The palaeogeographic and palaeoclimatic significance of climate proxies for data-model comparisons

Palaeoclimate interpretations based on geological proxies of climate are fundamental to our understanding of climate change in the geological record. Most proxy definitions depend upon analogy with modern-day relationships, and the validity of this has long been questioned, especially for biological climate proxies. In the early 19th century the solution was to assume that if multiple proxies indicated the same climate then this increased the probability that the interpretation was correct. This probabilistic approach is advocated here. A further criticism has been that climate interpretations based on proxies are mainly qualitative. This is a problem when such data are used to constrain the output of computer-based numerical climate models, which are inherently quantitative. In this study, I examine the climatic and palaeoclimatic significance of a selection of climate proxies (including crocodilians, turtles, amphibians, coals and evaporites), illustrating how each can be quantitatively defined in the modern using the concept of ‘climate space’. ‘Climate space’ is a concept taken from ecology, analogous to petrological phase space, that defines the (usually multidimensional) environmental limits in which an organism can survive. This concept can be also used for any climate proxy, including sedimentological features (‘facies’ or ‘depositional’ space). I then show how these derived climate interpretations are applied to geological climate proxy occurrences, which are then used to evaluate climate model output directly using Geographical Information Systems (GIS). GIS combines the storage and querying functionality of relational databases with the spatial context provided by maps. Unfortunately, the geological record is neither a complete nor impartial witness to the past, and even quantitative interpretations include inherent uncertainties that must be recognized and stated: viz., temporal and spatial heterogeneities and imprecision. This requires the careful compilation of large databases, and the use of ‘control’ groups to constrain significant absences of climate proxies. These spatial heterogeneities are examined using a series of palaeogeographic maps for the Cretaceous to Recent, which are provided here. Such maps are also important because they also act as one of the main boundary conditions for palaeoclimate models. The interpretation of past climates is largely derived from geological phenomena, usually fossil or sedimentary, by analogy with the phenomena’s modern climate limits. In most cases, these climate proxies offer only the broadest guides to contemporary climates: for example, evaporites imply net evaporation during deposition (P , E: precipitation is less than evaporation), but do not relate to a specific value; coals and peats have often been interpreted to represent ‘wet’ conditions, without any reference to what ‘wet’ means, and such generalizations can be misleading. For example, peats are more an indication of contemporary productivity and local hydrological conditions, which may or may not reflect precipitation (Lottes & Ziegler 1994). Similarly, red beds are frequently associated with arid conditions; but while this is often the case, it is not always so, and the description of ‘red beds’ in the literature (without any other sedimentological information) cannot be assumed to indicate past arid conditions (Dubiel & Smoot 1994). Part of the problem is that there have been few studies that have examined any particular proxy in detail in terms of its palaeoclimatic significance (Bárdossy 1982; Gyllenhaal 1991; Lottes & Ziegler 1994; Markwick 1994, 1996; Price et al. 1997). With the advent of GCMs (General Circulation Models), the need for more quantitative climate proxies has become critical and the lack of well-constrained climate proxies has become readily apparent. The nature of the problem Computer-based climate models, specifically GCMs, are the key means of investigating the dynamics and pattern of future (Houghton et al. 2001) and past climate change. They also provide a powerful tool in frontier oil and gas exploration by modelling many of the environmental processes needed to predict the past distribution of source, From: WILLIAMS, M. , HAYWOOD, A. M. , GREGORY, F. J. & SCHMIDT, D. N. (eds) Deep-Time Perspectives on Climate Change: Marrying the Signal from Computer Models and Biological Proxies. The Micropalaeontological Society, Special Publications. The Geological Society, London, 251–312. 1747-602X/07/$15.00 # The Micropalaeontological Society 2007. reservoir and seal facies (Barron 1985; Huc 1990; Kruijs & Barron 1990; Barron & Moore 1994; Burggraf et al. 2006). But, models are experiments that must be constantly tested against observations to assess their veracity (Markwick 1998). For palaeoclimatology, these observations are represented by geological climate proxies: indirect indicators of the contemporary climate, which include fossil, sedimentary, mineralogical and geochemical evidence (Wolfe 1971, 1978, 1993, 1994; Ziegler et al. 1987, 2003; Price et al. 1995; Parrish 2001; Zachos et al. 2001). Models are also quantitative and therefore the proxies used to evaluate them must also be defined quantitatively. Numerical climate ranges have been assigned to many geological climate proxies (Bárdossy 1982; Gyllenhaal 1991; Lottes & Ziegler 1994; Markwick 1994, 1996; Price et al. 1997), but invariably these relationships have been defined in the modern, either through comparison of the climate represented by the geographic limits of each proxy’s modern distribution (Markwick 1996; Price et al. 1997), or through experiment (Colbert et al. 1946). This also applies to physiographic methods such as CLAMP (Wolfe 1993, 1994) or stomatal analysis (Beerling et al. 2001; Royer et al. 2001; Beerling 2002; Beerling & Royer 2002). It also pertains to demonstrably numerical methods, such as stable isotope geochemistry (Price & Sellwood 1997; Pearson et al. 2001; MacLeod & Bergen 2004), which is not without caveats, including the influence of diagenesis, ‘vital’ effects, spatial and depth-related isotope heterogeneity, and inter-species variations. In the early 19th century, Fleming (1829, 1830) questioned the validity of using modern-day climate relationships as the basis for interpreting past climate through analogy, especially for biological climate proxies (today often referred to as NLR, nearest living relative, or NLE, nearest living equivalent), and argued that there could be no way of proving beyond reasonable doubt that an organism in the past, no matter how similar it resembled a living form, had the same climate tolerances. This is a criticism that has been raised by numerous subsequent authors (Sloan & Barron 1992). Fleming’s (1829, 1830) objections to using climate proxies were countered by Conybeare (1829) who argued that geologists do not rely on any one climate proxy, but multiple proxies, and that the combined force of these analogues increases the probability that a palaeoclimate interpretation is correct. This probabilistic approach is still the most compelling (Markwick 1996, 1998; Sinka & Atkinson 1999) and is that advocated here (Fig. 1). But even accepting this, defining the climate represented by each proxy is problematic and a number of questions must be addressed, especially when using fossils. These include the following: (1) To what extent does the modern distribution of a proxy represent its full potential range, and therefore define the actual physical climate limits that can then be applied to the past? For biological systems, this can include the consequences of biogeographical and ecological limits independent of the immediate climate. (2) Which climate/environmental variables are the important limiting factors for any proxy? Most proxies are the consequence of a number of environmental factors and there is always the possibility of autocorrelation. (3) To what extent is an observed proxy a consequence of local conditions rather than the general state of the atmosphere at a resolution comparable to model output? (4) What is the consequence of temporal climate variability? This relates to the problem of equating climate in the meteorological sense (the state of the atmosphere over decades) to palaeoclimate, which on a regional or global scale can be a heterogeneic pastiche of various ‘climate’ states that existed over millions of years (Markwick & Lupia 2001). In order to more rigorously define modern climate tolerances, Markwick (1996, 1998) used Fig. 1. Schematic representation of 2D climate space (Markwick 1996, 1998) occupied by three different climate proxies, A, B and C. The union of the three (darker shaded area) is the mutual climate range (Sinka & Atkinson 1999). In reality, the climate space occupied by each individual proxy is multivariate, and like petrological phase space, the addition of more variables can change the shape of that space. The more climate proxies that can be used to refine the interpretation of palaeoclimate, the greater the probability that the interpretation is true. P. J. MARKWICK 252