The dynamics of the atmosphere (and of the climate system) is known to display the property of sensitivity to initial conditions. This property has considerable impact on our abilities to make predictions at short and medium-range weather time scale, and at seasonal-to-decadal ranges. This implies that climate predictions are in essence probabilistic problems that should be tackled with appropriate tools. Since the nineties, considerable efforts have been addressed to develop such an information, often through ensemble forecasts based on multiple model integrations starting from different initial or boundary conditions. Such an approach is now well settled for weather forecasts, but still in its infancy for seasonal to decadal forecasts.

The impact of both initial condition and boundary condition errors on ensemble forecasts is extensively explored in a reduced-order coupled extratropical ocean-atmosphere model -- that was developed over the years -- forced by a Tropical model (Vannitsem et al, 2015, 2021), with a particular emphasis on the impact of Tropical teleconnections on extratropical predictability. In this perfect model framework, the analysis reveals that the potential of teleconnections in improving the quality of climate predictions could only be realized provided that the Tropical dynamics is accurately predicted. The improvements that can be expected by ensemble and temporal averages are also explored.

References

Vannitsem, S., J. Demaeyer, L. De Cruz, M Ghil, Low-frequency variability and heat transport in a low-order nonlinear coupled ocean-atmosphere model. Physica D, 309, 71-85, 2015. https://doi.org/10.1016/j.physd.2015.07.006

Vannitsem, S., Demaeyer, J., & Ghil, M. Extratropical low-frequency variability with ENSO forcing: A reduced-order coupled model study. Journal of Advances in Modeling Earth Systems, 13, e2021MS002530, 2021. https://doi.org/10.1029/2021MS002530.

How to cite: Vannitsem, S.: Impact of ENSO teleconnections on the probabilistic prediction of the atmosphere at seasonal-to-decadal time scales: A reduced-order model perspective, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-232, https://doi.org/10.5194/ems2023-232, 2023.

Reliable forecasts of quasi-stationary, recurrent, and persistent large-scale circulation patterns – so-called weather regimes – are crucial for various sectors of society, including energy, health, and agriculture. Despite steady progress, probabilistic weather regime predictions still exhibit significant biases and are not reliable beyond 15 days of lead time. Thus, this study aims to advance their predictions through ensemble post-processing. Our approach is based on a year-round regime definition that distinguishes between four types of blocked regimes dominated by high-pressure situations in the North Atlantic-European region and three types of cyclonic regimes dominated by low-pressure situations. The manifestation of each regime can be expressed by a seven-dimensional weather regime index representing the projection of the 500-hPa geopotential height field onto the mean patterns of the seven weather regimes.
This index is calculated for ECMWF’s sub-seasonal reforecast ensemble data valid in the period 1999 to 2020 and verified against ERA5 reanalyses. To improve the accuracy and reliability of the multivariate probabilistic weather regime forecasts, we adjust the raw model outputs respective to their uncertainties and biases using a combination of Ensemble Model Output Statistics (EMOS) and Ensemble Copula Coupling (ECC). With EMOS, the year-round mean skill horizon (referring to the 0.1 level of the CRPSS compared to the climatological forecast) increases by 1.5 days compared to the current state-of-the-art weather regime forecast. We further replace the univariate EMOS method with a neural network-based distributional regression approach that provides greater flexibility in predictor intake.
Overall, our study reveals that statistical post-processing techniques are one way to improve weather regime forecasts, which can help plan and manage, reduce risks, and maximise societal benefits.

How to cite: Mockert, F., Grams, C. M., Quinting, J., and Lerch, S.: Multivariate post-processing of sub-seasonal weather regime forecasts, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-131, https://doi.org/10.5194/ems2023-131, 2023.

Heatwaves in Switzerland have various impacts on human health and ecosystems. Moreover, heat extremes might act as final triggers for high-Alpine hazards such as glacier break-offs and rockfalls, because they can accelerate the slowly growing disturbance of the Alpine permafrost layer due to climate warming. Heatwaves often occur concurrently with drought, which can impact agriculture, reduce lake and river shipping due to low water levels, and reduce nuclear power generation due to shortage of cooling water. As heatwaves have become and are expected to become even more common with climate change, it is crucial to predict their occurrence ahead of time and to issue warnings for stakeholders and the general public. The goal of this interdisciplinary project is, therefore, to assess the potential of heatwave prediction and warnings for Switzerland on timescales up to several weeks. The project consists of two parallel branches: on the one hand, we investigate if the forecast skill horizon for Switzerland can be extended by predicting heatwaves via statistical downscaling from larger-scale weather patterns compared to the prediction based on direct model output on a grid point level. On the other hand, we evaluate the potential benefits of early warning products for sectors that are directly linked to human lives and livelihoods. One focus is on predicting heat-related mortality by coupling a statistical temperature-mortality model to extended-range temperature forecasts. Another focus is on better understanding and predicting the penetration of heatwaves into Alpine glaciers, rocks, and sediments, which might ultimately support early-warning systems for heat-related high-Alpine hazards. The aim of our presentation is to introduce the multifaceted project in more detail and to provide some first results from the different branches.

How to cite: Büeler, D., Pyrina, M., Chavez, V., Imamovic, A., Liniger, M. A., Moret, L., Spirig, C., Vicedo-Cabrera, A. M., Lehning, M., and Domeisen, D. I. V.: Extended-range warnings for heatwaves in Switzerland, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-470, https://doi.org/10.5194/ems2023-470, 2023.

Increasing summer drought periods in Central Europe represent a serious threat for rain-fed agriculture. Recent drought events in 2018, 2019, 2020 and 2022 cause yield loss in Germany for wheat, corn, sugar beet and grassland production. In the future, climate projections show further increase of drought periods due to climate change. Hence, the requirement of long-range drought forecasts, which could provide useful predictions for agricultural applications over several month rises. Seasonal forecasts could offer guidance for medium-term management adjustments like irrigation planning or reduced fertilizer usage in case of expected severe drought periods. Unfortunately, long range precipitation forecasts often exhibit lower prediction skill. Here we assess another relevant parameter for drought prediction, the soil moisture, for its long range predictability.  Due to the small variability and persistence of soil moisture values, it is proposed, that this storage variable is well suited for climate services like agricultural drought predicting systems on seasonal time scales. We present a coupled modelling attempt, that combines seasonal forecasts from the German Climate Forecast System (GSFS) with the soil-vegetation-atmosphere-transfer (SVAT) impact model AMBAV to simulate the soil moisture for topsoil layers on a down scaled 5x5 km grid in Germany. A quality assessment of forecast ensemble means has been done with the corresponding hindcasts for the preceding 30 years. We used the mean squared error skill score (MSESS) of monthly averages to compare soil moisture forecasts in the upper 60 cm against long term reference climatology and other parameters, like precipitation. The results of this study reveal an adequate forecast skill over lead times up to several months. This shows the potential of seasonal soil moisture forecasts for agricultural applications, like fertilization advisement and drought prediction. Overall, the impact modelling system might contribute to the adaptation of agriculture to climate change in Germany.

How to cite: Leppelt, T., Wehring, S., Paxian, A., and Fröhlich, K.: Are seasonal soil moisture forecasts a reliable source for agricultural drought predictions in Germany?, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-152, https://doi.org/10.5194/ems2023-152, 2023.