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Register a new userSatellite altimetry reveals spatial patterns of variations in the Baltic Sea wave climate
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The main properties of the climate of waves in the seasonally ice-covered Baltic Sea and its decadal changes since 1990 are estimated from satellite altimetry data. The data set of significant wave heights (SWH) from all existing nine satellites, cleaned and cross-validated against in situ measurements, shows overall a very consistent picture. A comparison with visual observations shows a good correspondence with correlation coefficients of 0.6-0.8. The annual mean SWH reveals a tentative increase of 0.005 m yr-1, but higher quantiles behave in a cyclic manner with a timescale of 10-15 yr. Changes in the basin-wide average SWH have a strong meridional pattern: an increase in the central and western parts of the sea and decrease in the east. This pattern is likely caused by a rotation of wind directions rather than by an increase in the wind speed.
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The core properties of the wave climate and its changes in the Caspian Sea are established in terms of the annual mean significant wave height and its regional changes in 2002-2013 based on the outcome of the satellite altimetry mission JASON-1. Remotely estimated wave heights are validated against properties of the empirical distribution of instrumentally measured wave heights in the southern Caspian Sea and monthly averages of visually observed wave heights at three locations. A correction for systematic differences leads to very good correspondence between monthly averaged in situ and satellite data with a typical root mean square difference of 0.06 m. The average significant wave height in the Caspian Sea is 0.5-0.7 m in the northern basin of the sea, around 1.2 m in large parts of the central and southern basins and reaches up to 1.8 m in the northern segment of the central basin. The basin-wide average wave intensity varied insignificantly in the range of 1.02-1.14 m in 2002-2013. These estimates overestimate the wave heights by about 30% because low wave conditions are ignored. Substantial and statistically significant changes in the wave height occurred in certain areas. The wave height decreased by 0.019 +- 0.007 m/yr in the eastern segment of the central basin and by 0.04 +- 0.04 m/yr in the western segment of the southern basin. These changes can be explained by an increase in the frequency of westerly winds at the expence of southerly winds. Both basin-wide and regional extreme wave heights exhibit large interannual variations but do not show any significant trend. The patterns of changes in mean and extreme wave height are different. The average wave height has increased while the extreme wave height has decreased in the eastern segment of the southern basin.
We present a complete cross-validation of significant wave heights (SWH) extracted from altimetry data from all ten existing satellites with available in situ (buoy and echosounder) wave measurements for the Baltic Sea basin. The main purpose is to select an adequate altimetry data subset for a subsequent evaluation of the wave climate. The satellite measurements with the backscatter coefficients > 13.5 cdb, errors in the SWH normalized standard deviation > 0.5 m and snapshots with centroids closer than 0.2 degrees to the land are not reliable. The ice flag usually denotes the ice concentration of > 50%. The presence of ice affects the SWH data starting from concentrations 10%, but substantial effects are only evident for concentrations > 30%. The altimetry data selected based on these criteria have very good correspondence with in situ data, except for GEOSAT Phase 1 data (1985-1989) that could not be validated. The root-mean-square difference of altimetry and in situ data is in the range of 0.23-0.37, which is significant for the Baltic Sea, compared with an average wave height of ~1 m. The bias for CRYOSAT-2, ERS-2, JASON-1/2 and SARAL data is below 0.06 m. The ENVISAT, ERS-1, GEOSAT and TOPEX satellites revealed larger biases up to 0.23 m. The SWH time series from several satellite pairs (ENVISAT/JASON-1, SARAL/JASON-2, ERS-1/TOPEX) exhibit substantial mutual temporal drift and part of them evidently are not homogeneous in time. A new high-resolution SWH data set from the SARAL satellite reveals a very good correspondence with the in situ data and with the data stream from previous satellites.
The mesoscale eddy field plays a key role in the mixing and transport of physical and biological properties and redistribute energy budgets in the ocean. Eddy kinetic energy is commonly defined as the kinetic energy of the time-varying component of the velocity field. However, this definition contains all processes that vary in time, including coherent mesoscale eddies, jets, waves, and large-scale motions. The focus of this paper is on the eddy kinetic energy contained in coherent mesoscale eddies. We present a new method to decompose eddy kinetic energy into oceanic processes. The proposed method uses a new eddy-identification algorithm (TrackEddy). This algorithm is based on the premise that the sea level signature of a coherent eddy can be approximated as a Gaussian feature. The eddy Gaussian signature then allows for the calculation of kinetic energy of the eddy field through the geostrophic approximation. TrackEddy has been validated using synthetic sea surface height data, and then used to investigate trends of eddy kinetic energy in the Southern Ocean using Satellite Sea Surface Height anomaly (AVISO+). We detect an increasing trend of eddy kinetic energy associated with mesoscale eddies in the Southern Ocean. This trend is correlated with an increase of the coherent eddy amplitude and the strengthening of wind stress over the last two decades.
Cenozoic temperature, sea level and CO2 co-variations provide insights into climate sensitivity to external forcings and sea level sensitivity to climate change. Climate sensitivity depends on the initial climate state, but potentially can be accurately inferred from precise paleoclimate data. Pleistocene climate oscillations yield a fast-feedback climate sensitivity 3 +/- 1{deg}C for 4 W/m2 CO2 forcing if Holocene warming relative to the Last Glacial Maximum (LGM) is used as calibration, but the error (uncertainty) is substantial and partly subjective because of poorly defined LGM global temperature and possible human influences in the Holocene. Glacial-to-interglacial climate change leading to the prior (Eemian) interglacial is less ambiguous and implies a sensitivity in the upper part of the above range, i.e., 3-4{deg}C for 4 W/m2 CO2 forcing. Slow feedbacks, especially change of ice sheet size and atmospheric CO2, amplify total Earth system sensitivity by an amount that depends on the time scale considered. Ice sheet response time is poorly defined, but we show that the slow response and hysteresis in prevailing ice sheet models are exaggerated. We use a global model, simplified to essential processes, to investigate state-dependence of climate sensitivity, finding an increased sensitivity towards warmer climates, as low cloud cover is diminished and increased water vapor elevates the tropopause. Burning all fossil fuels, we conclude, would make much of the planet uninhabitable by humans, thus calling into question strategies that emphasize adaptation to climate change.
The application of Synthetic Aperture Radar (SAR) techniques to classical radar altimetry offers the potential for greatly improved Earth surface mapping. This paper provides an overview of the progress of SAMOSA, Development of SAR Altimetry Studies and Applications over Ocean, Coastal zones and Inland waters, an on-going ESA-funded project. The main objective of SAMOSA is to better quantify the improvement of SAR altimetry over conventional altimetry on water surfaces. More specifically, one of the tasks focuses on the reduction of SAR mode data to pulse-limited altimeter data, and a theoretical modelling to characterize the expected gain between high Pulse Repetition Frequency (PRF) reduced SAR mode data and low PRF classical Low-Resolution Mode (LRM) data. To this end, theoretical modelling using the Cramer-Rao bound (CRB) will be used and the results will be compared to previous theoretical estimates [7], using an analysis akin to that in [8].
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