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  • 1
    Publication Date: 2016-12-16
    Description: Ensemble simulation propagates a collection of initial states forward in time in a Monte Carlo fashion. Depending on the fidelity of the model and the properties of the initial ensemble, the goal of ensemble simulation can range from merely quantifying variations in the sensitivity of the model all the way to providing actionable probability forecasts of the future. Whatever the goal is, success depends on the properties of the ensemble, and there is a longstanding discussion in meteorology as to the size of initial condition ensemble most appropriate for Numerical Weather Prediction. In terms of resource allocation: how is one to divide finite computing resources between model complexity, ensemble size, data assimilation and other components of the forecast system. One wishes to avoid undersampling information available from the model’s dynamics, yet one also wishes to use the highest fidelity model available. Arguably, a higher fidelity model can better exploit a larger ensemble; nevertheless it is often suggested that a relatively small ensemble, say ~16 members, is sufficient and that larger ensembles are not an effective investment of resources. This claim is shown to be dubious when the goal is probabilistic forecasting, even in settings where the forecast model is informative but imperfect. Probability forecasts for a ‘simple’ physical system are evaluated at different lead times; ensembles of up to 256 members are considered. The pure density estimation context (where ensemble members are drawn from the same underlying distribution as the target) differs from the forecasting context, where one is given a high fidelity (but imperfect) model. In the forecasting context, the information provided by additional members depends also on the fidelity of the model, the ensemble formation scheme (data assimilation), the ensemble interpretation and the nature of the observational noise. The effect of increasing the ensemble size is quantified by its relative information content (in bits) using a proper skill score. Doubling the ensemble size is demonstrated to yield a non-trivial increase in the information content (forecast skill) for an ensemble with well over 16 members; this result stands in forecasting a mathematical system and a physical system. Indeed, even at the largest ensemble sizes considered (128 and 256), there are lead times where the forecast information is still increasing with ensemble size. Ultimately, model error will limit the value of ever larger ensembles. No support is found, however, for limiting design studies to the sizes commonly found in seasonal and climate studies. It is suggested that ensemble size be considered more explicitly in future design studies of forecast systems on all time scales. Keywords: chaotic systems, data assimilation, ensemble forecasting, forecast value, predictability, probabilistic forecasting, scoring rule, probability (Published: 15 December 2016) Citation: Tellus A 2016, 68, 28393, http://dx.doi.org/10.3402/tellusa.v68.28393
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  • 2
    Publication Date: 2016-12-09
    Description: The Tibetan Plateau (TP) is covered by thousands of lakes which affect the regional and global heat and mass budget with important implications for the current and future climate change. However, the heat and mass budget of TP lakes and the performance of contemporary lake models over TP have not been quantified to date. We utilise 3-yr observations from Ngoring Lake, the largest lake in the Yellow River source region of TP, to investigate the typical properties of the lake–air boundary layer and to evaluate the performance of a simplified lake scheme from the Community Land Model version 4.5 (SLCLM) as one of the most popular lake parameterization schemes in atmospheric models. The strong boundary layer instability during the entire open-water period is a distinguishing feature of the air–lake exchange, similar to the situation over tropical and subtropical lakes, while contrasting to the generally stable atmospheric conditions commonly observed over ice-free temperate and boreal lakes from spring to summer. The rather simple algorithm of SLCLM demonstrated good performance in these conditions. A series of sensitivity simulations with SLCLM revealed strong shortwave solar radiation and cold air temperatures because of high altitude as the primary factors causing the boundary layer instability. The outcomes of the study (1) demonstrate the role of TP lakes as accumulators of shortwave solar radiation releasing the heat into the atmosphere during the entire open-water period; (2) justify the use of simple lake models for the Tibetan highlands, while revealing remarkable uncertainties in the estimations of the latent heat flux; (3) qualify the strong cool-skin effect on the lake surface which results from permanent negative air–lake temperature difference, and should be taken into account when interpreting remote sensing data from highland areas. Keywords: unstable boundary layer, air–lake interaction, Ngoring Lake, CLM, lake modelling, Tibetan Plateau (Published: 8 December 2016) Citation: Tellus A 2016, 68, 31091, http://dx.doi.org/10.3402/tellusa.v68.31091
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  • 3
    Publication Date: 2016-12-09
    Description: Data assimilation methods that work in high-dimensional systems are crucial to many areas of the geosciences: meteorology, oceanography, climate science and so on. The equivalent weights particle filter (EWPF) has been designed for, and recently shown to scale to, problems that are of use to these communities. This article performs a systematic comparison of the EWPF with the established and widely used local ensemble transform Kalman filter (LETKF). Both methods are applied to the barotropic vorticity equation for different networks of observations. In all cases, it was found that the LETKF produced lower root mean–squared errors than the EWPF. The performance of the EWPF is shown to depend strongly on the form of nudging used, and a nudging term based on the local ensemble transform Kalman smoother is shown to improve the performance of the filter. This indicates that the EWPF must be considered as a truly two-stage filter and not only by its final step which avoids weight collapse. Keywords: equivalent weights particle filter, non-linear data assimilation, EMPIRE, LETKF, nudging, LETKS relaxation (Published: 8 December 2016) Citation: Tellus A 2016, 68, 30466, http://dx.doi.org/10.3402/tellusa.v68.30466
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  • 4
    Publication Date: 2016-12-08
    Description: We document a pilot stochastic re-analysis computed by assimilating sea surface temperature (SST) anomalies into the ocean component of the coupled Norwegian Climate Prediction Model (NorCPM) for the period 1950–2010 (doi: 10.11582/2016.00002 ). NorCPM is based on the Norwegian Earth System Model and uses the ensemble Kalman filter for data assimilation (DA). Here, we assimilate SST from the stochastic HadISST2 historical reconstruction. The accuracy, reliability and drift are investigated using both assimilated and independent observations. NorCPM is slightly overdispersive against assimilated observations but shows stable performance through the analysis period. It demonstrates skills against independent measurements: sea surface height, heat and salt content, in particular in the Equatorial and North Pacific, the North Atlantic Subpolar Gyre (SPG) region and the Nordic Seas. Furthermore, NorCPM provides a reliable monitoring of the SPG index and represents the vertical temperature variability there, in good agreement with observations. The monitoring of the Atlantic meridional overturning circulation is also encouraging. The benefit of using a flow-dependent assimilation method and constructing the covariance in isopycnal coordinates are investigated in the SPG region. Isopycnal coordinates discretisation is found to better capture the vertical structure than standard depth-coordinate discretisation, because it leads to a deeper influence of the assimilated surface observations. The vertical covariance shows a pronounced seasonal and decadal variability that highlights the benefit of flow-dependent DA method. This study demonstrates the potential of NorCPM to compute an ocean re-analysis for the 19th and 20th centuries when SST observations are available. Keywords: Ocean re-analysis, EnKF, isopycnal ocean model, SST coupled re-analysis, weakly coupled data assimilation, NorCPM, Flow dependent assimilation (Published: 7 December 2016) Citation: Tellus A 2016, 68, 32437, http://dx.doi.org/10.3402/tellusa.v68.32437
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  • 5
    Publication Date: 2016-12-02
    Description: Several studies have shown a statistically significant correlation between Atlantic multidecadal variability (AMV) and Indian summer monsoon rainfall (ISMR) since 1871 when instrumental data are available. In the instrumental records, both ISMR and North Atlantic sea surface temperatures (SSTs) have multidecadal variability with a period close to 60 yr, where periods of warm (cold) North Atlantic SSTs are accompanied by periods of wetter (dryer) ISMR and lower (higher) frequencies of dry years. We have studied both AMV and ISMR for the period from 1481 to present using several proxy reconstructions from both regions, as well as an extended instrumental data set for ISMR, to investigate multidecadal variability in the ISMR and the teleconnection to AMV. Previous studies investigating the relationship between AMV and ISMR in instrumental data have only used the period from 1871 onwards, whereas rain gauge data from the year 1844 are studied here, extending the instrumental record by 26 yr. We find that the observed link between AMV and ISMR is present in the extended instrumental data. We also find that multidecadal variability is present in the ISMR in all proxy records; however, all the proxy records for both ISMR and AMV diverge before the 1800s. In addition, the observed correlation between AMV and ISMR has weakened in the last decade. These results emphasise that it is not appropriate to use single proxy reconstructions to study past climates. Keywords: Atlantic multidecadal variability, Indian summer monsoon, proxy reconstructions, teleconnection, multidecadal variability, correlation analysis (Published: 1 December 2016) Citation: Tellus A 2016, 68, 31717, http://dx.doi.org/10.3402/tellusa.v68.31717
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  • 6
    Publication Date: 2016-12-01
    Description: A computational simplification of the Kalman filter (KF) is introduced – the parametric Kalman filter (PKF). The full covariance matrix dynamics of the KF, which describes the evolution along the analysis and forecast cycle, is replaced by the dynamics of the error variance and the diffusion tensor, which is related to the correlation length-scales. The PKF developed here has been applied to the simplified framework of advection–diffusion of a passive tracer, for its use in chemical transport model assimilation. The PKF is easy to compute and computationally cost-effective than an ensemble Kalman filter (EnKF) in this context. The validation of the method is presented for a simplified 1-D advection–diffusion dynamics. Keywords: data assimilation, Kalman filter, covariance dynamics, parameterisation of analysis (Published: 30 November 2016) Citation: Tellus A 2016, 68, 31547, http://dx.doi.org/10.3402/tellusa.v68.31547
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  • 7
    Publication Date: 2016-11-26
    Description: Inter-annual variation of meteorological conditions and their effects on snow and ice thickness in an Arctic lake Unari (67.14° N, 25.73° E) were investigated for winters 1980/1981–2012/2013. The lake snow and ice thicknesses were modelled applying a thermodynamic model, and the results were compared with observations. Regression equations were derived for the relationships between meteorological parameters and modelled snow and ice properties. The composite differences of large-scale atmospheric circulation patterns between seasons of thin and thick ice were analysed. The air temperature had an increasing trend (statistical significance p 〈0.05) during the freezing season (1.0° C/decade), associated with an increasing trend of liquid precipitation ( p 〈0.05) in winter. Both observed and modelled average and maximum ice thicknesses showed a decreasing trend ( p 〈0.05). The model results were statistically more reliable (1) for lake ice than snow and (2) for seasonal means than maxima. Low temperature with less precipitation prompted the formation of columnar ice, whereas strong winds and heavy snowfall were in favour of granular ice formation. The granular (columnar) ice thickness was positively (negatively) correlated with precipitation. The seasonal mean and maximum modelled lake ice and snow thicknesses were controlled by precipitation and temperature history, with 58–86 % of the inter-annual variance explained. Using regression equations derived from data from 1980 to 2013, snow and ice thickness for the following winter seasons was statistically forecasted, yielding errors of 9–12 %. Among large-scale climate indices, the Pacific Decadal Oscillation was the only one that correlated with inter-annual variations in the seasonal average ice thickness in Lake Unari. Keywords: Snow, ice, temperature, precipitation, modelling Arctic lake (Published: 25 November 2016) Citation: Tellus A 2016, 68, 31590, http://dx.doi.org/10.3402/tellusa.v68.31590
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  • 8
    Publication Date: 2016-11-22
    Description: The North Atlantic thermohaline circulation (THC) carries heat and salt towards the Arctic. This circulation is partly sustained by buoyancy loss and is generally believed to be inhibited by northern freshwater input as indicated by the ‘box-model’ of Stommel ( 1961 ). The inferred freshwater-sensitivity of the THC, however, varies considerably between studies, both quantitatively and qualitatively. The northernmost branch of the Atlantic THC, which forms a double estuarine circulation in the Arctic Mediterranean, is one example where both buoyancy loss and buoyancy gain facilitate circulation. We have built on Stommel’s original concept to examine the freshwater-sensitivity of a double estuarine circulation. The net inflow into the double estuary is found to be more sensitive to a change in the distribution of freshwater than to a change in the total freshwater input. A double estuarine circulation is more stable than a single overturning, requiring a larger amount and more localised freshwater input into regions of buoyancy loss to induce a thermohaline ‘collapse’. For the Arctic Mediterranean, these findings imply that the Atlantic inflow may be relatively insensitive to increased freshwater input. Complementing Stommel’s thermal and haline flow regimes, the double estuarine circulation allows for a third: the throughflow regime. In this regime, a THC with warm poleward surface flow can be sustained without production of dense water; a decrease in high-latitude dense water formation does therefore not necessarily affect regional surface conditions as strongly as generally thought. Keywords: Box-model, Arctic Mediterranean, freshwater-sensitivity, thermohaline circulation (Published: 21 November 2016) Citation: Tellus A 2016, 68, 31051, http://dx.doi.org/10.3402/tellusa.v68.31051
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  • 9
    Publication Date: 2016-11-22
    Description: A new development in the field of reanalyses is the incorporation of uncertainty estimation capabilities. We have developed a probabilistic regional reanalysis system for the CORDEX-EUR11 domain that is based on the numerical weather prediction model COSMO at a 12-km grid spacing. The lateral boundary conditions of all ensemble members are provided by the global reanalysis ERA-Interim. In the basic implementation of the system, uncertainties due to observation errors are estimated. Atmospheric assimilation of conventional observations perturbed by means of random samples of observation error yields estimates of the reanalysis uncertainty conditioned to observation errors. The data assimilation employed is a new scheme based on observation nudging that we denote ensemble nudging. The lower boundary of the atmosphere is regularly updated by external snow depth, sea surface temperature and soil moisture analyses. One of the most important purposes of reanalyses is the estimation of so-called essential climate variables. For regional reanalyses, precipitation has been identified as one of the essential climate variables that are potentially better represented than in other climate data sets. For that reason, we assess the representation of precipitation in our system in a pilot study. Based on two experiments, each of which extends over one month, we conduct a preliminary comparison to the global reanalysis ERA-Interim, a dynamical downscaling of the latter and the high-resolution regional reanalysis COSMO-REA6. In a next step, we assess our reanalysis system’s probabilistic capabilities versus the ECMWF-EPS in terms of six-hourly precipitation sums. The added value of our probabilistic regional reanalysis system motivates the current production of a 5-year-long test reanalysis COSMO-EN-REA12 in the framework of the FP7-funded project Uncertainties in Ensembles of Regional Re-Analyses (UERRA). Keywords: climate dynamics, essential climate variables, ensemble data assimilation, CORDEX, uncertainty estimation, precipitation (Published: 21 November 2016) Citation: Tellus A 2016, 68, 32209, http://dx.doi.org/10.3402/tellusa.v68.32209
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  • 10
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    Co-Action Publishing
    In: Tellus A
    Publication Date: 2016-11-16
    Description: The Defence Centre for Operational Oceanography runs operational forecasts for the Danish waters. The core setup is a 60-layer baroclinic circulation model based on the General Estuarine Transport Model code. At intervals, the model setup is tuned to improve ‘model skill’ and overall performance. It has been an area of concern that the uncertainty inherent to the stochastical/chaotic nature of the model is unknown. Thus, it is difficult to state with certainty that a particular setup is improved, even if the computed model skill increases. This issue also extends to the cases, where the model is tuned during an iterative process, where model results are fed back to improve model parameters, such as bathymetry. An ensemble of identical model setups with slightly perturbed initial conditions is examined. It is found that the initial perturbation causes the models to deviate from each other exponentially fast, causing differences of several PSUs and several kelvin within a few days of simulation. The ensemble is run for a full year, and the long-term variability of salinity and temperature is found for different regions within the modelled area. Further, the developing time scale is estimated for each region, and great regional differences are found – in both variability and time scale. It is observed that periods with very high ensemble variability are typically short-term and spatially limited events. A particular event is examined in detail to shed light on how the ensemble ‘behaves’ in periods with large internal model variability. It is found that the ensemble does not seem to follow any particular stochastic distribution: both the ensemble variability (standard deviation or range) as well as the ensemble distribution within that range seem to vary with time and place. Further, it is observed that a large spatial variability due to mesoscale features does not necessarily correlate to large ensemble variability. These findings bear impact on the way data assimilation should be addressed – especially in relation to operational forecasts. Keywords: Baroclinic, ensemble modelling, salinity, temperature, elevation, Skagerrak, Baltic Sea (Published: 15 November 2016) Citation: Tellus A 2016, 68, 30417, http://dx.doi.org/10.3402/tellusa.v68.30417
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