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Este es un informe dentro del Programa para la Investigación del Clima Mundial (World Climate Research Programme, WCRP) cuya misión es facilitar el análisis y la predicción de la variabilidad de la Tierra para proporcionar un valor añadido a la sociedad a nivel práctica. La WCRP tiene varios proyectos centrales, de los cuales el de Intercambio Global de Energía y Agua (Global Energy and Water Exchanges, GEWEX) es uno de ellos. Este proyecto se centra en estudiar el ciclo hidrológico global y regional, así como sus interacciones a través de la radiación y energía y sus implicaciones en el cambio global. Dentro de GEWEX existe el proyecto de Evaluación del Vapor de Agua (VAP, Water Vapour Assessment) que estudia las medidas de concentraciones de vapor de agua en la atmósfera, sus interacciones radiativas y su repercusión en el cambio climático global. El vapor de agua es, de largo, el gas invernadero más importante que reside en la atmósfera. Es, potencialmente, la causa principal de la amplificación del efecto invernadero causado por emisiones de origen humano (principalmente el CO2). Las medidas precisas de su concentración en la atmósfera son determinantes para cuantificar este efecto de retroalimentación positivo al cambio climático. Actualmente, se está lejos de tener medidas de concentraciones de vapor de agua suficientemente precisas para sacar conclusiones significativas de dicho efecto. El informe del WCRP titulado "GEWEX water vapor assessment. Final Report" detalla el estado actual de las medidas de las concentraciones de vapor de agua en la atmósfera. AEMET ha colaborado en la generación de este informe y tiene a unos de sus miembros, Xavier Calbet, como co-autor de este informe.

We have compared monthly mean cloud and radiation fields from the EUMETSAT Climate Monitoring SAF (CM-SAF, http://www.cmsaf.eu) data base with the clouds and radiation simulated by the Rossby Centre regional climate model (RCA) and by the European Centre Medium range Weather Forecast model (ECMWF) over Europe and North Africa for the time period January 2005 to December 2006.ECMWF and RCA overestimate the cloud fraction by 20% over snow covered regions in the north east of Europe and overestimate the surface downwelling longwave radiation (SDL) by 20-40W/m2 and surface outgoing longwave radiation by 10-30W/m2. The RCA-simulated clouds have too much cloud water in northern Europe in summer and in autumn and they therefore reflect too much shortwave radiation at the TOA (TRS) and this also leads to an underestimation of the incoming shortwave radiation (SIS) at the surface. Over most of Europe and over sea ECMWF (all year) and RCA (in winter-spring) underestimate the cloud fraction which could explain a corresponding underestimate of TRS, overestimate of SIS and underestimate of SDL. The satellites overestimate cloud cover over sea due to problems in the treatment of sub-pixel cloudiness and therefore the models underestimates are larger over sea. Mainly RCA but also ECMWF overestimate cloud fraction on top of mountains and underestimate it along mountain ranges and have corresponding differences in the TOA and surface radiation fluxes compared to the CM-SAF data.Over North Africa RCA underestimates TRS by -11W/m2 and overestimates the TOA emitted thermal radiation (TET) by 8W/m2. ECMWF underestimates TRS by -28W/m2 and overestimates TET by 14W/m2. These errors are similar to what has been found for many other global models and are attributed to clear sky errors either due to too high surface temperatures, errors in emissivity, albedo or lack of aerosols. Adding clear and cloudy skies radiation fluxes to the CM-SAF data base would help us to understand the reasons for ECMWF and RCA errors. The polar orbiting satellite retrieval for 2005-2006 erroneously overestimated cloud fraction over North Africa, which also affects the CM-SAF derived surface radiation fluxes.

This report presents the latest version of the Rossby Centre regional atmospheric model, RCA3, with focus on model improvements since the earlier version, RCA2. The main changes in RCA3 relate to the treatment of land surface processes. Apart from the changes in land surface parameterizations several changes in the calculation of radiation, clouds, condensate and precipitation have been made. The new parameterizations hold a more realistic description of the climate system.Simulated present day climate is evaluated compared to observations. The new model version show equally good, or better, correspondence to observational climatologies as RCA2, when forced by perfect boundary conditions. Seasonal mean temperature errors are generally within ±1oC except during winter in north-western Russia where a larger positive bias is identified. Both the diurnal temperature range and the annual temperature range are found to be underestimated in the model. Precipitation biases are generally smaller than in the corresponding reanalysis data used as boundary conditions, showing the benefit of a higher horizontal resolution.The model is used for the regionalization of two transient global climate change projections for the time period 1961- 2100. The radiative forcing of the climate system is based on observed concentrations of greenhouse gases until 1990 and on the IPCC SRES B2 and A2 emissions scenarios for the remaining time period. Long-term averages as well as measures of the variability around these averages are presented for a number of variables including precipitation and near-surface temperature. It is shown that the changes in variability sometimes differ from the changes in averages. For instance, in north-eastern Europe, the mean increase in wintertime temperatures is followed by an even stronger reduction in the number of very cold days in winter. This kind of performance of the climate system implies that methods of inferring data from climate change projections to other periods than those actually simulated have to be used with care, at least when it comes to variables that are expected to change in a non-linear way. Further, these new regional climate change projections address the whole 21st century.

A series of six general circulation model (GCM) driven regional climate simulations made at the Rossby Centre, SMHI, during the year 2002 are documented. For both the two driving GCMs HadAM3H andECHAM4/OPYC3, a 30-year (1961-1990) control run and two 30-year (2071-2100) scenario runs have been made. The scenario runs are based on the IPCC SRES A2 and B2 forcing scenarios. These simulations were made at 49 km atmospheric resolution and they are part of the European PRUDENCE project.Many aspects of the simulated control climates compare favourably with observations, but some problems are also evident. For example, the simulated cloudiness and precipitation appear generally too abundant in northern Europe (although biases in precipitation measurements complicate the interpretation), whereas too clear and dry conditions prevail in southern Europe. There is a lot of similarity between the HadAM3Hdriven (RCAO-H) and ECHAM4/OPYC3-driven (RCAO-E) control simulations, although the problems associated with the hydrological cycle and cloudiness are somewhat larger in the latter.The simulated climate changes (2071-2100 minus 1961-1990) depend on both the forcing scenario (the changes are generally larger for A2 than B2) and the driving global model (the largest changes tend to occur in RCAO-E). In all the scenario simulations, the warming in northern Europe is largest in winter or autumn. In central and southern Europe, the warming peaks in summer and reaches in the RCAO-E A2 simulation locally 10°C. The four simulations agree on a general increase in precipitation in northern Europe especiallyin winter and on a general decrease in precipitation in southern and central Europe in summer, but the magnitude and the geographical patterns of the change differ a lot between RCAO-H and RCAO-E. Thisreflects very different changes in the atmospheric circulation during the winter half-year, which also have a large impact on the simulated changes in windiness. A very large increase in the lowest minimumtemperatures occurs in a large part of Europe, most probably due to reduced snow cover. Extreme daily precipitation increases even in most of those areas where the mean annual precipitation decreases.

Results of the first 10-year climate change experiment made with the Rossby Centre regional climate model (RCA) are described. The boundary data for this experiment were derived from two simulations with the .global HadCM2 ocean-atmosphere GCM, a control run anda scenario run with 150% higher equivalent CO2 and 2.6°C higher global mean surface air temperature. Some of the climate changes (scenario run - control run) simulated by RCA are substantial. The annual mean temperature in the Nordic region increases by roughly 4°C, with largest warming in winter. Annual absolute minimum temperatures increase even more than the winter mean temperature, presumably due to greatly reduced snow and ice cover. Precipitation is also simulated to increase in northern Europe, locally by 40% in the annual mean in Swedish Lappland. The larger time mean precipitation is accompanied by a marked increase in the number of days with heavy precipitation. The large-scale temperature and precipitation changes simulated by RCA are similar to those in HadCM2. Unlike HadCM2, however, RCA simulates a strong local maximum of wintertime warming over the northern parts of the Baltic Sea. This is caused by radically reduced ice cover, but the crude treatment of the Baltic Sea and its ice even in RCA complicates the interpretation. Large differences between the models occur in the simulated changes of winter mean total cloudiness and near-surface wind speed, demonstrating the sensitivity of these to differences in resolution and/or physical parameterizations. The significance of the simulated climate changes against interannual variability depends on the parameter considered. Of highest statistical significance are changes in surface air temperature and strongly temperature-related variables such as snow and ice cover. In general, changes in annual means are more commonly significant than those in seasonal means. The impact of the limited averaging period is also studied by comparing the 10-year mean climate changes simulated by the driving HadCM2 mode! with climate changes inferred from much longer HadCM2 integrations.