Reconstruction of the Late Holocene climate and environmental history from North Bolgoda Lake,Sri Lanka,using lipid biomarkers and pollen records

The catastrophic impact and unpredictability of the Indian Ocean Monsoon (IOM) over South Asia are evident from devastating floods, mudslides and droughts in one of the most densely populated regions of the globe. However, our understanding as to how the IOM has varied in the past, as well as its impact on local environments, remains limited. This is particularly the case for Sri Lanka, where erosional landscapes have limited the availability of well-stratified, high-resolution terrestrial archives. Here, we present novel data from an undisturbed sediment core retrieved from the coastal Bolgoda Lake. This includes the presentation of a revised Late Holocene age model as well as an innovative combination of pollen, source-specific biomarkers, and compound-specific stable carbon isotopes of n-alkanes to reconstruct the shifts in precipitation, salinity and vegetation cover. Our record documents variable climate between 3000 years and the present, with arid conditions c. 2334 and 2067 cal a bp. This extreme dry period was preceded and followed by more wet conditions. The high-resolution palaeoenvironmental reconstruction fills a major gap in our knowledge on the ramifications of IOM shifts across South Asia and provides insights during a time of major redistribution of dense human settlements across Sri Lanka.     

Midsummer Energy Balance for the Southern Seas

During a ship voyage from Tasmania to Antarctica in summer 2000/01, radiative and meteorological measurements were continuously made, from which the surface energy budget was calculated. Sea conditions throughout the voyage ranged from open water to broken pack and finally to snow-covered unbroken sea ice in McMurdo Sound. The global radiation increased on average during the trip (to higher latitudes) as we travelled poleward. The net radiation, which was positive (toward the surface) on average, decreased however, mostly due to the increase in surface albedo. For open water, most of the net radiation is used for evapouration (61%), while for broken sea-ice conditions, nearly all energy is used for melting of the sea ice or heating of the ocean (96%). For unbroken snow-covered sea ice, the net radiation lies close to zero, due to the high surface albedo, which reached a mean value of 0.81. The sensible heat flux becomes the largest heat source and nearly all the energy is used for warming of the surface. Finally, a Radarsat image, on which the ship track was visible, was used to compare the ship observations with satellite derived ice types.     

Lava bubble-wall fragments formed by submarine hydrovolcanic explosions on Lō'ihi Seamount and Kīlauea Volcano

 Glassy bubble-wall fragments, morphologically similar to littoral limu o Pele, have been found in volcanic sands erupted on Lō'ihi Seamount and along the submarine east rift zone of Kīlauea Volcano. The limu o Pele fragments are undegassed with respect to H₂O and S and formed by mild steam explosions. Angular glass sand fragments apparently form at similar, and greater, depths by cooling-contraction granulation. The limu o Pele fragments from Lō'ihi Seamount are dominantly tholeiitic basalt containing 6.25–7.25% MgO. None of the limu o Pele samples from Lō'ihi Seamount contains less than 5.57% MgO, suggesting that higher viscosity magmas do not form lava bubbles. The dissolved CO₂ and H₂O contents of 7 of the limu o Pele fragments indicate eruption at 1200±300 m depth (120±30 bar). These pressures exceed that generally thought to limit steam explosions. We conclude that hydrovolcanic eruptions are possible, with appropriate pre-mixing conditions, at pressures as great as 120 bar. Received: 22 December 1998 / Accepted: 16 July 1999     

On trend detection

A main obstacle to trend detection in time series occurs when they are autocorrelated. By reducing the effective sample size of a series, autocorrelation leads to decreased trend significance. Numerous recipes attempt to mitigate the effect of autocorrelation, either by adjusting for the reduced effective sample size or by removing the autocorrelated components of a series. This short note deals with the latter, also called prewhitening (PW). It is known that removal of autocorrelation also removes part of the trend, which may affect the signal‐to‐noise ratio. Two popular methods have dealt with this problem, the trend‐free prewhitening (TFPW) and the iterative prewhitening. Although it is generally accepted that both methods reduce the adverse effects of PW on the trend magnitude, corresponding effects on statistical significance have not been clearly stated for TFPW. Using a Monte Carlo approach, it is demonstrated that both methods entail quite different Type‐I error rates. The iterative prewhitening produces rates that are generally close to the nominal significance level. The TFPW, however, shows very high Type‐I error rates with increasing autocorrelation. The corresponding rate of false trend detections is unacceptable for applications, so that published trends based on TFPW need to be reassessed.     

High resolution regional climate model simulations for Germany: part I—validation

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution.