Large ensembles of uncoupled and coupled model experiments on the influence of Arctic sea ice decline on mid-latitude weather and climate

We have conducted a series of idealized atmosphere-only and coupled model experiments on time scales from weather to climate and with different methods to address the question how the large scale circulation of the Northern mid-latitudes is affected by the shrinking Arctic sea ice. A recurring response feature to declined Arctic sea ice is the slowdown and southward shift of the jet stream with less cyclone activity north of it leading to around 0.5 K colder conditions over some limited regions of North America and North Siberia in winter. This happens despite the tendency of less intense cold advection due to the warmer Arctic in cases of anomalous northerly flow. It should be noted that for robust responses large ensemble simulations are needed due to low signal-to-noise ratio. In this respect it has been proven helpful to perform simulations in a Numerical Weather Prediction setting as the short simulation time enables us to easily run ensembles of several hundreds of realizations. Furthermore, in such a setting the initial response to a suddenly changed Arctic sea ice cover can be studied giving us hints how anomalies in the atmosphere develop. Coupled simulations hint at no discernable influence of shrinking Arctic sea ice on the ocean on time scales of a year while on decadal to centennial time scales the ocean starts to react with possible feedbacks to the atmosphere

Rising Mediterranean Sea Surface Temperatures Amplify Extreme Summer Precipitation in Central Europe

The beginning of the 21st century was marked by a number of severe summer floods in Central Europe associated with extreme precipitation (e.g., Elbe 2002, Oder 2010 and Danube 2013). Extratropical storms, known as Vb-cyclones, cause summer extreme precipitation events over Central Europe and can thus lead to such floodings. Vb-cyclones develop over the Mediterranean Sea, which itself strongly warmed during recent decades. Here we investigate the influence of increased Mediterranean Sea surface temperature (SST) on extreme precipitation events in Central Europe. To this end, we carry out atmosphere model simulations forced by average Mediterranean SSTs during 1970–1999 and 2000–2012. Extreme precipitation events occurring on average every 20 summers in the warmer-SST-simulation (2000–2012) amplify along the Vb-cyclone track compared to those in the colder-SST-simulation (1970–1999), on average by 17% in Central Europe. The largest increase is located southeast of maximum precipitation for both simulated heavy events and historical Vb-events. The responsible physical mechanism is increased evaporation from and enhanced atmospheric moisture content over the Mediterranean Sea. The excess in precipitable water is transported from the Mediterranean Sea to Central Europe causing stronger precipitation extremes over that region. Our findings suggest that Mediterranean Sea surface warming amplifies Central European precipitation extremes

Southern Ocean mesocyclones and polar lows from manually tracked satellite mosaics

A new reference dataset of mesocyclone activity over the Southern Ocean has been developed from the manual analysis of high resolution infrared satellite mosaics for winter 2004. Of the total 1735 mesocyclones which were identified and analyzed about three quarters were classified as being ‘polar lows’ (i.e. intense systems; see Rasmussen and Turner 2003). The dataset includes mesocyclone track, size, associated cloud vortex type and background synoptic conditions. Maxima in track density were observed over the Bellingshausen Sea and around East Antarctica and are highly correlated with cyclogenesis regions. A comparison against QuikSCAT and reanalyses wind characteristics shows that the reanalyses, while capturing mesocyclone events, tend to considerably underestimate their wind speed (by up to 10 ms-1). This mesocyclone dataset is available as a reference for further analysis of mesocyclones and for the evaluation and development of cyclone-tracking algorithms

Are greenhouse gas signals of Northern Hemisphere winter extra-tropical cyclone activity dependent on the identification and tracking algorithm?

For Northern Hemisphere extra-tropical cyclone activity, the dependency of a potential anthropogenic climate change signal on the identification method applied is analysed. This study investigates the impact of the used algorithm on the changing signal, not the robustness of the climate change signal itself. Using one single transient AOGCM simulation as standard input for eleven state-of-the-art identification methods, the patterns of model simulated present day climatologies are found to be close to those computed from re-analysis, independent of the method applied. Although differences in the total number of cyclones identified exist, the climate change signals (IPCC SRES A1B) in the model run considered are largely similar between methods for all cyclones. Taking into account all tracks, decreasing numbers are found in the Mediterranean, the Arctic in the Barents and Greenland Seas, the mid-latitude Pacific and North America. Changing patterns are even more similar, if only the most severe systems are considered: the methods reveal a coherent statistically significant increase in frequency over the eastern North Atlantic and North Pacific. We found that the differences between the methods considered are largely due to the different role of weaker systems in the specific methods

IMILAST: a community effort to intercompare extratropical cyclone detection and tracking algorithms

The variability of results from different automated methods of detection and tracking of extratropical cyclones is assessed in order to identify uncertainties related to the choice of method. Fifteen international teams applied their own algorithms to the same dataset—the period 1989–2009 of interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERAInterim) data. This experiment is part of the community project Intercomparison of Mid Latitude Storm Diagnostics (IMILAST; see www.proclim.ch/imilast/index.html). The spread of results for cyclone frequency, intensity, life cycle, and track location is presented to illustrate the impact of using different methods. Globally, methods agree well for geographical distribution in large oceanic regions, interannual variability of cyclone numbers, geographical patterns of strong trends, and distribution shape for many life cycle characteristics. In contrast, the largest disparities exist for the total numbers of cyclones, the detection of weak cyclones, and distribution in some densely populated regions. Consistency between methods is better for strong cyclones than for shallow ones. Two case studies of relatively large, intense cyclones reveal that the identification of the most intense part of the life cycle of these events is robust between methods, but considerable differences exist during the development and the dissolution phases

Seasonal atmospheric responses to reduced Arctic sea ice in an ensemble of coupled model simulations

Arctic sea ice decline is expected to continue throughout the 21st century as a result of increased greenhouse gas concentrations. Here we investigate the impact of a strong Arctic sea ice decline on the atmospheric circulation and low pressure systems in the Northern Hemisphere through numerical experimentation with a coupled climate model. More specifically, a large ensemble of 1-year long integrations, initialized on 1 June with Arctic sea ice thickness artificially reduced by 80%, is compared to corresponding, unperturbed control experiments. The sensitivity experiment shows an ice-free Arctic from July to October; during autumn the largest near-surface temperature increase of about 15 K is found in the central Arctic, which goes along with a reduced meridional temperature gradient, a decreased jet stream, and a southward shifted Northern Hemisphere storm track; and the near-surface temperature response in winter and spring reduces substantially due to relatively fast sea ice growth during the freezing season. Changes in the maximum Eady growth rate are generally below 5% and hardly significant, with reduced vertical wind shear and reduced vertical stability counteracting each other. The reduced vertical wind shear manifests itself in a decrease of synoptic activity by up to 10% and shallower cyclones while the reduced vertical stability along with stronger diabatic heating due to more available moisture may be responsible for the stronger deepening rates and thus faster cyclone development once a cyclone started to form. Furthermore, precipitation minus evaporation decreases over the Arctic because the increase in evaporation outweighs that for precipitation with implications for the ocean stratification and hence ocean circulation

Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics

Polar mesocyclones (MCs) are small marine atmospheric vortices. The class of intense MCs, called polar lows, are accompanied by extremely strong surface winds and heat fluxes and thus largely influencing deep ocean water formation in the polar regions. Accurate detection of polar mesocyclones in high-resolution satellite data, while challenging, is a time-consuming task, when performed manually. Existing algorithms for the automatic detection of polar mesocyclones are based on the conventional analysis of patterns of cloudiness and they involve different empirically defined thresholds of geophysical variables. As a result, various detection methods typically reveal very different results when applied to a single dataset. We develop a conceptually novel approach for the detection of MCs based on the use of deep convolutional neural networks (DCNNs). As a first step, we demonstrate that DCNN model is capable of performing binary classification of 500 × 500 km patches of satellite images regarding MC patterns presence in it. The training dataset is based on the reference database of MCs manually tracked in the Southern Hemisphere from satellite mosaics. We use a subset of this database with MC diameters falling in the range of 200⁻400 km. This dataset is further used for testing several different DCNN setups, specifically, DCNN built “from scratch„, DCNN based on VGG16 pre-trained weights also engaging the Transfer Learning technique, and DCNN based on VGG16 with Fine Tuning technique. Each of these networks is further applied to both infrared (IR) and a combination of infrared and water vapor (IR + WV) satellite imagery. The best skills (97% in terms of the binary classification accuracy score) is achieved with the model that averages the estimates of the ensemble of different DCNNs. The algorithm can be further extended to the automatic identification and tracking numerical scheme and applied to other atmospheric phenomena that are characterized by a distinct signature in satellite imagery

Wind waves in the North Atlantic and Arctic from ship navigational radar (SeaVision system) and wave buoy Spotter during three research cruises in 2020 and 2021

The purpose of this dataset is to provide in situ wind waves observations in the open ocean. The dataset consists of significant wave heights, wave periods, wave directions, wave energy frequency spectra, meteorological data and other related parameters. Parameters of the wind waves measured with Spotter wave buoy and SeaVision system on the basis of navigational ship X-band radar. The dataset was collected in almost 50 locations during three research cruises on the research vessels Akademik Sergey Vavilov and Akademik Ioffe in the North Atlantic (August 2020 and June 2021) and Arctic (August 2021). The dataset is the supplement to the manuscript "Wind waves in the North Atlantic from ship navigational radar: SeaVision development and its validation with Spotter wave buoy and WaveWatch III" (Tilinina et al., 2022). Technical details, maps with expedition tracks and detailed methodology of the wind wave parameters calculations from both SeaVision and Spotter raw data are described in the manuscript

RAS-NAAD: 40-yr High-Resolution North Atlantic Atmospheric Hindcast for Multipurpose Applications (New Dataset for the Regional Mesoscale Studies in the Atmosphere and the Ocean)

International audienceWe present in this paper the results of the Russian Academy of Sciences North Atlantic Atmospheric Downscaling (RAS-NAAD) project, which provides a 40-yr 3D hindcast of the North Atlantic (108-808N) atmosphere at 14-km spatial resolution with 50 levels in the vertical direction (up to 50 hPa), performed with a regional setting of the WRF-ARW 3.8.1 model for the period 1979-2018 and forced by ERA-Interim as a lateral boundary condition. The dataset provides a variety of surface and free-atmosphere parameters at sigma model levels and meets many demands of meteorologists, climate scientists, and oceanographers working in both research and operational domains. Three-dimensional model output at 3-hourly time resolution is freely available to the users. Our evaluation demonstrates a realistic representation of most characteristics in both datasets and also identifies biases mostly in the ice-covered regions. High-resolution and nonhydrostatic model settings in NAAD resolve mesoscale dynamics first of all in the subpolar latitudes. NAAD also provides a new view of the North Atlantic extratropical cyclone activity with a much larger number of cyclones as compared with most reanalyses. It also effectively captures highly localized mechanisms of atmospheric moisture transports. Applications of NAAD to ocean circulation and wave modeling are demonstrated

Generative Modeling of Atmospheric Convection

While cloud-resolving models can explicitly simulate the details of small-scale storm formation and morphology, these details are often ignored by climate models for lack of computational resources. Here, we explore the potential of generative modeling to cheaply recreate small-scale storms by designing and implementing a Variational Autoencoder (VAE) that performs structural replication, dimensionality reduction, and clustering of high-resolution vertical velocity fields. Trained on ∼6 · 106 samples spanning the globe, the VAE successfully reconstructs the spatial structure of convection, performs unsupervised clustering of convective organization regimes, and identifies anomalous storm activity, confirming the potential of generative modeling to power stochastic parameterizations of convection in climate models