Hindcast Data Research Articles

Uncertainty in wave hindcasts in the North Atlantic Ocean

Accurate knowledge of ocean surface waves is crucial for ship design. With the significant advancements in model physics and numerical resources, recent numerical wave hindcast data has the potential to provide environmental conditions for estimating the wave load in the ship design process. To help estimate extreme wave loads with quantified uncertainty for strength assessments of ship structures, this study aims to quantify model uncertainty in the state-of-the-art numerical wave hindcast products. Model uncertainty was deduced from inter-model comparisons with four numerical wave hindcast products after model evaluations. For model evaluations, wave buoys distributed over the Northwest Atlantic and Northeast Atlantic were used as wave buoy arrays. In general, all four models accurately estimated the significant wave height Hs. However, as the wave condition becomes extreme with the increase of Hs, the model estimations of Hs deviate from the buoy data and differ from each other. For the extreme wave conditions, two wave hindcast products consistently showed better performance based on the buoy observations for both the Northwest Atlantic and Northeast Atlantic. In addition to Hs, the mean wave period Tm02 from the hindcast products are also examined in this study. Although evaluations of Tm02 are not often seen in previous studies, Tm02 is another crucial wave parameter because the ship response is often calculated as a function of Hs and Tm02. Based on careful comparisons, it is found that all the wave hindcast products underestimate Tm02 under the extreme wave conditions of Hs>10m. The joint probabilities of Hs and Tm02 for extreme wave conditions of Hs>10m and 8s<Tm02<14s are further calculated as a scatter diagram for extreme wave conditions (SDE). Among the SDEs from the buoys and four model products, the total number of samples varied by 50%, although the mean value of the four model products is close to the result from the buoys. The significance of the variation is further described in the context of the extreme wave load estimation for strength assessments of ship structures.

Power Performance and Response Analysis of a Semi-Submersible Wind Turbine Combined With Flap-Type and Torus Wave Energy Converters

Abstract An integrated offshore wind and wave energy system is an attractive concept in areas with abundant wind and wave energy resources. The sharing of supporting platform and facilities, e.g., mooring systems, offers significant cost savings. This will effectively lower the levelized cost of energy (LCOE). In the present study, a conceptual design consisting of a braceless semi-submersible floating horizontal axis wind turbine (FHAWT), three flap-type wave energy converters (WECs), as well as a torus (donut-shaped) point absorber-type WEC is proposed. The flap-type WECs harvest wave energy through the flap motion caused by oscillating wave surge, while the torus WEC absorbs wave energy generated from its heaving motion. The absorbed mechanical power of the power take-off (PTO) systems is calculated based on linear damping forces and the motions of the WECs relative to the supporting platform. Hydrodynamic interaction between the WECs and the supporting platform is considered by including the coupling terms in the added mass and potential damping coefficient matrices. A fully coupled aero-servo-hydro-elastic numerical model of the concept is constructed. The feasibility study of the concept is carried out using time-domain simulations. Only operational environmental conditions are simulated based on simultaneous wind and wave hindcast data of a selected offshore site. The effects of the WECs on the wind turbine, platform motions, and WEC power take-off are examined. Based on the power performance of WECs, recommendations are also provided for optimum power absorption.

Standard Wave Scatter Table Limitation for Evaluating SGISC Based on Hindcast Data Analysis

The operational measure (OM) of the Second-Generation Intact Stability Criteria (SGISC) is the initial step toward the design of a performance-based dynamic stability assessment of the ship by considering the vessel’s operation, loading condition and weather parameters. The SGISC recommends the standard wave scatter table (WST) for the environmental data, an indefinite requirement for a simplified assessment pathway, which provides the probability of wave occurrence. The existing standard WST was developed based on the North Atlantic Ocean. The study aims to identify the discrepancy in the probability of wave occurrence in the IMO-recommended WST when compared with developed hindcast WST for smaller regions of the North Atlantic Ocean for the application of the SGISC. This study is one of a kind and very significant, given that the assessment using the standard WST provided unrestricted operational ability to ship under the SGISC. The findings of this research provide insights into significant differences in the existing standard WST when compared graphically with hindcast data, especially across different seasons of the North Atlantic Ocean. A case study of OMs on the C11 class post-Panamax container ship for excessive acceleration is provided to better represent the study. In the case study, the operational limitation defined by the hindcast WST for individual sea areas and seasons is compared with the standard WST. This investigation shows that regional and seasonal operational limitations are required for season 4 operation beyond the standard WST safety estimation and that the operational limitation achieved with standard WST is not adequate. The findings suggest that the identified limitations significantly limit the use of a standard WST for unrestricted operational validation in the SGISC. The provided recommendation is very relevant in improving the safety assessment using vulnerability criteria, given that the hindcast data are reliable and available for the season and are also region-specific, and hence the accuracy of the ship stability can be improved while using the SGISC.

Investigation of Correlation Between Suspended Sediment Discharge and Activity Concentration of Natural Radionuclides in Langat River Basin

Investigation of the correlation between natural radionuclide activity concentrations (RC) in river sediment and suspended sediment discharge (SD) is carried out for Langat river basin over a period of 4 years. Three river stations with suspended sediment discharge rate derived from validated sediment rating equation are paired with three radionuclide observation stations by geographical proximity. Long-term correlation of the quarterly RC observation with maximum SD is generally poor. However, for specific time window, the data trend correlation ranges from moderate to good, indicative of the influence of SD on the transport process of NORM within the river basin. Examination of the seasonal variation shows that Station 2 exhibits good correlation of RC and SD in the months of March and August/September, corresponding to the pre-storm seasons, with R2 value as high as 0.9799. Despite the limitation of the undated RC observation and the use of hindcasted SD data, the present study shows the increase of radionuclide activity concentration of Ra-226, Ra-228 and K-40 in river sediment can be associated with the suspended sediment discharge rate and hence, the river streamflow especially during the pre-storm season.

How to choose credible ensemble members for the sub-seasonal to seasonal prediction of precipitation?

The sub-seasonal to seasonal (S2S) prediction of precipitation is not only a hot topic but also a challenge. The traditional ensemble mean and ensemble probabilistic forecast methods cannot avoid the uncertainty of the initial value in the S2S prediction. Is there a more suitable ensemble postprocessing method for the S2S prediction? In this study, the hindcast data during the 1999–2010 summers from nine operational models in the international S2S prediction project has been evaluated. Based on the quantitative objective precipitation evaluation methods, such as the Equitable Threat Score and frequency bias methods, the climatological spatio-temporal distribution of the optimal probabilistic threshold on the S2S scale is proven to exist, and it can be used as the standard to judge how many ensemble members are credible. Then, different ensemble forecast strategies are adopted in different regions to construct a Deterministic Ensemble Forecast using an Optimal Probabilistic Threshold (DEFOPT) method for precipitation prediction. The hindcast data of eight S2S models outside the period 1999–2010 are used to verify the applicability of the DEFOPT method by using the historical optimal probabilistic threshold during 1999–2010. The results show that the DEFOPT outperforms the deterministic forecast from one initial value, the ensemble mean, and the deterministic ensemble forecast using a probabilistic threshold for the occurrence days of rainfall at the 1 mm and 5 mm thresholds (≥ 1 mm and ≥ 5 mm) over China during each pentad in most S2S models.

Using a Bayesian joint probability approach to improve the skill of medium-range forecasts of the Indian summer monsoon rainfall

Study regionGanga, Mahanadi, Godavari, Narmada, and Tapti River basins of India. Study focusThe manuscript focuses on improving skills of the Indian summer-monsoon precipitation forecasts obtained from National Center for Medium-Range Weather Forecasting (NCMRWF) at both sub-basin and gridded scale. A well-established Bayesian Joint Probability (BJP) based statistical post-processing approach, operational in Australia, is used for the first time in India throughout diverse geographical extent. The work evaluates how the post-processor can be used in a summer-monsoon dominated region like India. The study informs whether annual or seasonal precipitation forecasts should be used as the length of data will play crucial role in both the cases. The spread-skill of the ensembles obtained from BJP approach and the NCMRWF is explored. New hydrological insights for the regionIntroduction of the BJP-based post-processing approach in India could help the forecast community to implement more robust approach in improving the skills of the forecasts. Our results show that instead of using the data of whole year, only monsoonal precipitation forecasts are adequate to setup the BJP approach. The calibrated forecasts obtained using three years of hindcast and observations data at grids and at the centroid of 177 sub-basins are found to be more skillful. The calibrated forecasts can discriminate between extreme and low precipitation events, and have appropriate ensemble spread to capture precipitation peaks. This study presents a guideline for water managers and forecasters to apply BJP approach to improve the forecasts.

Analyzing wave direction scenarios for large waves in deep water and its impact on littoral drift

In this study, four types of wave direction scenarios in large waves greater than 3 m in deep water are examined by analyzing NOAA's wave hindcast data spanning over 40 years. The results reveal that wave direction changes at the starting, peak and ending of large/storm waves, accompanying by changing wave heights with wave direction becoming in perpendicular to the coastal orientation when waves reaching a peak. Within the bulk of NOAA wave data which last 1.65 days, 55.1% of the large/storm waves had wave direction returning to the starting direction on the wane of the waves, while the remaining 44.9% reversed to the opposite side of the starting direction. Based on this finding, littoral drift is calculated for each scenario by the CERC equation, on the basis of deep water wave parameters. The resulting littoral drift is then used in a numerical model to calculate shoreline changes at Jeongdongjin Beach in Korea, where a natural groin block the longshore sediment transport. The result of shoreline change in the vicinity of the groin suggests beach may become stable about one day after the end of a short-term large wave/storm event, or 2 days after the peak wave height.

Nearshore Wave Energy Resource Assessment for Off-Grid Islands: A Case Study in Cuyo Island, Palawan, Philippines

Electrifying off-grid and isolated islands in the Philippines remains one of the challenges that hinders community development, and one of the solutions seen to ensure energy security, energy access and promote a low-carbon future is the use of renewable energy sources. This study determines the nearshore wave energy resource during monsoon seasons in Cuyo Island using a 40-year wave hindcast and 8-year on-site wind speed data as inputs to develop a high-resolution wave energy model using SWAN and assesses its annual energy production through matching with wave energy devices. The results show that the average significant wave height (Hs), peak period (Tp) and wave power density (Pd) during a northeast monsoon are Hs = 1.35 m, Tp = 4.79 s and Pd = 4.05 kW/m, respectively, while a southwest monsoon, which is sheltered by the mainland, results in Hs = 0.52 m, Tp = 3.37 s and Pd = 0.34 kW/m. While the simulated model was observed to overestimate the significant wave height (bias = 0.398, RMSE = 0.54 and SI = 1.34), it has a strong relationship with the “observed values” (average r = 0.9). The annual energy production for Wave Dragon, Archimedes Wave Swing and Seawave Slot-Cone Generator are highest at 1970.6 MWh, 2462.04 MWh, 62.424 MWh and 4099.23 MWh, respectively.

Environmental contours using nonparametric copulas

The use of nonparametric copulas for modeling joint probability distributions of metocean variables and building environmental contours is examined. Provided that no assumption is made about a particular functional form, nonparametric copulas may be an alternative to improve modeling dependence patterns, which is critical to develop environmental contours. The transformation method with local log-quadratic likelihood kernel estimator and nearest-neighbor bandwidth (TLL2nn), the mirror-reflection and beta kernel methods, and Bernstein polynomials are used for nonparametric estimation. For comparison purposes, well known parametric copulas are also estimated. Hindcast data from tropical cyclones and storms at three sites in the southern Gulf of Mexico is used. The adequacy of the copula models is assessed first using graphical diagnostics tools. Data fitting is then evaluated using Cramér-von Mises statistics, and the Akaike and Bayesian information criteria are applied for model selection. The estimated copulas are also compared in terms of their adequacy to model upper and lower tail dependence. The results showed that whereas different parametric copulas were needed to model the different data sets, the nonparametric TLL2nn model was able to give adequate results for the three sites. Environmental contours using the nonparametric, parametric, and empirical copulas are compared.

A one-year comparison of new wind atlases over the North Sea

The New European Wind Atlas (NEWA) and the Norwegian hindcast archive (NORA3) database have become publicly available since the end of 2019 and mid-2021, respectively. They aim to model the long-term wind climatology with a spatial resolution of ca. 3 km and a temporal resolution of 1 h (NORA3) or 30 min (NEWA). Both products have a high potential for wind energy applications. Although their geographical coverages partly overlap, an inter-comparison of the NEWA and NORA3 databases in an offshore environment is still lacking. The paper compares the hourly mean wind speed and wind direction recorded in 2009 at the FINO1 platform (North Sea) with hindcast data from the NEWA and the NORA3 database. Both products were found to provide reliable estimates of the mean wind speed at 101 m above sea level. However, NORA3 shows slightly better performances than NEWA for the mean wind speed in terms of root-mean-square error, bias, earth mover’s distance (EMD) and Pearson correlation coefficient. For the mean wind direction, a larger circular EMD than previously documented is found, which could be due to a directional bias in the wind vane data. Finally, the Brunt-Väisälä frequency is computed using sea-surface temperature analyses and the air temperature from NORA3 and NEWA at 101 m above sea level. The encouraging description of the static atmospheric stability by the wind atlases opens the possibility to study in more detail thermally-induced wind events for wind resource assessment or wind turbine design.

Modelling and simulation of extreme wave heights around agatti island of lakshadweep, west coast of India

Small islands experience variation in wave energy regime along shorter shoreline due to refraction, diffraction and variability in direction of wave travel. Nearshore wave climate around these islands is quite complex due to wave transformation associated with configuration of islands, and high steepness in the bathymetry. Understanding wave energy will help in identifying suitable locations for coastal structures, wave energy devices and sediment budgeting. Assessment of extreme wave climate around the islands play a crucial role in the design and survival of coastal infrastructure for any developmental activities in islands. This study investigates the extreme wave climate around the Agatti island of Union Territory of Lakshadweep located in west coast of India. The spatial variability of wave power distribution around the Agatti island is analyzed based on 10 years of wave hindcast data from spectral wave modelling for the period between 2011 and 2020 using spectral wave model. In this study, third-generation spectral wave model was used to simulate the wave climate over the Arabian Sea. Regional model was established using ECMWF model winds and the offshore wave parameters were validated with deep water ocean observations AD07 and AD09 deployed in the Arabian Sea. To understand the nearshore wave transformation along the Agatti island, local wave model of fine resolution was established using high resolution bathymetry and is validated using the coastal buoy (CB02) deployed near Agatti. Nearshore wave parameters such as Significant wave height, Peak wave period (Tp) and Mean wave direction (θm) were analyzed. The wave power distribution around the island was quantified and demarcated for siting of offshore structures for developmental activities. The study reveals that the south zone of the island is more energetic with annual mean wave power of 8–12 kW/m and during monsoon, wave power more than 28 kW/m is observed. The extreme value theory based on Generalized Extreme Value distribution is applied to the extreme wave height estimate based on modelled wave data around the Agatti island. The extreme wave heights for different return periods were estimated around the Agatti island for design and analysis of coastal infrastructure.

Investigating ocean deoxygenation and the oxygen minimum zone in the Central Indo Pacific region based on the hindcast datasets.

Deoxygenation is increasingly recognized as a significant environmental threat to the ocean following sea temperature rises due to global warming and climate change. Considering the cruciality of the deoxygenation impacts, it is important to assess the current status and predict the future possibility of ocean deoxygenation, for instance, within the Central Indo Pacific (CIP) regions represent climate-regulated marine areas. This study divided CIP into five regions then investigated the deoxygenation parameters (dissolved oxygen, temperature, salinity, and pH) collected from 1993 to 2021 sourced from in situ measurement and long-term hindcast data. The overall error statistics indicate a good accuracy of hindcast data and are comparable to the in situ values. The surface waters were identified to have the most fluctuated seasonal pattern of dissolved oxygen (DO) concentration compared to other water columns. However, DO fluctuation occurred in a different pattern in each region. The study shows ocean deoxygenation is accelerated in all five regions over decades. The strongest oxygen minimum zone (OMZ) is identified in the Bay of Bengal and the Arafura Sea with the lowest oxygen concentration less than 0.5mmol/m3, while the weakest OMZ is located in the South China Sea with 60mmol/m3 of DO concentration. The deoxygenation rates in CIP regions are varied by 0.09-2.75mmol/m3/year depending on the different controlling factors that work in the five regions. Our results indicate that deoxygenation is attributed to ocean warming, seawater salinity increases, and ocean acidification and possibly continue in the future with continued global changes.

Evaluation of ship operational effect based on long-term encountered sea states using wave hindcast combined with storm avoidance model

Ships avoid severe sea states for the safety of lives and property and to save energy. Therefore, the actual sea states encountered by ships differ from nature and are affected by human operational effects that we refer to as the “ship operational effect”. The evaluation of the ship operational effect is crucial for the rational design of structural strength of the hull. In this study, we derived the sea states encountered over 25 years in the North Atlantic, by considering storm avoidance by merchant ships. We then performed long-term predictions of the ship motions and wave loads of 75 merchant ships. Existing pioneering studies used automatic identification system (AIS)-based ship locations and timestamps to subsample wave hindcast data along ship tracks for a limited duration. Unlike existing studies, we used a statistical storm avoidance model of merchant ships to reconstruct the sea states, encountered over 25 years, in the North Atlantic. Owing to the ship operational effect, the long-term prediction values of roll motion, pitch motion, vertical bending moment amidships, and hydrodynamic pressure on bottom centerline amidships of merchant ships, at the probability level 10−8, were reduced by approximately 10–20%, and the coefficient of the ship operational effect was 0.8–0.9. Furthermore, we evaluated the uncertainties in the ship operational effect. The values deviated by as much as 6%, depending on the choice of wave hindcast products (TodaiWW3-NK, IOWAGA, and ERA5). The variation based on the parameter settings of the storm avoidance model (severity of the storm, radius of influence of the storm, and number of ship locations) was up to approximately 1 m at a probability level of 10−5 of the encountered sea states for 25 years.

Multi-model seasonal prediction of global surface temperature based on partial regression correction method

The increased climate change is having a huge impact on the world, with the climatic change sensitive and vulnerable regions at significant risk particularly. Effective understanding and integration of climate information are essential. It helps to reduce the risks associated with adverse weather conditions and to better adapt to the impacts of climate variability and change. Using the hindcast data from Japan Meteorological Agency/Meteorological Research Institute (JMA/MRI) coupled prediction system version 2 (JMA/MRI-CPS2), National Centers for Environmental Prediction (NCEP) Climate Forecast System model version 2 (CFSv2), and Canadian Centre for Climate Modeling and Analysis (CCCma) Coupled Climate Model, versions 3 (CanCM3) seasonal prediction model systems, the performance of seasonal prediction for global surface temperature in boreal summer and winter is comprehensively evaluated and compared for 1982–2011 from the perspective of deterministic and probabilistic forecast skills in this study, and a partial regression correction (PRC) method is introduced to correct seasonal predictions. The results show high prediction skills in the tropics, particularly in the equatorial Pacific, while poor skills on land. In general, JMA/MRI-CPS2 has slightly better prediction performance than CFSv2 and CanCM3 in the tropics. CFSv2 is generally superior to JMA/MRI-CPS2 and CanCM3 in the extratropical northern hemisphere and East Asia, especially for the abnormal low winter temperature prediction in East Asia. CanCM3 shows good deterministic forecast skills in extra-tropics but performs slightly worse in probabilistic forecasting. Based on the respective strengths of each seasonal prediction model, an ensemble forecast correction with observational constraint is implemented by partial regression, and the improvement of skills in ensemble predicting has been analyzed. Compared to the simple multi-model ensemble (MME), the correction improved the global-average temporal correlation coefficient and multi-year mean anomaly correlation coefficient by about 0.1 and 0.13, respectively. The validation tests indicate that the corrected ensemble forecast has higher ranked probability skill scores than that of the MME, which is improved by more than 0.06 in the tropics. Meanwhile, when the training period is sufficiently long, it may have the potential for future seasonal temperature predictions from the perspective of stable zonal partial regression coefficients.

Spatial-temporal wave height forecast using deep learning and public reanalysis dataset

A novel data-driven model is proposed for efficient spatial–temporal significant wave height forecast in West Pacific using deep learning. A multivariate 3-layer LSTM-based architecture following a matrix-to-matrix mapping fashion is applied to establish the underlying spatial–temporal relationship between wind components and significant wave height field for long-term and ocean-scale forecasts. A parallel modeling strategy is employed to reduce the computational expense in which the forecasting domain is flexibly partitioned into finite windows and trained independently as a sub-model. A 30-year hindcast wave dataset released by EMC (Environmental Modeling Center) Operational Wave Models associated with NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) wind data is used to drive and test the model. A rectangle sea region covering over 9.33 × 106 km2 in West Pacific is regarded as a modeling domain. The data-driven model is validated against a 12-year period historical hindcast data, and global errors are carefully discussed. The forecasting performance of the established model on tropical cyclone wave events is investigated in terms of both spatial pattern and temporal variance of significant wave height. The extreme significant wave height, rough sea occurrence, as well as seasonal and annual characteristics of significant wave height in the West Pacific are carried out using the proposed model and compared with the original hindcast results. The data-driven model shows promising potential to accurately capture the ambiguous patterns and features that are variant in both spatial and temporal dimensions and has obvious superiority over numerical wave modeling in terms of computational efficiency.

Assessment of hot weather seasonal temperatures over India using Monsoon Mission Coupled Forecasting System hindcasts

AbstractThe present study evaluates the skill of seasonal forecasts of temperatures over India during April to June using the Monsoon Mission Coupled Forecasting System (MMCFS) model hindcasts, which are initialized with February initial conditions. Model hindcast data of 1981–2017 period have been used for the analysis. The India Meteorological Department (IMD) gridded temperature dataset has been used for model verifications. The MMCFS model captures the annual cycle of temperatures reasonably well, but with a higher mean and smaller variability compared to observations. The model hindcasts show a significant skill for seasonal forecasts of temperatures over most of northwest and central India. Empirical Orthogonal Function (EOF) analysis suggests that the model captures temporal and spatial characteristics of different modes of maximum temperatures but with less accuracy. The model teleconnections of maximum temperatures with Indian Ocean sea surface temperatures (SSTs) and El Niño–Southern Oscillation (ENSO) are weakly represented. The model is also found capable of predicting the spatial distribution of heat wave characteristics such as heat wave frequency (HWF) and heat wave duration (HWD) reasonably well. The present study suggests that the MMCFS Model can be used to generate a useful outlook of hot weather seasonal temperatures and heat waves over India.

Systematization of short-term forecasts of regional wave heights using a machine learning technique and long-term wave hindcast

Machine-learning techniques have been applied to wave forecasting to address the implementational and computational challenges of conventional numerical wave models. Most previous studies emphasized the accuracy of approaches based on in situ wave measurements, but such approaches are site-specific and lack the spatial and temporal fidelity required by regional wave climate forecasts. This study presents a novel systematization of forecasting regional wave heights at sufficient spatial resolution and coverage, using long-term wave hindcasts generated by numerical wave models as a training dataset for a long short-term memory (LSTM). This systematization not only ensures the accuracy of the model's forecasts but demonstrates the applicability of long-term wave hindcasts as input data. It involves thoroughly investigating the dependence of model accuracy on multi-task LSTM structures (training variables, input data record periods, and forecast lead times), and on regional wave climates (different wave sites and sea states). The resulting model performance is spatially heterogeneous in global wave forecasts, underlining the need for validation of wave forecasting models to global wave climates. This study reveals that machine-learning techniques that leverage validated long-term wave hindcasts can successfully forecast regional wave climates. As access to high-resolution regional wave hindcast data improves, accuracy will also increase.

Variability of wave power production of the M4 machine at two energetic open ocean locations: Off Albany, Western Australia and at EMEC, Orkney, UK

Since intermittent and highly variable power supply is undesirable, quantifying power yield fluctuations of wave energy converters (WECs) aids with assessment of potential deployment sites. This paper presents analysis of 3-hourly, monthly, seasonal, and inter-annual variability of power output of the M4 WEC. We compare expected performance from deployment at two wave energy hotspots: off Albany on the south-western coast of Australia and off the European Marine Energy Centre (EMEC) at Orkney, UK. We use multi-decadal wave hindcast data to predict the power that would have been generated by M4 WEC machines. The M4 machine, as a floating articulated device which extracts energy from flexing motion about a hinge, is sized according to a characteristic wavelength of the local wave climate. Using probability distributions, production duration curves, and coefficients of variation we demonstrate larger variability of the 3-hourly power yield at Orkney compared to Albany. At longer timescales, seasonal trends are highlighted through average monthly power values. From a continuity of supply perspective, we investigate occurrences of low production at three different threshold levels and calculate duration and likelihood of such events. Orkney is found to suffer from more persistent lows, causing a more intermittent power output. We also consider the effect of machine size on its power performance. Smaller machines are found to more effectively smooth out the stochastic nature of the underlying wave resource.

Dynamical Seasonal Prediction of the Asian Summer Monsoon in the China Meteorological Administration Climate Prediction System Version 3

Based on the 20 years of ensemble hindcast data, we evaluated the performance of the new version climate prediction system developed by the China Meteorological Administration (CMA CPSv3) on the Asian summer monsoon (ASM) seasonal prediction in this study. Many major features of the ASM are well predicted by CPSv3, including the intensity and location of the heavy precipitation centers, large-scale monsoon circulations, monsoon onset, and the interannual variation of dynamical monsoon indices. The model captures realistically interannual variability of the summer western North Pacific subtropical high (WNPSH) and is highly skillful for the WNPSH index. Compared with its predecessor, the prediction skill of summer precipitation over Asia in CPSv3 is obviously improved, especially over eastern China. The improvement mainly benefits from skillful predictions of the tropical Pacific Ocean and tropical Indian Ocean sea surface temperatures and ocean–atmosphere coupling associated with them.

Predictive Skill of Teleconnection Patterns in Twentieth Century Seasonal Hindcasts and Their Relationship to Extreme Winter Temperatures in Europe

European winter weather is dominated by several low-frequency teleconnection patterns, the main ones being the North Atlantic Oscillation, East Atlantic, East Atlantic/Western Russia and Scandinavian patterns. We analyze the century-long ERA-20C reanalysis and ASF-20C seasonal hindcast datasets and find that these patterns are subject to decadal variability and fluctuations in predictive skill. Using indices for determining periods of extreme cold or warm temperatures, we establish that the teleconnection patterns are, for some regions, significantly correlated or anti-correlated to cold or heat waves. The seasonal hindcasts are however only partly able to capture these relationships. There do not seem to be significant changes to the observed links between large-scale circulation patterns and extreme temperatures between periods of higher and lower predictive skill.