Dr Antje Weisheimer (she)

Abstract:

Based on skill estimates from hindcasts made over the last couple of decades, recent studies have suggested that considerable success has been achieved in forecasting winter climate anomalies over the Euro-Atlantic area using current-generation dynamical forecast models. However, previous-generation models had shown that forecasts of winter climate anomalies in the 1960s and 1970s were less successful than forecasts of the 1980s and 1990s. Given that the more recent decades have been dominated by the North Atlantic Oscillation (NAO) in its positive phase, it is important to know whether the performance of current models would be similarly skilful when tested over periods of a predominantly negative NAO. To this end, a new ensemble of atmospheric seasonal hindcasts covering the period 1900–2009 has been created, providing a unique tool to explore many aspects of atmospheric seasonal climate prediction. In this study we focus on two of these: multi-decadal variability in predicting the winter NAO, and the potential value of the long seasonal hindcast datasets for the emerging science of probabilistic event attribution. The existence of relatively low skill levels during the period 1950s–1970s has been confirmed in the new dataset. The skillof the NAO forecasts is larger, however, in earlier and later periods. Whilst these inter-decadal differences in skill are, by themselves, only marginally statistically significant, the variations in skill strongly co-vary with statistics of the general circulation itself suggesting that such differences are indeed physically based. The mid-century period of low forecast skill coincides with a negative NAO phase but the relationship between the NAO phase/amplitude and forecast skill is more complex than linear. Finally, we show how seasonal forecast reliability can be of importance for increasing confidence in statements of causes of extreme weather and climate events, including effects of anthropogenic climate change.

Abstract:

Uncertainties in parametrized processes in general circulation models can be represented as stochastic perturbations to the model formulation. The European Centre for Medium-Range Weather Forecasts (ECMWF) has pioneered approaches to represent these model errors in forecasting systems. In particular, the stochastically perturbed physical tendency (SPPT) scheme for the atmosphere is used in their operational ensemble system for medium- and long-range predictions. Recent studies have shown that these stochastic approaches can both increase the reliability of the probabilistic forecasts and reduce long-term mean biases of the model climate. Towards developing a seamless prediction system in the future, these benefits of stochastic parametrization for both short-term and long-term forecasts make it an essential component of the next generation Earth System models. We present results of the impact of different configurations of the SPPT scheme in ECMWF's seasonal forecasting System 4 on the mean and variability in tropical precipitation. Small-scale perturbations in the SPPT scheme play a significant role in reducing the mean biases in tropical precipitation. The stochastic physics also nonlinearly rectify the convection and precipitation during different phases of El Niño Southern Oscillation events and improve the reliability of the ensemble forecasts for the Madden–Julian Oscillation (MJO). They impact the MJO dynamics by modulating the convective and suppressed phases of the MJO. Finally, we discuss some of the caveats to this analysis and some future prospects.

Abstract:

© 2015, The Author(s). The winter 2009/2010 was remarkably cold and snowy over North America and across Eurasia, from Europe to the Far East, coinciding with a pronounced negative phase of the North Atlantic Oscillation (NAO). While previous studies have investigated the origin and persistence of this anomalously negative NAO phase, we have re-assessed the role that the Eurasian snowpack could have played in contributing to its maintenance. Many observational and model studies have indicated that the autumn Eurasian snow cover influences circulation patterns over high northern latitudes. To investigate that role, we have performed a suite of forecasts with the coupled ocean–atmosphere ensemble prediction system from the European Centre for Medium-Range Weather Forecasts. Pairs of 2-month ensemble forecasts with either realistic or else randomized snow initial conditions are used to demonstrate how an anomalously thick snowpack leads to an initial cooling over the continental land masses of Eurasia and, within 2 weeks, to the anomalies that are characteristic of a negative NAO. It is also associated with enhanced vertical wave propagation into the stratosphere and deceleration of the polar night jet. The latter then exerts a downward influence into the troposphere maximizing in the North Atlantic region, which establishes itself within 2 weeks. We compare the forecasted NAO index in our simulations with those from several operational forecasts of the winter 2009/2010 made at the ECWMF, and highlight the importance of relatively high horizontal resolution.

Abstract:

Soil moisture memory is a key component of seasonal predictability. However, uncertainty in current memory estimates is not clear and it is not obvious to what extent these are dependent on model uncertainties. To address this question, we perform a global sensitivity analysis of memory to key hydraulic parameters, using an uncoupled version of the H-TESSEL land surface model. Results show significant dependency of estimates of memory and its uncertainty on these parameters, suggesting that operational seasonal forecasting models using deterministic hydraulic parameter values are likely to display a narrower range of memory than exists in reality. Explicitly incorporating hydraulic parameter uncertainty into models may then give improvements in forecast skill and reliability, as has been shown elsewhere in the literature. Our results also show significant differences with previous estimates of memory uncertainty, warning against placing too much confidence in a single quantification of uncertainty.