Retrieval of effective ice crystal size in the infrared: sensitivity study and global measurements from TIROS-N Operational Vertical Sounder

The difference between cirrus emissivities at 8 and 11 μm is sensitive to the mean effective ice crystal size of the cirrus cloud, De. By using single scattering properties of ice crystals shaped as planar polycrystals, diameters of up to about 70 μm can be retrieved, instead of up to 45 μm assuming spheres or hexagonal columns. The method described in this article is used for a global determination of mean effective ice crystal sizes of cirrus clouds from TOVS satellite observations. A sensitivity study of the De retrieval to uncertainties in hypotheses on ice crystal shape, size distributions, and temperature profiles, as well as in vertical and horizontal cloud heterogeneities shows that uncertainties can be as large as 30%. However, the TOVS data set is one of few data sets which provides global and long-term coverage. Having analyzed the years 1987–1991, it was found that measured effective ice crystal diameters De are stable from year to year. For 1990 a global median De of 53.5 μm was determined. Averages distinguishing ocean/land, season, and latitude lie between 23 μm in winter over Northern Hemisphere midlatitude land and 64 μm in the tropics. In general, larger Des are found in regions with higher atmospheric water vapor and for cirrus with a smaller effective emissivity.

Climate change and heat-related mortality in six cities Part 1: model construction and validation

Heat waves are expected to increase in frequency and magnitude with climate change. The first part of a study to produce projections of the effect of future climate change on heat-related mortality is presented. Separate city-specific empirical statistical models that quantify significant relationships between summer daily maximum temperature (T max) and daily heat-related deaths are constructed from historical data for six cities: Boston, Budapest, Dallas, Lisbon, London, and Sydney. ‘Threshold temperatures’ above which heat-related deaths begin to occur are identified. The results demonstrate significantly lower thresholds in ‘cooler’ cities exhibiting lower mean summer temperatures than in ‘warmer’ cities exhibiting higher mean summer temperatures. Analysis of individual ‘heat waves’ illustrates that a greater proportion of mortality is due to mortality displacement in cities with less sensitive temperature–mortality relationships than in those with more sensitive relationships, and that mortality displacement is no longer a feature more than 12 days after the end of the heat wave. Validation techniques through residual and correlation analyses of modelled and observed values and comparisons with other studies indicate that the observed temperature–mortality relationships are represented well by each of the models. The models can therefore be used with confidence to examine future heat-related deaths under various climate change scenarios for the respective cities (presented in Part 2).

Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis

Global hydrological models (GHMs) model the land surface hydrologic dynamics of continental-scale river basins. Here we describe one such GHM, the Macro-scale - Probability-Distributed Moisture model.09 (Mac-PDM.09). The model has undergone a number of revisions since it was last applied in the hydrological literature. This paper serves to provide a detailed description of the latest version of the model. The main revisions include the following: (1) the ability for the model to be run for n repetitions, which provides more robust estimates of extreme hydrological behaviour, (2) the ability of the model to use a gridded field of coefficient of variation (CV) of daily rainfall for the stochastic disaggregation of monthly precipitation to daily precipitation, and (3) the model can now be forced with daily input climate data as well as monthly input climate data. We demonstrate the effects that each of these three revisions has on simulated runoff relative to before the revisions were applied. Importantly, we show that when Mac-PDM.09 is forced with monthly input data, it results in a negative runoff bias relative to when daily forcings are applied, for regions of the globe where the day-to-day variability in relative humidity is high. The runoff bias can be up to - 80% for a small selection of catchments but the absolute magnitude of the bias may be small. As such, we recommend future applications of Mac-PDM.09 that use monthly climate forcings acknowledge the bias as a limitation of the model. The performance of Mac-PDM.09 is evaluated by validating simulated runoff against observed runoff for 50 catchments. We also present a sensitivity analysis that demonstrates that simulated runoff is considerably more sensitive to method of PE calculation than to perturbations in soil moisture and field capacity parameters.

Lagrangian validation of numerical drifter trajectories using drifting buoys: Application to the Agulhas system

The skill of numerical Lagrangian drifter trajectories in three numerical models is assessed by comparing these numerically obtained paths to the trajectories of drifting buoys in the real ocean. The skill assessment is performed using the two-sample Kolmogorov–Smirnov statistical test. To demonstrate the assessment procedure, it is applied to three different models of the Agulhas region. The test can either be performed using crossing positions of one-dimensional sections in order to test model performance in specific locations, or using the total two-dimensional data set of trajectories. The test yields four quantities: a binary decision of model skill, a confidence level which can be used as a measure of goodness-of-fit of the model, a test statistic which can be used to determine the sensitivity of the confidence level, and cumulative distribution functions that aid in the qualitative analysis. The ordering of models by their confidence levels is the same as the ordering based on the qualitative analysis, which suggests that the method is suited for model validation. Only one of the three models, a 1/10° two-way nested regional ocean model, might have skill in the Agulhas region. The other two models, a 1/2° global model and a 1/8° assimilative model, might have skill only on some sections in the region

Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments

During the past 15 years, a number of initiatives have been undertaken at national level to develop ocean forecasting systems operating at regional and/or global scales. The co-ordination between these efforts has been organized internationally through the Global Ocean Data Assimilation Experiment (GODAE). The French MERCATOR project is one of the leading participants in GODAE. The MERCATOR systems routinely assimilate a variety of observations such as multi-satellite altimeter data, sea-surface temperature and in situ temperature and salinity profiles, focusing on high-resolution scales of the ocean dynamics. The assimilation strategy in MERCATOR is based on a hierarchy of methods of increasing sophistication including optimal interpolation, Kalman filtering and variational methods, which are progressively deployed through the Syst`eme d’Assimilation MERCATOR (SAM) series. SAM-1 is based on a reduced-order optimal interpolation which can be operated using ‘altimetry-only’ or ‘multi-data’ set-ups; it relies on the concept of separability, assuming that the correlations can be separated into a product of horizontal and vertical contributions. The second release, SAM-2, is being developed to include new features from the singular evolutive extended Kalman (SEEK) filter, such as three-dimensional, multivariate error modes and adaptivity schemes. The third one, SAM-3, considers variational methods such as the incremental four-dimensional variational algorithm. Most operational forecasting systems evaluated during GODAE are based on least-squares statistical estimation assuming Gaussian errors. In the framework of the EU MERSEA (Marine EnviRonment and Security for the European Area) project, research is being conducted to prepare the next-generation operational ocean monitoring and forecasting systems. The research effort will explore nonlinear assimilation formulations to overcome limitations of the current systems. This paper provides an overview of the developments conducted in MERSEA with the SEEK filter, the Ensemble Kalman filter and the sequential importance re-sampling filter.