Subseasonal Time Scales Research Articles

Investigating the influence of synoptic circulation patterns on regional dry and moist heat waves in North China

Summer (June–August) heat waves in North China are found to be either primarily dry or moist, based on surface meteorological observations. This study characterizes synoptic circulation patterns (i.e., 500 hPa geopotential height) using the self-organizing map (SOM) method and investigates the influence of synoptic circulation patterns on these two types of heat waves. Results show that regional dry and moist heat waves are associated with different circulation patterns, which significantly modulate the advection of water vapor within the low-level atmosphere, and soil moisture and evaporation conditions at the surface. Regional dry heat waves are associated with times when a continental high pressure ridge is situated to the northwest of North China, and when the northern edge of the western North Pacific subtropical high (WNPSH) is south of 30° N. Regional moist heat waves are associated with a northward shift of the WNPSH. Long term variations of dry and moist heat wave occurrences correlated significantly with the occurrences of their associated circulation patterns at 0.38 (p = 0.02) and 0.71 (p = 0.00), respectively. On sub-seasonal time scales, the dominant heat wave type transforms from dry in June to moist in late July, which is in accordance with summer north–south WNPSH shifts. In addition, training the SOM with absolute geopotential height results in representative circulation patterns that are closely related to surface heat wave conditions in North China rather than the anomaly field, which mixes different circulation regimes.

Land‐Atmosphere Interactions Exacerbated the Drought and Heatwave Over Northern Europe During Summer 2018

AbstractThe 2018 drought and heatwave over northern Europe were exceptional, with unprecedented forest fires in Sweden, searing heat in Germany and water restrictions in England. Monthly, daily, and hourly data from ERA5, verified with in situ soil water content and surface flux measurements, are examined to investigate the subseasonal‐to‐seasonal progression of the event and the diurnal evolution of tropospheric profiles over Britain to quantify the anomalous land surface contribution to heat and drought. Data suggest the region entered an unprecedented condition of becoming a “hot spot” for land‐atmosphere coupling, which exacerbated the heatwave across much of northern Europe. Land‐atmosphere feedbacks were prompted by unusually low soil water over wide areas, which generated moisture limitations on surface latent heat fluxes, suppressing cloud formation, increasing surface net radiation, and driving temperatures higher during several multiweek episodes of extreme heat. We find consistent evidence in field data and reanalysis of a threshold of soil water content at most locations, below which surface fluxes and daily maximum temperatures become hypersensitive to declining soil water. Similar recent heatwaves over various parts of Europe in 2003, 2010, and 2019, combined with dire climate change projections, suggest such events could be on the increase. Land‐atmosphere feedbacks may play an increasingly important role in exacerbating extremes, but could also contribute to their predictability on subseasonal time scales.

Seasonal Estimates and Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

Extended Range Arctic Sea Ice Forecast with Convolutional Long-Short Term Memory Networks

AbstractOperational Arctic sea ice forecasts are of crucial importance to science and to society in the Arctic region. Currently, statistical and numerical climate models are widely used to generate the Arctic sea ice forecasts at weather time-scales. Numerical models require near real-time input of relevant environmental conditions consistent with the model equations and they are computationally expensive. In this study, we propose a deep learning approach, namely Convolutional Long Short Term Memory Networks (ConvLSTM), to forecast sea ice in the Barents Sea at weather to sub-seasonal time scales. This is an unsupervised learning approach. It makes use of historical records and it exploits the covariances between different variables, including spatial and temporal relations. With input fields from reanalysis data, we demonstrate that ConvLSTM is able to learn the variability of the Arctic sea ice and can forecast regional sea ice concentration skillfully at weekly to monthly time scales. It preserves the physical consistency between predictors and predictands, and generally outperforms forecasts with climatology, persistence and a statistical model. Based on the known sources of predictability, sensitivity tests with different climate fields as input for learning were performed. The impact of different predictors on the quality of the forecasts are evaluated and we demonstrate that the surface energy budget components have a large impact on the predictability of sea ice at weather time scales. This method is promising to enhance operational Arctic sea ice forecasting in the near future.

Observational Evidence of Distinguishable Weather Patterns for Three Types of Sudden Stratospheric Warming During Northern Winter

Sudden stratospheric warming (SSW) events often lead to a cold surface air temperature anomaly over the extratropical regions. In this study, we propose, through observational evidence, that the types of SSW determine the severity of the cold anomaly. Based on the three-type classification of SSW, it is found that the surface air temperature drops notably over central to eastern North America following an SSW-type transition, especially from displacement to split. Note, however, that the differences in mean surface air temperature anomalies between SSW types are not statistically significant, even though after SSW-type transition from displacement to split, surface air temperature anomalies are colder than the other two types. The development of an anomalous tropospheric ridge in the North Pacific Arctic sector, associated with the difference in the vertical and zonal propagation of planetary waves, characterizes the post-warming period of the displacement–split type. After the occurrence of the displacement–split type transition of SSW events, upward propagation of planetary waves of zonal wavenumber 1 is suppressed, whereas planetary waves of zonal wavenumber 2 increase in the troposphere. Accompanying the ridge in the North Pacific, a trough developed downstream over North America that carries cold polar air therein. The results in this study are relevant for the subseasonal time scale, within 20 days after an SSW occurrence.

When Does the Lorenz 1963 Model Exhibit the Signal‐To‐Noise Paradox?

AbstractSeasonal prediction systems based on Earth System Models exhibit a lower proportion of predictable signal to unpredictable noise than the actual world. This puzzling phenomena has been widely referred to as the signal‐to‐noise paradox (SNP). Here, we investigate the SNP in a conceptual framework of a seasonal prediction system based on the Lorenz, 1963 Model (L63). We show that the SNP is not apparent in L63, if the uncertainty assumed for the initialization of the ensemble is equal to the uncertainty in the starting conditions. However, if the uncertainty in the initialization overestimates the uncertainty in the starting conditions, the SNP is apparent. In these experiments the metric used to quantify the SNP also shows a clear lead‐time dependency on subseasonal timescales. We therefore, formulate the alternative hypothesis to previous studies that the SNP could also be related to the magnitude of the initial ensemble spread.

Evaluation of subseasonal impacts of the MJO/BSISO in the East Asian extended summer

The Madden–Julian Oscillation (MJO)/Boreal Summer Intraseasonal Oscillation (BSISO) has been considered an important climate mode of variability on subseasonal timescales for East Asian summer. However, it is unclear how well the MJO/BSISO indices would serve as guidance for subseasonal forecasts. Using a probabilistic forecast model determined through multiple linear regression (MLR) with MJO, ENSO, and long-term trend as predictors, we examine lagged impacts of each predictor on East Asia extended summer (May–October) climate from 1982 to 2015. The forecast skills of surface air temperature (T2m) contributed by each predictor is evaluated for lead times out to five weeks. We also provide a systematic evaluation of three commonly used, real-time MJO/BSISO indices in the context of lagged temperature impacts over East Asia. It is found that the influence of the trend provides substantial summertime skill over broad regions of East Asia on subseasonal timescales. In contrast, the MJO influence shows regional as well as phase dependence outside the tropical band of the main action centers of the MJO convective anomalies. All three MJO/BSISO indices generate forecasts that yield high skill scores for week 1 forecasts. For some initial phases of the MJO/BSISO, skill reemerges over some regions for lead times of 3–5 weeks. This emergence indicates the existence of windows of opportunity for skillful subseasonal forecasts over East Asia in summer. We also explore the dynamics that contribute to the elevated skills at long lead times over Tibet and Taiwan–Philippine regions following the initial state of phases 7 and 5, respectively. The elevated skill is rooted in a wave train forced by the MJO convective heating over the Arabian Sea and feedbacks between MJO convection and SSTs in Taiwan–Philippine region. Two out of the three commonly used MJO/BSISO indices tend to identify MJO events that evolve consistently in time, allowing them to serve as reliable predictors for subseasonal forecasts for up to 5 weeks.

Impacts of Amazon Fire Aerosols on the Subseasonal Circulations of the Mid-High Latitudes

The strong radiative effects of fire aerosols have been well accepted in the climate community. However, there have been few studies on the aerosol effects at a monthly to subseasonal range. We used the National Centers for Environmental Prediction (NCEP) reanalysis datasets and Community Atmosphere Model Version 5 (CAM5) to explore the impacts of Amazon fire aerosols on the subseasonal climate. With the reanalysis datasets, we found that most of the abnormal high emissions tended to happen more frequently/intensely under a dry and warm condition during the La Niña years. And the composite analysis of the abnormal high emissions showed that there is a La Niña-like pattern of sea surface temperature anomalies (SSTA), precipitation, and circulation anomalies. To isolate the aerosol impacts from the SSTA, we removed the linear regressions of the Nino3.4 SST index, and found that significant anomalies in the pressure field still persisted in the midlatitude. Five wavetrains can be found in the mid-high latitudes of both hemispheres induced by Amazon fire aerosols. Through prescribing climatological mean SST and La Niña-like SSTA in CAM5 simulations respectively, we found that only the latter could reproduce the aerosol impact on circulation in the mid-high latitudes, i.e., five-wave anomalies, although with biased locations. This indicates that the Amazon wildfire aerosol impacts are highly coupled with the La Niña-like SSTA. This study emphasizes that Amazon fire aerosols indeed result in significant circulation anomalies in the mid-high latitudes and including fire aerosols may improve model forecasting skills at the monthly to subseasonal timescale.

Fidelity of Regional Climate Model v4.6 in capturing seasonal and subseasonal variability of Indian summer monsoon

Climate modeling studies in the context of Indian summer monsoon (ISM) variability have usually been performed on the seasonal and interannual timescales. The present study assesses the fidelity of the Regional Climate Model (RegCM v4.6) in capturing the subseasonal active and break spells along with the seasonal mean rainfall during the ISM season. The model fields are obtained from 24 years (1982–2005) of simulation and validated against the observations and latest reanalyzed ERA5 data products. Our analysis indicates that RegCM v4.6 fairly captures the large scale features of ISM and improvement in seasonal rainfall is noted as compared to its precedent RegCM v4.4. At subseasonal timescales, though the model captures the active and break spells of ISM, the length and frequency of these events seem inconsistent as compared to the observations. Occurrences of breaks and associated circulation features are mostly consistent but the active spells are significantly misconstrued in the model. The dry air intrusion from the western region and lack of monsoon low over the mainland and Bay of Bengal seem to suppress the precipitation in the model. This subseasonal bias might persist due to systematic errors linked to the lack of ocean coupling, inefficiency of land surface and cumulus parameterization schemes in the model. Overall, RegCM v4.6 offers improvements at seasonal timescale but needs further improvements to realistically represent the subseasonal variability of ISM.

Intermittency in phytoplankton bloom triggered by modulations in vertical stability

Seasonal surface chlorophyll (SChl) blooms are very chaotic in nature, but traditional bloom paradigms have climbed out of these subseasonal variations. Here we highlight the leading order role of wind bursts, by conjoining two decades of satellite SChl with atmospheric reanalysis in the Northwestern Mediterranean Sea. We demonstrate that weekly SChl fluctuations are in phase with weekly changes in wind stress and net heat flux during the intial state of the bloom in winter and early spring, thus expanding the convection shutdown hypothesis of bloom onset to subseasonal timescales. We postulate that the mechanism reflected by this link is intermittency in vertical stability due to short-term episodes of calm weather in winter or to stormy conditions in early spring, leading to short-term variations in light exposure or to events of vertical dilution. This strong intermittency in phytoplankton bloom may probably have important consequences on carbon export and trophic web structure and should not be overlooked.

Understanding the combined effects of global warming and anthropogenic aerosol forcing on the South Asian monsoon

There is growing evidence of the influence of northern hemisphere (NH) anthropogenic aerosols (AA) on the observed regional climate change over Asia in the recent few decades. While the radiative effects of AA can regionally offset the greenhouse gas (GHG) forcing, their combined impacts on the South Asian monsoon (SAM) are not adequately understood. The present study examines the individual and combined effects of GHG and AA forcing on the SAM precipitation response, based on numerical experiments conducted using the IITM Earth System Model version2 (IITM-ESMv2). We performed the following three sets of 50-year simulations based on the CMIP6 forcing (a) AER: where the atmospheric CO2 concentration is fixed to the pre-industrial level (i.e., year 1850) and AA levels correspond to the year 2005 (b) GHG: CO2 concentration corresponds to the year 2005 and AA levels correspond to the year 1850 (c) COMB: Both CO2 concentration and AA levels correspond to the year 2005. The three experiments are compared against the pre-industrial control (PICTL) run of the IITM-ESMv2. An intensification of SAM precipitation is noted in GHG relative to PICTL, whereas AER exhibits a decrease of SAM precipitation and weakening of monsoon circulation in response to the AA forcing. The results show that absorption of shortwave radiation above the atmospheric boundary layer over the Asian region creates surface radiation deficit and stabilizes the lower troposphere, leading to a slowdown of monsoonal winds, reduced evaporation over the Indian Ocean and decreased moisture convergence over South and Southeast Asia. A striking finding from our analysis is the reinforced suppression of SAM precipitation and organized monsoon convection in COMB, which results from an enhanced inter-hemispheric asymmetry in radiative forcing under the combined influence of the elevated CO2 and AA forcing. Furthermore, both the AER and COMB experiments reveal a widespread suppression of large-scale organized monsoon convective activity on sub-seasonal time-scales over South and Southeast Asia.

Projected near-term changes in temperature extremes over China in the mid-twenty-first century and underlying physical processes

This study investigates the near-term future changes of temperature extremes in summer (June–August) and winter (December–February) seasons over mainland China in the mid-twenty-first century (FP; 2045–2055) under representative concentration pathway (RCP) 4.5 scenario relative to the present day (PD; 1994–2011) by using an atmosphere–ocean-mixed-layer coupled model MetUM-GOML1. The projected changes in hot extremes exhibit a rise in hottest day temperature (TXx) and warmest night temperature (TNx) and an increase in frequencies of summer days (SU) and tropical nights (TR). The projected changes in cold extremes show a rise in coldest day temperature (TXn) and coldest night temperature (TNn) and a decrease in frequencies of ice days (ID) and frost days (FD). The projected changes in temperature extremes in both seasons are primarily determined by changes in seasonal mean daily maximum and minimum temperature while changes in temperature variability from daily to sub-seasonal time scales play a minor role. The future changes in temperature extremes over China, being consistent with the rise in seasonal temperature, are partly due to the increase in surface downward clear sky longwave radiation through the increased greenhouse gas concentrations and enhanced water vapor in the atmosphere, and partly due to the increase in net surface shortwave radiation as a result of the decreased aerosol emissions over Asia via aerosol-radiation interactions. Moreover, the seasonal mean surface warming can further be amplified with positive feedbacks by reducing the cloud cover, leading to positive changes in shortwave radiative effect through aerosol-cloud interactions and surface-atmosphere feedbacks during summer, and by positive changes in surface clear sky shortwave radiation through snow-albedo feedbacks over northern China and southwestern China during winter.

The Influence of Sudden Arctic Sea-Ice Thinning on North Atlantic Oscillation Events

ABSTRACT Utilizing the Community Atmosphere Model, version 4 (CAM4), the influence of sudden Arctic sea-ice thinning on North Atlantic Oscillation (NAO) events is investigated on a sub-seasonal time scale. First, the control simulation with CAM4 using prescribed daily boundary conditions produces realistic descriptions for the frequency and evolution of NAO events. Second, sensitivity experiments are then conducted to investigate the impact of sudden Arctic sea-ice thinning on NAO events. Numerical results show that the NAO has obvious responses to sudden sea-ice thinning over 10 days. Finally, the NAO responses to sudden sea-ice thinning are related to the positions of the NAO event. Further diagnostic analysis reveals that when the poleward centre of a positive NAO event is located west (east) of Greenland, cold (warm) advection would be induced because of the sudden sea-ice thinning along with the northward (southward) wind. This cooling (warming) causes the air to contract (expand) and further results in a decrease (increase) in the geopotential height and sea level pressure fields, which corresponds to a positive NAO (negative NAO) response. The results of this investigation show that the response of atmospheric circulation to boundary conditions is determined by both boundary condition changes and the atmospheric circulation regime, which provides an understanding of the effect on an NAO event of sudden sea-ice thinning on a sub-seasonal time scale.

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

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

North American cold events following sudden stratospheric warming in the presence of low Barents-Kara Sea sea ice

While the relationship between the Arctic sea ice loss and midlatitude winter climate has been well discussed, especially on the seasonal mean scale, it remains unclear whether the Arctic sea ice condition affects the predictability of North American cold weather on the subseasonal time scale. Here we find that, in the presence of low Barents-Kara Sea (BKS) sea ice, sudden stratospheric warmings (SSWs) can favor surface cold spells over North America at the subseasonal timescale based on observations and model experiments. A persistent ridge of wave-2 pattern emerges over the Bering Sea-Gulf of Alaska several weeks after the SSW onset, with a coherent structure from the stratosphere to the surface, which, in turn, is conducive to synoptic cold air outbreaks in Canada and midwestern USA. This highlights a planetary wave pathway relating to BKS sea ice changes, by which the stratospheric polar vortex impacts the regional surface temperature on the subseasonal scale. In contrast, this mechanism does not occur with positive BKS sea ice anomaly. These findings help to improve the subseasonal predictability over North America, especially under the background of rapid change of Arctic sea ice in a warming world.

Subseasonal Forecast of Surface Air Temperature Using Superensemble Approaches: Experiments over Northeast Asia for 2018

AbstractBridging the gap between weather forecasting and climate prediction, subseasonal to seasonal (S2S) forecasts are of great importance yet currently of relatively poor quality. Using the S2S Prediction Project database, the study evaluates products derived from four operational centers of CMA, KMA, NCEP, and UKMO, and superensemble experiments including the straightforward ensemble mean (EMN), bias-removed ensemble mean (BREM), error-based superensemble (ESUP), and Kalman filter superensemble (KF), in forecasts of surface air temperature with lead times of 6–30 days over northeast Asia in 2018. Validations after the preprocessing of a 5-day running mean suggest that the KMA model shows the highest skill for either the control run or the ensemble mean. The nonequal weighted ESUP is slightly superior to BREM, whereas they both show larger biases than EMN after a lead time of 22 days. The KF forecast constantly outperforms the others, decreasing mean absolute errors by 0.2°–0.5°C relative to EMN. Forecast experiments of the 2018 northeast Asia heat wave reveal that the superensembles remarkably improve the raw forecasts featuring biases of >4°C. The prominent advancement of KF is further confirmed, showing the regionally averaged bias of ≤2°C and the hit rate of 2°C reaching up to 60% at a lead time of 22 days. The superensemble techniques, particularly the KF method of dynamically adjusting the weights in accordance with the latest information available, are capable of improving forecasts of spatiotemporal patterns of surface air temperature on the subseasonal time scale, which could extend the skillful prediction lead time of extreme events such as heat waves to about 3 weeks.

Post-processing sub-seasonal precipitation forecasts at various spatiotemporal scales across China during boreal summer monsoon

Post-processing outputs from Coupled Global Circulation Models (CGCM) is required to generate skillful and reliable sub-seasonal precipitation forecasts. However, it is not currently known at what spatiotemporal scale can sub-seasonal precipitation forecasts be improved after post-processing. In this study, a Bayesian joint probability (BJP) method is used to post-process European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal precipitation forecasts at various spatiotemporal scales across China during the boreal summer monsoon. The relationships between forecasts and observations are built at daily, weekly, and fortnight temporal scales for each grid cell and each hydroclimatic region after normalization. The results suggest that the skills and reliability of ECMWF raw daily precipitation forecasts are improved after post-processing. The forecast skills are further improved at larger spatial scales or longer temporal scales, especially in the Upper and Middle Yangtze River, Southeast Rivers, and Pearl River. Although the forecast skills decrease rapidly as lead time increases, the uncertainty spread of the BJP calibrated sub-seasonal precipitation forecasts is highly reliable at all lead times and spatiotemporal scales. This work will help to realize the potential of CGCM forecasts at sub-seasonal time scales. Future works will investigate the benefits of post-processing precipitation forecasts for sub-seasonal streamflow predictions.

Simulation of Central Indian Ocean Mode in S2S Models

AbstractSimulation of monsoon intraseasonal oscillations (MISO) during the Indian summer monsoon (ISM) is a grand scientific challenge. The Subseasonal‐to‐Seasonal (S2S) prediction project provides a unique way to examine the key dynamics of MISO. Recently, a Central Indian Ocean (CIO) mode was proposed as an intrinsic climate mode over the Indian Ocean, and it has a close relation with MISO during the ISM. In this study, the simulations of the CIO mode events in S2S models are examined. The results confirm that a better rendition of the CIO mode in S2S models tends to result in a better simulation of northward propagating MISO and leads to stronger subseasonal rainfall during the ISM. The positive CIO mode cases are classified into well‐simulated and poorly simulated groups. It is shown that the barotropic energy conversion due to the meridional shear of background zonal winds ( ) enhances the kinetic energy at subseasonal timescales during the CIO mode in the well‐simulated group, which also agrees with observations. As a result, the enhanced meridional wind anomalies transport moisture from the tropics to the subtropics and reinforce midtropospheric moisture loading in the subtropics, which nourishes the northward propagating MISO. Therefore, a better simulation of and moisture transport by subseasonal meridional wind anomalies are required to improve the CIO mode simulation, which is expected to benefit the simulation and prediction of MISO during the ISM.

Comment on “Unraveling the Mystery of Indian Summer Monsoon Prediction: Improved Estimate of Predictability Limit” by Saha et al.

AbstractIn a recent study, Saha et al. (2019, https://doi.org/10.1029/2018JD030082) examine the correlation between boreal summer seasonal mean rainfall over India and rainfall variance as a function of subseasonal time scale and find that the correlation has a local maximum (exceeding a value of 0.6) for synoptic time scales (2–5 day periods). They claim these results to be a major advancement in understanding monsoon predictability but do not provide a clear physical explanation. Here we examine the sensitivity of this relationship to the details of the analysis and only consider the observed correlation identified by Saha et al. (2019, https://doi.org/10.1029/2018JD030082). There is large sensitivity in the correlation maximum between spatially averaged seasonal mean rainfall and synoptic scale rainfall variance averaged over the same domain. The correlation maximum is weaker over the longer period of 1901–2015, and more notably it is highly sensitive to the domain of prior averaging. A correlation peak is not found outside central India and is neither found within central India when averaging over a smaller domain. Averaging over a larger domain results in a disproportionate reduction in synoptic variance (that is of most interest). The peak in correlation between seasonal mean and 2–5 day variance only emerges after first averaging over a sufficiently large region; thus, the maximum appears to be an artifact of spatial averaging. It is further pointed out that a positive mean‐variance relationship is an intrinsic property of rainfall, and thus, its existence alone does not necessarily imply any physical, causal, and/or predictive connection between time scales.

Subseasonal Prediction with and without a Well-Represented Stratosphere in CESM1

AbstractThere is a growing demand for understanding sources of predictability on subseasonal to seasonal (S2S) time scales. Predictability at subseasonal time scales is believed to come from processes varying slower than the atmosphere such as soil moisture, snowpack, sea ice, and ocean heat content. The stratosphere as well as tropospheric modes of variability can also provide predictability at subseasonal time scales. However, the contributions of the above sources to S2S predictability are not well quantified. Here we evaluate the subseasonal prediction skill of the Community Earth System Model, version 1 (CESM1), in the default version of the model as well as a version with the improved representation of stratospheric variability to assess the role of an improved stratosphere on prediction skill. We demonstrate that the subseasonal skill of CESM1 for surface temperature and precipitation is comparable to that of operational models. We find that a better-resolved stratosphere improves stratospheric but not surface prediction skill for weeks 3–4.