Validation Test Report for GDEM4

Carnes, Michael R.; Helber, Robert W.; Barron, Charlie N.; Dastugue, J. M.

doi:10.21236/ada530343

Cited by 26 publications

(20 citation statements)

References 14 publications

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“…The fields for months February, May, August, and November are described in section 6 and the rest of the months are available in the . The gridding procedure for the climatology follows Carnes et al [2010] and is based on a cost function designed to minimize the squared slope and data misfit to the gridded value,

J = \sum_{m} \sum_{n} \{{((), \frac{T_{n + 1, m} - T_{n, m}}{Δ x_{m} / Δ y})}^{2} + {((), \frac{T_{n, m + 1} - T_{n, m}}{1})}^{2} + \sum_{k} {true(T_{m, n} - θ_{m, n, k} true)}^{2}\} .

…”

Section: Methodsmentioning

confidence: 99%

Temperature versus salinity gradients below the ocean mixed layer

Helber¹,

Kara²,

Richman³

et al. 2012

J. Geophys. Res.

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[1] We characterize the global ocean seasonal variability of the temperature versus salinity gradients in the transition layer just below the mixed layer using observations of conductivity temperature and depth and profiling float data from the National Ocean Data Center's World Ocean Data set. The balance of these gradients determines the temperature versus salinity control at the mixed layer depth (MLD). We define the MLD as the shallowest of the isothermal, isohaline, and isopycnal layer depths (ITLD, IHLD, and IPLD), each with a shared dependence on a 0.2 C temperature offset. Data are gridded monthly using a variational technique that minimizes the squared analysis slope and data misfit. Surface layers of vertically uniform temperature, salinity, and density have substantially different characteristics. By examining differences between IPLD, ITLD, and IHLD, we determine the annual evolution of temperature or salinity or both temperature and salinity vertical gradients responsible for the observed MLD. We find ITLD determines MLD for 63% and IHLD for 14% of the global ocean. The remaining 23% of the ocean has both ITLD and IHLD nearly identical. It is found that temperature tends to control MLD where surface heat fluxes are large and precipitation is small. Conversely, salinity controls MLD where precipitation is large and surface heat fluxes are small. In the tropical ocean, salinity controls MLD where surface heat fluxes can be moderate but precipitation is very large and dominant.

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J = \sum_{m} \sum_{n} \{{((), \frac{T_{n + 1, m} - T_{n, m}}{Δ x_{m} / Δ y})}^{2} + {((), \frac{T_{n, m + 1} - T_{n, m}}{1})}^{2} + \sum_{k} {true(T_{m, n} - θ_{m, n, k} true)}^{2}\} .

…”

Section: Methodsmentioning

confidence: 99%

Temperature versus salinity gradients below the ocean mixed layer

Helber¹,

Kara²,

Richman³

et al. 2012

J. Geophys. Res.

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“…There are twin experiments performed with and without the Greenland freshwater flux imposed along the coast. Both experiments are initialized from a spin‐up simulation started from the ocean climatology fields from the Generalized Digital Environmental Model version 4 (Carnes et al, ) and are integrated for 1993–2016. One experiment is conducted without any freshwater flux from Greenland (hereinafter referenced as “GR−”).…”

Section: Model Configuration and Numerical Experimentsmentioning

confidence: 99%

Role of Greenland Freshwater Anomaly in the Recent Freshening of the Subpolar North Atlantic

et al. 2019

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The cumulative Greenland freshwater flux anomaly has exceeded 5,000 km 3 since the 1990s. The volume of this surplus freshwater is expected to cause substantial freshening in the North Atlantic. Analysis of hydrographic observations in the subpolar seas reveals freshening signals in the 2010s. The sources of this freshening are yet to be determined. In this study, the relationship between the surplus Greenland freshwater flux and this freshening is tested by analyzing the propagation of the Greenland freshwater anomaly and its impact on salinity in the subpolar North Atlantic based on observational data and numerical experiments with and without the Greenland runoff. A passive tracer is continuously released during the simulations at freshwater sources along the coast of Greenland to track the Greenland freshwater anomaly. Tracer budget analysis shows that 44% of the volume of the Greenland freshwater anomaly is retained in the subpolar North Atlantic by the end of the simulation. This volume is sufficient to cause strong freshening in the subpolar seas if it stays in the upper 50–100 m. However, in the model the anomaly is mixed down to several hundred meters of the water column resulting in smaller magnitudes of freshening compared to the observations. Therefore, the simulations suggest that the accelerated Greenland melting would not be sufficient to cause the observed freshening in the subpolar seas and other sources of freshwater have contributed to the freshening. Impacts on salinity in the subpolar seas of the freshwater transport through Fram Strait and precipitation are discussed.

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“…A probability of error is then calculated based on the difference between the observation and background data. The sea ice concentration climatology is obtained from the European Centre for Medium Weather Forecast (ECMWF) climatology formed from years 1979 to 1996 [ Fernandez et al ., ], while ocean climatologies are obtained from Generalized Digital Environmental Model (GDEM) [ Carnes et al ., ]. If the observation probability of error reaches a specified threshold (usually 5%), the data are not assimilated.…”

Section: The Arctic Cap Nowcast/forecast System (Acnfs)mentioning

confidence: 99%

Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

et al. 2015

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In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1–7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short‐term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

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Validation Test Report for GDEM4

Cited by 26 publications

References 14 publications

Temperature versus salinity gradients below the ocean mixed layer

Temperature versus salinity gradients below the ocean mixed layer

Role of Greenland Freshwater Anomaly in the Recent Freshening of the Subpolar North Atlantic

Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

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