A reprocessing for climate of sea surface temperature from the Along-Track Scanning Radiometers: a new retrieval scheme

We present a new coefficient-based retrieval scheme for estimation of sea surface temperature (SST) from the Along Track Scanning Radiometer (ATSR) instruments. The new coefficients are banded by total column water vapour (TCWV), obtained from numerical weather prediction analyses. TCWV banding reduces simulated regional retrieval biases to < 0.1 K compared to biases ~ 0.2 K for global coefficients. Further, detailed treatment of the instrumental viewing geometry reduces simulated view-angle related biases from ~ 0.1 K down to < 0.005 K for dual-view retrievals using channels at 11 and 12 μm. A novel analysis of trade-offs related to the assumed noise level when defining coefficients is undertaken, and we conclude that adding a small nominal level of noise (0.01 K) is optimal for our purposes. When applied to ATSR observations, some inter-algorithm biases appear as TCWV-related differences in SSTs estimated from different channel combinations. The final step in coefficient determination is to adjust the offset coefficient in each TCWV band to match results from a reference algorithm. This reference uses the dual-view observations of 3.7 and 11 μm. The adjustment is independent of in situ measurements, preserving independence of the retrievals. The choice of reference is partly motivated by uncertainty in the calibration of the 12 μm of Advanced ATSR. Lastly, we model the sensitivities of the new retrievals to changes to TCWV and changes in true SST, confirming that dual-view SSTs are most appropriate for climatological applications

Estimation of sea surface temperature from the Spinning Enhanced Visible and Infra Red Imager, improved using numerical weather prediction

Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5 K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). The simulated errors, used as correction terms, reduce significantly the regional biases of the NL algorithm as well as the standard deviation of the differences with drifting buoy measurements. The availability of atmospheric profiles associated with observed satellite-buoy differences allows us to analyze the origins of the main algorithmic errors observed in the SEVIRI field of view: a negative bias in the inter-tropical zone, and a mid-latitude positive bias. We demonstrate how these errors are explained by the sensitivity of observed brightness temperatures to the vertical distribution of water vapour, propagated through the SST retrieval algorithm.

Parameterised diabatic processes in numerical simulations of an extratropical cyclone

The parameterisation of diabatic processes in numerical models is critical for the accuracy of weather forecasts and for climate projections. A novel approach to the evaluation of these processes in models is introduced in this contribution. The approach combines a suite of on-line tracer diagnostics with off-line trajectory calculations. Each tracer tracks accumulative changes in potential temperature associated with a particular parameterised diabatic process in the model. A comparison of tracers therefore allows the identification of the most active diabatic processes and their downstream impacts. The tracers are combined with trajectories computed using model-resolved winds, allowing the various diabatic contributions to be tracked back to their time and location of occurrence. We have used this approach to investigate diabatic processes within a simulated extratropical cyclone. We focus on the warm conveyor belt, in which the dominant diabatic contributions come from large-scale latent heating and parameterised convection. By contrasting two simulations, one with standard convection parameterisation settings and another with reduced parameterised convection, the effects of parameterised convection on the structure of the cyclone have been determined. Under reduced parameterised convection conditions, the large-scale latent heating is forced to release convective instability that would otherwise have been released by the convection parameterisation. Although the spatial distribution of precipitation depends on the details of the split between parameterised convection and large-scale latent heating, the total precipitation amount associated with the cyclone remains largely unchanged. For reduced parameterised convection, a more rapid and stronger latent heating episode takes place as air ascends within the warm conveyor belt.

Analyses of single nucleotide polymorphisms in selected nutrient-sensitive genes in weight-regain prevention: the DIOGENES study

BACKGROUND: Differences in the interindividual response to dietary intervention could be modified by genetic variation in nutrient-sensitive genes. OBJECTIVE: This study examined single nucleotide polymorphisms (SNPs) in presumed nutrient-sensitive candidate genes for obesity and obesity-related diseases for main and dietary interaction effects on weight, waist circumference, and fat mass regain over 6 mo. DESIGN: In total, 742 participants who had lost ≥ 8% of their initial body weight were randomly assigned to follow 1 of 5 different ad libitum diets with different glycemic indexes and contents of dietary protein. The SNP main and SNP-diet interaction effects were analyzed by using linear regression models, corrected for multiple testing by using Bonferroni correction and evaluated by using quantile-quantile (Q-Q) plots. RESULTS: After correction for multiple testing, none of the SNPs were significantly associated with weight, waist circumference, or fat mass regain. Q-Q plots showed that ALOX5AP rs4769873 showed a higher observed than predicted P value for the association with less waist circumference regain over 6 mo (-3.1 cm/allele; 95% CI: -4.6, -1.6; P/Bonferroni-corrected P = 0.000039/0.076), independently of diet. Additional associations were identified by using Q-Q plots for SNPs in ALOX5AP, TNF, and KCNJ11 for main effects; in LPL and TUB for glycemic index interaction effects on waist circumference regain; in GHRL, CCK, MLXIPL, and LEPR on weight; in PPARC1A, PCK2, ALOX5AP, PYY, and ADRB3 on waist circumference; and in PPARD, FABP1, PLAUR, and LPIN1 on fat mass regain for dietary protein interaction. CONCLUSION: The observed effects of SNP-diet interactions on weight, waist, and fat mass regain suggest that genetic variation in nutrient-sensitive genes can modify the response to diet. This trial was registered at clinicaltrials.gov as NCT00390637.

On the reprocessing and reanalysis of observations for climate

The long observational record is critical to our understanding of the Earth’s climate, but most observing systems were not developed with a climate objective in mind. As a result, tremendous efforts have gone into assessing and reprocessing the data records to improve their usefulness in climate studies. The purpose of this paper is to both review recent progress in reprocessing and reanalyzing observations, and summarize the challenges that must be overcome in order to improve our understanding of climate and variability. Reprocessing improves data quality through more scrutiny and improved retrieval techniques for individual observing systems, while reanalysis merges many disparate observations with models through data assimilation, yet both aim to provide a climatology of Earth processes. Many challenges remain, such as tracking the improvement of processing algorithms and limited spatial coverage. Reanalyses have fostered significant research, yet reliable global trends in many physical fields are not yet attainable, despite significant advances in data assimilation and numerical modeling. Oceanic reanalyses have made significant advances in recent years, but will only be discussed here in terms of progress toward integrated Earth system analyses. Climate data sets are generally adequate for process studies and large-scale climate variability. Communication of the strengths, limitations and uncertainties of reprocessed observations and reanalysis data, not only among the community of developers, but also with the extended research community, including the new generations of researchers and the decision makers is crucial for further advancement of the observational data records. It must be emphasized that careful investigation of the data and processing methods are required to use the observations appropriately.

Northern Hemisphere atmospheric blocking as simulated by 15 atmospheric general circulation models in the period 1979-1988

As a part of the Atmospheric Model Intercomparison Project (AMIP), the behaviour of 15 general circulation models has been analysed in order to diagnose and compare the ability of the different models in simulating Northern Hemisphere midlatitude atmospheric blocking. In accordance with the established AMIP procedure, the 10-year model integrations were performed using prescribed, time-evolving monthly mean observed SSTs spanning the period January 1979–December 1988. Atmospheric observational data (ECMWF analyses) over the same period have been also used to verify the models results. The models involved in this comparison represent a wide spectrum of model complexity, with different horizontal and vertical resolution, numerical techniques and physical parametrizations, and exhibit large differences in blocking behaviour. Nevertheless, a few common features can be found, such as the general tendency to underestimate both blocking frequency and the average duration of blocks. The problem of the possible relationship between model blocking and model systematic errors has also been assessed, although without resorting to ad-hoc numerical experimentation it is impossible to relate with certainty particular model deficiencies in representing blocking to precise parts of the model formulation.

Northern Hemisphere atmospheric blocking as simulated by 15 atmospheric general circulation models in the period 1979-1988 (Results from an AMIP diagnostic subproject)

As a part of the Atmospheric Model Intercomparison Project (AMIP), the behaviour of 15 general circulation models has been analysed in order to diagnose and compare the ability of the different models in simulating Northern Hemisphere midlatitude atmospheric blocking. In accordance with the established AMIP procedure, the 10-year model integrations were performed using prescribed, time-evolving monthly mean observed SSTs spanning the period January 1979–December 1988. Atmospheric observational data (ECMWF analyses) over the same period have been also used to verify the models results. The models involved in this comparison represent a wide spectrum of model complexity, with different horizontal and vertical resolution, numerical techniques and physical parametrizations, and exhibit large differences in blocking behaviour. Nevertheless, a few common features can be found, such as the general tendency to underestimate both blocking frequency and the average duration of blocks. The problem of the possible relationship between model blocking and model systematic errors has also been assessed, although without resorting to ad-hoc numerical experimentation it is impossible to relate with certainty particular model deficiencies in representing blocking to precise parts of the model formulation.

The influence of ocean flow on newly forming sea ice

The heat and mass balance of the Arctic Ocean is very sensitive to the growth and decay of sea ice and the interaction between the heat and salt fields in the oceanic boundary layer. The hydraulic roughness of sea ice controls the detailed nature of turbulent fluxes in the boundary layer and hence is an important ingredient in model parameterizations. We describe a novel mechanism for the generation of corrugations of the sea ice–ocean interface, present a mathematical analysis elucidating the mechanism, and present numerical calculations for geophysically relevant conditions. The mechanism relies on brine flows developing in the sea ice due to Bernoulli suction by flow of ocean past the interface. For oceanic shears at the ice interface of 0.2 s−1, we expect the corrugations to form with a wavelength dependent upon the permeability structure of the sea ice which is described herein. The mechanism should be particularly important during sea ice formation in wind-maintained coastal polynyas and in leads. This paper applies our earlier analyses of the fundamental instability to field conditions and extends it to take account of the anisotropic and heterogeneous permeability of sea ice.

Analytical and numerical solutions describing the inward solidification of a binary melt

We present a mathematical model describing the inward solidification of a slab, a circular cylinder and a sphere of binary melt kept below its equilibrium freezing temperature. The thermal and physical properties of the melt and solid are assumed to be identical. An asymptotic method, valid in the limit of large Stefan number is used to decompose the moving boundary problem for a pure substance into a hierarchy of fixed-domain diffusion problems. Approximate, analytical solutions are derived for the inward solidification of a slab and a sphere of a binary melt which are compared with numerical solutions of the unapproximated system. The solutions are found to agree within the appropriate asymptotic regime of large Stefan number and small time. Numerical solutions are used to demonstrate the dependence of the solidification process upon the level of impurity and other parameters. We conclude with a discussion of the solutions obtained, their stability and possible extensions and refinements of our study.

Communicating probabilistic information from climate model ensembles—lessons from numerical weather prediction

Climate model ensembles are widely heralded for their potential to quantify uncertainties and generate probabilistic climate projections. However, such technical improvements to modeling science will do little to deliver on their ultimate promise of improving climate policymaking and adaptation unless the insights they generate can be effectively communicated to decision makers. While some of these communicative challenges are unique to climate ensembles, others are common to hydrometeorological modeling more generally, and to the tensions arising between the imperatives for saliency, robustness, and richness in risk communication. The paper reviews emerging approaches to visualizing and communicating climate ensembles and compares them to the more established and thoroughly evaluated communication methods used in the numerical weather prediction domains of day-to-day weather forecasting (in particular probabilities of precipitation), hurricane and flood warning, and seasonal forecasting. This comparative analysis informs recommendations on best practice for climate modelers, as well as prompting some further thoughts on key research challenges to improve the future communication of climate change uncertainties.

Objective determination of feature resolution in two sea surface temperature analyses

Considerable effort is presently being devoted to producing high-resolution sea surface temperature (SST) analyses with a goal of spatial grid resolutions as low as 1 km. Because grid resolution is not the same as feature resolution, a method is needed to objectively determine the resolution capability and accuracy of SST analysis products. Ocean model SST fields are used in this study as simulated “true” SST data and subsampled based on actual infrared and microwave satellite data coverage. The subsampled data are used to simulate sampling errors due to missing data. Two different SST analyses are considered and run using both the full and the subsampled model SST fields, with and without additional noise. The results are compared as a function of spatial scales of variability using wavenumber auto- and cross-spectral analysis. The spectral variance at high wavenumbers (smallest wavelengths) is shown to be attenuated relative to the true SST because of smoothing that is inherent to both analysis procedures. Comparisons of the two analyses (both having grid sizes of roughly ) show important differences. One analysis tends to reproduce small-scale features more accurately when the high-resolution data coverage is good but produces more spurious small-scale noise when the high-resolution data coverage is poor. Analysis procedures can thus generate small-scale features with and without data, but the small-scale features in an SST analysis may be just noise when high-resolution data are sparse. Users must therefore be skeptical of high-resolution SST products, especially in regions where high-resolution (~5 km) infrared satellite data are limited because of cloud cover.

Implications of model error for numerical climate prediction

Numerical climate models constitute the best available tools to tackle the problem of climate prediction. Two assumptions lie at the heart of their suitability: (1) a climate attractor exists, and (2) the numerical climate model's attractor lies on the actual climate attractor, or at least on the projection of the climate attractor on the model's phase space. In this contribution, the Lorenz '63 system is used both as a prototype system and as an imperfect model to investigate the implications of the second assumption. By comparing results drawn from the Lorenz '63 system and from numerical weather and climate models, the implications of using imperfect models for the prediction of weather and climate are discussed. It is shown that the imperfect model's orbit and the system's orbit are essentially different, purely due to model error and not to sensitivity to initial conditions. Furthermore, if a model is a perfect model, then the attractor, reconstructed by sampling a collection of initialised model orbits (forecast orbits), will be invariant to forecast lead time. This conclusion provides an alternative method for the assessment of climate models.