Abstract

Night-minus day-measured relative humidity differences are used to calculate mean temperature differences between the hygristor of the U.S. National Weather Service rawinsondes and the ambient air. Calculations are made on four types of tropical weather systems in the western Pacific. The dependency of hygristor daytime temperature on solar radiation is shown and its relationship with the cloud distribution in each weather system (typhoon, cloud cluster, cloud cluster environment, clear region) is discussed. The total opaque sky cover proved to be a useful parameter in determining the day vs night hygristor temperature differences.

Abstract

The North Atlantic Oscillation (NAO) represents the dominant mode of atmospheric variability in the North Atlantic region. In the present study, the role of the synoptic systems (cyclones and anticyclones) in generating the NAO pattern is investigated. To study the intermonthly variations of the NAO, NCEP–NCAR reanalysis data are used, and for the interdecadal variations the results of a 300-yr control integration under present-day conditions of the coupled model ECHAM4/OPYC3 are analyzed. A filtering method is developed for the sea level pressure anomalies. Application of this method to each grid point yields the low-frequency variability in the sea level pressure field that is due to the synoptic systems. The low-frequency variability of the filtered and the original data are in high agreement. This indicates that the low-frequency pressure variability, and with it the variability of the NAO, is essentially caused by the distribution of the synoptic systems. The idea that the distribution of the synoptic systems is the cause of the variation of the NAO is confirmed by high correlation between the latitudinal position of the polar front over the North Atlantic and the NAO index. Since most of the low-frequency variability in sea level pressure can be explained through the distribution of the synoptic systems, the NAO seems to be a reflection of the distribution of the synoptic systems, rather than the source for variations in the cyclone tracks.

Abstract

A method is presented to reconstruct decadal variations of the North Atlantic Oscillation (NAO). The spectral characteristics of the NAO on time scales of decades and longer are of particular interest for the understanding of North Atlantic ocean–atmosphere interactions. The reconstruction is based on a transfer model calibration that uses bandpass-filtered time series. The maximum overlap discrete wavelet transform (MODWT) is applied for decomposing the time series variance into different time scales. A total of 43 proxies, including Greenland ice cores and European tree-ring chronologies, are selected and regionally grouped providing four independent reconstructions for the period 1700–1978. The mean reconstruction agrees well with two recently published reconstructions during most of the time period. However, there are considerable differences in the earliest part before 1750. Running correlations between the reconstructions indicate that time-dependent relations exist among the different NAO reconstructions. The results suggest that the geographical distribution of proxies strongly affects the reconstruction and could explain some of the apparent discrepancies among the reconstructions recently published in literature. In the early eighteenth century, external forcing (solar, volcanic) seems to mask the NAO signature within the proxies.

Abstract

North Atlantic synoptic-scale processes are analyzed by bandpassing 6-hourly NCEP–NCAR reanalysis data (1958–98) for several synoptic ranges corresponding to ultrahigh-frequency variability (0.5–2 days), synoptic-scale variability (2–6 days), slow synoptic processes (6–12 days), and low-frequency variability (12–30 days). Climatological patterns of the intensity of synoptic processes are not collocated for different ranges of variability, especially in the lower troposphere. Intensities of synoptic processes demonstrate opposite trends between the North American coast and in the northeast Atlantic. Although north of 40°N the intensity of ultrahigh-frequency variability and synoptic-scale processes show similar interannual variability, further analysis indicates that secular changes, and decadal-scale and interannual variability in the intensities of synoptic processes may not be necessarily consistent for different synoptic timescales. Magnitudes of winter ultrahigh-frequency variability are highly correlated with the intensity of synoptic-scale processes in the 1960s and early 1970s. However, they show little agreement with each other during the last two decades, pointing to the remarkable change in atmospheric variability over the North Atlantic in late 1970s. North Atlantic ultrahigh-frequency variability in winter is highly correlated with surface temperature gradient anomalies in the Atlantic–American sector. These gradients are computed from the merged fields of SST and surface temperature over the continent. They demonstrate a dipolelike pattern associated with the North American coast on one hand, with the subpolar SST front and continental Canada on the other. High-frequency variability and its synoptic counterpart demonstrate different relationships with the North Atlantic Oscillation. Reliability of these results and their sensitivity to the filtering procedures are addressed by comparison to radiosonde data and application of alternative filters.

Abstract

Relative humidity and temperature information is presented for satellite-observed western Pacific and West Indies summertime cloud clusters, cloud cluster environments, clear regions, typhoons, and pre-typhoon cloud clusters. Information is stratified by weather system, region, and time of day. Data are presented as differences from the Jordan (1958) mean summertime West Indies sounding. Inner weather system deviations of humidity and temperature are also given.

Abstract

A neural network (NN) has been developed in order to retrieve the cloud liquid water path (LWP) over the oceans from Special Sensor Microwave/Imager (SSM/I) data. The retrieval with NNs depends crucially on the SSM/I channels used as input and the number of hidden neurons—that is, the NN architecture. Three different combinations of the seven SSM/I channels have been tested. For all three methods an NN with five hidden neurons yields the best results. The NN-based LWP algorithms for SSM/I observations are intercompared with a standard regression algorithm. The calibration and validation of the retrieval algorithms are based on 2060 radiosonde observations over the global ocean. For each radiosonde profile the LWP is parameterized and the brightness temperatures (Tb’s) are simulated using a radiative transfer model.

The best LWP algorithm (all SSM/I channels except T85V) shows a theoretical error of 0.009 kg m⁻² for LWPs up to 2.8 kg m⁻² and theoretical “clear-sky noise” (0.002 kg m⁻²), which has been reduced relative to the regression algorithm (0.031 kg m⁻²). Additionally, this new algorithm avoids the estimate of negative LWPs.

An indirect validation and intercomparison is presented that is based upon SSM/I measurements (F-10) under clear-sky conditions, classified with independent IR-Meteosat data. The NN-based algorithms outperform the regression algorithm. The best LWP algorithm shows a clear-sky standard deviation of 0.006 kg m⁻², a bias of 0.001 kg m⁻², nonnegative LWPs, and no correlation with total precipitable water. The estimated accuracy for SSM/I observations and two of the proposed new LWP algorithms is 0.023 kg m⁻² for LWP ⩽ 0.5 kg m⁻².

Abstract

A neural network is used to calculate the longwave net radiation (L _net) at the sea surface from measurements of the Special Sensor Microwave/Imager (SSM/I). The neural network applied in this study is able to account largely for the nonlinearity between L _net and the satellite-measured brightness temperatures (TB). The algorithm can be applied for instantaneous measurements over oceanic regions with the area extent of satellite passive microwave observations (30–60 km in diameter). Comparing with a linear regression method the neural network reduces the standard error for L _net from 17 to 5 W m⁻² when applied to model results. For clear-sky cases, a good agreement with an error of less than 5 W m⁻² for L _net between calculations from SSM/I observations and pyrgeometer measurements on the German research vessel Poseidon during the International Cirrus Experiment (ICE) 1989 is obtained. For cloudy cases, the comparison is problematic due to the inhomogenities of clouds and the low and different spatial resolutions of the SSM/I data. Global monthly mean values of L _net for October 1989 are computed and compared to other sources. Differences are observed among the climatological values from previous studies by H.-J. Isemer and L. Hasse, the climatological values from R. Lindau and L. Hasse, the values of W. L. Darnell et al., and results from this study. Some structures of L _net are similar for results from W. L. Darnell et al. and the present authors. The differences between both results are generally less than 15 W m⁻². Over the North Atlantic Ocean the authors found a poleward increase for L _net, which is contrary to the results of H.-J. Isemer and L. Hasse.

Abstract

Nimbus-7 SMMR data and ship observations are combined to compute the latent heat flux using the bulk aerodynamic method. Sea surface temperature (SST) and the surface humidity are determined with the microwave data. The surface wind field is derived from an analysis of ship observations of wind speed and surface pressure by means of a boundary-layer model by Bumke and Hasse. The microwave-derived SSTs are calibrated against those calculated from Advanced Very High-Resolution Radiometer (AVHRR) data. To get reliable results in the northern parts of the North Atlantic, only ascending (daytime) orbits of Nimbus-7 were used. Daytime data show a larger bias due to solar heating of the instrument but lack the complicating effects of differential cooling when the satellite enters the earth's shadow at the beginning of the descending orbits.

The evaporation fields are derived over the North Atlantic for individual overpasses of the satellite during July 1983, with a spatial resolution of 1° × 1°. High temporal and spatial gradients are observed, which are consistent with the prevailing synoptic situations. In the area south of Greenland and east of Canada, where the Labrador Current is located, latent heat flux (LE) is negative even in the monthly mean. The reliability of the negative values is demonstrated by a case study. They coincide well with ship observations of fog events.

The flux of latent heat can be determined with an acceptable accuracy of 25–40 W m⁻² for individual values if the bias of the SMMR data can be reliably removed.

Abstract

Using the same approach as in Part I, here it is shown how sampling problems in voluntary observing ship (VOS) data affect conclusions about interannual variations and secular changes of surface heat fluxes. The largest uncertainties in linear trend estimates are found in relatively poorly sampled regions like the high-latitude North Atlantic and North Pacific as well as the Southern Ocean, where trends can locally show opposite signs when computed from the regularly sampled and undersampled data. Spatial patterns of shorter-period interannual variability, quantified through the EOF analysis, also show remarkable differences between the regularly sampled and undersampled flux datasets in the Labrador Sea and northwest Pacific. In particular, it is shown that in the Labrador Sea region, in contrast to regularly sampled NCEP–NCAR reanalysis fluxes, VOS-like sampled NCEP–NCAR reanalysis fluxes neither show significant interannual variability nor significant trends. These regions, although quite localized covering small parts of the globe, play a crucial role for the coupled atmosphere–ocean system. In the Labrador Sea, for instance, interannual and decadal-scale changes of the surface net heat fluxes are known to affect oceanic convection and, thus, the meridional overturning circulation of the Atlantic Ocean. From a discussion of current atmospheric data assimilation systems it is argued that in poorly sampled regions reanalysis products are superior to VOS-based products for studying interannual and interdecadal variations of atmosphere–ocean interaction. In well-sampled regions, on the other hand, conclusions about surface heat flux variations are relatively insensitive to the choice of the flux products used (VOS versus reanalysis data). The results are confirmed for two different datasets, that is, ECMWF 40-yr Re-Analysis (ERA-40) data and seasonal integrations with a recent version of the ECMWF model in which no actual data were assimilated.

Abstract

Sampling uncertainties in the voluntary observing ship (VOS)-based global ocean–atmosphere flux fields were estimated using the NCEP–NCAR reanalysis and ECMWF 40-yr Re-Analysis (ERA-40) as well as seasonal forecasts without data assimilation. Air–sea fluxes were computed from 6-hourly reanalyzed individual variables using state-of-the-art bulk formulas. Individual variables and computed fluxes were subsampled to simulate VOS-like sampling density. Random simulation of the number of VOS observations and simulation of the number of observations with contemporaneous sampling allowed for estimation of random and total sampling uncertainties respectively. Although reanalyses are dependent on VOS, constituting an important part of data assimilation input, it is assumed that the reanalysis fields adequately reproduce synoptic variability at the sea surface. Sampling errors were quantified by comparison of the regularly sampled (i.e., 6 hourly) and subsampled monthly fields of surface variables and fluxes. In poorly sampled regions random sampling errors amount to 2.5°–3°C for air temperature, 3 m s⁻¹ for the wind speed, 2–2.5 g kg⁻¹ for specific humidity, and 15%–20% of the total cloud cover. The highest random sampling errors in surface fluxes were found for the sensible and latent heat flux and range from 30 to 80 W m⁻². Total sampling errors in poorly sampled areas may be higher than random ones by 60%. In poorly sampled subpolar latitudes of the Northern Hemisphere and throughout much of the Southern Ocean the total sampling uncertainty in the net heat flux can amount to 80–100 W m⁻². The highest values of the uncertainties associated with the interpolation/extrapolation into unsampled grid boxes are found in subpolar latitudes of both hemispheres for the turbulent fluxes, where they can be comparable with the sampling errors. Simple dependencies of the sampling errors on the number of samples and the magnitude of synoptic variability were derived. Sampling errors estimated from different reanalyses and from seasonal forecasts yield qualitatively comparable spatial patterns, in which the actual values of uncertainties are controlled by the magnitudes of synoptic variability. Finally, estimates of sampling uncertainties are compared with the other errors in air–sea fluxes and the reliability of the estimates obtained is discussed.