Prediction Skill Research Articles

Process Evaluation of Subseasonal North Atlantic Oscillation Prediction in the ECMWF Ensemble Forecast System

AbstractThis study evaluates the prediction skill of the North Atlantic Oscillation (NAO) pattern and its associated energy budget as simulated by the European Center for Medium‐Range Weather Forecasts ensemble forecast system. By classifying NAO events into high‐ and low‐skill cases, we analyzed the stationarity of NAO patterns and the role of baroclinic energy conversion in NAO prediction. In both positive and negative NAO phases, high‐skill cases exhibited more stationary NAO patterns than low‐skill cases. The analysis of processes indicates that high‐skill NAO cases are due to stronger baroclinic maintenance of NAO, with its initial position at the climatological thermal trough, whereas low‐skill NAO cases result from forecast biases in wave propagation from the North Pacific. Specifically, biases in baroclinic energy conversion in the meridional direction from week 2 lead to weak advection of the eddy available potential energy (EAPE), resulting in lower prediction skill.

Tropical Interbasin Interaction as Effective Predictors of Late-Spring Precipitation Variability in the Southern Great Plains

Abstract The southern Great Plains experience fluctuating precipitation extremes that significantly impact agriculture and water management. Despite ongoing efforts to enhance forecast accuracy, the underlying causes of these climatic phenomena remain inadequately understood. This study elucidates the relative influence of the tropical Pacific and Atlantic basins on April–May–June precipitation variability in this region. Our partial ocean assimilation experiments using the Community Earth System Model unveil the prominent role of interbasin interaction, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these interbasin contrasts. Our statistical analyses suggest that these tropical interbasin contrasts could serve as a more reliable indicator for late-spring precipitation anomalies than El Niño–Southern Oscillation. The conclusions are reinforced by analyses of seven climate forecasting systems within the North American Multi-Model Ensemble, offering an optimistic outlook for enhancing real-time forecasting of late-spring precipitation in the southern plains. However, the current predictive skills of the interbasin contrasts across the prediction systems are hindered by the lower predictability of the tropical Atlantic Ocean, pointing to the need for future research to refine climate prediction models further. Significance Statement Agriculture and infrastructure in the southern plains face challenges from severe late-spring precipitation extremes. Traditional predictors like El Niño–Southern Oscillation (ENSO) lose effectiveness during the critical spring-to-summer transition, creating a forecasting gap. This study introduces the concept of tropical interbasin interactions, known to enhance seasonal predictability for late-spring precipitation in the southern plains. Novel climate model experiments highlight contributions from the tropical Pacific and Atlantic, offering a promising predictability that potentially surpasses the limitations of ENSO-based predictions. These outcomes hold the potential for developing operational forecasts of late-spring precipitation anomalies in the southern plains, enabling proactive risk management.

Systematic Upstream Large-Scale Control of Subtropical Low-Cloud Properties

Abstract It has recently been shown that the slow adjustment of the atmospheric boundary layer (ABL) to perturbations of the large-scale atmospheric conditions translate into an upstream control of the low-cloud cover (LCC) variability at climatological timescales in the subtropics. In this study we expand upon this recent study to investigate upstream control of the climatology of low-cloud radiative effect (CRE), as well as cloud properties relevant to their radiative effect: cloud liquid water path (LWP), and cloud optical depth (COD).We use machine-learning statistical models with feature selection capabilities (random-forests) to determine the influence of the local and upstream large-scale conditions in monthly data. These conditions are determined using back-trajectories in monthly-mean wind fields. Total CRE, dominated by the shortwave contribution, exhibits a dependence on upstream Estimated Inversion Strength (EIS), but not on upstream Sea-Surface Temperature (SST) as LCC does. Upstream control of COD was present but was not as consistent as LCC or CRE across different regions and cloud regimes, while LWP does not exhibit strong upstream control at all. This implies that the control of other boundary layer properties could explain the differences in response between LCC and CRE to SST and EIS. Looking at five subtropical eastern basins, it appears that key lead times are region-specific. The inclusion of upstream control provides significant improvements to the predictive skill of statistical models over local linear regression, with improvements in variance explained well over 20% in many cases.

Subseasonal-to-seasonal (S2S) prediction of atmospheric rivers in the Northern Winter

Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.

Global Prediction of Flash Drought Using Machine Learning

AbstractFlash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub‐seasonal time scales remains a great challenge. Current state‐of‐the‐art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Here, we develop a machine learning‐based framework that uses meteorological forecasts as inputs to predict global root‐zone soil moisture and flash droughts from 1 day to 2 week lead times. The results indicate that 33% and 24% global flash drought onset and termination events can be correctly predicted by machine learning at 7 day lead time, versus 19% and 11% fractions by state‐of‐the‐art dynamic model. The developed machine learning model demonstrates substantial improvements over dynamic model in global soil moisture prediction, and thus enhances global flash drought forecasting skills in space and time. The presented framework may benefit global flash drought prediction and early warning at sub‐seasonal scales.

A New ENSO Statistical Prediction Model considering Extratropical Effects and Its Application to the Prediction of the 2015/16 El Niño Event

Abstract Understanding the evolution mechanisms and providing accurate predictions for ENSO continue to be a challenge for the research community. In recent years, some studies pointed out that the extratropical Pacific atmosphere and ocean variations may affect the evolution of ENSO, which is different from the classical theoretical framework of ENSO. In this study, by combining tropical atmosphere and ocean predictors with extratropical precursors, an ENSO statistical prediction model was constructed. It was found that including extratropical predictors can significantly enhance the prediction skills of the model. The Victoria mode index (VMI) can significantly improve the prediction skills in the 10–12-month lead model and can effectively help the model cross the spring predictability barrier (SPB). When predicting 6–8 months in advance, the South Pacific Oscillation index (SPOI) can improve the prediction skills, but the degree of the improvement is relatively small. The model was also applied to conduct prediction experiments on the 2015/16 El Niño event. It was found that the model can successfully predict the event nearly 1 year in advance, mainly due to the inclusion of the VMI. Due to the interdecadal variation in the relationship between predictors and ENSO, when constructing the 6-month lead model, using data since 2000 and replacing the 6-month lead VMI with a 10-month lead, one can effectively improve the model’s prediction skills, especially in predicting the intensity of 2015/16 El Niño events, which has shown significant improvement.

Advancing the Seasonal Outlook of the Wet Seasons of Florida

Abstract In this study, we introduce an ensemble approach to provide a probabilistic seasonal outlook of the length and seasonal rainfall anomaly of the wet season over Florida using the observed variations of the onset date of the season at the granularity of ∼10-km grid resolution (which is the spatial resolution of the observed rainfall data used for this work). The time series of daily precipitation at the grid resolution of NASA’s Global Precipitation Mission is randomly perturbed 1000 times to account for the uncertainty of synoptic to mesoscale variations on the diagnosis of the onset and demise date of the wet season. The strong covariability of the onset date with the seasonal length and seasonal rainfall anomaly of the wet season is then leveraged to provide the seasonal outlooks by monitoring the onset date of the wet season. This simple seasonal outlook is effective in predicting extreme tercile and even extreme pentile anomalies across Florida. We suggest that the proposed approach to the seasonal outlook of the wet season of Florida provides a viable alternative in the absence of strong external forcing like ENSO or tropical Atlantic variability that potentially limits the predictability of numerical climate models used for seasonal prediction. Significance Statement Earlier studies have shown that the seasonal prediction from the numerical climate models even at zero lead time has very limited prediction skills over the summer in Florida, which also coincides with the wettest part of the year. Florida’s wet season exhibits significant interannual variations, which exert its influence on water management decisions for subsequent drier seasons of the year. Therefore, strategies to improve this skill are highly relevant. We propose in this study that by monitoring the onset of the wet season variations, we can usefully provide a probabilistic seasonal outlook of the season over Florida. This is done by leveraging the observed linear relationships between the onset date variations with the length and the seasonal rainfall anomaly of the season. Furthermore, the outlook is provided at the spatial resolution of the observed dataset, which in this case is at 10-km grid resolution.

Aeolus Winds Improve Arctic Weather Prediction

AbstractIt has been proven that assimilating winds from the Aeolus global Doppler Wind Lidar (DWL) would enhance the predictive skill of weather forecast models. In this study, we use a series of observing system experiments to examine how currently assimilated “operational” wind products and Aeolus winds impact environment and climate change Canada's global forecast system over the data‐sparse Arctic region. Aeolus winds improve the tropospheric wind and temperature forecasts by about 0.7%–0.9% of error reduction (a 15%–20% effect compared to the impact of operational wind products) while having little impact on specific humidity. In particular, Aeolus winds have an impact on forecasts of strong wind days on the wind and temperature fields that is double the impact for weaker wind days. On the other hand, the operational wind products do not show as large a discrepancy in impact between forecasts of disturbed and normal days. These findings suggest significant potential for global DWL observations to enhance severe weather prediction in polar regions.

Advancing Subseasonal Surface Air Temperature and Heat Wave Prediction Skill in China by Incorporating Scale Interaction in a Deep Learning Model

AbstractAccurate subseasonal predictions of high surface air temperature (SAT) and heat wave events 10–30 days in advance are crucial for mitigating the risks of extreme weather; however, they pose a challenge for current operational models. In this study, we trained a convolutional neural network (CNN)‐based deep learning model to exploit the modulations in China's SAT by precursor signals across different timescales to improve predictions of future SAT and heat wave events. This CNN model demonstrated superior capability in capturing the evolution of SAT anomalies and the occurrence of heat wave events with forecast lead times beyond 20 days, compared with that of the operational models of the China Meteorological Administration and European Centre for Medium‐Range Weather Forecasts. Explainability analysis highlighted that subseasonal SAT predictability in China is driven primarily by large‐scale intraseasonal perturbations from both lower‐ and higher‐latitude regions of Eurasia and interannual variability.

Statistical seasonal prediction of Arctic sea ice concentration based on spatiotemporal anomaly persistent method

Abstract Accurate prediction of Arctic sea ice is crucial for high-latitude and even mid-latitude climate prediction. It significantly affects atmospheric circulation, the environment, ecology, and maritime transport. This study developed a statistical prediction model to predict monthly Arctic sea ice concentration (SIC) for up to one year based on the season-reliant empirical orthogonal functions (SEOFs) technique. Its prediction skill was compared with that of a dynamical prediction model. The spatiotemporal pattern of sea ice anomalies, which exhibit strong seasonality and are maintained for a significant period above the seasonal time scale by atmosphere-ocean interactions, was extracted using SEOFs. A prediction model was constructed by extrapolating from the recent anomalous state of sea ice to predict the future. Experimental retrospective predictions with monthly time resolution for 1982–2021 were performed to validate the prediction skill of Arctic SIC and areal extent. Statistically significant prediction skills were achieved over several months, even up to six months, exceeding the skill of the dynamical model.

Understanding the Factors Controlling MJO Prediction Skill across Events

Abstract The prediction skill for individual Madden–Julian oscillation (MJO) events is highly variable, but the key factors behind this remain unclear. Using the latest hindcast results from the subseasonal-to-seasonal (S2S) phase II models, this study attempts to understand the diverse prediction skill for the MJO events with an enhanced convective anomaly over the eastern Indian Ocean (IO) at the forecast start date, by investigating the preference of the prediction skill to the MJO-associated convective anomalies and low-frequency background states (LFBS). Compared to the low-skill MJO events, the high-skill events are characterized by a stronger intraseasonal convection–circulation couplet over the IO before the forecast start date, which could result in a longer zonal propagation range during the forecast period, thereby leading to a higher score for assessing the prediction skill. The difference in intraseasonal fields can further be attributed to the LFBS of IO sea surface temperature (SST) and quasi-biannual oscillation (QBO), with the high-skill (low-skill) events corresponding to a warmer (colder) IO and easterly (westerly) QBO phase. The physical link is that a warm IO could increase the low-level convective instability and thus amplify MJO convection over the IO, whereas an easterly QBO phase could weaken the Maritime Continent barrier effect by weakening the static stability near the tropopause, thus favoring eastward propagation of the MJO. It is also found that the combined effects of IO SST and QBO phases are more effective in influencing MJO prediction skill than individual LFBS. Significance Statement Given the importance of the Madden–Julian oscillation (MJO) in the subseasonal predictability of global weather and climate, how well the MJO itself can be predicted is a matter of concern. This study reveals the critical observational factors that determine the variation in MJO prediction skill across events. Without making any presumptions about what the factors would be, the observed MJO events are separated according to their individual prediction skill, and the difference between the MJO events with higher and lower skill is then investigated. The results show that the low-frequency background states of the Indian Ocean sea surface temperature and the quasi-biannual oscillation are good indicators for MJO prediction skill, for their modulatory role in the MJO propagation range.

Atmospheric Precursors of Skillful SST Prediction in the Northeast Pacific

Abstract Forecasts of sea surface temperature anomalies (SSTAs) provide essential information to stakeholders of marine resources in coastal ecosystems, such as the California Current Large Marine Ecosystem (CCLME), at management-relevant monthly-to-annual time scales. Diagnosing dynamical sources of predictability and the mechanisms differentiating skill among forecasts is required for verification and improvement in operational forecasting systems. Using retrospective forecasts (1982–2020) from a four-member subset of the North American Multi-Model Ensemble (NMME), we evaluate the conditional skill of SSTA forecasts in the CCLME at monthly resolution for lead times up to 10.5 months. Forecasts from ensemble members with relatively small SSTA errors at shorter lead times retain higher skill at longer lead times, with the most substantial and long-lasting increases for forecasts initialized in the fall and early spring. The “best” low-error SSTA forecasts are characterized by increased skill in the prediction of North Pacific atmospheric circulation [sea level pressure (SLP) and 200-hPa geopotential height] the month prior to the evaluation of SSTA errors in the CCLME and exhibit more realistic progressions of anomalous SLP. The Pacific meridional mode (PMM) emerges as a diagnostic of skillful North Pacific atmosphere–ocean coupling, as forecasts that correctly simulate the PMM and its associated SLP variability increase the SSTA prediction skill in the CCLME in the fall through spring. Predictable coupled ocean–atmosphere modes provide a target for enhancing predictability with early detection of the onset of a deterministic progression emerging from stochastic atmospheric variability. Significance Statement Global forecast systems provide near-term climate predictions that inform the management of marine resources, such as those of the California Current Large Marine Ecosystem. In this study, we probe the processes which lead forecasts to succeed or fail at predicting sea surface temperatures in the California Current at seasonal time scales among retrospective forecasts from the North American Multimodel Ensemble. We demonstrate that forecasts which best simulate sea surface temperatures at the earliest lead times sustain advantages in forecast skill and find that correctly simulating extratropical atmospheric circulation increases the predictive skill of sea surface temperatures in the northeast Pacific in the following lead times. Our results offer North Pacific atmospheric circulation as a target for forecast model improvement that would additionally enhance ocean forecasts.

Contrasting influence of the 1997 and 2015 El Niño on the Indian Summer Monsoon Rainfall: Role of the Southern Hemisphere

This study provides comprehensive analysis on the contrasting effects caused by the 1997 and 2015 historical El Niño events linked with the Indian Summer Monsoon (ISM) rainfall. The presence of strong southeast-northwest oriented cold Sea Surface Temperature (SST) anomalies during 1997, that spatially extended from southwest Pacific to Southeast Indian Ocean (SEIO) in comparison to the 2015 event is the robust feature. Evidently, these anomalies are closely related to interaction between cyclonic (anticyclonic) circulation over South Pacific Convergence Zone (south Australia). During 2015, the conjunction of Modoki II and classical El Niño triggered an asymmetrical equatorial circulation throughout the Indo-western Pacific (IWP) and thereby stimulated the Southern Annular Mode (SAM) through troposphere and stratospheric pathway mechanism. In addition, in 2015, SAM impacted the Indian Ocean, which intern affected ISM rainfall. Positive SAM associated with westward shift of anticyclone over south of Australia alters the circulation by inducing westerly winds over the South Indian Ocean, thereby suppressing Indian Ocean Dipole (IOD), and inducing drought conditions over India during 2015. Moreover, the AUS index, an indicator for IOD strength in boreal summer, is a bridging factor prevalent over mid-latitude regions in the southern hemisphere. Results from this study indicate the complex interaction of southern hemisphere atmospheric flow and its role in modulating the Indian Ocean region thereby ISM rainfall. A better understanding of these underlying mechanism can significantly enhance the predictive skills and projections of monsoon variability and extremes.

Forecasting malaria dynamics based on causal relations between control interventions, climatic factors, and disease incidence in western Kenya.

Malaria remains one of the deadliest diseases worldwide, especially among young children in sub-Saharan Africa. Predictive models are necessary for effective planning and resource allocation; however, statistical models suffer from association pitfalls. In this study, we used empirical dynamic modelling (EDM) to investigate causal links between climatic factors and intervention coverage with malaria for short-term forecasting. Based on data spanning the period from 2008 to 2022, we used convergent cross-mapping (CCM) to identify suitable lags for climatic drivers and investigate their effects, interaction strength, and suitability ranges on malaria incidence. Monthly malaria cases were collected at St. Elizabeth Lwak Mission Hospital. Intervention coverage and population movement data were obtained from household surveys in Asembo, western Kenya. Daytime land surface temperature (LSTD), rainfall, relative humidity (RH), wind speed, solar radiation, crop cover, and surface water coverage were extracted from remote sensing sources. Short-term forecasting of malaria incidence was performed using state-space reconstruction. We observed causal links between climatic drivers, bed net use, and malaria incidence. LSTD lagged over the previous month; rainfall and RH lagged over the previous two months; and wind speed in the current month had the highest predictive skills. Increases in LSTD, wind speed, and bed net use negatively affected incidence, while increases in rainfall and humidity had positive effects. Interaction strengths were more pronounced at temperature, rainfall, RH, wind speed, and bed net coverage ranges of 30-35°C, 30-120 mm, 67-80%, 0.5-0.7 m/s, and above 90%, respectively. Temperature and rainfall exceeding 35°C and 180 mm, respectively, had a greater negative effect. We also observed good short-term predictive performance using the multivariable forecasting model (Pearson correlation coefficient = 0.85, root mean square error = 0.15). Our findings demonstrate the utility of CCM in establishing causal linkages between malaria incidence and both climatic and non-climatic drivers. By identifying these causal links and suitability ranges, we provide valuable information for modelling the impact of future climate scenarios.

Opportunities and barriers for skillful sub-seasonal prediction of East Asian summer precipitation

Abstract Accurate sub-seasonal (2-8 weeks) prediction of monsoon precipitation is crucial for mitigating flood and heatwave disasters caused by intra-seasonal variability (ISV). However, current state-of-the-art sub-seasonal-to-seasonal (S2S) models have limited prediction skills beyond one week when predicting weekly precipitation. Our findings suggest that predictability primarily arises from strong ISV events, and the prediction skills for ISV events depend on the propagation stability of preceding signals, regardless of models. This allows us to identify opportunities and barriers (OBs) within S2S models, clarifying what the models can and cannot achieve in ISV event prediction. Focusing on the complex East Asian summer monsoon (EASM), we discover that stable propagation of Eurasian and tropical atmospheric wave trains towards East Asia serves as an opportunity. This opportunity offers a one-week leading prediction skill of up to 0.85 and skillful prediction up to 13 days ahead for 43% of all ISV events. However, the Tibetan Plateau barrier highlights the limitation of EASM predictability. Identifying these OBs will help us gain confidence in making more accurate sub-seasonal prediction.

On the skill of Indo-Pacific decadal sea level predictions and its connection with skilful AMO and PDO predictions

On the skill of Indo-Pacific decadal sea level predictions and its connection with skilful AMO and PDO predictions

THE DEVELOPMENT OF PEDAGOGICAL SKILLS OF PEDAGOGICAL UNIVERSITY GRADUATE STUDENTS IN COOPERATION WITH STUDENTS OF SPECIALISED PSYCHOLOGICAL AND PEDAGOGICAL CLASSES

The article describes the results of a study on the development of pedagogical skills among final year students in interaction with students of specialised psychological and pedagogical classes. The main groups of teaching skills are described and categorised. It was established that participation in practical work, which involves active participation and organisation of pedagogical interaction, contributes to the development of the main groups of pedagogical skills - analytical, predictive, perceptual, developmental and projective skills. The results obtained can be used to create conditions for graduates’ professionalisation and to organise interaction between specialised universities and classes of psychological and pedagogical orientation.

Description and Evaluation of the CNRM‐Cerfacs Climate Prediction System (C3PS)

AbstractThe CNRM‐Cerfacs Climate Prediction System (C3PS) is a new research modeling tool for performing climate reanalyzes and seasonal‐to‐multiannual predictions for a wide array of Earth system variables. C3PS is based on the CNRM‐ESM2‐1 model including interactive aerosols and stratospheric chemistry schemes as well as terrestrial and marine biogeochemistry enabling a comprehensive representation of the global carbon cycle. C3PS operates through a seamless coupled initialization for the atmosphere, land, ocean, sea ice and biogeochemistry components that allows a continuum of predictions across seasonal to multiannual time‐scales. C3PS has also contributed to the Decadal Climate Prediction Project (DCPP‐A) as part of the sixth Coupled Model Intercomparison Project (CMIP6). Here we describe the main characteristics of this novel Earth system‐based prediction platform, including the methodological steps for obtaining initial states to produce forecasts. We evaluate the entire C3PS initialization procedure with the most up‐to‐date observations and reanalyzes over 1960–2021, and we discuss the overall performance of the system in the light of the lessons learned from previous and actual prediction platforms. Regarding the forecast skill, C3PS exhibits comparable seasonal predictive skill to other systems. At the multiannual scale, C3PS shows significant predictive skill in surface temperature during the first 2 years after initialization in several regions of the world. C3PS also exhibits potential predictive skill in Net primary production (NPP) and carbon fluxes several years in advance. This expands the possibility of applications of forecasting systems, such as the possibility of performing multiannual predictions of marine ecosystems and carbon cycle.

Random Forests’ Postprocessing Capability of Enhancing Predictive Skill on Subseasonal Time Scales—A Flow-Dependent View on Central European Winter Weather

Abstract Weather predictions 2–4 weeks in advance, called the subseasonal time scale, are highly relevant for socioeconomic decision-makers. Unfortunately, the skill of numerical weather prediction models at this time scale is generally low. Here, we use probabilistic random forest (RF)-based machine learning models to postprocess the subseasonal to seasonal (S2S) reforecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF). We show that these models are able to improve the forecasts slightly in a 20-winter mean at lead times of 14, 21, and 28 days for wintertime central European mean 2-m temperatures compared to the lead-time-dependent mean bias-corrected ECMWF’s S2S reforecasts and RF-based models using only reanalysis data as input. Predictions of the occurrence of cold wave days are improved at lead times of 21 and 28 days. Thereby, forecasts of continuous temperatures show a better skill than forecasts of binary occurrences of cold wave days. Furthermore, we analyze if the skill depends on the large-scale flow configuration of the atmosphere at initialization, as represented by weather regimes (WRs). We find that the WR at the start of the forecast influences the skill and its evolution across lead times. These results can be used to assess the conditional improvement of forecasts initialized during one WR in comparison to forecasts initialized during another WR. Significance Statement Forecasts of winter temperatures and cold waves 2–4 weeks in advance done by numerical weather prediction (NWP) models are often unsatisfactory due to the chaotic characteristics of the atmosphere and limited predictive skill at this time range. Here, we use statistical methods, belonging to the so-called machine learning (ML) models, to improve forecast quality by postprocessing predictions of a state-of-the-art NWP model. We compare the forecasts of the NWP and ML models considering different weather regimes (WRs), which represent the large-scale atmospheric circulation such as the typical westerly winds in Europe. We find that the ML models generally yield better temperature forecasts for 14, 21, and 28 days in advance and better forecasts of cold wave days 21 and 28 days in advance. The quality of forecasts depends on the WR present at the forecast start. This information can be used to assess the conditional improvement of forecasts.

The Role of Antecedent Winter Soil Moisture Carryover on Spring Runoff Predictability in Snow-Influenced Western U.S. Catchments

Abstract Spring runoff is critical to agricultural, industrial, and municipal water supply as well as environmental uses in the western United States. Although spring runoff in this region has long been predicted based on late winter and early spring montane snow water storage, the role of soil moisture carryover from the previous winter is less understood and utilized in forecasts. We quantify the relationship between antecedent winter soil moisture and spring runoff for 85 unmanaged catchments in the western United States. Using a regression-based approach, we estimate the proportional reduction in error in seasonal runoff forecasts associated with soil moisture. We classify the catchments into two regimes: interior (cold and relatively dry) and maritime (warmer and wetter), based on seasonal variations in soil moisture. Soil moisture generally is relatively unchanged through the winter period in interior catchments, whereas it increases (mostly gradually, but occasionally rapidly) in maritime catchments. We find that winter temperature dominates soil moisture variability in both types of catchments. We find two patterns in the predictability of spring runoff. First, including antecedent winter soil moisture as a predictor enhances forecast accuracy and variance explanation in spring runoff for both interior and maritime catchments, particularly and with greater significance in interior catchments. Second, spring runoff prediction skill from antecedent winter soil moisture increases with elevation. Overall, we find strong evidence that the use of antecedent soil moisture can improve spring runoff forecast skill for catchments in the western United States. Significance Statement Spring runoff forecasts are crucial for water supply in the western United States, but typically exclude information about soil moisture despite its key role in the water balance. Our study explores how and why incorporating information about winter’s soil moisture levels affects spring runoff forecasts across 85 catchments in the western United States. We find that doing so can produce modest improvements in the prediction skill of spring runoff for both interior and maritime catchments but is particularly noticeable in interior catchments and at higher elevations where temperatures are lower. Overall, our research strongly supports the idea that using information about soil moisture from the previous winter can improve accuracy in spring runoff forecasts for the western United States.