Global Observations of the Land Breeze

Transcription

1 Global Observations of the Land Breeze Sarah T. Gille, 1,2 Stefan G. Llewellyn Smith, 2 Nicholas M. Statom 2 1 Scripps Institution of Oceanography, UCSD, 9500 Gilman Drive, La Jolla, CA , USA 2 Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, UCSD, 9500 Gilman Drive, La Jolla, CA , USA To whom correspondence should be addressed; sgille@ucsd.edu. Satellite wind observations are used to study the land/sea breeze circulation, the daily cycle of coastal winds associated with daytime heating of the land surface. This provides the first ever comprehensive global view of diurnal wind variations over the ocean, in contrast with past observational studies which have been limited to measurements from isolated locations, mostly over land. Results from this study agree with frictional linear theory in showing how winds follow an elliptical hodograph through the course of the day, but indicate that diurnal wind variations propagate offshore progressively and are detectable several hundred kilometers from the coast. In coastal regions, the land surface warms and cools more rapidly than the ocean in response to the daily cycle in solar heating. The resulting diurnal cycle in air-sea temperature difference drives an onshore wind during the day and an offshore wind at night. These wind patterns, 1

2 known as the sea breeze and land breeze respectively, have important implications for coastal meteorology, for air quality and pollutant dispersal in densely populated coastal regions (1), and for climate processes influenced by diurnal forcing, such as ocean mixed-layer heat storage. Here we use scatterometer winds from the SeaWinds instruments aboard the QuikSCAT and ADEOS-II satellites to study for the first time the diurnal evolution of the wind over the global ocean. We show that a land breeze occurs along most coastlines, that it is detectable hundreds of kilometers offshore, and that its qualitative features agree surprisingly well with simple theories. Past observational studies of the land and sea breeze have focused on describing diurnal variations in limited regions where meteorological data were available (1 6). Because in situ observations have limited geographic range, these studies identified processes that depend on orography or other local conditions, and could not distinguish local variations from global variations. SeaWinds scatterometers are designed to measure winds with an accuracy of 2 m s 1 in speed and 20 in direction. Wind observations are reported in 25 km pixels, along a grid centered along the satellite ground track (7,8). The QuikSCAT and ADEOS-II satellites functioned as a tandem mission between April and October 2003, providing a total of 198 days of data, before the untimely demise of ADEOS-II. Both followed sun synchronous orbits, with QuikSCAT observing at roughly 6:00 and 18:00 local time and ADEOS-II observing at 10:30 and 22:30 local time. Earlier analyses based on QuikSCAT observations alone have shown that morning and evening wind observations are statistically different along most coastlines of the world and throughout the tropical ocean (9, 10). The addition of ADEOS-II observations allows us to examine the details of the diurnal variability. Studies of wind variability represent the wind as a hodograph, a curve whose locus as a function of time is the tip of the wind vector. In theory, single-frequency variability should produce elliptical hodographs (11), and measurements 2

3 have found near-elliptical hodographs (3, 11, 12). For this analysis, wind observations were least-squares fitted to an elliptical hodograph (see the supporting online text for details). The semi-major axis of the ellipse (a) is oriented in the direction of maximum diurnal variation. The semi-minor axis (b) is orthogonal to a and in the direction of minimum variation. The present analysis allows us to study the global features of diurnal variability in sea-level winds. The resulting global structure is given in Figure 1a, which shows a and a selection of wind ellipses. A strong diurnal cycle is visible across the entire Pacific and Atlantic Oceans equatorward of 30 latitude and along coastlines at all latitudes. The amplitude of the wind is strongest in pixels closest to the coast and attenuates with distance from the shore, as predicted by theory (13, 14). Poleward of 30 latitude, statistically significant diurnal cycles are more evident along Northern Hemisphere coastlines than along South American coastlines. This is expected, because the land breeze is strongest in summer (9). We are able for the first time to measure the offshore extent of the land breeze, shown as a function of latitude in Figure 2a. It is visible up to 300 km offshore at 30 latitude, and up to 100 km offshore poleward of 40 latitude. Its extent consistently exceeds the onshore extent of the sea breeze, which has been reported to be between 15 km on the Beaufort Sea coast in Alaska (15) and 200 km in northern Australia (1, 4). This indicates that winds are able to propagate further over the ocean than over land; presumably this is because of differences in surface roughness, or because regional meteorological phenomena on land make sea breeze effects difficult to detect far from the coast. The most visible feature of the offshore extent is the strong diurnal cycle spanning the tropical oceans, which might be interpreted as unbounded offshore propagation of the land breeze. In the central Pacific and Atlantic Oceans, the diurnal wind ellipses have a northsouth orientation that appears unrelated to the presence of land. Thus the open ocean diurnal variation is likely to be controlled by local convective effects rather than by daytime heating of 3

4 the land (16). (Diurnal variations do not appear statistically significant in the tropical Indian Ocean, because of the high variance associated with monsoonal fluctuations.) Nonetheless, the influence of land appears to extend several hundred kilometers offshore equatorward of 30 latitude. This is not as far as implied by inviscid linear theory, which predicts infinite propagation equatorward of 30 latitude (17), but it is further than implied by frictional linear theory, which predicts a gradual change in offshore extent between high and low latitudes (14) (see Figure 1a). The shortcomings of linear theory found here imply that nonlinear advective terms need to be invoked to capture the essential dynamics of offshore propagation. In coastal regions, wind ellipses are expected to be roughly perpendicular to the coastline, with the exact orientation predicted to depend on several factors including latitude and friction (3,12). However, topography can steer the winds, so for example, along the west coast of South America, wind ellipses are oriented parallel to the Andes. Figure 2b shows that on average the angular orientation of the major axis relative to a normal to the coastline varies linearly with latitude. These findings are consistent with the theoretical predictions of Alpert et al. (3), who obtained a near-linear dependence with latitude. Our study provides the first global verification of this prediction, and shows that the best fit is obtained when the linear drag term has magnitude roughly equal to the angular rotation rate of the Earth. Latitude also affects the wind ellipse eccentricity, e a 2 b 2 /a. The eccentricity exceeds 0.5 everywhere and is almost always greater than 0.9. Hence the dominant wind motion is a pattern of alternating onshore and offshore flow. As shown in Figure 2c, on average scatterometer eccentricities roughly match Alpert et al. s predictions of high eccentricity near the equator, a minimum at mid-latitudes, and higher eccentricity at the poles. A simple physical argument indicates that latitude affects the ellipse s direction of rotation, which should be anticylonic (i.e. clockwise in the Northern Hemisphere) since the Coriolis force deflects the wind (to the right in the Northern Hemisphere). We find that the direction of rota- 4

5 tion is consistently clockwise in the Northern Hemisphere and counterclockwise in the Southern Hemisphere (see Figure 1b). Regional observations of the sea breeze have often reported winds rotating in the opposite direction (3). Our analysis indicates that on a global scale cyclonic rotation is unusual and is probably linked to the details of regional meteorology and orography. Along the equator where the Coriolis force is zero, both clockwise and counterclockwise rotation occur. Figure 1c shows a global map of the observed time of day when winds reach their maximum strength. Maximum air-sea temperature differences are typically thought to occur around 2 pm (11). Figure 1c indicates the maximum land breeze occurs between 4 pm (yellow pixels) and 8 pm (magenta pixels) at scatterometer measurements closest to the coast. With increasing distance from the coast, the time of day when the maximum wind occurs becomes progressively later, so that several hundred kilometers offshore, the maximum wind speed occurs around 10 pm (blue pixels). Exceptions occur near major topographic features such as the Andes and the Namibian highlands and along the east coast of Africa, where the nearshore maximum may occur after the offshore maximum. Finally, from the phase information shown in Figure 1c, we estimate the propagation rate of the land breeze maximum, which typically travels about 300 km over 8 hours. Equatorward of 30 propagation rates averaged 9 m s 1, with standard deviation 4 m s 1 (see methods section), while at higher latitudes fewer measurements are available so propagation rates are difficult to calculate but often appear dominated by large outliers. No clear latitudinal trend is seen. These propagation rates are roughly an order of magnitude greater than the major axis amplitudes. They also exceed the component of mean flow perpendicular to the coast, which averages 0.6 m s 1 with standard deviation 2.9 m s 1 for locations within 550 km of the coast and equatorward of 30 that have a statistically significant diurnal cycle. Theory and laboratory experiments predict that the land breeze front should behave as a gravity current with 5

6 velocity u = k g d, where k is a constant, g = g T/T is reduced gravity, and d is a scale height. In contrast with the linear theories used to predict eccentricity and angular orientation of the tidal ellipses, gravity currents depend crucially on nonlinear processes. For typical values k = 0.62 (18), T = 300 K, T = 5 K, and d = 1000 m, u is 8 m s 1, in rough agreement with our observations. This study describes the high-resolution structure of diurnal variability in winds, providing the first global spatio-temporal observations of the land breeze. Simple linear theory (3), with appropriately chosen drag parameters, is surprisingly good at explaining the orientation, eccentricity, and direction of rotation of the wind hodographs. In addition, simple scalings derived from laboratory gravity current experiments are able to predict the propagation speed of the land breeze. The significant offshore extent of the land breeze system over the ocean has important implications for our understanding of pollutant dispersal. Since the land breeze signal propagates with gravity wave speeds, coastal pollutants will propagate out to sea fast and far. The results shown here are also important for upper ocean coastal mixed-layer dynamics, because they show that the land s influence is felt a long way offshore. Since land breeze wind forcing and solar heating are in phase, this may influence global upper ocean heat storage and gas exchange processes. This study also demonstrates the important role that a tandem mission can play in diagnosing geophysical phenomena. A multi-year tandem scatterometer mission, though not currently planned, would allow careful investigation of the seasonal and interannual variations in the land breeze. References 1. S. T. K. Miller, B. D. Keim, R. W. Talbot, H. Mao, Rev. Geophys. 41 (2003). Doi: /2003RG

7 2. C. E. Dorman, J. Geophys. Res. 87, 9636 (1982). 3. P. Alpert, M. Kusuda, N. Abe, J. Atmos. Sci. 41, 3568 (1984). 4. J. E. Simpson, Sea Breeze and Local Wind (Cambridge University Press, Cambridge, England, 1994). 5. J. A. Lerczak, M. C. Hendershott, C. D. Winant, J. Geophys. Res. 106, 19,715 (2001). 6. R. J. Wichink Kruit, A. A. M. Holtslag, A. B. C. Tijm, Boundary-Layer Met. 112, 369 (2004). 7. T. Lungu, QuikSCAT science data product user s manual, overview and geophysical data products, Version 2.2 D-18053, Jet Propulsion Laboratory (2001). 95 pp. 8. T. Lungu, Seawinds science data product user s manual, Version 1.0 D-21551, Jet Propulsion Laboratory (2002). 128 pp. 9. S. T. Gille, S. G. Llewellyn Smith, S. M. Lee, Geophys. Res. Lett. 30 (2003) /2002GL N. Perlin, R. M. Samelson, D. B. Chelton, Mon. Wea. Rev. 132, 2110 (2004). 11. B. Haurwitz, J. Meteorol. 4, 1 (1947). 12. F. H. Schmidt, J. Meteor. 4, 9 (1947). 13. J. E. Walsh, J. Atmos. Sci. 31, 2012 (1974). 14. H. Niino, J. Meteorol. Soc. Japan 65, 901 (1987). 15. T. L. Kozo, J. Appl. Meteorol. 21, 891 (1982). 16. A. Dai, C. Deser, J. Geophys. Res. 104, 31,109 (1999). 7

8 17. R. Rotunno, J. Atmos. Sci. 40, 1999 (1983). 18. J. E. Simpson, Q. J. R. Meteorol. Soc. 95, 758 (1969). 19. This work was supported by the National Science Foundation and by the NASA Ocean Vector Wind Science Team. Supporting Online Material Materials and Methods 8

9 Figure Captions Fig. 1. (a) Strength of diurnal wind cycle, with major axis plotted in color in locations where it is statistically significant, and wind ellipses plotted every 6 in locations that are either within 10 of land or equatorward of 30 latitude. (b) Direction of rotation of wind, with red indicating clockwise rotation and blue counter-clockwise rotation. (c) Time of day when wind is aligned with major axis. Since winds are aligned with the major axis twice a day, the time scale runs from 0 to 12. Fig. 2. (a) Maximum offshore extent of the land breeze as a function of latitude. Also shown are predictions from Niino s linear theory (14), arbitrarily scaled to match observed offshore extents poleward of 30. (b) Angular orientation of the wind ellipse relative to a normal to the coastline. Positive angles represent a rotation to the right of the normal. Also shown are angular orientation predictions from Alpert et al. (3), which uses a linear drag k that is scaled relative to the rotation rate ω. (c) Eccentricity of wind ellipses, again shown with predictions from (3). 9

10 Fig. 1. See caption, p

11 Offshore Distance (km) Angle (degrees) Latitude a b scatterometer Niino (1987) scatterometer k/ω = 0.5 k/ω = 1.0 k/ω = 2.0 Eccentricity c Latitude Fig. 2. (a) Maximum offshore extent of the land breeze as a function of latitude. Also shown are predictions from Niino s linear theory (14), arbitrarily scaled to match observed offshore extents poleward of 30. (b) Angular orientation of the wind ellipse relative to a normal to the coastline. Positive angles represent a rotation to the right of the normal. Also shown are angular orientation predictions from Alpert et al. (3), which uses a linear drag k that is scaled relative to the rotation rate ω. (c) Eccentricity of wind ellipses, again shown with predictions from (3). 11

12 Supporting Online Material Methods Data Processsing For this analysis, all scatterometer wind measurements collected during the six month tandem mission were sorted geographically into 0.25 latitude by 0.25 longitude bins, and temporally into one of four time bins centered at approximately 6:00, 10:30, 18:00, or 22:30, depending on whether the measurements were from an ascending or descending satellite pass of QuikSCAT or ADEOS-II. Winds were averaged to produce mean zonal (u) and meridional (v) components in each bin. Because of the Earth s rotation relative to the satellite orbit, the mean time of local observations at 60 latitude differs by 68 minutes from the mean observation time at the equator. Since the satellite swath is several hundred kilometers wide, the exact local time of observation spans a range of 64 minutes at the equator and 136 minutes at 60 latitude. Hence an average measurement time was computed for each spatiotemporal bin. The binned components were then least-squares fitted to sinusoidal functions: u = u 0 + u 1 cos ωt + u 2 sin ωt, (1) v = v 0 + v 1 cos ωt + v 2 sin ωt, (2) where ω = 2π day 1. The least-squares fit is slightly overdetermined, with 8 quantities (four values each of u and v) used to determine six parameters. The ellipses are defined by u 1, u 2, v 1 and v 2. Winds align with the major axis of the ellipse at time: τ = 1 [ ] 2 atan 2(u1 u 2 + v 1 v 2 ). (3) u v1 2 u 2 2 v2 2 The semi-major axis has length a = (u 1 cos ωτ + u 2 sin ωτ) 2 + (v 1 cos ωτ + v 2 sin ωτ) 2, (4) 12

13 and the semi-minor axis has length b = ( u 1 sin ωτ + u 2 cos ωτ) 2 + ( v 1 sin ωτ + v 2 cos ωτ) 2. (5) The ellipse tilts relative to the parallels of latitude by an angle: [ ] v1 cos ωτ + v 2 sin ωτ φ = atan. (6) u 1 cos ωτ + u 2 sin ωτ By looking at the angular orientation of the wind at midnight and 6:00 am, we can also identify whether the winds rotate clockwise or counterclockwise. Error estimation Because the parameters that define the wind ellipse are calculated through a set of non-linear equations, error bars were estimated using a Monte Carlo procedure. One hundred realizations of random data were created by adding to the observations normally distributed perturbations with a standard deviation equal to the standard error in the observations. In Figure 1a, pixels are white if the semi-major axis a is less than 3 times the standard deviation of a determined from this Monte Carlo procedure. Sea breeze statistics The offshore extent, angular orientation, and eccentricity shown in Figure 2 were computed in 1 latitude bins. The choice of bin width does not have any significant impact on the results reported here. The maximum extent at each latitude was determined by finding the first 25 km interval from the shore in which no statistically significant land breeze was observed at any longitude. Angular orientation and eccentricity were bin averaged by latitude for all statistically significant sea breeze locations within 3 of the coast. Since wind data are spatially correlated, standard errors were estimated by dividing the standard deviation by N/16, where N is the number of quarter degree bins available, i.e. by assuming that data decorrelate over 1 lengthscales in coastal regions. 13

14 Offshore propagation rates were computed by averaging distances from the coast by time of maximum wind, also in 1 latitudinal bins. The change in average distance from the coast over time was then used to estimate the speed of the land breeze front. Equtorward of 30 propagation rates varied between 2 and 15 m s 1, with one outlier of 29 m s 1. At higher latitudes, fewer data are available and outliers are more common. 14