Global Seasonal Forecast System Research Articles

Prediction Performance of Ocean Temperature and Salinity in Global Seasonal Forecast System Version 5 (GloSea5) on ARGO Float Data

Prediction Performance of Ocean Temperature and Salinity in Global Seasonal Forecast System Version 5 (GloSea5) on ARGO Float Data

Generation of state‐dependent ensemble perturbations based on time‐varying seawater density for GloSea5 initialization

AbstractIn this study, we developed a flow‐dependent oceanic initialization system for initializing the oceanic temperature and salinity in the Global Seasonal forecast system version 5 (GloSea5). Our algorithm overcomes the limitation of stationary perturbations for Ensemble Optimal Interpolation (EnOI) by spreading observed information along isopycnal lines to create three‐dimensional snapshot density states. The proposed algorithm, which we call state‐dependent ensemble‐based EnOI (SD‐EnOI), takes into account changes in the background error covariance over time without relying on ensemble model simulations. To evaluate the quality of the oceanic initial conditions (ICs) produced by SD‐EnOI, we compared them with those generated by the Global Ocean Data Assimilation and Prediction System version 1 (GODAPS1) operated by the Korea Meteorological Agency (KMA) throughout January 2017 to December 2017. Our findings show that the thermal construction of the SD‐EnOI ICs is more realistic than that of GODAPS1, particularly in the tropical Pacific region. The strong warm bias in sea surface temperature (SST) and the shallow mixed‐layer depth bias observed in the GODAPS1 ICs are not shown in SD‐EnOI. Due to the more realistic oceanic thermal structure present in the SD‐EnOI ICs, their use in retrospective forecast experiments resulted in a systematic reduction in climatological SST drift in the central‐eastern Pacific for forecasts up to four lead months compared to using GODAPS1 ICs. This demonstrates the significant impact of the initialization process on the quality of dynamical seasonal forecasts.

Assessment of seasonal forecasting potential for springtime Asian dust in South Korea using the KMA global seasonal forecasting system

This study aims to assess the potential of seasonal forecasting for springtime Asian dust days in South Korea using the Korea Meteorological Administration's Global Seasonal Forecasting System version 6 (KMA GloSea6). The system demonstrated similar predictive performance for springtime Asian dust days compared to GloSea5-ADAM, which incorporates the Asian Dust and Aerosol Model's emission algorithm into GloSea5. While KMA GloSea6 exhibited strong wind, cold, and moist biases in the Asian dust source region during the spring of the hindcast period, similar to GloSea5-ADAM, it showed an improved spatial distribution of ACC. The anomaly correlation coefficient for the annual variability of Asian dust occurrence frequency anomalies in the dust source region was also similar, with values of 0.41 for KMA GloSea6 and 0.43 for GloSea5-ADAM. Furthermore, in evaluating the seasonal forecasting of springtime Asian dust days using hindcast datasets, KMA GloSea6 accurately predicted 12 out of 24 instances, resulting in a forecast accuracy of 0.5, consistent with GloSea5-ADAM. When applying the criteria to spring 2022 and 2023 forecast data not used in the derivation, the predicted Asian dust days of 6.14 days in spring 2022 led to a “Near normal” prediction, differing from the observed “Below normal.” However, the spatial distribution showed “Below normal” conditions, aligning with observations in some areas. In spring 2023, the predicted 9.78 Asian dust days led to an “Above normal” prediction, consistent with observed conditions. These findings will contribute to advancing seasonal Asian dust forecasting in South Korea.

Understanding winter windstorm predictability over Europe

Abstract. Winter windstorms belong to the most damaging meteorological events in the extra-tropics. Their impact on society makes it essential to understand and improve seasonal forecasts of these extreme events. Skilful predictions on a seasonal timescale have been shown in previous studies by investigating hindcasts from various forecast centres. This study aims to explain storm forecast skill based on relevant dynamical factors. Therefore, a number of factors which are known to influence either windstorms directly or their synoptic relevant systems, mid-latitude cyclones, are investigated. These factors are analysed for their relation to windstorm forecast performance based on a reanalysis (ERA5) and the seasonal hindcast of the UK Met Office (Global Seasonal forecasting system version 5, GloSea5). Within GloSea5, relevant dynamical factors are (1) validated with respect to their physical connections to windstorms, (2) investigated with respect to the seasonal forecast skill of the factors themselves, and (3) assessed on the relevance and influence of their forecast performance to and on windstorm forecast skill. Although not all investigated factors reveal a clear and consistent influence on windstorm forecast skill over Europe, core factors like mean sea level pressure gradient, sea surface temperature, equivalent potential temperature and Eady growth rate show consistent results within these three steps: their physical connection is well represented in the model; these factors are skilfully predicted in storm-relevant regions, and, consequently, this skill leads to increased forecast skill of winter windstorms over Europe. This study thus explains existing forecast skill in winter windstorms but also indicates potential for further model developments to improve seasonal winter windstorm predictions.

Seasonal and interannual variation of equatorial waves in ERA5 and GloSea5

AbstractThis study presents an analysis of the equatorial waves in the ERA5 reanalysis and forecasts of the Met Office Global Seasonal Forecast System version 5 (GloSea5), by projecting dynamical fields onto theoretical equatorial wave modes. The seasonal and interannual variation of equatorial wave activity in GloSea5 is evaluated against ERA5 waves. We find that in ERA5 the seasonal and spatial variations of eastward‐moving Kelvin wave activity are mainly determined by the ambient zonal flow, with wave activity being stronger in easterlies than westerlies. For the westward‐moving mixed Rossby–gravity waves and equatorial Rossby waves, the seasonal and spatial variations of the upper‐tropospheric wave activity are determined by both the ambient flow and extratropical forcing with stronger wave activity generally in the westerlies, whilst the lower‐tropospheric wave activity is related to the ambient flow and tropical convection. In GloSea5, the Kelvin wave activity is not well simulated in the upper troposphere, and in general the wave activity and wave‐related convective signal are too weak. In contrast, GloSea5 performs better for the westward‐moving equatorial waves, including their Doppler‐shifted eastward‐moving components, despite a significant overestimation in the Atlantic and African region. The phase of ENSO has a substantial impact on equatorial waves in both ERA5 and GloSea5. The mechanism of the impact is through changes in the ENSO‐related ambient flow, upper‐tropospheric extratropical forcing, and tropical convection. GloSea5 can capture the low‐level Kelvin wave–ENSO relationship reasonably well, with stronger wave activity over the eastern Pacific in El Niño, although the ENSO‐driven variation is too weak. The wave–ENSO relationship for westward‐moving waves is well represented in GloSea5, especially in the upper‐level eastern‐Pacific westerly duct region in the extended boreal winter and the low‐level Indo‐Pacific in all seasons. We conclude that in GloSea5 the representation of the wave–ENSO teleconnection is the key driver for the performance of interannual variability in the wave activity.

Taylor expansion of the correlation metric for an individual forecast evaluation and its application to East Asian sub-seasonal forecasts

This study develops a skill evaluation metric for an individual forecast by applying a Taylor expansion to the commonly-used temporal correlation skill. In contrast to other individual forecast evaluation metrics, which depend on the amplitude of forecasted and observed anomalies, the so-called “association strength (AS) skill” is less affected by the anomaly amplitude and mainly depends on the degree of similarity between the forecasted and the observed values. Based on this newly developed index, the forecast skill is evaluated for an individual case, then, a group is categorized with respect to the AS skill. The cases with the highest AS skill exhibit the highest correlation skill than any group randomly selected, indicating that the AS skill is a powerful metric to evaluate the non-dimensionalized forecast skill. This strategy is adopted for the subseasonal East Asian summer precipitation forecasts produced by the UK Met Office’s ensemble Global Seasonal forecast system version 5 (GloSea5). In the group with the highest AS skill of the East Asian summer precipitation index (i.e., highest AS cases), the geopotential height anomalies showed quasi-stationary Rossby waves from the North Atlantic to East Asia. The spatial distribution of the dominant subseasonal anomalies for cases with the highest AS is distinct from the cases or groups with the lowest AS skill. Furthermore, the dominant pattern with the highest AS is not solely explained by any well-known typical subseasonal climate patterns, such as the Madden–Julian Oscillation, circumglobal teleconnection pattern, Pacific-Japan pattern, or the Summer North Atlantic Oscillation. This implies that the excitation of well-known climate patterns only partly contributes to increasing the mid-latitude climate predictability in the GloSea5.

Seasonal forecasting of snow resources at Alpine sites

Abstract. Climate warming in mountain regions is resulting in glacier shrinking, seasonal snow cover reduction, and changes in the amount and seasonality of meltwater runoff, with consequences on water availability. Droughts are expected to become more severe in the future with economical and environmental losses both locally and downstream. Effective adaptation strategies involve multiple timescales, and seasonal forecasts can help in the optimization of the available snow and water resources with a lead time of several months. We developed a prototype to generate seasonal forecasts of snow depth and snow water equivalent with a starting date of 1 November and a lead time of 7 months, so up to 31 May of the following year. The prototype has been co-designed with end users in the field of water management, hydropower production and mountain ski tourism, meeting their needs in terms of indicators, time resolution of the forecasts and visualization of the forecast outputs. In this paper we present the modelling chain, based on the seasonal forecasts of the ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S) Climate Data Store. Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and relative humidity are bias-corrected and downscaled to three sites in the Western Italian Alps and finally used as input for the physically based multi-layer snow model SNOWPACK. Precipitation is bias-corrected with a quantile mapping method using ERA5 reanalysis as a reference and then downscaled with the RainFARM stochastic procedure in order to allow an estimate of uncertainties due to the downscaling method. The impacts of precipitation bias adjustment and downscaling on the forecast skill have been investigated. The skill of the prototype in predicting the deviation of monthly snow depth with respect to the normal conditions from November to May in each season of the hindcast period 1995–2015 is demonstrated using both deterministic and probabilistic metrics. Forecast skills are determined with respect to a simple forecasting method based on the climatology, and station measurements are used as reference data. The prototype shows good skills at predicting the tercile category, i.e. snow depth below and above normal, in the winter (lead times: 2–3–4 months) and spring (lead times: 5–6–7 months) ahead: snow depth is predicted with higher accuracy (Brier skill score) and higher discrimination (area under the relative operating characteristics (ROC) curve skill score) with respect to a simple forecasting method based on the climatology. Ensemble mean monthly snow depth forecasts are significantly correlated with observations not only at short lead times of 1 and 2 months (November and December) but also at lead times of 5 and 6 months (March and April) when employing the ECMWFS5 forcing. Moreover the prototype shows skill at predicting extremely dry seasons, i.e. seasons with snow depth below the 10th percentile, while skills at predicting snow depth above the 90th percentile are model-, station- and score-dependent. The bias correction of precipitation forecasts is essential in the case of large biases in the global seasonal forecast system (MFS6) to reconstruct a realistic snow depth climatology; however, no remarkable differences are found among the skill scores when the precipitation input is bias-corrected, downscaled, or bias-corrected and downscaled, compared to the case in which raw data are employed, suggesting that skill scores are weakly sensitive to the treatment of the precipitation input.

Large-scale circulation patterns and their influence on European winter windstorm predictions

Severe winter windstorms are amongst the most damaging weather events for Europe and show significant interannual variability. While surface variables (temperature, precipitation) have been successfully predicted for some time now, predictability of severe windstorms caused by extra-tropical cyclones remains less well explored. This study investigates windstorm prediction skill of the UK Met Office Global Seasonal Forecast System Version 5 (GloSea5) for the Northeast-Atlantic and European region. Based on an objective Lagrangian tracking of severe, damage relevant windstorms, three storm parameters are analysed: windstorm frequency and two intensity measures. Firstly, skill based on direct tracking of simulated windstorms is diagnosed. Significant positive skill for storm frequency and intensity is found over an extended area at the downstream end of the storm track, i.e., from the UK to southern Scandinavia. The skill for frequency agrees well with previous studies for older model versions, while the results of event-based intensity are novel. Receiver Operating Characteristic Curves for three smaller regions reveal significant skill for high and low storm activity seasons. Second, skill of windstorm characteristics based on their multi-linear regressions to three dominant large-scale circulation patterns [i.e., the North Atlantic Oscillation (NAO), the Scandinavian Pattern (SCA), and the East-Atlantic Pattern (EA)] are analysed. Although these large-scale patterns explain up to 80% of the interannual variance of windstorm frequency and up to 60% for intensity, the forecast skill for the respectively linear-regressed windstorms do not show systematically higher skill than the direct tracking approach. The signal-to-noise ratio of windstorm characteristics (frequency, intensity) is also quantified, confirming that the signal-to-noise paradox extends to windstorm predictions.

Predictability of European winter 2020/2021: Influence of a mid‐winter sudden stratospheric warming

AbstractBoreal winter (December–February) 2020/2021 in the North Atlantic/European region was characterised by a negative North Atlantic Oscillation (NAO) index. Although this was captured within the ensemble spread of predictions from the Met Office Global Seasonal forecast system (GloSea5), with 17% of ensemble members predicting an NAO less than zero, the forecast ensemble mean was shifted towards a positive NAO phase. The observed monthly NAO anomalies were particularly negative in January and February, following an early January sudden stratospheric warming (SSW), and a prolonged period of Phase 6 or 7 of the Madden Julian Oscillation (MJO) in late January/early February. In contrast, predictions showed the expected teleconnection from the observed La Niña, with a positive NAO signal resulting from a weakening of the Aleutian Low leading to a reduction in tropospheric wave activity, an increase in polar vortex strength and a reduced chance of an SSW. Forecasts initialised later in the winter season successfully predicted the negative NAO in January and February once the SSW and MJO were within the medium range timescale. GloSea5 likely over‐predicted the strength of the La Niña which we estimate caused a small negative bias in the SSW probability. However, this error is smaller than the uncertainty in SSW probability from the finite forecast ensemble size, emphasising the need for large forecast ensembles. This case study also demonstrates the advantage of continuously updated lagged ensemble forecasts over a ‘burst’ ensemble started on a fixed date, since a change in forecast signal due to events within the season can be detected early and promptly communicated to users.

Impact of stratospheric ozone on the subseasonal prediction in the southern hemisphere spring

Antarctic ozone has been regarded as a major driver of the Southern Hemisphere (SH) circulation change in the recent past. Here, we show that Antarctic ozone can also affect the subseasonal-to-seasonal (S2S) prediction during the SH spring. Its impact is quantified by conducting two reforecast experiments with the Global Seasonal Forecasting System 5 (GloSea5). Both reforecasts are initialized on September 1st of each year from 2004 to 2020 but with different stratospheric ozone: one with climatological ozone and the other with year-to-year varying ozone. The reforecast with climatological ozone, which is common in the operational S2S prediction, shows the skill re-emergence in October after a couple of weeks of no prediction skill in the troposphere. This skill re-emergence, mostly due to the stratosphere–troposphere dynamical coupling, becomes stronger in the reforecast with year-to-year varying ozone. The surface prediction skill also increases over Australia. This result suggests that a more realistic stratospheric ozone could lead to improved S2S prediction in the SH spring.

Atmosphere-driven cold SST biases over the western North Pacific in the GloSea5 seasonal forecast system

The predictability of the sea surface temperature (SST) in seasonal forecast systems is crucial for accurate seasonal predictions. In this study, we evaluated the prediction of SST in the Global Seasonal forecast system version 5 (GloSea5) hindcast, particularly focusing on the western North Pacific (WNP), where the SST can modify atmospheric convection and the East Asian weather. GloSea5 has a cold SST bias in the WNP that grows over at least 7 months. The bias originates from the surface net heat flux. At the beginning of model integration, the ocean receives excessive heat from the atmosphere because of the predominant positive bias in the downward shortwave radiation (SW), which rapidly decreased within a few days as cloud cover builds. Then, the negative bias in the latent heat (LH) flux increases over time and induces a negative bias in the surface net heat flux. Although the magnitude of the negative bias in LH flux gradually decreases, it remains the most significant contributor to the negative bias in the net heat flux bias for more than 250 days. Uncoupled ocean model experiments showed that the ocean model is unlikely to be the primary source of the SST bias.

Characteristics of Subseasonal Winter Prediction Skill Assessment of GloSea5 for East Asia

In this study, the characteristics of systematic errors in subseasonal prediction for East Asia are investigated using an ensemble hindcast (1991–2010) produced by the Global Seasonal Forecasting System version 5 (GloSea5). GloSea5 is a global prediction system for the subseasonal-to-seasonal time scale, based on a fully coupled atmosphere, land, ocean, and sea ice model. To examine the fidelity of the system with respect to reproducing and forecasting phenomena, this study assesses the systematic biases in the global prediction model focusing on the prediction skill for the East Asian winter monsoon (EAWM), which is a major driver of weather and climate variability in East Asia. To investigate the error characteristics of GloSea5, the hindcast period is analyzed by dividing it into two periods: 1991–2000 and 2001–2010. The main results show that the prediction skill for the EAWM with a lead time of 3 weeks is significantly decreased in the 2000s compared to the 1990s. To investigate the reason for the reduced EAWM prediction performance in the 2000s, the characteristics of the teleconnections relating to the polar and equatorial regions are examined. It is found that the simulated excessive weakening of the East Asian jet relating to the tropics and a failure in representing the Siberian high pressure relating to the Arctic are mainly responsible for the decreased EAWM prediction skill.

Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times

Abstract. Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of ensemble streamflow prediction (ESP) for a diverse sample of 46 catchments using the GR4J (Génie Rural à 4 paramètres Journalier) hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 d to 12 months for each of the 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with a continuous ranked probability skill score (CRPSS) of 0.8 (1 d), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ=0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and the East, being those where slowly responding, high-storage catchments are located. Forecast reliability and discrimination were also assessed with respect to low- and high-flow events. In addition to our benchmarking experiment, we conditioned ESP with the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office's Global Seasonal Forecasting System version 5. We found gains in winter forecast skill (CRPSS) of 7 %–18 % were possible over lead times of 1 to 3 months and that improved reliability and discrimination make NAO-conditioned ESP particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.

How well can a seasonal forecast system represent three hourly compound wind and precipitation extremes over Europe?

Extreme precipitation and winds can have a severe impact on society, particularly when they occur at the same place and time. In this study the Met Office’s Global Seasonal forecast system version 5 (GloSea5) model ensembles are evaluated against the reanalysis dataset ERA5, to find out how well they represent three hourly extreme precipitation, extreme wind and extreme co-occurring events over Europe. Although substantial differences in magnitude are found between precipitation and wind extremes between the datasets, the conditional probability of exceedance above the 99th percentile, which measures the co-occurrence between the two extremes, compares well spatially over Europe. However, significant differences in frequency are found around and over some areas of high topography. Generally GloSea5 underestimates this co-occurrence over sea. The model’s co-occurring events at individual locations investigated occur with very similar synoptic patterns to ERA5, indicating that the compound extremes are produced for the correct reasons.

Diagnosing ISO Forecast from GloSea5 Using Dynamic-Oriented ISO Theory

A Madden–Jillian oscillation (MJO) and boreal summer intraseasonal oscillation (BSISO) are important climate variabilities, which affect a forecast of weather and climate. In this study, the MJO and the BSISO hindcasts from the Global Seasonal Forecast System, version 5 (GS5) were diagnosed using dynamic-oriented theories. We additionally analyzed the GS5 climatological run to identify whether the weakness of the GS5 hindcast results from the model physics or initialization processes. The GS5 hindcast captures three-dimensional dynamics and thermodynamics structure of MJO eastward propagation well in the Indian Ocean. The model produces the boundary layer (BL) moisture convergence anomalies to the east of the MJO deep precipitation with easterly anomalies associated with the Kelvin wave. The enhanced BL moisture convergence increases upward transport of moisture from the surface to the lower troposphere, inducing the moist lower troposphere and the positive convective instability by destabilization of the lower atmosphere and, thus, generating the next convection to the east of MJO deep convection and promoting MJO eastward propagation. However, the signal for eastward propagation is relatively weak in the Maritime Continent (MC) and the Western Pacific (WP). To improve the MJO eastward propagation in the MC and WP, improved heating induced by shallow (or congestus) clouds interacting with enhanced BL dynamics may be required. On the other hand, the GS5 hindcast reproduces the BSISO northward propagation reasonably well in the Indian Ocean, which is attributed to positive vorticity anomalies induced by strong vertical shear.

Co-designing Indices for Tailored Seasonal Climate Forecasts in Malawi

In central and southern Malawi, climate variability significantly impacts agricultural production and food availability owing to a high dependence on rain-fed maize production. Seasonal climate forecast information has the potential to inform farmers' agricultural planning, thereby improving preparedness to extreme events. In this paper we describe and evaluate an approach to co-designing and testing agro-climatic indices for use in seasonal forecasts that are tailored to farmer-defined decision-making needs in three districts of central and southern Malawi. Specifically, we aim to (a) identify critical maize specific agro-climatic indices by engaging key stakeholders and farmers; (b) compare and triangulate these indices with the historical climate record in study districts; and (c) analyze empirical relationships between seasonal total rainfall and maize specific indices in order to assess the potential for forecasting them at appropriate seasonal timescales. The identified agro-climatic indices include critical temperature/rainfall thresholds that are directly associated with phenological stages of maize growth with direct implications for maize yield and quality. While there are statistically significant relationships between observed wet season rainfall totals and several agro-climatic indices (e.g., heavy rainfall days and dry spell), the forecast skill of the UK Met Office's coupled initialized global seasonal forecasting system (GloSea5) over Malawi is currently low to provide confident predictions of total wet season rainfall and the agro-climatic indices correlated with it. We reflect on some of the opportunities and challenges associated with integrating farmers' information needs into a seasonal forecast process, through the use of agro-climatic indices.

Western North Pacific Tropical Cyclones in the Met Office Global Seasonal Forecast System: Performance and ENSO Teleconnections

AbstractThe performance of the Met Office Global Seasonal Forecast System (GloSea5-GC2) for tropical cyclone (TC) frequency for the western North Pacific (WNP) in July–October is evaluated, using 23 years of ensemble forecasts (1993–2015). Compared to observations, GloSea5 overpredicts the climatological TC frequency in the eastern WNP and underpredicts it in the western and northern WNP. These biases are associated with an El Niño–type bias in TC-related environmental conditions (e.g., low-level convergence and steering flow), which encourages too many TCs to form throughout the tropical Pacific and slows TC propagation speed. For interannual TC frequency variability, GloSea5 overestimates the observed negative TC–ENSO teleconnection in the western and northern WNP, associated with an eastward shift in the ENSO teleconnection to environmental conditions. Consequently, GloSea5 fails to predict interannual TC variability in the northeast WNP (south of Japan); performance is higher in the southwest WNP (e.g., the South China Sea) where the sign of the TC–ENSO teleconnection is correct. This study suggests the need to reduce biases in environmental conditions and associated ENSO teleconnections in GloSea5 to improve the TC prediction performance in the NWP.

Prediction Skill for East Asian Summer Monsoon Indices in a KMA Global Seasonal Forecasting System (GloSea5)

Prediction Skill for East Asian Summer Monsoon Indices in a KMA Global Seasonal Forecasting System (GloSea5)

Investigation of the 2016 Eurasia heat wave as an event of the recent warming

This study investigates the physical mechanisms that contributed to the 2016 Eurasian heat wave during boreal summer season (July–August, JA), characterized by much higher than normal temperatures over eastern Europe, East Asia, and the Kamchatka Peninsula. It is found that the 2016 JA mean surface air temperature, upper-tropospheric height, and soil moisture anomalies are characterized by a tri-pole pattern over the Eurasia continent and a wave train-like structure not dissimilar to recent (1980–2016) trends in those quantities. A series of forecast experiments designed to isolate the impacts of the land, ocean, and sea ice conditions on the development of the heat wave is carried out with the Global Seasonal Forecast System version 5. The results suggest that the tri-pole blocking pattern over Eurasia, which appears to be instrumental in the development of the 2016 summer heat wave, can be viewed as an expression of the recent trends, amplified by record-breaking oceanic warming and internal land-atmosphere interactions.

Development of a Hydrological Drought Forecasting Model Using Weather Forecasting Data from GloSea5

This study developed a hydrological drought forecasting framework linked to the meteorological model and land surface model (LSM) considering hydrologic facilities and evaluated the feasibility of the Modified Surface Water Supply Index (MSWSI) for drought forecasts in South Korea. The Global Seasonal Forecast System version 5 (GloSea5) and variable infiltration capacity (VIC) models were adapted for meteorological and hydrological models for ensemble weather forecasts and corresponding hydrologic river and dam inflow forecasts, respectively. Instead of direct use for weather and runoff forecasts, the anomaly between the ensemble forecast and hindcast data for each month was computed. Then, the monthly forecasted weather and runoff were obtained by adding this anomaly and the statistical nominal values obtained from the average monthly runoff during the last 30 years. For the selection of drought index duration, past historical observation data and drought records were used, and the 3-month period of the MSWSI outperformed any other durations in the study area. In addition, the simulated monthly river and dam inflows agreed well with the observed inflows; therefore, the model-driven runoff data from the VIC model were usable for hydrological drought forecasts. A case study result for the 2015–2016 drought event demonstrated that the hydrological drought forecasting framework suggested in this study is reliable for drought forecasting up to a 2-month forecast lead time. It is therefore concluded that the proposed framework linked with GloSea5, the VIC model and MSWSI(3) provides useful information for supporting decision-making related to water supply and management.