Quality Control Methods in Ocean-Sea ice Coupled Data Assimilation

Since 2012, the National Centers for Environmental Prediction’s Global Ensemble Forecast System (GEFS) has undergone two major upgrades. Version 11 was introduced in December 2015, with a new dynamic scheme, improved physics, increased horizontal and vertical resolution, and a more accurate initialization method. Prior to implementation, retrospective model runs over four years were made, covering multiple hurricane seasons. The second major upgrade was implemented in May 2016, when the data assimilation system for the deterministic Global Forecast System (GFS) was upgraded. Because the GEFS initialization is taken from the deterministic GFS, this upgrade had a direct impact on the GEFS. Unlike the previous upgrade, the model was rerun for only a few tropical cyclones. Hurricane Edouard (2014) was the storm for which the most retrospective runs (4) were made for the new data assimilation system. In this paper, the impact of the GEFS upgrades is examined using seasonal data for the 2014–17 hurricane seasons, and detailed data from the four model runs made for Hurricane Edouard. Both upgrades reduced the spread between ensemble member tracks. The first upgrade reduced the spread but did not reduce the likelihood that the actual track would be included in the family of member tracks. The second upgrade both reduced the spread further and reduced the chance that the real storm track would be within the envelope of member tracks.

Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short-term corrections, or analysis increments, closely mirror the systematic bias patterns of the dynamical model. In this work, we show that Convolutional Neural Networks (CNNs) can be used to learn a mapping from model state variables to analysis increments, thus promoting the feasibility of a data-driven model parameterization which predicts state-dependent model errors. We showcase this problem using an ice-ocean data assimilation system within the fully coupled Seamless system for Prediction and EArth system Research (SPEAR) model at the Geophysical Fluid Dynamics Laboratory (GFDL), which assimilates satellite observations of sea ice concentration. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are sea ice concentration, sea-surface temperature, ice velocities, ice thickness, net shortwave radiation, ice-surface skin temperature, and sea-surface salinity. We show that the CNN is able to make skilful predictions of the increments, particularly between December and February in both the Arctic and Antarctic, with average daily spatial pattern correlations of 0.72 and 0.79, respectively. Initial investigation of implementation of the CNN into the fully coupled SPEAR model shows that the CNN can reduce biases in retrospective seasonal sea ice forecasts by emulating a data assimilation system, further suggesting that systematic sea ice biases could be reduced in a free-running climate simulation.

The National Centers for Environmental Prediction (NCEP) produces operational reanalysis products based on the Global Ocean Data Assimilation System (GODAS) with the Modular Ocean Model (MOM3/MOM4p0d) as a physical model and 3D-Var as the data assimilation method. The Indian National Center for Ocean Information Services (INCOIS) also produces operational ocean analysis with the same GODAS with MOM4p0d. In this study, GODAS is upgraded with MOM5. The improved reanalysis is compared with respect to the EN4 and Ocean ReAnalysis System 5 (ORAS5) for the subsurface temperature and salinity. The microwave-based satellite-derived sea surface temperature (SST) is employed for the independent evaluation of the reanalysis SST products. We have assimilated observed temperature and salinity profiles from all in situ platforms over the global ocean and also assimilated along-track sea level anomaly from altimeter (Jason1 and Jason2) to produce the improved reanalysis. There is a significant improvement in the SST with biases of temperatures reduced from 1.5 to ~ 0.2 °C as compared to without assimilation. Moreover, a significant improvement is found in the subsurface temperature and salinity fields over the northwest Atlantic and Pacific Ocean, Nino 3.4, and Indian Ocean thermocline ridge regions. The biases in this new, improved reanalysis are even lower than ORAS5 when compared with EN4 analysis. Thermocline depth also shows improvement in terms of better representation and capturing seasonal variability. Altimeter assimilation further reduces Root Mean Square Deviation (RMSD) in SST over the global ocean. We also show the improvement in the new reanalysis with respect to the reanalysis based on MOM4p0d.

The Arctic is the most rapidly warming region on Earth, and it is facing considerable environmental change. These changes are beginning to alter the way humanity uses and exploits the Arctic region, with commercial activities such as tourism, fishing, mineral and oil extraction, and shipping on the increase. Increased human activity in the Arctic has implications for safety and environmental conservation, and can present difficulties for local communities who rely on subsistence hunting and fishing, and community resupply. Environmental prediction for the Arctic is therefore becoming increasingly important, but our ability to accurately predict the Arctic atmosphere–sea-ice–ocean system is relatively immature, with short-term forecasts significantly less accurate than those produced for the mid-latitudes. There are numerous challenges with respect to forecasting systems in the Arctic: there is a relatively sparse in-situ observing network; some satellite-based observations are hard to use because of the snow and ice-covered surfaces; it is difficult and expensive to make detailed research-quality observations; and the dominant physical processes are different to those at mid-latitudes and over the tropics. To address these issues, the Year of Polar Prediction (YOPP1) was developed as one of the key elements of the WMO's Polar Prediction Project2. On Wednesday 21 November 2018, the Royal Meteorological Society hosted a meeting at the Society of Chemical Industry (SCI) in London to discuss recent advances and challenges associated with the understanding and prediction of key Arctic processes using numerical models and observations – including new insights provided by several field campaigns and modelling experiments carried out under the YOPP umbrella. The first talk was entitled: What are the challenges and priorities for improved prediction and climate monitoring of the Arctic? (Irina Sandu, ECMWF). Irina explained that in order to improve Arctic predictions and reanalyses, which constitute a great tool for Arctic climate monitoring, work is needed in three areas: (i) enhanced coupled modelling, (ii) data assimilation methods and (iii) the effective use of observations in the numerical weather prediction systems. Arctic regions pose specific challenges for each of these three areas because model errors are large, model uncertainty is not necessarily well represented, and in-situ observations are sparse. Moreover, while the Arctic is very rich in terms of remote sensing observations from polar orbiting satellites, some of these observations are difficult to use in data assimilation because of ambiguous signal properties, particularly over snow and sea-ice and in cloudy situations. Irina introduced the challenges and priorities in each of these three key areas and described recent and ongoing efforts to evaluate and improve predictions in the Arctic, and beyond, made in the framework of the YOPP modelling activities and the EU-APPLICATE3 project. She highlighted the fact that, compared to previous similar initiatives, YOPP puts an additional emphasis on numerical experimentation, in a concerted effort to exploit observations for model improvement and drive developments in data assimilation and the design of observing systems. The next talk presented was: What are the limitations of Arctic sea ice remote sensing products, and what opportunities can they provide for improving predictive skill of Arctic forecasts? (Ed Blockley, Met Office). Ed explained that, in response to declining sea-ice cover, human activity in the Arctic is increasing, and that accurate forecasts of Arctic sea ice – on a variety of different timescales – are therefore becoming increasingly important for the safety of human activities in the region. He then provided a brief overview of how sea ice has been traditionally initialised in short-range forecasting and seasonal prediction systems, and explained that most operational systems initialise sea ice purely using passive microwave sea ice concentration products. He gave an overview of the passive microwave remote sensing products typically used for initialisation of sea ice concentration, as well as the (relatively new) satellite-derived sea ice thickness products currently available. Information was provided about how these observations are derived, what the uncertainties and limitations are, and what new avenues of research are currently underway to improve measurements of Arctic sea ice from space. Ed also illustrated the potential impact of assimilating satellite sea ice thickness products into our operational prediction systems. Using the Met Office GloSea seasonal prediction system, he showed how sea ice thickness values inferred from the CryoSat-2 satellite altimetry could be used within the initialisation process to considerably improve forecasts of Arctic summer sea ice on seasonal timescales. Next up was: Chasing the source of the AMOC (Atlantic Meridional Overturning Circulation) atmosphere–ocean coupling in the Iceland and Greenland Seas (Ian Renfrew, University of East Anglia) Ian described a coordinated meteorological and oceanographic field campaign over the Iceland and southern Greenland Seas that took place in February and March 2018. The aim of the campaign was to characterise the atmospheric forcing and the ocean response of coupled atmosphere–ocean processes – in particular, cold-air outbreaks in the vicinity of the marginal-ice-zone, and their triggering of oceanic heat loss and the generation of dense waterbody body masses. He described how the team observed the spatial structure and variability of surface flux fields in the region, and the weather systems that dictate these fluxes, through the first meteorological field campaign in the Iceland Sea. This was done as part of a coupled atmosphere–ocean field campaign in winter 2018 involving a rare wintertime research cruise, airborne observations and a host of ocean and atmosphere observing systems. In-situ observations of air–sea interaction processes were made from several platforms. Ian presented some highlights from the field campaign and introduced some early findings from the post-campaign analysis – most notably a comprehensive set of observations of a marine cold air outbreak which were obtained via radiosonde releases and the research aircraft. Mixing it up: What connects Arctic clouds and sea ice? was a talk presented by Ian Brooks (University of Leeds). Ian explained that the single largest source of uncertainty in climate models arises from the representation of clouds; this is particularly true in the Arctic where, for much of the year, low-level boundary-layer clouds dominate the surface energy budget. The time-evolution and properties of these clouds are intimately linked their interactions with the surface and boundary layer structure. The properties of both clouds and boundary layer (and indeed the surface) are essentially sub-grid-scale in climate and weather forecast models, and thus many important features are poorly represented by current parameterisations. Ian emphasised that observations show that the summertime boundary-layer over sea ice is often decoupled below cloud base, inhibiting turbulent mixing – and hence the transport of aerosols and water vapour – between cloud and the surface. This decoupling is not reproduced by models, and the controls on this turbulent structure are poorly understood. He provided a review of the state of knowledge, alongside some of the recent research on Arctic boundary layer processes and their effects on clouds and the surface energy budget. A unique feature of polar boundary layers is the occurrence of a humidity inversion, where absolute humidity increases across the top of the boundary layer. This allows boundary-layer clouds to extend into the temperature inversion and results in a weakening of turbulence generated by cloud-top radiative cooling. Finally, he showed how recent field measurements have improved the parameterisation of surface turbulent fluxes over marginal sea ice, which will ultimately affect the model representation of the evolution of sea ice. The view from above Arctic snow at 89-325GHz: What can surface emissivity on these channels tell us about snowpack stratigraphy? was the next talk by Chawn Harlow ( Met Office). Chawn introduced Measurements of Arctic Cloud, Snow and Sea Ice in the Marginal Ice ZonE (MACSSIMIZE) – an airborne campaign carried out by the Met Office in March 2018 that focussed on evaluation of snow microwave emission models in the 89–325GHz frequency range. He described how the campaign included a ground-based component to collect snow pit measurements co-located with the airborne radiometric measurements, and that these were now being used to improve snow physical models for use in future Met Office numerical weather prediction systems. An explanation of the main motivation for MACSSIMIZE followed, both from the point of view of snow remote sensing and for improving atmospheric data assimilation, along with a description of the emissivity retrievals obtained during the campaign at 89, 118, 157, 183.3, 243 and 325GHz. Chawn then described how the MACSSIMIZE observations will be used to evaluate the snow thermodynamic and emissivity models used at the Met Office in the short term, and how they will eventually be used to improve assimilation of microwave atmospheric sounding data over snow-covered surfaces in the polar regions. Finally, Richard Essery (University of Edinburgh) presented: Why tundra snow is upside down in models, and why it matters?. Richard described how Earth System and Numerical Weather Prediction models are beginning to use more sophisticated representations of terrestrial snow, drawing on snow physics models that were first developed for forecasting avalanche risk. He explained that, having been developed for deep mid-latitude mountain snow, these models neglect important physical processes occurring in shallow tundra snow subjected to high winds and low temperatures. Understanding these processes is important for representing the thermal properties of Arctic snow and exploiting information from microwave remote sensing over snow-covered surfaces. Richard provided an overview of the ground-based component of the YOPP-endorsed MACSSIMIZE campaign, carried out at Trail Valley Creek in northwestern Canada. He presented results obtained from the snow pit measurements within MACSSIMIZE and drew attention to the existence of a granular depth hoar layer located below the wind-slab, which is a common feature of Arctic tundra snow. The resulting vertical density profile – with low-density depth hoar below a high-density wind-slab layer – is not reproduced by current snow models. Field instruments that can replace visual and subjective measurements of snow structure are now becoming available and can provide data for improving models. With speakers focussing on operational forecasting, Earth observation, and on observation and modelling of key Arctic processes such as clouds, sea ice and snow, this meeting illustrated the broad range of the UK's expertise and interest in Arctic prediction, and highlighted the significant contribution that scientists in the UK are making to YOPP. This RMetS meeting was convened by Ed Blockley and Chawn Harlow (Met Office) and was chaired by Ed Blockley (Met Office), with Jonathan Day (ECMWF) as rapporteur. The event was sponsored by the Centre for Polar Observation and Modelling (CPOM).

The quality control of observation data is an important link in the entire meteorological observation service system chain. The reactive gas observation is different from the observation of other meteorological elements in general, especially the special operations such as periodic zero / span inspection and multi-point calibration involved in instrument observation, and the quality control corresponding to its data is also special character. The main purpose of this article is to find the most effective quality control method for quality control of reactive gas observation data. Six types of quality control methods are used to perform quality control on reactive gas observation data, including extreme value check, triple standard deviation method, zero-span identification method, internal consistency check, time change check, and comprehensive identification check. By comprehensively identifying the data quality control result identification code given by each method, the quality control status of the data is finally determined. The quality control data used are the observation data of Beijing Shangdianzi atmospheric background station in 2018. Conclusion: A comparison of longsequence, multi-site analysis of the data after quality control using 6 methods and the data processed by a single method can draw conclusions. The multi-method comprehensive quality control results are superior to a single traditional meteorological observation data quality control method, which will cause fewer false judgments on the data and ensure the best quality control results of the observation data

We review current progress on an operational ocean forecasting system for the North West Atlantic. The Canadian Newfoundland Operational Ocean Forecasting System (C-NOOFS) is being developed under a national coordinated effort through the DFO Virtual Center for Ocean Modeling and Data Assimilation. The model development is reviewed along with the various data assimilation components and output. The model domain covers the North West Atlantic with open boundary conditions that are nested within a basin or global scale model from the French Operational Oceanography Service MERCATOR-Ocean. The model is forced by Canadian Meteorological Service wind forecasts with initial conditions taken from MERCATOR-Ocean ocean analysis. The initialization from a best estimate through ocean analysis with realistic boundary conditions as opposed to climatology will improve the model forecast. We demonstrate the improvement in oil drift dispersion by the use of ocean currents derived from a data assimilative model that includes the assimilation of sea surface height from a Satellite Altimeter and in-situ data. Useful outputs from this operational ocean forecasting system include enhancing search and rescue drift calculations and oil spill dispersion in near real time. Furthermore, model hind casts permit the determination of potential oil drift envelopes for specific regions throughout the Newfoundland Shelf. This enables scenario testing to better prepare for environmental responses regarding oil spills.Keywordsmodelingocean forecastingoil spill response

At the National Centers for Environmental Prediction (NCEP), a new three-dimensional variational data assimilation (3DVAR) analysis system was implemented into the operational Global Data Assimilation System (GDAS) on 1 May 2007. The new analysis system, the Gridpoint Statistical Interpolation (GSI), replaced the Spectral Statistical Interpolation (SSI) 3DVAR system, which had been operational since 1991. The GSI was developed at the Environmental Modeling Center at NCEP as part of an effort to create a more unified, robust, and efficient analysis scheme. The key aspect of the GSI is that it formulates the analysis in model grid space, which allows for more flexibility in the application of the background error covariances and makes it straightforward for a single analysis system to be used across a broad range of applications, including both global and regional modeling systems and domains. Due to the constraints of working with an operational system, the final GDAS package included many changes other than just a simple replacing of the SSI with the new GSI. The new GDAS package contained an upgrade to the Global Forecast System model, including a new vertical coordinate, as well as new features in the GSI that were never developed for the SSI. Some of these new features included changes to the observation selection, quality control, minimization algorithm, dynamic balance constraint, and assimilation of new observation types. The evaluation of the new system relative to the SSI-based system was performed for nearly an entire year of analyses and forecasts. The objective and subjective evaluations showed that the new package exhibited superior forecast performance relative to the old SSI-based system. The new system has been shown to improve forecast skill in the tropics and substantially reduce the short-term forecast error in the extratropics. This implementation has laid the groundwork for future scientific advancements in data assimilation at NCEP.

Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice.

U.S. National Weather Service (NWS) River Forecast Centers (RFCs) have produced monthly to seasonal hydrologic forecast ensemble outlooks for many years using historical precipitation and temperature as the forcing data for the ensembles within the Advanced Hydrologic Prediction System (AHPS). This has worked well for many water resources applications relying on long lead-time ensembles of greater than thirty days. In the eastern United States, there is considerable interest in shorter lead-time ensembles, particularly in the period of one to seven days that convey hydrologic forecast uncertainty resulting directly from the uncertainties in precipitation and temperature forecasts, with a focus on flood forecasting. This paper demonstrates the use of numerical weather prediction (NWP) model ensembles as the forcing for hydrologic models within the NWS hydrologic forecasting system, the Deltares Flood Early Warning System (FEWS) based Community Hydrologic Prediction System (CHPS), utilizing Ensemble Streamflow Prediction (ESP) system techniques. This approach makes it possible to produce probabilistic hydrologic forecasts that incorporate the skill and uncertainty of atmospheric models. Significant issues exist with directly using the meteorological ensembles, including biases and spatial resolution of the available data, but the approach allows for the generation of valuable information for end-user decision makers, covering a time frame for which there is little current probabilistic hydrologic guidance available. This paper presents a description of a joint project among four NWS RFCs to incorporate ensemble forecasts from a variety of meteorological model ensembles. These include the National Oceanic and Atmospheric Administration (NOAA) NWS National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS), North American Ensemble Forecast System (NAEFS), and Short Range Ensemble Forecast System (SREF). Verification results are presented for operational NAEFS and SREF based MMEFS real-time probabilistic hydrologic forecasts covering multi-year period. Improvements to the MMEFS are also recommended.

The available ecological and palaeoecological information for two sea ice-related marine diatoms (Bacillariophyceae), Thalassiosira antarctica Comber and Porosira glacialis (Grunow) Jørgensen, suggests that these two species have similar sea surface temperature (SST), sea surface salinity (SSS) and sea ice proximity preferences. From phytoplankton observations, both are described as summer or autumn bloom species, commonly found in low SST waters associated with sea ice, although rarely within the ice. Both species form resting spores (RS) as irradiance decreases, SST falls and SSS increases in response to freezing ice in autumn. Recent work analysing late Quaternary seasonally laminated diatom ooze from coastal Antarctic sites has revealed that sub-laminae dominated either by T. antarctica RS, or by P. glacialis RS, are nearly always deposited as the last sediment increment of the year, interpreted as representing autumn flux. In this study, we focus on sites from the East Antarctic margin and show that there is a spatial and temporal separation in whether T. antarctica RS or P. glacialis RS form the autumnal sub-laminae. For instance, in deglacial sediments from the Mertz Ninnis Trough (George V Coast) P. glacialis RS form the sub-laminae whereas in similar age sediments from Iceberg Alley (Mac.Robertson Shelf) T. antarctica RS dominate the autumn sub-lamina. In the Dumont d'Urville Trough (Adélie Land), mid-Holocene (Hypsithermal warm period) autumnal sub-laminae are dominated by T. antarctica RS whereas late Holocene (Neoglacial cool period) sub-laminae are dominated by P. glacialis RS. These observations from late Quaternary seasonally laminated sediments would appear to indicate that P. glacialis prefers slightly cooler ocean–climate conditions than T. antarctica. We test this relationship against two down-core Holocene quantitative diatom abundance records from Dumont d'Urville Trough and Svenner Channel (Princess Elizabeth Land) and compare the results with SST and sea ice concentration results of an Antarctic and Southern Ocean Holocene climate simulation that used a coupled atmosphere–sea ice–vegation model forced with orbital parameters and greenhouse gas concentrations. We find that abundance of P. glacialis RS is favoured by higher winter and spring sea ice concentrations and that a climatically-sensitive threshold exists between the abundance of P. glacialis RS and T. antarctica RS in the sediments. An increase to > 0.1 for the ratio of P. glacialis RS: T. antarctica RS indicates a change to increased winter sea ice concentration (to >80% concentration), cooler spring seasons with increased sea ice, slightly warmer autumn seasons with less sea ice and a change from ~ 7.5 months annual sea ice cover at a site to much greater than 7.5 months. In the East Antarctic sediment record, an increase in the ratio from <0.1 to above 0.1 occurs at the transition from the warmer Hypsithermal climate into the cooler Neoglacial climate (~ 4 cal kyr) indicating that the ratio between these two diatoms has the potential to be used as a semi-quantitative climate proxy.

It is quite common that in a regional ensemble system the large-scale initial condition (IC) perturbations and the lateral boundary condition (LBC) perturbations are taken from a global ensemble prediction system (EPS). The choice of global EPS as a driving model can have a significant impact on the performance of the regional EPS. This study investigates the impact of large-scale IC/LBC perturbations obtained from different global EPSs on the forecast quality of a regional EPS. For this purpose several experiments are conducted where the Aire Limitée Adaption dynamique Développement International–Limited Area Ensemble Forecasting (ALADIN-LAEF) regional ensemble is forced by two of the world’s leading global ensembles, the European Centre for Medium-Range Weather Forecasts’ Ensemble Prediction System (ECMWF-EPS) and the Global Ensemble Forecasting System (GEFS) from the National Centers for Environmental Prediction (NCEP), which provide the IC and LBC perturbations. The investigation is carried out for a 51-day period during summer 2010 over central Europe. The results indicate that forcing of the regional ensemble with GEFS performs better for surface parameters, whereas at upper levels forcing with ECMWF-EPS is superior. Using perturbations from GEFS lead to a considerably higher spread in ALADIN-LAEF, which is beneficial near the surface where regional EPSs are usually underdispersive. At upper levels, forcing with GEFS leads to an overdispersion of ALADIN-LAEF as a result of the large spread of some parameters, where forcing ALADIN-LAEF with ECMWF-EPS provides statistically more reliable forecasts. The results indicate that the best global EPS might not always provide the best ICs and LBCs for a regional ensemble.

The search for precipitation quality control (QC) methods has proven difficult. The high spatial and temporal variability associated with precipitation data causes high uncertainty and edge creep when regression-based approaches are applied. Precipitation frequency distributions are generally skewed rather than normally distributed. The commonly assumed normal distribution in QC methods is not a good representation of the actual distribution of precipitation and is inefficient in identifying the outliers. This paper first explores the use of a single gamma distribution, fit to all precipitation data, in a quality assurance test. A second test, the multiple intervals gamma distribution (MIGD) method, is introduced. It assumes that meteorological conditions that produce a certain range in average precipitation at surrounding stations will produce a predictable range at the target station. The MIGD bins the average of precipitation at neighboring stations; then, for the events in a specific bin, an associated gamma distribution is derived by fit to the same events at the target station. The new gamma distributions can then be used to establish the threshold for QC according to the user-selected probability of exceedance. This paper also explores a test (Q test) for precipitation, which uses a metric based on comparisons with neighboring stations. The performance of the three approaches is evaluated by assessing the fraction of “known” errors that can be identified in a seeded error dataset. The single gamma distribution and Q-test approach were found to be relatively efficient at identifying extreme precipitation values as potential outliers. However, the MIGD method outperforms the other two QC methods. This method identifies more seeded errors and results in fewer type I errors than the other methods. It will be adopted in the Applied Climatic Information System (ACIS) for precipitation quality control.

Sea surface salinity (SSS) is one of the key components of the Earth’s global water cycle. Reliable information on SSS is very important for ocean modelling, data assimilation, and ocean and climate research applications. In this study, SSS variability in the tropical Indian Ocean (TIO) was analysed using the Aquarius instrument on board the SAC-D satellite and in situ observations from the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) buoys and Array for Real-Time Geostrophic Oceanography (ARGO) data sets for the period 2012–2013. Comparison of two recent versions (V2 and V3) of Aquarius-based SSS estimates to nine RAMA buoys on a daily timescale showed excellent mutual agreement. The systematic underestimation of SSS by satellite-based V2 products over the TIO shows a clear advantage for the new version product (V3). A larger root-mean-square error of the order of 0.50 psu in the satellite-based SSS was observed over the highly variable (larger standard deviation) Bay of Bengal region as compared with ARGO data sets. In the eastern equatorial Indian Ocean region, satellite-based SSS overestimated SSS below 34 psu and underestimated SSS of 34–35 psu as compared with ARGO data. However, the V3 SSS from Aquarius showed marginal improvement over V2 SSS. Monthly variation and fast Fourier analysis of the satellite-based SSS estimates are in reasonably good agreement with in situ observations which suggest that Aquarius is able to capture SSS variability in the TIO. The Aquarius-based V3 SSS showed a temporal autocorrelation of 0.6 over most parts of the TIO up to day 10, and decreased gradually with time. Overall analysis suggests that Aquarius-derived V3 SSS can detect variability in SSS satisfactorily in the TIO and is in reasonably good agreement with in situ observations.

Abstract. Eurasian rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice concentration. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. Previous in situ and modelling studies suggest that local wind forcing is a driver of variability in Laptev sea surface salinity (SSS) but there is no consensus on the roles of Lena River discharge and sea ice cover in contributing to this variability or on the dominant driver of variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. In this region, satellite-based SSS is found to agree well with in situ data (r&gt;0.8) and provides notable improvements compared to the reanalysis product used in this study (r&gt;0.7) in capturing patterns and variability observed in in situ data. This study demonstrates a novel method of identifying the dominant drivers of interannual variability in Laptev Sea dynamics within reanalysis products and testing if these relationships appear to hold in satellite-based SSS, sea surface temperature (SST) data, and in situ observations. The satellite SSS data firmly establish what is suggested by reanalysis products and what has previously been subject to debate due to the limited years and locations analysed with in situ data; the zonal wind is the dominant driver of offshore or onshore Lena River plume transport. The eastward wind confines the plume to the southern Laptev Sea and drives alongshore transport into the East Siberian Sea, and westward wind drives offshore plume transport into the northern Laptev Sea. This finding is affirmed by the strong agreement in SSS pattern under eastward and westward wind regimes in all reanalyses and satellite products used in this study, as well as with in situ data. The pattern of SST also varies with the zonal wind component and drives spatial variability in sea ice concentration.

The surface heat budget over the Weddell Sea ice cover in 1996 was studied on the basis of data from Argos buoys equipped with meteorological sensors. In addition, a thermodynamic sea ice model, satellite‐based data on the sea ice concentration, sonar results on ice thickness distribution, and output from large‐scale meteorological models were all utilized. Applying the buoy data, the sensible heat flux over sea ice was calculated by Monin‐Obukhov theory using the gradient method, and the latent heat flux was obtained by the bulk method. A second estimate for the surface fluxes was obtained from the thermodynamic sea ice model, which was forced by the buoy observations. The results showed a reasonable agreement. The dominating component in the heat budget over ice was the net longwave radiation, which had a mean annual cooling effect of −28 W m−2. This was balanced by the net shortwave radiation (annual mean 13 W m−2), the sensible (13 W m−2) and latent (−3 W m−2) heat fluxes, and the conductive heat flux through the ice (5 W m−2). The regional surface fluxes over the fractured ice cover were estimated using the buoy data and Special Sensor Microwave Imager (SSMI)‐derived ice concentrations. In winter the regional surface sensible heat flux was sensitive to the ice concentration and thickness distribution. The estimate for the area‐averaged formation rate of new ice in leads in winter varies from 0.05 to 0.21 m per month depending on the SSMI processing algorithm applied. Countergradient fluxes occurred 8–10% of the time. The buoy observations were compared with the operational analyses of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the reanalyses of the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR). The 2 m air temperature and surface temperature were 3.5° and 4.4°C too high, respectively, in the ECMWF and 3.2° and 3.0°C too low in the NCEP/NCAR fields, but the models reproduced the synoptic‐scale temperature variations well. The errors seem to be related to the cloud cover and the surface boundary conditions. Neither of the models recognizes leads in the ice pack, and the ice and snow thicknesses are often far from reality. The distribution of the cloud cover in the both models differed a lot from observations.