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\"A comprehensive introduction to the most commonly used statistical methods relevant in atmospheric, oceanic and climate sciences. Each method is described step-by-step using plain language, and illustrated with concrete examples, with relevant statistical and scientific concepts explained as needed. Particular attention is paid to nuances and pitfalls, with sufficient detail to enable the reader to write relevant code. Topics covered include hypothesis testing, time series analysis, linear regression, data assimilation, extreme value analysis, Principal Component Analysis, Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. The specific statistical challenges that arise in climate applications are also discussed, including model selection problems associated with Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. Requiring no previous background in statistics, this is a highly accessible textbook and reference for students and early-career researchers in the climate sciences\"-- Provided by publisher.

Sea surface wind speed is a key parameter in marine meteorology, navigation safety, and offshore engineering. Traditional marine radar wind speed retrieval algorithms often suffer from poor environmental adaptability and limited applicability across different radar systems, while existing empirical models face challenges in accuracy and generalization. To address these issues, this study proposes a novel wind speed retrieval method based on X-band marine radar image sequences and texture features derived from the Gray-Level Co-occurrence Matrix (GLCM). A three-stage preprocessing pipeline—comprising noise suppression, geometric correction, and interpolation—is employed to extract small-scale wind streaks that reflect wind field characteristics, ensuring high-quality image data. Two key GLCM texture features of wind streaks, energy and entropy, are identified, and their stable values are used to construct a segmented dual-parameter wind speed model with a division at 10 m/s. Experimental results show that both energy- and entropy-based models outperform traditional empirical models, reducing mean errors by approximately 49.3% and 16.7%, respectively. The energy stable model achieves the best overall performance with a correlation coefficient of 0.89, while the entropy stable model demonstrates superior performance at low wind speeds. The complementary nature of the two models enhances robustness under varying conditions, providing a more accurate and efficient solution for sea surface wind speed retrieval.

Marine atmospheric boundary-layer height (MABLH) is crucial for ocean heat, momentum, and substance transfer, affecting ocean circulation, climate, and ecosystems. Due to the unique geographical location of the South China Sea (SCS), coupled with its complex atmospheric environment and sparse ground-based observation stations, accurately determining the MABLH remains challenging. Coherent Doppler wind lidar (CDWL), as a laser-based active remote sensing technology, provides high-resolution wind profiling by transmitting pulsed laser beams and analyzing backscattered signals from atmospheric aerosols. In this study, we developed a stacking optimal ensemble model (SOEM) to estimate MABLH in the vicinity of the site by integrating CDWL measurements from a representative SCS site with ERA5 (fifth-generation reanalysis dataset from the European Centre for Medium-Range Weather Forecasts) data from December 2019 to May 2021. Based on the categorization of the total cloud cover data into weather conditions such as clear/slightly cloudy, cloudy/transitional, and overcast/rainy, the SOEM demonstrates enhanced performance with an average mean absolute percentage error of 3.7%, significantly lower than the planetary boundary-layer-height products of ERA5. The SOEM outperformed random forest, extreme gradient boosting, and histogram-based gradient boosting models, achieving a robustness coefficient (R2) of 0.95 and the lowest mean absolute error of 32 m under the clear/slightly cloudy condition. The validation conducted in the coastal city of Qingdao further confirmed the superiority of the SOEM in resolving meteorological heterogeneity. The predictions of the SOEM aligned well with CDWL observations during Typhoon Sinlaku (2020), capturing dynamic disturbances in MABLH. Overall, the SOEM provides a precise approach for estimating convective boundary-layer height, supporting marine meteorology, onshore wind power, and coastal protection applications.

The Joint Committee for Oceanography and Marine Meteorology (JCOMM), a joint technical commission of IOC of UNESCO and WMO, has devised a coordination mechanism for the fit-for-purpose delivery of an end-to-end system, from ocean observations to met-ocean operational services. This paper offers a complete overview of the activities carried out by JCOMM and the status of the achievements up to 2017. The JCOMM stakeholders are the WMO Members and the IOC Member States, their research and operational Institutions, which mandated JCOMM to devise an international strategy to advance toward the achievement of the United Nations Sustainable Development Goals. The three activity areas, namely the Observation Program Area-OPA, the Data Management Program Area-DMPA and the Services and Forecasting Services Program Area-SFSPA have established several expert teams to contribute to the international coordination. OPA is organized in observing networks connected with different observing technologies, DMPA organizes the overall near-real time and delayed mode data assembly and delivery methodology and architecture and the SFSPA coordinates the met-ocean services stemming out of observations and data management. The future developments should strengthen the coordination in the three program areas considering the inclusion of new and emergent observing technologies, the interoperability of met-ocean data assembly centers and the establishment of efficient research to operations protocols, as well as better fit-for-purpose customized services for the public and private sectors.

Storm surges pose significant danger and havoc to the coastal residents’ safety, property, and lives, particularly at offshore locations with shallow water levels. Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans. In addition to experienced predictions and numerical models, artificial intelligence (AI) techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations. Convolutional neural network (CNN) and long short-term memory (LSTM) are two of the most important models among AI techniques. However, they have been scarcely utilised for surge level (SL) forecasting, and combinations of the two models are even rarer. This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information. The architectures of the CNN, LSTM, and two sequential techniques of combining the models (LSTM-CNN and CNN-LSTM) were constructed via a trial-and-error approach and knowledge obtained from previous studies. As a case study, 11 a of hourly observed SL and wind data of the Xiuying Station, Hainan Province, China, were organised as inputs for training to verify the feasibility and superiority of the proposed models. The results show that CNN and LSTM had evident advantages over support vector regression (SVR) and multilayer perceptron (MLP), and the combined models outperformed the individual models (CNN and LSTM), mostly by 4%–6%. However, on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges, the accuracy was found to improve by over 10% at all forecasting steps.

The present study aims to address a disconnect between science and the public in the form of a potential misalignment in the supply and demand of information known as the usability gap. In this case, we explore the salience of marine meteorological (metocean) information as perceived by users in two Southern Hemisphere countries: South Africa and New Zealand. Here, the focus is not only on the perceptions, usability and uptake of extreme event forecasts but rather focused on general, routine forecast engagement. The research was conducted by means of a survey, designed around three research questions. The research questions covered topics ranging from forecasting tool ergonomics, accuracy and consistency, usability, institutional reputation, and uncertainties related to climate change (to name but a few). The online questionnaire was widely distributed to include both recreational and commercial users. The study focused on identifying potential decision-making cultures that uniquely impact coastal ocean users' information needs. Cultural consensus analysis (CCA) was used to investigate shared understandings and variations in perceptions within the total group of respondents as well as in sectoral and country-based subgroups. We found varying degrees of consensus in the whole group (participants from both countries and all sectors combined) versus different subgroups of users. All participants taken together exhibited an overall moderate cultural consensus regarding the issues presented but with some variations in perspectives at the country-level, suggesting potential subcultures. Analysing national and sectoral subgroups separately, we found the most coherent cultural consensus in the South African users' cohort, with strong agreement regardless of sectoral affiliation. New Zealand's commercial users' cohort had the weakest agreement with all other subgroups. We discuss the implications from our findings on important factors in service uptake and therefore on the production of salient forecasts. Several priorities for science-based forecasts in the future are also reflected on, considering anticipated climate change impacts. We conclude by proposing a conceptual diagram to highlight the important interplay between forecast product co-development and scientific accuracy/consistency.

The Icelandic Meteorological Office (IMO) conducted meteorological buoy measurements in the central Iceland Sea in the time period 2007–2009, specifically in the northern Dreki area on the southern segment of the Jan Mayen Ridge. Due to difficulties in deployment and operations, in situ measurements in this region are sparse. Here the buoy, deployment and measurements are described with the aim of giving a future user of the data set information that is as comprehensive as possible. The data set has been quality-checked, suspect data removed and the data set made publicly available from PANGAEA Data Publisher (https://doi.org/10.1594/PANGAEA.876206).

The general features of the Great Whirl (GW) off the Somali Coast in 2017 and its influences on chlorophyll a (Chl a ) concentration were studied by using satellite data and model outputs. Results show that GW, which initiated at 7°N, 53°E on June 13, had a lifetime of 153 d with an average amplitude of 16 cm and an average radius of 205 km. After the formation of GW, the concentration of Chl a in the interior of GW showed a downward trend throughout its life cycle, except in early July and mid-October. In early July, the Chl a blooms in the interior of GW were attributed to the combined effect of three processes. They are eddy horizontal transportation, the deepening of the mixed layer caused by the monsoon and eddy pumping, and the upward transportation of nutrients caused by eddy-induced Ekman pumping. In October, the Chl a blooms were probably due to the weakening of GW. During the period, water exchange occurred more frequently across the eddy, thus phytoplanktons were imported into the interior of GW.

The effects of biological heating on the upper-ocean temperature of the global ocean are investigated using two ocean-only experiments forced by prescribed atmospheric fields during 1990–2007, on with fixed constant chlorophyll concentration, and the other with seasonally varying chlorophyll concentration. Although the existence of high chlorophyll concentrations can trap solar radiation in the upper layer and warm the surface, cooling sea surface temperature (SST) can be seen in some regions and seasons. Seventeen regions are selected and classified according to their dynamic processes, and the cooling mechanisms are investigated through heat budget analysis. The chlorophyll-induced SST variation is dependent on the variation in chlorophyll concentration and net surface heat flux and on such dynamic ocean processes as mixing, upwelling and advection. The mixed layer depth is also an important factor determining the effect. The chlorophyll-induced SST warming appears in most regions during the local spring to autumn when the mixed layer is shallow, e.g., low latitudes without upwelling and the mid-latitudes. Chlorophyll-induced SST cooling appears in regions experiencing strong upwelling, e.g., the western Arabian Sea, west coast of North Africa, South Africa and South America, the eastern tropical Pacific Ocean and the Atlantic Ocean, and strong mixing (with deep mixed layer depth), e.g., the mid-latitudes in winter.

The sea ice conditions in the Kara Sea have important impacts on Arctic shipping, oil and gas production, and marine environmental changes. In this study, sea ice coverage ( C R ) less than 30% is considered as open water, its onset and end dates are defined as T open and T close , respectively. The sea ice melt onset ( T melt ) is defined as the date when ice-sea freshwater flux initially changes from ice into the ocean. Satellite-based sea ice concentration (SIC) from 1989 to 2019 shows a negative correlation between T open and T close ( r = −0.77, p < 0.01) in the Kara Sea. This phenomenon is also obtained through analyzing the hindcast simulation from 1994 to 2015 by a coupled ocean and sea-ice model (NAPA1/4). The model results reveal that thermodynamics dominate the sea ice variations, and ice basal melt is greater than the ice surface melt. Heat budget estimation suggests that the heat flux is significant correlated with T open ( r = −0.95, p < 0.01) during the melt period (the duration of multi-year averaged T melt to T open ) influenced by the sea ice conditions. Additionally, this heat flux is also suggested to dominate the interannual variation of the heat input during the whole heat absorption process ( r = 0.81, p < 0.01). The more heat input during this process leads to later T close ( r = 0.77, p < 0.01). This is the physical basis of the negative correlation between T open and T close . Therefore, the duration of open water can be predicted by T open and thence support earlier planning of marine activities.