Subseasonal Experiment Research Articles

Statistical Postprocessing of Week-1 and Week-2 Precipitation Forecasts over Taiwan

Abstract The predictability of precipitation is hindered by finer-scale processes not captured explicitly in global numerical models, such as convective interactions, cloud microphysics, and boundary layer dynamics. However, there is growing demand across various sectors for medium- (3–10-day) and extended-range (10–30-day) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs). This study uses a novel statistical postprocessing technique, APPM, that combines analog postprocessing (AP) with probability matching (PM) to produce week-1 and week-2 accumulated precipitation forecasts over Taiwan. AP searches for historical predictions that closely resemble the current forecast and create an AP ensemble using the observed high-resolution precipitation patterns corresponding to these forecast analogs. Frequency counting and PM are then separately applied to the AP ensemble to produce calibrated and downscaled PQPFs and bias-reduced QPFs, respectively. Evaluation over a 22-yr (1999–2020) period shows that raw ensemble forecasts from the GEFS of NOAA/NWS/Environmental Modeling Center, collected for the subseasonal experiment, are underdispersive with a wet bias. In contrast, the AP ensemble spread well represents forecast uncertainty, leading to substantially more reliable and skillful probabilistic forecasts. Furthermore, the AP-based PQPF demonstrates superior discrimination ability and yields notably greater economic benefits for a wider range of users, with the maximum economic value increasing by 30%–50% for the week-2 forecast. Compared to the raw ensemble mean forecast, the calibrated QPF exhibits lower mean absolute error and explains 3–8 times more variance in observations. Overall, the APPM technique significantly improves week-1 and week-2 QPFs and PQPFs over Taiwan. Significance Statement There are two significant challenges in improving precipitation forecasts beyond a few days in Taiwan. First, large-scale numerical models often struggle with accurately predicting precipitation locations, magnitudes, and providing sufficient detail. Second, probabilistic precipitation forecasts have been unreliable, failing to convey accurate uncertainty information to users. In response to these challenges, this study has developed a relatively simple yet effective technique that corrects the spatiotemporal distribution of predicted precipitation and downscales the forecasts from a 1° to 1-km spatial resolution. Our results demonstrate that this technique significantly alleviates these two issues, resulting in more accurate precipitation forecasts and more reliable probabilistic precipitation forecasts within a 2-week timeframe.

Subseasonal Forecast Skill of Evaporative Demand, Soil Moisture, and Flash Drought Onset from Two Dynamic Models over the Contiguous United States

Abstract Flash droughts are rapidly developing subseasonal climate extreme events that are manifested as suddenly decreased soil moisture, driven by increased evaporative demand and/or sustained precipitation deficits. Over each climate region in the contiguous United States (CONUS), we evaluated the forecast skill of weekly root-zone soil moisture (RZSM), evaporative demand (ETo), and relevant flash drought (FD) indices derived from two dynamic models [Goddard Earth Observing System model V2p1 (GEOS-V2p1) and Global Ensemble Forecast System version 12 (GEFSv12)] in the Subseasonal Experiment (SubX) project between years 2000 and 2019 against three reference datasets: Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), North American Land Data Assimilation System, phase 2 (NLDAS-2), and GEFSv12 reanalysis. The ETo and its forcing variables at lead week 1 have moderate-to-high anomaly correlation coefficient (ACC) skill (∼0.70–0.95) except downwelling shortwave radiation, and by weeks 3–4, predictability was low for all forcing variables (ACC < 0.5). RZSM (0–100 cm) for model GEFSv12 showed high skill at lead week 1 (∼0.7–0.85 ACC) in the High Plains, West, Midwest, and South CONUS regions when evaluated against GEFSv12 reanalysis but lower skill against MERRA-2 and NLDAS-2 and ACC skill are still close to 0.5 for lead weeks 3–4, better than ETo forecasts. GEFSv12 analysis has not been evaluated against in situ observations and has substantial RZSM anomaly differences when compared to NLDAS-2, and our analysis identified GEFSv12 reforecast prediction limit, which can maximally achieve ACC ∼0.6 for RZSM forecasts between lead weeks 3 and 4. Analysis of major FD events reveals that GEFSv12 reforecast inconsistently captured the correct location of atmospheric and RZSM anomalies contributing to FD onset, suggesting the needs for improving the dynamic models’ assimilation and initialization procedures to improve subseasonal FD predictability. Significance Statement Flash droughts are rapidly developing climate extremes which reduce soil moisture through enhanced evaporative demand and precipitation deficits, and these events can have large impacts on the ecosystem and crop health. We evaluated the subseasonal forecast skill of soil moisture and evaporative demand against three reanalysis datasets and found that evaporative demand skill was similar between forecasts and reanalyses while soil moisture skill is dependent on the reference dataset. Skill of evaporative demand decreases rapidly after week 1, while soil moisture skill declines more slowly after week 1. Case studies for the 2012, 2017, and 2019 United States flash droughts identified that forecasts could capture rapid decreases in soil moisture in some regions but not consistently, implying that long-lead forecasts still need improvements before being used in early warning systems. Improvements in flash drought predictability at longer lead times will require less biased initial conditions, better model parameterizations, and improved representations of large-scale teleconnections.

MJO Influence on Subseasonal-to-Seasonal Prediction in the Northern Hemisphere Extratropics

Abstract The impacts of the Madden–Julian oscillation (MJO) on the subseasonal-to-seasonal (S2S) prediction in the Northern Hemisphere extratropics are examined using the reforecasts from the S2S Project and Subseasonal Experiment Project (SubX). When forecasts are initialized during an active MJO, extratropical prediction skill becomes significantly higher at 3–4-week windows compared to inactive MJO. Such prediction skill improvement is evident in the 500-hPa geopotential height over the Pacific–North America region and the North Atlantic and in surface temperature over North America, especially when the model is initialized during the MJO phases 6–7 and 8–1. However, the extratropical prediction skill is not modulated by the MJO phases 2–3 and 4–5. This phase dependency is likely determined by the arrival time of the MJO at the Maritime Continent (MC) barrier that substantially enhances the MJO amplitude error. This result suggests that only MJO phases whose convection lies east of the MC are a source of wintertime S2S predictability in the extratropics.

The Soil Moisture–Surface Flux Relationship as a Factor for Extreme Heat Predictability in Subseasonal to Seasonal Forecasts

Abstract Thresholds of soil moisture exist below which the atmosphere becomes hypersensitive to land surface drying, inducing thermal feedbacks that can exacerbate heatwaves. Realistic representation of threshold transitions in forecast models could improve extreme heat predictability and understanding of the role of land–atmosphere coupling. This study evaluates the performance of several forecast models from the Subseasonal Experiment (SubX) and several prototype versions of the Unified Forecast System (UFS) in their representation of threshold transitions by validation against reanalysis data. A metric of skill (true skill score) is applied to soil moisture breakpoint values, which mark the transition to heatwave hypersensitivity for drying soils. Forecast models have poor skill at being initialized on the correct side of the breakpoint, but show improvement when normalized to account for deficiencies in their soil moisture climatologies. Regionally, models performed best in the U.S. Northwest and worst in the Southwest. They effectively capture the tendency of western regions to spend more summer days in the hypersensitive regime than the eastern United States. Models represent well extreme heat as a consequence of atmospheric initial state for the first week of the forecast, but struggle to represent the soil moisture feedback regime. Forecast models generally perform better at extreme heat prediction when they are already dry and in the hypersensitive regime, even when erroneously so, implying that errors or biases exist in model parameterizations. Nevertheless, composite analysis shows encouraging model performance of the “hit” category, suggesting that an improvement in soil moisture initialization could further improve extreme heat forecast skill.

Advances in tropical cyclone prediction on subseasonal time scales during 2019–2022

This review describes advances in understanding and forecasting tropical cyclone (TC) subseasonal variability during the past four years. A large effort by the scientific community has been in understanding the sources of predictability at subseasonal timescales beyond the well-known modulation of TC activity by the Madden-Julian Oscillation (MJO). In particular, the strong modulation of TC activity over the western North Pacific by the Boreal Summer Intra-Seasonal Oscillation (BSISO) has been documented. Progress has also been realized in understanding the role of tropical-extratropical interactions in improving subseasonal forecasts. In addition, several recent publications have shown that extratropical wave breaking may have a role in the genesis and development of TCs. Analyses of multi-model ensemble data sets such as the Subseasonal to Seasonal (S2S) and Subseasonal Experiment (SubX) have shown that the skill of S2S models in predicting the genesis of TCs varies strongly among models and regions but is often tied to their ability to simulate the MJO and its impacts. The skill in select models has led to an increase over the past four years in the number of forecasting centers issuing subseasonal TC forecasts using various techniques (statistical, statistical-dynamical and dynamical). More extensive verification studies have been published over the last four years, but often only for the North Atlantic and eastern North Pacific.

A Multimodel Real-Time System for Global Probabilistic Subseasonal Forecasts of Precipitation and Temperature

Abstract A global multimodel probabilistic subseasonal forecast system for precipitation and near-surface temperature is developed based on three NOAA ensemble prediction systems that make their forecasts available publicly in real time as part of the Subseasonal Experiment (SubX). The weekly and biweekly ensemble means of precipitation and temperature of each model are individually calibrated at each grid point using extended logistic regression, prior to forming equal-weighted multimodel ensemble (MME) probabilistic forecasts. Reforecast skill of week-3–4 precipitation and temperature is assessed in terms of the cross-validated ranked probability skill score (RPSS) and reliability diagram. The multimodel reforecasts are shown to be well calibrated for both variables. Precipitation is moderately skillful over many tropical land regions, including Latin America, sub-Saharan Africa and Southeast Asia, and over subtropical South America, Africa, and Australia. Near-surface temperature skill is considerably higher than for precipitation and extends into the extratropics as well. The multimodel RPSS skill of both precipitation and temperature is shown to exceed that of any of the constituent models over Indonesia, South Asia, South America, and East Africa in all seasons. An example real-time week-3–4 global forecast for 13–26 November 2021 is illustrated and shown to bear the hallmarks of the combined influences of a moderate Madden–Julian oscillation event as well as weak–moderate ongoing La Niña event. Significance Statement This paper develops a system for forecasting of precipitation and temperatures globally over land, several weeks in advance, with a focus on biweekly averaged conditions between three and four weeks ahead. The system provides the likelihood of biweekly and weekly conditions being below, near, or above their long-term averages, as well the probability of exceeding (or not exceeding) any threshold value. Using historical data, the precipitation forecasts are demonstrated to have skill in many tropical regions, and the temperature forecasts are more widely skillful. While weather and seasonal range forecasts have become quite generally available, this is one of the first examples of a publicly available, calibrated multimodel probabilistic real-time forecasting system for the subseasonal range.

Evaluation of Subseasonal Drought Forecast Skill over the Coastal Western United States

Abstract Predictions of drought onset and termination at subseasonal (from 2 weeks to 1 month) lead times could provide a foundation for more effective and proactive drought management. We used reforecasts archived in NOAA’s Subseasonal Experiment (SubX) to force the Noah Multiparameterization (Noah-MP), which produced forecasts of soil moisture from which we identified drought levels D0–D4. We evaluated forecast skill of major and more modest droughts, with leads from 1 to 4 weeks, and with particular attention to drought termination and onset. We find usable drought termination and onset forecast skill at leads 1 and 2 weeks for major D0–D2 droughts and limited skill at week 3 for major D0–D1 droughts, with essentially no skill at week 4 regardless of drought severity. Furthermore, for both major and more modest droughts, we find limited skill or no skill for D3–D4 droughts. We find that skill is generally higher for drought termination than for onset for all drought events. We also find that drought prediction skill generally decreases from north to south for all drought events.

Sub-Seasonal Experiment (SubX) Model-based Assessment of the Prediction Skill of Recent Multi-Year South Korea Droughts

Reliable sub-seasonal forecast of precipitation is essential to manage the risk of multi-year droughts in a timely manner. However, comprehensive assessments of sub-seasonal prediction skill of precipitation remain limited, particularly during multi-year droughts. This study used various verification metrics to assess the sub-seasonal prediction skill of hindcasts of five Sub-seasonal Experiment (SubX) models for precipitation during two recent multi-year South Korea droughts (2007 − 10 and 2013 − 16). Results show that the sub-seasonal prediction skill of the SubX models were stage-, event-, and model-dependent over the recent multi-year droughts. According to the Brier skill scores, SubX models show a more skillful in one to four lead weeks during the drought onset and persistence stages, than the recovery stage. While the prediction skill of the SubX models in the first two initial weeks show more skillful prediction during the 2007–10 drought, the impact of the forecast initial time on the prediction skill is relatively weak during the 2013–16 drought. Overall, the EMC-GEFSv12 model with the 11 ensemble members (the largest among the five SubX models) show the most skillful forecasting skill. According to the sensitivity test to the ensemble member size, the EMC-GEFSv12 model had no gain for biweekly precipitation forecast with the nine ensemble members or more. This study highlights the importance of a robust evaluation of the predictive performance of sub-seasonal climate forecasts via multiple verification metrics.

The Impact of Mean-State Moisture Biases on MJO Skill in the Navy ESPC

Abstract The Madden–Julian oscillation (MJO) is a key source of predictability in the subseasonal time scale (weeks to months) and influences a wide range of weather and climate phenomena. Although there have been enormous gains in simulating the MJO, many climate and forecast models still have biases in MJO behavior and structure. In this study, we examine the MJO in the Navy Earth System Prediction Capability (Navy ESPC) forecasts performed for the Subseasonal Experiment (SubX) using process-based diagnostics and a moisture budget analysis that uses wavenumber–frequency filtering to isolate the MJO. The MJO in the Navy ESPC is too strong in both boreal winter and summer. This amplitude bias is driven by biases in the vertical moisture advection in the Navy ESPC, which is too strong and deep, driven by a more bottom-heavy vertical motion profile and too steep lower-tropospheric vertical moisture gradient. Additionally, the convective moisture adjustment time scale in the Navy ESPC is faster than observed, such that for a given moisture anomaly the precipitation response is greater than observed. In the Navy ESPC, the MJO propagation shows strong agreement with observations in the Indian Ocean, followed by too rapid propagation east of the Maritime Continent in both seasons. This MJO acceleration east of the Maritime Continent is linked to an acceleration of moisture anomalies driven by biases in anomalous moisture tendency. The mechanisms that drive this bias have seasonal differences, with excess evaporation in the western Pacific dominating in boreal winter and horizontal moisture advection dominating in boreal summer.

Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration

Sub-seasonal forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to advance SSF. In this paper, for the first time, we perform a fine-grained comparison of a suite of modern ML models with start-of-the-art physics-based dynamical models from the Subseasonal Experiment (SubX) project for SSF in the western contiguous United States. Additionally, we explore mechanisms to enhance the ML models by using forecasts from dynamical models. Empirical results illustrate that, on average, ML models outperform dynamical models while the ML models tend to generate forecasts with conservative magnitude compared to the SubX models. Further, we illustrate that ML models make forecasting errors under extreme weather conditions, e.g., cold waves due to the polar vortex, highlighting the need for separate models for extreme events. Finally, we show that suitably incorporating dynamical model forecasts as inputs to ML models can substantially improve the forecasting performance of the ML models. The SSF dataset constructed for the work and code for the ML models are released along with the paper for the benefit of the artificial intelligence community.

Combining precipitation forecasts and vegetation health to predict fire risk at subseasonal timescale in the Amazon

Current forecast systems provide reliable deterministic forecasts at the scale of weather (1–7 days) and probabilistic outcomes at the scale of seasons (1–9 months). Only in recent years research has begun transitioning to operational settings to provide numerical predictions for a lead time of 2–4 weeks, a timescale known as subseasonal. The Subseasonal Experiment (SubX) multi-model ensemble mean precipitation forecast (2017–2021) for days 8–14 (week-2 forecast) is used as a covariate in logistic regression models to predict fire risk in the Amazon. In a complementary experiment, a vegetation health index (VHI) is added to SubX precipitation forecasts as a predictor of fires. We find that fire risk can be skillfully assessed in most of the Amazon where fires occur regularly. In some sectors, SubX week-2 precipitation alone is a reliable predictor of fire risk, but the addition of VHI as a predictor results both in (a) a larger portion of the Amazon domain with skillful forecasts and; (b) higher skill in some sectors. By comparing two sectors of the Amazon, we find that the added information provided by VHI is most relevant where the mosaic of land covers includes savannas and grassland, whereas SubX precipitation can be used as the sole predictor for week-2 fire risk forecast in areas where the mosaic of land cover is dominated by forests. Our results illustrate the potential for using numerical model forecasts, at the subseasonal timescale, in combination with satellite remote sensing of vegetation to obtain skillful fire risk forecasts in the Amazon. The operationalization of the methods presented in this study could allow for better preparedness and fire risk reduction in the Amazon with a lead time greater than a week.

Subseasonal Earth System Prediction with CESM2

Abstract Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available. Significance Statement We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.

GEFSv12 Reforecast Dataset for Supporting Subseasonal and Hydrometeorological Applications

Abstract For the newly implemented Global Ensemble Forecast System, version 12 (GEFSv12), a 31-yr (1989–2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System, version 15.1, and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–99) and Phase 2 (2000–19) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast dataset was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500-hPa geopotential height, tropical storm track, precipitation, 2-m temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.

Sub‐Seasonal Prediction of Drought and Streamflow Anomalies for Water Management in India

AbstractMeteorological and hydrologic prediction at short to sub‐seasonal scales is essential for reservoir operations to mitigate droughts. We examine the skills in the meteorological forecast from the Subseasonal Experiment (SubX) and Extended Range Forecast System (ERFS) for precipitation, maximum and minimum temperatures at 1, 7, 15, and 30 days lead. We bias‐corrected meteorological forecasts using the multivariate bias correction method for hydrologic prediction. The Variable Infiltration Capacity model was used to simulate total runoff and root‐zone soil moisture for India. We also developed a streamflow forecast for the five major river basins that have large reservoirs. Bias correction of meteorological forecast (precipitation, maximum and minimum temperatures) resulted in a considerable improvement in hydrologic and meteorological forecast skills. The Environmental Modeling Center (EMC) model from the SubX provides either better or equal forecast skills for the raw meteorological forecast compared to ERFS, which is an operational product in India. We examined the forecast skills of the meteorological and hydrological products for the two major droughts that occurred recently. We find that most forecast models effectively captured the onset, peak, and termination of the North Indian drought in 2015–2016 and the South Indian drought in 2016–2017 at a 30‐day lead. Bias correction of the meteorological forecast improved the streamflow forecast for the selected drought event upstream of the major reservoirs. The EMC model showed better forecast skills for the two major droughts than other forecast products. Overall, the SubX products show potential for short‐to‐sub‐seasonal scale hydrologic prediction that can assist water management in India.

The Role of Atmospheric Drivers in a Sudden Transition of California Precipitation in the 2012/13 Winter

AbstractCalifornia experienced a sharp transition from wet to historically dry conditions during the 2012/13 winter. Such transitions in the precipitation regime have strong consequences for water management, but predicting them at the seasonal time scale remains very challenging as little is known about what drives them. The 2012/13 winter was characterized by a neutral El Niño Southern Oscillation, strong Madden‐Julian Oscillation (MJO) activity, and sudden stratospheric warming. This study examines the potential influence of these different drivers in atmospheric global climate model experiments with prescribed sea surface temperature (SST) and sea ice concentration and constrained tropical and/or Arctic variability. Our model results are compared to reforecasts from the North American Multi‐Model Ensemble (NMME) and Subseasonal Experiment (SubX)/Subseasonal to Seasonal (S2S) projects to determine which important processes may have been missed by the seasonal prediction systems. Our simulations suggest that the tropical warm‐west, cool‐east SST anomaly played an active role in the wet conditions of November–December 2012 through a typical tropical‐extratropical teleconnection. The tropical SST anomalies were not accurately predicted by the NMME models, and none of them predicted the wet anomalies in November–December. In contrast, the sudden transition toward dry conditions in January is found to be independent of tropical SST variability, and rather influenced by strong MJO activity over the western Pacific. The SubX/S2S models with a better MJO prediction tend to predict the dry conditions in January better. Our study highlights how intraseasonal processes superimpose on more persistent seasonal anomalies and induce regime shifts such as the wet‐to‐dry transition of the 2012/13 winter.

Sources of Subseasonal Skill and Predictability in Wintertime California Precipitation Forecasts

AbstractThe relation between the El Niño–Southern Oscillation (ENSO) and California precipitation has been studied extensively and plays a prominent role in seasonal forecasting. However, a wide range of precipitation outcomes on seasonal time scales are possible, even during extreme ENSO states. Here, we investigate prediction skill and its origins on subseasonal time scales. Model predictions of California precipitation are examined using Subseasonal Experiment (SubX) reforecasts for the period 1999–2016, focusing on those from the Flow-Following Icosahedral Model (FIM). Two potential sources of subseasonal predictability are examined: the tropical Pacific Ocean and upper-level zonal winds near California. In both observations and forecasts, the Niño-3.4 index exhibits a weak and insignificant relationship with daily to monthly averages of California precipitation. Likewise, model tropical sea surface temperature and outgoing longwave radiation show only minimal relations with California precipitation forecasts, providing no evidence that flavors of El Niño or tropical modes substantially contribute to the success or failure of subseasonal forecasts. On the other hand, an index for upper-level zonal winds is strongly correlated with precipitation in observations and forecasts, across averaging windows and lead times. The wind index is related to ENSO, but the correlation between the wind index and precipitation remains even after accounting for ENSO phase. Intriguingly, the Niño-3.4 index and California precipitation show a slight but robust negative statistical relation after accounting for the wind index.

Multi-model Subseasonal Precipitation Forecasts over the Contiguous United States: Skill Assessment and Statistical Postprocessing

AbstractThis study assessed multi-model subseasonal precipitation forecasts (SPFs) from eight subseasonal experiment (SubX) models over the contiguous United States (CONUS) and explored the generalized extreme value distribution (GEV)-based ensemble model output statistics (EMOS) framework for postprocessing multi-model ensemble SPF. The results showed that the SubX SPF skill varied by location and season, and the skill were relatively high in the western coastal region, north-central region, and Florida peninsula. The forecast skill was higher during winter than summer seasons, especially for lead week 3 in the northwest region. While no individual model consistently outperformed the others, the simple multi-model ensemble (MME) demonstrated a higher skill than any individual model. The GEV-based EMOS approach dramatically improved the MME subseasonal precipitation forecast skill at long lead times. The continuous ranked probability score (CRPS) was improved by approximately 20% in week 3 and 43% in lead week 4; the 5-mm Brier skill score (BSS) was improved by 59.2% in lead week 3 and 50.9% in lead week 4, with the largest improvements occurring in the northwestern, north-central, and southeastern CONUS. Regarding the relative contributions of the individual SubX model to the predictive skill, the NCEP model was given the highest weight at the shortest lead time, but the weight decreased dramatically with the increase in lead time, while the CESM, EMC, NCEP, and GMAO models were given approximately equal weights for lead weeks 2-4. The presence of active MJO conditions notably increased the forecast skill in the north-central region during weeks 3-4, while the ENSO phases influenced the skill mostly in the southern regions.

Evaluation of the subseasonal forecast skill of floods associated with atmospheric rivers in coastal Western U.S. watersheds

AbstractAtmospheric rivers (ARs) are responsible for up to 90% of major flood events along the U.S. West Coast. The timescale of subseasonal forecasting (two weeks to one month) is a critical lead time for proactive mitigation of flood disasters. The NOAA/Climate Testbed Subseasonal Experiment (SubX) is a research-to-operations project with almost immediate availability of forecasts. It has produced a reforecast database that facilitates evaluation of flood forecasts at these subseasonal lead times. Here, we examine the SubX driven forecast skill of AR-related flooding out to 4-week lead using the Distributed Hydrology Soil Vegetation Model (DHSVM), with particular attention to the role of antecedent soil moisture (ASM), which modulates the relationship between meteorological and hydrological forecast skill. We study three watersheds along a transect of the U.S. West Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. We find that the SubX driven flood forecast skill drops quickly after week 1, during which there is relatively high deterministic forecast skill. We find some probabilistic forecast skill relative to climatology as well as ensemble streamflow prediction (ESP) in week 2, but minimal skill in weeks 3-4, especially for annual maximum floods, notwithstanding some probabilistic skill for smaller floods in week 3. Using ESP and reverse-ESP experiments to consider the relative influence of ASM and SubX reforecast skill, we find that ASM dominates probabilistic forecast skill only for small flood events at week 1, while SubX reforecast skill dominates for large flood events at all lead times.

Subseasonal Prediction of Wintertime Northern Hemisphere Extratropical Cyclone Activity by SubX and S2S Models

AbstractThe prediction of wintertime extratropical cyclone activity (ECA) on subseasonal time scales by models participating in the Subseasonal Experiment (SubX) and the Seasonal to Subseasonal Prediction (S2S) is assessed. Consistent with a previous study that investigated the S2S models, the SubX models have skillful predictions of ECA over regions from central North Pacific across North America to western North Atlantic, as well as East Asia and northern and southern part of eastern North Atlantic at 3–4 weeks lead time. SubX provides daily mean data, while S2S provides instantaneous data at 0000 UTC each day. This leads to different variance of ECA. Different S2S and SubX models have different reforecast initialization times and reforecast time periods. These factors can all lead to differences in prediction skill. To fairly compare the prediction skill between different models, we develop a novel way to evaluate the prediction of individual model across the two ensembles by comparing every model to the Climate Forecast System, version 2 (CFSv2), as CFSv2 has 6-hourly output and forecasts initialized every day. Among the S2S and SubX models, the European Centre for Medium-Range Weather Forecasts model exhibits the best prediction skill, followed by CFSv2. Our results also suggest that while the prediction skill is sensitive to forecast lead time, including forecasts up to 4 days old into the ensemble may still be useful for weeks 3–4 predictions of ECA.

Utility of Sub-seasonal Experiment (SubX) forecast for hydrologic prediction in India

Utility of Sub-seasonal Experiment (SubX) forecast for hydrologic prediction in India