Complex Earth System Models Research Articles

Dynamics of the terrestrial biosphere, climate and atmospheric CO<sub>2</sub> concentration during interglacials: a comparison between Eemian and Holocene

Abstract. A complex earth system model (atmosphere and ocean general circulation models, ocean biogeochemistry and terrestrial biosphere) was used to perform transient simulations of two interglacial sections (Eemian, 128–113 ky B.P., and Holocene, 9 ky B.P.–present). The changes in terrestrial carbon storage during these interglacials were studied with respect to changes in the earth's orbit. The effects of different climate factors on changes in carbon storage were studied in offline experiments in which the vegetation model was forced only with temperature, hydrological parameters, radiation, or CO2 concentration from the transient runs. The largest anomalies in terrestrial carbon storage were caused by temperature changes. However, the increase in storage due to forest expansion and increased photosynthesis in the high latitudes was nearly balanced by the decrease due to increased respiration. Large positive effects on carbon storage were caused by an enhanced monsoon circulation in the subtropics between 128 and 121 ky B.P. and between 9 and 6 ky B.P., and by increases in incoming radiation during summer for 45° to 70° N compared to a control simulation with present-day insolation. Compared to this control simulation, the net effect of these changes was a positive carbon storage anomaly in the terrestrial biosphere of about 200 Pg C for 125 ky B.P. and 7 ky B.P., and a negative anomaly around 150 Pg C for 116 ky B.P. Although the net increases for Eemian and Holocene were rather similar, the magnitudes of the processes causing these effects were different. The decrease in terrestrial carbon storage during the experiments was the main driver of an increase in atmospheric CO2 concentration during both the Eemian and the Holocene.

Centennial‐scale interactions between the carbon cycle and anthropogenic climate change using a dynamic Earth system model

A complex Earth system model including atmosphere, ocean, ice sheets, marine carbon cycle and terrestrial vegetation was used to study the long‐term response (100–1000 yrs) of the climate to different increased atmospheric CO2 concentrations. A 3.2 K global mean surface temperature increase is simulated for a 3xCO2 experiment. The freshwater input by melting of the Greenland Ice Sheet due to global warming is of minor importance compared to hydrological changes in the atmosphere. Increased equatorial upwelling enhances the tropical outgassing of CO2 from the oceans, lowering the total marine carbon uptake by 16–22%. On land, carbon release due to increase in soil temperature reduces the anthropogenic carbon uptake from CO2 fertilization up to 43%. Thus, we show that both marine and terrestrial carbon cycle have a positive feedback on climate, which has to be considered for future carbon emission scenarios.

PRISM and ENES: a European approach to Earth system modelling

Europe's widely distributed climate modelling expertise, now organized in the European Network for Earth System Modelling (ENES), is both a strength and a challenge. Recognizing this, the European Union's Program for Integrated Earth System Modelling (PRISM) infrastructure project aims at designing a flexible and friendly user environment to assemble, run and post-process Earth System models. PRISM was started in December 2001 with a duration of three years. This paper presents the major stages of PRISM, including: 1 [(1)] the definition and promotion of scientific and technical standards to increase component modularity; 2 [(2)] the development of an end-to-end software environment (graphical user interface, coupling and I/O system, diagnostics, visualization) to launch, monitor and analyse complex Earth system models built around state-of-art community component models (atmosphere, ocean, atmospheric chemistry, ocean bio-chemistry, sea-ice, land-surface); and 3 [(3)] testing and quality standards to ensure high-performance computing performance on a variety of platforms. PRISM is emerging as a core strategic software infrastructure for building the European research area in Earth system sciences. Copyright © 2005 John Wiley & Sons, Ltd.

Advances in large‐scale crop modeling

Intensified human activity and a growing population have changed the climate and the land biosphere. One of the most widely recognized human perturbations is the emission of carbon dioxide (C02) by fossil fuel burning and land‐use change. As the terrestrial biosphere is an active player in the global carbon cycle, changes in land use feed back to the climate of the Earth through regulation of the content of atmospheric CO2, the most important greenhouse gas,and changing albedo (e.g., energy partitioning).Recently, the climate modeling community has started to develop more complex Earthsystem models that include marine and terrestrial biogeochemical processes in addition to the representation of atmospheric and oceanic circulation. However, most terrestrial biosphere models simulate only natural, or so‐called potential, vegetation and do not account for managed ecosystems such as croplands and pastures, which make up nearly one‐third of the Earth's land surface.

An efficient climate forecasting method using an intermediate complexity Earth System Model and the ensemble Kalman filter

We present the implementation and results of a model tuning and ensemble forecasting experiment using an ensemble Kalman filter for the simultaneous estimation of 12 parameters in a low resolution coupled atmosphere-ocean Earth System Model by tuning it to realistic data sets consisting of Levitus ocean temperature/salinity climatology, and NCEP/NCAR atmospheric temperature/humidity reanalysis data. The resulting ensemble of tuned model states is validated by comparing various diagnostics, such as mass and heat transports, to observational estimates and other model results. We show that this ensemble has a very reasonable climatology, with the 3-D ocean in particular having comparable realism to much more expensive coupled numerical models, at least in respect of these averaged indicators. A simple global warming experiment is performed to investigate the response and predictability of the climate to a change in radiative forcing, due to 100 years of 1% per annum atmospheric CO2 increase. The equilibrium surface air temperature rise for this CO2 increase is 4.2±0.1°C, which is approached on a time scale of 1,000 years. The simple atmosphere in this version of the model is missing several factors which, if included, would substantially increase the uncertainty of this estimate. However, even within this ensemble, there is substantial regional variability due to the possibility of collapse of the North Atlantic thermohaline circulation (THC), which switches off in more than one third of the ensemble members. For these cases, the regional temperature is not only 3–5°C colder than in the warmed worlds where the THC remains switched on, but is also 1–2°C colder than the current climate. Our results, which illustrate how objective probabilistic projections of future climate change can be efficiently generated, indicate a substantial uncertainty in the long-term future of the THC, and therefore the regional climate of western Europe. However, this uncertainty is only apparent in long-term integrations, with the initial transient response being similar across the entire ensemble. Application of this ensemble Kalman filtering technique to more complete climate models would improve the objectivity of probabilistic forecasts and hence should lead to significantly increased understanding of the uncertainty of our future climate.

Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter

We describe the development of an efficient method for parameter estimation and ensemble forecasting in climate modelling. The technique is based on the ensemble Kalman filter and is several orders of magnitude more efficient than many others which have been previously used to address this problem. As well as being theoretically (near-)optimal, the method does not suffer from the `curse of dimensionality' and can comfortably handle multivariate parameter estimation. We demonstrate the potential of this method in identical twin testing with an intermediate complexity coupled AOGCM. The model's climatology is successfully tuned via the simultaneous estimation of 12 parameters. Several minor modifications arc described by which the method was adapted to a steady state (temporally averaged) case. The method is relatively simple to implement, and with only O(50) model runs required, we believe that optimal parameter estimation is now accessible even to computationally demanding models.