Errors in the simulation of clouds in general circulation models (GCMs) remain a long-standing issue in climate projections, as discussed in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. This highlights the need for developing new analysis techniques to improve our knowledge of the physical processes at the root of these errors. The Cloud Feedback Model Intercomparison Project (CFMIP) pursues this objective, and under that framework the CFMIP Observation Simulator Package (COSP) has been developed. COSP is a flexible software tool that enables the simulation of several satellite-borne active and passive sensor observations from model variables. The flexibility of COSP and a common interface for all sensors facilitates its use in any type of numerical model, from high-resolution cloud-resolving models to the coarser-resolution GCMs assessed by the IPCC, and the scales in between used in weather forecast and regional models. The diversity of model parameterization techniques makes the comparison between model and observations difficult, as some parameterized variables (e.g., cloud fraction) do not have the same meaning in all models. The approach followed in COSP permits models to be evaluated against observations and compared against each other in a more consistent manner. This permits a more detailed diagnosis of the physical processes that govern the behavior of clouds and precipitation in numerical models. The World Climate Research Programme (WCRP) Working Group on Coupled Modelling has recommended the use of COSP in a subset of climate experiments that will be assessed by the next IPCC report. In this article we describe COSP, present some results from its application to numerical models, and discuss future work that will expand its capabilities.

https://hal.archives-ouvertes.fr/hal-01117353/document

COSP: Satellite simulation software for model assessment

Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program

Satellite observations and their corresponding instrument simulators are used to document global cloud biases in the Community Atmosphere Model (CAM) versions 4 and 5. The model–observation comparisons show that, despite having nearly identical cloud radiative forcing, CAM5 has a much more realistic representation of cloud properties than CAM4. In particular, CAM5 exhibits substantial improvement in three long-standing climate model cloud biases: 1) the underestimation of total cloud, 2) the overestimation of optically thick cloud, and 3) the underestimation of midlevel cloud. While the increased total cloud and decreased optically thick cloud in CAM5 result from improved physical process representation, the increased midlevel cloud in CAM5 results from the addition of radiatively active snow. Despite these improvements, both CAM versions have cloud deficiencies. Of particular concern, both models exhibit large but differing biases in the subtropical marine boundary layer cloud regimes that are kn...

https://opensky.ucar.edu:/islandora/object/articles%3A12186/datastream/PDF/view

Exposing Global Cloud Biases in the Community Atmosphere Model (CAM) Using Satellite Observations and Their Corresponding Instrument Simulators

The properties of clouds that may be observed by satellite instruments, such as optical depth and cloud top pressure, are only loosely related to the way clouds m-e represented in models of the atmosphere. One way to bridge this gap is through "instrument simulators," diagnostic tools that map the model representation to synthetic observations so that differences between simulator output and observations can be interpreted unambiguously as model error. But simulators may themselves be restricted by limited information available from the host model or by internal assumptions. This paper considers the extent to which instrument simulators are able to capture essential differences between MODIS and ISCCP, two similar but independent estimates of cloud properties. The authors review the measurements and algorithms underlying these two cloud climatologies, introduce a MODIS simulator, and detail data sets developed for comparison with global models using ISCCP and MODIS simulators, In nature MODIS observes less mid-level doudines!> than ISCCP, consistent with the different methods used to determine cloud top pressure; aspects of this difference are reproduced by the simulators running in a climate modeL But stark differences between MODIS and ISCCP observations of total cloudiness and the distribution of cloud optical thickness can be traced to different approaches to marginal pixels, which MODIS excludes and ISCCP treats as homogeneous. These pixels, which likely contain broken clouds, cover about 15 k of the planet and contain almost all of the optically thinnest clouds observed by either instrument. Instrument simulators can not reproduce these differences because the host model does not consider unresolved spatial scales and so can not produce broken pixels. Nonetheless, MODIS and ISCCP observation are consistent for all but the optically-thinnest clouds, and models can be robustly evaluated using instrument simulators by excluding ambiguous observations.

/pdf/reconciling-simulated-and-observed-views-of-clouds-modis-q800wzi80m.pdf

Reconciling Simulated and Observed Views of Clouds: MODIS, ISCCP, and the Limits of Instrument Simulators

Using five climate model simulations of the response to an abrupt quadrupling of CO2, the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. Upon CO2 quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m−2 net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically thick clouds are important in reducing planetary albedo in every model. As the planet warms, clouds become fewer, higher, and thicker, and global mean net cloud feedback is positive in all but one model and results primarily from increased trapping of longwave radiation. As was true for earlier models, high cloud changes are the largest contributor to intermodel spread in longwave and shortwave cloud feedbacks, but low ...

Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5

[1] There are notable differences in the joint histograms of cloud top height and optical depth being produced from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectro-Radiometer (MISR) and by the International Satellite Cloud Climatology Project (ISCCP). These differences have their roots in the different retrieval approaches used by the three projects and are driven largely by responses of the retrievals to (1) stratocumulus (or more broadly low-level clouds under temperature inversions), (2) small (subpixel) or broken low-level clouds, and (3) multilayer clouds. Because each data set has different strengths and weakness, the combination tells us more about the observed cloud fields than any of the three by itself. In particular, the MISR stereo height retrieval provides a calibration insensitive approach to determining cloud height that is especially valuable in combination with ISCCP or MODIS because the combination provides a means to estimate the amount of multilayer cloud, where the upper cloud is optically thin. In this article we present a review of the three data sets using case studies and comparisons of annually averaged joint histograms on global and regional scales. Recommendations for using these data in climate model evaluations are provided.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2009JD013422

A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS

Remote sensing of the atmosphere is typically achieved through measurements that are high-resolution radiance spectra. In this article, our goal is to characterize the first-moment and second-moment properties of the errors obtained when solving the regularized inverse problem associated with space-based atmospheric CO2 retrievals, specifically for the dry air mole fraction of CO2 in a column of the atmosphere. The problem of estimating (or retrieving) state variables is usually ill posed, leading to a solution based on regularization that is often called Optimal Estimation (OE). The difference between the estimated state and the true state is defined to be the retrieval error; error analysis for OE uses a linear approximation to the forward model, resulting in a calculation where the first moment of the retrieval error (the bias) is identically zero. This is inherently unrealistic and not seen in real or simulated retrievals. Nonzero bias is expected since the forward model of radiative transfer is strongly nonlinear in the atmospheric state. In this article, we extend and improve OE's error analysis based on a first-order, multivariate Taylor series expansion, by inducing the second-order terms in the expansion. Specifically, we approximate the bias through the second derivative of the forward model, which results in a formula involving the Hessian array. We propose a stable estimate of it, from which we obtain a second-order expression for the bias and the mean square prediction error of the retrieval.

/pdf/statistical-bias-and-variance-for-the-regularized-inverse-3tngthzg55.pdf

Statistical bias and variance for the regularized-inverse problem: application to Space-based atmospheric CO2 retrievals

During the standard geo-rectification processing of MISR imagery, all four spectral bands belonging to each of the nine MISR cameras are required to be geolocated and co- registered automatically to approximately one pixel accuracy. Two steps of processing are designed to accomplish this goal: (1) a complex multi-camera geolocation and co- registration of the red spectral band data for all nine cameras, and (2) the co-registration of the other three spectral bands of MISR imagery of each camera using their relationship with the already geolocated red band imagery. This paper addresses the second processing step. The geometry of the satellite orbit, the ellipsoid rotating earth, and the separation of the view angles between different spectral bands are combined in a mathematical model which describes the band-to-band line and sample parallaxes. The sensitivity study of this model to numerous error sources, such as variations in the orbit and earth radius, orbit perturbation, and navigation errors, leads to a practical polynomial band-to-band transform solution, and the decision on the usage of either a static or dynamic band-to-band transform as well as the application range of the transform.

Michael M. Smyth

Papers

A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS

Statistical bias and variance for the regularized-inverse problem: application to Space-based atmospheric CO2 retrievals

MISR band-to-band registration