Thermostads and circulation in the upper layer of the Atlantic Ocean

There are three major permanent thermostads with roughly the same potential densities in the upper layer of the Atlantic Ocean. One is the thermostad of the 13°C Water in the equatorial Atlantic. The original type of the 13°C Water is formed in the thermocline in the eastern sector of the South Atlantic subtropical gyre by vertical mixing of dense, low-salinity water from the winter outcrop farther south and overlying less dense, high-salinity water. There might also be a lateral contribution of relatively high-salinity water from the Indian Ocean. The original 13°C Water thus formed is transported northwestward along the northern edge of the subtropical gyre and fed into the North Brazilian Current, which flows equatorward along the coast of Brazil. In the region of the equator, the Equatorial Undercurrent and the subsurface North and South Equatorial countercurrents branch off from the North Brazilian Current and carry the 13°C Water eastward to the thermostad region. Vertical mixing does not explain the development of the thermostad, but is found to be essential in determining the ultimate characteristics of the 13°C Water. The other two thermostads are those of the 18°C Water in the Sargasso Sea and the Subantarctic Mode Water in the western South Atlantic. Unlike the 13°C Water, both of these mode waters are formed as thermostads in the surface layer by winter convection, but vertical mixing in the subtropical gyres may play a role in determining their characteristics. All the three thermostads appear to be required to balance the system of flows in opposing directions.     

Global assessment of the effectiveness of adaptation in coastal areas based on RCP/SSP scenarios

Climatic Change - The objective of this study is to assess the global impact of sea level rise and to evaluate the effectiveness of adaptation. Global areas of inundation due to sea level rise are...     

Short-term variability of Jupiter’s extended sodium nebula

Ground-based optical observations of D₁ and D₂ line emissions from Jupiter’s sodium nebula, which extend over several hundreds of jovian radii, were carried out at Mt. Haleakala, Maui, Hawaii using a wide field filter imager from May 19 to June 21, 2007. During this observation, the east-west asymmetry of the nebula with respect to the Io’s orbital motion was clearly identified. Particularly, the D₁+D₂ brightness on the western side of Jupiter is strongly controlled by the Io phase angle. The following scenario was developed to explain this phenomenon as follows: First, more ionospheric ions like NaX⁺, which are thought to produce fast neutral sodium atoms due to a dissociative recombination process, are expected to exist in Io’s dayside hemisphere rather than in the nightside one. Second, it is expected that more NaX⁺ ionospheric ions are picked up by the jovian co-rotating magnetic field when Io’s leading hemisphere is illuminated by the Sun. Third, the sodium atom ejection rate varies with respect to Io’s orbital position as a result of the first two points. Model simulations were performed using this scenario. The model results were consistent with the observation results, suggesting that Io’s ionosphere is expected to be controlled by solar radiation just like Earth.     

An international turbulence comparison experiment (ITCE 1976)

Turbulence data for the International Turbulence Comparison Experiment (ITCE) held at Conargo, N.S.W. (35° 18′ S., 145° 10′ E.) during October, 1976 are analysed. The standard deviation % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaaiikaiqado% hagaqbamaaCaaaleqabaGaaGOmaaaakiaacMcadaahaaWcbeqaaiaa% igdacaGGVaGaaGOmaaaaaaa!3B93!\[(s'^2 )^{1/2} \] and covariance % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaa0aaaeaace% WG3bGbauaaceWGZbGbauaaaaaaaa!3809!\[\overline {w's'} \] measured by a number of instruments and instrument arrays have been compared to assess their field performance and calibration accuracy. Satisfactory agreement, i.e. typically 5% for % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaaiikamaana% aabaGabm4CayaafaWaaWbaaSqabeaacaaIYaaaaaaakiaacMcadaah% aaWcbeqaaiaaigdacaGGVaGaaGOmaaaaaaa!3BA4!\[(\overline {s'^2 } )^{1/2} \] (except in humidity) and of the order of 20% for % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaaiikaiqado% hagaqbamaaCaaaleqabaGaaGOmaaaakiaacMcadaahaaWcbeqaaiaa% igdacaGGVaGaaGOmaaaaaaa!3B93!\[(s'^2 )^{1/2} \], was achieved, but only after consideration of:

1. Instrumental response at high frequencies.
2. Flow distortion induced by instruments and supporting structures.
3. Spatial separation of instruments used for covariance measurements.
4. Statistical errors associated with single point measurements over a finite averaging time, and with lateral separation of two sensor arrays being compared.

     

Estimation of urban heat island intensity using biases in surface air temperature simulated by a nonhydrostatic regional climate model

This study demonstrates that urban heat island (UHI) intensity can be estimated by comparing observational data and the outputs of a well-developed high-resolution regional climate model. Such an estimate is possible because the observations include the effects of UHI, whereas the model used does not include urban effects. Therefore, the errors in the simulated surface air temperature, defined as the difference between simulated and observed temperatures (simulated minus observed), are negative in urban areas but 0 in rural areas. UHI intensity is estimated by calculating the difference in temperature error between urban and rural areas. Our results indicate that overall UHI intensity in Japan is 1.5 K and that the intensity is greater in nighttime than in daytime, consistent with the previous studies. This study also shows that root mean square error and the magnitude of systematic error for the annual mean temperature are small (within 1.0 K).