EGU Journal Logos (RGB) Open Access Open Access Open Access Advances in Annales Nonlinear Processes Geosciences Geophysicae in Geophysics Open Access Open Access Natural Hazards Natural Hazards and Earth System and Earth System Sciences Sciences Discussions Open Access Open Access Atmospheric Atmospheric Chemistry Chemistry and Physics and Physics Discussions Open Access Open Access Atmospheric Atmospheric Measurement Measurement Techniques Techniques Discussions Open Access Open Access Biogeosciences Biogeosciences Discussions Open Access Open Access Clim. Past, 9, 367–376, 2013 Climate www.clim-past.net/9/367/2013/ Climate doi:10.5194/cp-9-367-2013 of the Past of the Past © Author(s) 2013. CC Attribution 3.0 License. Discussions Open Access Open Access Earth System Earth System Dynamics Dynamics Discussions A new global reconstruction of temperature changes Open Access Open Access at the Last Glacial Maximum Geoscientific Geoscientific Instrumentation Instrumentation J. D. Annan and J. C. Hargreaves Methods and Methods and Research Institute for Global Change, Yokohama, Japan Data Systems Data Systems Discussions Open Access Correspondence to: J. D. Annan ([email protected]) Open Access Geoscientific Geoscientific Received: 11 September 2012 – Published in Clim. Past Discuss.: 10 October 2012 Model Development Revised: 23 January 2013 – Accepted: 23 January 2013 – Published: 13 FebruaryModel 2013 Development Discussions ◦ Open Access Abstract. Some recent compilations of proxy data both on globally-averaged value of 2.3 and 0.8 C in theOpen Access tropics (all land and ocean (MARGO Project Members, 2009; Bartlein temperature anomaliesHydrology are presented here and as pre-industrial Hydrology and et al., 2011; Shakun et al., 2012), have provided a new op- climate minus LGM). However,Earth theseSystem values were argued Earth System portunity for an improved assessment of the overall climatic to be substantially too small by subsequent analyses, which state of the Last Glacial Maximum. In this paper, we com- presented tropical LGM SST estimatesSciences of around 2.5–3 ◦C Sciences bine these proxy data with the ensemble of structurally di- colder than present (Crowley, 2000; Ballantyne et al., 2005). Discussions Open Access verse state of the art climate models which participated in Simulations of the LGM using state of the artatmosphere–Open Access the PMIP2 project (Braconnot et al., 2007) to generate a spa- ocean global climate models (GCMs) generally generate Ocean Science tially complete reconstruction of surface air (and sea surface) global mean surface airOcean temperature Science (SAT) anomalies in the temperatures. We test a variety of approaches, and show that range of 3–5 ◦C colder than present (Braconnot et al., 2007), Discussions multiple linear regression performs well for this application. but these values are thought to be biased warm due to the ex- Our reconstruction is significantly different to and more ac- perimental design, which omits the likely negative forcings Open Access curate than previous approaches and we obtain an estimated of vegetation and dust changes (Crucifix and HewittOpen Access , 2005; ◦ global mean cooling of 4.0 ± 0.8 C (95 % CI). Schneider von Deimling et al., 2006a). These results can be Solid Earth interpreted as implying a model-basedSolid Earth range of around 4– 7 ◦C if these extra forcings were to be accounted for (Jansen Discussions et al., 2007). 1 Introduction The first attempts at directly constraining model results with proxy data produced results consistent withOpen Access this range, Open Access The Last Glacial Maximum (LGM, 19–23 ka BP) represents with resulting best estimates for the global mean SAT The Cryosphere the most recent interval when the global climate was sub- anomaly of aroundThe 6 ◦C( SchneiderCryosphere von Deimling et al., Discussions stantially different to the present, and therefore provides us 2006a; Holden et al., 2009). However, a new analysis has re- with a key target in testing the response of climate models cently challenged this emerging consensus with a remarkably to large changes in radiative forcing. There is, however, sig- mild estimate of 3.0 ◦C (90 % range 1.7–3.7 ◦C) (Schmittner nificant disagreement even over first-order diagnostics such et al., 2011), based on the fit of an intermediate complex- as the global average of the annual mean temperature at that ity climate model to the most recent comprehensive proxy ◦ time, with estimates ranging from as much as 6 C to as little syntheses. Such a mild climate state, if confirmed in other ◦ as 3 C colder than the modern (pre-industrial) climate (e.g. studies, would be difficult to reconcile with GCM simula- Schneider von Deimling et al., 2006a; Holden et al., 2009; tions. The response of the climate system to a large forcing Schmittner et al., 2011). This uncertainty limits our ability to is of fundamental importance to understanding future climate critically assess climate model performance. change, and therefore the large discrepancy between these An early reconstruction of the global sea surface tempera- analyses requires further investigation. ture (SST) anomaly at the LGM was made by the CLIMAP project (Climap Project Members, 1976), which estimated a Published by Copernicus Publications on behalf of the European Geosciences Union. 368 J. D. Annan and J. C. Hargreaves: Temperature changes at the Last Glacial Maximum Fig. 1. Reconstruction of Last Glacial Maximum surface air temperature anomaly (◦C) based on multi-model regression. Proxy data are represented as coloured dots. In this paper we present a new model-data synthesis, com- 2 Data and models bining the recent comprehensive compilation of proxy data as used by Schmittner et al. (2011), together with the en- The proxy data which we use here consist of a multiproxy semble of state of the art GCMs which participated in the analysis of SST anomalies presented on a 5◦ grid (MARGO PMIP2 project (Braconnot et al., 2007). The data and mod- Project Members, 2009), and SAT anomalies on a 2◦ grid els are introduced more fully in Sect. 2. In Sect. 3 we de- over land based on pollen and plant macrofossils (Bartlein scribe several approaches to reconstructing the climate state: et al., 2011), with some additional points from a variety of increasing complexity and accuracy. In order to test the re- sources including Antarctic and Greenland ice cores (Shakun construction methods, and to estimate their uncertainties, we et al., 2012). The data are displayed as the dots in Figs. 1 perform extensive cross-validation using each of the PMIP2 and 2. While the land data of Bartlein et al. (2011) are pro- simulations in turn as the target, extracting pseudoproxy data vided with uncertainty estimates, the ocean data are not, in- from the appropriate locations, and calculating the accuracy stead being associated with a nondimensional “reliability in- of the resulting reconstruction based on these data. Confi- dex”. One common interpretation of this parameter is to treat dence intervals are presented at the 95 % level based on the it as the one standard deviation uncertainty of a Gaussian er- spread of cross-validation results unless otherwise stated. We ror (Hargreaves et al., 2011; Schmittner et al., 2011). Analy- start with an attempt to simply smooth the data in Sect. 3.1; ses presented in Sect. 4.2 cast some doubt on the accuracy of this being a commonly used approach to generate climate these uncertainty estimates, but our results are not sensitive field reconstructions. However, the sparseness of the data, to this factor. and in particular the non-random nature of large data void We use the outputs of nine models which participated in areas, limits the performance of this approach. In Sect. 3.2 the PMIP2 project (Braconnot et al., 2007), being all of those we consider the pattern scaling approach, in which a single for which both atmosphere and sea surface temperatures are model anomaly field is scaled to optimally fit to the data. available. The models used for this were predominantly state While this method improves on the smoothing, the results are of the art atmosphere–ocean GCMs, with some models also still rather moderate. Our main result, presented in Sect. 3.3, including an interactive vegetation component and one be- is based on multiple linear regression of the ensemble of cli- ing an intermediate-complexity model with simplified atmo- mate model fields. This method performs substantially better sphere. Model outputs were typically calculated as 100 yr av- than the other two approaches. Detailed validation and some erages to minimise the effect of internal variability. The ex- sensitivity analyses are presented in Sect. 4. We summarise perimental protocol for the LGM accounts for the largest and and discuss some implications of our result in Sect. 5. best-quantified forcings at that time, which include reduced greenhouse gas concentrations, minor changes in orbital pa- rameters, and extensive increases in Northern Hemisphere Clim. Past, 9, 367–376, 2013 www.clim-past.net/9/367/2013/ J. D. Annan and J. C. Hargreaves: Temperature changes at the Last Glacial Maximum 369 Fig. 2. Reconstruction of Last Glacial Maximum sea surface temperature anomaly (◦C) based on multi-model regression. Proxy data are represented as coloured dots. Land areas are masked as brown. ice sheets. The experimental design and main results are de- performed for the ocean alone by CLIMAP (Climap Project scribed more fully by Braconnot et al. (2007). Despite some Members, 1976) and which is commonly used for modern limitations in the forcing protocol (Schneider von Deimling temperature anomaly fields (Hansen and Lebedeff, 1987; et al., 2006a), the model outputs appear to generally provide Smith et al., 2008) – could give good results. However, we a reasonable representation of the Last Glacial Maximum find this not to be the case. The performance of smoothing (Hargreaves et al., 2011). The global surface air temperature was investigated through the use of pseudoproxy data taken anomalies simulated by these models at the LGM range from from the PMIP2 models.