The representation of the effect of tropical deep convective (DC) systems on upper-tropospheric moist processes and outgoing longwave radiation is evaluated in the EC-Earth3, ECHAM6, and CAM5 (Community Atmosphere Model) climate models using satellite-retrieved data. A composite technique is applied to thousands of deep convective systems that are identified using local rain rate maxima in order to focus on the temporal evolution of the deep convective processes in the model and satellite-retrieved data. The models tend to over-predict the occurrence of rain rates that are less than approximate to 3 mm h(-1) compared to Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA). While the diurnal distribution of oceanic rain rate maxima in the models is similar to the satellite-retrieved data, the land-based maxima are out of phase. Despite having a larger climatological mean uppertropospheric relative humidity, models closely capture the satellite-derived moistening of the upper troposphere following the peak rain rate in the deep convective systems. Simulated cloud fractions near the tropopause are larger than in the satellite data, but the ice water contents are smaller compared with the satellite-retrieved ice data. The models capture the evolution of ocean-based deep convective systems fairly well, but the land-based systems show significant discrepancies. Over land, the diurnal cycle of rain is too intense, with deep convective systems occurring at the same position on subsequent days, while the satellite-retrieved data vary more in timing and geographical location. Finally, simulated outgoing longwave radiation anomalies associated with deep convection are in reasonable agreement with the satellite data, as well as with each other. Given the fact that there are strong disagreements with, for example, cloud ice water content, and cloud fraction, between the models, this study supports the hypothesis that such agreement with satellite-retrieved data is achieved in the three models due to different representations of deep convection processes and compensating errors.

An earlier method to determine the mean response of upper-tropospheric water to localised deep convective systems (DC systems) is improved and applied to the EC-Earth climate model. Following Zelinka and Hartmann (2009), several fields related to moist processes and radiation from various satellites are composited with respect to the local maxima in rain rate to determine their spatio-temporal evolution with deep convection in the central Pacific Ocean. Major improvements to the earlier study are the isolation of DC systems in time so as to prevent multiple sampling of the same event, and a revised definition of the mean background state that allows for better characterisation of the DC-system-induced anomalies. The observed DC systems in this study propagate westward at similar to 4 ms(-1). Both the upper-tropospheric relative humidity and the outgoing longwave radiation are substantially perturbed over a broad horizontal extent and for periods > 30 h. The cloud fraction anomaly is fairly constant with height but small maximum can be seen around 200 hPa. The cloud ice water content anomaly is mostly confined to pressures greater than 150 hPa and reaches its maximum around 450 hPa, a few hours after the peak convection. Consistent with the large increase in upper-tropospheric cloud ice water content, albedo increases dramatically and persists about 30 h after peak convection. Applying the compositing technique to EC-Earth allows an assessment of the model representation of DC systems. The model captures the large-scale responses, most notably for outgoing longwave radiation, but there are a number of important differences. DC systems appear to propagate east-ward in the model, suggesting a strong link to Kelvin waves instead of equatorial Rossby waves. The diurnal cycle in the model is more pronounced and appears to trigger new convection further to the west each time. Finally, the modelled ice water content anomaly peaks at pressures greater than 500 hPa and in the upper troposphere between 250 hPa and 500 hPa, there is less ice than the observations and it does not persist as long after peak convection. The modelled upper-tropospheric cloud fraction anomaly, however, is of a comparable magnitude and exhibits a similar longevity as the observations.

Tropical upper tropospheric humidity, clouds, and ice water content, as well as outgoing longwave radiation (OLR), are evaluated in the climate model EC Earth with the aid of satellite retrievals. The Atmospheric Infrared Sounder and Microwave Limb Sounder together provide good coverage of relative humidity. EC Earth's relative humidity is in fair agreement with these observations. CloudSat and CALIPSO data are combined to provide cloud fractions estimates throughout the altitude region considered (500-100 hPa). EC Earth is found to overestimate the degree of cloud cover above 200 hPa and underestimate it below. Precipitating and non-precipitating EC Earth ice definitions are combined to form a complete ice water content. EC Earth's ice water content is below the uncertainty range of CloudSat above 250 hPa, but can be twice as high as CloudSat's estimate in the melting layer. CERES data show that the model underestimates the impact of clouds on OLR, on average with about 9 W m(-2). Regionally, EC Earth's outgoing longwave radiation can be similar to 20 W m(-2) higher than the observation. A comparison to ERA-Interim provides further perspectives on the model's performance. Limitations of the satellite observations are emphasised and their uncertainties are, throughout, considered in the analysis. Evaluating multiple model variables in parallel is a more ambitious approach than is customary.

During the August-September 2003 Autonomous Ocean Sampling Network-II experiment, the Harvard Ocean Prediction System (HOPS) and Error Subspace Statistical Estimation (ESSE) system were utilized in real-time to forecast physical fields and uncertainties, assimilate various ocean measurements (CTD, AUVs, gliders and SST data), provide suggestions for adaptive sampling, and guide dynamical investigations. The qualitative evaluations of the forecasts showed that many of the surface ocean features were predicted, but that their detailed positions and shapes were less accurate. The root-mean-square errors of the real-time forecasts showed that the forecasts had skill out to two days. Mean one-day forecast temperature RMS error was 0.26 degrees C less than persistence RMS error. Mean two-day forecast temperature RMS error was 0.13 degrees C less than persistence RMS error. Mean one- or two-day salinity RMS error was 0.036 PSU less than persistence RMS error. The real-time skill in the surface was found to be greater than the skill at depth. Pattern correlation coefficient comparisons showed, on average, greater skill than the RMS errors. For simulations lasting 10 or more days, uncertainties in the boundaries could lead to errors in the Monterey Bay region. Following the real-time experiment, a reanalysis was performed in which improvements were made in the selection of model parameters and in the open-boundary conditions. The result of the reanalysis was improved long-term stability of the simulations and improved quantitative skill, especially the skill in the main thermocline (RMS simulation error 1 degrees C less than persistence RMS error out to five days). This allowed for an improved description of the ocean features. During the experiment there were two-week to 10-day long upwelling events. Two types of upwelling events were observed: one with plumes extending westward at point Ano Nuevo (AN) and Point Sur (PS); the other with a thinner band of upwelled water parallel to the coast and across Monterey Bay. During strong upwelling events the flows in the upper 10-20 m had scales similar to atmospheric scales. During relaxation, kinetic energy becomes available and leads to the development of mesoscale features. At 100-300 m depths, broad northward flows were observed, sometimes with a coastal branch following topographic features. An anticyclone was often observed in the subsurface fields in the mouth of Monterey Bay. (C) 2008 Elsevier Ltd. All rights reserved.

The Satellite Application Facility on Climate Monitoring (CM-SAF) aims at the provision of satellite-derived geophysical parameter data sets suitable for climate monitoring. CM-SAF provides climatologies for Essential Climate Variables (ECV), as required by the Global Climate Observing System implementation plan in support of the UNFCCC. Several cloud parameters, surface albedo, radiation fluxes at the top of the atmosphere and at the surface as well as atmospheric temperature and humidity products form a sound basis for climate monitoring of the atmosphere. The products are categorized in monitoring data sets obtained in near real time and data sets based on carefully intercalibrated radiances. The CM-SAF products are derived from several instruments on-board operational satellites in geostationary and polar orbit as the Meteosat and NOAA satellites, respectively. The existing data sets will be continued using data from the instruments on-board the new joint NOAA/EUMETSAT Meteorological Operational Polar satellite. The products have mostly been validated against several ground-based data sets both in situ and remotely sensed. The accomplished accuracy for products derived in near real time is sufficient to monitor variability on diurnal and seasonal scales. The demands on accuracy increase the longer the considered time scale is. Thus, interannual variability or trends can only be assessed if the sensor data are corrected for jumps created by instrument changes on successive satellites and more subtle effects like instrument and orbit drift and also changes to the spectral response function of an instrument. Thus, a central goal of the recently started Continuous Development and Operations Phase of the CM-SAF (2007-2012) is to further improve all CM-SAF data products to a quality level that allows for studies of interannual variability.

The purpose of this study was to quantify the impact of using ancillary data from Numerical Weather Prediction (NWP) models in the derivation of cloud parameters from satellite data in the Climate Monitoring Satellite Application Facility (CM-SAF) project. In particular, the sensitivity to the NWP-analysed surface temperature parameter was studied.A one-year dataset of satellite images over the Scandinavian region from the Advanced Very High Resolution Radiometer (AVHRR) on the polar orbiting NOAA satellites was studied. Cloud products were generated by use of the Polar Platform System (PPS) cloud software and the sensitivity to perturbations of the NWP-analysed surface temperature was investigated. In addition, a study on the importance of the chosen NWP model was also included. Results based on three different NWP models (ECMWF, HIRLAM and GME) were analysed.It was concluded that the NWP model influence on the results appears to be small. An interchange of NWP model analysis input data to the PPS cloud processing method did only lead to marginal changes of the resulting CM-SAF cloud products. Thus, the current CM-SAF cloud algorithmsproduce robust results that are not heavily dependent on NWP model background information. Nevertheless, the study demonstrated a natural high sensitivity to the NWP-analysed surface skin temperature. This parameter is crucial for the a priori determination of the thresholds used for the infrared cloud tests of the PPS method. It was shown that a perturbation of the surface skin temperature of one K generally resulted in a change of cloud cover of about 0.5-1 % in absolute cloud amount units. However, if perturbations were in the range 5-10 K the change in cloud cover increased to values between 1 to 2 % per degree, especially for positive perturbations. Important here is that a positive surface temperature perturbation always leads to an increase in the resulting cloud amounts and vice versa.

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The goals of this study are to compare the MSG SEVIRI and PPS AVHRR monthly mean cloud products of the CM-SAF. The study was done in two parts: first comparing the cloud mask products and then comparing the cloud top temperature and height products. This was done over a region from Greenland to eastern Russia and as far south as the Sahara. The study covered four seasonally-representative months. For the cloud mask using PPS version 1.0, the results showed large problems over the Sahara and parts of Spain during the summer months. This was primarily due to the high reflectances in channel 3a and mostprominent with NOAA 17.Much larger differences were found over water than over land surfaces, with the exception of Scandinavia where the differences were comparable to those found over water. The cloud-contaminated values were removed in one plot and this revealed that PPS had a larger number of cloud-contaminated pixels than MSG. This agrees with the concept that MSG reports increased cloudiness at higher viewing angles. This also explains why the differences over Scandinavia were so large and positive in value. The NOAA images at high latitudes have better spatial resolution and reports fewer cloudy and cloud-contaminated pixels than MSG.Sub-pixel and thin clouds greatly affected how well the two products converged. An attempt to use a weighted factor to adjust the effect of cloud-contaminated pixels on the total cloud cover failed to improve the convergence between the two cloud masks. The effect of the MSG viewing angle and the subsequent effects of reporting more cloudy pixels (or cloud-contaminated pixel – to include thin clouds) could be seen throughout all four months in the form of larger positive differences at latitudes approaching 80 degrees. Significant changes were seen with results from the PPS version 1.1. A significant decrease in the difference over the Sahara was the most discernable change. On the other hand, for NOAA 17, the agreement with MSG during twilight conditions was reduced by almost one half. The comparison of the cloud top temperature and height products revealed that MSG reported more low clouds during the summer months than PPS. This was mostly like due to the presence of convective clouds and the angle at which they are viewed (small cumulus clouds when viewed from nadir has a smaller diameter than when viewed slantwise).