The SPECIAL research group has led or contributed to numerous initiatives that have produced scientific resources. The outputs are openly available, for use with proper citation.
The datasets maintained by the SPECIAL group include:
- PMIP2 diagnostic for mid-Holocene precipitation over northern Africa
- SISAL (Speleothem Isotopes Synthesis and AnaLysis Working Group) database
- EMBSeCBIO modern pollen biomisation
- Modern Pollen Data for Climate Reconstructions, version 1 (SMPDS)
- BIOME 6000 vegetation reconstructions
- The China plant trait database
- A new multi-variable benchmark for Last Glacial Maximum climate simulations
- The climatic space of European pollen taxa
- Fossil pollen data for climate reconstructions from El Cañizar de Villarquemado
Details and links to the datasets are provided below.
PMIP 2 diagnostic for mid-Holocene precipitation over northern Africa
This dataset provides the latitudinal distribution of biomes (reconstructed at individual pollen sites) in northern Africa today and during the mid-Holocene (6000 ±500 yr B.P.) and the maximum and minimum estimates of the increase in precipitation (ΔP) required to support grassland at each latitude. As a model benchmark, the simulated change in ΔP is required to lie above/within the ΔP required to support grassland at all latitudes from 0-23° N.
Link to the dataset:
- Harrison, Sandy and Prentice, Iain Colin (2018): PMIP 2 diagnostic for mid-Holocene precipitation over northern Africa. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.176
- Joussaume, S., Taylor, K. E., Braconnot, P., Mitchell, J. F. B., Kutzbach, J. E., Harrison, S. P., Prentice, I. C., Broccoli, A. J., Abe-Ouchi, A., Bartlein, P. J., Bonfils, C., Dong, B., Guiot, J., Herterich, K., Hewitt, C. D., Jolly, D., Kim, J. W., Kislov, A., Kitoh, A., Loutre, M. F., Masson, V., McAvaney, B., McFarlane, N., de Noblet, N., Peltier, W. R., Peterschmitt, J. Y., Pollard, D., Rind, D., Royer, J. F., Schlesinger, M. E., Syktus, J., Thompson, S., Valdes, P., Vettoretti, G., Webb, R. S., and Wyputta, U. (1999). Monsoon changes for 6000 years ago: Results of 18 simulations from the Paleoclimate Modeling Intercomparison Project (PMIP). Geophysical Research Letters 26(7), 859-862.
- Haxeltine, A., and Prentice, I. C. (1996). BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. Global Biogeochemical Cycles 10(4), 693-709.
- Jolly, D., Harrison, S. P., Damnati, B., and Bonnefille, R. (1998a). Simulated climate and biomes of Africa during the Late Quaternary: Comparison with pollen and lake status data. Quaternary Science Reviews 17(6-7), 629-657.
- Jolly, D., Prentice, I. C., Bonnefille, R., Ballouche, A., Bengo, M., Brenac, P., Buchet, G., Burney, D., Cazet, J. P., Cheddadi, R., Edorh, T., Elenga, H., Elmoutaki, S., Guiot, J., Laarif, F., Lamb, H., Lezine, A. M., Maley, J., Mbenza, M., Peyron, O., Reille, M., Reynaud-Farrera, I., Riollet, G., Ritchie, J. C., Roche, E., Scott, L., Ssemmanda, I., Straka, H., Umer, M., Van Campo, E., Vilimumbalo, S., Vincens, A., and Waller, M. (1998b). Biome reconstruction from pollen and plant macrofossil data for Africa and the Arabian peninsula at 0 and 6000 years. Journal of Biogeography 25(6), 1007-1027.
SISAL (Speleothem Isotopes Synthesis and AnaLysis Working Group) database Version 1b
Stable isotope records from speleothems provide information on past climate changes, most particularly information that can be used to reconstruct past changes in precipitation and atmospheric circulation. SISAL (Speleothem Isotope Synthesis and Analysis) is an international working group of the Past Global Changes (PAGES) project. The working group aims to provide a comprehensive compilation of speleothem isotope records for climate reconstruction and model evaluation. Version 1b of the SISAL database contains oxygen and carbon isotope measurements from 440 individual and 15 composite speleothem records from 221 cave systems worldwide, as well as metadata describing their cave settings and age-depth models. New records have been added and some metadata has been amended. The SISAL working group has also created SISAL chronologies for 20 entities, all of which had no published chronologies. In order to assure traceability, any presentation, report, or publication that uses the SISALv1b database should cite Atsawawaranunt et al. (2018) (The SISAL database: a global resource to document oxygen and carbon isotope records from speleothems; https://doi.org/10.5194/essd-10-1687-2018) and Comas-Bru et al. (2019) (Evaluating model outputs using integrated global speleothem records of climate change since the last glacial; https://doi.org/10.5194/cp-2019-25). If using individual sites or speleothems, the literature citations for published work provided in the database should also be cited. Contact information of data contributors of unpublished data is also provided and these should be contacted when unpublished records are used on an individual basis.
Link to the dataset:
- Atsawawaranunt, Kamolphat, Harrison, Sandy and Comas-Bru, Laia (2019): SISAL (Speleothem Isotopes Synthesis and AnaLysis Working Group) database version 1b. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.189
- Comas-Bru, L., Harrison, S. P., Werner, M., Rehfeld, K., Scroxton, N., Veiga-Pires, C., and SISAL working group members (2019): Evaluating model outputs using integrated global speleothem records of climate change since the last glacial, Clim. Past Discuss., https://doi.org/10.5194/cp-2019-25.
- Atsawawaranunt K, Comas-Bru L, Amirnezhad Mozhdehi S, Deininger M, Harrison SP, Baker A, Boyd M, Kaushal N, Ahmad SM, Ait Brahim Y, Arienzo M, Bajo P, Braun K, Burstyn Y, Chawchai S, Duan W, Hatvani IG, Hu J, Kern Z, Labuhn I, Lachniet M, Lechleiter FA, Lorrey A, Pérez-Mejías C, Pickering R, Scroxton N & SISAL Working Group Members, 2018. The SISAL database: a global resource to document oxygen and carbon isotope records from speleothems. Earth System Science Data, 10, 3, 1687-1713, https://doi.org/10.5194/essd-10-1687-2018.
Superseded version of the dataset:
- Atsawawaranunt, Kamolphat , Harrison, Sandy and Comas-Bru, Laia (2018): SISAL (Speleothem Isotopes Synthesis and AnaLysis Working Group) database Version 1.0. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.147
EMBSeCBIO modern pollen biomisation
The data set contains metadata describing modern pollen samples for the Eastern Mediterranean-Black Sea-Caspian-Corridor region and biome reconstructions made using these data. Observed vegetation at the sites, according to three different data sources, is also given for comparison.
Link to dataset:
- Harrison, Sandy and Marinova, Elena (2017): EMBSeCBIO modern pollen biomisation. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.109
- Marinova, E., Harrison, S.P., Bragg, F., Connor, S., De Laet, V., Leroy, S.A., Mudie, P., Atanassova, J., Bozilova, E., Caner, H. and Cordova, C., 2018. Pollen‐derived biomes in the Eastern Mediterranean–Black Sea–Caspian‐Corridor. Journal of Biogeography, 45(2), pp.484-499.
Modern pollen data for climate reconstructions, version 1 (SMPDS)
The dataset contains percentage counts for the 247 most important European pollen taxa from individual modern samples from Europe and northern Eurasia, and supporting metadata about each sample. The dataset has been specifically designed for use for quantitative climate reconstructions.
Link to dataset:
- Harrison, Sandy (2019): Modern pollen data for climate reconstructions, version 1 (SMPDS). University of Reading. Dataset. http://dx.doi.org/10.17864/1947.194
- Wei, D., González-Sampériz, P., Gil-Romera, G., Harrison, S. P., and Prentice, I. C., (in review, 2019). Climate changes in interior semi-arid Spain from the last interglacial to the late Holocene, Clim. Past Discuss., https://doi.org/10.5194/cp-2019-16.
BIOME 6000 vegetation reconstructions
This dataset contains BIOME 6000 reconstructions of vegetation at 0, 6, and 21ka at individual sites, where the original published nomenclature for individual regions has been converted to a globally-applicable standardized classification (BIOME 6000 Consolidated Name). Two other standardized classifications are also given: common biome names between BIOME 6000 and the BIOME 4.2 model (BIOME 4.2 BIOME 6000 common names) and the megabiome scheme used by Harrison and Bartlein (2012) (MegaBiome Scheme 2). Additional information to translate BIOME 4.2 outputs into either BIOME 6000 Consolidated Names or BIOME 4.2 BIOME 6000 common names is also given.
Link to dataset:
- Harrison, Sandy (2017): BIOME 6000 DB classified plotfile version 1. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.99
- Harrison, S. P. and Bartlein, P. (2012) Records from the past, lessons for the future: what the palaeo-record implies about mechanisms of global change. In: Henderson-Sellers, A. and McGuffie, K. (eds.) The Future of the World’s Climate. 2nd ed. Elsevier, Amsterdam, pp. 403-436. ISBN 9780123869173
- Harrison, S. P. and Prentice, C. I. (2003), Climate and CO2 controls on global vegetation distribution at the last glacial maximum: analysis based on palaeovegetation data, biome modelling and palaeoclimate simulations. Global Change Biology, 9, 983-1004. doi: 10.1046/j.1365-2486.2003.00640.x
- Prentice, I. C., and Jolly, D., (2000), Mid‐Holocene and glacial‐maximum vegetation geography of the northern continents and Africa. Journal of Biogeography, 27, 507-519. doi: 10.1046/j.1365-2699.2000.00425.x
- 2003), Climate change and Arctic ecosystems: 1. Vegetation changes north of 55°N between the last glacial maximum, mid‐Holocene, and present, J. Geophys. Res., 108, 8170, doi: 10.1029/2002JD002558, D19. , et al. (
- Pickett, E. J., Harrison, S. P., Hope, G. , Harle, K. , Dodson, J. R., Peter Kershaw, A. , Colin Prentice, I. , Backhouse, J. , Colhoun, E. A., D’Costa, D. , Flenley, J. , Grindrod, J. , Haberle, S. , Hassell, C. , Kenyon, C. , Macphail, M. , Martin, H. , Martin, A. H., McKenzie, M. , Newsome, J. C., Penny, D. , Powell, J. , Ian Raine, J. , Southern, W. , Stevenson, J. , Sutra, J. , Thomas, I., Kaars, S. and Ward, J. (2004), Pollen‐based reconstructions of biome distributions for Australia, Southeast Asia and the Pacific (SEAPAC region) at 0, 6000 and 18,000 14C yr BP. Journal of Biogeography, 31, 1381-1444. doi: 10.1111/j.1365-2699.2004.01001.x
- Marchant, R., Cleef, A., Harrison, S. P., Hooghiemstra, H., Markgraf, V., Van Boxel, J., … & Behling, H. (2009). Pollen-based biome reconstructions for Latin America at 0, 6000 and 18 000 radiocarbon years ago. Climate of the Past, 5, 725-767.
- Prentice, C.I., Guiot, J., Huntley, B., Jolly D. and Cheddadi, R., (1996) Reconstructing biomes from palaeoecological data: a general method and its application to European pollen data at 0 and 6 ka. Climate Dynamics 12, 3,185-194.
The China Plant Trait Database
Plant functional traits provide information about adaptations to climate and environmental conditions, and can be used to explore the existence of alternative plant strategies within ecosystems. Trait data are also increasingly being used to provide parameter estimates for vegetation models. Here we present a new database of plant functional traits from China. Most global climate and vegetation types can be found in China, and thus the database is relevant for global modelling. The China Plant Trait Database contains information on morphometric, physical, chemical and photosynthetic traits from 122 sites spanning the range from boreal to tropical, and from deserts and steppes through woodlands and forests, including montane vegetation. Data collection at each site was based either on sampling the dominant species or on a stratified sampling of each ecosystem layer. The database contains information on 1215 unique species, though many species have been sampled at multiple sites. The original field identifications have been taxonomically standardized to the Flora of China. Similarly, derived photosynthetic traits, such as electron-transport and carboxylation capacities, were calculated using a standardized method. To facilitate trait-environment analyses, the database also contains detailed climate and vegetation information for each site.
Link to dataset:
- Wang, Han; Harrison, Sandy P; Prentice, Iain Colin; Yang, Yanzheng; Bai, Fan; Furstenau Togashi, Henrique; Wang, Meng; Zhou, Shuangxi; Ni, Jian (2017): The China Plant Trait Database. PANGAEA, https://doi.org/10.1594/PANGAEA.871819
- Wang, H., Harrison, S.P., Prentice, I.C., Yang, Y., Bai, F., Togashi, H.F., Wang, M., Zhou, S. and Ni, J., 2018. The China Plant Trait Database: toward a comprehensive regional compilation of functional traits for land plants. Ecology, 99, 2.
- Wang, H., Prentice, I.C. and Ni, J., 2013. Data-based modelling and environmental sensitivity of vegetation in China. Biogeosciences, 10, 9, 5817-5830.
A new multi-variable benchmark for Last Glacial Maximum climate simulations
Reconstruction of climate anomalies for the Last Glacial Maximum (LGM, c.a. 21,000 years ago), made by combining pollen based reconstructions (from Bartlein et al. 2011) and averaged outputs of LGM simulations from the 3rd round of the Palaeoclimate Model Intercomparison Project (PMIP, Braconnot et al. 2011), under a variational data assimilation technique. This reconstruction is designed to be used for data-model comparison, specifically against the results of PMIP4. The dataset consists of 6 variables: moisture index (the ratio precipitation and equilibrium evapotranspiration), mean annual precipitation (mm), mean annual temperature (degrees C), mean temperature of the coldest month (degrees C), mean temperature of the warmest month (degrees C), growing degree days above 5 degrees C (day degrees C). The standard deviation of these variables is also given.
Link to dataset:
- Cleator, Sean , Harrison, Sandy , Nichols, Nancy, Prentice, Iain Colin and Roulstone, Ian (2019): A new multi-variable benchmark for Last Glacial Maximum climate simulations. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.229
- Bartlein, P.J., Harrison, S.P., Brewer, S., Connor, S., Davis, B.A.S., Gajewski, K., Guiot, J., Harrison-Prentice, T.I., Henderson, A., Peyron, O. and Prentice, I.C., 2011. Pollen-based continental climate reconstructions at 6 and 21 ka: a global synthesis. Climate Dynamics, 37, 3-4, 775-802.
- Braconnot, P., Harrison, S.P., Otto-Bliesner, B., Abe-Ouchi, A., Jungclaus, J. and Peterschmitt, J.Y., 2011. The paleoclimate modeling intercomparison project contribution to CMIP5. CliVAR Exchanges, 56, 16, 15-19.
- Cleator, Sean, Harrison, Sandy, Nichols, Nancy, Prentice, Iain Colin and Roulstone, Ian (2019): A new multi-variable benchmark for Last Glacial Maximum climate simulations. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.206 (Status: superseded. The spatial and temporal correlations were based on a Gaussian function, instead of a Bessel function).
The climatic space of European pollen taxa
Pollen data are widely used to reconstruct past climate changes, using relationships between modern pollen abundance in surface samples and climate at the surface sample sites. Visualisation of these data in multi-dimensional climate space provides an important way to establish that pollen taxon abundances are well-behaved before using them in climate reconstructions, but visualisation can also be helpful for ecological interpretation of the pollen diagrams. Here we present data created using Generalized Additive Models (GAMs) on the distribution of 195 European pollen taxa in climate space defined by seasonal temperature, as defined by the mean temperature of the coldest month (MTCO) and growing degree days above a baseline of 0°C (GDD0), and an annual moisture index (MI) expressed as the ratio of annual precipitation to annual potential evapotranspiration. These models can be used to explore the realised climate niche of individual pollen taxa and to build statistical models for climate reconstruction.
Link to dataset:
- Wei, Dongyang, Harrison, Sandy and Prentice, Iain Colin (2019): The climatic space of European pollen taxa. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.204
- Wei, D., González-Sampériz, P., Gil-Romera, G., Harrison, S.P., Prentice, I.C. Climate changes in interior semi-arid Spain from the last interglacial to the late Holocene. Climate of the Past Discussions https://doi.org/10.5194/cp-2019-16, 2019.
- Harrison, S.P. Modern Pollen Data for Climate Reconstructions, version 1 (SMPDS). University of Reading Dataset. http://dx.doi.org/10.17864/1947.194, 2019.
Fossil pollen data for climate reconstructions from El Cañizar de Villarquemado
The sedimentary sequences from El Cañizar de Villarquemado provide a palaeoenvironmental record from the western Mediterranean Basin spanning the interval from the last part of MIS6 to the late Holocene. Wei et al. (2019) have used Weighted Averaging Partial Least-Squares (WA-PLS) regression to derive quantitative reconstructions of winter and summer temperature regimes from the pollen data, expressed in terms of the mean temperature of the coldest month (MTCO) and growing degree days above a baseline of 0° C (GDD0) respectively, and a moisture index (MI), the ratio of annual precipitation to annual potential evapotranspiration, taking account of the effect of low CO2 on water use efficiency. Since Wei et al. (2019) used the SMPDS (Harrison, 2019: http://dx.doi.org/10.17864/1947.194) to derive modern pollen-climate relationships, the fossil pollen data from El Cañizar de Villarquemado were assigned to the subset of pollen taxa recognised in the modern dataset. In addition to removing obligate aquatics, insectivorous plants, cultivated plants and non-native species, this involved amalgamating some taxa to a higher taxonomic level. There are 104 taxa represented in the final taxon list from El Cañizar de Villarquemado. This dataset contains the pollen counts for the subset of the taxa that have been used to make climate reconstructions by Wei et al. (2019).
Link to dataset:
- Harrison, Sandy, González-Sampériz, Penélope and Gil-Romera, Graciela (2019): Fossil pollen data for climate reconstructions from El Cañizar de Villarquemado. University of Reading. Dataset. http://dx.doi.org/10.17864/1947.219