AS1.4

A low sea surface temperature (SST) region extends southward in the central part of southern South China Sea during boreal winter, which is called the South China Sea cold tongue (SCS CT). This talk presents an analysis of the factors of interannual variation of SST in the SCS CT region and the individual and combined impacts of El Niño-Southern Oscillation (ENSO) and East Asian winter monsoon (EAWM) on the SCS CT intensity. During years with ENSO alone or with co-existing ENSO and anomalous EAWM, shortwave radiation and ocean horizontal advection play major roles in the interannual variation of the SCS CT intensity. Ocean advection contributes largely to the SST change in the region southeast of Vietnam. In strong CT years with anomalous EAWM alone, surface wind-related latent heat flux has a major role and shortwave radiation is secondary to the EAWM-induced change of the SCS CT intensity, whereas the role of ocean horizontal advection is relatively small. The above differences in the roles of ocean advection and latent heat flux are associated with the distribution of low level wind anomalies. In anomalous CT years with ENSO, low level anomalous cyclone/anticyclone-related wind speed change leads to latent heat flux anomalies with effects opposite to shortwave radiation. In strong CT years with anomalous EAWM alone, surface wind-related latent heat flux anomalies are large as anomalous winds are aligned with climatological winds.

How to cite: Wang, Z. and Wu, R.: Individual and combined impacts of ENSO and East Asian winter monsoon on the South China Sea cold tongue intensity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1446, https://doi.org/10.5194/egusphere-egu21-1446, 2021.

Central Southwest Asia (CSWA) is a region with the largest number of glaciers, outside the polar regions in its northeast and the Arabian desert to its southwest. The region receives precipitation from November to April period also known as the wet season, which contributes to the regional freshwater resources. Mainly, El Niño–Southern Oscillation (ENSO) modulates the wet season precipitation over CSWA, with a positive relationship. However, the intraseasonal characteristics of ENSO influence are largely unknown, which may be important to understand the regional sub-seasonal to seasonal hydroclimate variability. We noted that the ENSO‐CSWA teleconnection varies intraseasonally and is a combination of direct and indirect positive influences. The ENSO direct influence is through a Rossby wave‐like pattern in the tail months of the wet season, while the indirect influence is noted through an ENSO‐forced atmospheric dipole of diabatic heating anomalies in the tropical Indian Ocean (TIO), which also generates a Rossby wave‐like forcing and persists throughout the wet season. The stronger ENSO influence is found when both direct and indirect modes are in phase, while the relationship breaks down when the two modes are out of phase. Moreover, the idealized numerical simulations confirm and reproduce the observed circulation patterns. This suggests that improvements in sub-seasonal to seasonal scale predictability requires the better representation of intraseasonal variability of ENSO teleconnection, as well as the role of interbasin interactions in its propagation.

How to cite: Abid, M. A., Ashfaq, M., Kucharski, F., Evans, K. J., and Almazroui, M.: Indian Ocean mediates the ENSO teleconnections to the Central Southwest Asia during the wet season , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10315, https://doi.org/10.5194/egusphere-egu21-10315, 2021.

We investigated the spatial structure of the intraseasonal variation (15-30 day) in cloud cover in the mid-latitudes during winter. We attempted to interpret the spatial pattern of clouds in　the context of Rossby waves.

We used the total cloud cover in H-series dataset (1984-2016) by the International Satellite Cloud Climatology Project (ISCCP) based on the satellite observations, and ERA-Interim re-analysis data (1980-2016) including high, medium, and low cloud covers defined by σ coordinate.

We calculated correlation coefficients between the geopotential height at 300hPa (Z300) at a certain position and the cloud covers, meridional wind, and vertical velocity in the surrounding area. The positions of the maximum of high (0.45≧σ) and medium cloud cover (0.8≧σ＞0.45) relative to Z300 are longitudinally constant for all longitudes except the region from east Asia to western part of the Pacific. The position of the maximum of the high cloud cover is located just west of the ridge and just east of the maximum positions of the upward motions of re-analysis vertical velocity and its adiabatic component. These results suggest that the adiabatic upward motion in the southerly wind region west of the ridge contributes to the generation of high cloud cover. In contrast, the position of the maximum of medium cloud cover is located just east of the trough. The position of the maximum of diabatic upward motion, which is consider to be due to condensation process is located near the maximum of medium cloud cover. These results suggest that Rossby waves modulate activity of short-period disturbances with precipitation. Apart from high and medium cloud covers, the position of the maximum of low cloud cover (σ＞0.8) has large longitudinal dependency. While the position of the maximum is located at almost the same as that of medium cloud cover mainly over the continent, the position of the maximum is located just east of the ridge mainly over the ocean.

The correlation coefficients between ISCCP total cloud cover and Z300 are statistically significant only over the continent, where the positions of the maximum of high, medium, and low cloud covers are all located east of the trough and west of the ridge.

How to cite: Satoh, R., Nishi, N., and Mukougawa, H.:  Intraseasonal Variability of Cloud Cover in Midlatitudes during Boreal Winter, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3804, https://doi.org/10.5194/egusphere-egu21-3804, 2021.

Using the ensemble empirical mode decomposition (EEMD) method, this study systematically investigates the multiple timescales of the Southern Annular Mode (SAM) and identifies their relative contributions to the low-frequency persistence of SAM. Analyses show that the subseasonal sustaining of SAM mainly depends on the contribution of longer-timescale variabilities, especially the cross-seasonal variability. When subtracting the cross-seasonal variability from the SAM, the positive covariance between the eddy and zonal flow, which is suggested the positive eddy feedback in SAM, disappears. Composite analysis shows that only with strong cross-seasonal variability, the meridional shift of zonal wind, eddy momentum forcing and baroclinicity anomalies can be maintained for more than 20 days, mainly resulting from the longer-timescale (especially the cross-seasonal timescale) eddy-zonal flow interactions. This study further suggests that the dipolar sea surface temperature (SST) anomalies in the mid latitude of Southern Hemisphere (SH) is a possible cause for the cross-seasonal variability. Analysis shows that about half of the strong cross-seasonal timescale events are accompanied by evident dipolar SST anomalies, which mostly occur in austral summer. The cross-seasonal dependence of the eddy-zonal flow interactions suggests the longer-timescale (especially the cross-seasonal timescale) contribution cannot be neglected in subseasonal prediction of SAM.

How to cite: Zhang, Q., Zhang, Y., and Wu, Z.: Multiple Timescales of the Southern Annular Mode, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3667, https://doi.org/10.5194/egusphere-egu21-3667, 2021.

Realistic representation of large-scale stationary waves (SWs) in general circulation models is crucial, as they modulate the trajectories of mid-latitude storms, and shape the distribution of surface temperatures along comparable latitude bands over densely populated areas in the Northern Hemisphere.

In this work, we assess the fidelity of NH wintertime SWs in 5 operational subseasonal-to-seasonal models. In the troposphere, we found that biases in the North Pacific are more pronounced in NCEP, ECMWF and UKMO models compared to the North Atlantic, while in the CMA and BoM models, large biases in amplitude and phase are present in both sectors. These biases in tropospheric SWs directly affect the simulated SWs in the stratosphere.

Finally, we attribute the biases in the North Pacific sector, in part, to the mean state biases in the tropics. Longitudinal shifts in the time-mean tropical convection over the Maritime Continent and central Pacific, affect the longitudinal position of the North Pacific trough in the models.

How to cite: Schwartz, C. and Garfinkel, C.: Stationary Waves and Upward Troposphere-Stratosphere Coupling in Operational Subseasonal Forecasting Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5453, https://doi.org/10.5194/egusphere-egu21-5453, 2021.

The strength of the stratospheric polar vortex is a key contributor to subseasonal prediction during boreal winter. Anomalously weak polar vortex events can be induced by enhanced vertically propagating Rossby waves from the troposphere, driven by blocking and wave breaking. Here, we analyse a tropospheric pattern—the Scandinavia–Greenland (S–G) pattern—associated with both processes. The S–G pattern is defined as the second empirical orthogonal function (EOF) of mean sea‐level pressure in the northeast Atlantic. The first EOF is a zonal pattern resembling the North Atlantic Oscillation. We show that the S–G pattern is associated with a transient amplification of planetary wavenumber 2 and meridional eddy heat flux, followed by the onset of a persistently weakened polar vortex. We then analyse 10 different models from the S2S database, finding that, while all models represent the structure of the S–G pattern well, some models have a zonal bias with more than the observed variability in their first EOF, and accordingly less in their second EOF. This bias is largest in the models with the lowest resolution, and consistent with biases in blocking and Rossby wave breaking in these models. Skill in predicting the S–G pattern is not high beyond week 2 in any model, in contrast to the zonal pattern. We find that the relationship between the S–G pattern, enhanced eddy heat flux in the stratosphere, and a weakened polar vortex is initially well represented, but decays significantly with lead time in most S2S models. Our results motivate improved representation of the S–G pattern and its stratospheric response at longer lead times for improved subseasonal prediction of the stratospheric polar vortex.

How to cite: Lee, S., Charlton-Perez, A., Furtado, J., and Woolnough, S.: Representation of the Scandinavia-Greenland Pattern and its Relationship with the Polar Vortex in S2S Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1370, https://doi.org/10.5194/egusphere-egu21-1370, 2021.

How to cite: Kolstad, E. W., Wulff, C. O., Domeisen, D., and Woollings, T.: Tracing North Atlantic Oscillation Forecast Errors to Stratospheric Origins, with a new analysis of the 2021 winter, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14216, https://doi.org/10.5194/egusphere-egu21-14216, 2021.

The ECMWF sub-seasonal forecast model includes dynamic representations of the atmosphere, ocean, sea-ice, and ocean-waves. Coupling to a dynamic ocean model allows more a realistic representation of air-sea interaction, but also introduces the potential for systematic errors in sea surface temperatures (SST). Here, we show that North Atlantic SST biases associated with errors in the position of the Gulf Stream have a significant impact on initialized forecasts at the sub‐seasonal time range. Correcting these errors with an online SST bias‐correction scheme improves the mean state of the North Atlantic region and has a significant positive impact on forecasts of atmospheric circulation anomalies. Improvements to forecast skill extend beyond the North Atlantic into Europe and along the northern hemisphere subtropical waveguide. These impacts provide important evidence for the potential benefits to initialized coupled forecast systems of higher‐resolution ocean models that can better resolve the position of the Gulf Stream.

Reference: Roberts, C. D., Vitart, F., & Balmaseda, M. A. Hemispheric impact of North Atlantic SSTs in sub‐seasonal forecasts. Geophysical Research Letters, e2020GL091446.

How to cite: Roberts, C., Vitart, F., and Balmaseda, M.: Remote impact of North Atlantic sea surface temperature errors in sub‐seasonal forecasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-846, https://doi.org/10.5194/egusphere-egu21-846, 2021.

The Tibetan Plateau (TP), referred to as the “Asian water tower”, contains one of the largest land ice masses on Earth. The local glacier shrinkage and frozen-water storage are strongly affected by variations in surface air temperature over the TP (TPSAT), especially in springtime. This study reveals a distinct out-of-phase connection between the February North Atlantic Oscillation (NAO) and March TPSAT, which is non-stationary and regulated by the warm phase of the Atlantic Multidecadal Variability (AMV+). The results show that during the AMV+, the negative phase of the NAO persists from February to March, and is accompanied by a quasi-stationary Rossby wave train trapped along a northward-shifted subtropical westerly jet stream across Eurasia, inducing an anomalous adiabatic descent that warms the TP. However, during the cold phase of the AMV, the negative NAO does not persist into March. The Rossby wave train propagates along the well-separated polar and subtropical westerly jets, and the NAO−TPSAT connection is broken. Further investigation suggests that the enhanced synoptic eddy and low-frequency flow (SELF) interaction over the North Atlantic in February and March during the AMV+, caused by the enhanced and southward-shifted storm track, help maintain the NAO anomaly pattern via positive eddy feedback. This study provides a new detailed perspective on the decadal variability of the North Atlantic−TP connections in late winter−early spring.

How to cite: Li, J., Li, F., He, S., Wang, H., and Orsolini, Y. J.: The AMV phase-dependence of the connection between February NAO and March surface air temperature over the Tibetan Plateau, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9879, https://doi.org/10.5194/egusphere-egu21-9879, 2021.

European winter weather is dominated by several low-frequency teleconnection patterns, the main ones being the North Atlantic Oscillation (NAO), East Atlantic, East Atlantic/Western Russia and Scandinavian patterns. Through predicting these patterns, skillful forecasts of weather parameters like surface temperature can be generated, which in turn are used in a variety of applications (e.g., predictions of energy demand). A previous study (Weisheimer et.al., 2017) found that the NAO was subject to decadal variability during the twentieth century, affecting its long-term predictability. During recent decades, predictions for the NAO index have shown considerable skill, but this is likely to change during future periods of reduced predictability.

We analyze the century-long ERA-20C reanalysis and ASF-20C seasonal hindcast datasets to find if the other main teleconnection patterns also experience fluctuations in predictability, with potential implications for future skill and development of seasonal forecasting models. By linking the teleconnections to extreme cold and heat wave indices (Russo et al., 2015), we highlight the impact of these large-scale patterns on seasonal surface temperature in Europe during two periods of interest in the middle and end of the century. Our study shows that even though the predictability of the teleconnection patterns themselves fluctuates on a decadal scale, the links to winter surface temperatures are not significantly affected. However, the ability of the seasonal hindcasts to reproduce these patterns is quite limited.

References:

Russo, S., Sillmann, J., & Fischer, E. M. (2015). Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environmental Research Letters, 10(12), 124003. doi: 10.1088/1748-9326/10/12/124003

Weisheimer, A., Schaller, N., O’Reilly, C., MacLeod, D. A., & Palmer, T. (2017). Atmospheric seasonal forecasts of the twentieth century:  multi-decadal variability in predictive skill of the winter North Atlantic Oscillation (NAO) and their potential value for extreme event attribution. Quarterly Journal of the Royal Meteorological Society, 143(703), 917-926. doi: 10.1002/qj.29

How to cite: Schuhen, N., Schaller, N., Bloomfield, H. C., Brayshaw, D. J., Sillmann, J., Lledó, L., and Cionni, I.: Long-term predictability of winter teleconnection indices and their relationship to seasonal temperature extremes in Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3327, https://doi.org/10.5194/egusphere-egu21-3327, 2021.

Issuing skillful forecasts beyond the typical horizon of weather predictability remains a challenge actively addressed by the scientific community. This study evaluates winter subseasonal reforecasts delivered by the CNRM and ECMWF dynamical systems and identifies that the level of skill for predicting temperature in Europe varies fairly consistently in both systems. In particular, forecasts initialized during positive NAO phases tend to be more skillful over Europe at week three in both systems. Composite analyses performed in an atmospheric reanalysis, a long-term climate simulation and both forecast systems unveil very similar temperature and sea-level pressure patterns 3 weeks after NAO+ conditions. Furthermore, regressing these fields onto the 3-week previous NAO index in a reanalysis shows consistent patterns over Europe but also eastern North America, thereby revealing a lagged teleconnection, either related to the persistence or recurrence of the NAO+ weather regime. Since this feature is well captured by forecast systems, this is a key mechanism for determining a priori confidence in the skill of wintertime subseasonal forecasts over Europe and North America.

How to cite: Ardilouze, C., Specq, D., Batté, L., and Cassou, C.: Flow dependence of wintertime subseasonal prediction skill over Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8666, https://doi.org/10.5194/egusphere-egu21-8666, 2021.

The accuracy and reliability in predicting winter anomalies, particularly high-impact events, is crucial for economic sectors like energy production and trade. Sub-seasonal predictions can provide a useful tool for early detection of these events. In this context, this study aims to target atmospheric patterns leading to skillful winter predictions at S2S lead times.

With a focus on Europe, we explore extended range predictability (up to 35 days) in the ECMWF hindcast dataset (1999-2018). This dataset provides a sizable sample for assessing the winter months predictive skill of the model and can be considered as a preparatory step to the use of the more comprehensive real-time ensemble forecasts.

The verification is performed on geopotential and temperature fields against the ERA5 reanalysis. We first identify the most skillful predictions both in terms of lead-time and period of initialization. Later we assess whether these skillful predictions correspond to high-impact events, especially cold spells. Finally, in the attempt to identify the potential drivers of improved predictability, we track back to the dominant Euro-Atlantic modes of variability present in the initial atmospheric states of the well predicted events.

How to cite: Mastrangelo, D., Giuntoli, I., and Malguzzi, P.: Exploring winter predictability in Europe using the ECMWF hindcasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9538, https://doi.org/10.5194/egusphere-egu21-9538, 2021.

The skill of predicted wind speed at 100 m and temperature at 2 m has been assessed in extended-range forecasts and hindcasts of the European Center for Medium-Range Weather Forecasts, starting from December 2015 to November 2019. The assessment was carried out over Europe grid-point wise and also by considering several spatially averaged country-sized domains, using standard scores such as the Continuous Ranked Probability Score and Anomaly Correlation Coefficient. The (re-)forecasts showed skill over climatology in predicting weekly mean wind speed and temperature well beyond two weeks. Even at a lead time of 6 weeks, the probability of the (re-)forecasts being skillful is greater than 50%, encouraging the use of operational subseasonal forecasts in the decision making value chain. The analysis also exhibited​ significant differences in skill in the predictability of different variables, with temperature being more skillful than wind speed, and for different seasons, with winter allowing more skillful forecasts. The predictability also displayed a clear spatial pattern with forecasts for temperature having more skill for Eastern than for Western Europe, and wind speed forecasts having more skill in Northern than Southern Europe.

How to cite: Goutham, N., Plougonven, R., Omrani, H., Parey, S., Tankov, P., Tantet, A., and Drobinski, P.: Verification of the European subseasonal forecasts of wind speed and temperature, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14728, https://doi.org/10.5194/egusphere-egu21-14728, 2021.

How to cite: Büeler, D., Wandel, J., Quinting, J. F., and Grams, C. M.: Flow-dependent sub-seasonal forecast skill for Atlantic-European weather regimes and its relation to planetary- to synoptic-scale processes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12124, https://doi.org/10.5194/egusphere-egu21-12124, 2021.

How to cite: Newman, M. and Albers, J.: Subseasonal Predictability of the North Atlantic Oscillation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1427, https://doi.org/10.5194/egusphere-egu21-1427, 2021.

Midlatitude prediction on subseasonal timescales is difficult due to the chaotic nature of the atmosphere and often requires the identification of favorable atmospheric conditions that may lead to enhanced skill ("forecasts of opportunity"). Here, we demonstrate that an artificial neural network can identify such opportunities for tropical-extratropical teleconnections to the North Atlantic circulation at a lead of 22 days using the network's confidence in a given prediction. Furthermore, layer-wise relevance propagation, an ANN interpretability technique, pinpoints the relevant tropical features the ANN uses to make accurate predictions. We find that layer-wise relevance propagation identifies tropical hot spots that correspond to known favorable regions for midlatitude teleconnections and reveals a potential new pattern for prediction over the North Atlantic on subseasonal timescales.

How to cite: Mayer, K. and Barnes, E.: Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9015, https://doi.org/10.5194/egusphere-egu21-9015, 2021.

One of the emerging topics in climate prediction is the issue of the so-called “signal-to-noise paradox”, characterized by too small signal-to-noise ratio in current model predictions that cannot reproduce the realistic signal. Recent studies have suggested that seasonal-to-decadal climate can be more predictable than ever expected due to the paradox. But no studies, to the best of our knowledge, have been focused on whether the signal-to-noise paradox exists in subseasonal predictions. The present study seeks to address the existence of the paradox in subseasonal predictions based on (i) coupled model simulations participating in phase 5 and phase 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively), and (ii) subseasonal hindcast outputs from the Subseasonal Experiment (SubX) and the Subseasonal-to-Seasonal Prediction (S2S) projects. Of particular interest is the possible existence of the paradox in the new generation of GFDL SPEAR model, through the diagnosis of which may help identify potential issues in the new forecast system to guide future model development and initialization. Here we investigate the paradox issue using two methods: the ratio of predictable component defined as the ratio of predictable component in the real world to the signal-to-noise ratio in models and the persistence/dispersion characteristics estimated from a Markov model framework. The preliminary results suggest a potentially widespread occurrence of the signal-to-noise paradox in subseasonal predictions, further implying some room for improvement in future ensemble-based subseasonal predictions.

How to cite: Zhang, W., Xiang, B., Kirtman, B., and Becker, E.: Does the Signal-to-Noise Paradox Exist in Subseasonal Predictions?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6198, https://doi.org/10.5194/egusphere-egu21-6198, 2021.

Although there is an increasing interest in precipitation information at the subseasonal timescales in a wide range of sectors, the use of subseasonal precipitation forecasts from general circulation models is often impaired by poor reliability and low forecast skill. One crucial step to improve forecast quality is statistical correction and post-processing, which is particularly important for a parameterized variable like rainfall. This study introduces and evaluates a statistical-dynamical post-processing scheme, based on a Bayesian framework, that aims at providing more skillful and more reliable subseasonal forecasts of weekly precipitation. On the one hand, this method relies on the statistical relationship between observed and dynamically-forecast precipitation, that is determined in a set of reforecasts and depends on the lead time. On the other hand, it also takes advantage of the climatological impacts of large-scale drivers affecting rainfall, that are generally better represented by numerical models than rainfall itself. These two aspects of the method are respectively called calibration and bridging.

This statistical-dynamical prediction scheme is illustrated with an application to the austral summer precipitation in the southwest tropical Pacific, using the Météo-France and ECMWF reforecasts in the Subseasonal-to-seasonal (S2S) database. Indices representing El Niño Southern Oscillation and the Madden-Julian Oscillation – the major sources of predictability in the area – are used for bridging. Probabilistic forecasts of heavy rainfall spells are evaluated in terms of discrimination (ROC skill score) and reliability, which are both improved by the Bayesian method at all lead times (from week 1 to week 4). Additional results show that the calibration part of the method, using forecast precipitation as a predictor, is necessary to enhance forecast skill. The bridging part also provides additional discrimination skill, that is mostly due to the ENSO-related information.

How to cite: Specq, D. and Batté, L.: A statistical-dynamical approach to improve subseasonal precipitation forecasts: application to the southwest tropical Pacific, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1292, https://doi.org/10.5194/egusphere-egu21-1292, 2021.

Besides the ocean, the land surface is a crucial component for predictability at (sub-)seasonal time scales. While the prediction of 2m temperature up to several months is possible for some maritime regions, continental regions lack predictive skill. Improved representation of the land surface in seasonal forecasting systems could help to close this gap. Snow cover fraction and snow water equivalent (SWE) are essential properties of the land surface. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) and melting snow cools the surface air and contributes to soil moisture. First, we analyse the dynamical relationships between snow, 2m temperature and sensible/latent heat fluxes in reanalysis data in the northern hemisphere. Then we investigate whether these relationships are also present in operational seasonal forecast models provided by Copernicus Climate Change Service (C3S). First results show that the quality of the 2m temperature forecast over continental regions drops sharply after the first forecasted month, whereas anomalies in snow water equivalent can be predicted up to several months. Forecasted anomalies in sensible and latent heat fluxes of continental land surfaces show predictive skill during winter and spring only locally in some places, which reduces potential interactions between snow/land surface and the atmosphere in the models. The goal of this ongoing work is to assess the importance of snow initialisation and parameterisation for seasonal forecasting.

How to cite: Risto, D., Ahrens, B., and Fröhlich, K.: Snow as a source of predictability in seasonal forecasts?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7331, https://doi.org/10.5194/egusphere-egu21-7331, 2021.

The quality of weather forecasts is continuously improving for decades. However, increases in forecast skills have slowed down in recent years. This highlights the importance of exploring new avenues towards future forecast system improvements. Until now, (near) real-time information on vegetation anomalies is not used in most forecasting models. Addressing this gap, we explore the potential of the vegetation state for explaining the spatial and temporal variation in forecast accuracy globally across climate regions, seasons, and vegetation types. For this purpose, we employ re-forecasts from the European Centre of Medium-Range Weather Forecasting (ECMWF) and infer the vegetation status through the Enhanced Vegetation Index derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite observations during the 2000-2019 period. In particular, we focus on land surface variables such as evaporation and temperature to study the relationship between forecast errors and vegetation anomalies.

The results show a stronger correlation between forecast errors and vegetation anomalies in semi-arid and sub-humid regions during the growing season, which highlights that vegetation information has the potential to help advance weather forecast performance. To put these results into perspective, we will further perform a multivariate analysis to determine the relative roles of vegetation, hydrology and climate in explaining weather forecast errors. Thereby, our results can inform the future development of weather forecast models and underlying data assimilation schemes.

How to cite: Ruiz, M., Oh, S., Orth, R., and Balsamo, G.: Exploring the potential of vegetation information for improving weather forecast performance, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3105, https://doi.org/10.5194/egusphere-egu21-3105, 2021.

Indian summer monsoon seasonal reforecasts by CFSv2, initiated from January (4-month lead time, L4) through May (0-month lead time, L0) initial conditions (ICs), are analysed to investigate causes for the highest Indian summer monsoon rainfall (ISMR) forecast skill of CFSv2 with February (3-month lead time, L3) ICs. Although theory suggests forecast skill should degrade with increase in lead-time, CFSv2 shows highest skill with L3, due to its forecasting of ISMR excess of 1983 which other ICs failed to forecast. In contrast to observation, in CFSv2, ISMR extremes are largely decided by sea surface temperature (SST) variation over central Pacific (NINO3.4) associated with El Niño-Southern Oscillation (ENSO), where ISMR excess (deficit) is associated with La Niña (El Niño) or cooling (warming) over NINO3.4. In 1983, CFSv2 with L3 ICs forecasted strong La Niña during summer, which resulted in 1983 ISMR excess. In contrast, in observation, near normal SSTs prevailed over NINO3.4 and ISMR excess was due to variation of convection over equatorial Indian Ocean, which CFSv2 fails to capture with all ICs. CFSv2 reforecasts with late-April/early-May ICs are found to have highest deterministic ISMR forecast skill, if 1983 is excluded and Indian monsoon seasonal biases are also reduced. During the transitional ENSO in Boreal summer of 1983, faster and intense cooling of NINO3.4 SSTs in L3, could be due to larger dynamical drift with longer lead time of forecasting, compared to L0. Boreal summer ENSO forecast skill is also found to be lowest for L3 which gradually decreases from June to September. Rainfall occurrence with strong cold bias over NINO3.4, is because of the existence of stronger ocean-atmosphere coupling in CFSv2, but with a shift of the SST-rainfall relationship pattern to slightly colder SSTs than the observed. Our analysis suggests the need for a systematic approach to minimize bias in SST boundary forcing in CFSv2, to achieve improved ISMR forecasts.

How to cite: Varghese, S. J., Rajendran, K., Surendran, S., and Chakraborty, A.: Dependence of Indian summer monsoon rainfall forecast skill of CFSv2 on initial conditions and the role of bias in SST boundary forcing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7046, https://doi.org/10.5194/egusphere-egu21-7046, 2021.

Traditionally, monsoon teleconnections are measured in terms of the strength of a simultaneous linear relationship. Such associative metrics do not quantify precipitation variations through physical parameters directly related to the moisture budget of the atmosphere. In this study, for the first time, we develop a linear model for the Indian summer monsoon rainfall (ISMR) based on surface pressure over regions surrounding it and sea surface temperature (SST) forcing from tropics and midlatitude. This surface pressure acts as a dynamical link between SST forcing and convective processes over the Indian region, which was missing in previous studies. We also use this novel approach to understand the ISMR prediction skill in the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). We find that the interannual variability of ISMR does not rely solely on tropical processes, but the midlatitude phenomenon also plays a crucial role in modulating it. The model, however, derived most of its variability from the ENSO mode. The understated midlatitude forcing in the model can be attributed to its low prediction skill. 

How to cite: Singhai, P., Chakraborty, A., Rajendran, K., and Surendran, S.: Global Teleconnections to the Indian Summer Monsoon in CFSv2 model: Tropics vs. Midlatitude, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4880, https://doi.org/10.5194/egusphere-egu21-4880, 2021.

It is well known that climate models commonly show biases in the Tropical Atlantic including reduced cold tongue development in the boreal summer. This work investigates whether these biases are present in the Met Office Seasonal Forecast System (GloSea5) at seasonal lead times and the impact they have on teleconnections to the North Atlantic, a key area for forecasting for Northern Europe.

GloSea5 hindcasts covering the period 1993 – 2016 are analysed for winter and summer start dates and biases are calculated with comparison to ERA Interim for sea surface temperature, near surface winds and upper tropospheric winds, and the Global Precipitation Climatology Project (GPCP) for Rainfall Rate. In contrast to fully developed climate model biases, enhanced cold tongue development is found in the summer months, and a general cold bias occurs in the SST in both winter and summer. This shows that biases in initialised forecasts do not simply asymptote to the climate model error but show more complex behaviour including a change in the sign of the bias. Easterly winds are found to be strengthened throughout and signs of a double Inter Tropical Convergence Zone (ITCZ) are observed in the winter season. The ITCZ in both seasons is shown to be a narrower band of heavier rain in GloSea5 compared to the GPCP.  We investigate how these tropical biases propagate into the North Atlantic and change the forecast biases there.

How to cite: Collier, T., Kettleborough, J., Scaife, A., Hermanson, L., and Davis, P.: Biases in the Tropical Atlantic in Seasonal Forecast System GloSea5, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9791, https://doi.org/10.5194/egusphere-egu21-9791, 2021.

A series of reforecasts have been generated with prototype versions of the coupled Unified Forecast System (UFS) to evaluate progress in the model development. The forecast skill and biases of the UFS Prototypes 3 and 5 reforecast sets—called Benchmark 3 and Benchmark 5, respectively—are analyzed and compared with the NCEP Climate Forecast System version 2 (CFSv2) reforecasts from the Subseasonal Prediction Experiment (SubX). The evaluation focuses on surface variables typically provided in the subseasonal outlooks at weekly-averaged timescales, namely 2-meter air temperature, precipitation rate, and sea surface temperature. Additional assessment of the structure of the systematic error in total diabatic heating over three broad layers of the atmosphere (850-650 hPa, 650-450 hPa and 450-50 hPa) has been performed as a function of season and forecast lead. In terms of forecast skill, all models still experience a skill drop-off of varying degree by week 3. In general, however, the UFS prototypes considerably reduce the marked diminution of variability with lead time displayed in their predecessor, CFSv2. Moreover, the prototypes have reduced systematic error compared to CFSv2, particularly for 2-meter temperature and precipitation. A systematic overestimate of diabatic cooling is noted in the upper atmosphere (diabatic heating too negative compare to ERA-5 estimates) during boreal winter. 

How to cite: Kodama, K., Straus, D., and Kinter, J.: Assessment and Comparison of Subseasonal Bias and Forecast Skill in the Unified Forecast System (UFS) Benchmarks 3 and 5, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3607, https://doi.org/10.5194/egusphere-egu21-3607, 2021.

In November 2020, the new version of the German Climate Forecast System, GCFS2.1, became operational at Deutscher Wetterdienst (DWD), providing new seasonal forecasts every month. The system is based on the Max Planck Institute for Meteorology Earth-System Model (MPI-ESM-HR) and is developed jointly by DWD, the Max Planck Institute for Meteorology and Universität Hamburg.

In GCFS2.1, ERA5 and ORAS5 reanalyses are assimilated using atmospheric, oceanic and sea ice nudging, respectively. From the assimilation, 50-member 6-month forecast ensembles are initialized at the start of each month. Prediction skill is assessed with a 30-member 6-month hindcast ensemble covering the time period 1982-2019 for February, May, August and November start months, and 1990-2019 for the remaining start months. Both the forecast and hindcast ensembles are generated by oceanic bred vectors with additional physical perturbations applied to the upper atmospheric model layers.

Here, we investigate the performance of GCFS2.1 summer and winter forecasts over Europe. While our main focus is on the prediction of large scale patterns that control the weather regimes during these two seasons, e.g. European blockings, special emphasis is paid on the impact of the January 2021 sudden stratospheric warming (SSW) event on the performance of GCFS2.1. The inclusion of the early phases of the January 2021 SSW event in the forecast initialisation significantly changes the GCFS2.1 forecast for February 2021 European surface climate. Prediction skill of GCFS2.1 for summer European blocking events will be also compared to the previous version GCFS2.0.

How to cite: Fröhlich, K., Isensee, K., Brandt, S., Brune, S., Paxian, A., and Baehr, J.: The German Climate Forecast System 2.1: seasonal forecast performance over Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9820, https://doi.org/10.5194/egusphere-egu21-9820, 2021.

The Global Modeling and Assimilation Office (GMAO) is about to release a new version of the Goddard Earth Observing System (GEOS) Subseasonal to Seasonal prediction (S2S) system, GEOS‐S2S‐3, that represents an improvement in performance and infrastructure over the  previous system, GEOS-S2S-2. The system will be described briefly, highlighting some features unique to GEOS-S2S, such as the coupled interactive aerosol model and ensemble  perturbation strategy and size. Results are presented from forecasts and from climate  equillibrium simulations. GEOS-S2S-3 will be used to produce a long term weakly coupled reanalysis called MERRA-2 Ocean.

The climate or equillibrium state of the atmosphere and ocean shows a reduction in systematic error relative to GEOS‐S2S‐2, attributed in part to an increase in ocean resolution and to the upgrade in the glacier runoff scheme.  The forecast skill shows improved prediction  of the North Atlantic Oscillation, attributed to the increase in forecast ensemble members.  

With the release of GEOS-S2S-3 and MERRA-2 Ocean, GMAO will continue its tradition of maintaining a state‐of‐the‐art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as evaluating additional sources of predictability in the Earth system through the expanded coupling of the Earth system model and assimilation components.

How to cite: Molod, A. and the GMAO Seasonal Prediction Development Group: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12759, https://doi.org/10.5194/egusphere-egu21-12759, 2021.

Severe weather associated with convective thunderstorms is becoming more intense globally and is also observed in the Arabian Peninsula (AP). AP convective extremes are often observed during winter season (October to March). Improvements in extreme weather forecast for sub-seasonal to seasonal forecast increase the preparedness of convective extremes and related hazards. We designed a series of ensemble forecast downscaling using the Weather Research and Forecasting model (WRF) at convective-permitting spatial scale. The driving global sub-seasonal to seasonal reforecast is provided by the European Centre for Medium-Range Weather Forecasts (ECMWF).

Sub-seasonal WRF simulations are performed on the AP’s top 20 extreme precipitation events reported in the last 20 years, downscaling from the 11 ECMWF hindcast ensemble members. Each of the events recorded at least 20 mm/day rainfall in the Jeddah station. Several aspects of the simulated events are evaluated: (1) Precipitation forecast capability: determine forecast window of opportunity in the regional climate model at 1-week, 2-week and 3-week lead time, identify the value added using convective-permitting type modeling; (2) Teleconnection pattern forecast capability: determine forecast skill for the dominant large scale pattern related to the convective extremes in the driving ECMWF reforecasts and ERA-Interim reanalysis data; (3) Dominant synoptic patterns associated with the AP’s top 20 extreme events: identify forecast capability for different synoptic-driven extreme events. Historic data analysis identified 3 general synoptic patterns that lead to precipitation extreme. The top 20 extreme events are parsed into the 3 synoptic groups. Sub-seasonal forecast evaluations are then performed with statistical analysis tools commonly used in operational forecast evaluation, such as Probability of Detection (POD), False Alarm Rate (FAR) and Relative Operating Characteristics (ROC); (4) Mesoscale convective system (MCS) tracking: objectively tracking the MCS clouds in satellite observation and WRF downscaled reforecasts using cloud top temperature and precipitation. Through the designed analyses, we can collectively show the ensemble forecast skills for the largest convective events in the AP and advancement in forecast capability at sub-seasonal time scale.

How to cite: Chang, H.-I., Castro, C. L., Risanto, C. B., Luong, T., and Hoteit, I.: Sub-seasonal forecast capability for Arabian Peninsula convective extremes using convective-permitting regional climate modeling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8090, https://doi.org/10.5194/egusphere-egu21-8090, 2021.

Subseasonal-to-seasonal (S2S) forecasts are bridging the gap between weather forecasts and long-range predictions. Decisions in various sectors are made in this forecast timescale, therefore there is a strong demand for this new generation of predictions. While much of the focus in recent years has been on improving forecast skill, if S2S predictions are to be used effectively, it is important that along with scientific advances, we also learn how best to develop, communicate and apply these forecasts.

In this paper, we present recent progress in the applications of S2S forecasts, and provide an overview of ongoing and emerging activities and initiatives from across the wider weather and climate applications and user communities, as follows:

• To support an increased focus on applications, an additional science sub-project focused on S2S applications has been launched on the World Meteorological Organization WWRP-WCRP S2S Prediction Project: http://s2sprediction.net/. This sub-project will provide a focal point for research focused towards S2S applications by exploring the value of applications-relevant S2S forecasts and highlighting the opportunities and challenges facing their uptake.
• Also supported by the S2S Prediction Project, the ongoing Real-Time Pilot initiative http://s2sprediction.net/file/documents_reports/16Projects.pdf is making S2S forecasts available to 15 selected projects that are addressing user needs over a two year period (November 2019 through to November 2021). By making this real-time data available, the initiative is drawing on the collective experiences of the researcher and user communities from across the projects. The Real-Time Pilot will develop best practice guidelines for producing useful and useable, application-orientated forecasts and tools that can be used to guide future S2S application development. We will present an update on the initiative, including results from an initial set of questionnaires that focussed on engagement strategies and practices, supporting a review of how projects were designs, the roles and responsibilities of different project participants and the methods used to determine project success.
• To increase the uptake and use of S2S forecasts more widely across the research and user communities, we present a new initiative: a global network of researchers, modellers and practitioners focused on S2S applications, called S2Sapp.net – a community with a shared aim of exploring and promoting cross-sectoral services and applications of this new generation of predictions.
• Finally, we will provide an update on a recently-submitted applications community review paper, covering sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. Drawing from the experience of researchers and users working with S2S forecasts, we explore the value of applications-relevant S2S predictions through a series of sectoral cases where uptake is starting to occur.

How to cite: White, C., Robbins, J., Domeisen, D., and Robertson, A.: Applications of Subseasonal-to-Seasonal Forecasts: Progress and Future Plans, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15457, https://doi.org/10.5194/egusphere-egu21-15457, 2021.