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- Author or Editor: Yuhei Takaya x
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Abstract
The authors investigated the influence of the Madden–Julian oscillation (MJO) on extreme warm and cold events, which may have large social and economic impacts. The frequencies of extreme temperature events were analyzed and compared between active and inactive MJO periods by using the 7-day running average of the 850-hPa temperature during the extended boreal winter (November–April). The results show that the frequency of extreme events is significantly modulated (i.e., increased by a factor of more than 2) by the MJO with a time lag over some areas in the extratropics as well as in the tropics. In the extratropics, the modulation of the frequency of the extreme events is roughly associated with midlatitude wave responses to tropical forcing and anomalous lower-level circulation due to the MJO.
The relationship between the MJO and forecast skill of extreme temperature events was also investigated by using a suite of hindcasts made with the operational one-month ensemble prediction system of the Japan Meteorological Agency. Forecast skill of extreme events occurring after active MJO periods tend to be better over some areas, compared with after inactive MJO periods. These results suggest that a realistic representation of the MJO and of the atmospheric response to the MJO in forecast models is important for providing reliable early warning information about extreme events.
Abstract
The authors investigated the influence of the Madden–Julian oscillation (MJO) on extreme warm and cold events, which may have large social and economic impacts. The frequencies of extreme temperature events were analyzed and compared between active and inactive MJO periods by using the 7-day running average of the 850-hPa temperature during the extended boreal winter (November–April). The results show that the frequency of extreme events is significantly modulated (i.e., increased by a factor of more than 2) by the MJO with a time lag over some areas in the extratropics as well as in the tropics. In the extratropics, the modulation of the frequency of the extreme events is roughly associated with midlatitude wave responses to tropical forcing and anomalous lower-level circulation due to the MJO.
The relationship between the MJO and forecast skill of extreme temperature events was also investigated by using a suite of hindcasts made with the operational one-month ensemble prediction system of the Japan Meteorological Agency. Forecast skill of extreme events occurring after active MJO periods tend to be better over some areas, compared with after inactive MJO periods. These results suggest that a realistic representation of the MJO and of the atmospheric response to the MJO in forecast models is important for providing reliable early warning information about extreme events.
Abstract
This study investigates the influence of sea surface temperature (SST) in the northern tropical Atlantic (NTA) on the Indo–western Pacific summer climate by analyzing record-high NTA SSTs in summer 2010. In that time, a decaying El Niño and developing La Niña were accompanied by widespread anomalous climate conditions in the Indo–western Pacific. These conditions are typical of summers that follow El Niño events and are often explained as being due to the influence of Indian Ocean warming induced by El Niños. Meanwhile, the record-high NTA SSTs that resulted from the influence of El Niño, the negative phase of the North Atlantic Oscillation and the interdecadal-and-longer NTA SST variability are one of the possible causes of anomalous conditions in the Indo–western Pacific. The results of sensitivity experiments using a coupled atmosphere–ocean model clearly indicate that the high NTA SSTs had a considerable influence on the summer weather in the Indo–western Pacific via two tropical routes: an eastbound route that involved a reinforcement of the atmospheric equatorial Kelvin wave and a westbound route that involved altering the Walker circulation over the Atlantic–Pacific region. The altered Walker circulation facilitated the transition to La Niña, amplifying the impact on the western North Pacific monsoon. Further evaluation reveals that both the interannual and interdecadal-and-longer variability of the NTA SST contributed to the anomalous Indo–western Pacific summer. The results highlight the interannual to multidecadal predictability of the Indo–western Pacific summer climate that originates in the NTA.
Abstract
This study investigates the influence of sea surface temperature (SST) in the northern tropical Atlantic (NTA) on the Indo–western Pacific summer climate by analyzing record-high NTA SSTs in summer 2010. In that time, a decaying El Niño and developing La Niña were accompanied by widespread anomalous climate conditions in the Indo–western Pacific. These conditions are typical of summers that follow El Niño events and are often explained as being due to the influence of Indian Ocean warming induced by El Niños. Meanwhile, the record-high NTA SSTs that resulted from the influence of El Niño, the negative phase of the North Atlantic Oscillation and the interdecadal-and-longer NTA SST variability are one of the possible causes of anomalous conditions in the Indo–western Pacific. The results of sensitivity experiments using a coupled atmosphere–ocean model clearly indicate that the high NTA SSTs had a considerable influence on the summer weather in the Indo–western Pacific via two tropical routes: an eastbound route that involved a reinforcement of the atmospheric equatorial Kelvin wave and a westbound route that involved altering the Walker circulation over the Atlantic–Pacific region. The altered Walker circulation facilitated the transition to La Niña, amplifying the impact on the western North Pacific monsoon. Further evaluation reveals that both the interannual and interdecadal-and-longer variability of the NTA SST contributed to the anomalous Indo–western Pacific summer. The results highlight the interannual to multidecadal predictability of the Indo–western Pacific summer climate that originates in the NTA.
Abstract
Snow cover (SC) is an important contributor to atmospheric predictability on subseasonal to seasonal time scales. This paper evaluates the submonthly scale cause-and-effect relationship between SC and surface air temperature (SAT) in Eurasia, which has been typically overlooked in previous statistical analyses of subseasonal to seasonal atmospheric predictability. We focus on the November east–west dipolar SC pattern, a dominant large-scale SC phenomenon. We use an information flow analysis, based on information theory, to infer causal relations not revealed by conventional correlation analysis. This analysis indicates a one-way causality from SAT to SC around the west pole (Europe) in boreal autumn (November), implying that SC has little influence on the time evolution of SAT. In contrast, causality from SC to SAT is significant around the east pole (the Mongolian Plateau). An atmospheric model experiment suggests that the SAT response to SC can persist for a month via snow–albedo feedback, although the response of the upper atmosphere in the model is small. Furthermore, the subseasonal hindcasts show that the contribution of SC may affect the predictability of SAT for up to four weeks around the east pole. We suggest that the geographical and climatological atmospheric conditions are favorable for generating a positive albedo feedback as a “hotspot” of SC–SAT coupling around the east pole in autumn. The agreement of our causality analysis with other analytical and modeling approaches underscores the cause-and-effect relationship between SC and SAT and its contribution to the subseasonal predictability over autumnal Eurasia.
Abstract
Snow cover (SC) is an important contributor to atmospheric predictability on subseasonal to seasonal time scales. This paper evaluates the submonthly scale cause-and-effect relationship between SC and surface air temperature (SAT) in Eurasia, which has been typically overlooked in previous statistical analyses of subseasonal to seasonal atmospheric predictability. We focus on the November east–west dipolar SC pattern, a dominant large-scale SC phenomenon. We use an information flow analysis, based on information theory, to infer causal relations not revealed by conventional correlation analysis. This analysis indicates a one-way causality from SAT to SC around the west pole (Europe) in boreal autumn (November), implying that SC has little influence on the time evolution of SAT. In contrast, causality from SC to SAT is significant around the east pole (the Mongolian Plateau). An atmospheric model experiment suggests that the SAT response to SC can persist for a month via snow–albedo feedback, although the response of the upper atmosphere in the model is small. Furthermore, the subseasonal hindcasts show that the contribution of SC may affect the predictability of SAT for up to four weeks around the east pole. We suggest that the geographical and climatological atmospheric conditions are favorable for generating a positive albedo feedback as a “hotspot” of SC–SAT coupling around the east pole in autumn. The agreement of our causality analysis with other analytical and modeling approaches underscores the cause-and-effect relationship between SC and SAT and its contribution to the subseasonal predictability over autumnal Eurasia.
Abstract
Probabilistic forecasting is a common activity in many fields of the Earth sciences. Assessing the quality of probabilistic forecasts—probabilistic forecast verification—is therefore an essential task in these activities. Numerous methods and metrics have been proposed for this purpose; however, the probabilistic verification of vector variables of ensemble forecasts has received less attention than others. Here we introduce a new approach that is applicable for verifying ensemble forecasts of continuous, scalar, and two-dimensional vector data. The proposed method uses a fixed-radius near-neighbors search to compute two information-based scores, the ignorance score (the logarithmic score) and the information gain, which quantifies the skill gain from the reference forecast. Basic characteristics of the proposed scores were examined using idealized Monte Carlo simulations. The results indicated that both the continuous ranked probability score (CRPS) and the proposed score with a relatively small ensemble size (<25) are not proper in terms of the forecast dispersion. The proposed verification method was successfully used to verify the Madden–Julian oscillation index, which is a two-dimensional quantity. The proposed method is expected to advance probabilistic ensemble forecasts in various fields.
Significance Statement
In the Earth sciences, stochastic future states are estimated by solving a large number of forecasts (called ensemble forecasts) based on physical equations with slightly different initial conditions and stochastic parameters. The verification of probabilistic forecasts is an essential part of forecasting and modeling activity in the Earth sciences. However, there is no information-based probabilistic verification score applicable for vector variables of ensemble forecasts. The purpose of this study is to introduce a novel method for verifying scalar and two-dimensional vector variables of ensemble forecasts. The proposed method offers a new approach to probabilistic verification and is expected to advance probabilistic ensemble forecasts in various fields.
Abstract
Probabilistic forecasting is a common activity in many fields of the Earth sciences. Assessing the quality of probabilistic forecasts—probabilistic forecast verification—is therefore an essential task in these activities. Numerous methods and metrics have been proposed for this purpose; however, the probabilistic verification of vector variables of ensemble forecasts has received less attention than others. Here we introduce a new approach that is applicable for verifying ensemble forecasts of continuous, scalar, and two-dimensional vector data. The proposed method uses a fixed-radius near-neighbors search to compute two information-based scores, the ignorance score (the logarithmic score) and the information gain, which quantifies the skill gain from the reference forecast. Basic characteristics of the proposed scores were examined using idealized Monte Carlo simulations. The results indicated that both the continuous ranked probability score (CRPS) and the proposed score with a relatively small ensemble size (<25) are not proper in terms of the forecast dispersion. The proposed verification method was successfully used to verify the Madden–Julian oscillation index, which is a two-dimensional quantity. The proposed method is expected to advance probabilistic ensemble forecasts in various fields.
Significance Statement
In the Earth sciences, stochastic future states are estimated by solving a large number of forecasts (called ensemble forecasts) based on physical equations with slightly different initial conditions and stochastic parameters. The verification of probabilistic forecasts is an essential part of forecasting and modeling activity in the Earth sciences. However, there is no information-based probabilistic verification score applicable for vector variables of ensemble forecasts. The purpose of this study is to introduce a novel method for verifying scalar and two-dimensional vector variables of ensemble forecasts. The proposed method offers a new approach to probabilistic verification and is expected to advance probabilistic ensemble forecasts in various fields.
Abstract
Weather and climate variability associated with major climate modes is a main driver of interannual yield variability of commodity crops in global cropland areas. A global crop forecasting service that is currently in the test operation phase is based on temperature and precipitation forecasts, while recent literature suggests that crop forecasting services may benefit from the use of climate index forecasts. However, no consistent comparison is available on prediction skill between yield models relying on forecasts from temperature and precipitation and from climate indices. Here, we present a global assessment of 26-yr (1983–2008) within-season yield anomaly hindcasts for maize, rice, wheat, and soybean derived using different types of statistical yield models. One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model). For the TP models, three specifications with different model complexities are compared. The results show that the CI model is characterized by a small reduction in the skillful area from the reanalysis model to the hindcast model and shows the largest skillful areas for rice and soybean. In the TP models, the skill of the simple model is comparable to that of the more complex models. Our findings suggest that the use of climate index forecasts for global crop forecasting services in addition to temperature and precipitation forecasts likely increases the total number of crops and countries where skillful yield anomaly prediction is feasible.
Abstract
Weather and climate variability associated with major climate modes is a main driver of interannual yield variability of commodity crops in global cropland areas. A global crop forecasting service that is currently in the test operation phase is based on temperature and precipitation forecasts, while recent literature suggests that crop forecasting services may benefit from the use of climate index forecasts. However, no consistent comparison is available on prediction skill between yield models relying on forecasts from temperature and precipitation and from climate indices. Here, we present a global assessment of 26-yr (1983–2008) within-season yield anomaly hindcasts for maize, rice, wheat, and soybean derived using different types of statistical yield models. One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model). For the TP models, three specifications with different model complexities are compared. The results show that the CI model is characterized by a small reduction in the skillful area from the reanalysis model to the hindcast model and shows the largest skillful areas for rice and soybean. In the TP models, the skill of the simple model is comparable to that of the more complex models. Our findings suggest that the use of climate index forecasts for global crop forecasting services in addition to temperature and precipitation forecasts likely increases the total number of crops and countries where skillful yield anomaly prediction is feasible.
Abstract
Upper-tropospheric anticyclones (UTACs) emerge throughout the seasons with changing location and intensity. Here, the formation mechanisms of these UTACs, especially in the Asian–Australian–western Pacific sector, were investigated based on the diagnosis of the vorticity equation as well as the contribution of the planetary waves. During June–July–August (JJA), a vigorous UTAC corresponding to the South Asian high (SAH) forms over South Asia, to the south of the Tibetan Plateau, where intense heating associated with the Asian summer monsoon rainfall and the resultant baroclinic Rossby response are the important physical processes. Meanwhile, the produced anticyclonic vorticity is farther transported by the interhemispheric divergent wind toward the Southern Hemisphere (SH), creating the SH UTAC centered over the Maritime Continent. During December–January–February (DJF), two zonally elongated UTACs reside on each side of the equator (∼10° poleward), mainly over the Maritime Continent–western Pacific sector. Upon a closer look at the NH winter, we observed that the northern parts of UTAC cannot be explained by this vorticity balance alone. Diagnosis of the wave activity flux indicated that planetary waves emanating from the cold Eurasian continent converges around the northern parts of the UTAC with its peak in the NH winter, which weakens the subtropical jet, thus generating UTAC. Configuration of the SH summer (DJF) UTAC bears resemblance with that of SAH. These results suggest that the creation of anticyclonic vorticity and its interhemispheric transportation as well as the propagation of planetary wave are the selectively important agents for the genesis of seasonally varying UTACs.
Significance Statement
Recent studies have provided evidence that the South Asian high (formerly Tibetan high) is not a purely thermally driven system only maintained over the elevated Tibetan Plateau. This study aims to understand the physical processes responsible for the genesis of the upper-tropospheric anticyclone, especially in the Asian–Australian–western Pacific sector, throughout the season. During summer in the Northern Hemisphere, deep heating caused by South Asian monsoon rainfall plays a crucial role in the genesis of the South Asian high. The wintertime anticyclone emerging over the subtropical western North Pacific is caused via remote influences anchored with tropical convection and the cold Eurasian continent in which atmospheric teleconnections are important. These findings provide new avenues for research on tropical–extratropical interactions with respect to the formation and variability of important climate phenomena.
Abstract
Upper-tropospheric anticyclones (UTACs) emerge throughout the seasons with changing location and intensity. Here, the formation mechanisms of these UTACs, especially in the Asian–Australian–western Pacific sector, were investigated based on the diagnosis of the vorticity equation as well as the contribution of the planetary waves. During June–July–August (JJA), a vigorous UTAC corresponding to the South Asian high (SAH) forms over South Asia, to the south of the Tibetan Plateau, where intense heating associated with the Asian summer monsoon rainfall and the resultant baroclinic Rossby response are the important physical processes. Meanwhile, the produced anticyclonic vorticity is farther transported by the interhemispheric divergent wind toward the Southern Hemisphere (SH), creating the SH UTAC centered over the Maritime Continent. During December–January–February (DJF), two zonally elongated UTACs reside on each side of the equator (∼10° poleward), mainly over the Maritime Continent–western Pacific sector. Upon a closer look at the NH winter, we observed that the northern parts of UTAC cannot be explained by this vorticity balance alone. Diagnosis of the wave activity flux indicated that planetary waves emanating from the cold Eurasian continent converges around the northern parts of the UTAC with its peak in the NH winter, which weakens the subtropical jet, thus generating UTAC. Configuration of the SH summer (DJF) UTAC bears resemblance with that of SAH. These results suggest that the creation of anticyclonic vorticity and its interhemispheric transportation as well as the propagation of planetary wave are the selectively important agents for the genesis of seasonally varying UTACs.
Significance Statement
Recent studies have provided evidence that the South Asian high (formerly Tibetan high) is not a purely thermally driven system only maintained over the elevated Tibetan Plateau. This study aims to understand the physical processes responsible for the genesis of the upper-tropospheric anticyclone, especially in the Asian–Australian–western Pacific sector, throughout the season. During summer in the Northern Hemisphere, deep heating caused by South Asian monsoon rainfall plays a crucial role in the genesis of the South Asian high. The wintertime anticyclone emerging over the subtropical western North Pacific is caused via remote influences anchored with tropical convection and the cold Eurasian continent in which atmospheric teleconnections are important. These findings provide new avenues for research on tropical–extratropical interactions with respect to the formation and variability of important climate phenomena.
Abstract
The Asian Precipitation Experiment (AsiaPEX) was initiated in 2019 to understand terrestrial precipitation over diverse hydroclimatological conditions for improved predictions, disaster reduction, and sustainable development across Asia under the framework of the Global Hydroclimatology Panel (GHP)/Global Energy and Water Exchanges (GEWEX). AsiaPEX is the successor to GEWEX Asian Monsoon Experiment (GAME; 1995–2005) and Monsoon Asian Hydro-Atmosphere Scientific Research and Prediction Initiative (MAHASRI; 2006–16). While retaining the key objectives of the aforementioned projects, the scientific targets of AsiaPEX focus on land–atmosphere coupling and improvements to the predictability of the Asian hydroclimatological system. AsiaPEX was designed for both fine-scale hydroclimatological processes occurring at the land surface and the integrated Asian hydroclimatological system characterized by multiscale interactions. We adopt six approaches including observation, process studies, scale interactions, high-resolution hydrological modeling, field campaigns, and climate projection, which bridge gaps in research activities conducted in different regions. Collaboration with mesoscale and global modeling researchers is one of the core methods in AsiaPEX. We review these strategies based on the literature and our initial outcomes. These include the estimation and validation of high-resolution satellite precipitation, investigations of extreme rainfall mechanisms, field campaigns over the Maritime Continent and Tibetan Plateau, areas of significant impact on the entire AsiaPEX region, process studies on diurnal- to interdecadal-scale interactions, and evaluation of the predictabilities of climate models for long-term variabilities. We will conduct integrated observational and modeling initiative, the Asian Monsoon Year (AMY)-II around 2025–28, whose strategies are the subregional observation platforms and integrated global analysis.
Abstract
The Asian Precipitation Experiment (AsiaPEX) was initiated in 2019 to understand terrestrial precipitation over diverse hydroclimatological conditions for improved predictions, disaster reduction, and sustainable development across Asia under the framework of the Global Hydroclimatology Panel (GHP)/Global Energy and Water Exchanges (GEWEX). AsiaPEX is the successor to GEWEX Asian Monsoon Experiment (GAME; 1995–2005) and Monsoon Asian Hydro-Atmosphere Scientific Research and Prediction Initiative (MAHASRI; 2006–16). While retaining the key objectives of the aforementioned projects, the scientific targets of AsiaPEX focus on land–atmosphere coupling and improvements to the predictability of the Asian hydroclimatological system. AsiaPEX was designed for both fine-scale hydroclimatological processes occurring at the land surface and the integrated Asian hydroclimatological system characterized by multiscale interactions. We adopt six approaches including observation, process studies, scale interactions, high-resolution hydrological modeling, field campaigns, and climate projection, which bridge gaps in research activities conducted in different regions. Collaboration with mesoscale and global modeling researchers is one of the core methods in AsiaPEX. We review these strategies based on the literature and our initial outcomes. These include the estimation and validation of high-resolution satellite precipitation, investigations of extreme rainfall mechanisms, field campaigns over the Maritime Continent and Tibetan Plateau, areas of significant impact on the entire AsiaPEX region, process studies on diurnal- to interdecadal-scale interactions, and evaluation of the predictabilities of climate models for long-term variabilities. We will conduct integrated observational and modeling initiative, the Asian Monsoon Year (AMY)-II around 2025–28, whose strategies are the subregional observation platforms and integrated global analysis.
Abstract
Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.
Abstract
Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.
Abstract
Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.
Abstract
Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.
