1st Argo Science Workshop Design Requirements for an Argo Float Array in the Indian Ocean Inferred from Observing System Simulation Experiments 1st Argo Science Workshop Tokyo Andreas Schiller Susan Wijffels Gary Meyers CSIRO Marine Research

Status of Argo Floats in Indian Ocean (September 2003): (~170 floats by end of 2003, ~450 in 2005) Courtesy Helen Phillips, CSIRO Marine Research

Questions Are typical horizontal velocities at parking depths small enough so that Argo floats will be quasi-stationary over their anticipate lifetime of about 3 years? What are the required temporal and spatial sampling intervals for capturing seasonal and longer-term variability? Using Argo floats, can we simultaneously measure intraseasonal and longer-term variability in the Indian Ocean? Are there areas in the Indian Ocean that need to be more frequently sampled than other areas?

Current Argo Mission Recording T & S as float rises Surface Recording T & S as float rises - 500 m - 2000 m Profiling from 2000m every 10 days, drifting at 2000m

Proposed Argo Mission Surface - 500 m 5 days - 2000 m Profiling from 500m every 5 days, profiling from 2000m every 20 days

Anomaly Composites SST Model Anomaly Composites SST (Reynolds obs.) Schiller and Godfrey, Journal of Climate, 2003 °C

What are Typical Advection Velocities at Parking Depths? Simulated Lagrangian Floats at z=2000m : Jan 82 – May 94 Max. Velocity: < 1.2 cm/s (< 32km/month) Numbers indicate distances covered by floats over period of 12 years,5 months

Simulated Lagrangian Floats at z=500m : Jan 82 – May 94 Max. Velocity: < 3 cm/s (< 76 km/month)

Spatial Sampling Temporal Sampling Complete Observations = 200km = 3days = 600km = 150-300km = 1200km = 300-600km = 6 days = 9 days = 21 days Temporal Sampling

Spatial Sampling Temporal Sampling Complete Observations = 200km = 3days = 600km = 150-300km = 1200km = 300-600km = 6 days = 9 days = 21 days Temporal Sampling

Spatial Sampling Noise added Temporal Sampling Complete Observations = 200km = 50-100km = 3days = 600km = 150-300km = 1200km = 300-600km = 6 days = 9 days = 21 days Temporal Sampling

Seasonal Variability of Temp. at 100m Depth (21-day sampling) Variability Complete Obs. RMS Variability Argo (21 days) Signal-to-Noise Ratio Argo

Intraseasonal Variability of Temperature at 100m Depth Variability Complete Obs. 6-day Sampling 9-day Sampling 21-day Sampling Variability RMS SNR

Variability of Temperature at 80ºE Variability Complete Obs. Variability Argo (6-day sampling) Colour: Intraseasonal, Contour lines: Seasonal signal RMS Signal-to-Noise Ratio Argo

Equatorial Averages (5ºS- 5ºN) of Temporal* Sampling Strategies Seasonal Intraseasonal ARGO ARGO Variability Variability RMS Signal:Noise Ratio Complete Obs. Argo Complete Obs. Argo °C °C °C * 0-500m: 6 days, 500-2000m: 21 days. Full model resolution: = 200km = 50-150km

= 600km = 1200km Spatial Sampling Temporal Sampling X A B C Complete Observations = 200km = 50-100km = 3 days = 600km = 150-300km = 1200km = 300-600km = 6 days (0-500m) [and 18 days (500-2000m)] X = 9 days (0-2000m) = 15 days (0-2000m) A ~ 2500 floats B ~ 300 floats Temporal Sampling End of 2003: ~ 170 floats C ~ 80 floats

Equatorial Averages of Temporal and Spatial* Sampling Strategies Seasonal Intraseasonal ARGO ARGO Variability Variability RMS Signal:Noise Ratio dotted: 9 days sampl. Complete Obs. Argo Complete Obs. Argo °C °C °C * 0-500m: 6 days, 500-2000m: 21 days. Model resolution: = 600km = 150-300km

Basin Averages of Temporal and Spatial* Sampling Strategies Seasonal Intraseasonal ARGO ARGO Variability Variability RMS Signal:Noise Ratio dotted: 9 days sampl. Complete Obs. Argo Complete Obs. Argo °C °C °C * 0-500m: 6 days, 500-2000m: 21 days. Model resolution: = 600km = 150-300km

Estimating α: Time Series at 0º N, 81º E (Reppin et al.,1999) White Noise Error (ºC) Estimating α: Time Series at 0º N, 81º E (Reppin et al.,1999) Model-Observation:

White Noise: Signal-to-Noise Ratios of Argo Temperature Seasonal Seasonal Basin Equator Intraseasonal Intraseasonal Error Factor α Error Factor α 0-500m: 6 days, 500-2000m: 21 days. Model resolution: = 600km = 150-300km

White Noise, Large-Scale Smoothing, Asynchronous Sampling: Signal-to-Noise Ratio of Argo Temperature Seasonal Seasonal Basin Equator Intraseasonal Intraseasonal Error Factor α Error Factor α 0-500m: 6 days, 500-2000m: 21 days. Model resolution: = 600km = 150-300km

White Noise, Large-scale Smoothing, Asynchronous Sampling: Signal-to-Noise Ratio of Argo Temperature Seasonal Seasonal Basin Equator Intraseasonal Intraseasonal Error Factor α Error Factor α 0-500m: 9 days, 500-2000m: 21 days. Model resolution: = 600km = 150-300km

Summary (1) Velocities at Parking Depths (~30kms/month at 2000m, ~80kms/month at 500m) small compared to propagation of intraseasonal signals  assume stationary floats (less valid for seasonal and longer signals). Simulated Variability of Temperature on Intraseasonal-to-Seasonal Time Scales (Equator and Basin): (a) Temporal and Spatial Sub-Sampling (both on “typical” scales): temporal sampling causes RMS errors of order 0.1ºC for both seasonal and intraseasonal time scales spatial sampling significantly increases RMS errors for both seasonal signal (≤ 0.9ºC) and intraseasonal signal (≤ 0.1ºC)  Seasonal RMS error of mixed spatial/temporal sampling almost completely determined by spatial RMS error (SNR ≥ 10). However: Intraseasonal RMS error due to temporal sampling is as large as intraseasonal RMS error due to spatial sampling (0.5 ≤ SNR ≤ 5).

Summary (2) (b) with additional noise (within reasonable range of error estimates): Seasonal SNR ≥ 1 Intraseasonal SNR: 0.1 ≤ SNR ≤ 1* (c) with asynchronous profiling and large-scale smoothing: Seasonal SNR increases by ≈ 10-30% Intraseasonal SNR almost unchanged (≤ 10%) Caveat: Results are based on single OGCM. Model deficits exist due to: Limited horizontal and vertical resolution (non-eddy resolving) Errors in forcing fields Errors in model parameterizations/physics (e.g., lack of : internal wave energy, tidal, inertial and other high frequency noise). * simulated intraseasonal signals are only half the size of observed amplitudes Observed intraseasonal SNRs are likely to be larger than simulated

Recommendations: To capture variability on intraseasonal-to-seasonal scales in the Indian Ocean with Argo Floats the results suggest that spatial sampling of the ocean is of crucial importance everywhere irrespective of dynamical regime  ARGO floats should be deployed such that they resolve spatial scales of interest. A minimum requirement for resolving ISO in the ocean is given by the spatial scales of intraseasonal variability, i.e. temporal sampling becomes particularly important in dynamically active areas such as the WBC and the equatorial domain of the ocean. High frequency sampling is required to maintain an acceptable SNR on intraseasonal timescales (strongly depends on “noise” level)  to capture intraseasonal variability in the Indian Ocean the minimum sampling interval in the upper ocean should be 5 days or less.

The End

Supplementary Material

Composites of Observation-Based Intraseasonal Wind Stress (FSU/NCEP) and Precipitation (CDIAC MSU)Anomalies dynes/cm^2 mm/day

Impact of Sampling Error on Ocean Mixed-Layer Heat Budget:

Spatial resolution Δx, Δy (averages) Temporal resolution Δt (averages) Noise added (1) Complete Obs. 200km, 50-100km 3 days No (2) Exp. 6D 6 days (3) Exp. 9D 9 days (5) Exp. 21D 21 days (6) Exp. 3DX 600km, 150-300km (7) Exp. 3DXX 1200km, 300-600km (8) Exp. 6DX (9) Exp. 21DX (10) Exp. 3DN 0-3x WOA error (11) Exp. 6DXN 0-3x WOA (12) Exp. 21DXN

Temperature Time Series at 0ºN, 81º E, z = 160m Complete data Black: model data Red: Observations from Reppin et al., 1999 Sampling interval 3-days Filtering Seasonal (t 93 days) Intraseasonal (t < 93 days) Sampling interval: 6-days

Spectra of Pot. Temperature at 0ºN, 80º 30’E, z=160m Obs.: Reppin et al., 1999 (red) and model (black) 870

Observed u,v (Reppin et al Observed u,v (Reppin et al., 1999; red) and Model (black) at 0ºN, 80º 30’E, z=25m

u,v Spectra at 0ºN, 80º 30’E (red: Obs. , Reppin et al u,v Spectra at 0ºN, 80º 30’E (red: Obs., Reppin et al., 1999; black: Model)

What is the Required Temporal Sampling Interval for Capturing Seasonal-to-Interannual and Intraseasonal Variability? Assumption: Spatially stationary ARGO floats are available at each horizontal model grid point Approach: Sample model output (complete temperature available as 3-day mean output) at different sampling intervals, e.g. 21 days (500-2000m) 6 days (0-500m) 9 days (0-2000m) Separate signal into longer (seasonal-to-interannual) and shorter (intraseasonal) components Calculate standard deviation of full data and sub-sampled data Calculate RMS differences between full and sub-sampled data Estimate signal-to-noise ratios (Standard deviation : RMS difference)

Cross Spectral Analysis of Temperature Time Series at 0ºN, 81º E, z = 160m Intraseasonal | seasonal SNRs: 0.7|4.2 0.4|2.1 0.2|1.0 Black: complete model Red: sub-sampled (6days, Δx=600km, Δy=150km) with increasing noise levels Error factor: α = 0.5 α = 1.0 α = 2.0

intraseasonal (colour), seasonal (isolines) Variability at 8° S: intraseasonal (colour), seasonal (isolines) Java equatorial wave guide °C C.I. = 0.5° C Note different scales 9days (shown): S:N = 2:1 6 days: S:N = 5:1 C.I. = 0.02° C

Variability at 4° N: 9days (shown): S:N = 2:1 6 days: S:N = 5:1 > 0.65°C 9days (shown): S:N = 2:1 6 days: S:N = 5:1 Note changes in RMS amplitudes: intraseasonal RMS ( ) = seasonal RMS ( ) > > 0.35°C > 0.55°C

Variability at 90° E: 9days (shown): S:N = 2:1 6 days: S:N = 5:1

Where do we have to take measurements for capturing seasonal-to-interannual and intraseasonal variability (spatial sampling)? Assumption: Spatially stationary ARGO floats are available at each time step Approach (similar to temporal sampling): Sample model output at different spatial sampling intervals 9 days (0-2000m) 6 days (0-500m) [and 18 days (500-2000m)] Separate signal into longer (seasonal-to-interannual) and shorter (intraseasonal) components Anisotropic smoothing of signals Calculate variabilities of full data and spatially sub-sampled data Calculate RMS differences between full and sub-sampled data Estimate signal-to-noise ratios

Equatorial Averages (5ºS- 5ºN) of Spatial* Sampling Strategies Seasonal Intraseasonal ARGO ARGO Variability Variability RMS Signal:Noise Ratio Complete Obs. Argo Complete Obs. Argo °C °C °C * Full temporal resolution (3 days). Model resolution: = 600km = 150-300km

White Noise: Signal-to-Noise Ratios of Complete Observations Error (ºC) , observed: Basin Equator Seasonal Seasonal Intraseasonal Intraseasonal Error Factor α Error Factor α

White Noise, Large-Scale Smoothing, Asynchronous Sampling: Signal-to-Noise Ratios of Complete Observations Basin Equator Seasonal Seasonal Intraseasonal Intraseasonal Error Factor α Error Factor α

White Noise and Signal-to-Noise Ratios: Argo Salinity Seasonal Seasonal Basin Equator Intraseasonal Intraseasonal Error Factor α Error Factor α 0-500m: 6 days, 500-2000m: 21 days. Model resolution: = 600km = 150-300km

Related Activities in Pacific Ocean: Ed Harrison, Gabriel Vecchi [PMEL], Tony Lee [JPL]