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Consensus in climate classifications for present climate and global warming scenarios
Accepted Manuscript Consensus in climate classifications for present climate and global warming scenarios
Francisco J. Tapiador, Raúl Moreno, Andrés Navarro PII: DOI: Reference:
S0169-8095(18)30902-5 doi:10.1016/j.atmosres.2018.09.017 ATMOS 4376
To appear in:
Atmospheric Research
Received date: Revised date: Accepted date:
14 July 2018 17 September 2018 18 September 2018
Please cite this article as: Francisco J. Tapiador, Raúl Moreno, Andrés Navarro , Consensus in climate classifications for present climate and global warming scenarios. Atmos (2018), doi:10.1016/j.atmosres.2018.09.017
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Consensus in Climate Classifications for Present Climate and Global Warming Scenarios
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Francisco J. Tapiador*, Raúl Moreno and Andrés Navarro
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University of Castilla-La Mancha (UCLM), Institute of Environmental Sciences (ICAM), Faculty of Environmental Sciences and Biochemistry, Earth and Space Sciences Group. Avda. Carlos III s/n, 45071 Toledo, Spain
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*Corresponding Author. Email: [email protected]
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Abstract: Climate classifications of climate models’ outputs have been used to assess environmental changes but systematic analyses o f the
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differences between models, scenarios and classification methods are scarce. Here, the results of applying the most commonly used climate
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classifications to the outputs of 47 Global Climate Models (GCM) of different physical parameterizations and varied grid size are presented. The extent and intensity of changes for present climate, three different Representative Pathways Scenarios (RCP26, RCP45 and RCP85) and three
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increasingly-fine classification methods show that there is a consensus between models, and that climate classifications are indeed useful tools to translate physical climatology variables into environmental changes. The main conclusions are that climate classifications can indeed be used to
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gauge model performance at several grid sizes and that the classification method does not decisively affects the potential gl obal changes in future climates under increasing greenhouse gas emissions. The analyses also reveal that there are several uncertainties that are no t attributable to model
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grid size or to limitations in the reference datasets but more likely to deficiencies in the physics of the models.
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Keywords: Climate classification, Global Climate Models, Köppen classification, Köppen-Trewartha classification, K-means clustering
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1. Introduction
Classifications of atmospheric variables are useful tools in atmospheric research to unveil patterns, filter observational da ta and identify
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relationships buried in model outputs. Thus, they have been used in the analysis of urbanization effects (Lin et al 2010); evaluation of
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precipitation (Ramos 2001, Serra et al 2014, Miró et al 2017, Wen et al 2017, Kim et al 2017, Sharifi et al 2018), and temperature (Peña-Angulo et al 2016) products; modelling extreme weather and climatic events (Chu and Zhao 2011, Tramblay et al 2018); radiation (Rozwadowska 2004,
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Vindel et al 2015); convection (Dimitrova et al 2009; Aran et al 2011, Lack et al 2012); and deriving climatologies of processes such as fog (Cereceda et al 2008) and tornadoes (Giaiotti et al 2007).
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Climate classifications in particular can be characterized as techniques to perform a dimensional reduction of physical variables ( precipitation,
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temperature, evapotranspiration, etc.) into one of two index-classes that can be more readily related to the biota. They have been found useful for
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a variety of topics including Arctic research (Wang and Overland 2004) ; studies of ecosystem impacts
(Roderfeld et al 2008); biome
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distribution (Leemans et al 1996) and biodiversity analyses (Garcia et al 2014); hydrological cycle studies (Manabe and Holloway 1975); to compare vegetation distribution (Monserud 1990); analyze precipitation metrics (Tang et al. 2012); analyzing vegetation changes in the future (Jiang et al 2013); provide input to global models (Prentice 1990) and to visualize climate change (Jylhä et al 2010) to name but a few.
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Climate classifications such as Köppen’s date back to pioneer studies in the late 19 th century, and were not intended to explain environmental factors in terms of climate variables. Rather, Köppen’s specific aim was the other way around: his intention was to be able to define climates based on the observed distribution of vegetation at the time (Köppen 1900). In synthesis, his method defines a set of temperature and precipitation thresholds to derive a tripartite climate classification, which a 3-letter key identifying the major climate, the precipitation cycles,
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and the summer temperature. Part of its success (Köppen’s method is taught in Geography 101 everywhere) relies on its simplicity. Other indexbased classifications such as Budyko’s recently discussed by Caracciolo et al (2018), or the one proposed by Thornthwaite (1948) are less popular.
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The inception of coupled and dynamic climate models has allowed to turn upside down Köppen’s approach. While his intention was help to define climates using vegetation distribution because of scarce and unreliable meteorological data, data availability is no l onger an issue but the
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response of vegetation to ongoing global warming is indeed an active field of research. Coupled atmosphere-ocean Global Climate Models (GCMs) offer a wealth of atmospheric variables at appropriate spatial and temporal resolutions that compare well with observa tions (Jiang et al
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2012). Increasing computing power have made possible Earth System Models (ESM) including cryosphere dynamics, geobiochemical cycles and human population interactions (Navarro et al 2018) to provide outputs below 25 km grid size, having in fact superseding Regional Climate
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Models (RCMs) as these have issues responding to large scale circulations, among other critical issues.
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Drawing upon improvements in Köppen early theory, improvements such as those in Netzel and Stepinski (2016) have allowed a more detailed characterization of climates. Therein, Köppen or slightly-modified Köppen systems have been proposed to classify climates, evaluate model performances and as input data for climate change impact assessment. It has also been suggested that Köppen and Köppen-Trewartha
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classifications could be improved by objective clustering methods. Indeed, the application of these techniques to global (Hoffman et al 2005,
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Netzel and Stepinski 2016) and regional models (Tapiador et al 2011) outputs produces climates that compare well with observations and with satellite estimates of vegetation vigor, such as the NDVI index.
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The sources of uncertainties in climate classifications using GCMs are at least three: (1) biases in reference data; (2) limi tations of the GCMs to faithfully represent climates at the required accuracy and precision; and; (3) unsuitability of the classifi cation method. The first issue has been tackled by Phillips and Bonfils (2015) albeit the effects of using different reference datasets for model tuning and validation remains unexplored.
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These authors also found no crucial differences in terms of grid size for the 15 GCMs they used. Here, the two latest factors are explored using a
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quite comprehensive host of 47 GCMs of varied resolution and physics and the three major classification methods described in the literature.
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2. Data
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47 GCM from the Coupled Model Intercomparison Project (CMIP5) were used to generate the climate classifications. Applications of CMIP5
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models for environmental studies are numerous so there is no question about the suitability of such models for this purpose. In the physical
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realm, the CMIP5 dataset has been used to investigate historical changes in precipitation at aggregated scale (Sillmann et al 2013a, 2013b, Ren et
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al 2013) and in terms of seasonal changes (Li et al 2013). CMIP5 simulations have also been instrumental to advance our knowledge on atmospheric dynamics (Allen and Landuyt 2014, Lott et al 2014, Kitoh et al 2013), cloud-aerosol interactions (Ekman 2014), present and future biases and uncertainties (Cattiaux et al 2013) greenhouse gas forcing (Bellouin et al 2011, Cook and Seager 2013), and attribution to human
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activities of the disruptions in precipitation cycles (Tapiador et al 2016).
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The CMIP5 database contains data of unprecedented quality to analyze atmospheric variables at global scales and for extended periods. Since its publication in 2008 by more than 20 climate modeling groups under the aegis of the World Climate Research Programme’s (WCRP) Working Group on Coupled Modelling (WGCM), and the International Geosphere-Biosphere Programme’s (IGBP) Analysis, Integration and Modeling of
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the Earth System (AIMES) project, CMIP5 has generated a set of comprehensive multimodel experiments for 1) assessing the mechanisms responsible for model differences in poorly understood feedbacks associated with the carbon cycle and with clouds; 2) examining climate predictability and exploring the predictive capabilities of forecast systems on decadal time scales; and 3) determining why s imilarly forced
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models produce a range of responses (Taylor et al 2012).
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Table 1 gathers the models used in this paper. The grid spacing varies from 3.71º in the CMCC-CMS model to 0.56º in the MIROC4h model, the
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latest being a resolution commensurable with that of Regional Climate Models (RCMs) with the added advantage of GCMs properly responding to large scale circulation and avoiding the biases induced by nesting. Specific information about the ensemble and the characteristics of each
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model can be found in (Taylor et al. 2012) and references therein.
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The observation data for the analyses is from the CRU database (Harris et al 2014). A major reason to use CRU instead of other precipitation and
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temperature databases such as GPCP (Adler et al 2018), which also covers the oceans, is that most GCMs are tuned against the CRU dataset, so
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in principle a better agreement with observations can be expected. The practice and consequences of model tuning have been re cently explored by Hourdin et al (2017), Rotstayn (2000), and Suzuki et al (2015) and do not affect the results presented in this paper.
3. Methods
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Index-based climate classifications coexist with physically-based classification such as those of Alisov (1954), Borchert (1953), Flöhn (1950), or Strahler (1976). These are based on global atmospheric patterns and air masses distributions, and render a method for classifying climates w hich
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is independent of biota. These classifications are however less amenable for use in environmental studies as the explanatory variables are not immediate, as in the case of precipitation and temperature.
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Here, three indexes are used: classical Köppen (11 classes), Köppen-Trewartha (28 classes; also named as ‘Extended Köppen’) and K-Means
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objective classification for a predefined number of 28 classes. The first two are well known so their rationale and description will not be repeated here. The algorithmic followed for Köppen approach is as in the pioneer work of Lohmann et al. (1993), who first used climate classifications as
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diagnostic tool for climate models. For Köppen-Trewartha we used the same method as Baker et al. (2009) which classifies the ecoregions in six main climate groups, and in which every subgroup is clearly defined.
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Regarding the K-Means classification, just noting that the convergence of the iterative algorithm depends on the choice of initial centroids. Here,
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as an alternative to random initialization, the Köppen-Trewartha classification was used as a starting point. Such choice sets sensible pivotal
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centroids for the algorithm thus improving the rate of convergence to the final classes, which by construction are nonetheles s independent on the
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initial coordinates.
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4. Results and Discussion
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4.1 Present climate (1961-2005)
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Figure 1 exemplifies the results of the classifications for the best resolution GCM (MIROC4h model), and how they compare with CRU data, which is limited to land. Köppen classification (Figure 1, top row) shows the limitations of the GCM and the sort of is sues that come across with climate classifications from GCM data. While there is an overall agreement in the planetary pattern, a detailed inspection show that for instance
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Europe shows a homogeneous Cf climate, which is not at variance with the classical, isolines maps of Köppen system, but nonetheless unrealistic
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and of limited usefulness. Central Australia is also misclassified because of the precipitation bias of the models for low ra in amounts and the sensitivity of the classifications on the associated thresholds.
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Maps in the supplementary information (S1) show the results of applying the Köppen index to present climate conditions in all the 47 CMIP5
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models (plus one ensemble) and CRU. Some models perform better compared with CRU than others. The MRI-CGCM3, for instance, almost perfectly classifies the climate of Central Australia (but fails in other locations). However, no clear pattern of agreement/ disagreement arises in
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terms of grid size or model complexity. Full, coupled ESMs do not necessarily do better than simpler, coarser resolution GCMs. Nonetheless, all
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the models consistently depict the major climates of Köppen system in approximately their actual locations, illustrating a ce rtain degree of
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consensus. It is worth noting that quantitative analyses in form of scatterplots, while easy to build, are less informative for environmental research than the maps provided here because of the differences in grid size.
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The Extended Köppen classification, been more detailed (28 classes) shows an overall better agreement with observations. Figure 1 (middle row)
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shows that the MIROC4h model compares best with observations than in the previous classification, with many nuances correctly captured by the model. The systematic comparison for all models (in S2) shows an even greater consensus among the models, with some of them becoming very close of the observations. This result shows that the number of classes in the climate classifications is not immaterial , and that finer, more detailed classifications compare better with observations.
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Indeed, the K-means classification (Figure 1, bottom row) is more detailed and provides added value to the comparisons. As can be expected from the algorithm, the classes are more homogenously distributed and that translates to more details, which are clearly visible over the oceans
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where the method is capable of discerning known features such as the ENSO that are invisible for the two classical methods. Here, the
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correspondence with observations is better than in the previous methods. The trade-off, however, is that classes cannot be easily assigned to the biota. But the added detail may be worth the price for some applications such as marine research, for which both Köppen and Extended Köppen
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classifications provide little insight as they depict the oceans as almost homogeneous in longitude (cf. Figure 1, top and middle rows). It is worth recalling that in order to make the classes between models more comparable, the centroids of the K-means algorithm were initialized with the
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Extended Köppen classification. This choice was intended to preserve the spatial coherence in the many models, and indeed the maps in S3 prove that the iterations of the algorithm generate classes that are consistent between the models. In terms of the sui tability of the classification method
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issue raised above, it seems that this objective method is preferable than the pre-defined, classical, Köppen classification. The Extended Köppen
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classification, on the other hand, compares well with the objective method and has the advantage of using named, easy to remember classes for
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land.
4.2 Future climate (2010-2100)
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For future climates, three Representative Concentration Pathways (RCPs; van Vuuren et al 2011) were used. RCPs are not fully integrated socioeconomic parameterizations but rather estimates for describing plausible trajectories of human climate change drivers. They provide simplified accounts of human activities and processes, including population density and economic development, from non-coupled Integrated Assessment Models (IAMs), and have been widely used in the analysis of possible climate change, its impacts, and options to mitigate climate
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change. RCPs have replaced the scenarios in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES).
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The RCPs are intended to be consistent with a wide range of possible changes in future anthropogenic GHG emissions (Meinshausen et al 2011).
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RCP 2.6 (RCP26 hereafter) assumes that global annual GHG emissions (measured in CO2-equivalents) peak between 2010-2020, with emissions declining substantially thereafter. Emissions in RCP 4.5 (RCP45) have a maximum around 2040 and then reduce. In RCP 8.5 (RCP8 5),
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emissions continue to increase throughout the 21st century. RCP85 is therefore the worse-case scenario in terms of global warming, RCP26 is the most optimistic, and RCP45 stays in the middle notwithstanding that such pathway could indeed trigger no-turning back responses in the climate
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system.
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The number of models that use the RCPs varies for each pathway, as not all the modelers chose to simulate all the three. RCP85 is the most
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popular because the signal of increasing GHG emissions is larger and a rational use of computing resources directs to first e xploring this
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scenario: if no climate change signal is apparent in RCP85 it would be a waste running the others.
In order to provide a quantitative account of the expected changes in the future, flow diagrams were used (Figures 2, 3 and 4 ). These show the
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migration of present climates towards the new conditions. This representation permits to synthetically account for different grid size and number
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of models in a useful way so the three scenarios can be compared for all the CMIP5 models. The width of each climate (say Köp pen’s AF) is proportional to the mean number of points covered by such climate (AF) in either present or future conditions averaged over all available models. If a climate wanes, then flows go to other climates; and conversely, a waxing climate receives flows from other climates. The resulting plot shows in both an intuitive and precise way if the climate classes are changing, and in which direction.
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Flow diagrams for Köppen classification (Figure 2) shows that most climates experiment a noticeable change, which increases with GHGs concentration. The pattern of increasing transfer in the RCP85 is noticeable (clearer in Figures 3 and 4), indicating a quite different climate than
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the present one. S4 provides an individual account of the changes in the classes for all the models under Köppen scheme. Note that some classes
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may contain no points.
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The Extended Köppen classification (Figure 3) confirms the pattern observed in Köppen classification, and provides additional insight into the changes. As the topic of this paper is to gauge the agreement between models and show the associated uncertainty, it is out of the scope to
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identify individual trends, cases and transfers that feature in the figures but, corresponding to Figure 3, S4 shows a consis tent behavior of all individual models. While there are differences between GCMs, there is consensus in the direction and magnitude of the changes. As it is
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obviously not possible to compare future climates with observations, this consensus is deemed in the community as measure of models’ ability to
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respond to the forcings in the same direction, and helps to provide data useful for environmental studies.
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Figure 4 represents the flows for the K-Means classification. In spite of Tapiador et al (2011) observation of the K-Means classification method comparing better with observations in present climate, the difficulties in tracking the same class from present climate to future hinders direct
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applicability of this classification for environmental studies. However, while over land only a careful consideration of the meaning of each class
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would render this classification suitable for such applications, over the oceans the K-Means method provides new information and many details that do not feature in the classical classifications. In any case, the pattern of noticeable changes under the RCP85 is confirmed also in the KMeans climates illustrating the independence of the changes with the classification method, and the consensus between differe nt approaches.
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A quantitative analysis of the changes in the future under the three RCP is provided in tables 2, 3 and 4. The Extended Köppen classification method is applied to all the available models to evaluate the expected changes in each predefined class. It is apparent that some classes do not feature, as the spatial resolution of the models is still too coarse. More importantly is that the direction of the changes a nd the differences
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between the RCPs are now clear: there is a trend toward more changes with increasing GHG concentration, as fi gure 3 suggests, and the strength
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of the changes is larger in the RCP85, whose climate can be characterized as quite different from the present one.
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5. Conclusions
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Climate classifications are proved useful to reduce the dimensionality of climate models’ outputs into variables that can be directly related with
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the biota. Index-based methods such as Köppen and Köppen-Trewartha have the advantage of easier interpretation than objective classifiers such
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as the K-Means algorithm.
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Using 47, diverse GCMs, it is shown that there is a good agreement between modelled climates and observations. Assuming that the models can simulate the future as good as they can model the present, future climate classifications derived for three RCPs (RCP 2.6, RCP 4.5 and RCP 8.5)
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can be considered a good proxy for the climates types of the future, and provide useful input for environmental research. All the data in this paper
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can be downloaded from doi.pangaea.de/10.1594/PANGAEA.891597.
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The host of models shows a reasonable consensus regardless of the classification method used, and increasing GHG concentrations yields more and more profound changes, with large areas of the world changing to different climates than those in the present. The conseq uences of such changes are out of the scope of this paper but deserve attention in the environmental sciences realm.
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Differences, however, persists. In some cases, the climates from the GCMs do not precisely correspond with the climates deriv ed from observations because an incorrect estimation of temperature or, more often, precipitation. While there is a general agreement, there are large
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uncertainties for large parts of the world, and the ability of GCMs to recreate the spatial distribution of precipitation is still challenging. Indeed, the deficiencies in the physics of the models is known to greatly affect precipitation outputs, and that impacts the climate classification. Further
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improvement in the parameterization of the physics will result in more accurate representation of present and future climate and thus in a better typology of the world’s climates that is useful for environmental studies in several fields. Advances on that direction would definitely impact the
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second source of uncertainty mentioned above, namely the GCMs limitations to faithfully represent climates at the required accuracy and
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precision.
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Regarding the first source of uncertainty, the biases in the reference data, the difficulties in the precise estimation of pr ecipitation, especially solid precipitation, are the major contributor to the problem. Only better and more direct observations such as those provided by the Global
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Precipitation Measuring (GPM) mission could provide a more detailed picture of the actual precipitation on Earth and reduce c urrent
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uncertainties. The objective for the near future is to observe total precipitation to an average accuracy of 15% over oceans a nd/or 25% over land and ice surfaces averaged over a 100x100 km region and two to three day time period (National Academies of Scie nces 2018). Achieving such very important goal would certainly benefit climate modeling and therefore the classification of future climates.
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In relation with the third source of uncertainty, namely the suitability of the classification method, here it has been shown that increasing the number of categories reduces the distance between observations and models and that objective methods are as suitable to quantify the direction of changes ad-hoc as predefined classes. The major advantage of the Extended Köppen method, though, is the immediateness in the interpretation
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of the changes, as the classes can be more easily related to simple climatological categories for land (arid, humid, etc.) us ed in most
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environmental studies.
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Acknowledgements
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Funding from projects CGL2013-48367-P, CGL2016-80609-R (Ministerio de Economía y Competitividad, MINECO) are gratefully
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acknowledged. FJT also acknowledges the Joint Research Proposal for Precipitation Research in Spain within NASA’s Precipitation
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Measurement Missions (PMM) Research Program. RM acknowledges a FPI grant from the MINECO. AN acknowledges grant FPU13/02798.
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Thanks are due to Nabhonil Kar (Princeton University) for his help in the editing of the manuscript.
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Figure 1. A comparison of observational-based climate classifications (left) and GCM-based classifications (MIROC4h model, right), for present climate and for three classification methods: index-based Köppen (top), Extended Köppen (middle), and objective, 28 classes K-Means algorithm
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(bottom).
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Figure 2. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the Köppen classification.
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Figure 3. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the Extended Köppen classification.
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Figure 4. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the K-Means classification.
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Table 1. GCMs participating in CMIP5 experiments used in this study. Model Name
Resolution
Modeling Group
ACCESS1.0
1.25° x 1.88°
Commonwealth Scientific and Industrial Research. Organization (CSIRO) and Bureau of
ACCESS1.3
1.25° x 1.88°
Meteorology CSIRO-BOM (BOM), Australia
bcc-csm1-1
2.79° × 2.81°
bcc-csm1-1(m)
1.125° × 1.125°
BNU-ESM
2.79° x 2.81°
CanCM4
2.79° x 2.81°
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Beijing Climate Center, China Meteorological Administration
C S U
College of Global Change and Earth System Science, Beijing Normal University
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Canadian Centre for Climate Modelling and Analysis CanESM2
2.79° x 2.81°
CCSM4
0.94° x 1.25°
CESM1-BGC
0.94° x 1.25°
CESM1-CAM5
0.94° x 1.25°
CESM1-FASTCHEM CESM1-WACCM CMCC-CESM CMCC-CM CMCC-CMS
C A
D E
PT
E C
0.94° x 1.25°
M
National Center for Atmospheric Research (NCAR)
National Science Foundation, Department of Energy, National Center for Atmospheric Research (NSF-DOE-NCAR).
1.88° x 2.5°
3.44° x 3.75° 0.75° x 0.75° 3.71° x 3.75°
Centro Euro-Mediterraneo sui Cambiamenti Climatici
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CNRM-CM5
1.4° x 1.41°
CNRM-CM5-2
1.4° x 1.41°
CSIRO-Mk3-6-0
1.865° x 1.875°
EC-EARTH
1.125° × 1.125°
EC-Earth consortium. National weather services and universities from 11 countries in Europe.
FGOALS-g2
2.8° x 2.8°
Institute of Atmospheric Physics, Chinese Academy of Sciences
FIO-ESM
2.875° x 2.875°
The First Institute of Oceanography, SOA, China
GFDL-CM3
2° x 2.5°
GFDL-ESM2G
2° x 2°
GFDL-ESM2M
2° x 2.5°
GISS-E2-H
2° x 2.5°
GISS-E2-H-CC
2° x 2.5°
GISS-E2-R
2° x 2.5°
GISS-E2-R-CC
2° x 2.5°
HadCM3
2.5° x 3.75°
HadGEM2-CC HadGEM2-ES inmcm4
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Commonwealth Scientific and Industrial Research Organisation in Collaboration with the Queensland Climate Change Centre of Excellence.
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1.25° x 1.875°
A
Avancée en Calcul Scientifique.
NOAA Geophysical Fluid Dynamics Laboratory
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HadGEM2-AO
Centre National de Recherches Météorologiques / Centre Européen de Recherche et Formation
1.25° x 1.875°
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NASA Goddard Institute for Space Studies
Met Office Hadley Centre
1.25° x 1.875° 2.0° × 1.5°
IPSL-CM5A-LR
1.9° x 3.75°
IPSL-CM5A-MR
1.25° x 2.5°
Russian Institute for Numerical Mathematics Climate Model Institut Pierre-Simon Laplace
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IPSL-CM5B-LR
1.9° x 3.75°
MIROC4h
0.56° x 0.56°
MIROC5
1.4° x 1.4°
Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research
MIROC-ESM
2.79° x 2.81°
Institute (The University of Tokyo), and National Institute for Environmental Studies
MIROC-ESM-CHEM
2.79° x 2.81°
MPI-ESM-LR
1.87° x 1.88°
MPI-ESM-MR
1.87° x 1.88°
MPI-ESM-P
1.87° x 1.88°
MRI-CGCM3
1.12° x 1.125°
MRI-ESM1
1.12° x 1.125°
NorESM1-M
1.89° x 2.5°
NorESM1-ME
1.89° x 2.5°
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Max Planck Institute for Meteorology (MPI-M)
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Meteorological Research Institute
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Norwegian Climate Centre
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Table 2. Quantitative analysis of the changes in the climates of the World in a future climate (RCP26) as defined by the extended Köppen classification of the Global Climate Models outputs. EXTENDED KÖPPEN FUTURE CLIMATE (RCP26 )
EXTENDED KÖPPEN, PRESENT CLIMATE
Af
A w
Af
22.9 6
Aw
-
Am
0.37
As
-
2.1 5 0.1 6 0.0 6
Ai
-
Ag BW h BW k BW s BW w BW n
-
A m 0.3 5 0.1 8
As
A i
A g
BW h
BW k
BW s
BW w
1.9 0.0 5
0.0 1 0.0 8 0.0 6 1.0 9
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.15 0.02
BW n
BS h
-
-
0.14
-
-
-
-
-
-
0.09
-
-
-
-
-
-
0.06
-
-
-
-
0.2 1 0.0 3 0.0 2
-
-
0.19
-
-
-
-
-
-
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-
-
-
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0.1 4
-
-
-
-
-
-
-
2.36
0.11
0.0 1
-
-
-
-
-
-
-
0.22
-
-
-
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-
-
-
-
0.01
0.0 6
-
-
-
-
-
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-
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-
BSh
-
-
-
-
BSk
-
-
-
-
C A
BSs
-
-
-
BSw
-
-
BSn
-
-
E C 0.01
BS s
BS w
BS n
Cf a
Cf b
Cw a
Cw b
0.5 2
0.0 3
0.0 1
Cs b
Df
Df d
D w
Dw d
ET
EF
0.1 1
0.0 1
-
-
-
-
-
-
0.0 1
-
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T P Cs a
0.0 6 0.0 8 0.0 5
-
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0.0 1
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0.1 4 0.0 2
-
0.0 1 0.0 3
0.0 3
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0.0 4
-
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0.0 2 0.0 1
-
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0.0 4 0.0 2
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0.0 6 0.0 3
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0.0 1
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0.0 4
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0.0 2
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0.0 1
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BS k
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0.16
-
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0.1 5 0.5 1
0.0 1 0.0 1 0.0 9
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0.0 3
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Cfa
-
-
-
-
-
-
-
-
-
-
-
0.0 1
-
-
-
-
Cfb
-
-
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Cwa
-
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Cwb
-
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Csa
-
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-
0.0 8
Csb
-
-
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-
-
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0.0 1 0.0 3 0.3 2
Df
-
-
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-
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Dfd
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Dw Dw d
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EF
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T P E
D E
A
C C
-
-
-
-
2.6 7 0.0 4
0.7 9.1 5
0.0 3
-
0.0 3 0.1 6
0.1 8 0.2 1
-
-
-
0.8 3
0.0 5 0.0 3 7.2 8
-
0.0 5 0.1 4
-
-
SC
-
-
-
0.3 5 2.2 8
-
-
-
-
-
U N
0.4 6
0.0 9
-
I R
T P
-
-
-
-
-
-
-
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-
-
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-
-
-
-
0.0 2
-
-
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-
-
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-
-
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-
-
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0.0 2
-
-
-
-
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-
-
A M
-
-
-
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-
-
1.01
-
-
0.0 1
-
-
-
-
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0.01
-
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-
-
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-
0.0 9
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0.05
-
0.01
0.73
-
0.01
0.02
-
0.03
0.02
-
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EXTENDED KÖPPEN, PRESENT CLIMATE
Table 3. Quantitative analysis of the changes in the climates of the World in a future climate (RCP45) as defined by the exte nded Köppen classification of the Global Climate Models outputs. EXTENDED KÖPPEN FUTURE CLIMATE (RCP45) Af
A w
A m
Af
22.6 2
0.0 1
Aw
0.01
Am
0.49
As
0.01
2.2 0.1 7 0.0 8
0.4 0.2 3 1.9 8 0.0 9
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Table 4. Quantitative analysis of the changes in the climates of the World in a future climate (RCP85) as defined by the exte nded Köppen classification of the Global Climate Models outputs. EXTENDED KÖPPEN FUTURE CLIMATE (RCP85) A w
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Figure 1. A comparison of observational-based climate classifications (left) and GCM-based classifications (MIROC4h model, right), for present climate and for three classification methods: index-based Köppen (top), Extended Köppen (middle), and objective, 28 classes K-Means algorithm (bottom).
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Figure 2. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the Köppen classification.
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Figure 3. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the Extended Köppen classification.
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Figure 4. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the K-Means classification.
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Highlights This paper provides a systematic analysis of climate classifications for present and future climates. Several classifications methods are used to illustrate the consensus between models and observations. The complete dataset for all models and methods is available as supplementary materials at doi.pangaea.de/10.1594/PANGAEA.891597
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Francisco J. Tapiador, Raúl Moreno, Andrés Navarro PII: DOI: Reference:
S0169-8095(18)30902-5 doi:10.1016/j.atmosres.2018.09.017 ATMOS 4376
To appear in:
Atmospheric Research
Received date: Revised date: Accepted date:
14 July 2018 17 September 2018 18 September 2018
Please cite this article as: Francisco J. Tapiador, Raúl Moreno, Andrés Navarro , Consensus in climate classifications for present climate and global warming scenarios. Atmos (2018), doi:10.1016/j.atmosres.2018.09.017
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Consensus in Climate Classifications for Present Climate and Global Warming Scenarios
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Francisco J. Tapiador*, Raúl Moreno and Andrés Navarro
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University of Castilla-La Mancha (UCLM), Institute of Environmental Sciences (ICAM), Faculty of Environmental Sciences and Biochemistry, Earth and Space Sciences Group. Avda. Carlos III s/n, 45071 Toledo, Spain
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*Corresponding Author. Email: [email protected]
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Abstract: Climate classifications of climate models’ outputs have been used to assess environmental changes but systematic analyses o f the
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differences between models, scenarios and classification methods are scarce. Here, the results of applying the most commonly used climate
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classifications to the outputs of 47 Global Climate Models (GCM) of different physical parameterizations and varied grid size are presented. The extent and intensity of changes for present climate, three different Representative Pathways Scenarios (RCP26, RCP45 and RCP85) and three
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increasingly-fine classification methods show that there is a consensus between models, and that climate classifications are indeed useful tools to translate physical climatology variables into environmental changes. The main conclusions are that climate classifications can indeed be used to
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gauge model performance at several grid sizes and that the classification method does not decisively affects the potential gl obal changes in future climates under increasing greenhouse gas emissions. The analyses also reveal that there are several uncertainties that are no t attributable to model
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grid size or to limitations in the reference datasets but more likely to deficiencies in the physics of the models.
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Keywords: Climate classification, Global Climate Models, Köppen classification, Köppen-Trewartha classification, K-means clustering
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1. Introduction
Classifications of atmospheric variables are useful tools in atmospheric research to unveil patterns, filter observational da ta and identify
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relationships buried in model outputs. Thus, they have been used in the analysis of urbanization effects (Lin et al 2010); evaluation of
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precipitation (Ramos 2001, Serra et al 2014, Miró et al 2017, Wen et al 2017, Kim et al 2017, Sharifi et al 2018), and temperature (Peña-Angulo et al 2016) products; modelling extreme weather and climatic events (Chu and Zhao 2011, Tramblay et al 2018); radiation (Rozwadowska 2004,
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Vindel et al 2015); convection (Dimitrova et al 2009; Aran et al 2011, Lack et al 2012); and deriving climatologies of processes such as fog (Cereceda et al 2008) and tornadoes (Giaiotti et al 2007).
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Climate classifications in particular can be characterized as techniques to perform a dimensional reduction of physical variables ( precipitation,
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temperature, evapotranspiration, etc.) into one of two index-classes that can be more readily related to the biota. They have been found useful for
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a variety of topics including Arctic research (Wang and Overland 2004) ; studies of ecosystem impacts
(Roderfeld et al 2008); biome
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distribution (Leemans et al 1996) and biodiversity analyses (Garcia et al 2014); hydrological cycle studies (Manabe and Holloway 1975); to compare vegetation distribution (Monserud 1990); analyze precipitation metrics (Tang et al. 2012); analyzing vegetation changes in the future (Jiang et al 2013); provide input to global models (Prentice 1990) and to visualize climate change (Jylhä et al 2010) to name but a few.
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Climate classifications such as Köppen’s date back to pioneer studies in the late 19 th century, and were not intended to explain environmental factors in terms of climate variables. Rather, Köppen’s specific aim was the other way around: his intention was to be able to define climates based on the observed distribution of vegetation at the time (Köppen 1900). In synthesis, his method defines a set of temperature and precipitation thresholds to derive a tripartite climate classification, which a 3-letter key identifying the major climate, the precipitation cycles,
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and the summer temperature. Part of its success (Köppen’s method is taught in Geography 101 everywhere) relies on its simplicity. Other indexbased classifications such as Budyko’s recently discussed by Caracciolo et al (2018), or the one proposed by Thornthwaite (1948) are less popular.
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The inception of coupled and dynamic climate models has allowed to turn upside down Köppen’s approach. While his intention was help to define climates using vegetation distribution because of scarce and unreliable meteorological data, data availability is no l onger an issue but the
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response of vegetation to ongoing global warming is indeed an active field of research. Coupled atmosphere-ocean Global Climate Models (GCMs) offer a wealth of atmospheric variables at appropriate spatial and temporal resolutions that compare well with observa tions (Jiang et al
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2012). Increasing computing power have made possible Earth System Models (ESM) including cryosphere dynamics, geobiochemical cycles and human population interactions (Navarro et al 2018) to provide outputs below 25 km grid size, having in fact superseding Regional Climate
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Models (RCMs) as these have issues responding to large scale circulations, among other critical issues.
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Drawing upon improvements in Köppen early theory, improvements such as those in Netzel and Stepinski (2016) have allowed a more detailed characterization of climates. Therein, Köppen or slightly-modified Köppen systems have been proposed to classify climates, evaluate model performances and as input data for climate change impact assessment. It has also been suggested that Köppen and Köppen-Trewartha
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classifications could be improved by objective clustering methods. Indeed, the application of these techniques to global (Hoffman et al 2005,
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Netzel and Stepinski 2016) and regional models (Tapiador et al 2011) outputs produces climates that compare well with observations and with satellite estimates of vegetation vigor, such as the NDVI index.
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The sources of uncertainties in climate classifications using GCMs are at least three: (1) biases in reference data; (2) limi tations of the GCMs to faithfully represent climates at the required accuracy and precision; and; (3) unsuitability of the classifi cation method. The first issue has been tackled by Phillips and Bonfils (2015) albeit the effects of using different reference datasets for model tuning and validation remains unexplored.
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These authors also found no crucial differences in terms of grid size for the 15 GCMs they used. Here, the two latest factors are explored using a
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quite comprehensive host of 47 GCMs of varied resolution and physics and the three major classification methods described in the literature.
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2. Data
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47 GCM from the Coupled Model Intercomparison Project (CMIP5) were used to generate the climate classifications. Applications of CMIP5
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models for environmental studies are numerous so there is no question about the suitability of such models for this purpose. In the physical
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realm, the CMIP5 dataset has been used to investigate historical changes in precipitation at aggregated scale (Sillmann et al 2013a, 2013b, Ren et
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al 2013) and in terms of seasonal changes (Li et al 2013). CMIP5 simulations have also been instrumental to advance our knowledge on atmospheric dynamics (Allen and Landuyt 2014, Lott et al 2014, Kitoh et al 2013), cloud-aerosol interactions (Ekman 2014), present and future biases and uncertainties (Cattiaux et al 2013) greenhouse gas forcing (Bellouin et al 2011, Cook and Seager 2013), and attribution to human
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activities of the disruptions in precipitation cycles (Tapiador et al 2016).
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The CMIP5 database contains data of unprecedented quality to analyze atmospheric variables at global scales and for extended periods. Since its publication in 2008 by more than 20 climate modeling groups under the aegis of the World Climate Research Programme’s (WCRP) Working Group on Coupled Modelling (WGCM), and the International Geosphere-Biosphere Programme’s (IGBP) Analysis, Integration and Modeling of
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the Earth System (AIMES) project, CMIP5 has generated a set of comprehensive multimodel experiments for 1) assessing the mechanisms responsible for model differences in poorly understood feedbacks associated with the carbon cycle and with clouds; 2) examining climate predictability and exploring the predictive capabilities of forecast systems on decadal time scales; and 3) determining why s imilarly forced
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models produce a range of responses (Taylor et al 2012).
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Table 1 gathers the models used in this paper. The grid spacing varies from 3.71º in the CMCC-CMS model to 0.56º in the MIROC4h model, the
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latest being a resolution commensurable with that of Regional Climate Models (RCMs) with the added advantage of GCMs properly responding to large scale circulation and avoiding the biases induced by nesting. Specific information about the ensemble and the characteristics of each
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model can be found in (Taylor et al. 2012) and references therein.
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The observation data for the analyses is from the CRU database (Harris et al 2014). A major reason to use CRU instead of other precipitation and
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temperature databases such as GPCP (Adler et al 2018), which also covers the oceans, is that most GCMs are tuned against the CRU dataset, so
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in principle a better agreement with observations can be expected. The practice and consequences of model tuning have been re cently explored by Hourdin et al (2017), Rotstayn (2000), and Suzuki et al (2015) and do not affect the results presented in this paper.
3. Methods
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Index-based climate classifications coexist with physically-based classification such as those of Alisov (1954), Borchert (1953), Flöhn (1950), or Strahler (1976). These are based on global atmospheric patterns and air masses distributions, and render a method for classifying climates w hich
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is independent of biota. These classifications are however less amenable for use in environmental studies as the explanatory variables are not immediate, as in the case of precipitation and temperature.
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Here, three indexes are used: classical Köppen (11 classes), Köppen-Trewartha (28 classes; also named as ‘Extended Köppen’) and K-Means
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objective classification for a predefined number of 28 classes. The first two are well known so their rationale and description will not be repeated here. The algorithmic followed for Köppen approach is as in the pioneer work of Lohmann et al. (1993), who first used climate classifications as
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diagnostic tool for climate models. For Köppen-Trewartha we used the same method as Baker et al. (2009) which classifies the ecoregions in six main climate groups, and in which every subgroup is clearly defined.
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Regarding the K-Means classification, just noting that the convergence of the iterative algorithm depends on the choice of initial centroids. Here,
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as an alternative to random initialization, the Köppen-Trewartha classification was used as a starting point. Such choice sets sensible pivotal
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centroids for the algorithm thus improving the rate of convergence to the final classes, which by construction are nonetheles s independent on the
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initial coordinates.
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4. Results and Discussion
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4.1 Present climate (1961-2005)
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Figure 1 exemplifies the results of the classifications for the best resolution GCM (MIROC4h model), and how they compare with CRU data, which is limited to land. Köppen classification (Figure 1, top row) shows the limitations of the GCM and the sort of is sues that come across with climate classifications from GCM data. While there is an overall agreement in the planetary pattern, a detailed inspection show that for instance
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Europe shows a homogeneous Cf climate, which is not at variance with the classical, isolines maps of Köppen system, but nonetheless unrealistic
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and of limited usefulness. Central Australia is also misclassified because of the precipitation bias of the models for low ra in amounts and the sensitivity of the classifications on the associated thresholds.
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Maps in the supplementary information (S1) show the results of applying the Köppen index to present climate conditions in all the 47 CMIP5
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models (plus one ensemble) and CRU. Some models perform better compared with CRU than others. The MRI-CGCM3, for instance, almost perfectly classifies the climate of Central Australia (but fails in other locations). However, no clear pattern of agreement/ disagreement arises in
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terms of grid size or model complexity. Full, coupled ESMs do not necessarily do better than simpler, coarser resolution GCMs. Nonetheless, all
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the models consistently depict the major climates of Köppen system in approximately their actual locations, illustrating a ce rtain degree of
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consensus. It is worth noting that quantitative analyses in form of scatterplots, while easy to build, are less informative for environmental research than the maps provided here because of the differences in grid size.
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The Extended Köppen classification, been more detailed (28 classes) shows an overall better agreement with observations. Figure 1 (middle row)
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shows that the MIROC4h model compares best with observations than in the previous classification, with many nuances correctly captured by the model. The systematic comparison for all models (in S2) shows an even greater consensus among the models, with some of them becoming very close of the observations. This result shows that the number of classes in the climate classifications is not immaterial , and that finer, more detailed classifications compare better with observations.
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Indeed, the K-means classification (Figure 1, bottom row) is more detailed and provides added value to the comparisons. As can be expected from the algorithm, the classes are more homogenously distributed and that translates to more details, which are clearly visible over the oceans
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where the method is capable of discerning known features such as the ENSO that are invisible for the two classical methods. Here, the
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correspondence with observations is better than in the previous methods. The trade-off, however, is that classes cannot be easily assigned to the biota. But the added detail may be worth the price for some applications such as marine research, for which both Köppen and Extended Köppen
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classifications provide little insight as they depict the oceans as almost homogeneous in longitude (cf. Figure 1, top and middle rows). It is worth recalling that in order to make the classes between models more comparable, the centroids of the K-means algorithm were initialized with the
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Extended Köppen classification. This choice was intended to preserve the spatial coherence in the many models, and indeed the maps in S3 prove that the iterations of the algorithm generate classes that are consistent between the models. In terms of the sui tability of the classification method
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issue raised above, it seems that this objective method is preferable than the pre-defined, classical, Köppen classification. The Extended Köppen
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classification, on the other hand, compares well with the objective method and has the advantage of using named, easy to remember classes for
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land.
4.2 Future climate (2010-2100)
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For future climates, three Representative Concentration Pathways (RCPs; van Vuuren et al 2011) were used. RCPs are not fully integrated socioeconomic parameterizations but rather estimates for describing plausible trajectories of human climate change drivers. They provide simplified accounts of human activities and processes, including population density and economic development, from non-coupled Integrated Assessment Models (IAMs), and have been widely used in the analysis of possible climate change, its impacts, and options to mitigate climate
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change. RCPs have replaced the scenarios in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES).
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The RCPs are intended to be consistent with a wide range of possible changes in future anthropogenic GHG emissions (Meinshausen et al 2011).
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RCP 2.6 (RCP26 hereafter) assumes that global annual GHG emissions (measured in CO2-equivalents) peak between 2010-2020, with emissions declining substantially thereafter. Emissions in RCP 4.5 (RCP45) have a maximum around 2040 and then reduce. In RCP 8.5 (RCP8 5),
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emissions continue to increase throughout the 21st century. RCP85 is therefore the worse-case scenario in terms of global warming, RCP26 is the most optimistic, and RCP45 stays in the middle notwithstanding that such pathway could indeed trigger no-turning back responses in the climate
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system.
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The number of models that use the RCPs varies for each pathway, as not all the modelers chose to simulate all the three. RCP85 is the most
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popular because the signal of increasing GHG emissions is larger and a rational use of computing resources directs to first e xploring this
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scenario: if no climate change signal is apparent in RCP85 it would be a waste running the others.
In order to provide a quantitative account of the expected changes in the future, flow diagrams were used (Figures 2, 3 and 4 ). These show the
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migration of present climates towards the new conditions. This representation permits to synthetically account for different grid size and number
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of models in a useful way so the three scenarios can be compared for all the CMIP5 models. The width of each climate (say Köp pen’s AF) is proportional to the mean number of points covered by such climate (AF) in either present or future conditions averaged over all available models. If a climate wanes, then flows go to other climates; and conversely, a waxing climate receives flows from other climates. The resulting plot shows in both an intuitive and precise way if the climate classes are changing, and in which direction.
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Flow diagrams for Köppen classification (Figure 2) shows that most climates experiment a noticeable change, which increases with GHGs concentration. The pattern of increasing transfer in the RCP85 is noticeable (clearer in Figures 3 and 4), indicating a quite different climate than
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the present one. S4 provides an individual account of the changes in the classes for all the models under Köppen scheme. Note that some classes
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may contain no points.
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The Extended Köppen classification (Figure 3) confirms the pattern observed in Köppen classification, and provides additional insight into the changes. As the topic of this paper is to gauge the agreement between models and show the associated uncertainty, it is out of the scope to
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identify individual trends, cases and transfers that feature in the figures but, corresponding to Figure 3, S4 shows a consis tent behavior of all individual models. While there are differences between GCMs, there is consensus in the direction and magnitude of the changes. As it is
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obviously not possible to compare future climates with observations, this consensus is deemed in the community as measure of models’ ability to
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respond to the forcings in the same direction, and helps to provide data useful for environmental studies.
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Figure 4 represents the flows for the K-Means classification. In spite of Tapiador et al (2011) observation of the K-Means classification method comparing better with observations in present climate, the difficulties in tracking the same class from present climate to future hinders direct
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applicability of this classification for environmental studies. However, while over land only a careful consideration of the meaning of each class
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would render this classification suitable for such applications, over the oceans the K-Means method provides new information and many details that do not feature in the classical classifications. In any case, the pattern of noticeable changes under the RCP85 is confirmed also in the KMeans climates illustrating the independence of the changes with the classification method, and the consensus between differe nt approaches.
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A quantitative analysis of the changes in the future under the three RCP is provided in tables 2, 3 and 4. The Extended Köppen classification method is applied to all the available models to evaluate the expected changes in each predefined class. It is apparent that some classes do not feature, as the spatial resolution of the models is still too coarse. More importantly is that the direction of the changes a nd the differences
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between the RCPs are now clear: there is a trend toward more changes with increasing GHG concentration, as fi gure 3 suggests, and the strength
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of the changes is larger in the RCP85, whose climate can be characterized as quite different from the present one.
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5. Conclusions
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Climate classifications are proved useful to reduce the dimensionality of climate models’ outputs into variables that can be directly related with
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the biota. Index-based methods such as Köppen and Köppen-Trewartha have the advantage of easier interpretation than objective classifiers such
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as the K-Means algorithm.
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Using 47, diverse GCMs, it is shown that there is a good agreement between modelled climates and observations. Assuming that the models can simulate the future as good as they can model the present, future climate classifications derived for three RCPs (RCP 2.6, RCP 4.5 and RCP 8.5)
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can be considered a good proxy for the climates types of the future, and provide useful input for environmental research. All the data in this paper
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can be downloaded from doi.pangaea.de/10.1594/PANGAEA.891597.
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The host of models shows a reasonable consensus regardless of the classification method used, and increasing GHG concentrations yields more and more profound changes, with large areas of the world changing to different climates than those in the present. The conseq uences of such changes are out of the scope of this paper but deserve attention in the environmental sciences realm.
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Differences, however, persists. In some cases, the climates from the GCMs do not precisely correspond with the climates deriv ed from observations because an incorrect estimation of temperature or, more often, precipitation. While there is a general agreement, there are large
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uncertainties for large parts of the world, and the ability of GCMs to recreate the spatial distribution of precipitation is still challenging. Indeed, the deficiencies in the physics of the models is known to greatly affect precipitation outputs, and that impacts the climate classification. Further
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improvement in the parameterization of the physics will result in more accurate representation of present and future climate and thus in a better typology of the world’s climates that is useful for environmental studies in several fields. Advances on that direction would definitely impact the
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second source of uncertainty mentioned above, namely the GCMs limitations to faithfully represent climates at the required accuracy and
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precision.
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Regarding the first source of uncertainty, the biases in the reference data, the difficulties in the precise estimation of pr ecipitation, especially solid precipitation, are the major contributor to the problem. Only better and more direct observations such as those provided by the Global
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Precipitation Measuring (GPM) mission could provide a more detailed picture of the actual precipitation on Earth and reduce c urrent
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uncertainties. The objective for the near future is to observe total precipitation to an average accuracy of 15% over oceans a nd/or 25% over land and ice surfaces averaged over a 100x100 km region and two to three day time period (National Academies of Scie nces 2018). Achieving such very important goal would certainly benefit climate modeling and therefore the classification of future climates.
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In relation with the third source of uncertainty, namely the suitability of the classification method, here it has been shown that increasing the number of categories reduces the distance between observations and models and that objective methods are as suitable to quantify the direction of changes ad-hoc as predefined classes. The major advantage of the Extended Köppen method, though, is the immediateness in the interpretation
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of the changes, as the classes can be more easily related to simple climatological categories for land (arid, humid, etc.) us ed in most
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environmental studies.
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Acknowledgements
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Funding from projects CGL2013-48367-P, CGL2016-80609-R (Ministerio de Economía y Competitividad, MINECO) are gratefully
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acknowledged. FJT also acknowledges the Joint Research Proposal for Precipitation Research in Spain within NASA’s Precipitation
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Measurement Missions (PMM) Research Program. RM acknowledges a FPI grant from the MINECO. AN acknowledges grant FPU13/02798.
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Thanks are due to Nabhonil Kar (Princeton University) for his help in the editing of the manuscript.
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Figure 1. A comparison of observational-based climate classifications (left) and GCM-based classifications (MIROC4h model, right), for present climate and for three classification methods: index-based Köppen (top), Extended Köppen (middle), and objective, 28 classes K-Means algorithm
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(bottom).
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Figure 2. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the Köppen classification.
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Figure 3. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the Extended Köppen classification.
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Figure 4. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the K-Means classification.
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Table 1. GCMs participating in CMIP5 experiments used in this study. Model Name
Resolution
Modeling Group
ACCESS1.0
1.25° x 1.88°
Commonwealth Scientific and Industrial Research. Organization (CSIRO) and Bureau of
ACCESS1.3
1.25° x 1.88°
Meteorology CSIRO-BOM (BOM), Australia
bcc-csm1-1
2.79° × 2.81°
bcc-csm1-1(m)
1.125° × 1.125°
BNU-ESM
2.79° x 2.81°
CanCM4
2.79° x 2.81°
T P
I R
Beijing Climate Center, China Meteorological Administration
C S U
College of Global Change and Earth System Science, Beijing Normal University
N A
Canadian Centre for Climate Modelling and Analysis CanESM2
2.79° x 2.81°
CCSM4
0.94° x 1.25°
CESM1-BGC
0.94° x 1.25°
CESM1-CAM5
0.94° x 1.25°
CESM1-FASTCHEM CESM1-WACCM CMCC-CESM CMCC-CM CMCC-CMS
C A
D E
PT
E C
0.94° x 1.25°
M
National Center for Atmospheric Research (NCAR)
National Science Foundation, Department of Energy, National Center for Atmospheric Research (NSF-DOE-NCAR).
1.88° x 2.5°
3.44° x 3.75° 0.75° x 0.75° 3.71° x 3.75°
Centro Euro-Mediterraneo sui Cambiamenti Climatici
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CNRM-CM5
1.4° x 1.41°
CNRM-CM5-2
1.4° x 1.41°
CSIRO-Mk3-6-0
1.865° x 1.875°
EC-EARTH
1.125° × 1.125°
EC-Earth consortium. National weather services and universities from 11 countries in Europe.
FGOALS-g2
2.8° x 2.8°
Institute of Atmospheric Physics, Chinese Academy of Sciences
FIO-ESM
2.875° x 2.875°
The First Institute of Oceanography, SOA, China
GFDL-CM3
2° x 2.5°
GFDL-ESM2G
2° x 2°
GFDL-ESM2M
2° x 2.5°
GISS-E2-H
2° x 2.5°
GISS-E2-H-CC
2° x 2.5°
GISS-E2-R
2° x 2.5°
GISS-E2-R-CC
2° x 2.5°
HadCM3
2.5° x 3.75°
HadGEM2-CC HadGEM2-ES inmcm4
T P
Commonwealth Scientific and Industrial Research Organisation in Collaboration with the Queensland Climate Change Centre of Excellence.
I R
C S U
N A
D E
T P E
1.25° x 1.875°
A
Avancée en Calcul Scientifique.
NOAA Geophysical Fluid Dynamics Laboratory
C C
HadGEM2-AO
Centre National de Recherches Météorologiques / Centre Européen de Recherche et Formation
1.25° x 1.875°
M
NASA Goddard Institute for Space Studies
Met Office Hadley Centre
1.25° x 1.875° 2.0° × 1.5°
IPSL-CM5A-LR
1.9° x 3.75°
IPSL-CM5A-MR
1.25° x 2.5°
Russian Institute for Numerical Mathematics Climate Model Institut Pierre-Simon Laplace
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IPSL-CM5B-LR
1.9° x 3.75°
MIROC4h
0.56° x 0.56°
MIROC5
1.4° x 1.4°
Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research
MIROC-ESM
2.79° x 2.81°
Institute (The University of Tokyo), and National Institute for Environmental Studies
MIROC-ESM-CHEM
2.79° x 2.81°
MPI-ESM-LR
1.87° x 1.88°
MPI-ESM-MR
1.87° x 1.88°
MPI-ESM-P
1.87° x 1.88°
MRI-CGCM3
1.12° x 1.125°
MRI-ESM1
1.12° x 1.125°
NorESM1-M
1.89° x 2.5°
NorESM1-ME
1.89° x 2.5°
T P
I R
C S U
Max Planck Institute for Meteorology (MPI-M)
N A
Meteorological Research Institute
T P E
D E
A
C C
M
Norwegian Climate Centre
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Table 2. Quantitative analysis of the changes in the climates of the World in a future climate (RCP26) as defined by the extended Köppen classification of the Global Climate Models outputs. EXTENDED KÖPPEN FUTURE CLIMATE (RCP26 )
EXTENDED KÖPPEN, PRESENT CLIMATE
Af
A w
Af
22.9 6
Aw
-
Am
0.37
As
-
2.1 5 0.1 6 0.0 6
Ai
-
Ag BW h BW k BW s BW w BW n
-
A m 0.3 5 0.1 8
As
A i
A g
BW h
BW k
BW s
BW w
1.9 0.0 5
0.0 1 0.0 8 0.0 6 1.0 9
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.15 0.02
BW n
BS h
-
-
0.14
-
-
-
-
-
-
0.09
-
-
-
-
-
-
0.06
-
-
-
-
0.2 1 0.0 3 0.0 2
-
-
0.19
-
-
-
-
-
-
-
-
-
-
-
0.1 4
-
-
-
-
-
-
-
2.36
0.11
0.0 1
-
-
-
-
-
-
-
0.22
-
-
-
-
-
-
-
-
0.01
0.0 6
-
-
-
-
-
-
-
-
-
-
-
BSh
-
-
-
-
BSk
-
-
-
-
C A
BSs
-
-
-
BSw
-
-
BSn
-
-
E C 0.01
BS s
BS w
BS n
Cf a
Cf b
Cw a
Cw b
0.5 2
0.0 3
0.0 1
Cs b
Df
Df d
D w
Dw d
ET
EF
0.1 1
0.0 1
-
-
-
-
-
-
0.0 1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
I R
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
C S U
-
-
N A
T P Cs a
0.0 6 0.0 8 0.0 5
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 1
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 2
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 2
-
-
-
-
-
-
-
-
-
-
-
-
-
0.1 4 0.0 2
-
0.0 1 0.0 3
0.0 3
-
-
-
-
-
0.0 4
-
-
-
-
-
-
0.0 2 0.0 1
-
-
0.0 4 0.0 2
-
-
0.0 6 0.0 3
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 1
-
-
0.0 4
-
-
0.0 2
-
-
0.3 2
0.0 1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
M 0.0 2
-
-
D E
PT -
BS k
-
0.16
-
-
-
-
-
-
-
-
0.1 5 0.5 1
0.0 1 0.0 1 0.0 9
-
-
-
-
-
0.01
-
-
-
-
0.9 9
-
-
-
0.01
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 3
-
-
-
-
-
-
-
-
-
-
-
-
-
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Cfa
-
-
-
-
-
-
-
-
-
-
-
0.0 1
-
-
-
-
Cfb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Cwa
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Cwb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Csa
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 8
Csb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 1 0.0 3 0.3 2
Df
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Dfd
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Dw Dw d
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
ET
-
-
-
-
-
-
-
-
-
-
EF
-
-
-
-
-
-
-
-
-
-
T P E
D E
A
C C
-
-
-
-
2.6 7 0.0 4
0.7 9.1 5
0.0 3
-
0.0 3 0.1 6
0.1 8 0.2 1
-
-
-
0.8 3
0.0 5 0.0 3 7.2 8
-
0.0 5 0.1 4
-
-
SC
-
-
-
0.3 5 2.2 8
-
-
-
-
-
U N
0.4 6
0.0 9
-
I R
T P
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 2
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 2
-
-
-
-
-
-
-
-
-
-
-
-
A M
-
-
-
-
-
-
-
1.01
-
-
0.0 1
-
-
-
-
-
-
0.01
-
-
-
-
-
-
-
0.0 9
-
0.05
-
0.01
0.73
-
0.01
0.02
-
0.03
0.02
-
0.2 0.1 1 -
0.6 8
-
-
-
0.0 4
-
-
-
-
-
0.01 14.5 7
-
-
-
-
-
0.03
1.93 18.1 5
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EXTENDED KÖPPEN, PRESENT CLIMATE
Table 3. Quantitative analysis of the changes in the climates of the World in a future climate (RCP45) as defined by the exte nded Köppen classification of the Global Climate Models outputs. EXTENDED KÖPPEN FUTURE CLIMATE (RCP45) Af
A w
A m
Af
22.6 2
0.0 1
Aw
0.01
Am
0.49
As
0.01
2.2 0.1 7 0.0 8
0.4 0.2 3 1.9 8 0.0 9
Ai
-
-
Ag BW h BW k BW s BW w BW n
-
-
As 0.0 2 0.1 1 0.0 9
A i
A g
BW h
BW k
BW s
BW w
BW n
BS h
BS k
BS s
BS w
-
-
0.0 1
-
-
-
-
-
-
BS n
Cf a
Cf b
Cw a
Cw b
-
0.7 5
0.0 4
0.0 2
-
0.16
0.01
-
-
-
-
-
0.14
-
-
-
-
-
-
0.09
-
-
-
-
1.1
-
-
0.27
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
C S U
-
-
-
-
-
-
-
-
-
-
-
-
0.23
-
0.1 9
0.01
-
-
-
-
-
-
-
2.29
0.13
0.0 1
-
-
-
-
-
-
-
0.2
0.0 1
-
-
-
-
-
-
-
0.01
0.0 7
-
-
-
-
-
-
-
-
-
-
-
BSh
-
-
-
-
BSk
-
-
-
-
BSs
-
-
-
BSw
-
-
BSn
-
-
E C 0.02
Df
Df d
D w
Dw d
ET
EF
0.1 5
0.0 1
-
-
-
-
-
-
0.0 1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 9 0.0 7 0.0 5
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 3
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 2
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 4
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 2
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 7
-
-
0.0 4
0.0 1
-
0.3 5
0.0 7 0.0 1 0.0 1
-
-
0.0 3 0.0 1
-
-
0.0 6 0.0 2
-
-
0.0 1 0.0 3
-
0.2 0.0 1
0.0 7 0.0 2
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
M 0.0 2
-
-
D E
PT -
Cs b
I R
-
0.2 4 0.0 4 0.0 4
N A
T P Cs a
-
0.18
-
-
-
-
C A
-
-
-
-
0.1 7 0.4 4
0.0 1
-
-
-
-
-
0.01
-
-
-
-
0.8 4
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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0.0 3
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2.4 4
0.0 2
-
0.3 6
0.0 4
-
0.1
-
-
-
0.8 8 7.8 3 0.0 1 0.0 2 0.0 6 0.4 3
SC
-
-
-
-
-
Cfa
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Cfb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Cwa
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Cwb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Csa
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 9
Csb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Df
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Dfd
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Dw Dw d
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
ET
-
-
-
-
-
-
-
-
-
-
EF
-
-
-
-
-
-
-
-
-
-
T P E
D E
A
C C
-
-
-
-
0.0 4
U N
0.0 9 -
0.0 3 0.1 3
0.2 1 0.2 5
-
-
-
-
0.8 6 -
0.4 7 2.2 7
-
-
-
0.0 9 0.0 3 6.6 3
I R
T P
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 1
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 1
-
-
-
-
-
-
-
-
-
-
-
-
A M
-
-
-
-
-
-
-
1.43
-
-
-
-
-
0.0 1 0.0 1
-
0.01
-
-
-
-
-
-
0.3 7 0.0 9
0.0 7
-
0.07
-
0.02
1.06
-
0.01
0.02
-
0.06
0.05
-
-
0.6 3
-
-
-
0.0 5
-
-
-
-
-
0.01 13.6 5
-
-
-
-
-
-
3.34 17.6 7
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Table 4. Quantitative analysis of the changes in the climates of the World in a future climate (RCP85) as defined by the exte nded Köppen classification of the Global Climate Models outputs. EXTENDED KÖPPEN FUTURE CLIMATE (RCP85) A w
A m
As
0.0 2 2.0 9 0.1 9
As
22. 5 0.0 2 0.6 1 0.0 2
0.1
0.4 2 0.2 7 1.8 3 0.1 2
0.0 3 0.1 5 0.0 9 1.2 9
Ai
-
-
-
Ag BW h BW k BW s BW w BW n
-
-
-
Af
Af Aw
EXTENDED KÖPPEN, PRESENT CLIMATE
Am
A i
A g
BW h
BW k
BW s
BW w
BW n
BS h
-
-
0.21
0.01
-
-
-
-
-
0.15
-
-
-
-
-
-
0.13
-
-
-
-
-
-
0.43
-
-
-
-
0.3 4 0.0 5 0.0 5 0.0 1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
1.88
0.15
0.0 1
0.22
-
-
-
-
-
-
0.16
0.0 1
0.01 -
-
-
-
-
-
-
-
-
0.0 4
-
-
-
-
-
-
-
0.02
-
-
-
-
-
-
-
-
BSh
-
-
-
-
-
BSk
-
-
-
-
-
BSs
-
-
-
-
-
BSw
-
-
-
-
BSn
-
-
-
Cfa
-
-
-
BS s
BS w
BS n
Cf a
Cf b 0.1 -
Cw a
Cw b 0.0 3 0.0 1 0.0 1
Cs a
Cs b
Df
Df d
D w
Dw d
ET
EF
0.2 6
0.0 4
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
T P
-
-
-
-
0.9 8
-
-
-
-
-
-
-
-
-
-
-
0.1 2 0.1 4 0.0 7
-
-
-
-
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-
-
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-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.1 9
0.0 4
-
0.0 7
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 3
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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-
-
-
-
-
-
0.0 3
-
-
-
-
-
-
-
-
-
-
-
-
-
0.2 1 0.0 1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 9 0.0 2
-
-
0.0 9 0.0 3
-
0.1 0.0 4
0.0 2
-
-
0.0 3 0.0 4
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0 9
-
-
0.0 2
0.0 2
-
0.2 2
0.1 0.0 1 0.0 2
-
-
-
-
-
-
-
-
-
0.0 5
0.3 5
0.0 1
-
-
-
0.0 8
-
-
0.0 3
-
-
1.2 5
-
-
2.0 5
-
-
-
D E -
-
M
-
-
0.08
-
-
-
-
-
PT
-
-
-
-
-
0.0 1 0.0 1 0.0 7
-
0.01
-
-
-
-
0.6 4
-
-
0.01
-
-
-
-
-
-
-
-
-
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-
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-
-
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-
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-
-
-
-
-
-
0.0 1
0.2 0.3 6 0.0 3
I R
C S U
N A
0.0 1
E C
C A
BS k
-
-
-
ACCEPTED MANUSCRIPT
8.4 8 0.0 1 0.0 1 0.0 9 0.4 9
Cfb
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Cwa
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Cwb
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Csa
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0.0 8
Csb
-
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Df
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Dfd
-
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Dw Dw d
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ET
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EF
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A M
T P E
D E
A
C C
-
-
0.0 4
-
0.1 5
0.3 2
-
-
-
-
-
0.5 9 -
0.6 4 1.9 2
0.0 9 0.0 4 6.7 3
-
0.0 6 0.0 9
-
-
-
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-
-
-
-
-
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-
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SC
-
-
-
-
-
-
-
-
-
-
-
-
-
0.01
-
-
-
0.0 1
-
-
-
-
12.8 9
-
-
-
-
-
-
-
-
0.01
U N
-
-
-
-
-
-
-
-
-
-
0.0 1
-
-
-
-
-
-
0.2 7
I R
T P
0.0 1
-
1.81
-
-
-
-
-
0.0 2 0.0 1
-
0.01
-
-
-
-
-
-
0.3 4 0.0 1
0.1 6
-
0.08
-
0.04
1.12
-
0.01
0.01
-
-
0.5 3
0.04
0.05
5.34 15.1 6
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Figure 1. A comparison of observational-based climate classifications (left) and GCM-based classifications (MIROC4h model, right), for present climate and for three classification methods: index-based Köppen (top), Extended Köppen (middle), and objective, 28 classes K-Means algorithm (bottom).
ACCEPTED MANUSCRIPT
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Figure 2. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the Köppen classification.
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Figure 3. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the Extended Köppen classification.
ACCEPTED MANUSCRIPT
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Figure 4. Flow diagrams from present climate to future RCP26, RCP45 and RCP85 climates for the K-Means classification.
ACCEPTED MANUSCRIPT
Highlights This paper provides a systematic analysis of climate classifications for present and future climates. Several classifications methods are used to illustrate the consensus between models and observations. The complete dataset for all models and methods is available as supplementary materials at doi.pangaea.de/10.1594/PANGAEA.891597
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