Subseasonal Forecasts Research Articles

Downscaled subseasonal fire danger forecast skill across the contiguous United States

AbstractThe increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead‐times of several weeks can help in wildland fire resource allocation as well as complement short‐term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble‐based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly‐to‐monthly fire business decision making for suppression‐based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.

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.

Advances in application of sub-seasonal weather forecast in Indian agriculture

Under the climatic variability and climate change, skilful weather forecast in different spatial and temporal scale encourages the farmers to organize and activate their own resources in the best possible way to increase the crop production. Though medium range weather forecast is used extensively in operational agromet advisory services, sub-seasonal forecast provides additional decision-relevant information to support the timing of crop planting, irrigation scheduling, and harvesting, particularly in water-stressed regions. In view of that, dynamical and statistical and sub-seasonal seasonal forecast is generated and delivered to the farmers as climate information services in number of countries in the world. Under the Gramin Krishi Mausam Sewa (GKMS) Project, Agricultural Meteorology Division, India Meteorological Department (IMD) in collaboration with Indian Institute of Tropical Meteorology (IITM), Pune, All India Coordinated Research Project on Agrometeorology (AICRPAM), CRIDA, Indian Council of Agricultural Research, Hyderabad prepared Agromet Advisory fortnightly taking into consideration realized rainfall during previous fortnight and extended range rainfall forecast for next fortnight and crop information i.e., state and stage of the crops. In the present article agricultural applications of sub-seasonal forecasts on agricultural management in India has been explored. It has been showed how the extended range weather forecast i.e., sub-seasonal forecast has been developed and translated into agromet advisories for the farming communities to increase crop production in India and whether the present state of accuracy could be used for generating advisory under contingent crop planning conditions and other advisories by citing different case studies and ultimately helping the farming communities to improve their economic conditions. It has been demonstrated here that sub-seasonal forecasts are increasingly being used across agriculture in the country. The sub-seasonal forecasting time scale is therefore a new concept for many users. Because of the additional value of sub-seasonal forecasts for decision-making, it is increasingly gaining interest among users. Present case studies clearly suggest the forecast at sub-seasonal time scale is need of the hour.

The unprecedented Pacific Northwest heatwave of June 2021

In late June 2021 a heatwave of unprecedented magnitude impacted the Pacific Northwest region of Canada and the United States. Many locations broke all-time maximum temperature records by more than 5 °C, and the Canadian national temperature record was broken by 4.6 °C, with a new record temperature of 49.6 °C. Here, we provide a comprehensive summary of this event and its impacts. Upstream diabatic heating played a key role in the magnitude of this anomaly. Weather forecasts provided advanced notice of the event, while sub-seasonal forecasts showed an increased likelihood of a heat extreme with lead times of 10-20 days. The impacts of this event were catastrophic, including hundreds of attributable deaths across the Pacific Northwest, mass-mortalities of marine life, reduced crop and fruit yields, river flooding from rapid snow and glacier melt, and a substantial increase in wildfires—the latter contributing to landslides in the months following. These impacts provide examples we can learn from and a vivid depiction of how climate change can be so devastating.

Assessing the Impact of Ocean In Situ Observations on MJO Propagation Across the Maritime Continent in ECMWF Subseasonal Forecasts

AbstractDespite the well‐recognized initial value nature of the subseasonal forecasts, the role of subsurface ocean initialization in subseasonal forecasts remains underexplored. Using observing system experiments, this study investigates the impact of ocean in situ data assimilation on the propagation of Madden–Julian Oscillation (MJO) events across the Maritime Continent in the European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal forecast system. Two sets of twin experiments are analyzed, which only differ on the use or not of in situ ocean observations in the initial conditions. Besides using the Real‐time Multivariate MJO Index (RMMI) to evaluate the forecast performance, we also develop a new MJO tracking method based on outgoing longwave radiation anomalies (OLRa) for forecast evaluation. We find that the ocean initialization with in situ data assimilation, though having an impact on the forecasted ocean mean state, does not improve the relatively low MJO forecast skill across the Maritime Continent. Moist static energy budget analysis further suggests that a significant underestimation in the meridional moisture advection in the model forecast may hinder the potential role played by the ocean state differences associated with data assimilation. Bias of the intraseasonal meridional winds in the model is a more important factor for such underestimation than the mean state moisture biases. This finding suggests that atmospheric model biases dominate the forecast error growth, and the atmospheric circulation bias is one of the major sources of the MJO prediction error and should be a target for improving the ECMWF subseasonal forecast model.

Predicting Subseasonal Tropical Cyclone Activity Using NOAA and ECMWF Reforecasts

Abstract Tropical cyclones are extreme events with enormous and devastating consequences to life, property, and our economies. As a result, large-scale efforts have been devoted to improving tropical cyclone forecasts with lead times ranging from a few days to months. More recently, subseasonal forecasts (e.g., 2–6-week lead time) of tropical cyclones have received greater attention. Here, we study whether bias-corrected, subseasonal tropical cyclone reforecasts of the GEFS and ECMWF models are skillful in the Atlantic basin. We focus on the peak hurricane season, July–November, using the reforecast years 2000–19. Model reforecasts of accumulated cyclone energy (ACE) are produced, and validated, for lead times of 1–2 and 3–4 weeks. Week-1–2 forecasts are substantially more skillful than a 31-day moving-window climatology, while week-3–4 forecasts still exhibit positive skill throughout much of the hurricane season. Furthermore, the skill of the combination of the two models is found to be an improvement with respect to either individual model. In addition to the GEFS and ECMWF model reforecasts, we develop a statistical modeling framework that solely relies on daily sea surface temperatures. The reforecasts of ACE from this statistical model are capable of producing better skill than the GEFS or ECMWF model, individually, and it can be leveraged to further enhance the model combination reforecast skill for the 3–4-week lead time.

Incorporating Seasonality into an Agroeconomic Model of Ethiopia

Ethiopia’s agriculture-based economy generates highly seasonal outputs, with most production occurring during the long-rains Meher season and a lesser amount during the short-rains Belg season. Despite numerous studies detailing the economic impacts of Meher precipitation, there is relatively little consideration of other seasonal or subseasonal climate impacts in Ethiopia, with most economic models only considering aggregated annual-scale production to simulate the Ethiopian economy. This paper serves to address both seasonal and subseasonal effects of climate using a partial equilibrium agroeconomic model of Ethiopia. First, an annual-scale model is disaggregated into a seasonal time step, corresponding to the two major cropping seasons in Ethiopia. Second, the effect of yield gains from a subseasonal forecast–based planting strategy is considered in the updated model. We found that crop yields and corresponding economic indicators varied widely by season and location, and that subseasonal forecast–based management strategies can, on average, increase gross domestic product (GDP), per capita calorie consumption, and lead toward reduced poverty, warranting the inclusion of seasonal and subseasonal processes in economic models and agricultural planning.

Increased vertical resolution in the stratosphere reveals role of gravity waves after sudden stratospheric warmings

Abstract. Sudden stratospheric warmings (SSWs) have a long-lasting effect within the stratosphere as well as impacts on the underlying troposphere. However, sub-seasonal forecasts of the winter polar stratosphere fail to use their full potential for predictability as they tend to underestimate the magnitude and persistence of these events already within the stratosphere. The origin of this underestimation is unknown. Here, we demonstrate that the associated polar stratospheric cold bias following SSW events in sub-seasonal hindcasts can be halved by increasing vertical model resolution, suggesting a potential sensitivity to gravity wave forcing. While the predictability of the planetary Rossby wave flux into the stratosphere at lead times longer than a week is limited, the existence of a critical layer for gravity waves with a low zonal phase speed caused by the disturbed polar vortex provides predictability to the upper stratosphere. Gravity wave breaking near that critical layer can, therefore, decelerate the zonal flow consistently with anomalous subsidence over the polar cap leading to warmer temperatures in the middle polar stratosphere. Since the spectrum of gravity waves involves vertical wavelengths of less than 4000 m, as estimated by wavelet analysis, a high vertical model resolution is needed to resolve the positive feedback between gravity wave forcing and the state of the polar vortex. Specifically, we find that at a spectral resolution of TCo639 (approximate horizontal grid spacing of 18 km) at least 198 levels are needed to correctly resolve the spectrum of gravity waves in the ECMWF Integrated Forecasting System. Increasing vertical resolution in operational forecasts will help to mitigate stratospheric temperature biases and improve sub-seasonal predictions of the stratospheric polar vortex.

Stationary Waves Weaken and Delay the Near-Surface Response to Stratospheric Ozone Depletion

Abstract An intermediate-complexity moist general circulation model is used to investigate the factors controlling the magnitude of the surface impact from Southern Hemisphere springtime ozone depletion. In contrast to previous idealized studies, a model with full radiation is used; furthermore, the model can be run with a varied representation of the surface, from a zonally uniform aquaplanet to a configuration with realistic stationary waves. The model captures the observed summertime positive Southern Annular Mode response to stratospheric ozone depletion. While synoptic waves dominate the long-term poleward jet shift, the initial response includes changes in planetary waves that simultaneously moderate the polar cap cooling (i.e., a negative feedback) and also constitute nearly one-half of the initial momentum flux response that shifts the jet poleward. The net effect is that stationary waves weaken the circulation response to ozone depletion in both the stratosphere and troposphere and also delay the response until summer rather than spring when ozone depletion peaks. It is also found that Antarctic surface cooling in response to ozone depletion helps to strengthen the poleward shift; however, shortwave surface effects of ozone are not critical. These surface temperature and stationary wave feedbacks are strong enough to overwhelm the previously recognized jet latitude/persistence feedback, potentially explaining why some recent comprehensive models do not exhibit a clear relationship between jet latitude/persistence and the magnitude of the response to ozone. The jet response is shown to be linear with respect to the magnitude of the imposed stratospheric perturbation, demonstrating the usefulness of interannual variability in ozone depletion for subseasonal forecasting.

Stratospheric PULSE–continental cold air outbreak coupling relationships: Interannual and interdecadal changes

Stratospheric processes and their role in weather and climate have attracted increasing interests. The correspondence between the occurrence of pulse-like, stronger stratospheric poleward warm airmass transport (PULSE) events and the continental-scale cold air outbreak (CAO) events in northern hemispheric winter is found to be unstable from year to year. This increases the difficulties in utilizing the more predictable stratospheric variability in the sub-seasonal forecasts of CAOs, which can cause cold hazards. Using the ERA5 reanalysis data covering 37 winters (November–March) in the period 1979–2015, this study categorizes the CAO events over mid-latitudes of Eurasia (CAO_EA) and those over North America (CAO_NA) into two groups: those coupled with and those decoupled with the PULSE events. The coupled CAOs are further categorized into events that are, respectively, lead-coupled and lag-coupled with PULSEs. The intensity and affected area of extremely cold temperatures tend to be larger during CAOs that are coupled with PULSEs, particularly during the CAO_NA events that are lag-coupled with PULSEs and the CAO_EA events that are lead-coupled with PULSEs. Remarkable interannual and interdecadal variations are observed in the percentage of CAOs that are coupled with PULSEs for each winter, which is an important reference for determining the window of opportunity for skillful sub-seasonal forecasts of CAO by using the stratospheric signals. At both interdecadal and interannual timescales, a warm phase of the El Niño–Southern Oscillation (ENSO) in winter is favorable for the higher lag-coupling rate of CAO_NA and the lead-coupling rate of CAO_EA, and vice versa. The ENSO signals related to the interdecadal changes of the CAO coupling rate in winter can be traced back to the previous winter, while an ENSO phase transition from the previous winter to the current winter is closely related to the interannual changes of the CAO coupling rate.

Northward propagation of convection over the Indian region in multiphysics multimodel ensemble

An assessment of a multiphysics multimodel ensemble (MPMME) strategy is provided to simulate the critical aspects of the Indian summer monsoon and its intraseasonal variability. Using various physics combinations of Climate Forecast System (CFS) and its atmospheric component Global Forecast System (GFS), a 15‐year hindcast for May–October is generated. Three different convective parametrizations, simplified Arakawa–Shubert (sas), revised deep‐convection sas (nsas), and revised sas with modified shallow‐convection (nsas_sc) are coupled with two microphysics schemes Zhao and Carr (zc) and Ferrier. Spatiotemporal characteristics of predicted Indian summer monsoon climatology and 20–70‐day periodic intraseasonal oscillations (ISOs) are evaluated using observations. MPMME members reproduce the overall characteristics of the seasonal mean, but they have significant biases over different regions. Pattern correlations reveal that CFS_nsaszc performs best among MPMME in simulating the observed characteristics of rainfall ISOs and providing significant ISO forecast up to pentad 3 lead. A diagnostic based on the vorticity budget equation during strong convective events (SCEs) associated with ISOs is used to understand better the mechanism of northward‐propagating ISOs and the responsible factors that develop a vorticity tendency to the north of convection maxima. The tilting term in the vorticity equation shows northward propagation and leads precipitation maxima by about a week over the Bay of Bengal. Vertical shear of mean zonal winds and meridional gradients of vertical winds are found to be essential in developing vorticity tendency. SCEs are better represented in CFS than in GFS. Notably, along with CFS_nsaszc, two CFS_sas members capture the occurrence of SCEs reasonably well. However, errors in vertical shear of mean zonal winds are remarkably high after pentad 2 lead in CFS_sas and GFS, explaining their relative weakness in simulating ISOs during June–September. This study demonstrates that the MPMME strategy could utilize individual physical schemes' strengths to provide better subseasonal forecasts.

The Winter North Pacific Teleconnection in Response to ENSO and the MJO in Operational Subseasonal Forecasting Models Is Too Weak

Abstract Teleconnection patterns associated with the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO) impact weather and climate phenomena in the Pacific–North American region and beyond, and therefore accurately simulating these teleconnections is of importance for seasonal and subseasonal forecasts. Systematic biases in boreal midwinter ENSO and MJO teleconnections are found in eight subseasonal to seasonal (S2S) forecast models over the Pacific–North America region. All models simulate an anomalous 500-hPa geopotential height response that is too weak. This overly weak response is associated with overly weak subtropical upper-level convergence and a too-weak Rossby wave source in most models, and in several models there is also a biased subtropical Pacific jet, which affects the propagation of Rossby waves. In addition to this overly weak response, all models also simulate ENSO teleconnections that reach too far poleward toward Alaska and northeastern Russia. The net effect is that these models likely underestimate the impacts associated with the MJO and ENSO over western North America, and suffer from a reduction in skill from what could be achieved.

Weather patterns in Southeast Asia: Enhancing high‐impact weather subseasonal forecast skill

AbstractWhile skilful forecasts of heavy rainfall are highly desirable for weather warnings and mitigating impacts, forecasting such events is notoriously difficult, even with the most advanced numerical weather prediction models, due to the strong dependence on convective‐scale processes. The large‐scale circulation, on the other hand, is typically more predictable. Weather patterns (WPs) are a set of circulation types obtained statistically that can be used to characterize regional weather and harness the predictability of the large‐scale circulation. In this work we produce pattern‐conditioned probabilistic rainfall forecasts by projecting the horizontal winds from the Met Office GloSea5 prediction system on to WPs and then using the observed relationship between each WP and rainfall estimated by satellite. The WPs are derived following a novel two‐tier clustering technique: the WPs in the first tier represent planetary‐scale variability, such as El Niño–Southern Oscillation (ENSO), while the WPs in the second tier capture synoptic‐scale variability. We investigate WP predictability as well as the improvement in skill of subseasonal rainfall forecasts gained by this technique. GloSea5 predicts the WP occurrence with skill extending beyond lead day 10. The pattern‐conditioned rainfall forecasts were evaluated against climatological forecasts and model‐simulated rainfall hindcasts. We show that the pattern‐conditioned forecasts are skilful and outperform the model‐simulated rainfall hindcasts for lead times extending to days 10–20, depending on the specific exceedance criteria and region. Spatial aggregation leads to increased levels of skill, but not to a significant extension of the skilful prediction horizon. These results constitute a fundamental step for the development of subseasonal prediction systems for Southeast Asia.

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.

Prediction Skill of GEFSv12 in Depicting Monthly Rainfall and Associated Extreme Events over Taiwan during the Summer Monsoon

Abstract The skillful prediction of monthly scale rainfall in small regions like Taiwan is one of the challenges of the meteorological scientific community. Taiwan is one of the subtropical islands in Asia. It experiences rainfall extremes regularly, leading to landslides and flash floods in/near the mountains and flooding over low-lying plains, particularly during the summer monsoon season [June–September (JJAS)]. In September 2020, NOAA/NCEP implemented Global Ensemble Forecast System, version 12 (GEFSv12), to support stakeholders for subseasonal forecasts and hydrological applications. In the present study, the performance evaluation of GEFSv12 for monthly rainfall and associated extreme rainfall (ER) events over Taiwan during JJAS against CMORPH has been done. There is a marginal improvement of GEFSv12 in depicting the East Asian summer monsoon index (EASMI) as compared to GEFS-SubX. The GEFSv12 rainfall raw products have been calibrated with a quantile–quantile (QQ) mapping technique for further prediction skill improvement. The results reveal that the spatial patterns of climatological features (mean, interannual variability, and coefficient of variation) of summer monsoon monthly rainfall over Taiwan from QQ-GEFSv12 are very similar to CMORPH than Raw-GEFSv12. Raw-GEFSv12 has an enormous wet bias and overforecast wet days, while QQ-GEFSv12 is close to reality. The prediction skill (correlation coefficient and index of agreement) of GEFSv12 in depicting the summer monsoon monthly rainfall over Taiwan is significantly high (>0.5) in most parts of Taiwan and particularly more during peak monsoon months, September, and August, followed by June and July. The calibration method significantly reduces the overestimation (underestimation) of wet (ER) events from the ensemble mean and probabilistic ensemble forecasts. The predictability of extreme rainfall events (>50 mm day−1) has also improved significantly.

The February 2021 Cold Air Outbreak in the United States: A Subseasonal Forecast of Opportunity

Abstract The sources of predictability for the February 2021 cold air outbreak (CAO) over the central United States, which led to power grid failures and water delivery shortages in Texas, are diagnosed using a machine learning–based prediction model called a linear inverse model (LIM). The flexibility and low computational cost of the LIM allows its forecasts to be used for identifying and assessing the predictability of key physical processes. The LIM may also be run as a climate model for sensitivity and risk analysis for the same reasons. The February 2021 CAO was a subseasonal forecast of opportunity, as the LIM confidently predicted the CAO’s onset and duration four weeks in advance, up to two weeks earlier than other initialized numerical forecast models. The LIM shows that the February 2021 CAO was principally caused by unpredictable internal atmospheric variability and predictable La Niña teleconnections, with nominally predictable contributions from the previous month’s sudden stratospheric warming and the Madden–Julian oscillation. When run as a climate model, the LIM estimates that the February 2021 CAO was in the top 1% of CAO severity and suggests that similarly extreme CAOs could be expected to occur approximately every 20–30 years.

The Simulation and Subseasonal Forecasting of Hydrological Variables: Insights from a Simple Water Balance Model

Abstract Past work has shown that a land surface model’s (LSM) implicit (not explicitly coded) relationships between soil moisture and both evapotranspiration (ET) and runoff largely determine the LSM’s hydrological behavior. Here we estimate the relationships that appear to be operating in the real world and compare them to those of the LSM component of a state-of-the-art Earth system model (ESM). The two sets of relationships are determined by calibrating them within a simple water balance model (WBM): once using stream gauge observations from small, unregulated rivers over the eastern half of the United States, and once using the runoffs generated by the LSM as part of a state-of-the-art atmospheric reanalysis. Hydrological simulations and subseasonal hydrological forecasts performed with the two calibrated versions of the WBM provide two key results. First, the version calibrated to the LSM-generated runoffs does successfully reproduce, to first order, the hydrological behavior of the full LSM within its ESM environment. Second, of the two WBM versions, the one calibrated to the observations reproduces more accurately a broad collection of fully independent streamflow observations as well as a similarly broad collection of in situ soil moisture measurements. Taken together, the two results suggest that the observations-calibrated ET and runoff efficiency functions do successfully represent, at least to some degree, soil moisture controls over hydrological variability in nature and can serve as potentially useful targets for further LSM development. Significance Statement For all their complexity, and for all the work that underlies their development, the land surface model components of Earth system models may be suboptimal in fundamental yet unstudied ways. Here we estimate how the joint control of soil moisture over evapotranspiration and runoff processes in nature differs from that built implicitly into a state-of-the-art land model. Validation exercises demonstrate how this difference appears to lead to reduced accuracy in the land model’s simulation and forecasting of such hydrological variables as streamflow and soil moisture. Our results indicate that the relationships estimated for nature could serve as a potentially valuable target for further land model development.

The Role of Wave Breaking in the Development and Subseasonal Forecasts of the February 2021 Great Plains Cold Air Outbreak

AbstractThe February 2021 cold air outbreak (CAO) was a high‐impact event in the South‐Central Plains of the United States. This study examines important precursors to the event that likely impacted its predictability in subseasonal forecasts. We use reanalysis to show that the CAO was facilitated by two distinct wave breaks—an East Siberian Sea anticyclonic wave break and a Labrador Sea cyclonic wave break. We also use European Center for Medium‐Range Weather Forecasts and National Center for Environmental Prediction subseasonal‐to‐seasonal models to investigate the impact of the wave breaks on the forecast skill of the event at a ∼2–3 weeks lead time. Ensemble members successfully simulating these features produce more negative temperature anomalies across the Great Plains, corresponding to better positioning of anomalous ridging. These results demonstrate that successfully simulating persistent anticyclones can improve central US extreme cold forecasts at long leads.

Subseasonal precipitation forecasts of opportunity over central southwest Asia

Abstract. Subseasonal forecasts of opportunity (SFOs) for precipitation over southwest Asia during January–March at lead times of 3–6 weeks are identified using elevated expected forecast skill from a linear inverse model (LIM), an empirical dynamical model that uses statistical relationships to infer the predictable dynamics of a system. The expected forecast skill from this LIM, which is based on the atmospheric circulation, tropical outgoing longwave radiation, and sea surface temperatures, captures the predictability associated with many relevant signals as opposed to just one. Two modes of variability, El Niño–Southern Oscillation (ENSO) and the Madden–Julian Oscillation (MJO), which themselves are predictable because of their slow variations, are related to southwest Asia precipitation SFOs. Strong El Niño events, as observed in 1983, 1998, and 2016, significantly increase the likelihood by up to 3-fold of an SFO 3–4 and 5–6 weeks in advance. Strong La Niña events, as observed in 1989, 1999, 2000, also significantly increase the likelihood of an SFO at those same lead times. High-amplitude MJO events in phases 2–4 and 6–8 of greater than one standardized departure also significantly increase the likelihood of an SFO 3–4 weeks in advance. Predictable atmospheric circulation patterns preceding anomalously wet periods indicate a role for enhanced tropical convection in the South Pacific convergence zone (SPCZ) region, while suppressed convection is observed preceding predictable dry periods. Anomalous heating in this region is found to distinguish wet and dry periods during both El Niño and La Niña conditions, although the atmospheric circulation response to the heating differs between each ENSO phase.

Improved sub-seasonal forecasts to support preparedness action for meningitis outbreak in Africa

West African countries are hit annually by meningitis outbreaks which occur during the dry season and are linked to atmospheric variability. This paper describes an innovative co-production process between the African Centre of Meteorological Applications for Development (ACMAD; forecast producer) and the World Health Organisation Regional Office for Africa (WHO AFRO; forecast user) to support awareness, preparedness and response actions for meningitis outbreaks. Using sub-seasonal to seasonal (S2S) forecasts, this co-production enables ACMAD and WHO AFRO to build initiative that increases the production of useful climate services in the health sector. Temperature and relative humidity forecasts are combined with dust forecasts to operationalize a meningitis early warning system (MEWS) across the African meningitis belt with a two-week lead time. To prevent and control meningitis, the MEWS is produced from week 1 to 26 of the year. This study demonstrates that S2S forecasts have good skill at predicting dry and warm atmospheric conditions precede meningitis outbreaks. Vigilance levels objectively defined within the MEWS are consistent with reported cases of meningitis. Alongside developing a MEWS, the co-production process provided a framework for analysis of climate and environmental risks based on reanalysis data, meningitis burden, and health service assessment, to support the development of a qualitative roadmap of country prioritization for defeating meningitis by 2030 across the WHO African region. The roadmap has enabled the identification of countries most vulnerable to meningitis epidemics, and in the context of climate change, supports plans for preventing, preparing, and responding to meningitis outbreaks.