Abstracts

SEARCH Open Science Meeting

October 27, 2003
Seattle, Washington, USA

Climate and Land-Surface Systems Interaction Centre (CLASSIC)

Brian Huntley1, Mike Barnsley2, Peter Cox3, Richard Harding4, Heiko Balzter5, Robert Baxter6, Sietse Los7, Adrian Luckman8, Peter North9, Chris Taylor10, Chris Thomas11, Barry Wyatt12
1School of Biological and Biomedical Sciences, University of Durham, South Road, Durham, DH1 3LE, UK, Phone +44-1913341200, Fax +44-1913341201, brian.huntley@durham.ac.uk
2Department of Geography, University of Wales Swansea , Singleton Park, Swansea, SA2 8PP, UK, Phone +44-1792295228, Fax +44-1792295955, M.Barnsley@swansea.ac.uk

3Hadley Centre for Climate Prediction and Research, Meterological Office, London Road, Bracknell, RG12 2SY, UK, Phone +44-8453-000300, Fax +44-1344-855681, pmcox@meteo.gov.uk
4Centre for Ecology and Hydrology, Maclean Building, Wallingford, OX10 8BB, UK, Phone +44-1491-838800, Fax +44-1491-692424, rjh@ceh.ac.uk
5Centre for Ecology and Hydrology, Monks Wood, Abbots Ripton, Huntingdon, PE28 2LS, UK, Phone +44-1487-772471, Fax +44-1487-773467, hbal@ceh.ac.uk
6School of Biological and Biomedical Sciences, University of Durham, South Road, Durham, DH1 3LE, UK
7Department of Geography, University of Wales Swansea, Singleton Park, Swansea, SA2 8PP, UK
8Department of Geography, University of Wales Swansea, Singleton Park, Swansea, SA2 8PP, UK
9Department of Geography, University of Wales Swansea, Singleton Park, Swansea, SA2 8PP, UK
10Centre for Ecology and Hydrology, Monks Wood, Abbots Ripton, Huntingdon, PE28 2L5, UK
11School of Biological and Biomedical Sciences, University of Durham, South Road, Durham, DH1 3LE, UK
12Centre for Ecology and Hydrology, Monks Wood, Abbots Ripton, Huntingdon, PE28 2L5, UK

INTRODUCTION

The IPCC and others [1, 2] identify as a priority the need to reduce uncertainty in assessing actual and potential effects of climate change. If this is to be achieved, our understanding of the feedbacks that exist between the land surface and the atmosphere must be greatly enhanced beyond the current state-of-the-art. In particular, current Land-Surface Parameterizations and Dynamic Vegetation Models must be improved, within both Global and Regional Climate Models, to reproduce fully these interactions. These models should be able to exploit dynamic, spatially comprehensive data on the terrestrial biosphere, such as those provided from Earth Observation (EO). A new NERC Centre of Excellence, CLASSIC, has been established this year (2003) to address the scientific challenges that this raises. CLASSIC consists of a core consortium of four institutions, combining expertise in EO science, satellite-sensor technology, and environmental (hydrological, ecological and climatological) modelling and analysis, namely: (1) the University of Durham; (2) the University of Wales Swansea; (3) the Hadley Centre for Climate Change Prediction and Research; and (4) the NERC Centre for Ecology and Hydrology. The scientific objectives of the Centre will be delivered via a coordinated programme of fundamental research, a scientific exchange scheme and a series of education and training initiatives. CLASSIC will act as a focal point for land-surface observation and modelling within the UK and internationally. It aims to be outward looking and inclusive — exchanging data, methods, software and knowledge through active collaboration with other institutions in the UK and overseas — and to build upon existing international links, participating in activities such as EOS, ISLSCP, GOFC, GCP, GEWEX, IGBP, PILPS, CEOS and collaborative land-surface calibration and validation activities.

CONTEXT

Climate-Land Surface Feedbacks

Feedbacks between the land surface and the atmosphere are key determinants of climate at a range of spatial (local–global) and temporal (seasonal–centennial) scales. Since the pioneering work of Charney et al., who demonstrated the potential rôle of vegetation removal in maintaining drought in sub-Saharan Africa [3], numerous studies have shown a sensitivity of climate to both natural and human-induced changes in land-surface properties [4–10]. Similarly, many of the properties involved — e.g., vegetation type and cover, soil moisture, and snow cover — evolve continuously in response to atmospheric/climatic forcing, while the initial forcing may be amplified or dampened as a consequence of their interaction [11–13]. Cox et al. [14], for example, suggest that die-back of the Amazonian rainforests over the next 50–100 years, caused by ‘greenhouse’ warming, may accelerate global climate change. Similarly, Zeng et al. [15, 16] demonstrate the rôle of vegetation dynamics in enhancing regional climate variability at interannual and inter-decadal time scales, while presenting evidence to suggest that soil moisture stress on vegetation may contribute to the persistence of regional droughts. An enhanced understanding of these feedback mechanisms would greatly improve the ‘skill’ of climate model predictions and, hence, assessment of the actual and potential effects of climate change.

An assessment of variations in land-surface properties and processes that are affected by climate oscillators, such as the El Niño Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), is also of paramount importance. Since these oscillators have a degree of predictability, studying their interactions with land-surface processes and investigating the feedback mechanisms involved would improve our ability to assess their likely impact, perhaps months in advance. Recent studies also suggest that climate oscillators operating on different time-scales interfere with one another, enhancing or negating each other’s effect [17]. Understanding these interferences and interactions will assist in the development of improved land-management strategies to cope with their adverse impacts and, where possible, to make maximum use of their beneficial effects.

Representation of Land Surface/Climate Feedbacks in GCMs

In the 1980s, greatly improved land-surface parameterizations (LSPs) were developed in which the transfer of mass, heat and momentum between the land surface and the atmosphere was linked with variations in biophysical properties in an integrated framework [18, 19]. Thus, for example, changes in leaf area index (LAI) not only alter interception and transpiration, as had previously been the case, but also albedo and surface roughness, altering both the surface energy balance and momentum transfer. LSPs are regulated by a set of inter-dependent biophysical properties, the values of which were initially based on land-cover classifications derived from conventional atlases [20] and existing ecological data sets. Because of their static nature, however, these data do not account for the full spatial and temporal variability of the biosphere, so that important within-class spatial heterogeneity, as well as interannual and longer-term (decadal to centennial) variations, cannot readily be modelled.

One way to obtain dynamic biophysical properties for LSPs is to use Dynamic Vegetation Models (DVMs) [21, 22]. These calculate key biophysical properties as a function of climate, soils, and competition between species. DVMs are an attractive alternative to the use of prescribed biophysical properties because they can interact fully with a GCM and, hence, provide a means to explore potential feedbacks between vegetation and climate. They enable plant growth and competition to be simulated interactively and the related land-surface properties to be updated accordingly [23–25]. As a result, they can simulate the terrestrial (land) carbon budget and the broad distribution of biomes across the globe. Even so, considerable uncertainties remain, notably in terms of plant and soil respiration, and soil water storage [26]. Enhancements are also required to the representation of the surface radiation balance, sub-grid-scale spatial heterogeneity, and seasonal/regional vegetation patterns.

The Rôle of Earth Observation and Considerations of Sub-Grid Scale Effects

The representation of biophysical properties in current LSPs can be further improved through the use of satellite sensors [27–29]. These produce spatially comprehensive (m–km) and temporally explicit (daily–interannual) information on the biosphere — e.g., vegetation type, cover/amount and phenology, with ongoing research into the retrieval of properties such as surface roughness, land surface temperature and soil water content—that can be incorporated into LSPs through model initialization, forcing and validation, or by means of data assimilation [30]. Sellers et al. [25] have, for example, adapted their Simple Biosphere (SiB) model to take advantage of satellite-sensor data by incorporating the photosynthesis formulations of Farqhuar, Berry and Collatz. The revised model, SiB2, uses fAPAR (fraction Absorbed Photosynthetically-Active Radiation) as the key parameter to calculate photosynthesis: fAPAR is also linked with LAI, surface roughness length and albedo. Estimates of fAPAR are obtained from satellite sensors such as NOAA/AVHRR [17, 25, 31].

The use of EO data in this context has been made possible by continuing increases in the computational power available to climate modelling. This has allowed the specification of finer spatial grids, in both GCMs and RCMs, that are more appropriate to an analysis of land-surface processes under changing environmental and climatic conditions. As a result, there is an increasing awareness of the sensitivity of spatially-averaged meteorological parameters to sub-grid-scale land-surface variability and of the need to incorporate such variability within climate models to improve seasonal to interannual forecasting [32]. Indeed, a new generation of LSP has recently been developed that accounts for mixtures of vegetation types within a single grid box and that allows these components to interact with the overlying atmosphere [33]. Although relatively crude at present, these models provide the framework for a more explicit description of sub-grid scale variability and feedback.

Importantly, advances in climate modelling have been matched by developments in both the science and technology of Earth Observation (EO). Specifically, the latest generation of satellite sensors produce data that are better calibrated, are more accurately geo-referenced, have finer spectral and spatial resolution and, hence, are more appropriate to the needs of the climate modelling community. At the same time, improvements in our understanding of, and ability to model, the physics of radiation transport at the Earth surface mean that we are now better able to convert remotely-sensed measurements of surface-leaving radiation into accurate estimates of the key land-surface properties, or to assimilate EO data directly into LSPs. Moreover, the archive of EO data is now sufficiently long to detect and represent interannual cycles and trends in the global biosphere [34].

Despite this, the full potential of EO data has yet to be realized. Most LSPs still obtain a substantial part of their input from land-cover classifications, while satellite data are seldom used in studies with DVMs. Thus, it is only recently that climate-related, interannual variations in vegetation — readily detected in satellite-sensor data — have been investigated using LSPs. Similarly, few LSPs can exploit directly information on episodic and seasonal changes in vegetation contained in EO data. Implementing these features in LSPs/DVMs would greatly increase their realism and would provide an additional means by which to validate the results produced by such models.

SCIENTIFIC OBJECTIVES

The objectives of CLASSIC are (i) to examine how EO data can be used to improve LSPs and DVMs in GCMs/RCMs, (ii) to increase our understanding of land surface/climate feedbacks, and (iii) through enhancements to climate model predictions, to improve our assessment of the actual and potential effects of climate change. More specifically, CLASSIC will address a number of scientific challenges identified as priorities by the IPCC and others [1, 2], including the need to:
1. Improve the representation of sub-grid scale land-surface processes in current LSPs/DVMs, based on EO data, to enhance the ‘skill’ of GCM/RCM predictions of future climate change;
2. Improve the understanding and representation of the feedback mechanisms that enhance or suppress the effects of climatic oscillators on land-surface properties and processes, as detected in EO data;
3. Achieve tighter coupling of EO data with explicit hydrological and ecological sub-models within LSPs and DVMs;
4. Understand the causes of, and hence attempt to resolve, inconsistencies between modelled and observed climates, particularly in terms of the shortcomings of current climate model simulations of observed interannual and sub-decadal patterns of change over land; and
5. Understand and predict the regional consequences of global climate and environmental change, including climate variability.

ACKNOWLEDGMENTS

CLASSIC is funded by the UK NERC through an award made as part of their ‘Centres of Excellence in Earth Observation’ programme (see: http://www.nerc.ac.uk/funding/earthobs/coex/index.shtml).

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