Evaluation of the atmospheric water vapor content in a regional climate model using ground-based GPS measurements

Ground-based GPS measurements can provide independent data for the assessment of climate models. We use the atmospheric integrated water vapor (IWV) obtained from GPS measurements at 99 European sites to evaluate the regional Rossby Centre Atmospheric climate model (RCA) driven at the boundaries by the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA Interim). The GPS data were compared to the RCA simulation and the ERA Interim data. The comparison was first made using the monthly mean values. Averaged over the domain and the 14 years covered by the GPS data, IWV differences of about 0.47 kg/m^2 and 0.39 kg/m^2 are obtained for RCA-GPS and ECMWF-GPS, respectively. The RCA-GPS standard deviation is 0.98 kg/m^2 whereas it is 0.35 kg/m^2 for the ECMWF-GPS comparison. The IWV differences for RCA are positively correlated to the differences for ECMWF. However, this is not the case for two sites in Italy where a wet bias is seen for ECMWF, while a dry bias is seen for RCA, the latter being consistent with a cold temperature bias found for RCA in that region by other authors. Comparisons of the estimated diurnal cycle and the spatial structure function of the IWV were made between the GPS data and the RCA simulation. The RCA captures the geographical variation of the diurnal peak in the summer. Averaged over all sites, a peak at 17 local solar time is obtained from the GPS data while it appears later, at 18, in the RCA simulation. The spatial variation of the IWV obtained for an RCA run with a resolution of 11 km gives a better agreement with the GPS results than does the spatial variation from a 50 km resolution run

New generation of climate models track recent unprecedented changes in Earth's radiation budget observed by CERES

We compare top‐of‐atmosphere (TOA) radiative fluxes observed by the Clouds and the Earth's Radiant Energy System (CERES) and simulated by seven general circulation models forced with observed sea‐surface temperature (SST) and sea‐ice boundary conditions. In response to increased SSTs along the equator and over the eastern Pacific (EP) following the so‐called global warming “hiatus” of the early 21st century, simulated TOA flux changes are remarkably similar to CERES. Both show outgoing shortwave and longwave TOA flux changes that largely cancel over the west and central tropical Pacific, and large reductions in shortwave flux for EP low‐cloud regions. A model's ability to represent changes in the relationship between global mean net TOA flux and surface temperature depends upon how well it represents shortwave flux changes in low‐cloud regions, with most showing too little sensitivity to EP SST changes, suggesting a “pattern effect” that may be too weak compared to observations

GEWEX water vapor assessment (G-VAP): final report

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

The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6

The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.Peer reviewe

Consistency of satellite climate data records for Earth system monitoring

Climate Data Records (CDRs) of Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS) derived from satellite instruments help to characterize the main components of the Earth system, to identify the state and evolution of its processes, and to constrain the budgets of key cycles of water, carbon and energy. The Climate Change Initiative (CCI) of the European Space Agency (ESA) coordinates the derivation of CDRs for 21 GCOS ECVs. The combined use of multiple ECVs for Earth system science applications requires consistency between and across their respective CDRs. As a comprehensive definition for multi-ECV consistency is missing so far, this study proposes defining consistency on three levels: (1) consistency in format and metadata to facilitate their synergetic use (technical level); (2) consistency in assumptions and auxiliary datasets to minimize incompatibilities among datasets (retrieval level); and (3) consistency between combined or multiple CDRs within their estimated uncertainties or physical constraints (scientific level). Analysing consistency between CDRs of multiple quantities is a challenging task and requires coordination between different observational communities, which is facilitated by the CCI program. The inter-dependencies of the satellite-based CDRs derived within the CCI program are analysed to identify where consistency considerations are most important. The study also summarizes measures taken in CCI to ensure consistency on the technical level, and develops a concept for assessing consistency on the retrieval and scientific levels in the light of underlying physical knowledge. Finally, this study presents the current status of consistency between the CCI CDRs and future efforts needed to further improve it

Preliminary use of CM-SAF cloud and radiation products for evaluation of regional climate simulations : Visiting Scientist Report Climate Monitoring SAF (CM-SAF)

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

Quantifying the clear-sky temperature inversion frequency and strength over the Arctic Ocean during summer and winter seasons from AIRS profiles

Temperature inversions are one of the dominant features of the Arctic atmosphere and play a crucial role in various processes by controlling the transfer of mass and moisture fluxes through the lower troposphere. It is therefore essential that they are accurately quantified, monitored and simulated as realistically as possible over the Arctic regions. In the present study, the characteristics of inversions in terms of frequency and strength are quantified for the entire Arctic Ocean for summer and winter seasons of 2003 to 2008 using the AIRS data for the clear-sky conditions. The probability density functions (PDFs) of the inversion strength are also presented for every summer and winter month. Our analysis shows that although the inversion frequency along the coastal regions of Arctic decreases from June to August, inversions are still seen in almost each profile retrieved over the inner Arctic region. In winter, inversions are ubiquitous and are also present in every profile analysed over the inner Arctic region. When averaged over the entire study area (70 degrees N-90 degrees N), the inversion frequency in summer ranges from 69 to 86% for the ascending passes and 72-86% for the descending passes. For winter, the frequency values are 88-91% for the ascending passes and 89-92% for the descending passes of AIRS/AQUA. The PDFs of inversion strength for the summer months are narrow and right-skewed (or positively skewed), while in winter, they are much broader. In summer months, the mean values of inversion strength for the entire study area range from 2.5 to 3.9 K, while in winter, they range from 7.8 to 8.9 K. The standard deviation of the inversion strength is double in winter compared to summer. The inversions in the summer months of 2007 were very strong compared to other years. The warming in the troposphere of about 1.5-3.0K vertically extending up to 400 hPa was observed in the summer months of 2007

The First Rossby Centre Regional Climate Scenario - Dynamical Downscaling of CO2-induced Climate Change in the HadCM2 GCM

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

The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model

The Cloud Climate Change Initiative (Cloud_cci) satellite simulator has been developed to enable comparisons between the Cloud_cci climate data record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth global climate model as well as the Regional Atmospheric Climate Model (RACMO) regional climate model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100&thinsp;% error in liquid water path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces total cloud fraction (TCF) globally by about 10&thinsp;% for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian subcontinent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean but lower than retrieved over the European continent, where in addition the cloud top pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modeled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.</p