WCRP 111 : Proceedings of the third PMIP Workshop



Sandy P. Harrison

Max Planck Institute for Biogeochemistry,
P.O. Box 100164, D-07701 Jena, Germany



Physically-based models provide the unique means to predict the likely impacts of anthropogenic changes in atmospheric composition and land use (Intergovernmental Panel on Climate Change, 1997). The accurate simulation of current climate is an important benchmark but does not guarantee that a model will simulate climate changes correctly (Joussaume et al., 1999). It difficult to evaluate model performance solely on the basis of the instrumental record because the changes in climate since the middle of the last century have been relatively modest (e.g. Mann et al., 1995; Tett et al., 1999). Evaluating model performance under the extreme climate conditions of the last glacial maximum (LGM: ca 21,000 calendar yr B.P., equivalent to ca 18,000 14C yr B.P.) and the mid-Holocene (ca 6000 yr B.P.) provides an opportunity to evaluate how models respond to larger changes in forcing, and ultimately provides a key credibility test for modelling the future (Joussaume and Taylor, this volume). However, such an evaluation is crucially dependent on the existence of spatially-explicit data sets which can be compared with output from the model simulations. Thus, one goal of the Palaeoclimate Modelling Intercomparison Project (PMIP) has been to foster the creation of well-documented, spatially-explicit data sets explicitly designed for use in model evaluation. Although the construction of palaeoenvironmental data sets for model evaluation began prior to PMIP (see e.g. Street and Grove, 1976; Peterson et al., 1979; COHMAP Members, 1988; Wright et al., 1993), PMIP has played a key role in stimulating the continued development and improvement of such data sets and has been instrumental in the creation of two new data sets: the BIOME 6000 data set (Prentice and Webb, 1998; Prentice et al., in press) and the 21ka Tropical Terrestrial Data Synthesis (Farrera et al., 1999).


Two basic approaches to comparing observations and simulations have been used within PMIP: inverse techniques and forward-modelling techniques. Inverse and forward-modelling techniques provide complementary approaches to data-model comparisons. Inverse methods are particularly useful when the geological data are abundant. The forward modelling approach maximizes the use of relatively sparse data sets for model evaluation.

In the inverse technique, palaeodata are translated into climatic parameters via statistical techniques (e.g. transfer functions, response surfaces or modern analog analyses: see Guiot, 1991 for a review of these techniques) which rely on establishing empirical relationships between modern observations and environments. Initially, inverse techniques were used to reconstruct standard climate parameters (e.g. mean July temperature, mean January temperature, mean annual precipitation). This approach yields robust reconstructions, with acceptably small error bars, in circumstances where the sensor is directly controlled by aspects of the climate that are well correlated with the standard climate parameters. This is not always the case. For example, one of the most important controls on vegetation growth is the accumulated temperature sum during the growing season (growing degree-days, GDD: Prentice et al., 1992). GDD is well correlated with mean July temperature when July temperatures are <18°C but not in warmer climates, and nor is GDD exactly predictable as an empirical combination of July and January temperatures (Kohfeld and Harrison, 2000). Furthermore, when GDD is used to predict vegetation limits it is being used as a proxy for net primary production (NPP) which can be influenced by low atmospheric CO2 concentrations (Jolly and Haxeltine, 1997; Street-Perrott et al., 1997; Cowling, 1999; Cowling and Sykes, 1999). In order to avoid such oversimplifications of the climate-vegetation relationship, PMIP has strongly encouraged reconstructions of non- standard climatic variables that are more closely related to the underlying controls on specific palaeoenvironmental indicators. Evaluation data sets derived from inverse reconstructions of climatic or bioclimatic parameters for the PMIP 6000 yr B.P. and LGM timeslices have been constructed for a number of continental-scale regions from which there is abundant fossil data, including Europe (Guiot et al., 1993; Cheddadi et al., 1997; Peyron et al., 1998), eastern North America (Webb et al., 1993; Williams et al., this volume), Russia (Tarasov et al., 1999; Tarasov et al, in press), China (Yu and Qin, 1997) and the tropics (Farrera et al., 1999). Guiot et al. (submitted; see also Guiot et al., this volume) have used explicit inversion of a forward model to reconstruct palaeotemperatures from LGM pollen data taking into account the physiological effects of low atmospheric CO2 concentrations at the LGM.

In the forward-modelling approach, process-based models are used to predict the response of palaeoenvironmental indicators (e.g. vegetation, hydrology) to the simulated climate. The predicted response is then compared directly with palaeo-observations. Although additional uncertainties about the cause of mismatches between simulations and observations can be caused by the use of a second model, forward modelling has been used within the PMIP project to facilitate comparisons with terrestrial vegetation data and with lake data. Thus, we have used terrestrial biosphere models from the BIOME model family (e.g. BIOME1: Prentice et al., 1992; BIOME3: Haxeltine and Prentice, 1996; BIOME4: Kaplan et al., in prep.) to simulate the distribution of major vegetation types (biomes) (see e.g. Harrison et al., 1995; Kutzbach et al., 1996; TEMPO, 1996; Harrison et al., 1998; Kutzbach et al., 1998; Harrison et al., in prep.) for direct comparison with biomes reconstructed from palaeovegetation data from the BIOME 6000 data set (see below). Terrestrial hydrological models, which predict the surface area of lakes and wetlands, and river discharge, using land-surface morphology and simulated runoff, precipitation, and evaporation (e.g. HYDRA: Coe, 1998; Coe, in press) have been used in a similar manner and directly compared with palaeolake area and river discharge data (Coe and Harrison, this volume) derived from the Global Lake Status Data Base (see below).


There are three types of data that have been widely used for model evaluation within PMIP: the Global Lake Status Data Base, the BIOME 6000 datasets, and the 21ka Tropical Terrestrial Data Synthesis.

3.1 The Global Lake Status Data Base

Lakes respond in a simple and well-understood fashion (see Cheddadi et al., 1997) to changes in the balance of precipitation and evaporation (P-E) over the lake and catchment by changing in volume (and hence normally monotonically in area or level). In overflowing lakes in humid regions, runoff from the catchment primarily determines the equilibrium lake level and hence the magnitude of lake discharge. Sustained changes in lake discharge are only produced when lake volume is also changed. In closed lakes from semi-arid regions, the area of the lake simply represents an equilibrium between runoff from the catchment and the water deficit over the lake. The Global Lake Status Data Base (GLSDB: Qin et al., 1998; Kohfeld and Harrison, 2000) is a long-standing international effort to compile the geomorphic and biostratigraphic data (see Harrison and Digerfeldt, 1993 for discussion of types of evidence) for changes in lake level, area, or volume (collectively referred to as lake status), in order to document changes in regional water balance during the last 30,000 years. Developed with data-model comparisons as a primary objective, the GLSDB builds on the earlier Oxford Lake Level Data Base (Street and Grove, 1976; Street-Perrott and Harrison, 1985; Street-Perrott et al., 1989), and contains data both from closed-basin lakes in now-arid regions and from currently overflowing lakes in temperate and wet tropical regions (Yu and Harrison, 1996; Tarasov et al., 1994, 1996; Jolly et al., 1998a; Qin et al., 1998; Kohfeld and Harrison, 2000; see also Yu et al., this volume).

Lake status data from the GLSDB for 6000 yr B.P. (Figure 1a) show that conditions were wetter than today across northern Africa, the Arabian Peninsula, northern India, and southwest China, indicating expansion of the Afro-Asian summer monsoons. Conditions were slightly wetter or comparable to today in central America, SW USA, central Eurasia, in the mid-latitudes of the southern hemisphere, and in the high northern latitudes. Exceptions to this pattern, where lakes show conditions were drier than today, are seen in interior North America and western Europe.

Lake status data from the GLSDB for the LGM (Figure 1b) show conditions as dry or drier than today over much of the world as a result of the generally weaker than present hydrological cycle. However, lakes register wetter conditions in western North America and the circum- Mediterranean region, and in the Southern Hemisphere mid-latitudes.

Figure 1(Click on the figure to get a bigger version)
Figure 1 - Changes in lake status at (a) 6000 14C yr B.P. and (b) at 18,000 14C yr B.P. (~21,000 calendar yr B.P.) compared to present. Only sites with dating control of <= 4 for 6000 14C yr B. P., and < 7 for 18,000 14C yr B.P. are included.

3.2. BIOME 6000

The Palaeovegetation Mapping Project (known as BIOME 6000: Prentice and Webb, 1998) was established in 1994 with the aim of developing global palaeovegetation data sets for the LGM (here defined as 18,000 ± 2000 14C yr B.P.) and the mid-Holocene (6000 ± 500 14C yr B.P.) which could be explicitly used to evaluate palaeoclimate model simulations. Broadscale vegetation types (biomes) are reconstructed from pollen or plant-macrofossil data using a standardized, objective method (biomization) based on plant functional types (PFTs: Steffen et al., 1992; Prentice et al., 1996). Plant taxa are first assigned to PFTs, and then the set of PFTs that can occur in each biome is specified. The allocation of pollen or plant-macrofossil assemblages to biomes is made on the basis of an affinity-score procedure which takes into account both the diversity and the abundance of taxa belonging to each PFT in the sample. Extensive tests using modern surface samples have shown that the method is capable of reproducing natural vegetation patterns even in regions heavily impacted by human activities (Figure 2a).

The BIOME 6000 data set for 6000 yr B.P. (Figure 2b) shows that the Arctic forest limit was north of its present position in the Mackenzie Delta region (Edwards et al., in press), Europe (Prentice et al., 1996) and western and central Siberia (Texier et al., 1997; Tarasov et al., 1998), unchanged in Beringia (Edwards et al., in press), northern Canada and Keewatin (Williams et al., in press) and south of its present position in Quebec-Labrador (Williams et al., in press). The northward expansion of northern temperate forest zones was more dramatic than the relatively modest change in the Arctic forest limit. Warmer winters (as well as summers) are required to explain some of these shifts in northern temperate forests (Prentice et al., in press). Temperate deciduous forests were greatly extended in Europe, southwards into the Mediterranean region as well as to the north (Prentice et al., 1996). Steppe vegetation occurred in areas occupied today by forests in North America in response to drier conditions (Williams et al., in press), but forest biomes encroached on the present-day steppe in southeastern Europe and Central Asia (Tarasov et al., 1998). Enhanced monsoons extended forest biomes inland in China (Yu et al., 1998; Yu et al., in press; see also Yu et al., this volume) and Sahelian vegetation into the Sahara, while the African rainforest was reduced (Jolly et al., 1998a, b) consistent with a more seasonal climate in the equatorial zone.

The BIOME 6000 data set for the LGM (Figure 2c) shows that cold and dry conditions at the LGM favoured extensive tundra and steppe vegetation (Prentice et al., in press). Northern hemisphere boreal and temperate forest biomes were displaced southward and fragmented. Tropical moist forests in Africa were also reduced in extent (Elenga et al., in press). Open conifer woodlands were more extensive than today in southwestern North America, however, indicating conditions wetter than today in this region (Thompson and Anderson, in press) in contrast to the general trend towards aridity.

Figure 2(Click on the figure to get a bigger version)
Figure 2 - Biome reconstructions for (a) modern, (b) 6000 14C yr B.P. and (c) 18,000 14C yr B.P. (from Prentice et al., in press).

3.3. 21 ka Tropical Terrestrial Data Synthesis

The 21 ka Tropical Terrestrial Data Synthesis (Farrera et al., 1999) contains quantitative reconstructions of mean temperature of the coldest month (MTCO) and mean annual ground temperature (MAT), and qualitative reconstructions of plant-available moisture (PAM) and runoff (P-E) from radiocarbon-dated terrestrial sites between 32°N and 33°S with records for 18,000±2000 14C yr B.P. The dataset combines multiple indicators of quantitative changes in land surface temperature (pollen and plant macrofossil records of MTCO, and noble gas and speleothem records of MAT) and qualitative indicators of moisture balance parameters (pollen and plant macrofossil records of PAM and lake status records of P-E). The use of multiple indicators allows the consistency of the temperature and moisture balance reconstructions to be evaluated.

PAM and P-E are complementary measures of the water balance: runoff is the water that is not used by plants (Prentice et al., 1993). However, in regions where there are both kinds of data, the observed changes at the LGM within the 32°N and 33°S band are in the same direction (Figure 3). Both indicators show conditions drier than today at the LGM across most of the tropics and subtropics, except in the Great Basin, E. Africa and at high elevations in South America and Papua New Guinea. Lakes in northern Africa, supplied by runoff from the Atlas Mountains (Street-Perrott et al., 1989), also show wetter conditions.

Figure 3(Click on the figure to get a bigger version)
Figure 3 - Reconstructions of PAM (circles) and P-E (squares) at the LGM, from Farrera et al. (1999).

The LGM changes in MAT and MTCO are broadly similar at low latitudes because the low seasonality of insolation in the tropics offers little scope for seasonal changes in temperature, but the two variables differ systematically in the northern subtropics as seasonal differences become greater. Both variables indicate that temperatures in the tropics were on average 2.5-3°C cooler than today at modern sea level (Figure 4). However, the magnitude of the cooling is not spatially uniform even in the lowlands: the Neotropical region (Central and northern South America) cooled by 5-6°C; the cooling at circum-Indian Ocean sites (South and East Africa, India and Indonesia) was ca 2-3°C; the cooling was less than 2°C in the circum-Pacific region (Papua New Guinea, and the islands of the western Pacific Ocean). Spatial differences are also apparent in the lapse rates at tropical sites. Lapse rates were steeper by ca 2?/km in the circum-Pacific region, by ca 1?/km in the Indian Ocean region, while there was no discernable change in lapse rate in the Neotropical region.

Figure 4(Click on the figure to get a bigger version)
Figure 4 - Comparison of DELTA(MTCO) reduced to sea level (circles) based on lowland terrestrial from the Farrera et al. (1999) data set and alkenone-based estimates of DELTA(SST) (diamonds) from the TEMPUS compilation.


These three global data sets, and the various regional climate reconstructions described above, have been widely used to evaluate the PMIP baseline and complementary experiments (see e.g. Yu and Harrison, 1996; Texier et al., 1997; Broström et al., 1998; Vettoretti and Peltier, 1998; Joussaume et al., 1999; Masson et al., 1999; Pinot et al., 1999; Guiot et al., in press; Bonfils et al., this volume; Braconnot et al., this volume; Coe and Harrison, this volume; Guiot et al., this volume; Kageyama et al., this volume; Williams et al., this volume; Yu et al., this volume). The aim of this section is not to document all that we have learnt from such evaluations but rather to provide an example of how the data can be used as a benchmark for model evaluation. Specifically, we will show how data from northern Africa has been used as a benchmark for the evaluation of PMIP baseline and complementary simulations of the mid- Holocene monsoon system, and then how the new data from the tropics has been used to evaluate PMIP LGM simulations.

4.1 Evaluation of the PMIP 6000 yr B.P. experiment

The expansion of the area influenced by the Afro-Asian summer monsoons at 6000 yr B.P. is one of the most striking features shown by palaeoenvironmental data. The mechanisms underlying the enhancement of the African monsoon have been known for some time (Kutzbach and Street-Perrott, 1985; COHMAP Members, 1988). The PMIP simulations (Joussaume et al., 1999) confirm that the orbitally-induced enhancement of northern-hemisphere summer insolation at 6000 yr B.P. resulted in increased heating over the northern hemisphere continents and thus intensified the thermal contrast between the land and the ocean. The increased heating over northern Africa resulted in the northward displacement of the ITCZ and hence of the monsoon front over northern Africa, while the enhanced land-sea contrast increased the flux of moisture from the ocean to the continent. However, comparisons of the spatial patterns in the simulated P- E fields with lake data from the GLSDB (Yu and Harrison, 1996) indicate that the PMIP simulations consistently underestimate the northward shift in the monsoon front. Similarly, BIOME3 simulations made with output from the PMIP simulations consistently fail to reproduce the observed northward shift in the Sahara/Sahel boundary (Harrison et al., 1998). The precipitation required to generate the observed latitudinal distribution of steppe (grassland) vegetation in northern Africa at 6000 yr B.P. has been estimated using a combination of forward-modelling and inverse techniques. Joussaume et al (1999) showed that the PMIP simulations underestimate the required precipitation at ca 23°N by at least 100mm (Figure 5a), i.e. by ca 50% of the minimum amount required to support steppe. When output from the PMIP experiments is used to simulate the extent of lakes across northern Africa using the HYDRA model, the observed area of Lake Chad (350,000 km2) is significantly underestimated by all of the models (Coe and Harrison, this volume). Thus, data-model comparisons show that the PMIP simulations consistently underestimate both (1) the northward shift in the monsoon belt shown by palaeoenvironmental data, and (2) the magnitude of the precipitation required to produce the observed lake and vegetation changes in northern Africa.

Figure 5(Click on the figure to get a bigger version)
Figure 5. Zonally-averaged simulated annual precipitation anomalies (6000 cal. yr B.P. - control) vs latitude for northern Africa (land grid cells, 20°W-30°E). Precipitation anomalies include the effects of: (a) radiative forcing (R) alone for the 18 climate models participating in PMIP (Joussaume et al., 1999); (b) radiative forcing plus land-surface feedbacks (soil: S, vegetation: V, lakes: L, and wetlands: W) simulated using CCM3 (Broström et al., 1998); (c) radiative forcing plus ocean feedbacks (DELTA(SST)) for an asynchronous coupling of GENESIS2 and MOM1 (Kutzbach and Liu, 1997). (d) radiative forcing plus ocean- and vegetation-feedbacks from an asynchronous coupling of the IPSL AOGCM and BIOME1 (Braconnot et al., 1999). The hatched lines in (a-d) represent upper and lower estimates of the additional precipitation (excess over modern) required to support the steppe vegetation observed in northern Africa at 6000 14C yr (see Joussaume et al., 1999). (e) Latitudinal distribution of biome types (desert, steppe, xerophytic woods/scrub, and savannah) for 6000 14C yr and modern over the longitudes 20W-30E (Jolly et al., 1998b; Joussaume et al., 1999).

4.2. Evaluation of Complementary 6000 yr B.P. PMIP Experiments

In the baseline PMIP simulations land-surface conditions and ocean surface temperatures were prescribed to be the same as today (Joussaume and Taylor, this volume). PMIP has also investigated the importance of feedbacks associated with changes in land-surface or ocean- surface conditions e.g. for the simulation of the African monsoon, using palaeodata as a benchmark. A sensitivity study which sequentially examined the response to realistic changes in (a) vegetation and soils, (b) the extent of lakes and (c) the extent of wetlands across northern Africa showed that land-surface feedbacks amplify the orbitally-induced monsoon by increasing precipitation during the peak of the monsoon season and extending the monsoon season by 2-3 months, resulting in a shift of the monsoon front ca 300 km further north than by insolation changes alone (Broström et al., 1998). Nevertheless, comparisons with the benchmark data (Figure 5b) show that the feedbacks were insufficient to produce the full observed northward expansion of grasslands into regions occupied today by desert. Similar evaluations (not shown) of experiments in which the land-surface feedbacks are simulated either through asynchronous- coupling of an equilibrium vegetation model with an AGCM (e.g. Texier et al., 1997; Claussen and Gaylor, 1997; de Noblet et al., in press) or using dynamically coupled atmosphere-vegetation models (e.g. Doherty et al., in press) confirm that land-surface feedbacks are insufficient to explain the full observed expansion of the African monsoon.

Sensitivity experiments is which sea-surface temperatures in the tropical and subtropical Atlantic were prescribed to be 1-3°C colder than today show ocean feedbacks increase monsoon rainfall in the Sahara/Sahel by enhancing evaporation from the ocean surface and increasing moisture flux to the continent (Texier et al., 2000). In asynchronously coupled atmosphere-ocean experiments (Kutzbach and Liu, 1997), maximum monsoon precipitation shifts northward to 15- 20°N and precipitation over northern Africa increases by 25% compared to simulations made with prescribed modern SSTs. However, the precipitation increase induced by the combined effect of orbital forcing and ocean feedbacks is still not enough to support the observed grassland vegetation north of 23°N (Figure 5c). Simulations using fully-coupled atmosphere-ocean models (e.g. Hewitt and Mitchell, 1998; Otto-Bleisner, 1999; Braconnot et al., in press; Braconnot et al., this volume) confirm that ocean-surface feedbacks produce a significant enhancement of the orbitally-induced changes in the African monsoon but again the changes are apparently insufficient to explain its observed expansion.

PMIP participating groups have begun to explore whether synergistic feedbacks involving land-atmosphere-ocean interactions are involved the observed expansion of the African monsoon during the mid-Holocene using asynchronously or fully-coupled ocean-atmosphere-vegetation models (Ganopolski et al., 1998; Braconnot et al., 1999) and benchmark palaeoclimate data sets (Figure 5d).

4.3 Evaluation of the PMIP 21,000 yr B.P. Experiments

Palaeoenvironmental data show that conditions were colder and, except in regions influenced by the southward displacement of the Westerlies by the Laurentide Ice Sheet, considerably drier than today (COHMAP Members, 1988; Kohfeld and Harrison, 2000). In the tropics, land-based estimates indicate temperatures were on average 2.5-3°C cooler than today at modern sea level, although there were large meridional differences in the magnitude of the cooling ranging from 1- 2°C in the circum-Pacific to 5-6°C in the Neotropics (Farrera et al., 1999). These land-based estimates of cooling at sea level are consistent with reconstructions of the cooling based on marine alkenone (Rostek et al., 1993; Schneider et al., 1995; Bard et al., 1997; Rosell-Melé et al., 1998; Sonzogni et al., 1998) data (Figure 4). The PMIP simulations with prescribed CLIMAP SSTs show only a weak cooling in the tropics (Figure 6) and an increase in continental aridity that is less than shown by the observations. Analyses of the tropical response of the PMIP models (Pinot et al., 1999) thus confirm Rind and Peteet's (1985) conclusions that AGCMs with prescribed CLIMAP SSTs underestimate the observed magnitude of tropical cooling at the LGM inferred from palaeodata. The simulations with computed SSTs show a wide range in the magnitude of tropical cooling, with some models underestimating and others overestimating the mean cooling (Pinot et al., 1999). However, at least some of the simulations made with computed SSTs produce a tropical cooling that is consistent with the temperature changes shown by terrestrial (Figure 4; Farrera et al., 1999) and marine alkenone (Figure 4; Rostek et al., 1993; Schneider et al., 1995; Bard et al., 1997; Rosell-Melé et al., 1998; Sonzogni et al., 1998) data. Most of the models produce a rather uniform zonal cooling, which is inconsistent with the distinct differences in the magnitude of the cooling shown by data from e.g. the Neotropics and the western Pacific (Figure 6). However, the UKMO model produces a meridional patterning in tropical temperature changes consistent with the terrestrial and the alkenone data (Figure 6).

Figure 6(Click on the figure to get a bigger version)
Figure 6. Comparison of the simulated tropical (30°N-30°S) cooling over land as a function of the intensity of the tropical cooling over the ocean as simulated by the PMIP models with prescribed (green circles) and computed (blue squares) SSTs (from Pinot et al., 1999), and reconstructions of the land cooling based on terrestrial data (from Farrera et al., 1999) and ocean cooling based on alkenone data (from the TEMPUS data set).

4.4. Evaluation of Complementary LGM PMIP Experiments

Land-surface conditions in the PMIP LGM simulations were prescribed to be the same as in the modern control simulation. Thus these experiments (like those for 6000 yr B.P.) neglect the potential role of land-surface feedbacks on glacial climates. Levis et al. (in press) have shown that vegetation feedbacks, and specifically a simulated reduction in tropical forests, in a coupled atmosphere-vegetation-mixed layer ocean model LGM simulation cooled the tropics by several degrees. Precipitation in the wet tropics and in eastern Asia was substantially reduced in this simulation, in good agreement with the widespread reduction of PAM in the tropics (Farrera et al., 1999) and the replacement of forests with desert and steppe vegetation in China shown in the BIOME 6000 data set (Yu et al., in press).

Vegetation distribution and structure play an important role in determining dust emissions, and the known changes in vegetation cover at the LGM could have caused changes in atmospheric dust loading that might have had a significant impact on the LGM climate. Ice cores, marine sediments and loess deposits show that glacial periods were 2-20 times dustier than interglacial periods (Figure 7a: Kohfeld and Harrison, 2000; Harrison et al., in press). Mahowald et al. (1999) have examined the relative importance of the direct effects of the LGM climate (increased wind strengths, decreased hydrological cycle) and the indirect effects of the changed climate through increasing dust source areas on the production and distribution of dust using climate output from one of the PMIP simulations (ECHAM 3.2, driven by CLIMAP SSTs: Lorenz et al., 1996). They estimated (1) potential dust source areas using a terrestrial biosphere model (BIOME3: Haxeltine and Prentice, 1996); (2) entrainment using a simple source function related to soil texture and wind velocity (Marticorena and Bergametti, 1996; Schulz et al., 1998); and (3) transport using an atmospheric transport model (TM3: Heimann, 1995). Mahowald et al. (1999) showed that, although changes in wind strength and precipitation could explain the increased dust deposition registered in marine cores off western Africa (7b), the expansion of source area in high-northern latitudes, Asia and Patagonia are required to account for the 2-5 fold increase in the global atmospheric dust content and 20-fold increase at the poles at the LGM. (Figure 7c). An offline assessment of the radiative impact of the relatively realistic dust fields simulated by Mahowald et al. (1999) show that the simulated dust loading caused a reduction in forcing (-0.4 to -1.1 W m-2) in the tropics (Harrison et al., in press; Claquin et al., submitted). This forcing represents an effect of comparable magnitude to the tropical cooling effect of low atmospheric CO2 concentrations (Hewitt and Mitchell, 1997).

Figure 7(Click on the figure to get a bigger version)
Figure 7 - Dust deposition simulated (a) under modern climate conditions, (b) in response to LGM climate changes, and (c) in response to LGM climate and expanded source areas (from Mahowald et al., 1999) compared with (d) modern dust deposition and (e) LGM dust deposition, based on geological records from the DIRTMAP data base (Kohfeld and Harrison, 2000; Harrison et al., in press).


The PMIP community, through systematic comparisons of model simulations against benchmarks provided by regional or global syntheses of palaeoenvironmental data, has demonstrated that the observed large changes in mid-Holocene and LGM climates cannot be simulated without explicitly considering ocean- and land-surface feedbacks. There may be a number of other important potential feedbacks (e.g. radiative forcing by mineral aerosols). COHMAP results (Kutzbach et al., 1998) indicating that the apparent mismatches between observed and simulated climate changes during the transition from glacial to interglacial conditions are greater than at either the LGM or the mid-Holocene suggest that the incorporation of these feedbacks may be even more important in attempts to simulate times of rapid climate change when there is a strong disequilibrium between insolation and other conditions. The fully- coupled ocean-atmosphere-vegetation models (OAVGCMs) that will be required to explore these issues will need to be rigorously benchmarked against palaeoenvironmental data. The PMIP project therefore remains committed to improving existing data sets, developing better analytical tools and encouraging additional syntheses of palaeoenvironmental data in order to be able to evaluate the models that will subsequently be used to simulate potential future climate changes.


The GLSDB is sponsored by IGBP through its programme element Past Global Changes (PAGES) Palaeo-Mapping Project (PMAP). BIOME 6000 is sponsored by IGBP through its programme elements Global Analysis, Intercomparison and Modelling (GAIM), the Data and Information System (DIS), Global Change and Terrestrial Ecosystems (GCTE) and PAGES. The members of the BIOME 6000 project are: Afanas'eva, N.B., Ager, T., Anderson, K., Anderson, P.M., Andrieu, V., Andreev, A.A., Ballouche, A., Bartlein, P.J., de Beaulieu, J.L., Bengo, M., Berezina, N.A., Bezusko, L.G., Bezusko, T.V., Bigelow, N.H., Blyakharchuk, T.A., Bolikhovskaya, N.S., Bonnefille, R., Bottema, S., Brénac, P., Brubaker, L.B., Buchet, G., Burney, D., Bykova, G.V., Cheddadi, R., Chen, X., Chernavskaya, M.M., Chernova, G.M., Cwynar, L.C., Dorofeyuk, N.I., Dirksen, V.G., Edorh, T., Edwards, M.E., Eisner, W.J., Elenga, H., Elina, G.A., Elmoutaki, S., Filimonova, L.V., Glebov, F.Z., Guiot, J., Gunova, V.S., Hamilton, A.C., Han, H., Harrison, S.P., Hu, F.-S., Huang, C., Huntley, B., Jolly, D., Jonson, H., Ke, M., Khomutova, V.I., Kong Z., Kvavadze, E.V., Laarif, F., Lamb, H.E., Lézine, A-M., Li, S., Li, W., Liew, P., Liu, G., Liu, J., Liu, Q., Liu, K.-B., Lozhkin, A.V., Maley, J., Marchant, R., Mbenza, M., MacDonald, G.M., Miyoshi, N., Mock, C.J., Morita, Y., Newby, P., Ni, J., Osipova, I.R., Panova, N.K., Perez- Obiol, R., Peyron, O., Prentice, I.C., Qiu, W., Reille, M., Ren, G., Reynaud-Farrera, I., Richard, P.J.H., Riollet, G., Ritchie, J.C., Roche, E., Saarse, L., Scott, L., Sevastyanov, D.V., Sher, A.V., Song, C., Spear, R.W., Ssemmanda, I., Straka, H., Sugita, S., Sun, X., Takahara, H., Tang, L., Tarasov, P.E., Taylor, D., Thompson, R.S., Uchiyama, T., Van Campo, E., Vilimumbalo, S., Vincens, A., Volkova, V.S., Waller, M., Webb III, T., Williams, J.W., Xia, Y., Xu, Q., Yan, S., Yang, X., Yu, G., Zernitskaya, V.P., Zhao, J. and Zheng, Z. The 21 ka Tropical Terrestrial Data Synthesis was initiated in response to a request by PMIP and compiled by P.J. Bartlein, R. Bonnefille, M. Bush, I. Farrera, J. Guiot, S.P. Harrison, K. Holmgren, H. Hooghiemstra, G. Hope, D. Jolly, S.-E. Lauritzen, Y. Ono, S. Pinot, I.C. Prentice, G. Ramstein, M. Stute, U. von Grafenstein and G. Yu.


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