Periodic solutions for soil carbon dynamics equilibriums with time-varying forcing variables

Martin, MP Cordier, S Balesdent, J Arrouays, D

ECOLOGICAL MODELLING 204:3-4 523-530

Numerical models that simulate the dynamics of carbon in soil are increasingly used to improve our knowledge and help our management of the carbon cycle. Calculation of the long-term behavior of these models is necessary in many applications but encounters the difficulty of managing the periodic forcing variables, e.g. seasonal variations, such as carbon inputs and decomposition rates. This calculation is conventionally done by running the model over large time durations or by assuming constant forcing variables. Two methods, which make it possible to rapidly compute periodic solutions taking into account the time variations of these variables, are proposed. The first one works on discrete-time models and the second one on continuous-time models involving Fourier transforms. Both methods were tested on the Rothamsted carbon model (RothC), a discrete-time model which has also been given a continuous approximation, using realistic and unrealistic sets of time-varying forcing functions. Both methods provided an efficient way to compute the periodic solutions of the RothC model within the application domain of the model. Compared to running the discrete model to the equilibrium, reduction in the computational cost was of up to 95% at the expense of a maximum absolute error of 1% for the estimation of carbon stocks. For specific distributions of the forcing variables the use of Fourier transform of zero order, which was equivalent to assume constant forcing variables, led to a maximum absolute error of SS% in the estimation of the long-term behavior of the model. There, a Fourier transform of order higher than zero is required. (C) 2007 Elsevier B.V. All rights reserved.

SPotGS:methods | /neutral/methods | 022

Simulation of seasonal precipitation and raindays over Greece: a statistical downscaling technique based on artificial neural networks (ANNs)

Tolika, K Maheras, P Vafiadis, M Flocasc, HA Arseni-Papadimitriou, A

INTERNATIONAL JOURNAL OF CLIMATOLOGY 27:7 861-881

A statistical downscaling technique based on artificial neural network (ANN) was employed for the estimation of local changes on seasonal (winter, spring) precipitation and raindays for selected stations over Greece. Empirical transfer functions were derived between large-scale predictors from the NCEP/NCAR reanalysis and local rainfall parameters. Two sets of predictors were used: (1) the circulation-based 500 hPa and (2) its combination along with surface specific humidity and raw precipitation data (nonconventional predictor). The simulated time series were evaluated against observational data and the downscaling model was found efficient in generating winter and spring precipitation and raindays. The temporal evolution of the estimated variables was well captured, for both seasons. Generally, the use of the nonconventional predictors are attributed to the improvement of the simulated results. Subsequently, the present day and future changes on precipitation conditions were examined using large-scale data from the atmospheric general circulation model HadAM3P to the statistical model. The downscaled climate change signal for both precipitation and raindays, partly for winter and especially for spring, is similar to the signal from the HadAM3P direct output: a decrease of the parameters is predicted over the study area. However, the amplitude of the changes was different. Copyright (c) 2006 Royal Meteorological Society

SPotGS:methods | /neutral/methods | 008

Dominant factors controlling glacial and interglacial variations in the treeline elevation in tropical Africa

Wu, HB Guiot, J Brewer, S Guo, ZT Peng, CH

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 104:23 9720-9724

The knowledge of tropical palaeoclimates is crucial for understanding global climate change, because it is a test bench for general circulation models that are ultimately used to predict future global warming. A longstanding issue concerning the last glacial maximum in the tropics is the discrepancy between the decrease in sea-surface temperatures reconstructed from marine proxies and the high-elevation decrease in land temperatures estimated from indicators of treeline elevation. In this study, an improved inverse vegetation modeling approach is used to quantitatively reconstruct palaeoclimate and to estimate the effects of different factors (temperature, precipitation, and atmospheric CO2 concentration) on changes in treeline elevation based on a set of pollen data covering an altitudinal range from 100 to 3,140 m above sea level in Africa. We show that lowering of the African treeline during the last glacial maximum was primarily triggered by regional drying, especially at upper elevations, and was amplified by decreases in atmospheric CO2 concentration and perhaps temperature. This contrasts with scenarios for the Holocene and future climates, in which the increase in treeline elevation will be dominated by temperature. Our results suggest that previous temperature changes inferred from tropical treeline shifts may have been overestimated for low-CO2 glacial periods, because the limiting factors that control changes in treeline elevation differ between glacial and interglacial periods.

SPotGS:paleo | /neutral/paleo | 014

Eustasy and sea water Sr composition: application to high-resolution Sr-isotope stratigraphy of Miocene shallow-water carbonates

Kroeger, KF Reuter, M Forst, MH Breisig, S Hartmann, G Brachert, TC

SEDIMENTOLOGY 54:3 565-585

Oceanic Sr-87/Sr-86-isotope ratios are strongly influenced by rates of silicate weathering and therefore linked not only to glaciation but also to sea-level change. The present study combines analysis of sequence stratigraphy and basin architecture with Sr-isotope stratigraphy in Miocene shallow-water sediments in southern Portugal and Crete (Greece). The common method is to use smoothed global sea water Sr-isotope reference curves but here a different approach is chosen. Instead, measured Sr-isotope curves are correlated with unsmoothed reference curves by identification of similar fluctuations in the order of several 100 kyr. Transgressive intervals are characterized by increasing Sr-isotope ratios interpreted as corresponding to intensified silicate weathering as a consequence of deglaciation, while lowstand deposits have low Sr-isotope ratios. Comparison of Sr-isotope curves and sedimentary sequences in the studied basins with independent global delta O-18 data and data on global sea-level might suggest a general relationship, supporting a connection to global climate change. Because of these relationships, the method presented herein has a high potential for use in high-resolution age dating and is also applicable in shallow-water sediments.

SPotGS:paleo | /neutral/paleo | 024

Expert judgements on the response of the Atlantic meridional overturning circulation to climate change

Zickfeld, K Levermann, A Morgan, MG Kuhlbrodt, T Rahmstorf, S Keith, DW

CLIMATIC CHANGE 82:3-4 235-265

We present results from detailed interviews with 12 leading climate scientists about the possible effects of global climate change on the Atlantic Meridional Overturning Circulation (AMOC). The elicitation sought to examine the range of opinions within the climatic research community about the physical processes that determine the current strength of the AMOC, its future evolution in a changing climate and the consequences of potential AMOC changes. Experts assign different relative importance to physical processes which determine the present-day strength of the AMOC as well as to forcing factors which determine its future evolution under climate change. Many processes and factors deemed important are assessed as poorly known and insufficiently represented in state-of-the-art climate models. All experts anticipate a weakening of the AMOC under scenarios of increase of greenhouse gas concentrations. Two experts expect a permanent collapse of the AMOC as the most likely response under a 4xCO(2) scenario. Assuming a global mean temperature increase in the year 2100 of 4 K, eight experts assess the probability of triggering an AMOC collapse as significantly different from zero, three of them as larger than 40%. Elicited consequences of AMOC reduction include strong changes in temperature, precipitation distribution and sea level in the North Atlantic area. It is expected that an appropriately designed research program, with emphasis on long-term observations and coupled climate modeling, would contribute to substantially reduce uncertainty about the future evolution of the AMOC.

SPotGS:discuss | /neutral/discuss | 050

A maximum entropy method for combining AOGCMs for regional intra-year climate change assessment

Laurent, R Cai, XM

CLIMATIC CHANGE 82:3-4 411-435

This paper deals with different responses from various Atmosphere-Ocean Global Climate Models (AOGCMs) at the regional scale. What can be the best use of AOGCMs for assessing the climate change in a particular region? The question is complicated by the consideration of intra-year month-to-month variability of a particular climate variable such as precipitation or temperature in a specific region. A maximum entropy method (MEM), which combines limited information with empirical perspectives, is applied to assessing the probability-weighted multimodel ensemble average of a climate variable at the region scale. The method is compared to and coupled with other two methods: the root mean square error minimization method and the simple multimodel ensemble average method. A mechanism is developed to handle a comprehensive range of model uncertainties and to identify the best combination of AOGCMs based on a balance of two rules: depending equally on all models versus giving higher priority to models more strongly verified by the historical observation. As a case study, the method is applied to a central US region to compute the probability-based average changes in monthly precipitation and temperature projected for 2055, based on outputs from a set of AOGCMs. Using the AOGCM data prepared by international climate change study groups and local climate observation data, one can apply the MEM to precipitation or temperature for a particular region to generate an annual cycle, which includes the effects from both global climate change and local intra-year climate variability.

SPotGS:methods | /neutral/methods | 052