WP 3 Applications
Climate attractor drifts (WP3a)
Climate model intercomparison initiatives have generated many long simulations with various forcing hypotheses (including astronomical, solar, volcanic and anthropogenic [Jansen et al., 2007]). If we consider general circulation models of the atmosphere, the basic equations of motion are the same for all models, which mainly differ in the physical schemes for small scale phenomena.
We shall first focus on control simulations of climate models (i.e. with no time varying forcing) from the CMIP5 and PMIP3 databases. We will assume that those control simulations sample the underlying climate attractor of an autonomous dynamical system. We will determine a “climatology” of analogues of sea-level pressure (SLP) for extra-tropical regions of the northern hemisphere covering the North Atlantic, the Arctic, North America and Asia. This climatology will include the probability distribution of analogue scores for various metrics and the probability distribution of dates of analogues. We will also determine the spatial probability distributions of composite temperatures and precipitations for those regions. This climatology describes the shadow of the attractor [Ghil et al., 2002] that is approximated by each trajectory (or model simulation). The distribution of poor scores is particularly interesting because it describes parts of the attractor that are seldom encountered. This climatology provides the baseline statistics against which we will test potential changes in the attractor of the system.
We will then determine the SLP analogues of future climate projection simulations (the target sets) from control simulations (reference sets) for each region that we identified. The statistics of scores will be compared to the “baseline attractor” probability distributions. In particular, we will examine the distribution of poor scores (large distance and/or low correlation value) that represent exceptional synoptic conditions found in the target sets, that are never encountered in the reference sets. Such adiagnostic will provide an assessment of an attractor deformation in climate change scenarios, and provide a basis for specific analyses of the atmospheric circulation. An ensemble of scoring tests, based on variance distances (Euclidean, Mahalanobis, Kullback-Leibler) or higher moments (e.g., madogram, entropy), and correlation tests will be performed.
In a second step, the analogue search will be performed solely on the future climate projection simulations (reference and target). We will examine clusters and trends in the dates of analogues, if an attractor deformation was identified in the previous step. This analysis of trend will permit the identification of the timing of the attractor deformation. This generalizes time of emergence notion [Giorgi and Bi, 2009]. We will provide atlases of deformation times for the regions of focus, when such changes are identified.
The attractor deformation can be due to the appearance of “new” synoptic patterns, or the deformation of already existing ones. Both hypotheses correspond to specific dynamical scenarios of bifurcation (Hopf or pitchfork bifurcations [Wiggins, 1990]). We will use the paradigm of weather regimes [Michelangeli et al., 1995] for the atmospheric circulation to propose dynamic scenarios of such drifts. Such an analysis will also be illustrated on simple dynamical systems showing various types of bifurcations [Lorenz, 1963].
We will then endeavor the attribution of such deformation of the atmospheric circulation patterns to external causes. The Paleoclimate Model Intercomparison Project phase 3 and other initiatives provide an invaluable ensemble of millennium climate simulations including forcings such as solar, volcanic, astronomical and land use [Schmidt et al., 2011]. Solar activity and major volcanic eruptions have induced changes in surface temperature distribution [Mann et al., 2009; Servonnat et al., 2010] and have been suspected to alter the extra-tropical atmospheric circulation [Shindell et al., 2001a; Shindell et al., 2001b], although such changes are certainly very subtle [Yiou et al., 2012]. An analogue analysis of the atmospheric circulation in millennium climate simulations will enable us to assess how the climate attractor responds to forcings that have durations of decades and less, and no particular trend (contrary to anthropogenic forcing during the last century).
Attribution of extreme events (WP3b)
Extreme climate events like the European summer heatwave in 2003 or the Russian summer heatwave in 2010 were connected to an anomalous anticyclonic flow and soil moisture feedbacks. The warm European winter in 2006/2007 or the cold winter in 2010 were also connected to anomalous atmospheric patterns.
It is in principle difficult to attribute long-term causes to the occurrence of extreme climate events. On the one hand, heatwaves or cold spells in the extra-tropics are generally connected to specific synoptic atmospheric patterns [Cassou et al., 2005; Cattiaux et al., 2010; Yiou et al., 2007], and are a major challenge for seasonal weather prediction because such patterns evolve on daily timescales. On the other hand, it has been shown that the probability distribution of extreme temperatures follows the evolution of mean temperatures [Parey et al., 2010; Yiou et al., 2009] on interannual time scales. We will explore how the framework of flow analogues permits to reconcile both points of view (seasonal vs. interannual) to understand the connection between extreme events that occur on short time scales, and secular climate and environmental variations.
The first direction is to investigate how flow analogues account for temperature anomalies in various regions of the world. We decide to start with four regions of the northern hemisphere (North Atlantic, North America, Arctic and East Asia). A systematic analysis of recent and coming (in a pseudo real time mode) extremes will be performed, as was done by [Cattiaux et al., 2010; Cattiaux and Yiou, 2012; Yiou et al., 2007] for European temperatures and using the NCEP reanalysis. Those case studies will be generalized by using various reference datasets (including high resolution simulations from CMIP5/PMIP3 databases). Hence we will explore how (and when) such extreme events can be reproduced in various possible trajectories of the climate system.
From a given case of extreme event (e.g. heat or cold spell, large-scale drought, extra-tropical storm) we will determine the atmospheric conditions that precede this event, from observations or reanalysis. We then use analogues of SLP and three-dimensional atmospheric circulation of those conditions to create an ensemble of coherent initial and boundary conditions. This ensemble is based on the best analogues of pressure related fields, from different models of the CMIP5/PMIP3 databases. In such models, the physical parameterizations (for convection for instance) and external forcings (solar, volcanic and anthropogenic) yield different implementations that make the computed ensemble of conditions test the structural stability of the trajectories. We then explore how the trajectories emerging from those analogue conditions (of the precursors of the observed extreme event) lead to an extreme event in each available simulation. In this way, we can evaluate the probability of obtaining an observed extreme event, given an initial condition, and a set of forcing hypotheses. We will use of tools of data mining [Hastie et al., 2009] to optimize the search of analogues of an observed pattern in a large multi-model database of simulations. We hence produce an estimate of the probability of obtaining an observed extreme event, conditional to choices of physical parameterizations and forcing factors. From long series of observations or control simulations, we can determine the baseline probability distribution of each type of extreme event. If the conditional probability of obtaining an observed event from the multi-model ensemble is significantly larger than the baseline distribution, we can evaluate the physical factor that is connected to the increased probability of this event. This procedure of attribution will be formalized in a rigorous fashion.
This methodology is similar to the one of [Pall et al., 2011] but it is much less demanding in terms of computer time because no new model simulation is required, and is not restricted to the analysis of single model experiments. This systematic and quasi-automatic analysis of extreme events will benefit from the design of the analogue platform (in WP2).