Climate models and satellite observations both indicate that the total amount of water in the atmosphere will increase at a rate of 7% per kelvin of surface warming. However, the climate models predict that global precipitation will increase at a much slower rate of 1 to 3% per kelvin. A recent analysis of satellite observations does not support this prediction of a muted response of precipitation to global warming. Rather, the observations suggest that precipitation and total atmospheric water have increased at about the same rate over the past two decades.

1] Near-surface wind speeds (u) measured by terrestrial anemometers show declines (a 'stilling') at a range of midlatitude sites, but two gridded u datasets (a NCEP/NCAR reanalysis output and a surface-pressure-based u model) have not reproduced the stilling observed at Australian stations. We developed Australia-wide 0.01°resolution daily u grids by interpolating measurements from an expanded anemometer network for 1975 -2006. These new grids represented the magnitude and spatialvariability of observed u trends, whereas grids from reanalysis systems (NCEP/NCAR, NCEP/DOE and ERA40) essentially did not, even when minimising the sea-breeze impact. For these new grids, the Australianaveraged u trend for 1975 -2006 was À0.009 m s À1 a À1 (agreeing with earlier site-based studies) with stilling over 88% of the land-surface. This new dataset can be used in numerous environmental applications, including benchmarking general circulation models to improve the representation of key parameters that govern u estimation.

In order to achieve the required accuracy in sea surface salinity (SSS) measurements from Lband radiometers such as the Aquarius/SAC-D or SMOS (Soil Moisture and Ocean Salinity) mission, it is crucial to accurately correct the radiation that is emitted from the ocean surface for roughness effects. We derive a geophysical model function (GMF) for the emission and backscatter of L-band microwave radiation from rough ocean surfaces. The analysis is based on radiometer brightness temperature and scatterometer backscatter observations both taken on board Aquarius. The data are temporally and spatially collocated with wind speeds from WindSat and F17 SSMIS (Special Sensor Microwave Imager Sounder) and wind directions from NCEP (National Center for Environmental Prediction) GDAS (Global Data Assimilation System). This GMF is the basis for retrieval of ocean surface wind speed combining L-band H-pol radiometer and HHpol scatterometer observations. The accuracy of theses combined passive/active L-band wind speeds matches those of many other satellite microwave sensors. The L-band GMF together with the combined passive/active L-band wind speeds is utilized in the Aquarius SSS retrieval algorithm for the surface roughness correction. We demonstrate that using these L-band wind speeds instead of NCEP wind speeds leads to a significant improvement in the SSS accuracy. Further improvements in the roughness correction algorithm can be obtained by adding VV-pol scatterometer measurements and wave height (WH) data into the GMF.

The forcing of equatorial waves by convective heating in the National Center for Atmospheric Research Community Climate Model (CCM3) is investigated and compared with the forcing deduced from observations of convective clouds. The analysis is performed on two different simulations, wherein convection is represented by the Zhang-McFarlane and the Hack parameterization schemes, respectively. Spectra of equatorial waves excited by convective heating (Rossby, Kelvin, and gravity waves) are obtained by projecting the heating field onto Hough modes; the dynamical response to the heating is then calculated in terms of the vertical component of the Eliassen-Palm flux, F z , focusing on waves that are able to propagate into the middle atmosphere. The same analysis is repeated using observations of outgoing longwave radiation as a proxy for tropical convection. Comparison of CCM3 results with those derived from observations indicates that high-frequency heating variability is underestimated in both CCM3 simulations, despite the fact that time-mean values of convective heating are well represented. Moreover, the two convective parameterization schemes differ substantially from each other: Compared to observations, F z is severely underestimated at most frequencies when CCM3 is run with the Zhang-McFarlane scheme. When the Hack scheme is used, F z at frequencies || Ͻ 0.5 cycles per day is comparable to the observations, but it is underestimated at higher frequencies. Misrepresentation of the variability of convective heating is likely to have important consequences for the dynamical simulation of the middle atmosphere and even the troposphere.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission was launched in January 2015 and has been providing science data since April 2015. Though designed to measure soil moisture, the SMAP radiometer has an excellent capability to measure ocean winds in storms at a resolution of 40 km with a swath width of 1,000 km. SMAP radiometer channels operate at a very low microwave frequency (L band, 1.41 GHz, 21.4 cm), which has good sensitivity to ocean surface wind speed even in very high winds and with very little impact by rain. This gives SMAP a distinct advantage over many spaceborne ocean wind sensors such as C-band [Advanced Scatterometer (ASCAT)] or Ku-band [Rapid Scatterometer (RapidScat)] scatterometers and radiometers operating at higher frequencies [Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), WindSat, Advanced Microwave Scanning Radiometer (AMSR), and Global Precipitation Measurement (GPM) Microwave Imager (GMI)], which either lose sensitivity at very high winds or degrade in rainy conditions. This article discusses the major features of a new ocean wind vector retrieval algorithm designed for SMAP. We compare SMAP wind fields in recent intense tropical cyclones with wind measurements from current scatterometer missions as well as WindSat. The most important validation source in hurricanes is the airborne stepped frequency microwave radiometer (SFMR), whose wind speeds are matched with SMAP in space and time. A comparison between SMAP and SFMR winds for eight storms in 2015, including Patricia, one of the strongest hurricanes ever recorded, shows excellent agreement up to 65 m s–1 without degradation in rain.