Comparative analysis of the long-term variability of winter surface temperature in the Black and Aegean Seas during 1982-2004 associated with the large-scale atmospheric forcing

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
Alexander S. Kazmin ◽  
Andrei G. Zatsepin ◽  
Harilaos Kontoyiannis
Keyword(s):  
Comparative Analysis ◽  
Surface Temperature ◽  
Large Scale ◽  
Atmospheric Forcing

scholarly journals Cities Exacerbate Climate Warming

Urban Science ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 27
Author(s):  
Lahouari Bounoua ◽  
Kurtis Thome ◽  
Joseph Nigro
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Warming Trend ◽  
Climate Mitigation ◽  
Temperature Changes ◽  
Surface Warming ◽  
The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

scholarly journals Comparison and assessment of large-scale surface temperature in climate model simulations

2019 ◽  
Vol 5 (1) ◽  
pp. 67-85
Author(s):  
Raquel Barata ◽  
Raquel Prado ◽  
Bruno Sansó
Keyword(s):  
Surface Temperature ◽  
Seasonal Changes ◽  
Large Scale ◽  
Climate Model ◽  
Internal Variability ◽  
Temperature Variability ◽  
Data Driven ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. We present a data-driven approach to assess and compare the behavior of large-scale spatial averages of surface temperature in climate model simulations and in observational products. We rely on univariate and multivariate dynamic linear model (DLM) techniques to estimate both long-term and seasonal changes in temperature. The residuals from the DLM analyses capture the internal variability of the climate system and exhibit complex temporal autocorrelation structure. To characterize this internal variability, we explore the structure of these residuals using univariate and multivariate autoregressive (AR) models. As a proof of concept that can easily be extended to other climate models, we apply our approach to one particular climate model (MIROC5). Our results illustrate model versus data differences in both long-term and seasonal changes in temperature. Despite differences in the underlying factors contributing to variability, the different types of simulation yield very similar spectral estimates of internal temperature variability. In general, we find that there is no evidence that the MIROC5 model systematically underestimates the amplitude of observed surface temperature variability on multi-decadal timescales – a finding that has considerable relevance regarding efforts to identify anthropogenic “fingerprints” in observational surface temperature data. Our methodology and results present a novel approach to obtaining data-driven estimates of climate variability for purposes of model evaluation.

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 403-419 ◽  
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

scholarly journals Surface atmospheric forcing as the driver of long-term pathways and timescales of ocean ventilation

2021 ◽  
Author(s):  
Alice Marzocchi ◽  
A. J. George Nurser ◽  
Louis Clément ◽  
Elaine L. McDonagh
Keyword(s):  
Large Scale ◽  
Sea Water ◽  
Anthropogenic Heat ◽  
Surface Mixed Layer ◽  
Atmospheric Forcing ◽  
Mode Water ◽  
The North ◽  
Ocean Ventilation ◽  
Passive Tracers

Abstract. The ocean takes up 93 % of the excess heat in the climate system and approximately a quarter of the anthropogenic carbon via air-sea fluxes. Ocean ventilation and subduction are key processes that regulate the transport of water (and associated properties) from the surface mixed layer, which is in contact with the atmosphere, to the ocean's interior which is isolated from the atmosphere for a timescale set by the large-scale circulation. Using numerical simulations with an ocean-sea-ice model using the NEMO framework, we assess where the ocean subducts water and thus takes up properties from the atmosphere and how ocean currents transport and redistribute them over time and how, where and when they are ventilated. Here, the strength and patterns of the net uptake of water and associated properties are analysed by including simulated sea water vintage dyes that are passive tracers released annually into the ocean surface layers between 1958 and 2017. The dyes' distribution is shown to capture years of strong and weak convection at deep and mode water formation sites in both hemispheres, especially when compared to observations in the North Atlantic subpolar gyre. Using this approach, relevant to any passive tracer in the ocean, we can evaluate the regional and depth distribution of the tracers, and determine their variability on interannual to multidecadal timescales. We highlight the key role of variations in subduction rate driven by changes in surface atmospheric forcing in setting the different sizes of the long-term inventory of the dyes released in different years and the evolution of their distribution. This suggests forecasting potential for determining how the distribution of passive tracers will evolve, from having prior knowledge of the surface air-sea fluxes, with implications for the uptake and storage of anthropogenic heat and carbon in the ocean.

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

2009 ◽  
Vol 6 (2) ◽  
pp. 1129-1171
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

Assessment of Hazardous Exogenous Processes of the Mondy Depression Based on Morphometric and Landscape Analysis

2018 ◽  
Vol 938 (8) ◽  
pp. 28-37
Author(s):  
O.V. Bezgodova ◽  
E.A. Istomina ◽  
E.V. Ovchinnikova
Keyword(s):  
Comparative Analysis ◽  
Large Scale ◽  
Space Images ◽  
Digital Terrain ◽  
Terrain Models ◽  
Term Change ◽  
Vector Basis ◽  
Landscape Map ◽  
Slope Class

The authors consider the application of geoinformation mapping in the analysis of hazardous processes using space images, digital terrain models, as well as comparative analysis of the landscape map and the map of hazardous exogenous processes. As a result, a large-scale map of hazardous processes within the Mondy depression was created. At the first stage, space images, physical and geographical conditions were analyzed, as well as the morphometric indicators of the territory. On the basis of that analysis classes of hazardous exogenous processes that affected the landscapes of the depression were identified. Further the classes of hazardous exogenous processes were compared with landscape facies, within the contours of which hazardous processes of different intensity are common. Calculation of hazardous exogenous processes using the data of morphometric indicators was made, the information on the long-term change in the main elements of the climate of the study area obtained. The method of spatial analysis in geographic information systems on a vector basis marked the main hazardous exogenous processes that have developed in the territory of the Mondy depression. The gravitational-slope class, represented by collapses and screes, is most widespread in the investigated area. Fluvial class of the dangerous exogenous processes is mostly indicated by mudflows in the valleys of small and temporary watercourses. Cryogenic class of these processes is least developed.

scholarly journals A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies

2016 ◽  
Vol 8 (1) ◽  
pp. 165-176 ◽  
Author(s):  
Viva Banzon ◽  
Thomas M. Smith ◽  
Toshio Mike Chin ◽  
Chunying Liu ◽  
William Hankins
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Sea Surface ◽  
Climate Data ◽  
Data Set ◽  
Volcanic Aerosols ◽  
Climate Data Record

Abstract. This paper describes a blended sea-surface temperature (SST) data set that is part of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program product suite. Using optimum interpolation (OI), in situ and satellite observations are combined on a daily and 0.25° spatial grid to form an SST analysis, i.e., a spatially complete field. A large-scale bias adjustment of the input infrared SSTs is made using buoy and ship observations as a reference. This is particularly important for the time periods when volcanic aerosols from the El Chichón and Mt. Pinatubo eruptions are widespread globally. The main source of SSTs is the Advanced Very High Resolution Radiometer (AVHRR), available from late 1981 to the present, which is also the temporal span of this CDR. The input and processing choices made to ensure a consistent data set that meets the CDR requirements are summarized. A brief history and an explanation of the forward production schedule for the preliminary and science-quality final product are also provided. The data set is produced and archived at the newly formed National Centers for Environmental Information (NCEI) in Network Common Data Form (netCDF) at doi:10.7289/V5SQ8XB5.

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