scholarly journals seNorge_2018, daily precipitation and temperature datasets over Norway

2019 ◽  
Author(s):  
Cristian Lussana ◽  
Ole Einar Tveito ◽  
Andreas Dobler ◽  
Ketil Tunheim
Keyword(s):  
Spatial Scale ◽  
Climate Model ◽  
Spatial Scales ◽  
Small Scale ◽  
Total Precipitation ◽  
Climate Data ◽  
Wide Range ◽  
Analysis Quality ◽  
Station Density ◽  
Daily Total

Abstract. seNorge_2018 is a collection of observational gridded datasets over Norway for: daily total precipitation; daily mean, maximum and minimum temperatures. The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a twenty-year long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology and meteorology. The observational dataset is based on MET Norway's climate data, which has been integrated by the European Climate Assessment and Dataset database. Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation. They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales are estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density. Mean, maximum and minimum temperatures are interpolated separately, then physical consistency among them is enforced. For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model. The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version. The analysis quality is presented as a function of the local station density. We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density. Temperature analyses over most of the domain are generally not affected by significant biases. However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 °C and 3 °C. Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures. The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5 % in data-dense regions, while it is approximately 30 % in data-sparse regions. he open-access datasets are available20for public download at: daily total precipitation (DOI: https://doi.org/10.5281/zenodo.2082320, Lussana, 2018b); daily mean (DOI: https://doi.org/10.5281/zenodo.2023997, Lussana, 2018c) , maximum (DOI: https://doi.org/10.5281/zenodo.2559372, Lussana, 2018e) and minimum (DOI: https://doi.org/10.5281/zenodo.2559354, Lussana, 2018d) temperatures.

scholarly journals seNorge_2018, daily precipitation, and temperature datasets over Norway

2019 ◽  
Vol 11 (4) ◽  
pp. 1531-1551 ◽  
Author(s):  
Cristian Lussana ◽  
Ole Einar Tveito ◽  
Andreas Dobler ◽  
Ketil Tunheim
Keyword(s):  
Spatial Scale ◽  
Climate Model ◽  
Spatial Scales ◽  
Small Scale ◽  
Total Precipitation ◽  
Climate Data ◽  
Wide Range ◽  
Analysis Quality ◽  
Station Density ◽  
Daily Total

Abstract. seNorge_2018 is a collection of observational gridded datasets over Norway for daily total precipitation: daily mean, maximum, and minimum temperatures. The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a 20-year-long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology, and meteorology. The observational dataset is based on MET Norway's climate data, which have been integrated by the “European Climate Assessment and Dataset” database. Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation. They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales is estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density. Mean, maximum, and minimum temperatures are interpolated separately; then physical consistency among them is enforced. For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model. The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version. The analysis quality is presented as a function of the local station density. We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density. Temperature analyses over most of the domain are generally not affected by significant biases. However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 ∘C and 3 ∘C. Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures. The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5% in data-dense regions, while it is approximately 30 % in data-sparse regions. The open-access datasets are available for public download at daily total precipitation (https://doi.org/10.5281/zenodo.2082320, Lussana, 2018b); and daily mean (https://doi.org/10.5281/zenodo.2023997, Lussana, 2018c), maximum (https://doi.org/10.5281/zenodo.2559372, Lussana, 2018e), and minimum (https://doi.org/10.5281/zenodo.2559354, Lussana, 2018d) temperatures.

Implementation of an Urban Parameterization Scheme into the Regional Climate Model COSMO-CLM

2013 ◽  
Vol 52 (10) ◽  
pp. 2296-2311 ◽  
Author(s):  
Kristina Trusilova ◽  
Barbara Früh ◽  
Susanne Brienen ◽  
Andreas Walter ◽  
Valéry Masson ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Climate Model ◽  
Spatial Scales ◽  
Regional Climate ◽  
Coupled Model ◽  
Urban Land ◽  
Urban Land Use ◽  
Small Scale ◽  
Model Simulations ◽  
Scale Modelling

AbstractAs the nonhydrostatic regional model of the Consortium for Small-Scale Modelling in Climate Mode (COSMO-CLM) is increasingly employed for studying the effects of urbanization on the environment, the authors extend its surface-layer parameterization by the Town Energy Budget (TEB) parameterization using the “tile approach” for a single urban class. The new implementation COSMO-CLM+TEB is used for a 1-yr reanalysis-driven simulation over Europe at a spatial resolution of 0.11° (~12 km) and over the area of Berlin at a spatial resolution of 0.025° (~2.8 km) for evaluating the new coupled model. The results on the coarse spatial resolution of 0.11° show that the standard and the new models provide 2-m temperature and daily precipitation fields that differ only slightly by from −0.1 to +0.2 K per season and ±0.1 mm day−1, respectively, with very similar statistical distributions. This indicates only a negligibly small effect of the urban parameterization on the model's climatology. Therefore, it is suggested that an urban parameterization may be omitted in model simulations on this scale. On the spatial resolution of 0.025° the model COSMO-CLM+TEB is able to better represent the magnitude of the urban heat island in Berlin than the standard model COSMO-CLM. This finding shows the importance of using the parameterization for urban land in the model simulations on fine spatial scales. It is also suggested that models could benefit from resolving multiple urban land use classes to better simulate the spatial variability of urban temperatures for large metropolitan areas on spatial scales below ~3 km.

scholarly journals Developing Gridded Climate Data Sets of Precipitation for Greece Based on Homogenized Time Series

Climate ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 68 ◽  
Author(s):  
Flora Gofa ◽  
Anna Mamara ◽  
Manolis Anadranistakis ◽  
Helena Flocas
Keyword(s):  
Climate Model ◽  
Interpolation Method ◽  
Region Of Interest ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Climate Data ◽  
Variable Nature ◽  
Wide Range ◽  
Realistic Representation ◽  
Space Data

The creation of realistic gridded precipitation fields improves our understanding of the observed climate and is necessary for validating climate model output for a wide range of applications. The challenge in trying to represent the highly variable nature of precipitation is to overcome the lack of density of observations in both time and space. Data sets of mean monthly and annual precipitations were developed for Greece in gridded format with an analysis of 30 arcsec (∼800 m) based on data from 1971 to 2000. One hundred and fifty-seven surface stations from two different observation networks were used to cover a satisfactory range of elevations. Station data were homogenized and subjected to quality control to represent changes in meteorological conditions rather than changes in the conditions under which the observations were made. The Meteorological Interpolation based on Surface Homogenized Data Basis (MISH) interpolation method was used to develop data sets that reproduce, as closely as possible, the spatial climate patterns over the region of interest. The main geophysical factors considered for the interpolation of mean monthly precipitation fields were elevation, latitude, incoming solar irradiance, Euclidian distance from the coastline, and land-to-sea percentage. Low precipitation interpolation uncertainties estimated with the cross-validation method provided confidence in the interpolation method. The resulting high-resolution maps give an overall realistic representation of precipitation, especially in fall and winter, with a clear longitudinal dependence on precipitation decreasing from western to eastern continental Greece.

Small spatial scale effects of wave action exposure on morphological traits of the limpet Lottia subrugosa

2019 ◽  
Vol 99 (06) ◽  
pp. 1309-1315
Author(s):  
Edson A. Vieira ◽  
Marília Bueno
Keyword(s):  
Spatial Scale ◽  
Scale Effects ◽  
Spatial Scales ◽  
Wave Exposure ◽  
Wave Action ◽  
Shell Shape ◽  
Small Scale ◽  
Small Spatial Scale ◽  
Large Spatial Scale ◽  
Exposed Shore

AbstractMany studies have already assessed how wave action may affect morphology of intertidal species among sites that vary in wave exposure, but few attempted to look to this issue in smaller scales. Using the most common limpet of the Brazilian coast, Lottia subrugosa, and assuming position on rocky boulders as a proxy for wave action at small scale, we tested the hypothesis that waves may also influence limpet morphology at a smaller spatial scale by investigating how individual size, foot area and shell shape vary between sheltered and exposed boulder sides on three shores in the coast of Ubatuba, Brazil. Limpets consistently showed a proportionally larger foot on exposed boulder sides for all shores, indicating that stronger attachment is an important mechanism to deal with wave action dislodgement at a smaller scale. Shell shape also varied in the scale investigated here, with more conical (dissipative) shells occurring in exposed boulder sides in one exposed shore across time and in the other exposed shore in one year. Shell shape did not vary regarding boulder sides across time in the most sheltered shore. Although we did not assess large spatial scale effects of wave action in this study, variations of the effect of waves at small spatial scale observed for shell shape suggest that it may be modulated by the local wave exposure regime. Our work highlights the importance of wave action at small spatial scales, and may help to understand the ecological variability of limpets inhabiting rocky shores.

scholarly journals Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

2011 ◽  
Vol 92 (9) ◽  
pp. 1181-1192 ◽  
Author(s):  
Frauke Feser ◽  
Burkhardt Rockel ◽  
Hans von Storch ◽  
Jörg Winterfeldt ◽  
Matthias Zahn
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Scale ◽  
Model Data ◽  
Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

scholarly journals Relationship between small-scale catch-per-unit-effort and abundance in New Zealand abalone (pāua, Haliotis iris) fisheries

2015 ◽  
Author(s):  
Edward R Abraham ◽  
Philipp Neubauer
Keyword(s):  
Spatial Scale ◽  
Spatial Information ◽  
Spatial Scales ◽  
Small Scale ◽  
Gps Data ◽  
Catch Per Unit Effort ◽  
Small Spatial Scale ◽  
Unit Effort ◽  
Data Loggers ◽  
Gps Data Loggers

Catch-per-unit-effort (CPUE) is commonly used as an index of abundance in fishery stock assessments, but CPUE may be misleading, as a number of global fishery collapses have been attributed to a hyper-stable CPUE. In abalone (Halitidae family) fisheries, CPUE at large spatial scales may be hyper-stable because of aggregating behaviour and serial-depletion, whereby fishers sequentially fish areas with no corresponding decline in CPUE. Obtaining detailed spatial information in abalone fisheries might mitigate this problem, allowing CPUE to be used more confidently in these fisheries. Here, we report on the use of newly-developed high-resolution Global Positioning System (GPS) data loggers in New Zealand's blacklip abalone (pāua, Haliotis iris) fisheries. Using these data loggers, we tested, via a fish-down experiment, if CPUE is a reliable indicator of abundance at a small spatial scale and over a period of months. In the experiment, hyper-stability at small spatial scales occurred at high abundance, but CPUE reflected the estimated depletion level at the end of experimental fishing. This experiment suggests that the GPS data loggers provide a promising avenue to track CPUE at a small spatial scale, and to assess spatial resource use in New Zealand's pāua fisheries.

scholarly journals Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 72 ◽  
Author(s):  
Abhishek Gaur ◽  
Michael Lacasse ◽  
Marianne Armstrong
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Internal Variability ◽  
Climatic Conditions ◽  
Climate Data ◽  
Wide Range ◽  
The Future

Buildings and homes in Canada will be exposed to unprecedented climatic conditions in the future as a consequence of global climate change. To improve the climate resiliency of existing and new buildings, it is important to evaluate their performance over current and projected future climates. Hygrothermal and whole building simulation models, which are important tools for assessing performance, require continuous climate records at high temporal frequencies of a wide range of climate variables for input into the kinds of models that relate to solar radiation, cloud-cover, wind, humidity, rainfall, temperature, and snow-cover. In this study, climate data that can be used to assess the performance of building envelopes under current and projected future climates, concurrent with 2 °C and 3.5 °C increases in global temperatures, are generated for 11 major Canadian cities. The datasets capture the internal variability of the climate as they are comprised of 15 realizations of the future climate generated by dynamically downscaling future projections from the CanESM2 global climate model and thereafter bias-corrected with reference to observations. An assessment of the bias-corrected projections suggests, as a consequence of global warming, future increases in the temperatures and precipitation, and decreases in the snow-cover and wind-speed for all cities.

scholarly journals Observed small spatial scale and seasonal variability of the CO<sub>2</sub> system in the Southern Ocean

2014 ◽  
Vol 11 (1) ◽  
pp. 75-90 ◽  
Author(s):  
L. Resplandy ◽  
J. Boutin ◽  
L. Merlivat
Keyword(s):  
Southern Ocean ◽  
Spatial Scale ◽  
Large Scale ◽  
Carbon Budget ◽  
Spatial Scales ◽  
Mixed Layer Depth ◽  
Small Scale ◽  
Spatiotemporal Resolution ◽  
Small Spatial Scale ◽  
Data Set

Abstract. The considerable uncertainties in the carbon budget of the Southern Ocean are largely attributed to unresolved variability, in particular at a seasonal timescale and small spatial scale (~ 100 km). In this study, the variability of surface pCO2 and dissolved inorganic carbon (DIC) at seasonal and small spatial scales is examined using a data set of surface drifters including ~ 80 000 measurements at high spatiotemporal resolution. On spatial scales of 100 km, we find gradients ranging from 5 to 50 μatm for pCO2 and 2 to 30 μmol kg−1 for DIC, with highest values in energetic and frontal regions. This result is supported by a second estimate obtained with sea surface temperature (SST) satellite images and local DIC–SST relationships derived from drifter observations. We find that dynamical processes drive the variability of DIC at small spatial scale in most regions of the Southern Ocean and the cascade of large-scale gradients down to small spatial scales, leading to gradients up to 15 μmol kg−1 over 100 km. Although the role of biological activity is more localized, it enhances the variability up to 30 μmol kg−1 over 100 km. The seasonal cycle of surface DIC is reconstructed following Mahadevan et al. (2011), using an annual climatology of DIC and a monthly climatology of mixed layer depth. This method is evaluated using drifter observations and proves to be a reasonable first-order estimate of the seasonality in the Southern Ocean that could be used to validate model simulations. We find that small spatial-scale structures are a non-negligible source of variability for DIC, with amplitudes of about a third of the variations associated with the seasonality and up to 10 times the magnitude of large-scale gradients. The amplitude of small-scale variability reported here should be kept in mind when inferring temporal changes (seasonality, interannual variability, decadal trends) of the carbon budget from low-resolution observations and models.

scholarly journals Optimal configurations of spatial scale for grid cell firing under noise and uncertainty

2014 ◽  
Vol 369 (1635) ◽  
pp. 20130290 ◽  
Author(s):  
Benjamin W. Towse ◽  
Caswell Barry ◽  
Daniel Bush ◽  
Neil Burgess
Keyword(s):  
Spatial Scale ◽  
Spatial Scales ◽  
Grid Cell ◽  
Spatial Uncertainty ◽  
Grid System ◽  
Grid Cells ◽  
Spatial Cues ◽  
Firing Patterns ◽  
Wide Range ◽  
Spatial Noise

We examined the accuracy with which the location of an agent moving within an environment could be decoded from the simulated firing of systems of grid cells. Grid cells were modelled with Poisson spiking dynamics and organized into multiple ‘modules’ of cells, with firing patterns of similar spatial scale within modules and a wide range of spatial scales across modules. The number of grid cells per module, the spatial scaling factor between modules and the size of the environment were varied. Errors in decoded location can take two forms: small errors of precision and larger errors resulting from ambiguity in decoding periodic firing patterns. With enough cells per module (e.g. eight modules of 100 cells each) grid systems are highly robust to ambiguity errors, even over ranges much larger than the largest grid scale (e.g. over a 500 m range when the maximum grid scale is 264 cm). Results did not depend strongly on the precise organization of scales across modules (geometric, co-prime or random). However, independent spatial noise across modules, which would occur if modules receive independent spatial inputs and might increase with spatial uncertainty, dramatically degrades the performance of the grid system. This effect of spatial uncertainty can be mitigated by uniform expansion of grid scales. Thus, in the realistic regimes simulated here, the optimal overall scale for a grid system represents a trade-off between minimizing spatial uncertainty (requiring large scales) and maximizing precision (requiring small scales). Within this view, the temporary expansion of grid scales observed in novel environments may be an optimal response to increased spatial uncertainty induced by the unfamiliarity of the available spatial cues.

scholarly journals The importance of different spatial scales in determining structure and function of deep-sea infauna communities

2013 ◽  
Vol 10 (1) ◽  
pp. 195-232 ◽  
Author(s):  
J. Ingels ◽  
A. Vanreusel
Keyword(s):  
Spatial Scale ◽  
Deep Sea ◽  
Spatial Ecology ◽  
Spatial Scales ◽  
Benthic Communities ◽  
Standing Stock ◽  
Small Scale ◽  
Sampling Locations ◽  
And Function ◽  
Structure Diversity

Abstract. The urge to understand spatial distributions of species and communities and their causative processes has continuously instigated the development and testing of conceptual models in spatial ecology. For the deep-sea, there is evidence that structure, diversity and function of benthic communities are regulated by a multitude of biotic and environmental processes that act in concert on different spatial scales, but the spatial patterns are poorly understood compared to those for other ecosystems. Deep-sea studies generally focus on very limited scale-ranges, thereby impairing our understanding of which spatial scales and associated processes are most important in driving diversity and ecosystem function of communities. Here, we used an extensive integrated dataset of free-living nematodes from deep-sea sediments to unravel which spatial scale is most important in determining benthic infauna communities. Multiple-factor multivariate permutational analyses were performed on different sets of community descriptors (structure, diversity, function, standing stock). The different spatial scales investigated cover two margins in the Northeast Atlantic, several submarine canyons/channel/slope areas, a bathymetrical range of 700–4300 m (represents different stations, 5–50 km apart), different sampling locations at each station (replication distances, 1–200 m), and vertical sediment profiles (cm layers). The results indicated that the most important spatial scale for diversity, functional and standing stock variability is the smallest one; infauna communities changed substantially more with differences between sediment depth layers than with differences associated to larger geographical or bathymetrical scales. Community structure differences were largest between stations at both margins. Important regulating ecosystem processes and the scale on which they occur are discussed. The results imply that, if we are to improve our understanding of ecosystem patterns of deep-sea infauna and the relevant processes driving their structure, diversity, function and standing stock, we must pay particular attention to the small-scale heterogeneity or patchiness and the causative mechanisms acting on that scale.

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