At a time when a new generation of satellite vertical sounders is going to be launched (including the Infrared Atmospheric Sounder Interferometer and Advanced Infrared Radiometric Sounder instruments), this paper assesses the possibilities of retrieving the vertical profiles of longwave clear-sky fluxes and cooling rates from the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) radiometers aboard the polar-orbiting National Oceanic and Atmospheric Administration satellites since 1979. It focuses on two different methodologies that have been developed at Laboratoire de Météorologie Dynamique (France). The first one uses a neural network approach for the parameterization of the links between the TOVS radiances and the longwave fluxes. The second one combines the geophysical variables retrieved by the Improved Initialization Inversion method and a forward radiative transfer model used in atmospheric general circulation models. The accuracy of these two methods is evaluated using both theoretical studies and comparisons with global observations.
Abstract. Evaluating land surface models (LSMs) using available observations is important for understanding the potential and limitations of current Earth system models in simulating water- and carbon-related variables. To reveal the error sources of a LSM, five essential climate variables have been evaluated in this paper (i.e., surface soil moisture, evapotranspiration, leaf area index, surface albedo, and precipitation) via simulations with the IPSL (Institute Pierre Simon Laplace) LSM ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) model, particularly focusing on the difference between (i) forced simulations with atmospheric forcing data (WATCH Forcing Data ERA-Interim – WFDEI) and (ii) coupled simulations with the IPSL atmospheric general circulation model. Results from statistical evaluation, using satellite- and ground-based reference data, show that ORCHIDEE is well equipped to represent spatiotemporal patterns of all variables in general. However, further analysis against various landscape and meteorological factors (e.g., plant functional type, slope, precipitation, and irrigation) suggests potential uncertainty relating to freezing and/or snowmelt, temperate plant phenology, irrigation, and contrasted responses between forced and coupled mode simulations. The biases in the simulated variables are amplified in the coupled mode via surface–atmosphere interactions, indicating a strong link between irrigation–precipitation and a relatively complex link between precipitation–evapotranspiration that reflects the hydrometeorological regime of the region (energy limited or water limited) and snow albedo feedback in mountainous and boreal regions. The different results between forced and coupled modes imply the importance of model evaluation under both modes to isolate potential sources of uncertainty in the model.
<p>Irrigated areas have increased, faster than the growth of the world population, from around 0.63 million km<sup>2</sup> at the start of the 20th century to 3.1 million km<sup>2</sup> of land in 2005, that is five times of area in 1900 (0.6 million km<sup>2</sup>). Irrigation is one of the land management practices with the largest biogeochemical and biogeophysical effects on climate. However, incorporating land management factors (including irrigation) into most of the state&#8208;of&#8208;the&#8208;art climate models under the Coupled Model Intercomparison Project, Phase 6 (CMIP6) coordinated by the World Climate Research Programme (WCRP) is still overlooked. To our best knowledge, three models, however, take into account irrigation activities: namely NorESM2&#8208;LM, GISS&#8208;E2&#8208;H, and CESM2. The overall objective of the study is to investigate the role of irrigation on climate change at the global scale by looking at temporal trends of Essential Climate variables (ECVs) that characterize the Earth's climate (Evapotranspiration, leaf area index, precipitation, soil moisture, radiation, and air temperature) over the last 115 years (i.e. 1900-2014). Within this investigation, we compared models with irrigation vs. models without irrigation using 20 models from different CMIP6 experiments: coupled land-atmosphere amip (observed sea surface temperatures and sea ice concentrations), coupled land-atmosphere-ocean historical simulation, and offline land-hist (land only simulations). Temporal trends over the 1900-2014 period were computed then spatially binned by the "FAO Global Map of Irrigation Areas", which represents area equipped for irrigation expressed as percentage of total area around the year 2005. For the three CMIP6 experiments, the three models with irrigation switched on showed similar and distinguished behavior from all other models with irrigation switched off over intensively irrigated areas: mean annual evapotranspiration and soil moisture increased over time (positive trends vs. negative or no trends for all other none-irrigation models). This increase in evapotranspiration over time was reflected in the negative trends (i.e. cooling) of annual maximum air temperature for the irrigation models vs. positive trends for most of the none-irrigation models. The ET temporal positive trends over intensively irrigated areas were detected and confirmed by four different satellite-based ET products. The consistent results among the three experiments and confirmed by different satellite data imply the importance of incorporating anthropogenic factors in the next generation of climate models.</p>
From 1979 to present, sensors aboard the NOAA series of polar meteorological satellites have provided continuous measurements of the earth's surface and atmosphere. One of these sensors, the TIROS-N Operational Vertical Sounder (TOVS), observes earth-emitted radiation in 27 wavelength bands within the infrared and microwave portions of the spectrum, thereby creating a valuable resource for studying the climate of our planet. The NOAA–NASA Pathfinder program was conceived to make these data more readily accessible to the community in the form of processed geophysical variables. The Atmospheric Radiation Analysis group at the Laboratoire de Météorologie Dynamique of the Centre National de la Recherche Scientifique of France was selected to process TOVS data into climate products (Path-B). The Improved Initialization Inversion (3I) retrieval algorithm is used to compute these products from the satellite-observed radiances. The processing technique ensures internal coherence and minimizes both observational and computational biases. Products are at a 1° × 1° latitude–longitude grid and include atmospheric temperature profiles (up to 10 hPa); total precipitable water vapor and content above four levels up to 300 hPa; surface skin temperature; and cloud properties (amount, type, and cloud-top pressure and temperature). The information is archived as 1-day, 5-day, and monthly means on the entire globe; a.m. and p.m. products for each satellite are stored separately. Eight years have been processed to date, and processing continues at the rate of approximately two satellite-months per day of computer time. Quality assessment studies are presented. They consist of comparisons to conventional meteorological data and to other remote sensing datasets.
The improved initialization inversion (3I) algorithms convert TIROS-N Operational Vertical Sounder observations from the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting environmental satellites into atmospheric temperature and water vapor profiles, together with cloud and surface properties. Their relatively good spectral resolution and coverage make IR sounders a very useful tool for the determination of cloud properties both day and night. The iterative process of detailed comparisons between cloud parameters obtained from this global dataset, which is available in the framework of the NOAA–National Aeronautics and Space Administration Pathfinder Program, with time–space-collocated observations of clouds from the recently reprocessed International Satellite Cloud Climatology Project (ISCCP) dataset has led to an improved 3I cloud analysis scheme based on a weighted-χ2 method described in the second article of this series. This process also provides a first evaluation of the ISCCP reanalysis. The new 3I cloud scheme obtains cloud properties very similar to those from ISCCP for homogeneous cloud scenes. Improvement is especially notable in the stratocumulus regimes where the new 3I scheme detects much more of the low-level cloudiness. Remaining discrepancies in cloud classification can now be explained by differences in cloud detection sensitivity, differences in temperature profiles used, and inhomogeneous or partly cloudy fields. Cirrus cloud identification during the daytime in the recent ISCCP dataset is improved relative to the first version of ISCCP, but is still an underestimate. At night only multispectral IR analyses like 3I can provide cirrus information. The reprocessed ISCCP dataset also shows considerable improvement in cloud cover at higher latitudes. Differences in 3I and ISCCP summertime cloud cover over deserts may be caused by different sensitivities to dust storms.
Abstract This study presents the development of a so‐called Turbulent Kinetic Energy (TKE)‐l, or TKE‐l, parameterization of the diffusion coefficients for the representation of turbulent diffusion in neutral and stable conditions in large‐scale atmospheric models. The parameterization has been carefully designed to be completely tunable in the sense that all adjustable parameters have been clearly identified and the number of parameters has been minimized as much as possible to help the calibration and to thoroughly assess the parametric sensitivity. We choose a mixing length formulation that depends on both static stability and wind shear to cover the different regimes of stable boundary layers. We follow a heuristic approach for expressing the stability functions and turbulent Prandlt number in order to guarantee the versatility of the scheme and its applicability for planetary atmospheres composed of an ideal and perfect gas such as that of Earth and Mars. Particular attention has been paid to the numerical stability and convergence of the TKE equation at large time steps, an essential prerequisite for capturing stable boundary layers in General Circulation Models (GCMs). Tests, parametric sensitivity assessments and preliminary tuning are performed on single‐column idealized simulations of the weakly stable boundary layer. The robustness and versatility of the scheme are assessed through its implementation in the Laboratoire de Météorologie Dynamique Zoom GCM and the Mars Planetary Climate Model and by running simulations of the Antarctic and Martian nocturnal boundary layers.
Abstract. This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).
