Hawaii, provided a picturesque back-drop to the PAGES-CLIVAR Intersection Panel workshop. The main objective of the workshop was to bring together modelers, theoreticians and paleocli-matologists to commence analysis of results from Phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulation database. The CMIP5 project is a community-wide effort to provide standard protocols for climate model simulations covering the historical instrumental period, future projections and a number of idealized simulations to aid the understanding, detection and attri-bution of climate change. Significantly, and for the first time, there is a concurrent paleoclimate component, in collaboration with the Paleoclimate Modelling Intercomparison Project Phase 3: PMIP3, that uses the same models for three specific experiments covering the Last Glacial Maximum (LGM, 20 ka ago), the Mid-Holocene (MH, 6 ka ago) and the Last Millennium (a transient simulation from 850 to 1850 AD; Taylor et al. 2012). Comparisons of paleoclimate simulations and proxy observation have a long history via earlier incarnations of PMIP and many individual studies, which motivated comprehensive data syntheses. However, it has been a challenge to quantitatively link the future simulations with skill or sensitivity in the paleocli-mate simulations. There are a number of reasons for this, not least because paleo-simulations were often not performed with the same models being used for future projections and through a lack of suitable paleoclimate metrics; predominantly large scale syntheses of the proxy data. The workshop focused specifically on this missing step-to make the quantitative connections, so that paleo-climate can become demonstrably useful for constraining future projections. The workshop began with a comprehensive discussion on the nature of the multi-model ensemble of opportunity and the techniques available for assessing model skill. The evidence indicates that the current models don't differ in kind from previous efforts (and so previous work can be analyzed in the same framework) and that there is sufficient reason to expect that, particularly for the LGM, the model spread likely encompasses the observations. However, it was widely acknowledged that it is challenging to find diagnostics of the models which can be compared to paleoclimate observations and that also correlate to model projections of the future. The remainder of the workshop was focused on specific uncertainties highlighted in IPCC AR4 for which there are some clear indications that paleo-climate might help. These included patterns of regional rainfall, temperature seasonality, climate sensitivity, ocean-atmosphere modes in the tropical Pacific, the response of the North Atlantic Meridional Circulation, and spectra of climate variability. Assessments of climate sensitivity using the LGM are very promising, with a large increase in available and relevant simulations over PMIP2. In the preliminary data there appears to be a correlation of verifiable temperature patterns at the...
Abstract. Bias correction methods are used to calibrate climate model outputs with respect to observational records. The goal is to ensure that statistical features (such as means and variances) of climate simulations are coherent with observations. In this article, a multivariate stochastic bias correction method is developed based on optimal transport. Bias correction methods are usually defined as transfer functions between random variables. We show that such transfer functions induce a joint probability distribution between the biased random variable and its correction. The optimal transport theory allows us constructing a joint distribution that minimizes an energy spent in bias correction. This extends the classical univariate quantile mapping techniques in the multivariate case. We also propose a definition of non-stationary bias correction as a transfer of the model to the observational world, and we extend our method in this context. Those methodologies are first tested on an idealized chaotic system with three variables. In those controlled experiments, the correlations between variables appear almost perfectly corrected by our method, as opposed to a univariate correction. Our methodology is also tested on daily precipitation and temperatures over 12 locations in southern France. The correction of the inter-variable and inter-site structures of temperatures and precipitation appears in agreement with the multi-dimensional evolution of the model, hence satisfying our suggested definition of non-stationarity.
Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events — such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events — require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks.
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