Data and Services at the Integrated Climate Data Center (ICDC) at the University of Hamburg

KlimawandelEarth observation data obtained from remote sensing sensors and in-situ data archives are fundamental for our current understanding of the Earth’s climate system. Such data are an important pre-requisite for Earth System research and should be easy to access and easy to use. In addition such data should be quality assessed and attached with information about uncertainties and long-term stability. If these data sets are stored in a self-explanatory, easy-to-use format, their usefulness and scientific value increase. This is the guideline for the Integrated Climate Data Center (ICDC) at the Center for Earth System Research and Sustainability (CEN), University of Hamburg. ICDC offers a reliable, quick and easy data access along with expert support for users and data providers. The ICDC provides several types of worldwide accessible in situ and satellite Earth observation data of the atmosphere, ocean, land surface, and cryosphere via the web portal http://icdc.zmaw.de. Recently, data from socio-economic sciences have been integrated into ICDC’s data base to enhance interdisciplinary collaboration. On ICDC’s web portal, each data set has its own page. It contains the data access points, a short data description, information about spatiotemporal coverage and resolution, data quality, important reference documents and contacts, and about how to cite the data set. The data are converted into netCDF or ASCII format. Consistency and quality checks are carried out – often in the framework of international collaborations. Literature studies are conducted to learn about potential limitations or preferred application areas of the data offered. The data sets can be accessed through the web page via FTP, HTTP or OPeNDAP. Using the Live Access Server, users can visualize data as maps, along transects and profiles, zoom into key regions, and create time series. In both fields, visualization and data access, ICDC tries to provide fast response times and high reliability

Decreasing intensity of open-ocean convection in the Greenland and Iceland seas

The air–sea transfer of heat and fresh water plays a critical role in the global climate system. This is particularly true for the Greenland and Iceland seas, where these fluxes drive ocean convection that contributes to Denmark Strait overflow water, the densest component of the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). Here we show that the wintertime retreat of sea ice in the region, combined with different rates of warming for the atmosphere and sea surface of the Greenland and Iceland seas, has resulted in statistically significant reductions of approximately 20% in the magnitude of the winter air–sea heat fluxes since 1979. We also show that modes of climate variability other than the North Atlantic Oscillation (NAO) are required to fully characterize the regional air–sea interaction. Mixed-layer model simulations imply that further decreases in atmospheric forcing will exceed a threshold for the Greenland Sea whereby convection will become depth limited, reducing the ventilation of mid-depth waters in the Nordic seas. In the Iceland Sea, further reductions have the potential to decrease the supply of the densest overflow waters to the AMOC

Spatial distribution of air-sea heat fluxes over the sub-polar North Atlantic Ocean

Author Posting. © American Geophysical Union, 2012. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 39 (2012): L18806, doi:10.1029/2012GL053097.On a variety of spatial and temporal scales, the energy transferred by air-sea heat and moisture fluxes plays an important role in both atmospheric and oceanic circulations. This is particularly true in the sub-polar North Atlantic Ocean, where these fluxes drive water-mass transformations that are an integral component of the Atlantic Meridional Overturning Circulation (AMOC). Here we use the ECMWF Interim Reanalysis to provide a high-resolution view of the spatial structure of the air-sea turbulent heat fluxes over the sub-polar North Atlantic Ocean. As has been previously recognized, the Labrador and Greenland Seas are areas where these fluxes are large during the winter months. Our particular focus is on the Iceland Sea region where, despite the fact that water-mass transformation occurs, the winter-time air-sea heat fluxes are smaller than anywhere else in the sub-polar domain. We attribute this minimum to a saddle point in the sea-level pressure field, that results in a reduction in mean surface wind speed, as well as colder sea surface temperatures associated with the regional ocean circulation. The magnitude of the heat fluxes in this region are modulated by the relative strength of the Icelandic and Lofoten Lows, and this leads to periods of ocean cooling and even ocean warming when, intriguingly, the sensible and latent heat fluxes are of opposite sign. This suggests that the air-sea forcing in this area has large-scale impacts for climate, and that even modest shifts in the atmospheric circulation could potentially impact the AMOC.GWKM was supported by the Natural Science and Engineering Research Council of Canada. IAR was funded in part by NCAS (the National Centre for Atmospheric Sciences) and by NERC grant NE/I005293/1. RSP was funded by grant OCE-0959381 fromthe US National Science Foundation.2013-03-2

Time-dependent propagators for stochastic models of gene expression: an analytical method

The inherent stochasticity of gene expression in the context of regulatory networks profoundly influences the dynamics of the involved species. Mathematically speaking, the propagators which describe the evolution of such networks in time are typically defined as solutions of the corresponding chemical master equation (CME). However, it is not possible in general to obtain exact solutions to the CME in closed form, which is due largely to its high dimensionality. In the present article, we propose an analytical method for the efficient approximation of these propagators. We illustrate our method on the basis of two categories of stochastic models for gene expression that have been discussed in the literature. The requisite procedure consists of three steps: a probability-generating function is introduced which transforms the CME into (a system of) partial differential equations (PDEs); application of the method of characteristics then yields (a system of) ordinary differential equations (ODEs) which can be solved using dynamical systems techniques, giving closed-form expressions for the generating function; finally, propagator probabilities can be reconstructed numerically from these expressions via the Cauchy integral formula. The resulting ‘library’ of propagators lends itself naturally to implementation in a Bayesian parameter inference scheme, and can be generalised systematically to related categories of stochastic models beyond the ones considered here

The SAMD Product Standard (Standardized Atmospheric Measurement Data)

The SAMD light data-product description-document includes the conventions for file names, variables and NetCDF-files. The standardized XML-file convention is included as well as all necessary abbreviations for institutes, instruments, variables, etc

The SAMD Product Standard (Standardized Atmospheric Measurement Data)

The SAMD light data-product description-document includes the conventions for file names, variables and NetCDF-files. The standardized XML-file convention is included as well as all necessary abbreviations for institutes, instruments, variables, etc

SPI - Standardized Precipitation Index from CRU for EU and USA

The "Standardized Precipitation Index" (SPI) is used to describe extremely dry or wet climate situations. The advantages of SPI usage are: Only precipitation data are needed for the calculation of the index. The index is a standardized measure for precipitation in different climatic regions and for seasonal differences. Calculated for different time scales: meteorological, agricultural-economic and hydrological. SPI Classes: SPI ≤ -2: Extremely dry, -2 < SPI ≤ -1.5: Severely dry, -1.5 < SPI ≤ -1: Moderately dry, -1 < SPI ≤ 1: Near normal, 1 < SPI ≤ 1.5: Moderately wet, 1.5 < SPI ≤ 2: Severely wet, SPI ≥ 2: Extremely wet. Calculation: The SPI, presented here, is different from the original SPI definition of McKee et al. 1993. An enhanced SPI is used, that significantly reduces errors resulting from the determination of the precipitation's distribution (Sienz et al. 2011). MC Kee et al. 1993 shifted the time series of the SPI one time step into the future, but this is not done for the calculation of the SPI presented here. The reference period used for calculation of all distributions is 1901-2020. The SPIs (1, 3, 6, 9, 12, 24, 48) were calculated from the Climate Research Unit (CRU) precipitation data set, Version: CRU TS 4.05 for the period 1901 - 2020 for Europe and USA. It is an update and replaces the SPI from CRU by Frank Sienz. As various changes were made to the scripts, comparisons with examples of the results were made to ensure the quality of the data. The date specified in the files always indicates the end of the period under consideration

The SAMD Product Standard (Standardized Atmospheric Measurement Data)

The SAMD light data-product description-document includes the conventions for file names, variables and NetCDF-files. The standardized XML-file convention is included as well as all necessary abbreviations for institutes, instruments, variables, etc

The Iceland-Lofotes pressure difference: different states of the North Atlantic low-pressure zone

The extended North Atlantic low-pressure zone exhibits two pressure minima in the long-term winter mean: the primary one west of Iceland and the secondary one near Norwegian Lofotes Islands. Based on the ERA-40 data set and on wintertime monthly sea level pressure (SLP) anomalies at both places, the states of co- and antivariability are investigated. The covariability represents states of a strongly or weakly developed North Atlantic low-pressure zone. The difference between these two states represents the NAO pattern. The antivariability is defined by an Iceland-Lofotes difference (ILD) index, which is positive (negative) when the anomaly in the Lofotes area is higher (lower) than that in the Iceland area. An ILD pattern is calculated as difference between SLP composites for high and low ILD indices. The ILD pattern extends horizontally beyond the two centers and affects other prominent Northern Hemisphere pressure centres: Aleutian low; Siberian high and Azores high. The pattern extends into the stratosphere and shows significant impacts on surface air temperature, Arctic sea ice concentration and sea ice motion