Simulation of the Argo observing systemIgor Kamenkovich,RSMAS, University of MiamiWei Cheng, E.S. Sarachik and D.E.HarrisonUniversity of Washington

Introduction. The Argo systemArgo array has been brought to the full strength of 3000 floats The floats reside at the depth of 1000-2000 meters, where they move with the flowEvery 10 days, the floats surface while taking vertical profiles of temperature and salinityThe density of global spatial coverage is unprecedentedSpatial resolution is still not sufficient for resolving sharp fronts and small-scale anomalies: average spacing between floats is ~ 300kmAdvection of the floats by oceanic currents has a dual affect: It increases spatial coverageIt shortens the time a float spends near any given location

Effects of advectionIncrease in spatial coverage:

What is the radius d/2 within which we are guaranteed to have a float within a time interval T? This is the case if D-d < UT. If D is 300km and T is 1month

d > D UT ~ 300km if U = 1cm/sec ~ 50km if U = 10cm/sec

Most large-scale currents (except western boundary currents and ACC) are too weak to impact coverage; especially in the direction of sharp gradientsStrong advection by mesoscale eddies has a more pronounced effect

Effects of advectionDecrease in time spent near a particular location:

Changes in float positions act to distort time dependency in sampled fields if UT is sufficiently large

For slow large-scale advection U ~ 1.0 - 0.1cm/sec, and the effects are noticeable only on long time scales (> 1 year)

Fast advection by eddies can noticeably redistribute floats within 1 monthExtreme case: random redistribution of floats every 10 days (upper bound on the effects of eddy advection):

The probability that within a month, at least one float is within a radius 100km of a given location is about 30%

Observing System Simulation ExperimentsThe goal is to estimate the expected accuracy and limitations of the Argo observing system, by analyzing its simulations in ocean general circulation models (GCMs) (Arnold and Dey 1986)

Has been used for different observing systems (Kindle 1986; Barth and Wunsch 1990; Bennett 1990; Hernandez et al. 1995; Hackert et al. 1998)For the Argo array, OSSEs concentrated on the Indian Ocean (Schiller et al. 2004; Oke and Schiller 2005; Ballabrera-Poy et al. 2007; Vecchi and Harrison 2007) and Mediterranean Sea (Griffa et al. 2006)

OSSE approach has several advantages: the actual (GCM-simulated) field is known and thus the errors in the reconstructed fields can be accurately estimatedparameters of the observing system and the observed oceanic state can be modified in sensitivity studies

We use two GCMs: global coarse-resolution and regional eddy-resolving

Global coarse-resolution GCMOcean model is based on the GFDL MOM with:Global geometry; 2o 2o resolution; 25 vertical layersGent-McWilliams parameterization of mesoscale eddiesKPP boundary layer mixing scheme Thermodynamic sea-ice model

Forcing:Surface fluxes calculated by bulk formulas Daily wind stress, wind speed, and air temperature/humidity are taken from 23 years of NCEP reanalysis

At this resolution, GCM underestimates the intensity of fronts and western boundary currents

The floats reside at 1500m depth, surface every 10 days while taking measurementsT/S values are reconstructed on the original model grid using an OA method (Mariano and Brown 1992)We analyze the reconstruction errors: the difference between the OA and actual GCM-simulated values

Errors in Upper Ocean Heat Content (UOHC)UOHC is calculated over the top 800m, in a 5-year runAnnual means: errors are small, except within the western boundary currents and ACC

Amplitude of the annual cycle (Sep-March) in flux units: errors exceed 25-50 Wm-2 in some locations, but are generally small

Amplitude of the interannual trend (year 5 year 1) in flux units: errors exceed 5-10 Wm-2 at high latitudesGlobally averaged error magnitude is 2.2 Wm-2

Errors in the Mixed layer Depth (MLD)Very sensitive to T/S values; negative biases in T lead to the underestimated MLD

Annual means, amplitude of the annual cycle and magnitude of the interannual trend all exhibit:

Large errors in ACC (especially the Indian sector)Large errors in the high-latitude North Atlantic

Sensitivity runs: Effects of float movementsThe parked floats case (float positions do not change in time):

Errors are larger in the standard run compare to the parked floats caseA large part of errors in the standard case is explained by advection

The random position case (floats are redistributed randomly every 10 days):

Errors are reduced as a result of redistribution, due to the increased spatial coverageDifference in the magnitude of errors in the interannual trend between the standard and parked floats cases Difference in the magnitude of errors in the interannual trend between the random position and standard cases

Eddy-resolving North Atlantic model250 floats are released9-year run, no seasonal cycle

Mesoscale variability is the strongest in the subpolar gyre region

Reconstruction errors are the largest in the subpolar and Gulf Stream regions

Errors in SST and UOHC have similar structure

The errors increase even further when the mesoscale variability in the model is amplified (to match the observed intensity)

Regional model of the North Atlantic: 14oN to 60oNEddy-resolving grid: 1/8o in latitude and longitude

Eddy-resolving sensitivity runsTime-mean case: mesoscale variability is removed by time-averagingThe errors are significantly reduced, in the subpolar gyre region

Are these eddy-induced errors due to the variability in advection or T/S fields?

Mean advection case: mesoscale variability is removed from the velocities onlyMost of the eddy-induced errors are due to eddy advection, not T/S variability

Difference in the magnitude of errors in the interannual trend between: (a) standard and time-mean; and (b) mean-advection and time-mean cases

ConclusionsOverall performance of the simulated Argo array is impressiveGlobal experiments, however, exhibit significant differences between the reconstructed and actual GCM-simulated fields within intense currentsThe errors in the interannual trend are particularly significant compare to the actual signal

The advection has a dual effect on the reconstruction errors. Movement of the floats (i) increases the overall spatial coverage, but (ii) negatively impacts the ability to resolve time dependence

Large-scale advection negatively impacts the ability of the Argo system to reconstruct the magnitude of the annual cycle and interannual trend Fast redistribution of floats increases the spatial coverage, which improves reconstruction of the interannual trend. These effects are similar to the effects of doubling the number of floats

Conclusions (cont.)Explicitly simulated mesoscale eddies act to increase the reconstruction errorsThese effects are mainly due to the strong, rapidly changing eddy velocities, rather than to the mesoscale variability in T/S

Further directions: Effects of mesoscale variability in ACCDetection of realistic patterns of climate variability

To remind what Argo is Argo was never intended to resolve these features Many factors complicate interpretation of data Depends on strength of advection quantified on a simple example 1D advection Lets say you want to have 1 measurement a month vigorous eddiesAs the floats are moving, the recovered variability at a given location is distorted UT has to be large enough eddy advection: extreme caseTo best understand what Argo can and cannot measure and study factors that are important Simple design; upper bound on accuracy the design makes it explicit, that small-scale features are not present (we are being fair to Argo)Examples what we can do As expected, but remember that this is a 2x2 model flux units AC errors are small (GA = ) INTV are big (GA = 4.4)Large errors where ML is deep underestimated winter-time deepeningAddress effects of advection remove it make it stronger --- extreme case of eddy effects In many aspects --- idealized model, but it has explicit eddy variability more BCL unstable reduce some errors using info from sat. data and we can do that Effects of mesoscale variability: remove it Two forms: advection and noise in T/S