Hindcast Results Research Articles

Northeast Atlantic fish stock productivity hindcasts and forecasts from a Bayesian framework reveal pronounced climate‐induced dynamics

AbstractClimate change continues to exert pressure on ocean ecosystems. The fisheries‐related responses, such as altered body growth, recruitment and spatial distribution of the targeted stock(s), have generally been reasonably well investigated. Nevertheless, there are still important knowledge gaps in how biophysical drivers impact stock productivity and thereby sustainable harvest levels. Considering this, we investigated 26 fish stocks in the Northeast Atlantic, a region characterized by accelerated climate change effects and a diverse set of fisheries. A novel, stepwise, Bayesian framework to quantify stock productivity was established to identify shared trends and project future patterns, aiming at determining essential baselines for adaptive fishery management in the face of climate change. Despite variation among large marine ecosystems and stocks, an overall declining trend in productivity over the past four decades was observed, especially in high‐latitude areas. These hindcast results were mainly attributed to higher temperatures posing negative effects on productivity, which was dependent on the stock's thermal preference. Contrastingly, the proxy for food availability – gross secondary production – exhibited less consistent impacts. In the forecast, the applied Shared Socio‐economic Pathways (SSPs) indicated that most stocks are likely to encounter adverse effects, with the worst cases expected to occur under SSP2‐4.5 in the 2050s, and under SSP5‐8.5 in the 2090s. Thus, this study generally not only supports earlier climate vulnerability assessments (‘scorings’) of experts but also provides evidence for revised directional effects under climate change, underlining the complexity of processes affecting stock productivity.

Improvement in Storm Surge Numerical Forecasting Based on Wave Buoys Data

The maximum wind speed radius of a strong typhoon making landfall is an important factor influencing the numerical forecasting of storm surges. A method for inverting the maximum wind speed radius of typhoons based on wave buoys data was designed to significantly reduce the error in 24 h storm surge forecasting in this paper, and an operation scheme was proposed to enhance the storm surge numerical forecasting system based on this method. Hangzhou Bay and the Yangtze River Estuary, which have been frequently impacted by typhoons over the past five years, were selected as the research area. Common schemes for the maximum wind speed radius were analyzed, and five ladder schemes (10, 15, 20, 25, and 30 km) were established for wave and storm surge numerical model verification of Typhoon Muifa in 2022. Based on a comparison of the wave hindcast results and wave buoys observation data, the wave hindcast result of the commonly used scheme (30 km) was significantly greater than that of the observation data, and the optimal scheme (15 km) closest to the observation data could be determined during the 48 h warning period. Moreover, it was difficult to identify the optimal scheme during the 48 h warning period based on the storm surge hindcast results. A 24 h storm surge numerical forecasting test was performed with the commonly used scheme (30 km) and the optimal scheme (15 km). The results showed that the root mean square error (RMSE) of the optimal scheme (15 km) was 34% lower than that of the commonly used scheme (30 km), while the maximum storm surge error was also reduced from 47.7% for the commonly adopted scheme (30 km) to 11.8% for the optimal scheme (15 km). The maximum storm surges under the optimal scheme (15 km) along Hangzhou Bay and the Yangtze River Estuary ranged from 1.9 to 2.2 m, which were closer to the observation data, and the maximum storm surge under the commonly used scheme (30 km) was 0.8~1.2 m greater than that under the optimal scheme (15 km).

Improving the hindcast of the northward shift of South Asian high in June with machine learning

Accurate prediction of the northward shift of the South Asian High (SAH) in June is crucial for improving the flood and drought management of Asian countries during the summer. This study investigates the ability of three supervised machine learning (ML) models in predicting the meridional index of the SAH (SAHI) in June. The ML models include the extreme gradient boosting (XGBoost), the support vector machine (SVM), and the multi-layer perceptron (MLP) neural network. The training data is derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model data that is significantly correlated with the reference data at a 99% confidence level. The hyperparameter optimization (HPO) is performed for each ML model using the particle swarm optimization (PSO). Six objective functions are defined for the HPO based on the conventional root-mean-square error (RMSE), the interannual variability skill score, and the temporal correlation coefficient (TCC). The performance of optimized ML models is evaluated with the TCC and the same sign rate (SSR). The top two models are the PSO-XGBoost model tuned with RMSE+IVS and the PSO-SVM model tuned with log(RMSE+TCC). Their stacked ensemble model has the TCC of 0.54 and the SSR of 72%. The average of the best model hindcasts has a higher TCC of 0.61 but a lower SSR of 67% than the ensemble model. Further investigation suggests that the ensemble model only preserves the predictor-predictand relationships for two predictors. To improve the representation of the predictor-predictand relationship, we divided the predictors into two groups and trained the ML models separately with interannual increments of predictors in Group 1 and standardized anomalies of predictors in Group 2. The average of the best model hindcasts from the two groups have the TCC of 0.63 and the SSR of 72%. The improvement in the SAHI hindcast is associated with a more realistic predictor-predictand relationship in the ML models. The hindcast results are comparable to the operational models and thus confirm the extensive application potential of the ML models in the SAH prediction.

Main Physical Processes Affecting the Residence Times of a Micro-Tidal Estuary

Residence time is an important parameter linked to the water quality in an estuary. In this paper, we identify and analyze the main processes that affect the residence time of the Caloosahatchee River Estuary, a micro-tidal and mixed diurnal-semidiurnal estuary located in western Florida. Multiyear validated hydrodynamic hindcast results were coupled with an offline particle tracking model to compute the residence time of the estuary, which showed a strong seasonality driven by the river discharge. The residence time reduced with increasing river flow. The wind velocity and direction also affected the residence time. The influence of the wind was dependent on the magnitude of the river discharge. In general, upstream-directed wind increased residence time, while downstream-directed wind decreased residence time. Downstream wind during the dry period reduced residence time on average by a week. Processes such as water density gradient-induced circulation and particle buoyancy also influenced the residence time of the estuary. The outcomes of this study can be used to better understand the influence of the main physical processes affecting the residence time at other similar estuaries and to help in the management of the estuaries to improve their water quality.

Hindcasting to inform the development of bottom-up electricity system models: The cases of endogenous demand and technology learning

Bottom-up, technology-rich electricity system models are commonly used to generate scenarios for policy support at a national or global level. In literature, previous hindcasting studies (also called retrospective modeling or ex-post modeling) evaluated existing models rather than sought to inform model development from the beginning. In this study, we present a hindcasting exercise with D-EXPANSE model for national electricity systems in 31 European countries over the 1990–2019 period. We develop several model versions with or without elastic electricity demand and with or without endogenous technology learning, and use hindcasting to choose the most accurate configuration of the bottom-up model. The hindcasting results show that a model with endogenous elastic demand can capture well the real-world evolution of electricity demand, if elasticity factor is chosen well and if the countries did not undergo severe structural changes. Endogenous technology learning, however, increases the uptake of new emerging technologies in cost-optimal scenarios, but still cannot fully capture the real-world dynamics and at times even introduces further inaccuracies.

Wave field predictions using a multi-layer perceptron and decision tree model based on physical principles: A case study at the Pearl River Estuary

In this study, a novel data-driven model is proposed for efficient spatial–temporal forecasting of the significant wave height (SWH), mean wave period (MWP), and mean wave direction (MWD) in the Pearl River Estuary, located in Southern China, using machine learning. The model utilizes machine learning techniques and employs a multi-layer perceptron (MLP) and decision tree (DT) based architecture following a matrix-to-matrix mapping strategy to establish the underlying spatial–temporal relationship between the wind and wave fields for four-day and ocean-scale forecasts. The training and testing of the model were carried out using long-term hindcast wave and wind datasets released by ERA5. A rectangular sea region covering an area of 5° × 5°in the Pearl River Estuary was regarded as the modeling domain. The data-driven model was validated against a 10 years of historical hindcast data, and the global errors were analyzed. The forecasting performance of the established model for tropical cyclone wave events was investigated in terms of both the spatial pattern and temporal variance of the SWH, MWP, and MWD. Extreme waves were generated using the proposed model and compared with the original hindcast results. The data-driven model showed promising potential for accurately capturing ambiguous patterns and features that are variant in both spatial and temporal dimensions, and exhibited obvious superiority over numerical wave modeling in terms of computational efficiency.

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.

S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections

Abstract A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

A forecast of staple crop production in Burkina Faso to enable early warnings of shortages in domestic food availability

Almost half of the Burkinabe population is moderately or severely affected by food insecurity. With climate change, domestic food production may become more under pressure, further jeopardizing food security. In this study, we focus on the production of maize, sorghum and millet as staple cereal crops in Burkina Faso to assess food availability as one component of food security. Based on a statistical weather-driven crop model, we provide a within-season forecast of crop production 1 month before the harvest. Hindcast results from 1984 to 2018 produce an r2 of 0.95 in case of known harvest areas and an r2 of 0.88 when harvest areas are modelled instead. We compare actually supplied calories with those usually consumed from staple crops, allowing us to provide early information on shortages in domestic cereal production on the national level. Despite the—on average—sufficient domestic cereal production from maize, sorghum and millet, a considerable level of food insecurity prevails for large parts of the population. We suggest to consider such forecasts as an early warning signal for shortages in domestic staple crop production and encourage a comprehensive assessment of all dimensions of food security to rapidly develop counteractions for looming food crises.

Superensembles of raw and bias‐adjusted regional climate models for Mediterranean region, Turkey

AbstractFor regional‐scale studies on climate change and relevant impact assessment, the projections of regional climate models (RCMs) are used due to their advantage of high resolution and better representation of the local climate relative to the global climate models. However, direct use of RCM outputs is prone to uncertainties and biases that may significantly diminish the accuracy of results. EURO‐COordinated Regional Downscaling EXperiment (CORDEX) initiative that is a part of the global Coordinated Regional Downscaling Experiment Project provides high‐resolution RCM projections for the European domain and bias‐adjusted regional projections under the “CORDEX‐Adjust” Project for climate change impact assessment studies. This study aims to perform a multi‐model analysis of precipitation data using bias‐adjusted and raw/non‐bias adjusted CORDEX RCMs to obtain an evaluation of their representativeness for local climate conditions and adequacy to be used for climate change impact assessment. For this purpose, the analysis focuses on four CORDEX RCMs and their 12 bias‐adjusted versions generated with cumulative distribution function transformation, quantile mapping, and distribution‐based scaling methodologies. For the analysis in total, 16 hindcast results of raw and bias‐adjusted RCMs, and three superensembles (SEs) generated through multiple linear regression are compared for their performance regarding their goodness of fit to the ground‐based precipitation monitoring data from eight meteorological stations in the Mediterranean region in Turkey. The analysis verified that the skill of individual simulations including the bias‐adjusted outputs is significantly variable in spatial and temporal means. On the other hand, SE formed by using all 16 hindcast outputs has the highest skill for the representation of variability in precipitation in time as well as for the reproduction of annual climatology at all stations, although potential drawbacks concerning seasonality and the range of anomaly may still exist which might be significant depending on the specific aim of impact assessment.

Prediction of accumulated cyclone energy in tropical cyclone over the western North Pacific in autumn

Tropical cyclones (TCs) are affected significantly by the climate system and can provide feedbacks. TC activities are important for weather forecasting and climate predictions. Here, we focused on the spatial distribution of accumulated cyclone energy (ACE) and its seasonal prediction. To predict the ACE distribution over the western North Pacific (WNP) in autumn, we established a physical-empirical model. Analyzing 36 years observations (1979–2014) of ACE over the WNP reveals two physically predictable modes. The sea surface temperature (SST) in the southwest Pacific and central Pacific affect the first mode through the low-level circulations. At the same time, the SST in the Gulf of Alaska and the sea-ice concentration in the Beaufort Sea affect the first mode through the circumglobal teleconnection. The development of the eastern Pacific El-Nino and anomalous SST over the North Pacific affect the second mode through the vertical wind shear and low-level circulation. The sea-ice concentration in the Greenland Sea induce an upper-level circulation anomaly over the WNP and affect the second mode. Physically meaningful predictors were selected according to the controlling mechanisms of the two modes. The cross-validated hindcast results demonstrated that the principal components of the two modes are predicted with correlation coefficients of 0.68 and 0.63. Thus, the two modes are predictable. The pattern correlation coefficient skill of the ACE spatial pattern is 0.26, which is significant at the 99% confidence level. The temporal correlation coefficient skill reaches 0.21 over major regions influenced by TCs. To validate the real-time predictability of the model, independent tests were performed on the last three years (2015, 2016 and 2017), and the results show that the pattern correlation coefficients between the observations and the predictions are 0.39, 0.70, and 0.41, respectively.

Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

In this paper, we evaluate the capability of the Beijing Climate Center Climate System Model (BCC-CSM) in simulating and forecasting the boreal summer intraseasonal oscillation (BSISO), using its simulation and sub-seasonal to seasonal (S2S) hindcast results. Results show that the model can generally simulate the spatial structure of the BSISO, but give relatively weaker strength, shorter period, and faster transition of BSISO phases when compared with the observations. This partially limits the model’s capability in forecasting the BSISO, with a useful skill of only 9 days. Two sets of hindcast experiments with improved atmospheric and atmosphere/ocean initial conditions (referred to as EXP1 and EXP2, respectively) are conducted to improve the BSISO forecast. The BSISO forecast skill is increased by 2 days with the optimization of atmospheric initial conditions only (EXP1), and is further increased by 1 day with the optimization of both atmospheric and oceanic initial conditions (EXP2). These changes lead to a final skill of 12 days, which is comparable to the skills of most models participated in the S2S Prediction Project. In EXP1 and EXP2, the BSISO forecast skills are improved for most initial phases, especially phases 1 and 2, denoting a better description for BSISO propagation from the tropical Indian Ocean to the western North Pacific. However, the skill is considerably low and insensitive to initial conditions for initial phase 6 and target phase 3, corresponding to the BSISO convection’s active-to-break transition over the western North Pacific and BSISO convection’s break-to-active transition over the tropical Indian Ocean and Maritime Continent. This prediction barrier also exists in many forecast models of the S2S Prediction Project. Our hindcast experiments with different initial conditions indicate that the remarkable model errors over the Maritime Continent and subtropical western North Pacific may largely account for the prediction barrier.

HF Radar Measurements of Surface Waves in the Gulf of Naples (Southeastern Tyrrhenian Sea): Comparison With Hindcast Results at Different Scales

HF radar systems wave measurements are evaluated against numerical simulations in the Gulf of Naples (Southeastern Tyrrhenian Sea). Wave measurements are obtained from three CODAR SeaSonde HF radars installed along the coast of the Gulf of Naples. The numerical models employed are WavewatchIII, implemented on a regional scale with a resolution of about 10 km in longitude and latitude in the whole Mediterranean Sea, and SWAN, implemented with a 200 m resolution in the area of interest. Numerical simulations are also validated against experimental data acquired by a buoy installed offshore the Gulf of Naples. The agreement between HF radar measurements and model hindcasts is evaluated through the estimate of statistical error indices for the main wave characteristics (significant wave height, mean period and mean direction). The consistency between wave parameters retrieved by HF radars and hindcasted by the models opens the way to future integration of the two systems as well as to the utilization of HF radar wave parameters that could be envisaged for data assimilation in wave models.

Wave energy resource assessment along the Algerian coast based on 39-year wave hindcast

This study investigates a long-term assessment of the wave energy resource propagated along the Algerian basin, based on a 39-year wave hindcast. The wave energy hindcast dataset was developed using the Simulating WAve Nearshore (SWAN) model, calibrated and validated [1] against wave measurements performed on the Algerian coast. A detailed spatial and local analysis was performed following the hindcast results. We have determined several parameters including; hourly, monthly, seasonal and annual variations of wave energy resources, the probability of occurrence distribution for different wave power ranges with different directions, the probability of calm sea states, the wave energy development index (WEDI) and the total annual wave energy and their distribution as a function of significant wave height and energy period. All these results enabled a very important benchmark for decision making regarding the future implementation and design of wave energy converters (WECs) and other offshore structures in the Algerian basin. Our findings have shown that the Algerian coasts are characterized by a considerable wave energy potential with a large hotspot area in the eastern coasts. Thus, we have recorded a significant variability in the wave energy characteristics available in each zone along the Algerian coast. The western zone was characterized by an average energy of ∼7.5 kW/m with a low monthly and seasonal variation (<1.2), the central zone was characterized by a significant total annual wave energy of 63 MWh/m/year and a considerable WEDI of 0.019, and the eastern Algerian coast was characterized by one of the highest energy potential in the Mediterranean basin with a total annual energy exceeding 100 MWh/m for less than 15 km from the coast and a calm sea state probability lower than 18%. Thus, it has been concluded that since 1995, wave energy resources have tended to increase further.

A statistical prediction model for summer extreme precipitation days over the northern central China

AbstractThe characteristics of summer precipitation over northern central China (NC China) and its associated atmospheric circulation have been documented in many previous studies. However, the prediction of extreme precipitation events, which is vital for water resource management and agricultural yield, has received little attention. In this study, we attempted to establish a statistical model to predict mid‐summer extreme precipitation days (EPDs) over NC China, based on a year‐to‐year increment (DY) approach. Three variables—preceding early‐spring snow cover DY over western China, previous early‐summer sea surface temperature (SST) DY in the North Pacific, and SST DY differences between the equatorial western and eastern Pacific—were selected as potential predictors. The first predictor exerted an impact on precipitable water content anomalies, South Asian high and vertical movement anomalies over NC China through anomalous soil moisture over northern China. The latter two predictors affected the EPDs over NC China via modulating moisture transport originating from tropical oceans. Further results showed that the prediction model performed well in predicting the inter‐annual variation of EPDs, with respective correlation coefficients of .79/.67 between the predicted and observed EPDs DY/EPDs for 1980–2014. The agreement index of the prediction model was 0.67 and the Nash–Sutcliffe efficiency was 0.37, suggesting good capabilities of the prediction model. Both the one‐year‐out cross‐validation test for 1980–2014 and independent hindcast for 1995–2014 demonstrated that these three predictors showed skilful predictions of EPDs over NC China. The cross‐validation (hindcast) simulated EPDs DY achieved a significant correlation coefficient of .74 (.78) with the observations, along with positive Nash–Sutcliffe efficiency values. Notably, the decadal shift of EPDs around the 2000s was successfully replicated in both cross‐validation and hindcast results.

Climate Prediction of Satellite-Based Spring Eurasian Vegetation Index (NDVI) using Coupled Singular Value Decomposition (SVD) Patterns

Satellite-based normalized difference vegetation index (NDVI) data are widely used for estimating vegetation greenness. Seasonal climate predictions of spring (April–May–June) NDVI over Eurasia are explored by applying the year-to-year increment approach. The prediction models were developed based on the coupled modes of singular value decomposition (SVD) analyses between Eurasian NDVI and climate factors. One synchronous predictor, the spring surface air temperature from the NCEP’s Climate Forecast System (SAT-CFS), and three previous-season predictors (winter (December–January–February) sea-ice cover over the Barents Sea (SICBS), winter sea surface temperature over the equatorial Pacific (SSTP), and winter North Atlantic Oscillation (NAO) were chosen to develop four single-predictor schemes: the SAT-CFS scheme, SICBS scheme, SSTP scheme, and NAO scheme. Meanwhile, a statistical scheme that involves the three previous-season predictors (i.e., SICBS, SSTP, and NAO) and a hybrid scheme that includes all four predictors are also proposed. To evaluate the prediction skills of the schemes, one-year-out cross-validation and independent hindcast results are analyzed, revealing the hybrid scheme as having the best prediction skill. The results indicate that the temporal correlation coefficients at 92% of grid points over Eurasia are significant at the 5% significance level in the hybrid scheme, which is the best among all the schemes. Furthermore, spatial correlation coefficients (SCCs) of the six schemes are significant at the 1% significance level in most years during 1983–2015, with the averaged SCC of the hybrid scheme being the highest (0.60). The grid-averaged root-mean-square-error of the hybrid scheme is 0.04. By comparing the satellite-based NDVI value with the independent hindcast results during 2010–2015, it can be concluded that the hybrid scheme shows high prediction skill in terms of both the spatial pattern and the temporal variability of spring Eurasian NDVI.

Reconstructing organic matter sources and rain rates in the southern West Pacific Warm Pool during the transition from the deglaciation period to early Holocene

Transient features in the organic carbon content of deep-sea sediment cores resulting from changes in the flux and/or quality of sedimentary organic matter are not uncommon. We examined the geochemical characteristics of sediments retrieved with a gravity corer from the northwestern Solomon Sea (3908 m water depth), southern West Pacific Warm Pool (WPWP). δ13C and δ15N of sedimentary organic matter, together with TOC/TN data suggest that the organic material is characterized by a mixture of marine and terrestrial origins with a higher contribution from marine algae. The data were analyzed with an inverse non-steady-state reaction-transport model to examine the variability and magnitude of particulate organic carbon (POC) flux to the seafloor during the transition between the deglaciation period and early Holocene. Measured POC content and porewater NO3−, NH4+, DIC and SO42− concentrations were used to constrain the model. Hindcast results revealed that POC flux decreased from 75 to 37.5 μg cm−2 yr−1 during the deglaciation–early Holocene transition. The rate of POC degradation in the present setting is slightly lower compared to that in the pre-Holocene setting. The synchronous decline in the relative contribution of terrestrial organic matter input and rapid sea level rise during this transition suggest that sea level, rather than surface productivity, is the dominant factor controlling the POC deposition flux in the Solomon Sea. This is conceivable because the sampling site is proximal to high-relief islands with high rainfall, a well-developed submarine canyon system and narrow and steep continental margins. Consequently, we suggest that deep-water basins in proximity to similar high-relief mountainous islands in the tropical Pacific may represent important sinks for terrestrial organic material, especially during sea level lowstands.

Sub-seasonal to Seasonal Hindcasts of Stratospheric Sudden Warming by BCC_CSM1.1(m): A Comparison with ECMWF

This study focuses on model predictive skill with respect to stratospheric sudden warming (SSW) events by comparing the hindcast results of BCC_CSM1.1(m) with those of the ECMWF’s model under the sub-seasonal to seasonal prediction project of the World Weather Research Program and World Climate Research Program. When the hindcasts are initiated less than two weeks before SSW onset, BCC_CSM and ECMWF show comparable predictive skill in terms of the temporal evolution of the stratospheric circumpolar westerlies and polar temperature up to 30 days after SSW onset. However, with earlier hindcast initialization, the predictive skill of BCC_CSM gradually decreases, and the reproduced maximum circulation anomalies in the hindcasts initiated four weeks before SSW onset replicate only 10% of the circulation anomaly intensities in observations. The earliest successful prediction of the breakdown of the stratospheric polar vortex accompanying SSW onset for BCC_CSM (ECMWF) is the hindcast initiated two (three) weeks earlier. The predictive skills of both models during SSW winters are always higher than that during non-SSW winters, in relation to the successfully captured tropospheric precursors and the associated upward propagation of planetary waves by the model initializations. To narrow the gap in SSW predictive skill between BCC_CSM and ECMWF, ensemble forecasts and error corrections are performed with BCC_CSM. The SSW predictive skill in the ensemble hindcasts and the error corrections are improved compared with the previous control forecasts.

Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation

The seasonal prediction of sea surface temperature (SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model (FIO-ESM) is assessed in this study. The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions. The average skill of the North Pacific variability (NPV) index from 1 to 6 months lead is as high as 0.72 (0.55) when El Nino-Southern Oscillation and NPV are in phase (out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6% (23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.

Model Development and Hindcast Simulations of NOAA’s Integrated Northern Gulf of Mexico Operational Forecast System

NOAA’s National Ocean Service is upgrading three existing northern Gulf of Mexico (GOM) operational nowcast/forecast systems (OFS) by integrating them into one single system (INGOFS) and developing additional domain coverage to encompass the lower Mississippi River, Lake Pontchartrain, Texas coastal embayments, and Mexican coastal waters. The system will produce real-time nowcast and short-range forecast guidance for water levels, 3-dimensional currents, water temperature, and salinity. INGOFS will be implemented using the Finite Volume Community Ocean Model (FVCOM). This paper describes the model configuration and results from a one-year (2 August 2016–1 August 2017) hindcast simulation. The model grid is composed of about 300,000 nodes and 600,000 elements, and has a spatial resolution ranging from 45 m near the coast to around 10 km on the open ocean boundary. It uses the FVCOM wetting and drying feature, the quadratic bottom friction scheme, and the two-equation model of the Mellor-Yamada Level 2.5 turbulence closure scheme. The hindcast results of water levels, surface temperatures, and salinity were verified by comparing the model time series with in situ observations. The root-mean-squared errors are about 0.08 m for water levels, about 1.1 °C for temperatures, and about 3.7 psu for salinity. The hindcast configuration will be further tested in a nowcast/forecast environment for a one-year period. The upgraded system is anticipated to be in operational production in mid-2020.