Nansen International Environmental and Remote Sensing Center

Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic’s response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of centuryand multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere–ice–ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased~8 · 105 km2 (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.

Recent research using microwave satellite remote sensing data has established that there has been a reduction of about 3 percent per decade in the areal extent of the Arctic sea ice cover since 1978, although it is unknown whether the nature of the perennial ice pack has changed. These data were used to quantify changes in the ice cover's composition, revealing a substantial reduction of about 14 percent in the area of multiyear ice in winter during the period from 1978 to 1998. There also appears to be a strong correlation between the area of multiyear ice and the spatially averaged thickness of the perennial ice pack, which suggests that the satellite-derived areal decreases represent substantial rather than only peripheral changes. If this apparent transformation continues, it may lead to a markedly different ice regime in the Arctic, altering heat and mass exchanges as well as ocean stratification.

While reported losses of climate-related hazards are at historically high levels, climate change is likely to enhance the risk posed by extreme weather events. Several regions are likely to be exposed to multiple climate hazards, yet their modeling in a joint scheme is still at the early stages. A multi-hazard framework to map exposure to multiple climate extremes in Europe along the twenty-first century is hereby presented. Using an ensemble of climate projections, changes in the frequency of heat and cold waves, river and coastal flooding, streamflow droughts, wildfires and windstorms are evaluated. Corresponding variations in expected annual exposure allow for a quantitative comparison of hazards described by different process characteristics and metrics. Projected changes in exposure depict important variations in hazard scenarios, especially those linked to rising temperatures, and spatial patterns largely modulated by local climate conditions. Results show that Europe will likely

Multiscale composite models based on the Bragg theory are widely used to study the normalized radar cross‐section (NRCS) over the sea surface. However, these models are not able to correctly reproduce the NRCS in all configurations and wind wave conditions. We have developed a physical model that takes into account, not only the Bragg mechanism, but also the non‐Bragg scattering mechanism associated with wave breaking. A single model was built to explain on the same physical basis both the background behavior of the NRCS and the wave radar Modulation Transfer Function (MTF) at HH and VV polarization. The NRCS is assumed to be the sum of a Bragg part (two‐scale model) and of a non‐Bragg part. The description of the sea surface is based on the short wind wave spectrum (wavelength from few millimeters to few meters) developed by Kudryavtsev et al. [1999] and wave breaking statistics proposed by Phillips [1985] . We assume that non‐Bragg scattering is supported by quasi‐specular reflection from very rough wave breaking patterns and that the overall contribution is proportional to the white cap coverage of the surface. A comparison of the model NRCS with observations is presented. We show that neither pure Bragg nor composite Bragg model is able to reproduce observed feature of the sea surface NRCS in a wide range of radar frequencies, wind speeds, and incidence and azimuth angles. The introduction of the non‐Bragg part in the model gives an improved agreement with observations. In Part 2, we extend the model to the wave radar MTF problem.

Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic’s response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of centuryand multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere–ice–ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased~8 · 105 km2 (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.

High‐resolution records from IMAGES core MD95‐2011 in the eastern Norwegian Sea provide evidence for relatively large‐ and small‐scale high‐latitude climate variability throughout the Holocene. During the early and mid‐Holocene a situation possibly driven by consistent stronger westerlies increased the eastward influence of Arctic intermediate and near‐surface waters. For the late Holocene a relaxation of the atmospheric forcing resulted in increased influence of Atlantic water. The main changes in Holocene climate show no obvious connection to changing solar irradiance, and spectral analysis reveals no consistent signature for any periodic behavior of Holocene climate at millennial or centennial timescales. There are, however, indications of consistent multidecadal variability.

A new radar imaging model of ocean current features is proposed. The simulated normalized radar cross section (NRCS) takes into account scattering from “regular” surfaces (by means of resonant Bragg scattering and specular reflections) and scattering from breaking waves. The description of background wind waves and their transformation in nonuniform medium is based on solution of the wave action conservation equation. Wave breaking plays a key role in the radar imaging model. Breaking waves scatter radio waves (thus directly contributing to the NRCS), provide energy dissipation in wind waves (thus defining the wave spectrum of intermediate scale waves), and generate short surface waves (thus affecting Bragg scattering). Surface current, surfactants accumulated in the convergence zone, and varying wind field are considered as the main sources for the NRCS manifestations of current features. The latter source can result from transformation of atmospheric boundary layer over the sea surface temperature front. It is shown that modulation of wave breaking significantly influences both radar returns and short wind waves. In the range of short gravity waves related to Ku‐ X‐, and C‐bands, the modulation of Bragg waves through wave breaking is the governing mechanism. The model is tested against well‐controlled experiments including JOWIP, SARSEX, and CoastWatch‐95. A reasonably good agreement between model and observations is obtained.

Previous analysis of Advanced Synthetic Aperture Radar (ASAR) signals collected by ESA's Envisat has demonstrated a very valuable source of high‐resolution information, namely, the line‐of‐sight velocity of the moving ocean surface. This velocity is estimated from a Doppler frequency shift, consistently extracted within the ASAR scenes. The Doppler shift results from the combined action of near surface wind on shorter waves, longer wave motion, wave breaking and surface current. Both kinematic and dynamic properties of the moving ocean surface roughness can therefore be derived from the ASAR observations. The observations are compared to simulations using a radar imaging model extended to include a Doppler shift module. The results are promising. Comparisons to coincident altimetry data suggest that regular account of this combined information would advance the use of SAR in quantitative studies of ocean currents.

In this paper, sea ice in the Central Arctic has been classified in synthetic aperture radar (SAR) images from ENVISAT using a neural network (NN)-based algorithm and a Bayesian algorithm. Since different sea ice types can have similar backscattering coefficients at C-band HH polarization, it is necessary to use textural features in addition to the backscattering coefficients. The analysis revealed that the most informative texture features for the classification of multiyear ice (MYI), deformed first-year ice (FYI) (DFYI), and level FYI (LFYI) and open water/nilas are correlation, inertia, cluster prominence, energy, homogeneity, and entropy, as well as third and fourth central statistical moments of image brightness. The optimal topology of the NN, trained for ENVISAT wide-swath SAR sea ice classification, consists of nine neurons in input layer, six neurons in hidden layer, and three neurons in output layer. The classification results for a series of 20 SAR images, acquired in the central part of the Arctic Ocean during winter months, were compared to expert analysis of the images and ice charts. The results of the NN classification show that the average correspondences with the expert analysis amount to 85 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\%$</tex></formula> , 83 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\%$</tex></formula> , and 80 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\%$</tex></formula> for LFYI, DFYI, and MYI, respectively. The Bayesian pixel-based method can provide a higher resolution in the classified image and, therefore, better capability to identify leads compared to the NN method. Both methods may be effectively used in the Central Arctic where MYI is predominant.

Abstract After extensive efforts over the course of a decade, convective-scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three-dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (10 3 km), with slowly evolving semigeostrophic dynamics and relatively long predictability on the order of a few days. Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), and parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues—the Liouville principle and Bayesian probability for probabilistic forecasts—and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows. The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided.

Abstract. Retrieval of Arctic sea ice thickness from CryoSat-2 radar altimeter freeboard data requires observational data to verify the relation between these two variables. In this study in-situ ice and snow data from 689 observation sites, obtained during the Sever expeditions in the 1980s, have been used to establish an empirical relation between thickness and freeboard of FY ice in late winter. Estimates of mean and variability of snow depth, snow density and ice density were produced on the basis of many field observations. These estimates have been used in the hydrostatic equilibrium equation to retrieve ice thickness as a function of ice freeboard, snow depth and snow/ice density. The accuracy of the ice thickness retrieval has been calculated from the estimated variability in ice and snow parameters and error of ice freeboard measurements. It is found that uncertainties of ice density and freeboard are the major sources of error in ice thickness calculation. For FY ice, retrieval of ≈ 1.0 m (2.0 m) thickness has an uncertainty of 46% (37%), and for MY ice, retrieval of 2.4 m (3.0 m) thickness has an uncertainty of 20% (18%), assuming that the freeboard error is ± 0.03 m for both ice types. For MY ice the main uncertainty is ice density error, since the freeboard error is relatively smaller than that for FY ice. If the freeboard error can be reduced to 0.01 m by averaging measurements from CryoSat-2, the error in thickness retrieval is reduced to about 32% for a 1.0 m thick FY floe and to about 18% for a 2.4 m thick MY floe. The remaining error is dominated by uncertainty in ice density. Provision of improved ice density data is therefore important for accurate retrieval of ice thickness from CryoSat-2 data.

A review of the main approaches developed for sea ice classification using satellite imagery is presented. Satellite data are the main and very often only information source for sea ice classification and charting in the remote arctic regions. The main techniques used for ice classification and ice charting in several national ice services are considered. Advantages and disadvantages of various SAR data-based methods for ice classification are analyzed. It is shown that an increase of SAR technical abilities contributes to the enhancement of sea ice classification reliability. The possible further development of satellite data-based methods for ice classification is discussed.

A continuous data set of Greenland Ice Sheet altimeter height from European Remote Sensing satellites (ERS-1 and ERS-2), 1992 to 2003, has been analyzed. An increase of 6.4 +/- 0.2 centimeters per year (cm/year) is found in the vast interior areas above 1500 meters, in contrast to previous reports of high-elevation balance. Below 1500 meters, the elevation-change rate is -2.0 +/- 0.9 cm/year, in qualitative agreement with reported thinning in the ice-sheet margins. Averaged over the study area, the increase is 5.4 +/- 0.2 cm/year, or approximately 60 cm over 11 years, or approximately 54 cm when corrected for isostatic uplift. Winter elevation changes are shown to be linked to the North Atlantic Oscillation.

This study revisits the problem of assimilation of asynchronous observations, or four-dimensional data assimilation, with the ensemble Kalman filter (EnKF). We show that for a system with perfect model and linear dynamics the ensemble Kalman smoother (EnKS) provides a simple and efficient solution for the problem: one just needs to use the ensemble observations (that is, the forecast observations for each ensemble member) from the time of observation during the update, for each assimilated observation. This recipe can be used for assimilating both past and future data; in the context of assimilating generic asynchronous observations we refer to it as the asynchronous EnKF. The asynchronous EnKF is essentially equivalent to the four-dimensional variational data assimilation (4D-Var). It requires only one forward integration of the system to obtain and store the data necessary for the analysis, and therefore is feasible for large-scale applications. Unlike 4D-Var, the asynchronous EnKF requires no tangent linear or adjoint model.

Case study of chapter 3 of report number 3 on Copernicus Marine Service Ocean State. The case studied consisted of the use of the satellite CMEMS and the Mediterranean Marine Protected Areas sentinel network to track ocean warming effects in coastal areas

At its seasonal peak the Amazon/Orinoco plume covers a region of 10 6 km 2 in the western tropical Atlantic with more than 1 m of extra freshwater, creating a near‐surface barrier layer (BL) that inhibits mixing and warms the sea surface temperature (SST) to &gt;29°C. Here new sea surface salinity (SSS) observations from the Aquarius/SACD and SMOS satellites help elucidate the ocean response to hurricane Katia, which crossed the plume in early fall, 2011. Its passage left a 1.5 psu high haline wake covering &gt;10 5 km 2 (in its impact on density, the equivalent of a 3.5°C cooling) due to mixing of the shallow BL. Destruction of this BL apparently decreased SST cooling in the plume, and thus preserved higher SST and evaporation than outside. Combined with SST, the new satellite SSS data provide a new and better tool to monitor the plume extent and quantify tropical cyclone upper ocean responses with important implications for forecasting.

The surface signatures of meandering fronts and eddies have been regularly observed and documented in synthetic aperture radar (SAR) images. Wave‐current interactions, the suppression of short wind waves by natural film, and the varying wind field resulting from atmospheric boundary layer changes across an oceanic temperature front all contribute to the radar image manifestation of such mesoscale features. The corresponding imaging mechanisms are quantitatively explored using a new radar imaging model (Kudryavtsev et al., 2005) that solves the energy balance equation where wind forcing, viscous and wave breaking dissipation, wave‐wave interactions, and generation of short waves by breaking waves are taken into account. High‐quality and synoptic in situ observations of the surface conditions should ideally be used in this model. However, such data are rarely available. Instead, the fields of temperature and ocean current are herein derived from two distinct numerical ocean models. SAR image expressions of current fronts and eddies are then simulated based on these fields. The comparison of simulated images with European Remote Sensing (ERS) SAR and Envisat advanced SAR (ASAR) images is favorable. We consequently believe that the new radar imaging model provides promising capabilities for advancing the quantitative interpretation of current features manifested in SAR images.

Abstract An algorithm has been developed for estimating total ice concentration from spaceborne high-frequency passive microwave instrumentation. The algorithm is intended for use with the coming Special Sensor Microwave/Imager (SSM/I) data giving a spatial resolution of 12 km. It is based on radiation physics and detailed millimetre wave surface signature measurements and can therefore be applied to other similar data. However, due to large effects on the signals caused by time varying atmospheric conditions and radiation properties of the ice, the algorithm is made self-adjusting. The atmospheric effects are implicitly treated as a smooth function of the ice concentration with tie points over open ocean and 100 per cent ice for each orbit. This means that the main errors are due to patches of heavy clouds and ice floes with atypical radiation properties. An error analysis indicates possible errors of the order of 5 percent for concentrations representative for the Arctic Basin, increasing with decreasing concentration.

Arctic amplification of temperature change is theorised to be an important feature of the Earth's climate system. For observational assessment and understanding of mechanisms of this amplification, which remain uncertain, thorough and detailed analyses of surface air temperature (SAT) variability and trends in the Arctic are needed. Here we present an analysis of Arctic SAT variability in comparison with mid-latitudes and the Northern Hemisphere (NH), based on an advanced SAT dataset – NansenSAT. We define an index for the Arctic amplification as the ratio between absolute values of the Arctic (65–90°N) and NH 30-yr running linear SAT trends. It is demonstrated that the temperature amplification in the Arctic is characteristic not only for the recent warming but also the early 20th century warming (ETCW) and subsequent cooling. The amplification appears to be weaker during the recent warming than in the ETCW, simply because the index values reflect the more pervasive nature of the recent warming that reflects the background of anthropogenic global warming. We also produced a new Arctic regionalisation created from hierarchical cluster analysis, which identifies six major natural regions in the Arctic that reflect SAT variability. Statistical comparison with several climate indices shows that the Atlantic Multidecadal Oscillation (AMO) is the mode of variability that is most significantly associated with the amplified warming–cooling in the Arctic, with a stronger correlation during the ETCW and recent warming than during the intermediate period. Regionally, differences are identified in terms of annual and seasonal rates of change and in their correlations with modes of variability.

Abstract. Changes to seawater inorganic carbon and nutrient concentrations in response to the deliberate CO2 perturbation of natural plankton assemblages were studied during the 2005 Pelagic Ecosystem CO2 Enrichment (PeECE III) experiment. Inverse analysis of the temporal inorganic carbon dioxide system and nutrient variations was used to determine the net community stoichiometric uptake characteristics of a natural pelagic ecosystem perturbed over a range of pCO2 scenarios (350, 700 and 1050 μatm). Nutrient uptake showed no sensitivity to CO2 treatment. There was enhanced carbon production relative to nutrient consumption in the higher CO2 treatments which was positively correlated with the initial CO2 concentration. There was no significant calcification response to changing CO2 in Emiliania huxleyi by the peak of the bloom and all treatments exhibited low particulate inorganic carbon production (~15 μmol kg−1). With insignificant air-sea CO2 exchange across the treatments, the enhanced carbon uptake was due to increase organic carbon production. The inferred cumulative C:N:P stoichiometry of organic production increased with CO2 treatment from 1:6.3:121 to 1:7.1:144 to 1:8.25:168 at the height of the bloom. This study discusses how ocean acidification may incur modification to the stoichiometry of pelagic production and have consequences for ocean biogeochemical cycling.