The molecular backscatter signal is necessary in aerosol lidars to determine the aerosol backscatter and extinction. It is derived using atmospheric temperature and pressure profiles from simultaneous soundings or climatological values from radiosonde measurements. In cases when no radiosonde measurements are available at or near the lidar stations the U.S. Standard Atmosphere is commonly used. Although the atmospheric reanalysis can also be used to obtain the atmospheric temperature and pressure profiles at any station, we found no reports of its use for this purpose. Therefore, the goal of this research is to compare molecular backscatter coefficients derived from radiosonde measurements with ones derived using the ERA-Interimreanalysis and the U.S. Standard Atmosphere 1976 (USSA-1976), for the locations of the lidar stations from the Latin America Lidar Network (LALINET). The differences between radiosonde measurements and ERA-Interim temperature profiles are smaller than the differences between radiosonde measurements and the USSA-1976 temperature profiles. In many stations, the differences between the temperature in radiosonde measurements and ERA-Interim are smaller than 2 K and the greatest differences are about 4 K. However, the differences between temperature in radiosonde measurements and in the USSA-1976 are smaller than 10 K only in three stations, and the greatest differences are about 20 K. The profiles of relative differences of the molecular backscatter coefficients between radiosonde measurements and ERA-Interim show negative values at most of the pressure levels. Positive values greater than 0.5 % are only observed above 8 km. However, the profiles of relative differences of the molecular backscatter coefficients between radiosonde measurements and the USSA-1976 shows positive values from surface to 12 km in all stations. Only in four stations are observed negative differences above 14 km with values smaller than -5 %. Comodoro Rivadavia presents the best behavior because is located near to 45 degree of latitude, but even in this station the differences between radiosonde measurements and the USSA-1976 are bigger than those observed between radiosonde measurements and ERA-Interim.
The annual cycle of precipitation in southern Mexico and Central America presents a maximum in June, a relative minimum in July-August, and a second maximum between September and early October. The minimum of this bimodal distribution is known as the midsummer drought or canícula. To investigate this process, the new version of the ERA-Interimreanalysis of the European Centre for Medium-Range Weather Forecast (ECMWF) was used. First, the climatology of precipitation, atmospheric pressure at sea level, wind vector at the surface and other variables of interest was obtained. The results represent the main large-scale weather systems in tropical latitudes. Subsequently, we determined the phases comprising the midsummer drought, where phase 1 corresponds to the first maximum in precipitation, phase 2 to the minimum, and phase 3 to the second maximum at the end of summer. Based on this separation, phase composites were obtained of mean conditions as well as anomalies from average summer conditions (June-September from 1979 to 2010) in order to analyze the weather conditions prevailing in each phase, as well as the local and large scale forcings important for the midsummer drought and the surface circulation.
- Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F. (2011), The ERA-Interimreanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553–597. doi: 10.1002/qj.828 - WMO-SPICE (Solid Precipitation Intercomparison Experiment)
Variability and changes in climate extremes affect the core crop region of Argentina and may increase its vulnerability leading to unprecedented disasters. This study investigates the long-term changes and interannual variability of daily temperature and precipitation climate extremes and assesses to what extent global reanalyses reproduce the observed variability in the recent past. Datasets include quality- controlled observations (1963-2013) and ERA-Interim and NCEP2 reanalyses (1979-2011). Climate extremes are characterized spatially and temporally by 11 indices proposed by the Expert Team on Climate Change Detection and Indices. A Singular Spectrum Analysis was applied to detect the leading modes of the area- averaged index time series. Nonparametric linear trends were fitted to each index time series to estimate the spatial distribution of mean changes. Temperature extremes are changing towards warmer conditions. Warm days has been increasing since 1990 while cold days has been decreasing. Warm and cold nights show a significant signal of warming that seems to be stabilizing in recent decades. Intense precipitation events in most of the region increased steadily since 1970. The annual maximum amount of 1-day precipitation events increased from the 1970s to the 2000s, stabilizing in recent years. The ERA-Interimreanalysis can recognize temperature extremes in time and space, while the older NCEP2 presents systematic biases. Both reanalyses reproduce the annual maximum 5-day precipitation with large biases. Although reanalyses would be expected to add information for climate extremes in areas of scarce observations, they still need to be used with great caution and only as a complement to observations.
The ability of regional climate models (RCMs) to accurately simulate current and future climate is increasingly important for impact assessment. This is the first evaluation of all reanalysis-driven RCMs within the CORDEX Australasia framework four configurations of the Weather Forecasting and Research (WRF) model, and single configurations of COSMO-CLM (CCLM) and the Conformal-Cubic Atmospheric Model (CCAM) to simulate the historical climate of Australia (1981–2010) at 50 km resolution. Simulations of near-surface maximum and minimum temperature and precipitation were compared with gridded observations at annual, seasonal, and daily time scales. The spatial extent, sign, and statistical significance of biases varied markedly between the RCMs. However, all RCMs showed widespread, statistically significant cold biases in maximum temperature which were the largest during winter. This bias exceeded − 5 K for some WRF configurations, and was the lowest for CCLM at ± 2 K. Most WRF configurations and CCAM simulated minimum temperatures more accurately than maximum temperatures, with biases in the range of ± 1.5 K. RCMs overestimated precipitation, especially over Australia’s populous eastern seaboard. Strong negative correlations between mean monthly biases in precipitation and maximum temperature suggest that the maximum temperature cold bias is linked to precipitation overestimation. This analysis shows that the CORDEX Australasia ensemble is a valuable dataset for future impact studies, but improving the representation of land surface processes, and subsequently of surface temperatures, will improve RCM performance. The varying RCM capabilities identified here serve as a foundation for the development of future regional climate projections and impact assessments for Australia.
Poor projects. The amount of extra funds needed by poor projects will in turn depend on poor entrepreneurs’ own characteristics: we assume that the class of poor entrepreneurs is itself not homogeneous. This heterogeneity comes from the fact that each poor entrepreneur has an idiosyncratic characteristic that reflects his ability to complete the project. At the contracting stage, a representative entrepreneur of this class knows he has a poor project, but he does not know how well he will perform in taking the project to completion. He needs to invest himself into the project, learn by doing, in order to know more and find out his level of ability. Hence, we assume that abilities are unknown at the contracting stage, and that this is particularly true for poor entrepreneurs themselves. At the interim stage, this process of information acquisition is completed and abilities become publicly observed 4 .
En el marco del proyecto nacional ”Generación de escenarios regionalizados de cambio climático en España con modelos de alta resolución, 2008-2011” (ESCENA) se ha anidado el RCM PROMES en el reanálisis de alta resolución del ECMWF (ERA Interim) y se han realizado simulaciones sobre un dominio centrado en la Península Ibérica con una resolución horizontal de 25 km (figura 1), comprendiendo los años de 1989 a 2008. Tras la validación del RCM para reproducir el clima presente observado, se usará dicho modelo para simulaciones de clima futuro que se realizarán dentro del proyecto, anidando el RCM en diferentes modelos globales y usando diferentes escenarios de emisiones.
“In Afghanistan...I watched as 3,000 schools across that war-torn country reopened, and 3 million children, boys as well as girls, streamed in, many for the fi rst time in six years. It was UNICEF’s largest logistical operation ever in support of education – and it succeeded because the interim government committed itself to a drive that mobilized teachers, registered children, readied school facilities and organized a curriculum and an entire educational structure virtually from scratch. It was a stirring affi rmation of hope and defi ance, and the universal spirit behind it has only reinforced my conviction that the future remains in our hands as never before.”
S2 tide amplitude and phase (measured by first maximum local time) have been plotted against time in Figure 4. We can observe some spurious values in NCEP reanalysis from 1948 to 1957, and to a lesser extent around 1997. The static tide amplitude calculated from the whole period will obviously lead to a greater than expected value in the amplitude, due to these outliers. The abnormally high values in the S2 amplitude reported by Ray (2001) and Ray and Ponte (2003) from NCEP reanalysis are probably related to this fact.