Climate Change Initiative Project Research Articles

A comparison between Envisat and ICESat sea ice thickness in the Southern Ocean

Abstract. The crucial role that Antarctic sea ice plays in the global climate system is strongly linked to its thickness. While field observations are too sparse in the Southern Ocean to determine long-term trends of the Antarctic sea ice thickness (SIT) on a hemispheric scale, satellite radar altimetry data can be applied with a promising prospect. The European Space Agency's Sea Ice Climate Change Initiative project (ESA SICCI) generates sea ice thickness derived from Envisat, covering the entire Southern Ocean year-round from 2002 to 2012. In this study, the SICCI Envisat Antarctic SIT is first compared with an Ice, Cloud, and land Elevation Satellite (ICESat) SIT product retrieved with a modified ice density algorithm. Both data sets are compared to SIT estimates from upward-looking sonar (ULS) in the Weddell Sea, showing mean differences (MDs) and standard deviations (SDs, in parentheses) of 1.29 (0.65) m for Envisat − ULS (− denotes “minus” and the same below), while we find 1.11 (0.81) m for ICESat − ULS. The inter-comparisons are conducted for all seasons except for winter, based on the ICESat operating periods. According to the results, the differences between Envisat and ICESat SIT reveal significant temporal and spatial variations. More specifically, the smallest seasonal SIT MD (SD) of 0.00 m (0.39 m) for Envisat − ICESat is found in spring (October–November), while a larger MD (SD) of 0.52 (0.68 m) and 0.57 m (0.45 m) exists in summer (February–March) and autumn (May–June). It is also shown that from autumn to spring, mean Envisat SIT decreases while mean ICESat SIT increases. Our findings suggest that both overestimation of Envisat sea ice freeboard potentially caused by radar backscatter originating from inside the snow layer and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E, where EOS stands for Earth Observing System) snow depth biases and sea ice density uncertainties can possibly account for the differences between Envisat and ICESat SIT.

Interference-sensitive coastal SAR altimetry retracking strategy for measuring significant wave height

Satellite altimetry is a radar remote sensing technology for the precise observation of the ocean surface and its changes over time. Its measurements allow the determination of geometric and physical parameters such as sea level, significant wave height or wind speed. This work presents a novel coastal retracking algorithm for SAR altimetry to estimate the significant wave height. The concept includes an adaptive interference masking scheme to sense and mitigate spurious interfering signals that typically arise from strongly reflective targets in the coastal zone. The described procedure aims at increasing the number of valid records in the coastal zone. The effectiveness of the novel retracking algorithm is validated using the methodology recently developed in the framework of the European Space Agency Sea State Climate Change Initiative project. Several different metrics were extracted as functions of sea state and distance to the nearest coast: outliers, number of valid records, intrinsic noise, power spectral density, and correlation statistics for the comparison with wave model and in-situ data. Two coastal case study scenarios complement the validation. The results show that with the presented novel retracking algorithm, the number of valid 20-Hz records in the near coastal zone of less than 5 km off the coast is increased by more than 25% compared to the best competing coastal retracking algorithm with no degradation of quality of the estimated records. We emphasise the importance of the correct choice of the quality flag that is provided together with the significant wave height. Our findings suggest that the strategy for the significant wave height quality flag of the official baseline Level-2 product of the Sentinel-3 mission can be redefined to obtain more robust significant wave height estimates in the coastal zone.

Using bottom trawls to monitor subsurface water clarity in marine ecosystems

Biophysical processes that affect subsurface water clarity play a key role in ecosystem function. However, subsurface water clarity is poorly monitored in marine ecosystems because doing so requires in-situ sampling that is logistically difficult to conduct and sustain. Novel solutions are thus needed to improve monitoring of subsurface water clarity. To that end, we developed a sampling method and data processing algorithm that enable the use of bottom trawl fishing gear as a platform for conducting subsurface water clarity monitoring using trawl-mounted irradiance sensors without disruption to fishing operations. The algorithm applies quality control checks to irradiance measurements and calculates the downwelling diffuse attenuation coefficient, Kd, and optical depth, ζ– apparent optical properties (AOPs) that characterize the rate of decrease in downwelling irradiance and relative irradiance transmission to depth, respectively. We applied our algorithm to irradiance measurements, obtained using bottom-trawl-mounted archival tags equipped with a photodiode collected during NOAA’s Alaska Fisheries Science Center annual summer bottom trawl surveys of the eastern Bering Sea continental shelf from 2004 to 2018. We validated our AOPs by quantitatively comparing surface-weighted Kd from tags to the multi-sensor Kd(490) product from the Ocean Colour Climate Change Initiative project (OC-CCI) and qualitatively evaluating whether tag Kd was consistent with patterns of subsurface chlorophyll-a concentrations predicted by a coupled regional physical-biological model (Bering10K-BESTNPZ). We additionally examined patterns and trends in water clarity in the eastern Bering Sea. Key findings are: 1) water clarity decreased significantly from 2004 to 2018; 2) a recurrent, pycnocline-associated, maximum in Kd occurred over much of the northwestern shelf, putatively due to a subsurface chlorophyll maximum; and 3) a turbid bottom layer (nepheloid layer) was present over a large portion of the eastern Bering Sea shelf. Our study demonstrates that bottom trawls can provide a useful platform for monitoring water clarity, especially when trawling is conducted as part of a systematic stock assessment survey.

The X-TRACK/ALES multi-mission processing system: New advances in altimetry towards the coast

In the context of the ESA Climate Change Initiative project, a new coastal sea level altimetry product has been developed in order to support advances in coastal sea level variability studies. Measurements from Jason-1,2&3 missions have been retracked with the Adaptive Leading Edge Subwaveform (ALES) Retracker and then ingested in the X-TRACK software with the best possible set of altimetry corrections. These two coastal altimetry processing approaches, previously successfully validated and applied to coastal sea level research, are combined here for the first time in order to derive a 16-year-long (June 2002 to May 2018), high-resolution (20-Hz), along-track sea level dataset in six regions: Northeast Atlantic, Mediterranean Sea, West Africa, North Indian Ocean, Southeast Asia and Australia. The study demonstrates that this new coastal sea level product called X-TRACK/ALES is able to extend the spatial coverage of sea level altimetry data ~3.5 km in the land direction, when compared to the X-TRACK 1-Hz dataset. We also observe a large improvement in coastal sea level data availability from Jason-1 to Jason-3, with data at 3.6 km, 1.9 km and 0.9 km to the coast on average, for Jason-1, Jason-2 and Jason-3, respectively. When combining measurements from Jason-1 to Jason-3, we reach a distance of 1.2–4 km to the coast. When compared to tide gauge data, the accuracy of the new altimetry near-shore sea level estimations also improves. In terms of correlations with a large set of independent tide gauge observations selected in the six regions, we obtain an average value of 0.77. We also show that it is now possible to derive from the X-TRACK/ALES product an estimation of the ocean current variability up to 5 km to the coast. This new altimetry dataset, freely available, will provide a valuable contribution of altimetry in coastal marine research community.

Tropical Instability Waves in the Atlantic Ocean: Investigating the Relative Role of Sea Surface Salinity and Temperature From 2010 to 2018

AbstractWe identify and analyze tropical instability waves (TIWs) in the equatorial Atlantic Ocean during 2010–2018 using satellite derived observations of sea surface salinity (SSS), sea surface temperature (SST), sea level anomaly, and Argo profiles. In particular, the weekly 50‐km resolution SSS time series from the climate change initiative project provides an unprecedented opportunity to observe the salinity structure at a scale closer to the SST scale. We examine the relative contributions of SSS and SST to the horizontal surface density gradient on seasonal and interannual time scales and how they contribute to the TIW properties and energetics. For the central Atlantic TIWs, the maximum of the SST contribution to the density anomaly lags the SSS one by approximately one month. Argo vertical profiles indicate that temperature and salinity both significantly contribute to TIW‐related density anomalies. In May–June, salinity contributes to 50% of the perturbation potential energy in the top 60 m, and between 30% and 45% from July to September. While variations in SST appear to be related to dynamic processes, the interannual variability of SSS is also influenced by precipitations. However, the two leading modes of variability in the region (Atlantic Meridional and Zonal modes) do not well explain at 1°N these interannual variations.

Coastal sea level rise at Senetosa (Corsica) during the Jason altimetry missions

Abstract. In the context of the ESA Climate Change Initiative project, we are engaged in a regional reprocessing of high-resolution (20 Hz) altimetry data of the classical missions in a number of the world's coastal zones. It is done using the ALES (Adaptive Leading Edge Subwaveform) retracker combined with the X-TRACK system dedicated to improve geophysical corrections at the coast. Using the Jason-1 and Jason-2 satellite data, high-resolution, along-track sea level time series have been generated, and coastal sea level trends have been computed over a 14-year time span (from July 2002 to June 2016). In this paper, we focus on a particular coastal site where the Jason track crosses land, Senetosa, located south of Corsica in the Mediterranean Sea, for two reasons: (1) the rate of sea level rise estimated in this project increases significantly in the last 4–5 km to the coast compared to what is observed further offshore, and (2) Senetosa is the calibration site for the TOPEX/Poseidon and Jason altimetry missions, which are equipped for that purpose with in situ instrumentation, in particular tide gauges and a Global Navigation Satellite System (GNSS) antenna. A careful examination of all the potential errors that could explain the increased rate of sea level rise close to the coast (e.g., spurious trends in the geophysical corrections, imperfect inter-mission bias estimate, decrease of valid data close to the coast and errors in waveform retracking) has been carried out, but none of these effects appear able to explain the trend increase. We further explored the possibility that it results from real physical processes. Change in wave conditions was investigated, but wave setup was excluded as a potential contributor because the magnitude was too low and too localized in the immediate vicinity of the shoreline. A preliminary model-based investigation about the contribution of coastal currents indicates that it could be a plausible explanation of the observed change in sea level trend close to the coast.

The Sea State CCI dataset v1: towards a sea state climate data record based on satellite observations

Abstract. Sea state data are of major importance for climate studies, marine engineering, safety at sea and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state climate data record, the Global Climate Observing System has listed “Sea State” as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a programme of the European Space Agency, whose objective is to realise the full potential of global Earth observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data, last access: 25 August 2020) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/ (last access: 25 August 2020). Three products are available: a multi-mission along-track L2P product (http://dx.doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (http://dx.doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c).

Round Robin Assessment of Radar Altimeter Low Resolution Mode and Delay-Doppler Retracking Algorithms for Significant Wave Height

Radar altimeters have been measuring ocean significant wave height for more than three decades, with their data used to record the severity of storms, the mixing of surface waters and the potential threats to offshore structures and low-lying land, and to improve operational wave forecasting. Understanding climate change and long-term planning for enhanced storm and flooding hazards are imposing more stringent requirements on the robustness, precision, and accuracy of the estimates than have hitherto been needed. Taking advantage of novel retracking algorithms, particularly developed for the coastal zone, the present work aims at establishing an objective baseline processing chain for wave height retrieval that can be adapted to all satellite missions. In order to determine the best performing retracking algorithm for both Low Resolution Mode and Delay-Doppler altimetry, an objective assessment is conducted in the framework of the European Space Agency Sea State Climate Change Initiative project. All algorithms process the same Level-1 input dataset covering a time-period of up to two years. As a reference for validation, an ERA5-based hindcast wave model as well as an in-situ buoy dataset from the Copernicus Marine Environment Monitoring Service In Situ Thematic Centre database are used. Five different metrics are evaluated: percentage and types of outliers, level of measurement noise, wave spectral variability, comparison against wave models, and comparison against in-situ data. The metrics are evaluated as a function of the distance to the nearest coast and the sea state. The results of the assessment show that all novel retracking algorithms perform better in the majority of the metrics than the baseline algorithms currently used for operational generation of the products. Nevertheless, the performance of the retrackers strongly differ depending on the coastal proximity and the sea state. Some retrackers show high correlations with the wave models and in-situ data but significantly under- or overestimate large-scale spectral variability. We propose a weighting scheme to select the most suitable retrackers for the Sea State Climate Change Initiative programme.

The effect of considering polar vortex dynamics in the validation of satellite total ozone observations

The main aim of this paper is to demonstrate the magnitude in which validation results of space-based total ozone column, TOC, measurements are affected by the location of the polar vortex. Potential Vorticity is used as an indicator to determine the polar vortex's boundary and surface area, provided by ERA-Interim reanalysis datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). The total ozone measurements that were examined fall within the hemispherical winter-spring period, focusing on middle and high latitudes on both hemispheres. The space-based total ozone data produced by the GODFIT (GOME-type Direct FITting) v4 algorithm as applied to the OMI/Aura, GOME-2/Metop-A and Metop-B observations, part of the European Space Agency's Ozone Climate Change Initiative project, are compared to TOC measurements from Brewer and Dobson spectrophotometers archived at the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) repository. The satellite-to-ground collocations were classified depending on whether both satellite and ground-based measurements are inside or outside the polar vortex (matched) or one measurement is inside whereas the other one is outside the polar vortex (mismatched). It is shown that the matched cases present an improved agreement between satellite and ground TOC measurements compared to the mismatched cases. Considering all examined stations for GOME2-A, GOME2-B, and OMI, the mean bias for the mismatched cases was found to be 2.22 ± 0.4%, 1.84 ± 0.57%, and 1.93 ± 0.39% respectively, while for the matched cases was found to be 1.46 ± 0.17%, 1.69 ± 0.22%, 1.7 ± 0.19% respectively. For the three satellite sensors, the mismatched cases do not exceed 3.3% of the total available collocations per station during the winter-spring period between years 2007 and 2017, where applicable. Hence, on a global scale, the exclusion of mismatched collocated measurements does not cause significant changes and the resulting impact on the validation of the satellite TOCs is small. However, when considering single stations consistently affected by the polar vortex, we conclude that mismatched cases should be excluded from the comparisons.

Evaluating GRACE Mass Change Time Series for the Antarctic and Greenland Ice Sheet—Methods and Results

Satellite gravimetry data acquired by the Gravity Recovery and Climate Experiment (GRACE) allows to derive the temporal evolution in ice mass for both the Antarctic Ice Sheet (AIS) and the Greenland Ice Sheet (GIS). Various algorithms have been used in a wide range of studies to generate Gravimetric Mass Balance (GMB) products. Results from different studies may be affected by substantial differences in the processing, including the applied algorithm, the utilised background models and the time period under consideration. This study gives a detailed description of an assessment of the performance of GMB algorithms using actual GRACE monthly solutions for a prescribed period as well as synthetic data sets. The inter-comparison exercise was conducted in the scope of the European Space Agency’s Climate Change Initiative (CCI) project for the AIS and GIS, and was, for the first time, open to everyone. GMB products generated by different groups could be evaluated and directly compared against each other. For the period from 2003-02 to 2013-12, estimated linear trends in ice mass vary between −99 Gt/yr and −108 Gt/yr for the AIS and between −252 Gt/yr and −274 Gt/yr for the GIS, respectively. The spread between the solutions is larger if smaller drainage basins or gridded GMB products are considered. Finally, findings from the exercise formed the basis to select the algorithms used for the GMB product generation within the AIS and GIS CCI project.

The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment

Abstract. The ECMWF OCEAN5 system is a global ocean and sea-ice ensemble of reanalysis and real-time analysis. This paper gives a full description of the OCEAN5 system, with the focus on upgrades of system components with respect to its predecessors, ORAS4 and ORAP5. An important novelty in OCEAN5 is the ensemble generation strategy that includes perturbation of initial conditions and a generic perturbation scheme for observations and forcing fields. Other upgrades include revisions to the a priori bias correction scheme, observation quality control and assimilation method for sea-level anomalies. The OCEAN5 historical reconstruction of the ocean and sea-ice state is the ORAS5 reanalysis, which includes five ensemble members and covers the period from 1979 onwards. Updated versions of observation data sets are used in ORAS5 production, with special attention devoted to the consistency of sea surface temperature (SST) and sea-ice observations. Assessment of ORAS5 through sensitivity experiments suggests that all system components contribute to an improved fit to observation in reanalyses, with the most prominent contribution from direct assimilation of ocean in situ observations. Results of observing system experiments further suggest that the Argo float is the most influential observation type in our data assimilation system. Assessment of ORAS5 has also been carried out for several key ocean state variables and verified against reference climate data sets from the ESA CCI (European Space Agency Climate Change Initiative) project. With respect to ORAS4, ORAS5 has improved ocean climate state and variability in terms of SST and sea level, mostly due to increased model resolution and updates in assimilated observation data sets. In spite of the improvements, ORAS5 still underestimates the temporal variance of sea level and continues exhibiting large SST biases in the Gulf Stream and its extension regions which are possibly associated with misrepresentation of front positions. Overall, the SST and sea-ice uncertainties estimated using five ORAS5 ensemble members have spatial patterns consistent with those of analysis error. The ensemble spread of sea ice is commensurable with the sea-ice analysis error. On the contrary, the ensemble spread is under-dispersive for SST.

Intercomparison and Validation of SAR-Based Ice Velocity Measurement Techniques within the Greenland Ice Sheet CCI Project

Ice velocity is one of the products associated with the Ice Sheets Essential Climate Variable. This paper describes the intercomparison and validation of ice-velocity measurements carried out by several international research groups within the European Space Agency Greenland Ice Sheet Climate Change Initiative project, based on space-borne Synthetic Aperture Radar (SAR) data. The goal of this activity was to survey the best SAR-based measurement and error characterization approaches currently in practice. To this end, four experiments were carried out, related to different processing techniques and scenarios, namely differential SAR interferometry, multi aperture SAR interferometry and offset-tracking of incoherent as well as of partially-coherent data. For each task, participants were provided with common datasets covering areas located on the Greenland ice-sheet margin and asked to provide mean velocity maps, quality characterization and a description of processing algorithms and parameters. The results were then intercompared and validated against GPS data, revealing in several cases significant differences in terms of coverage and accuracy. The algorithmic steps and parameters influencing the coverage, accuracy and spatial resolution of the measurements are discussed in detail for each technique, as well as the consistency between quality parameters and validation results. This allows several recommendations to be formulated, in particular concerning procedures which can reduce the impact of analyst decisions, and which are often found to be the cause of sub-optimal algorithm performance.

A new phase in the production of quality-controlled sea level data

Abstract. Sea level is an essential climate variable (ECV) that has a direct effect on many people through inundations of coastal areas, and it is also a clear indicator of climate changes due to external forcing factors and internal climate variability. Regional patterns of sea level change inform us on ocean circulation variations in response to natural climate modes such as El Niño and the Pacific Decadal Oscillation, and anthropogenic forcing. Comparing numerical climate models to a consistent set of observations enables us to assess the performance of these models and help us to understand and predict these phenomena, and thereby alleviate some of the environmental conditions associated with them. All such studies rely on the existence of long-term consistent high-accuracy datasets of sea level. The Climate Change Initiative (CCI) of the European Space Agency was established in 2010 to provide improved time series of some ECVs, including sea level, with the purpose of providing such data openly to all to enable the widest possible utilisation of such data. Now in its second phase, the Sea Level CCI project (SL_cci) merges data from nine different altimeter missions in a clear, consistent and well-documented manner, selecting the most appropriate satellite orbits and geophysical corrections in order to further reduce the error budget. This paper summarises the corrections required, the provenance of corrections and the evaluation of options that have been adopted for the recently released v2.0 dataset (https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612). This information enables scientists and other users to clearly understand which corrections have been applied and their effects on the sea level dataset. The overall result of these changes is that the rate of rise of global mean sea level (GMSL) still equates to ∼ 3.2 mm yr−1 during 1992–2015, but there is now greater confidence in this result as the errors associated with several of the corrections have been reduced. Compared with v1.1 of the SL_cci dataset, the new rate of change is 0.2 mm yr−1 less during 1993 to 2001 and 0.2 mm yr−1 higher during 2002 to 2014. Application of new correction models brought a reduction of altimeter crossover variances for most corrections.

Evaluating a new homogeneous total ozone climate data record from GOME/ERS‐2, SCIAMACHY/Envisat, and GOME‐2/MetOp‐A

AbstractThe European Space Agency's Ozone Climate Change Initiative (O3‐CCI) project aims at producing and validating a number of high‐quality ozone data products generated from different satellite sensors. For total ozone, the O3‐CCI approach consists of minimizing sources of bias and systematic uncertainties by applying a common retrieval algorithm to all level 1 data sets, in order to enhance the consistency between the level 2 data sets from individual sensors. Here we present the evaluation of the total ozone products from the European sensors Global Ozone Monitoring Experiment (GOME)/ERS‐2, SCIAMACHY/Envisat, and GOME‐2/MetOp‐A produced with the GOME‐type Direct FITting (GODFIT) algorithm v3. Measurements from the three sensors span more than 16 years, from 1996 to 2012. In this work, we present the latest O3‐CCI total ozone validation results using as reference ground‐based measurements from Brewer and Dobson spectrophotometers archived at the World Ozone and UV Data Centre of the World Meteorological Organization as well as from UV‐visible differential optical absorption spectroscopy (DOAS)/Système D′Analyse par Observations Zénithales (SAOZ) instruments from the Network for the Detection of Atmospheric Composition Change. In particular, we investigate possible dependencies in these new GODFIT v3 total ozone data sets with respect to latitude, season, solar zenith angle, and different cloud parameters, using the most adequate type of ground‐based instrument. We show that these three O3‐CCI total ozone data products behave very similarly and are less sensitive to instrumental degradation, mainly as a result of the new reflectance soft‐calibration scheme. The mean bias to the ground‐based observations is found to be within the 1 ± 1% level for all three sensors while the near‐zero decadal stability of the total ozone columns (TOCs) provided by the three European instruments falls well within the 1–3% requirement of the European Space Agency's Ozone Climate Change Initiative project.

Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative

Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.

Phytoplankton phenology indices in coral reef ecosystems: Application to ocean-color observations in the Red Sea

Phytoplankton, at the base of the marine food web, represent a fundamental food source in coral reef ecosystems. The timing (phenology) and magnitude of the phytoplankton biomass are major determinants of trophic interactions. The Red Sea is one of the warmest and most saline basins in the world, characterized by an arid tropical climate regulated by the monsoon. These extreme conditions are particularly challenging for marine life. Phytoplankton phenological indices provide objective and quantitative metrics to characterize phytoplankton seasonality. The indices i.e. timings of initiation, peak, termination and duration are estimated here using 15years (1997–2012) of remote sensing ocean-color data from the European Space Agency (ESA) Climate Change Initiative project (OC-CCI) in the entire Red Sea basin. The OC-CCI product, comprising merged and bias-corrected observations from three independent ocean-color sensors (SeaWiFS, MODIS and MERIS), and processed using the POLYMER algorithm (MERIS period), shows a significant increase in chlorophyll data coverage, especially in the southern Red Sea during the months of summer NW monsoon. In open and reef-bound coastal waters, the performance of OC-CCI chlorophyll data is shown to be comparable with the performance of other standard chlorophyll products for the global oceans. These features have permitted us to investigate phytoplankton phenology in the entire Red Sea basin, and during both winter SE monsoon and summer NW monsoon periods. The phenological indices are estimated in the four open water provinces of the basin, and further examined at six coral reef complexes of particular socio-economic importance in the Red Sea, including Siyal Islands, Sharm El Sheikh, Al Wajh bank, Thuwal reefs, Al Lith reefs and Farasan Islands. Most of the open and deeper waters of the basin show an apparent higher chlorophyll concentration and longer duration of phytoplankton growth during the winter period (relative to the summer phytoplankton growth period). In contrast, most of the reef-bound coastal waters display equal or higher peak chlorophyll concentrations and equal or longer duration of phytoplankton growth during the summer period (relative to the winter phytoplankton growth period). The ecological and biological significance of the phytoplankton seasonal characteristics are discussed in context of ecosystem state assessment, and particularly to support further understanding of the structure and functioning of coral reef ecosystems in the Red Sea.

The ozone climate change initiative: Comparison of four Level-2 processors for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS)

The MIPAS spectrometer onboard the Envisat platform observed infrared emission from the Earth's limb between 2002 and 2012. It recorded high-resolution spectra during day and night, from pole to pole and between 6 and 70km altitude in the nominal measurement mode or up to 170km in special measurement modes, producing daily more than 1000 vertical profiles of various trace gases. The operational Level-2 data are processed by ESA/DLR but there exist three other, independent research Level-2 processors that are hosted by ISAC-CNR/University of Bologna, Oxford University, and KIT IMK/IAA. All four Level-2 processors rely on the same Level-1b data provided by ESA but their retrieval schemes differ. As part of ESA's Ozone Climate Change Initiative project, an intercomparison of the four MIPAS processors took place, in which vertical ozone profiles retrieved by these four processors from MIPAS nominal mode measurements were compared for 2007 and 2008. We present the results of this comparison exercise, which consisted of five parts: an information content study of the vertical averaging kernels, an intercomparison of zonal seasonal means and spreads, a determination of biases through comparison to ozonesonde and lidar measurements, a comparison to other satellite records (bias estimation and precision assessment with respect to ACE-FTS and Aura-MLS data), and a geophysical validation of the provided error bars using MIPAS–MIPAS collocations.The four processors demonstrate similar performance. All processors use the same Level-1b data from ESA, apply global fits, and use microwindows instead of the full spectrum. The main differences in the processing schemes include the choice of microwindows, the regularization approach, the treatment of negative retrieved values, and the cloud detection threshold. The different regularization schemes lead to a different trade-off between noise and resolution, but without a clear average advantage for any particular data set. The vertical resolution is typically 3–5km and the single profile precision is about 2–3%. In the middle and upper stratosphere, at 25–45km, all four MIPAS processors clearly show a high bias of 2 to 5% relative to all reference instruments. The similarity of the structure and magnitude of the bias among the MIPAS data sets indicates that the bias is most likely linked to the use of microwindows of the MIPAS AB band. The satellite intercomparisons show furthermore that for the KIT dataset, the onset of the high bias starts at a somewhat higher altitude (only above 35km) than for the other three datasets. This is likely due to the more restrictive use of the AB band by the KIT processor, which comes at the cost of a coarser vertical resolution near the ozone volume mixing ratio (vmr) peak. In the troposphere, the Level-2 algorithms that suppress negative ozone values in the iterative retrieval process produce a larger positive bias than the algorithm that does not follow such a strategy. Our main conclusion is that the four MIPAS processors are more similar to each other than to any other reference instrument. This indicates that the observed biases are very likely instrument-related.

Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project

Abstract. Sea level is one of the 50 Essential Climate Variables (ECVs) listed by the Global Climate Observing System (GCOS) in climate change monitoring. In the past two decades, sea level has been routinely measured from space using satellite altimetry techniques. In order to address a number of important scientific questions such as "Is sea level rise accelerating?", "Can we close the sea level budget?", "What are the causes of the regional and interannual variability?", "Can we already detect the anthropogenic forcing signature and separate it from the internal/natural climate variability?", and "What are the coastal impacts of sea level rise?", the accuracy of altimetry-based sea level records at global and regional scales needs to be significantly improved. For example, the global mean and regional sea level trend uncertainty should become better than 0.3 and 0.5 mm year−1, respectively (currently 0.6 and 1–2 mm year−1). Similarly, interannual global mean sea level variations (currently uncertain to 2–3 mm) need to be monitored with better accuracy. In this paper, we present various data improvements achieved within the European Space Agency (ESA) Climate Change Initiative (ESA CCI) project on "Sea Level" during its first phase (2010–2013), using multi-mission satellite altimetry data over the 1993–2010 time span. In a first step, using a new processing system with dedicated algorithms and adapted data processing strategies, an improved set of sea level products has been produced. The main improvements include: reduction of orbit errors and wet/dry atmospheric correction errors, reduction of instrumental drifts and bias, intercalibration biases, intercalibration between missions and combination of the different sea level data sets, and an improvement of the reference mean sea surface. We also present preliminary independent validations of the SL_cci products, based on tide gauges comparison and a sea level budget closure approach, as well as comparisons with ocean reanalyses and climate model outputs.

Potential of Probabilistic Hydrometeorological Approach for Precipitation-Based Soil Moisture Estimation

AbstractIn this paper, a hydrometeorological approach to the probabilistic simulation of soil moisture is proposed. The time series of in situ soil moisture and meteorological variables at a monthly scale from different monitoring stations across India are utilized. Preliminary investigation with both precipitation and near-surface air temperature as meteorological variables reveals that the strength of association between soil moisture and precipitation is more significant compared to that between soil moisture and temperature. Precipitation-based probabilistic estimation of soil moisture using the proposed hydrometeorological approach is tested with in situ observed soil moisture data and with soil moisture data of the Climate Change Initiative project, funded by the European Space Agency. The results are found to be promising. In addition, the proposed hydrometeorological approach is able to provide the information on uncertainty associated with the estimation. It is also shown that the parameter of th...

On sampling uncertainty of satellite ozone profile measurements

Abstract. Satellite measurements sample continuous fields of atmospheric constituents at discrete locations and times. However, insufficient or inhomogeneous sampling, if not taken into account, can result in inaccurate average estimates and even induce spurious features. We propose to characterize the spatiotemporal inhomogeneity of atmospheric measurements by a measure, which is a linear combination of the asymmetry and entropy of a sampling distribution. It is shown that this measure is related to the so-called sampling uncertainty, which occurs due to non-uniform sampling patterns. We have estimated the sampling uncertainty of zonal mean ozone profiles for six limb-viewing satellite instruments participating in the European Space Agency Ozone Climate Change Initiative project using the high-resolution ozone field simulated with the FinROSE chemistry-transport model. It is shown that the sampling uncertainty for the instruments with coarse sampling is not negligible and can be as large as a few percent. It is found that the standard deviation of the sampling uncertainty in the monthly zonal mean data allows for a simple parameterization in terms of the product of the standard deviation of natural variations and the proposed inhomogeneity measure. The sampling uncertainty estimates improve the uncertainty quantification and can be used in comprehensive data analyses. The focus of this work is the vertical ozone distributions measured by limb-viewing satellite instruments, but the developed methods can also be applied to different satellite, ground-based and in situ measurements.