historical climate, satisfactory yield forecasting can be achieved one month before harvest with a bias of 5.50 %−5.63 %. Bias-correcting climate forecast data from dynamical weather forecast models has a larger benefit for yield prediction with a lower bias of 4.77 %−5.06 %. Furthermore, we found a better benefit of assimilating SCF when compared with GPP during the first three forecast phases, although its relative importance decreases substantially towards harvest. Finally, phase-dependent maps indicating the best model are produced for each county, with historical resampling methods performing best in 40 %, 32 %, 27 %, and 21 % of counties from Phase 1 to Phase 4 and dynamical weather forecast models showing greater predictability in 54 %, 58 %, 58 %, and 52 % of counties during Phases 1–4, respectively. This study provides a useful yield forecasting framework for the breadbasket of China, which can be extended to other crops.

Analyzing trend and forecasting of temperature and rainfall in Shimla district of Himachal Pradesh, India using non-parametric and bagging REPTree machine learning approaches

The changing pattern of climate variables has caused extreme weather events and severe disasters, especially in mountainous regions. Such events have a detrimental impact on resources, environment and society. Thus, it has become imperative to examine the trends and forecasts of meteorological variables using a scientific modelling approach. This study investigates temperature and rainfall trends using the modified Mann-Kendall test and Sen's slope estimator between 1980 and 2021. A Bagging-REPTree machine learning model was utilized for forecasting temperature and rainfall trends for the next 30 years (2022–2051) to understand the temporal dynamics in Shimla district of the Indian Himalayan state. The mean absolute percentage error, mean absolute error, root mean squared error and correlation coefficient were determined to assess the effectiveness and precision of the model. The findings revealed that the frequency of intense rainfall in the district has increased during the monsoon season (June–September) from 1980 to 2021. Significant trends were found in annual rainfall, maximum, minimum and mean temperatures while rainfall during the winter, summer and post-monsoon seasons has shown a declining trend. The forecast analysis revealed a significant trend for rainfall during the monsoon season and an increasing trend in the maximum temperature has been observed during the winter and summer seasons. The analysis has provided sufficient evidence of variability and uncertainty in the behavior of meteorological variables. The outcome of the study may help in devising suitable adaptation and mitigation strategies to combat climate change in hilly regions. The methodology adopted in the study may help in the future progression of the research in different geographical regions for trend and climate forecasting.

Forecasting Climate Change Patterns to Improving Rice Harvest Using SVR for Achieving Green Economy

The consistently declining rice harvest will cause several economic and environmental problems. The unstable and unpredictable climate change was believed as the main problem of the declining rice harvest. We proposed a method for forecasting climate change to help the farmer in their rice cultivation. We used Support Vector Regression (SVR) to improve algorithm steps such as normalizing the data and applying an Adaptive Linear Combiner (ALC) to optimize the dataset before we processed it with the algorithm. Our model gets 95% accuracy as measured with the confusion matrix. We believe our model will help the farmers in their rice cultivation with good climate forecasting. A further benefit of this research we belief that with the well-forecasted climate, the usage of pesticides will decrease and will help the vision of the Indonesian government with a green economy

Seasonal climate forecasts show skill in predicting winter chill for specialty crops in California

Many fruits and nuts crops in California require sufficient winter chill to break dormancy, and insufficient chill can harm fruit quantity and quality. Early information on winter chill forecast can help growers prepare for a low chill year. Here we evaluate use of dynamic climate models for chill accumulation forecast in California. Using temperature forecasts from seasonal prediction systems, we found that the multimodel forecasts can predict chill. This is evident from the anomaly correlation coefficients exceeding 0.5 between the model-predicted and reference chill values for most California regions. The forecasts correctly identified chill categories in over 50% instances in more than 40% of the Central Valley and southern parts of California. The forecasts also demonstrated skill in capturing the interannual variability of chill, especially during years with substantial decrease in chill. Additionally, the seasonal forecast can provide potentially useful crop specific chill sufficiency prediction. However, forecasts beyond a one-month lead time showed reduced forecast skills.

Potential cloud precipitation capacity in typical regions over China

Based on the CLoud, Albedo and RAdiation dataset, AVHRR-based, version 2 (CLARA-A2), Tropical Rainfall Measuring Mission 3B43 (TRMM-3B43), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) reanalysis data, the potential cloud precipitation capacity (PCPA) of typical regions in China is compared, and the relationship between impact factors and PCPA is discussed. Results have suggested that the Tarim Basin (TB) has scarce cloud water resources, while cloud water path (CWP) values are higher in South China (SC) and Sichuan Basin (SB) under the influence of the East Asian monsoon. Moreover, different typical regions of China exhibit varying dependencies on the ice water path (IWP) and liquid water path (LWP). There is a strong correlation between the IWP and precipitation in the Tibet Plateau (TP), Northeast China (NE), SC, and SB. The precipitation in TB demonstrates a more pronounced correlation with the LWP. Through a comparison of the correlation between PCPA and influencing factors in different typical regions of China, it is found that convective available potential energy (CAPE), surface latent heat flux (SLHF), surface sensible heat flux (SSHF), and 0–3 km relative humidity (RH) exhibit stronger correlation with PCPA than 2 m temperature (T2m) and 2–5 km vertical wind shear (SHEAR). Further investigation revealed that the joint effect of CAPE, RH, and SLHF has a pronounced effect on PCPA, particularly during spring and autumn. Additionally, the PCPA of TP exhibits significant dependency on the joint effect of these three influential factors. Furthermore, the ratio of LWP to IWP (RLI) also affects PCPA. In spring and autumn, the PCPA of TB and NC exhibits a positive correlation with RLI, whereas the PCPA of TP, SC, NE, and SB shows a negative correlation with RLI. In summer, the PCPA of TB and SC exhibits a notably negative correlation with RLI. This study deepens the understanding of the formation mechanism of cloud precipitation in typical regions of China, provides the basis for climate forecast and improves the accuracy of weather forecast.

Evaluation of precipitation and temperature of the CFSR (Climate Forecast System Reanalysis) in the Mayo River basin

La información climatológica es importante en la aplicación e investigación en distintas disciplinas relacionadas con los recursos naturales y el medio ambiente, sin embargo su adquisición puede ser difícil y demandar mucho tiempo, particularmente en zonas montañosas (Kouwen et al., 2005). Las estaciones meteorológicas terrestres no siempre representan de modo adecuado el clima que ocurre en las regiones de estudio o cuenca hidrográficas por su falta de cobertura geográfica, además de que presentan vacíos de información en sus series de datos. Ante tal problemática, en el presente trabajo de investigación se evaluaron datos mensuales de precipitación, temperatura máxima y mínima provenientes del conjunto de datos meteorológicos globales denominado “Reanálisis del Sistema de Predicción del Clima” (CFSR, por sus siglas en inglés), como una fuente de información de uso alternativo frente a las observadas in situ en estaciones convencionales (CLICOM), mediante un análisis estadístico en la cuenca alta del río Mayo, Chihuahua, México. Los indicadores estadísticos que se emplearon (sesgo porcentual, raíz del error cuadrático medio, error promedio absoluto y coeficiente de determinación) mostraron que, en general, existe un buen desempeño del CFSR en el modelamiento de la precipitación, temperatura máxima y mínima en época seca, y una sobreestimación durante los meses de lluvia.

Why East Asian monsoon anomalies are more robust in post El Niño than in post La Niña summers

The East Asian summer monsoon (EASM) supplies vital rainfall for over one billion people. El Niño-Southern Oscillation (ENSO) markedly affects the EASM, but its impacts are more robust following El Niño than La Niña. Here, we show that this asymmetry arises from the asymmetry in ENSO evolution: though most El Niño events last for one year, La Niña events often persist for 2-3 years. In the summers between consecutive La Niña events, the concurrent La Niña opposes the delayed effect of the preceding winter La Niña on the EASM, causing a reduction in the magnitude and coherence of climate anomalies. Results from a large ensemble climate model experiment corroborate and strengthen the observational analysis with an order of magnitude increase in sample size. The apparent asymmetry in the impacts of the ENSO on the EASM can be reduced by considering the concurrent ENSO, in addition to the ENSO state in the preceding winter. This has important implications for seasonal climate forecasts.

Preseason maize and wheat yield forecasts for early warning of crop failure

Provided the considerable logistical challenges of anticipatory action and disaster response programs, there is a need for early warning of crop failures at lead times of six to twelve months. But crop yield forecasts at these lead times are virtually nonexistent. By leveraging recent advances in climate forecasting, we demonstrate that global preseason crop yield forecasts are not only possible but are skillful over considerable portions of cropland. Globally, maize and wheat forecasts are skillful at lead times of up to a year ahead of harvest for 15% and 30% of harvested areas, respectively. Forecasts are most skillful in Southeast Africa and Southeast Asia for maize and parts of South and Central Asia, Australia, and Southeast South America for wheat. Wheat forecasts, furthermore, remain skillful at lead times of over 18 months ahead of harvest in some locations. Our results demonstrate that the potential for preseason crop yield forecasts is greater than previously appreciated.

Advancing polar motion prediction with derivative information

Abstract Earth Orientation Parameters (EOP) are essential for monitoring Earth’s rotational irregularities, impacting satellite navigation, space exploration, and climate forecasting. This study introduces a hybrid prediction model combining least-squares (LS) and vector autoregression (VAR) to improve Earth’s Pole Coordinates (x, y) forecast accuracy. Using daily sampled IERS EOP 20 C04 data from 2013 to 2023, we conducted 1,000 yearly random trials, performing 48 forecasts per year. Our method evaluates six data combinations, including primary variables (x, y) and their derivatives ( x ̇ , y ̇ $\dot{x},\dot{y}$ ). Results show a systematic improvement in prediction accuracy, especially for ultra-short-term forecasts (10 days into future), with derivative information stabilizing the solutions. The best-performing combination ( x , y , x ̇ , y ̇ $x,y,\dot{x},\dot{y}$ ) achieved a mean absolute prediction error (MAPE) reduction (with respect to the reference data combination – x, y) of up to 8 % for the y and 7 % for the x over a whole 30-day forecast horizon. These findings highlight the effectiveness of incorporating derivatives of polar motion time series into prediction procedure.

Harnessing Machine Learning to Decode the Mediterranean’s Climate Canvas and Forecast Sea Level Changes

Harnessing Machine Learning to Decode the Mediterranean’s Climate Canvas and Forecast Sea Level Changes

A multi-decadal 1 km gridded database of continental-scale spring onset products

The timing of spring onset is a particularly sensitive climate change indicator that also allows the study of weather interannual variations and extremes. This indicator can be derived from a suite of phenological models called the Extended Spring Indices (SI-x). These models transform daily minimum and maximum temperatures into a set of consistent indices that track the timing of first leaf and first bloom for key indicator plant species. The SI-x also enables the calculation of the so-called frost damage index. Using new computational technologies and high spatial resolution gridded weather data, here we present and evaluate a multi-decadal 1 km spatial resolution version of the SI-x models, which cover North American (1980 to 2022) and European areas (1950 to 2020). Because of the generally good agreement with ground phenological observations, this continental-scale and high spatial resolution product can support both scientists and decision makers in their quest to hind- and forecast weather and climate change impacts.