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Ongoing climate change can shift organism phenology in ways that vary depending on species, habitats and climate factors studied. To probe for large-scale patterns in associated phenological change, we use 70,709 observations from six decades of systematic monitoring across the former Union of Soviet Socialist Republics. Among 110 phenological events related to plants, birds, insects, amphibians and fungi, we find a mosaic of change, defying simple predictions of earlier springs, later autumns and stronger changes at higher latitudes and elevations. Site mean temperature emerged as a strong predictor of local phenology, but the magnitude and direction of change varied with trophic level and the relative timing of an event. Beyond temperature-associated variation, we uncover high variation among both sites and years, with some sites being characterized by disproportionately long seasons and others by short ones. Our findings emphasize concerns regarding ecosystem integrity and highlight the difficulty of predicting climate change outcomes., The work was financially supported by the Academy of Finland, grants 250243 (O.O.), 284601 (O.O.), 309581 (O.O.); the European Research Council, ERC Starting Grant 205905 (O.O.) and Synergy Grant 856506 – LIFEPLAN (to O.O. and T.R.); Nordic Environment Finance Corporation Grant (O.O.); Jane and Aatos Erkko Foundation Grant (O.O., T.R., M.H., L.A.); University of Helsinki HiLIFE Fellow Grant 2017–2020 (O.O.); and the Research Council of Norway through its Centres of Excellence Funding Scheme (223257) to O.O. via Centre for Biodiversity Dynamics; the Kone Foundation 44-6977 (M.D.) and 55-14839 (G.T.); a Spanish Ramon y Cajal grant RYC-2014-16263 (M.D.); the Federal Budget for the Forest Research Institute of Karelian Research Centre Russian Academy of Sciences 220-2017-0003, 0220-2017-0005 (L.V., S.S. and J.K.); the Russian Foundation for Basic Research Grant 16-08-00510 (L.K.), and the Ministry of Education and Science of the Russian Federation 0017-2019-0009 (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences) (N.I., M. Shashkov).

European Research Council (ERC) Starting Grant 205905 (to O.O.) and Synergy Grant 856506—LIFEPLAN (to O.O. and T.R.); Spanish Ramón y Cajal Grant RYC-2014-16263 (to M.M.D.) (...), Delgado, M.D.M., Roslin, T., Tikhonov, G., Meyke, E., Lo, C., Gurarie, E., Abadonova, M., Abduraimov, O., Adrianova, O., Akimova, T., Akkiev, M., Ananin, A., Andreeva, E., Andriychuk, N., Antipin, M., Arzamascev, K., Babina, S., Babushkin, M., Bakin, O., Barabancova, A., Basilskaja, I., Belova, N., Belyaeva, N., Bespalova, T., Bisikalova, E., Bobretsovz, A., Bobrov, V., Bobrovskyi, V., Bochkareva, E., Bogdanov, G., Bolshakov, V., Bondarchuk, S., Bukharova, E., Butunina, A., Buyvolov, Y., Buyvolova, A., Bykov, Y., Chakhireva, E., Chashchina, O., Cherenkova, N., Chistjakov, S., Chuhontseva, S., Davydov, E.A., Demchenko, V., Diadicheva, E., Dobrolyubov, A., Dostoyevskaya, L., Drovnina, S., Drozdova, Z., Dubanaev, A., Dubrovsky, Y., Elsukov, S., Epova, L., Ermakova, O.S., Ermakova, O., Esengeldenova, A., Evstigneev, O., Fedchenko, I., Fedotova, V., Filatova, T., Gashev, S., Gavrilov, A., Gaydys, I., Golovcov, D., Goncharova, N., Gorbunova, E., Gordeeva, T., Grishchenko, V., Gromyko, L., Hohryakov, V., Hritankov, A., Ignatenko, E., Igosheva, S., Ivanova, U., Ivanova, N., Kalinkin, Y., Kaygorodova, E., Kazansky, F., Kiseleva, D., Knorrem, A., Kolpashikov, L., Korobov, E., Korolyova, H., Korotkikh, N., Kosenkov, G., Kossenko, S., Kotlugalyamova, E., Kozlovsky, E., Kozsheechkin, V., Kozurak, A., Kozyr, I., Krasnopevtseva, A., Krugliko, S., Kuberskaya, O., Kudryavtsev, A., Kulebyakina, E., Kulsha, Y., Kupriyanova, M., Kurbanbagamaev, M., Kutenkov, A., Kutenkova, N., Kuyantseva, N., Kuznetsov, A., Larin, E., Lebedev, P., Litvinov, K., Luzhkova, N., Mahmudov, A., Makovkina, L., Mamontov, V., Mayorova, S., Megalinskaja, I., Meydus, A., Minin, A., Mitrofanov, O., Motruk, M., Myslenkov, A., Nasonova, N., Nemtseva, N., Nesterova, I., Nezdoliy, T., Niroda, T., Novikova, T., Panicheva, D., Pavlov, A., Pavlova, K., Van, P., Podolski, S., Polikarpova, N., Tatiana Polyanskaya81, Pospelov, I., Pospelova, E., Prokhorov, I., Prokosheva, I., Puchnina, L., Putrashyk, I., Raiskaya, J., Rozhkov, Y., Rozhkova, O., Rudenko, M., Rybnikova, I., Rykova, S., Sahnevich, M., Samoylov, A., Sank, V., Sapelnikova, I., Sazonov, S., Selyunina, Z., Shalaeva, K., Shashkov, M., Shcherbakov, A., Shevchyk, V., Shubin, S., Shujskaja, E., Sibgatullin, R., Sikkilah, N., Sitnikova, E., Sivkov, A., Skok, N., Skorokhodova, S., Smirnova, E., Sokolova, G., Sopin, V., Spasovski, Y., Stepanov, S., Stratiy, V., Strekalovskaya, V., Sukhov, A., Suleymanova, G., Sultangareeva, L., Teleganova, V., Teplov, V., Teplova, V., Tertitsa, T., Timoshkin, V., Tirski, D., Tolmachev, A., Tomilin, A., Tselishcheva, L., Turgunov, M., Tyukh, Y., Van, V., Ershkova, E., Vasin, A., Vasina, A., Vekliuk, A., Vetchinnikova, L., Vinogradov, V., Volodchenko, N., Voloshina, I., Xoliqov, T., Yablonovska-Grishchenko, E., Yakovlev, V., Yakovleva, M., Yantser, O., Yarema, Y., Zahvatov, A., Zakharov, V., Zelenetskiy, N., Zheltukhin, A., Zubina, T., Kurhinen, J., Ovaskainen, O.

This article describes the first step toward the development of the geophysical model function (GMF) for the retrieval of wind speed and wind stress in hurricanes, based on developing a relation between the cross-polarized satellite SAR data from Sentinel-1 and winds/stress observed from collocated NOAA GPS-dropsondes data. Field measurements and remote sensing data for tropical cyclones in the Atlantic Ocean were analyzed. Using the data measured by GPS-dropsondes, average wind velocity profiles were obtained, while the parameters of the wind boundary layer (drag coefficient and friction velocity) were restored from the “wake” part of the velocity profiles using the self-similarity property. The self-similarity of the velocity profile “defect” in the boundary layer, known from the fluid dynamics, was used to retrieve the parameters of the atmospheric boundary layer (the surface wind velocity, drag coefficient, and friction velocity) from the dropsonde wind velocity profiles in ten major hurricanes. Based on the processing of the measurements in the hurricanes Irma 2017/09/07 and Maria 2017/09/21 and 2017/09/23 at a time close to the time of acquisition of the Sentinel-1 images, the dependencies of the cross-polarized normalized radar cross section on the wind speed and wind friction velocity were obtained and used for constructing the GMFs.

In storm conditions, the atmosphere and the ocean are multiphase: the boundary layer of the atmosphere is saturated with splashes and raindrops, foamy areas are present on the surface of the water, and the near-surface water layer is saturated with air bubbles. This causes radical changes in the ocean-atmosphere exchange processes that must be taken into account when constructing models. Empirical data on the parameters of such media, obtained under natural conditions, are characterized by large errors. Laboratory modeling on large experimental facilities helps to reduce this uncertainty. In this regard, modern laboratory facilities for modeling hurricane wind conditions have been created in the USA and Japan. This article presents the results of recent studies of multiphase media at the Unique Scientific Installation “Complex of large-scale geophysical stands” of the IAP RAS.

This paper describes laboratory experiments (using a high-speed wind-wave flume) of the effects of water waves on heat and momentum exchange in the near-water atmospheric boundary layer at high win...

The work was funded by Academy of Finland, grants 250243, 284601, 309581 (OO); the European Research Council, ERC Starting Grant 205905 (OO); Nordic Environment Finance Corporation Grant (OO); Jane and Aatos Erkko Foundation Grant (OO and TR); University of Helsinki HiLIFE Fellow Grant 2017–2020 (OO); the Kone Foundation 44-6977 (MD); Spanish Ramon y Cajal grant RYC-2014-16263 (MD); the Federal Budget for the Forest Research Institute of Karelian Research Centre Russian Academy of Sciences 220-2017-0003,0220-2017-0005 (LV, SS and JK); the Russian Foundation for Basic Research Grant 16-08-00510 (LK), and the Ministry of Education and Science of the Russian Federation 0017-2019-0009 (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences) (NI, MSh)., Ovaskainen, O., Meyke, E., Lo, C., Tikhonov, G., Delgado, M.M., Roslin, T., Gurarie, E., Abadonova, M., Abduraimov, O., Adrianova, O., Akimova, T., Akkiev, M., Ananin, A., Andreeva, E., Andriychuk, N., Antipin, M., Arzamascev, K., Babina, S., Babushkin, M., Bakin, O., Barabancova, A., Basilskaja, I., Belova, N., Belyaeva, N., Bespalova, T., Bisikalova, E., Bobretsov, A., Bobrov, V., Bobrovskyi, V., Bochkareva, E., Bogdanov, G., Bolshakov, V., Bondarchuk, S., Bukharova, E., Butunina, A., Buyvolov, Y., Buyvolova, A., Bykov, Y., Chakhireva, E., Chashchina, O., Cherenkova, N., Chistjakov, S., Chuhontseva, S., Davydov, E.A., Demchenko, V., Diadicheva, E., Dobrolyubov, A., Dostoyevskaya, L., Drovnina, S., Drozdova, Z., Dubanaev, A., Dubrovsky, Y., Elsukov, S., Epova, L., Ermakova, O.S., Ermakova, O., Esengeldenova, A., Evstigneev, O., Fedchenko, I., Fedotova, V., Filatova, T., Gashev, S., Gavrilov, A., Gaydysh, I., Golovcov, D., Goncharova, N., Gorbunova, E., Gordeeva, T., Grishchenko, V., Gromyko, L., Hohryakov, V., Hritankov, A., Ignatenko, E., Igosheva, S., Ivanova, U., Ivanova, N., Kalinkin, Y., Kaygorodova, E., Kazansky, F., Kiseleva, D., Knorre, A., Kolpashikov, L., Korobov, E., Korolyova, H., Korotkikh, N., Kosenkov, G., Kossenko, S., Kotlugalyamova, E., Kozlovsky, E., Kozsheechkin, V., Kozurak, A., Kozyr, I., Krasnopevtseva, A., Kruglikov, S., Kuberskaya, O., Kudryavtsev, A., Kulebyakina, E., Kulsha, Y., Kupriyanova, M., Kurbanbagamaev, M., Kutenkov, A., Kutenkova, N., Kuyantseva, N., Kuznetsov, A., Larin, E., Lebedev, P., Litvinov, K., Luzhkova, N., Mahmudov, A., Makovkina, L., Mamontov, V., Mayorova, S., Megalinskaja, I., Meydus, A., Minin, A., Mitrofanov, O., Motruk, M., Myslenkov, A., Nasonova, N., Nemtseva, N., Nesterova, I., Nezdoliy, T., Niroda, T., Novikova, T., Panicheva, D., Pavlov, A., Pavlova, K., Petrenko, P., Podolski, S., Polikarpova, N., Polyanskaya, T., Pospelov, I., Pospelova, E., Prokhorov, I., Prokosheva, I., Puchnina, L., Putrashyk, I., Raiskaya, J., Rozhkov, Y., Rozhkova, O., Rudenko, M., Rybnikova, I., Rykova, S., Sahnevich, M., Samoylov, A., Sanko, V., Sapelnikova, I., Sazonov, S., Selyunina, Z., Shalaeva, K., Shashkov, M., Shcherbakov, A., Shevchyk, V., Shubin, S., Shujskaja, E., Sibgatullin, R., Sikkila, N., Sitnikova, E., Sivkov, A., Skok, N., Skorokhodova, S., Smirnova, E., Sokolova, G., Sopin, V., Spasovski, Y., Stepanov, S., Stratiy, V.І., Strekalovskaya, V., Sukhov, A., Suleymanova, G., Sultangareeva, L., Teleganova, V., Teplov, V., Teplova, V., Tertitsa, T., Timoshkin, V., Tirski, D., Tolmachev, A., Tomilin, A., Tselishcheva, L., Turgunov, M., Tyukh, Y., Vladimir, V., Vargot, E., Vasin, A., Vasina, A., Vekliuk, A., Vetchinnikova, L., Vinogradov, V., Volodchenkov, N., Voloshina, I., Xoliqov, T., Yablonovska-Grishchenko, E., Yakovlev, V., Yakovleva, M., Yantser, O., Yarema, Y., Zahvatov, A., Zakharov, V., Zelenetskiy, N., Zheltukhin, A., Zubina, T., Kurhinen, J.

This paper describes the results of an experimental and theoretical investigation into the mechanisms by which spume droplets are generated by high winds. The experiments were performed in a high-speed wind-wave flume at friction velocities between 0.8 and 1.5 m s−1 (corresponding to a 10-m wind speed of 18–33 m s−1 under field conditions). High-speed video of the air–water interface revealed that the main types of spray-generating phenomena near the interface are “bag breakup” (similar to fragmentation of droplets and jets in gaseous flows at moderate Weber numbers), breakage of liquid ligaments near the crests of breaking surface waves, and bursting of large submerged bubbles. Statistical analysis of these phenomena showed that at wind friction velocities exceeding 1.1 m s−1 (corresponding to a wind speed of approximately 22.5 m s−1), the main mechanism responsible for the generation of spume droplets is bag breakup fragmentation of small-scale disturbances that arise at the air–water interface under the strong wind. Based on the general principles of statistical physics, it was found that the number of bags arising at the water surface per unit area per unit time was dependent on the friction velocity of the wind. The statistics obtained for the bag breakup events and other data available on spray production through this type of fragmentation were employed to construct a spray generation function (SGF) for the bag breakup mechanism. The resultant bag breakup SGF is in reasonable agreement with empirical SGFs obtained under laboratory and field conditions.

This study describes an approximate quasi-linear model for the description of the turbulent boundary layer over steep surface waves. The model assumes that wave-induced disturbances of the atmospheric turbulent boundary layer could be reasonably described in a linear approximation with the momentum flux from wind to waves retained as the only nonlinear effect in the model. For the case of periodic long-crested waves, the model has been verified with a set of the original laboratory and numerical experiments. The laboratory experimental study of the airflow over the steep waves was performed by means of the PIV technique. The numerical study was performed with direct numerical simulation (DNS) of the turbulent airflow over waved surface at Re=15,000 for quasi-homogeneous waves, wave trains and parasitic capillaries riding on the crest of a steep waves. Examples are given of the application of the quasi-linear approximation to describe the turbulent boundary layer over waves with the continuous spectrum under the assumption of random phases of harmonics. In the latter case the quasi-linear model provides the growth rate of surface waves in the inertial interval of the surface wave spectrum proportional to w7/3 in agreement with predictions in [1].

Experiments on generation mechanisms of sea water spray under hurricane wind were performed using shadow illumination high-speed video recording from several camera positions. Classification of fragmentation mechanism leading to generation of spray was conducted. Statistics of the events causing generation of spray was studied. A phenomenological model was developed based on the methods of statistical physics to describe the “bag-breakup” generation mechanism. The function of spray generation was conducted on this basis, which describes well the experimental data obtained under hurricane winds in the natural and laboratory conditions.

Influence of the spray generation due to the fragmentation of the “bag-breakup” type on momentum exchange in the atmospheric boundary layer above the sea surface at hurricane winds was investigated on the basis of the analysis of the results of laboratory experiments. It was shown that aerodynamic drag is determined by the contribution of three factors: first, the drag of the “bag-breakup” canopies as obstacles; second, acceleration of the spray formed during fragmentation by the air flow; and the third factor is related to the stratification of the near-water atmospheric layer due to the presence of levitated water droplets. Combination of all three factors leads to a non-monotonous dependence of the aerodynamic drag coefficient on wind speed, which confirms the results of the field and laboratory measurements.

Showing the record strengths and growth-rates, recent hurricanes have highlighted needs for improving forecasts of tropical cyclone intensities most sensitive to models of the air-sea interaction. Especially challenging is the nature of sea-spray supposed to strongly affecting the momentum- and energy- air-sea fluxes at strong winds. Even the spray-generation mechanisms in extreme winds remained undetermined. Basing on high-speed video here we identify it as the bag-breakup mode of fragmentation of liquid in gaseous flows known in a different context. This regime is characterized by inflating and consequent bursting of the short-lived objects, bags, comprising sail-like water films surrounded by massive liquid rims then fragmented to giant droplets with sizes exceeding 500 micrometers. From first principles of statistical physics we develop statistical description of these phenomena and show that at extreme winds the bag-breakup is the dominant spray-production mechanism. These findings provide a new basis for understanding and modeling of the air-sea exchange processes at extreme winds.

The paper describes the first step towards development of GMF for retrieval wind speed and wind stress in hurricanes basing on calibration of the cross-polarized satellite SAR images from Sentinel-1 by the collocated NOAA GPS dropsondes data. The self-similarity of the velocity profile "defect" in the boundary layer known from the fluid dynamics was used to retrieve the parameters of the atmospheric boundary layer (the surface wind velocity and the wind friction velocity) from the dropsonde wind velocity profiles in 10 major hurricanes. Basing on preliminary data processing, the dependencies of the cross-polarized NRCS on the wind speed and wind friction velocity were obtained to be used for constructing the GMFs.

We have advanced the energy and flux budget (EFB) turbulence closure theory that takes into account a two-way coupling between internal gravity waves (IGW) and the shear-free stably stratified turbulence. This theory is based on the budget equation for the total (kinetic plus potential) energy of IGW, the budget equations for the kinetic and potential energies of fluid turbulence, and turbulent fluxes of potential temperature for waves and fluid flow. The waves emitted at a certain level, propagate upward, and the losses of wave energy cause the production of turbulence energy. We demonstrate that due to the nonlinear effects more intensive waves produce more strong turbulence, and this, in turns, results in strong damping of IGW. As a result, the penetration length of more intensive waves is shorter than that of less intensive IGW. The anisotropy of the turbulence produced by less intensive IGW is stronger than that caused by more intensive waves. The low amplitude IGW produce turbulence consisting up to 90 \% of turbulent potential energy. This resembles the properties of the observed high altitude tropospheric strongly anisotropic (nearly two-dimensional) turbulence., Comment: 13 pages, 11 figures, REVTEX4-1, revised

A series of laboratory and numerical experiments were carried out to study the structure of the turbulent flow over a rectangular obstacle for high Reynolds number. The results of numerical simulation performed within ANSYS CFX were verified on data obtained in the wind tunnel of IAP RAS by visualization methods including Particle Image Velocimetry. It was found that the airflow over the obstacle can be conditionally divided into main several regions: the region of the initiation of the detachment and formation shielding zone, the region of the maximum vertical separation and vortex development and the region of flow reattachment. Comparison of numerical calculations with the results of experiments showed that the physical processes of the airflow around model objects can be optimally modeled in a three-dimensional setting using the Detached Eddy turbulence model (DES) with central numerical differentiation scheme and allowed to select optimal parameters of calculation grid.

This paper presents data of laboratory experiments on a high-speed wind-wave flume of the Institute of Applied Physics Nizhny Novgorod, Russian Federation, which are devoted to the investigation of the X-band co-polarized and de-polarized radar return in a wide range of high wind speeds from 8 to 40 m s–1. Microwave measurements were accompanied by the measurements of airflow and wave field parameters. Experiments showed that alternatively to the co-polarized return, the dependency of the de-polarized return on the wind speed does not saturate, although the growth rate decreases at wind speed exceeding 30 m s–1. Comparison of the experimental data with the composite-surface Bragg scattering model for the measured parameters of the wind and waves showed that the model is in agreement with measurements of microwave co-polarized return, but fails to describe the de-polarized radar return. The obtained dependency of de-polarized radar return was compared with the empirical geophysical model function based on collocated airborne and satellite data.

Based on laboratory experiments, the logarithmic dependence of X-band cross-polarized NRCS on wind friction velocity was found for high wind conditions. Using this dependency, surface drag parameterizations at hurricane wind and available collocated field data on radar backscattering and wind speed, the geophysical model function (GMF) as NRCS via 10-m wind speed is derived. The GMP is applicable for wind speed retrieval at stormy and hurricane conditions.

The paper presents the results of investigations of the mechanisms of spray of droplets generation within wind wave interaction obtained under laboratory conditions on the High-speed Wind-Wave Flume of the Institute of Applied Physics of the Russian Academy of Sciences. For the research, a multi-angle high-speed video system used together shadow method, including underwater illumination. The results allowed for the classification of mechanismsleading to the formation of droplets. Three main types of phenomena responsible for the generation of the spume droplets near the wave crest were specified: breakage of liquid ligaments, bursting of large submerged bubbles, and bag breakup. The last and less known mechanism claims to be dominant for high wind speeds and it was described in detail.