Signal detectability in extreme precipitation changes assessed from twentieth century climate simulations

This study assesses the detectability of external influences in changes of precipitation extremes in the twentieth century, which is explored through a perfect model analysis with an ensemble of coupled global climate model (GCM) simulations. Three indices of precipitation extremes are defined from the generalized extreme value (GEV) distribution: the 20-year return value (P ₂₀), the median (P _m), and the cumulative probability density as a probability-based index (PI). Time variations of area-averages of these three extreme indices are analyzed over different spatial domains from the globe to continental regions. Treating all forcing simulations (ALL; natural plus anthropogenic) of the twentieth century as observations and using a preindustrial control run (CTL) to estimate the internal variability, the amplitudes of response patterns to anthropogenic (ANT), natural (NAT), greenhouse-gases (GHG), and sulfate aerosols (SUL) forcings are estimated using a Bayesian decision method. Results show that there are decisively detectable ANT signals in global, hemispheric, and zonal band areas. When only land is considered, the global and hemispheric detection results are unchanged, but detectable ANT signals in the zonal bands are limited to low latitudes. The ANT signals are also detectable in the P _m and PI but not in P ₂₀ at continental scales over Asia, South America, Africa, and Australia. This indicates that indices located near the center of the GEV distribution (P _m and PI) may give better signal-to-noise ratio than indices representing the tail of the distribution (P ₂₀). GHG and NAT signals are also detectable, but less robustly for more limited extreme indices and regions. These results are largely insensitive when model data are masked to mimic the availability of the observed data. An imperfect model analysis in which fingerprints are obtained from simulations with a different GCM suggests that ANT is robustly detectable only at global and hemispheric scales, with high uncertainty in the zonal and continental results.     

Statistical analysis of tropical climate anomaly simulations

The statistical analysis of two atmospheric general circulation simulations using the ECHAM3 GCM in permanent January conditions are presented. The two simulations utilize different oceanic surface temperatures in the Atlantic as boundary conditions: the cold simulation has SST representing the anomalous cold conditions during the decade 1904-13 while the warm simulation has SST representative for the decade 1951-60 where anomalous warm conditions have been observed. The analysis concentrates on the simulated differences between both experiments within the tropical belt to test the working hypothesis whether changes in the deep tropical heating initiated by the anomalous SST are responsible for the anomalies in the flow and mass field. We present a method which extracts the significant and dynamically consistent signal of the total difference using a multivariate statistical test based on the amplitudes of an a-priori specified mode expansion. These expansion modes are defined from a variant of the Matsuno-Gill linearized reduced gravity model for the tropical atmosphere. The application of the method shows a clear and well defined tropical signal in the flow and mass field which can be understood as the reponse of the ECHAM3 model to a deep heating anomaly not in the vicinity of the anomalous SST but on the neighboring continents especially South America and with opposite sign in remote areas between Indonesia and the dateline. The signal can be summarized as an enhancement of the GCM's tropical East-West circulation with the ascending branch over South America in the warm simulation compared to the cold run.     

Regional and interannual variability of ecosystem dynamics in the Southern Ocean

To investigate regional and interannual variability of the ecosystem in the Southern Ocean, a coupled circumpolar ice–ocean–plankton model has been developed. The ice–ocean component (known as BRIOS-2) is based on a modified version of the s-coordinate primitive equation model (SPEM) coupled to a dynamic–thermodynamic sea-ice model. The biological model (BIMAP) comprises two biogeochemical cycles – silica and nitrogen – and a prognostic iron compartment to include possible effects of micronutrient limitation. Simulations with the coupled ice–ocean–plankton model indicate that the physical–biological interaction is not limited to the effect of a varying surface mixed-layer depth. In the Pacific sector, large anomalies in winter mixed-layer depth cause an increased iron supply and enhance primary production and plankton biomass in the following summer, whereas in the Atlantic sector variability in primary production is caused mainly by fluctuations of oceanic upwelling. Thus, the Antarctic Circumpolar Wave (ACW) induces regional oscillations of phytoplankton biomass in both sectors, but not a propagating signal. Furthermore, interannual variability in plankton biomass and primary production is strong in the Coastal and Continental Shelf Zone and the Seasonal Ice Zone around the Antarctic continent. Interannual variability induced by the ACW has large effects on the regional scale, but the associated variability in biogenic carbon fluxes is small compared to the long-term carbon sequestration of the Southern Ocean.     

Anisotropic effective conductivity in fractured rocks by explicit effective medium methods

In this work, we assess the use of explicit methods for estimating the effective conductivity of anisotropic fractured media. Explicit methods are faster and simpler to use than implicit methods but may have a more limited range of validity. Five explicit methods are considered: the Maxwell approximation, the T‐matrix method, the symmetric and asymmetric weakly self‐consistent methods, and the weakly differential method, where the two latter methods are novelly constructed in this paper. For each method, we develop simplified expressions applicable to flat spheroidal “penny‐shaped” inclusions. The simplified expressions are accurate to the first order in the ratio of fracture thickness to fracture diameter. Our analysis shows that the conductivity predictions of the methods fall within known upper and lower bounds, except for the T‐matrix method at high fracture densities and the symmetric weakly self‐consistent method when applied to very thin fractures. Comparisons with numerical results show that all the methods give reliable estimates for small fracture densities. For high fracture densities, the weakly differential method is the most accurate if the fracture geometry is non‐percolating or the fracture/matrix conductivity contrast is small. For percolating conductive fracture networks, we have developed a scaling relation that can be applied to the weakly self‐consistent methods to give conductivity estimates that are close to the results from numerical simulations.     

Signal analysis of the atmospheric mean 500/1000 hPa temperature north of 55°N between 1949 and 1994

 The lower tropospheric mean temperature 500/1000 hPa is examined in the Northern Hemisphere high-latitude region north of 55°N with regard to a climate change signal due to anthropogenic climate forcing as a supplement to previous studies which concentrated on near surface temperatures. An observational data set of the German Weather Service is compared with several model simulations including different scenarios of greenhouse gas and sulfate aerosol forcing derived from the two recent versions of the coupled climate model in Hamburg, ECHAM-3/LSG and ECHAM-4/OPYC. The signal analysis is based on the optimal fingerprint method, which supplies a detection variable with optimal signal-to-noise ratio. The natural variability measures are derived from the corresponding long-term control experiments. From 1970 onward, we find high trend pattern analogies between the observational data and the greenhouse-gas induced model simulations. The fingerprint of this common temperature signal consists of a predominate warming with maximum over Siberia and a weak cooling over the North Atlantic reaching an estimated significance level of about 1%. A non-optimized approach has also been examined, leading to even closer trend pattern correlations. The additional forcing by sulfate aerosols decreases the correlation of this climate change simulation with the observations. The natural variability constitutes about 50% of the conforming trend patterns. The signal-to-noise ratio is best over the oceans while the tropospheric temperatures over the land masses are contaminated by strong noise. The trend pattern correlations look the same for both model versions and several ensemble members with different noise realizations. Received: 4 January 1999 / Accepted: 11 April 2001     

Food preferences of mangrove crabs related to leaf nitrogen compounds in the Segara Anakan Lagoon,Java, Indonesia

The large amounts of leaf litter produced by tropical mangrove forests serve as a major food source for the benthic fauna. The reasons for the preferential consumption of mangrove leaves by crabs are unclear as yet. We investigated the diet, food preferences and consumption rates of 8 dominant grapsoid crab species (Perisesarma spp., Episesarma spp., Metopograpsus latifrons, and Metaplax elegans) in mangroves of Segara Anakan, Java, Indonesia, by means of stomach-content analysis and feeding experiments. Leaves from the five most abundant mangrove tree species (Aegiceras corniculatum, Avicennia alba, Ceriops decandra, Rhizophora apiculata, and Sonneratia caseolaris) were analyzed for organic carbon, total nitrogen, δ¹³C, δ¹⁵N and amino acids and hexosamines. This study is the first that investigated crab food preferences related to the nitrogen compound composition of leaves.Our results show that Episesarma spp. and Perisesarma spp. are omnivorous crabs which mainly feed on detritus, mangrove litter and bark, and on a small amount of roots, algae and animal matter whereas M. elegans is a detritus feeder. In feeding experiments with green, yellow and brown leaves Perisesarma spp. and E. singaporense had the highest consumption rates for brown leaves of R. apiculata and S. caseolaris, and for green leaves of A. alba. Preferred leaves were characterized by a high amount and/or freshness of nitrogenous compounds and their biochemical composition was significantly different from that of disliked leaves (all leaves of A. corniculatum and C. decandra, green and yellow leaves of R. apiculata and S. caseolaris). The presence of the hexosamine galactosamine found only in brown leaves indicates that bacteria contribute to the amount of bioavailable nitrogen compounds. We infer that the nitrogen compound composition rather than the C/N ratio alone is a determinant for bioavailability of mangrove leaves and hence may partly explain the crabs' food preferences.     

Record occurrence and record values in daily and monthly temperatures

We analyze the occurrence and the values of record-breaking temperatures in daily and monthly temperature observations. Our aim is to better understand and quantify the statistics of temperature records in the context of global warming. Similar to earlier work we employ a simple mathematical model of independent and identically distributed random variables with a linearly growing expectation value. This model proved to be useful in predicting the increase (decrease) in upper (lower) temperature records in a warming climate. Using both station and re-analysis data from Europe and the United States we further investigate the statistics of temperature records and the validity of this model. The most important new contribution in this article is an analysis of the statistics of record values for our simple model and European reanalysis data. We estimate how much the mean values and the distributions of record temperatures are affected by the large scale warming trend. In this context we consider both the values of records that occur at a certain time and the values of records that have a certain record number in the series of record events. We compare the observational data both to simple analytical computations and numerical simulations. We find that it is more difficult to describe the values of record breaking temperatures within the framework of our linear drift model. Observations from the summer months fit well into the model with Gaussian random variables under the observed linear warming, in the sense that record breaking temperatures are more extreme in the summer. In winter however a significant asymmetry of the daily temperature distribution hides the effect of the slow warming trends. Therefore very extreme cold records are still possible in winter. This effect is even more pronounced if one considers only data from subpolar regions.     

The influence of metal content on the physical and mineralogical properties of pelagic manganese nodules

Deep sea manganese nodules are considered as important natural resources for the future because of their Ni, Cu and Co contents. Their different shapes cannot be correlated clearly with their chemical composition. Surface constitution, however, can be associated with the metal contents. A classification of the nodules is suggested on the basis of these results. The iron content of the nodules strikingly shows relations to the physical properties (e.g. density and porosity). The method of density-measurement is the reason for this covariance. The investigation of freeze-dried nodular substance does not give this result. The Fe-rich nodules lose more hydration water than the Fe-poor ones during heat drying. The reason for this effect is the different crystallinity, respectively the particle size. The mean particle size is calculated on the basis of geometrical models. The X-ray-diffraction analysis proves the variation of crystallinity in connection with the Fe-content, too. The internal nodular textures also show characteristic distinctions.     

Effects of Anthropogenic Estrogens Nonylphenol and 17α‐Ethinylestradiol in Aquatic Model Ecosystems

Microcosm tests were conducted to investigate the effects of the estrogenic substances nonylphenol (NP) and 17α‐ethinylestradiol (EE) on aquatic ecosystems. Maximum concentrations of 9 to 120 μg L^—1 NP resp. 49 to 724 ng L^—1 EE were induced by controlled release. The controlled release method allows the establishment of a continuous concentration course. The microcosms proved to run robustly with abiotic conditions close to natural. They developed biocenosis with similar characteristics as in natural ecosystems and, considering their given level of complexity, they can be used to describe possible risks for the environment. Both tested chemicals unveiled the potential to affect the plankton communities in the tested concentration range. NP exposure caused a reduction of Cladocera and Copepoda abundances and disturbed the phytoplankton structure. A NOEC_community of 30 μg L^—1 was calculated. In the first EE study, a flood in the lake where the microcosm water was collected caused additional stress and thereby a high variability, both between the microcosms and in each microcosm over time. Probably therefore the only effect found was a reduction of Copepoda abundance. In a second EE study Cladocera and Copepoda abundances were reduced, from which the phytoplankton benefited. Although a final interpretation is difficult for results of microcosm tests, there are indications that the found effects of EE and perhaps also NP may be caused at least partially by endocrine disruptive activity.