The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables

Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties. There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together. This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.

Above-water reflectance for the evaluation of adjacency effects in Earth observation data: initial results and methods comparison for near-coastal waters in the Western Channel, UK

Un-supervised hyperspectral remote-sensing reflectance data (<15 km from the shore) were collected from a moving research vessel. Two different processing methods were compared. The results were similar to concurrent Aqua-MODIS and Suomi-NPP-VIIRS satellite data.

The global distribution of phytoplankton size spectrum and size classes from their light-absorption spectra derived from satellite data

The absorption spectra of phytoplankton in the visible domain hold implicit information on the phytoplankton community structure. Here we use this information to retrieve quantitative information on phytoplankton size structure by developing a novel method to compute the exponent of an assumed power-law for their particle-size spectrum. This quantity, in combination with total chlorophyll-a concentration, can be used to estimate the fractional concentration of chlorophyll in any arbitrarily-defined size class of phytoplankton. We further define and derive expressions for two distinct measures of cell size of mixed. populations, namely, the average spherical diameter of a bio-optically equivalent homogeneous population of cells of equal size, and the average equivalent spherical diameter of a population of cells that follow a power-law particle-size distribution. The method relies on measurements of two quantities of a phytoplankton sample: the concentration of chlorophyll-a, which is an operational index of phytoplankton biomass, and the total absorption coefficient of phytoplankton in the red peak of visible spectrum at 676 nm. A sensitivity analysis confirms that the relative errors in the estimates of the exponent of particle size spectra are reasonably low. The exponents of phytoplankton size spectra, estimated for a large set of in situ data from a variety of oceanic environments (similar to 2400 samples), are within a reasonable range; and the estimated fractions of chlorophyll in pico-, nano- and micro-phytoplankton are generally consistent with those obtained by an independent, indirect method based on diagnostic pigments determined using high-performance liquid chromatography. The estimates of cell size for in situ samples dominated by different phytoplankton types (diatoms, prymnesiophytes, Prochlorococcus, other cyanobacteria and green algae) yield nominal sizes consistent with the taxonomic classification. To estimate the same quantities from satellite-derived ocean-colour data, we combine our method with algorithms for obtaining inherent optical properties from remote sensing. The spatial distribution of the size-spectrum exponent and the chlorophyll fractions of pico-, nano- and micro-phytoplankton estimated from satellite remote sensing are in agreement with the current understanding of the biogeography of phytoplankton functional types in the global oceans. This study contributes to our understanding of the distribution and time evolution of phytoplankton size structure in the global oceans.

Surface Ocean CO2 Atlas (SOCAT) gridded data products

As a response to public demand for a well-documented, quality controlled, publically available, global surface ocean carbon dioxide (CO2) data set, the international marine carbon science community developed the Surface Ocean CO2 Atlas (SOCAT). The first SOCAT product is a collection of 6.3 million quality controlled surface CO2 data from the global oceans and coastal seas, spanning four decades (1968–2007). The SOCAT gridded data presented here is the second data product to come from the SOCAT project. Recognizing that some groups may have trouble working with millions of measurements, the SOCAT gridded product was generated to provide a robust, regularly spaced CO2 fugacity (fCO2) product with minimal spatial and temporal interpolation, which should be easier to work with for many applications. Gridded SOCAT is rich with information that has not been fully explored yet (e.g., regional differences in the seasonal cycles), but also contains biases and limitations that the user needs to recognize and address (e.g., local influences on values in some coastal regions).

Coccolithophore surface distributions in the North Atlantic and their modulation of the air-sea flux of CO2 from 10 years of satellite Earth observation data

Coccolithophores are the primary oceanic phytoplankton responsible for the production of calcium carbonate (CaCO3). These climatically important plankton play a key role in the oceanic carbon cycle as a major contributor of carbon to the open ocean carbonate pump (similar to 50 %) and their calcification can affect the atmosphere-to-ocean (air-sea) uptake of carbon dioxide (CO2) through increasing the seawater partial pressure of CO2 (pCO(2)). Here we document variations in the areal extent of surface blooms of the globally important coccolithophore, Emiliania huxleyi, in the North Atlantic over a 10-year period (1998-2007), using Earth observation data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). We calculate the annual mean sea surface areal coverage of E. huxleyi in the North Atlantic to be 474 000 +/- 104 000 km(2), which results in a net CaCO3 carbon (CaCO3-C) production of 0.14-1.71 Tg CaCO3-C per year. However, this surface coverage (and, thus, net production) can fluctuate inter-annually by -54/+81% about the mean value and is strongly correlated with the El Nino/Southern Oscillation (ENSO) climate oscillation index (r = 0.75, p < 0.02). Our analysis evaluates the spatial extent over which the E. huxleyi blooms in the North Atlantic can increase the pCO(2) and, thus, decrease the localised air-sea flux of atmospheric CO2. In regions where the blooms are prevalent, the average reduction in the monthly air-sea CO2 flux can reach 55%. The maximum reduction of the monthly air-sea CO2 flux in the time series is 155 %. This work suggests that the high variability, frequency and distribution of these calcifying plankton and their impact on pCO(2) should be considered if we are to fully understand the variability of the North Atlantic air-to-sea flux of CO2. We estimate that these blooms can reduce the annual N. Atlantic net sink atmospheric CO2 by between 3-28 %.

Real-time sea surface air temperature retrieval over Southern Ocean using AMSR-E data

The AMSR-E satellite data and in-situ data were applied to retrieve sea surface air temperature (Ta) over the Southern Ocean. The in-situ data were obtained from the 24~(th) -26~(th) Chinese Antarctic Expeditions during 2008-2010. First, Ta was used to analyze the relativity with the bright temperature (Tb) from the twelve channels of AMSR-E, and no high relativity was found between Ta and Tb from any of the channels. The highest relativity was 0.38 (with 23.8 GHz). The dataset for the modeling was obtained by using in-situ data to match up with Tb, and two methods were applied to build the retrieval model. In multi-parameters regression method, the Tbs from 12 channels were used to the model and the region was divided into two parts according to the latitude of 50°S. The retrieval results were compared with the in-situ data. The Root Mean Square Error (RMS) and relativity of high latitude zone were 0.96℃and 0.93, respectively. And those of low latitude zone were 1.29 ℃ and 0.96, respectively. Artificial neural network (ANN) method was applied to retrieve Ta.The RMS and relativity were 1.26 ℃ and 0.98, respectively.

Composite front maps for improved visibility of dynamic sea-surface features on cloudy SeaWiFS and AVHRR data

Novel techniques have been developed for increasing the value of cloud-affected sequences of Advanced Very High Resolution Radiometer (AVHRR) sea-surface temperature (SST) data and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean colour data for visualising dynamic physical and biological oceanic processes such as fronts, eddies and blooms. The proposed composite front map approach is to combine the location, strength and persistence of all fronts observed over several days into a single map, which allows intuitive interpretation of mesoscale structures. This method achieves a synoptic view without blurring dynamic features, an inherent problem with conventional time-averaging compositing methods. Objective validation confirms a significant improvement in feature visibility on composite maps compared to individual front maps. A further novel aspect is the automated detection of ocean colour fronts, correctly locating 96% of chlorophyll fronts in a test data set. A sizeable data set of 13,000 AVHRR and 1200 SeaWiFS scenes automatically processed using this technique is applied to the study of dynamic processes off the Iberian Peninsula such as mesoscale eddy generation, and many additional applications are identified. Front map animations provide a unique insight into the evolution of upwelling and eddies.

Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data

The detection of dense harmful algal blooms (HABs) by satellite remote sensing is usually based on analysis of chlorophyll-a as a proxy. However, this approach does not provide information about the potential harm of bloom, nor can it identify the dominant species. The developed HAB risk classification method employs a fully automatic data-driven approach to identify key characteristics of water leaving radiances and derived quantities, and to classify pixels into “harmful”, “non-harmful” and “no bloom” categories using Linear Discriminant Analysis (LDA). Discrimination accuracy is increased through the use of spectral ratios of water leaving radiances, absorption and backscattering. To reduce the false alarm rate the data that cannot be reliably classified are automatically labelled as “unknown”. This method can be trained on different HAB species or extended to new sensors and then applied to generate independent HAB risk maps; these can be fused with other sensors to fill gaps or improve spatial or temporal resolution. The HAB discrimination technique has obtained accurate results on MODIS and MERIS data, correctly identifying 89% of Phaeocystis globosa HABs in the southern North Sea and 88% of Karenia mikimotoi blooms in the Western English Channel. A linear transformation of the ocean colour discriminants is used to estimate harmful cell counts, demonstrating greater accuracy than if based on chlorophyll-a; this will facilitate its integration into a HAB early warning system operating in the southern North Sea.

Comparison of new and primary production models using SeaWiFS data in contrasting hydrographic zones of the northern North Atlantic.

The accuracy of two satellite models of marine primary (PP) and new production (NP) were assessed against 14C and 15N uptake measurements taken during six research cruises in the northern North Atlantic. The wavelength resolving model (WRM) was more accurate than the Vertical General Production Model (VGPM) for computation of both PP and NP. Mean monthly satellite maps of PP and NP for both models were generated from 1997 to 2010 using SeaWiFS data for the Irminger basin and North Atlantic. Intra- and inter-annual variability of the two models was compared in six hydrographic zones. Both models exhibited similar spatio-temporal patterns: PP and NP increased from April to June and decreased by August. Higher values were associated with the East Greenland Current (EGC), Iceland Basin (ICB) and the Reykjanes Ridge (RKR) and lower values occurred in the Central Irminger Current (CIC), North Irminger Current (NIC) and Southern Irminger Current (SIC). The annual PP and NP over the SeaWiFS record was 258 and 82 gC m-2 yr-1 respectively for the VGPM and 190 and 41 gC m-2 yr-1 for the WRM. Average annual cumulative sum in the anomalies of NP for the VGPM were positively correlated with the North Atlantic Oscillation (NAO) in the EGC, CIC and SIC and negatively correlated with the multivariate ENSO index (MEI) in the ICB. By contrast, cumulative sum of the anomalies of NP for the WRM were significantly correlated with NAO only in the EGC and CIC. NP from both VGPM and WRM exhibited significant negative correlations with Arctic Oscillation (AO) in all hydrographic zones. The differences in estimates of PP and NP in these hydrographic zones arise principally from the parameterisation of the euphotic depth and the SST dependence of photo-physiological term in the VGPM, which has a greater sensitivity to variations in temperature than the WRM. In waters of 0 to 5C PP using the VGPM was 43% higher than WRM, from 5 to 10C the VGPM was 29% higher and from 10 to 15C the VGPM was 27% higher.

Seasonality and interannual variability of the European Slope Current from 20 years of altimeter data compared with in situ measurements

The European Slope Current (SC) is a major section of the warm poleward flow from the Atlantic to the Arctic, which also moderates the exchange of heat, salt, nutrients and carbon between the deep ocean and the European shelf seas. The mean structure of the geostrophic flow, seasonality, interannual variability and long-term trend of SC are appraised with an unprecedented continuous 20-year satellite altimeter dataset. Comparisons with long term in situ data showed a maximum correlation of r2=0.51 between altimeter and Acoustic Doppler Current Profilers (ADCP), with similar results for drogued buoy data. Mean geostrophic currents were appraised more comprehensively than previous attempts, and the paths of 4 branches of the North Atlantic Current (NAC) and positions of 5 eddies in the region were derived quantitatively. A consistent seasonal cycle in the flow of the SC was found at all 8 sections along the European shelf slope, with maximum poleward flow in the winter and minimum in the summer. The seasonal difference in the altimetry current speed amounted to ~8-10 cm s-1 at the northern sections, but only ~5 cm s-1 on the Bay of Biscay slopes. This extended altimeter dataset indicates significant regional and seasonal variations, and has revealed new insights into the interannual variability of the SC. It is shown that there is a peak poleward flow at most positions along a ~2000 km stretch of the continental slope from Portugal to Scotland during 1995-1997, but this did not clearly relate to the extreme negative North Atlantic Oscillation (NAO) in the winter of 1995-1996. The speed of the SC exhibited a long term decreasing trend of ~1% per year. By contrast the NAC showed no significant trend over the 20-year period. Major changes in the NAC occurred three times, and these changes followed decreases in the NAO index.

High-resolution view of the spring bloom initiation and net community production in the Subantarctic Southern Ocean using glider data

In the Southern Ocean, there is increasing evidence that seasonal to subseasonal temporal scales, and meso- to submesoscales play an important role in understanding the sensitivity of ocean primary productivity to climate change. This drives the need for a high-resolution approach to re- solving biogeochemical processes. In this study, 5.5 months of continuous, high-resolution (3 h, 2 km horizontal resolution) glider data from spring to summer in the Atlantic Subantarctic Zone is used to investigate: (i) the mechanisms that drive bloom initiation and high growth rates in the region and (ii) the seasonal evolution of water column production and respiration. Bloom initiation dates were analysed in the context of upper ocean boundary layer physics highlighting sensitivities of different bloom detection methods to different environmental processes. Model results show that in early spring (September to mid-November) increased rates of net community production (NCP) are strongly affected by meso- to submesoscale features. In late spring/early summer (late-November to mid-December) seasonal shoaling of the mixed layer drives a more spatially homogenous bloom with maximum rates of NCP and chlorophyll biomass. A comparison of biomass accumulation rates with a study in the North Atlantic highlights the sensitivity of phytoplankton growth to fine-scale dynamics and emphasizes the need to sample the ocean at high resolution to accurately resolve phytoplankton phenology and improve our ability to estimate the sensitivity of the biological carbon pump to climate change.

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data.