Could an ocean climate atlas generated by a model compete with an observational one?

2020 ◽  
Author(s):  
Georgy I. Shapiro ◽  
Jose M. Gonzalez-Ondina ◽  
Xavier Francis ◽  
Hyee S. Lim ◽  
Ali Almehrezi
Keyword(s):  
Standard Error ◽  
Sea Surface ◽  
Vertical Profiles ◽  
Individual Data ◽  
Model Simulations ◽  
Ocean Climate ◽  
Data Points ◽  
In Situ Observations ◽  
The Mean

<p>Modern numerical ocean models have matured over the last decades and are able to provide accurate fore- and hind-cast of the ocean state. The most accurate data could be obtained from the reanalysis where the model run in a hindcast mode with assimilation of available observational data. An obvious benefit of model simulation is that it provides the spatial density and temporal resolution which cannot be achieved by in-situ observations or satellite derived measurements. It is not unusual that even a relatively small area of the ocean model can have in access of 100,000 nodes in the horizontal, each containing vertical profiles of temperature, salinity, velocity and other ocean parameters with a temporal resolution theoretically as high as a few minutes. Remotely sensed (satellite) observations of sea surface temperature can compete with the models in terms of spatial resolution, however they only produce data at the sea surface not the vertical profiles. On the other hand, in-situ observations have a benefit of being much more precise than model simulations. For instance a widely used CTD profiler SBE 911plus has accuracy of about 0.001 °C, which is not achievable by models.</p><p>In the creation of a climatic atlas the higher accuracy of individual profiles provided by in-situ measurements may become less beneficial. Assuming the normal distribution of data at each location, the standard error of the mean (SEM) is calculated as SE=S/SQRT(N), where S is the standard deviation of individual data points around the mean, and N is the number of data points. The climatic data are obtained by averaging a large number of individual data points, and here the benefit of having more data points may become a greater advantage than the accuracy of a single observation.  </p><p>In this study we have created an ocean climate atlas for the northern part of the Indian Ocean including the Red Sea and the Arabian Gulf using model generated data. The data were taken from Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis product GLOBAL_REANALYSIS_PHY_001_030 with 1/12° horizontal resolution and 50 vertical levels for the period 1998 to 2017. The model component is the NEMO platform driven at the surface by ECMWF ERA-Interim reanalysis. The model assimilates along track altimeter data, satellite Sea Surface Temperature, as well as in-situ temperature and salinity vertical profiles where available. The monthly data from CMEMS were then averaged over 20 years to produce an atlas at the surface, 10, 20, 30, 75, 100, 125, 150, 200, 250, 300, 400, and 500 m depths.  The standard error of the mean has been calculated for each point and each depth level on the native grid (1/12 degree).</p><p>The atlas based on model simulations was compared with the latest version of the World Ocean Atlas (WOA)  2018 published by the NCEI.  WOA has objectively analysed climatological mean fields on a ¼  degree grid. The differences between the mean values and SEMs from observational and simulated atlases are analysed, and the potential causes of mismatch are discussed.</p>

scholarly journals Impacts of mean dynamic topography on a regional ocean assimilation system

2015 ◽  
Vol 12 (3) ◽  
pp. 1083-1105
Author(s):  
C. Yan ◽  
J. Zhu ◽  
C. A. S. Tanajura
Keyword(s):  
Sea Surface Height ◽  
Sea Surface ◽  
Dynamic Topography ◽  
Altimetry Data ◽  
Mean Dynamic Topography ◽  
Mean Sea Surface ◽  
In Situ Observations ◽  
The Mean

Abstract. An ocean assimilation system was developed for the Pacific-Indian oceans with the aim of assimilating altimetry data, sea surface temperature, and in-situ measurements from ARGO, XBT, CTD, and TAO. The altimetry data assimilation requires the addition of the mean dynamic topography to the altimetric sea level anomaly to match the model sea surface height. The mean dynamic topography is usually computed from the model long-term mean sea surface height, and is also available from gravimeteric satellite data. In this study, different mean dynamic topographies are used to examine their impacts on the sea level anomaly assimilation. Results show that impacts of the mean dynamic topography cannot be neglected. The mean dynamic topography from the model long-term mean sea surface height without assimilating in-situ observations results in worsened subsurface temperature and salinity estimates. The gravimeter-based mean dynamic topography results in an even worse estimate. Even if all available observations including in-situ measurements, sea surface temperature measurements, and altimetry data are assimilated, the estimates are still not improved. This further indicates that the other types of observations do not compensate for the shortcoming due to the altimetry data assimilation. The mean dynamic topography computed from the model's long-term mean sea surface height after assimilating in-situ observations presents better results.

scholarly journals Seasonal and annual mass balances of Mera and Pokalde glaciers (Nepal Himalaya) since 2007

2013 ◽  
Vol 7 (4) ◽  
pp. 3337-3378 ◽  
Author(s):  
P. Wagnon ◽  
C. Vincent ◽  
Y. Arnaud ◽  
E. Berthier ◽  
E. Vuillermoz ◽  
...  
Keyword(s):  
Mass Balance ◽  
Cross Sections ◽  
Mass Balances ◽  
Equilibrium Line Altitude ◽  
Nepal Himalaya ◽  
Limited Mass ◽  
In Situ Observations ◽  
The Mean ◽  
Southern Flank

Abstract. In the Everest region, Nepal, ground-based monitoring programs were started on the debris-free Mera Glacier (27.7° N, 86.9° E; 5.1 km2, 6420 to 4940 m a.s.l.) in 2007 and on the small Pokalde Glacier (27.9° N, 86.8° E; 0.1 km2, 5690 to 5430 m a.s.l., ∼ 25 km North of Mera Glacier) in 2009. These glaciers lie on the southern flank of the central Himalaya under the direct influence of the Indian monsoon and receive more than 80% of their annual precipitation in summer (June to September). Despite a large inter-annual variability with glacier-wide mass balances ranging from −0.77± 0.40 m w.e. in 2011–2012 (Equilibrium-line altitude (ELA) at ∼ 6055 m a.s.l.) to + 0.46 ± 0.40 m w.e. in 2010–2011 (ELA at ∼ 5340 m a.s.l.), Mera Glacier has been shrinking at a moderate mass balance rate of −0.10± 0.40 m w.e. yr−1 since 2007. Ice fluxes measured at two distinct transverse cross sections at ∼ 5350 m a.s.l. and ∼ 5520 m a.s.l. confirm that the mean state of this glacier over the last one or two decades corresponds to a limited mass loss, in agreement with remotely-sensed region-wide mass balances of the Everest area. Seasonal mass balance measurements show that ablation and accumulation are concomitant in summer which in turn is the key season controlling the annual glacier-wide mass balance. Unexpectedly, ablation occurs at all elevations in winter due to wind erosion and sublimation, with remobilized snow likely being sublimated in the atmosphere. Between 2009 and 2012, the small Pokalde Glacier lost mass more rapidly than Mera Glacier with respective mean glacier-wide mass balances of −0.72 and −0.26 ± 0.40 m w.e. yr−1. Low-elevation glaciers, such as Pokalde Glacier, have been usually preferred for in-situ observations in Nepal and more generally in the Himalayas, which may explain why compilations of ground-based mass balances are biased toward negative values compared with the regional mean under the present-day climate.

scholarly journals What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

2019 ◽  
Vol 147 (7) ◽  
pp. 2433-2449
Author(s):  
Laura C. Slivinski ◽  
Gilbert P. Compo ◽  
Jeffrey S. Whitaker ◽  
Prashant D. Sardeshmukh ◽  
Jih-Wang A. Wang ◽  
...  
Keyword(s):  
El Niño ◽  
Large Scale ◽  
El Nino ◽  
Mean Square ◽  
Observational Network ◽  
In Situ Observations ◽  
The Mean ◽  
The Impact ◽  
Assimilation System

Abstract Given the network of satellite and aircraft observations around the globe, do additional in situ observations impact analyses within a global forecast system? Despite the dense observational network at many levels in the tropical troposphere, assimilating additional sounding observations taken in the eastern tropical Pacific Ocean during the 2016 El Niño Rapid Response (ENRR) locally improves wind, temperature, and humidity 6-h forecasts using a modern assimilation system. Fields from a 50-km reanalysis that assimilates all available observations, including those taken during the ENRR, are compared with those from an otherwise-identical reanalysis that denies all ENRR observations. These observations reveal a bias in the 200-hPa divergence of the assimilating model during a strong El Niño. While the existing observational network partially corrects this bias, the ENRR observations provide a stronger mean correction in the analysis. Significant improvements in the mean-square fit of the first-guess fields to the assimilated ENRR observations demonstrate that they are valuable within the existing network. The effects of the ENRR observations are pronounced in levels of the troposphere that are sparsely observed, particularly 500–800 hPa. Assimilating ENRR observations has mixed effects on the mean-square difference with nearby non-ENRR observations. Using a similar system but with a higher-resolution forecast model yields comparable results to the lower-resolution system. These findings imply a limited improvement in large-scale forecast variability from additional in situ observations, but significant improvements in local 6-h forecasts.

scholarly journals Satellite-based time-series of sea-surface temperature since 1981 for climate applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Christopher J. Merchant ◽  
Owen Embury ◽  
Claire E. Bulgin ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Model ◽  
Sea Surface ◽  
Climate Data ◽  
Satellite Measurements ◽  
Data Record ◽  
The Mean ◽  
Climate Data Record

Abstract A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

scholarly journals Evaluation of global monitoring and forecasting systems at Mercator Océan

Ocean Science ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 57-81 ◽  
Author(s):  
J.-M. Lellouche ◽  
O. Le Galloudec ◽  
M. Drévillon ◽  
C. Régnier ◽  
E. Greiner ◽  
...  
Keyword(s):  
Quality Control ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Altimeter Data ◽  
Open Boundary ◽  
Vertical Profiles ◽  
Validation Procedure

Abstract. Since December 2010, the MyOcean global analysis and forecasting system has consisted of the Mercator Océan NEMO global 1/4° configuration with a 1/12° nested model over the Atlantic and the Mediterranean. The open boundary data for the nested configuration come from the global 1/4° configuration at 20° S and 80° N. The data are assimilated by means of a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. A 3-D-Var scheme provides a correction for the slowly evolving large-scale biases in temperature and salinity. Altimeter data, satellite sea surface temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. In addition to the quality control performed by data producers, the system carries out a proper quality control on temperature and salinity vertical profiles in order to minimise the risk of erroneous observed profiles being assimilated in the model. This paper describes the recent systems used by Mercator Océan and the validation procedure applied to current MyOcean systems as well as systems under development. The paper shows how refinements or adjustments to the system during the validation procedure affect its quality. Additionally, we show that quality checks (in situ, drifters) and data sources (satellite sea surface temperature) have as great an impact as the system design (model physics and assimilation parameters). The results of the scientific assessment are illustrated with diagnostics over the year 2010 mainly, assorted with time series over the 2007–2011 period. The validation procedure demonstrates the accuracy of MyOcean global products, whose quality is stable over time. All monitoring systems are close to altimetric observations with a forecast RMS difference of 7 cm. The update of the mean dynamic topography corrects local biases in the Indonesian Throughflow and in the western tropical Pacific. This improves also the subsurface currents at the Equator. The global systems give an accurate description of water masses almost everywhere. Between 0 and 500 m, departures from in situ observations rarely exceed 1 °C and 0.2 psu. The assimilation of an improved sea surface temperature product aims to better represent the sea ice concentration and the sea ice edge. The systems under development are still suffering from a drift which can only be detected by means of a 5-yr hindcast, preventing us from upgrading them in real time. This emphasizes the need to pursue research while building future systems for MyOcean2 forecasting.

scholarly journals Vertical profiles of pure dust and mixed smoke–dust plumes inferred from inversion of multiwavelength Raman/polarization lidar data and comparison to AERONET retrievals and in situ observations

2013 ◽  
Vol 52 (14) ◽  
pp. 3178 ◽  
Author(s):  
Detlef Müller ◽  
Igor Veselovskii ◽  
Alexei Kolgotin ◽  
Matthias Tesche ◽  
Albert Ansmann ◽  
...  
Keyword(s):  
Lidar Data ◽  
Vertical Profiles ◽  
In Situ Observations

scholarly journals Blending Sea Surface Temperatures from Multiple Satellites and In Situ Observations for Coastal Oceans

2009 ◽  
Vol 26 (7) ◽  
pp. 1415-1426 ◽  
Author(s):  
Yi Chao ◽  
Zhijin Li ◽  
John D. Farrara ◽  
Peter Hung
Keyword(s):  
Coastal Upwelling ◽  
Sea Surface ◽  
High Temporal Resolution ◽  
Potential Applications ◽  
Independent Observations ◽  
Ocean Flows ◽  
In Situ Observations ◽  
Continuous Fields ◽  
Mean Square Errors

Abstract A two-dimensional variational data assimilation (2DVAR) method for blending sea surface temperature (SST) data from multiple observing platforms is presented. This method produces continuous fields and has the capability of blending multiple satellite and in situ observations. In addition, it allows specification of inhomogeneous and anisotropic background correlations, which are common features of coastal ocean flows. High-resolution (6 km in space and 6 h in time) blended SST fields for August 2003 are produced for a region off the California coast to demonstrate and evaluate the methodology. A comparison of these fields with independent observations showed root-mean-square errors of less than 1°C, comparable to the errors in conventional SST observations. The blended SST fields also clearly reveal the finescale spatial and temporal structures associated with coastal upwelling, demonstrating their utility in the analysis of finescale flows. With the high temporal resolution, the blended SST fields are also used to describe the diurnal cycle. Potential applications of this SST blending methodology in other coastal regions are discussed.

scholarly journals Development of Observation-based Global Multi-layer Soil Moisture Products for 1970 to 2016

2021 ◽  
Author(s):  
Yaoping Wang ◽  
Jiafu Mao ◽  
Mingzhou Jin ◽  
Forrest M. Hoffman ◽  
Xiaoying Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Earth System Model ◽  
System Model ◽  
Surface Model ◽  
Earth System ◽  
Model Simulations ◽  
The Earth ◽  
Wide Range ◽  
In Situ Observations

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling/retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multi-layer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5° resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations and the latest gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Assessed against in situ observations, the global mean bias of the synthesized SM data ranged from −0.044 to 0.033 m3/m3, root mean squared error from 0.076 to 0.104 m3/m3, and Pearson correlation from 0.35 to 0.67. The merged SM datasets also showed the ability to capture historical large-scale drought events and physically plausible global sensitivities to observed meteorological factors. Three of the new SM products, produced by applying any of the three merging methods onto the source datasets excluding the Earth system models, were finally recommended for future applications because of their better performances than the Earth system model–dependent merged estimates. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

scholarly journals On the performance of satellite-based observations of <i>CO</i><sub>2</sub> in capturing the NOAA Carbon Tracker model and ground-based flask observations over Africa land mass

2019 ◽  
Author(s):  
Anteneh Getachew Mengistu ◽  
Gizaw Mengistu Tsidu
Keyword(s):  
Model Simulation ◽  
Satellite Observation ◽  
Satellite Observations ◽  
Lower Altitude ◽  
Equatorial Africa ◽  
In Situ Observations ◽  
The Mean ◽  
Data Gap ◽  
Good Agreement

Abstract. Africa is one of the most data-scarce regions as satellite observation at the equator is limited by cloud cover and there are a very limited number of ground-based measurements. As a result, the use of simulations from models are mandatory to fill this data gap. A comparison of satellite observation with model and available in-situ observations will be useful to estimate the performance of satellites in the region. In this study, GOSAT XCO2 is compared with the NOAA CT2016 and six flask observations over Africa using five years of data covering the period from May 2009 to April 2014. Ditto for OCO-2 XCO2 against NOAA CT16NRT17 and eight flask observations over Africa using two years of data covering the period from January 2015 to December 2016. The analysis shows that the XCO2 from GOSAT is higher than XCO2 simulated by CT2016 by 0.28 ppm whereas OCO-2 XCO2 is lower than CT16NRT17 by 0.34 ppm on African landmass on average. The mean correlations of 0.83 and 0.60 and average RMSD of 2.30 and 2.57 ppm are found between the model and the respective datasets from GOSAT and OCO-2 implying the existence of a reasonably good agreement between CT and the two satellites over Africa's land region. However, significant variations were observed in some regions. For example, OCO-2 XCO2 are lower than that of CT16NRT17 by up to 3 ppm over some regions in North Africa (e.g., Egypt, Libya, and Mali) whereas it exceeds CT16NRT17 XCO2 by 2 ppm over Equatorial Africa (10° S–10° N). This regional difference is also noted in the comparison of model simulations and satellite observations with flask observations over the continent. For example, CT shows a better sensitivity in capturing flask observations over sites located in Northern Africa. In contrast, satellite observations have better sensitivity in capturing flask observations in lower altitude island sites. CT2016 shows a high spatial mean of seasonal mean RMSD of 1.91 ppm during DJF with respect to GOSAT while CT16NRT17 shows 1.75 ppm during MAM with respect to OCO-2. On the other hand, low RMSD of 1.00 and 1.07 ppm during SON in the model XCO2 with respect to GOSAT and OCO-2 are determined respectively indicating better agreement during autumn. The model simulation and satellite observations exhibit similar seasonal cycles of XCO2 with a small discrepancy over Southern Africa and during wet seasons over all regions.

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