UP3.3

Temperature extremes in the high Arctic have made the headlines in recent years, with wintertime warm spells approaching 0 °C at the North Pole. In the first part of this presentation, I will outline some salient large-scale and synoptic atmospheric drivers of wintertime warm and cold spells in the high Arctic. The warm spells are systematically associated with a large-scale circulation pattern that creates a natural pathway for extreme moisture intrusions from the Atlantic sector into the Arctic. Anomalies in the distribution of synoptic cyclones then favour a deep penetration of these intrusions across the Arctic basin. The large-scale circulation pattern associated with the warm spells further favours the advection of cold air across central-northern Eurasia. On the contrary, cold Arctic extremes are associated with a persistent low-pressure system over the pole. This effectively isolates the high latitudes from mid-latitude air masses, favouring an intense radiative cooling of the polar region. In the second part of the presentation, I will discuss return times of the wintertime warm spells, using a novel approach grounded in extreme value theory. This approach explicitly takes into account the spatial structure of the moisture intrusions driving the temperature extremes, and I will try to convince you that it provides a more realistic set of estimates than conventional return-time algorithms.

How to cite: Messori, G., Woods, C., Wada, R., and Caballero, R.: Wintertime temperature extremes in the high Arctic: drivers, statistics and implications for the mid-latitudes, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-4, https://doi.org/10.5194/ems2021-4, 2021.

Thanks to the recent advances in climate modelling, seasonal predictions are becoming more skilful at anticipating the future state of near-surface climate variables over extratropics. Nevertheless, such predictions are delivered on too coarse grids with horizontal resolutions of hundreds of kilometres so that local events happening at much finer scales cannot be reproduced. This is particularly noted for variables with high spatial variability like wind or precipitation: wind speeds can vary substantially over a few kilometres, from the top of a mountain to a valley floor. The differences in magnitude might be relevant for the deriving sectoral indicators, for example, within the wind industry and at a wind farm level.

This work presents and applies a downscaling methodology to generate fine-scale seasonal forecasts ---up to station scale--- for near-surface wind speeds in Europe. The hybrid forecasts are based on a statistical downscaling with a perfect prognosis approach, fitting a multi-linear regression with the four main Euro-Atlantic Teleconnections (EATC) indices as predictors. Seasonal predictions of EATC indices, which are predictable with relatively good skill levels, are later inserted into the multi-linear model. This results in skilful seasonal predictions of surface wind speeds. Indeed, the comparison of the hybrid forecasts against the dynamical forecasts of wind speed shows that the skill of such forecasts is not only maintained but also increased over most of Europe. The hybrid forecasts are generated at 17 locations where tall tower wind speed data are available and at a pan-European scale using the 100-metre wind speeds from the ERA5 reanalysis. Improving the accuracy of seasonal predictions is an essential step to inform weather-and-climate-vulnerable socio-economic sectors of seasonal anomalies a few months ahead.

How to cite: Ramon, J., Lledó, L., Bretonnière, P.-A., Samsó, M., and Doblas-Reyes, F. J.: Local-scale wind speed features captured by seasonal forecasts, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-10, https://doi.org/10.5194/ems2021-10, 2021.

Wildfires cause substantial losses to socio-economic and natural assets, especially in Mediterranean-climate regions. Despite human activity is the main cause of wildfires in Mediterranean European countries, lightning-ignited wildfires should be also considered a major disruptive agent as they can trigger large fires. Besides, recent studies on the potential climate change effects on wildfires pointed out that lightning-ignited wildfires may gain relevance in Mediterranean areas in the years to come.

In this regard, the present study analyses the meteorological conditions favouring lightning-ignited wildfires in Catalonia (NE Iberian Peninsula). Gaining insight into circulation types favouring thunderstorms that ignite wildfires can be useful in the forest protection tactical decision-making process, i.e. locating ignitions and potential holdover fires, preparing for days with multiple ignitions or routing detection flight paths.

It is worth noticing that one of the reasons why lightning-caused wildfires are difficult to manage is that they can survive for several days before flaring up. That is, even if forest fuels remain damp after the thunderstorm’ rainfall, lightning ignitions may survive smouldering underneath, emerging days later as surface vegetation becomes dry enough to support sustained combustion.

For this reason, on a first step, a reliable lightning-wildfire association is needed to properly identify the date and time of the firestarter for each wildfire. Afterwards, the circulation types on the days of ignition are analysed.

The study relies on a dataset of more than 750 lightning-ignited wildfires, gathered by the Forest Protection Agency of the autonomous government of Catalonia between 2005 and 2018. Lightning data comes from the Lightning Location System operated by the Meteorological Service of Catalonia.

How to cite: Pineda, N., Soler, A., Peña, J. C., Aran, M., Soler, X., and Pérez-Zanón, N.: Synoptic patterns favouring lightning-ignited wildfires in Catalonia, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-88, https://doi.org/10.5194/ems2021-88, 2021.

Rotated principal component analysis (RPCA) is a commonly used tool to detect modes of low-frequency atmospheric circulation variability, also referred to as teleconnections. Teleconnections manifest themselves as distant areas of high negative or positive correlations in sea level pressure, geopotential height, or another variable describing atmospheric circulation. For outputs of RPCA to be valid representations of teleconnections, their spatial patterns (loadings) must correspond to underlying correlation / covariance structures, that is, be in agreement with autocorrelation maps.

When comparing teleconnections identified in different datasets (e.g., between reanalyses, between outputs of climate models, between different periods, between different seasons), the spatial similarity of loadings is evaluated and quantified; if it is low, the datasets are said to disagree in the representation of a particular teleconnection. However, things appear to be less straightforward: It may happen that although the loadings pertaining to the same teleconnection differ, the maps of correlations with the action centres (i.e., points with highest positive or negative loadings) are identical. This may suggest that while the autocorrelation structures are the same in the two datasets, they appear with different weight (intensity). This issue appears to be unrelated to uncertainty due to the number of principal components to rotate; it typically occurs for various reasonable numbers of components.

In our contribution, we (i) introduce the above described issue on several examples (RPCA of different reanalyses, of sliding time periods, and of sliding 93-day seasons), (ii) discuss what is a correct interpretation of such cases (should we consider the teleconnections to be equal or different when the autocorrelation maps agree but the loadings disagree?), and (iii) suggest possible ways out of it (to use oblique instead of orthogonal rotation, to return back to autocorrelation maps).

How to cite: Huth, R., Hynčica, M., Piskala, V., and Pokorná, L.: Troubles with teleconnections, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-252, https://doi.org/10.5194/ems2021-252, 2021.

Wind direction is an important meteorological parameter, however, its analysis is made difficult by it being a circular variable that cannot easily be averaged. The goal of this study was to identify the main features of wind direction climate over the Baltic States in a methodical way. We used Principal Component Analysis (PCA) for this purpose.

Two data sets were used: UERRA re-analysis with 11 km horizontal resolution and surface wind direction observations from Latvian stations. We used PCA on both of these datasets and analyzed the results together. Such an approach enabled comparison of the wind direction climate of the reanalysis with the observations. However, preliminary results suggested applying PCA also on the subset of UERRA data that corresponds to observation stations. This eliminates effects caused by differences in spatial coverage between  gridded and station datasets.

To verify the quality of the reanalysis independently of the PCA method, Earth Mover’s Distance (EMD) was used to directly compare wind direction distributions at the station grid points with observations.

Results show good correspondence overall between the reanalysis data and the observations. The PCA method identifies SW as the prevailing wind direction, in good agreement with the expectations. The PCA results enable identification of the main wind direction features of the region, such as increased frequency of northern winds during the summer and increased frequency of southern winds during the winter that can be explained by synoptic scale processes. Additionally, the PCA method identifies coast parallel flows created by mesoscale interaction between the Baltic Sea and the dry land, and wind deflection around terrain (hills up to 300 m above sea level).

This approach could be generalized to other regions and help create a more systematic understanding about wind direction climate, as well as assist in quantifying the performance of reanalysis and identify meteorological processes that need to be investigated further.

Corresponding author is grateful to the project “Mathematical modelling of weather processes - development of methodology and applications for Latvia (1.1.1.2/VIAA/2/18/261)” for financial support.

How to cite: Sile, T., Pogumirskis, M., Seņņikovs, J., and Bethers, U.: Quantifying the Wind Direction Climate over the Baltic States using Principal Component Analysis, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-334, https://doi.org/10.5194/ems2021-334, 2021.

Extra-tropical cyclones constitute a large part of the circulation in the mid-latitudes and can lead to high impact weather. Therefore, it is beneficial to society to determine how these storms and their associated weather may change in the future. We focus on precipitation associated with extra-tropical cyclones (ETCs) and first aim to determine how the relationship between dynamical measures (e.g. maximum relative vorticity) of cyclone intensity and ETC related precipitation will response to climate change. Secondly, because not all ETCs are the same, we investigate whether the relationship between ETC precipitation and ETC intensity depends on the type of cyclone. Finally, we examine whether certain types of ETCs, in terms of their precipitation patterns, are likely to become more or less common in the future. We address these questions using aqua-planet simulations performed using an atmosphere-only model (OpenIFS) with fixed sea surface temperatures (SSTs). The simulations are run at T255 resolution (~ 80 km) and are 10 years long which generates a very large sample size of ETCs (> 14,000). The three simulations differ only in terms of the specific SST distribution: a control simulation is performed with the well-known “QObs” SST distributions, the second simulation has a uniform warming of 4K applied everywhere, and the third simulation is a polar amplification experiment with a 5K warming poleward of 45 degrees. In each experiment, all ETCs are objectively identified and tracked. Different types of cyclones are identified by applying k-means clustering to the precipitation pattern within a 12-degree radius of the cyclone centre. In all three experiments, more dynamically intense ETCs have more precipitation associated with them but there is considerable spread. Uniform warming strengthens this relationship and hence a ETC of a certain dynamical intensity will have more precipitation associated with it in a warmer climate. Clustering identifies 4 distinct types of ETCs in terms of their precipitation patterns: ETCs with most precipitation associated with the warm front; ETCs dominated by cold front precipitation; ETCs dominated by cyclone-centred precipitation; ETCs with very little precipitation. All 4 cyclone types appear in each experiment. Uniform warming causes a notable increase in the number of ETCs with precipitation concentrated on the warm front and a decrease in the number of ETCs with weak precipitation. In contrast, polar warming causes a large increase in the number of ETCs with weak precipitation and ETCs dominated by cold front precipitation decrease in number. These results, and others, will be presented along with dynamical interpretations.

How to cite: Sinclair, V. and Catto, J.: Precipitation associated with extra-tropical cyclones: response to uniform global warming and to polar amplification, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-340, https://doi.org/10.5194/ems2021-340, 2021.