Evaluating global climate models for the Pacific island region

While the practice of reporting multi-model ensemble climate projections is well established, there is much debate regarding the most appropriate methods of evaluating model performance, for the purpose of eliminating and/or weighting models based on skill. The CMIP3 model evaluation undertaken by the Pacific Climate Change Science Program (PCCSP) is presented here. This includes a quantitative assessment of the ability of the models to simulate 3 climate variables: (1) surface air temperature, (2) precipitation and (3) surface wind); 3 climate features: (4) the South Pacific Convergence Zone, (5) the Intertropical Convergence Zone and (6) the West Pacific Monsoon; as well as (7) the El Niño Southern Oscillation, (8) spurious model drift and (9) the long term warming signal. For each of 1 to 9, it is difficult to identify a clearly superior subset of models, but it is generally possible to isolate particularly poor performing models. Based on this analysis, we recommend that the following models be eliminated from the multi-model ensemble, for the purposes of calculating PCCSP climate projections: INM-CM3.0, PCM and GISS-EH (consistently poor performance on 1 to 9); INGV-SXG (strong model drift); GISS-AOM and GISS-ER (poor ENSO simulation, which was considered a critical aspect of the tropical Pacific climate). Since there are relatively few studies in the peer reviewed literature that have attempted to combine metrics of model performance pertaining to such a wide variety of climate processes and phenomena, we propose that the approach of the PCCSP could be adapted to any region and set of climate model simulations

From top to bottom: Do Lake Trout diversify along a depth gradient in Great Bear Lake, NT, Canada?

<div><p>Depth is usually considered the main driver of Lake Trout intraspecific diversity across lakes in North America. Given that Great Bear Lake is one of the largest and deepest freshwater systems in North America, we predicted that Lake Trout intraspecific diversity to be organized along a depth axis within this system. Thus, we investigated whether a deep-water morph of Lake Trout co-existed with four shallow-water morphs previously described in Great Bear Lake. Morphology, neutral genetic variation, isotopic niches, and life-history traits of Lake Trout across depths (0–150 m) were compared among morphs. Due to the propensity of Lake Trout with high levels of morphological diversity to occupy multiple habitat niches, a novel multivariate grouping method using a suite of composite variables was applied in addition to two other commonly used grouping methods to classify individuals. Depth alone did not explain Lake Trout diversity in Great Bear Lake; a distinct fifth deep-water morph was not found. Rather, Lake Trout diversity followed an ecological continuum, with some evidence for adaptation to local conditions in deep-water habitat. Overall, trout caught from deep-water showed low levels of genetic and phenotypic differentiation from shallow-water trout, and displayed higher lipid content (C:N ratio) and occupied a higher trophic level that suggested an potential increase of piscivory (including cannibalism) than the previously described four morphs. Why phenotypic divergence between shallow- and deep-water Lake Trout was low is unknown, especially when the potential for phenotypic variation should be high in deep and large Great Bear Lake. Given that variation in complexity of freshwater environments has dramatic consequences for divergence, variation in the complexity in Great Bear Lake (i.e., shallow being more complex than deep), may explain the observed dichotomy in the expression of intraspecific phenotypic diversity between shallow- vs. deep-water habitats. The ambiguity surrounding mechanisms driving divergence of Lake Trout in Great Bear Lake should be seen as reflective of the highly variable nature of ecological opportunity and divergent natural selection itself.</p></div

Length at age of Lake Trout captured in three depth zones.

<p>Length at age of Lake Trout captured in three depth zones (0−20 m = ○ and dotted line; 21−50 m = gray ○ and dashed line; 51–150 m = ♦ and solid line) and classified into three morphs (Morph 1 = ○ and dotted line; Morph 2 = ● and dashed line; Morph 3 = gray ○ and solid line) and four composite groups (Comp 1 = * and dotted line; Comp 2 = —and dashed line, Comp 3 = <b>×</b> and solid line; and Comp 4 = gray ▲ and long-dashed line) in Great Bear Lake.</p

Admixture coefficient plots of the Bayesian clustering analysis for Lake Trout using STRUCTURE.

<p>Admixture coefficient plots of the Bayesian clustering analysis for Lake Trout from Great Bear Lake using STRUCTURE. Population structure was examined by groups defined by depth zone (0-20m, 21–50 m, 51–150 m), morphological data (Morph1, Morph 2 and Morph 3), and the composite dataset (Comp 1, Comp 2, Comp 3, and Comp 4). Each individual is represented as a vertical line partitioned into colored segments representative of an individual’s fractional membership in any given cluster (K). The most likely number of genetic clusters based on the ΔK statistic of Evanno et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0193925#pone.0193925.ref063" target="_blank">63</a>] was three, six and four for depth, morphology, and composite grouping respectively. The most likely number of clusters based on the traditional statistic mean LnP(K) was K = 1 for each scenario.</p

Trend between C:N ratio and δ¹³C (‰) in individual Lake Trout from three depth strata.

<p>Trend between C:N ratio and δ¹³C (‰) in individual Lake Trout from Great Bear Lake caught from three depth strata: open circle = 0–20 m, light grey square = 21–50 m, and black diamond = 51–150 m. A polynomial trend line was fitted for the overall data. C:N ratios are an indirect representation of lipid content (index of buoyancy).</p

Global phenotypic trait divergence (<i>Pst</i>).

<p>Global phenotypic trait divergence (<i>Pst</i>) ± SE for individual variable for Lake Trout from Great Bear Lake based on groups established based on depth strata, morphological data, and composite data.</p