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K?ppen-Trewartha climate types for the period 2070?2099, derived from the CRU TS3.22 observational dataset and the CMIP5 ensemble RCP4.5 scenario using the delta method
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Climate classifications can provide an effective tool for integrated assessment of climate model results. We present an analysis of future global climate projections performed in the framework of the Coupled Model Intercomparison Project Phase 5 (CMIP5) project by means of Köppen-Trewartha classification. Maps of future climate type distributions w...
Citations
... Global climate models (GCMs) represent invaluable instruments for various purposes, in particular, analysis of climate system dynamics (e.g., Yang et al. 2022;Dai and Deng 2022), evolution of past climates (e.g., Askjaer et al. 2022;Wang et al. 2023) and climate change projections (e.g., Coppola et al. 2021;Belda et al. 2016). The newest set of GCM simulations has been produced under the Coupled Model Intercomparison Project Phase 6 (CMIP6) initiative coordinated by the World Climate Research Programme's (WCRP) Working Group on Coupled Modelling (Eyring et al. 2016). ...
Global climate models (GCMs) are essential for studying the climate system and climate change projections. Due to their coarse spatial resolution, downscaling is necessary on the regional scale. Regional climate models (RCMs) represent a standard solution for this issue. Nevertheless, the boundary conditions provided by GCMs unavoidably influence the outputs of RCMs. This study evaluates CMIP6 GCMs regarding the variables relevant to RCM boundary conditions. Particular focus is on the simulation of CNRM-ESM2-1, which is being used as a driving model for convection-permitting ALARO-Climate RCM, used as one source feeding new Czech climate change scenarios. The analysis is conducted over the boundaries and inside the RCM integration domain. Firstly, an evaluation of CFSR and ERA5 reanalyses against radiosondes is performed to choose an appropriate reference dataset for upper air variables. A high correlation between the two studied reanalysis and radiosondes was revealed, and it slightly decreases at the upper tropospheric levels. ERA5 is then chosen as the reference for the boundary analysis. Over the inner region, the simulated mean annual cycle of impact-relevant variables is validated against E-OBS. The CNRM-ESM2-1 performs well regarding near-surface variables over the Czech Republic, but it exhibits larger errors along the boundaries, especially for air temperature and specific humidity. The GCM performance in simulating the upper air atmospheric variables used as RCM boundary conditions relates rather weakly to the GCM performance in simulating the near-surface parameters in the inner region in terms of parameters relevant for impact studies.
... Projected changes in temperature and precipitation in the twenty-first century may cause significant climate zone shifts in the global land area. A growing body of literature applies the Köppen climate classification scheme to model simulations to predict future changes in Köppen climate zones (Belda et al., 2016;Feng et al., 2014;Rohli et al., 2015;Cui et al., 2021). ...
Knowing climate characteristics enables the detection of particular climate characteristics and their boundaries. This situation is essential in terms of providing sustainable use of areal resources and directing land use plans. For this reason, in this study, climate boundary maps of the Safranbolu district were created based on the need to form a basis for planning. For this purpose, measurement data of all meteorological stations in the district for the last 30 years were obtained; data were associated with the location, and the water balance of each station was calculated using the Thornthwaite climate classification method. In addition, the climate type was determined using different climate classification methods, and the results were compared. All applied methods have shown that Safranbolu has a humid climate; however, the humidity value in the north of Safranbolu is slightly higher than that in the central and southern parts. In addition, water shortage in the north of Safranbolu is observed in July–August, while water shortage in the central and southern parts is observed in July–August–September. Considering the long-term precipitation average of the Safranbolu district, the highest annual precipitation is observed in March and the lowest in August. Etp and Etr throughout the district are highest in July and lowest in January. Surplus water and surface flow occur in the months between December and May, with the highest amount of surface flow occurring in March. There is no month without rain in Safranbolu. Safranbolu, which is on the UNESCO World Heritage List, is a visiting area for local and foreign tourists because of its cultural, architectural, and historical features and geotourism potential. In addition to its current agricultural activities, the cultivation of the “Saffron” plant, which gives its name to the district, and its forest assets cause an increase in both the tourism capacity and population of the district. Considering all of these, studies on climate change risk management and water resources management in Safranbolu have been conducted.
... Previous efforts, such as WorldClim (Hijmans et al., 2005), WorldClim2 (Fick and Hijmans, 2017), and CHELSA (Karger et al., 2017) for global land surface, PRISM (Daly et al., 2002;Daly et al., 2008) and Daymet (Thornton et al., 1997) for North America, had developed several high-quality baseline climatology surfaces with 1 km spatial resolution. Although these baseline climatology surfaces were widely used for basic and applied studies (Belda et al., 2017;Ray et al., 2015), a gap between these gridded climate datasets and weather stations was still observed in many areas (New et al., 2002;Fick and Hijmans, 2017). For example, data quality of WorldClim depends on local climate variability, quality and density of observations, and the degree of the fitted spline (Hijmans et al., 2005). ...
Long-term climate data and high-quality baseline climatology surface with high resolution are essential to investigate climate change and its effect on hydrological processes and ecosystem functioning. However, large uncertainties remain in the climate products in China owing to lacking of high-density distribution network of weather stations. Here, the thin plate spline (TPS) algorithm was used to produce new baseline climatology surfaces (ChinaClim_baseline) using more than 2000 freely available weather stations. Then, climatologically aided interpolation (CAI) was employed to generate a 1km monthly precipitation and temperatures dataset for China during 1952-2019 (ChinaClim_time-series) via superimposing ChinaClim_baseline and monthly anomaly surface. Our finding showed that ChinaClim_baseline performed exceptionally well in four climatic regions, with RMSEs for precipitation and temperature element estimation of 1.276~28.439 mm and 0.310~2.040 °C, respectively. The correlations among ChinaClim_baseline and WorldClim2 and CHELSA were high, but there were clearly spatial differences. For ChinaClim_time-series, precipitation and temperature elements had average RMSEs between 7.502 mm~52.307 mm, and 0.461 °C~0.939 °C for all months, respectively. In comparison to Peng’s climatic surface and CHELSAcruts, R2 increased by 7%, RMSE and MAE dropped by 17% for precipitation; R2 hardly increased, while RMSE and MAE decreased by 50% for temperature elements. Our findings indicated that ChinaClim_baseline improved the accuracy of time-series climatic elements estimation obviously, and the satellite-driven data can greatly improve the accuracy of time-series precipitation estimation, but not the accuracy of time-series temperature estimation. Overall, ChinaClim_baseline as an excellent baseline climatology surface can be used for obtaining high-quality and long-term climate datasets from past to future. Meantime, ChinaClim_time-series of 1km spatial resolution is appropriate for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China.
... Köppen's climate classification method (Köppen 1936) was implemented in this study to classify the climate zone and identify the climate shift to arid conditions in Asia. The method is popularly used to detect regional climates depending on their temperature and precipitation patterns due to the availability and simplicity of this method (Belda et al. 2014(Belda et al. , 2016 ...
A warming climate cause changes in regional climate and climate extremes. However, understanding how future warming can affect the arid climate and drought in Asia is still unclear. Global warming (GW) impacts on shifts in arid regions and change patterns of drought in the Asia region are investigated using the shared socioeconomic pathway 5–8.5 of the Coupled Model Intercomparison Project Phase 6. The variations in the arid region are explored under potential GW targets above preindustrial levels (+ 0.66 °C for the reference period and + 1.5 °C, + 2.5 °C, + 3.0 °C for future periods). We examine changes in precipitation and temperature indices related to drought, the drought patterns corresponding to consistent arid zones and arid-wet shifting zones based on GW scenarios. The results indicate that warming climates influence temperature and precipitation, and consequently alter climate distribution toward an expansion of the arid region. Due to shifts in climate zones, arid regions may expand by 2.6% relative to the reference climate in response to 3.0 °C of warming. Changes in precipitation indices contribute to the spatial patterns and magnitude of drought changes. Future drought features induced by meteorological conditions exhibit different behaviors according to arid-wet shifts and drought events. Especially, drought features throughout Asia are likely to be aggravated in terms of both severity and frequency in response to a greater degree of GW, especially in arid regions and regarding extreme droughts. This study provides valuable information on the relationship between regional climate and drought features in a world affected by GW.
... Otto-Bliesner et al., 2017) and climate change projections (e.g. Belda et al., 2017;Coppola et al., 2021a;Thomas et al., 2022). However, model simulations of climate are subject to many uncertainties. ...
... Continentality of climate generally characterizes the influence of the distance of ocean on the climate of a place (Driscoll and Yee Fong, 1992). Previous studies based on Köppen-Trewartha climatic classification showed projected transition from continental temperate climate to oceanic temperate climate over Central Europe (Feng et al., 2012;Belda et al., 2017). This is seemingly in contrast to our results. ...
The multi-model ensembles like CMIP5 or CMIP6 provide a tool to analyze structural uncertainty of climate simulations. Currently developed regional and local climate change scenarios for the Czech Republic assess the uncertainty based on state-of-the-art Global Climate Model (GCM) and Regional Climate Model (RCM) ensembles. Present study focuses on multi-model spread of projected changes in long-term monthly means and inter-annual variability of monthly mean minimum, mean and maximum daily air temperature and monthly mean precipitation. We concentrate in more detail on the simulation of CNRM-ESM2-1, the driving GCM for the convection permitting ALADIN-Climate/CZ simulation contributing to the local scenarios in very high resolution. For this GCM, we also analyze a mini-ensemble with perturbed initial conditions to evaluate the range of internal climate variability. The results for the Czech Republic reveal minor differences in model performance in the reference period whereas quite substantial inter-generation shift in projected future change towards higher air temperature and lower summer precipitation in CMIP6 comparing to CMIP5. One of the prominent features across GCM generations is the pattern of summer precipitation decrease over central Europe. Further, projected air temperature increase is higher in summer and autumn than in winter and spring, implying increase of thermal continentality of climate. On the other hand, slight increase of winter precipitation and tendency towards decrease of summer precipitation lead to projected decrease of ombric continentality. The end of 21st century projections also imply higher probability of dry summer periods, higher precipitation amounts in the cold half of the year and extremely high temperature in summer. Regarding the CNRM-ESM2-1, it is often quite far from the multi-model median. Therefore, we strictly recommend to accompany any analysis based on the simulation of nested Aladin-CLIMATE/CZ with proper uncertainty estimate. The range of uncertainty connected to internal climate variability based on one GCM is often quite large in comparison to the range of whole CMIP6 ensemble. It implies that when constructing climate change scenarios for the Central Europe region, attention should be paid not only to structural uncertainty represented by inter-model differences and scenario uncertainty, but also to the influence of internal climate variability.
... This algorithm sorts the data according to the climate values of the biome (temperature and precipitation) and sets the borders between them (Fick & Hijmans, 2017;Gotway et al., 1996;Orus et al., 2005;Timpano et al., 2011;Valjarević et al., 2018;Zabel et al., 2014). The main four maps present the climate properties for the four future periods: 2021-2040, 2041-2060, 2061-2080and 2081-2100(Belda et al., 2016. The overlap of the two grids was made possible by the proximity algorithm. ...
The Updated Trewartha climate classification (TWCC) at global level shows the changes that are expected as a consequence of global temperature increase and imbalance of precipitation. This type of classification is more precise than the Köppen climate classification. Predictions included the increase in global temperature (T in °C) and change in the amount of precipitation (PA in mm). Two climate models MIROC6 and IPSL-CM6A-LR were used, along with 4261 meteorological stations from which the data on temperature and precipitation were taken. These climate models were used because they represent the most extreme models in the CMIP6 database. Four scenarios of climate change and their territories were analysed in accordance with the TWCC classification. Four scenarios of representative concentration pathway (RCP) by 2.6, 4.5, 6.0 and 8.5 W/m2 follow the increase of temperature between 0.3°C and 4.3°C in relation to precipitation and are being analysed for the periods 2021–2040, 2041–2060, 2061–2080
and 2081–2100. The biggest extremes are shown in the last grid for the period 2081–2100, reflecting the increase of T up to 4.3°C. With the help of GIS (geographical
information systems) and spatial analyses, it is possible to estimate the changes in
climate zones as well as their movement. Australia and South East Asia will suffer
the biggest changes of biomes, followed by South America and North America.
Climate belts to undergo the biggest change due to such temperature according to
TWCC are Ar, Am, Aw and BS, BW, E, Ft and Fi. The Antarctic will lose 11.5% of
the territory under Fi and Ft climates within the period between 2081 and 2100.
The conclusion is that the climates BW, Bwh and Bwk, which represent the deserts,
will increase by 119.8% with the increase of T by 4.3°C.
... Applications of CMIP models to environmental studies began in the early stages of the project. Some common research topics have been: landscape analysis Guan et al., 2021;Zhou et al., 2021), climate zonation (Rubel and Kottek, 2010;Kriticos et al., 2012;Tapiador et al., 2019a), changes in environmental conditions (Belda et al., 2016;Akinsanola et al., 2020a;Dong et al., 2021), and adaptation to climate change (Läderach et al., 2017;Chen et al., 2020;Mondal et al., 2021). Table 1 lists CMIP6 models used in our work. ...
... Overall, the shifts were at the limits of climate zones and were clearer over the Arctic Ocean. The shrinkage of polar climates was not restricted to high latitudes but were also in high-elevation areas, as suggested by previous studies (Belda et al., 2016;Cui et al., 2021). As expected, changes towards wetter climate types were testimonial. ...
Climate classifications are useful to synthesize the physical state of the climate with a proxy that can be directly related to biota. However, they present a potential drawback, namely a strong sensitivity because of the use of hard thresholds (step functions). Thus, minor discrepancies in the base data produce large differences in the type of climate. However, such an a priori limitation is also a strength because such sensitivity can be used to better gauge model performance. Although previous attempts of classifying climates of the world using global climate model outputs were encouraging, the applicability of their classifications to impact studies has been limited by past model issues. Notwithstanding the persistence of certain imperfections and limitations in current models, the high‐quality physical simulations from phase six of the Coupled Intercomparison Project (CMIP6) has opened new possibilities in the field, thanks to their improved representation of atmospheric and oceanic physics. The purpose of this paper is twofold: to show that climate classifications from CMIP6 are sufficiently robust for use in impact studies, and to use those classifications for identifying error sources and potential issues that deserve further attention in models. Thus, 52 CMIP6 climate models were evaluated by using three climate classifications schemes, classical Köppen, extended‐Köppen, and modified Thornthwaite. We first assessed model ability to reproduce present climate types by comparing their outputs with observational data. Models performed best for the Köppen and extended‐Köppen classification methods (Cohen's kappa κ = 0.7), and had moderate scores for the Thornthwaite climate classification (κ = 0.4). By tracing back the observed biases, we were able to pinpoint the misrepresentation of dry climates as a major source of error. Another finding was that most models still had some difficulties in representing the seasonal variability of precipitation over several monsoonal regions, thereby yielding the wrong climate type there. Models were also evaluated for future climate. Substantial changes in climate types are projected in the SSP5‐8.5 scenario. These changes include a shrinkage of polar/frigid climates (22%) and an increase of dry climates (7%). Simulations arising from global climate models can be directly used to understand the global climate. They are however in the form of multidimensional matrices, which makes the outputs difficult to compare and validate. Conversely, climate classifications simplify the complex interactions of the climate system and serve as a single, aggregated parameter for environmental applications. The purpose of this work is to show that climate classifications from GCMs are robust enough to be used in impact studies, and use those classifications to identify potential issues deserving further attention in models.
... The Köppen climate classification has been used to set up dynamic global vegetation models (Poulter et al., 2011(Poulter et al., , 2015, to characterize species composition (Brugger and Rubel, 2013), to model the species range distribution (Tererai and Wood, 2014;Brugger and Rubel, 2013;Webber et al., 2011), and to analyze the species growth behavior (Tarkan and Vilizzi, 2015). The Köppen classification has also been applied to detect the shifts in geographical distributions of climate zones (Belda et al., 2016;Chan and Wu, 2015;Feng et al., 2014;Mahlstein et al., 2013). It also has the potential to aggregate climate information on warmth and precipitation seasonality into ecologically important climate classes, thereby simplifying spatial variability. ...
The Köppen–Geiger classification scheme provides an effective and ecologically meaningful way to characterize climatic conditions and has been
widely applied in climate change studies. Significant changes in the
Köppen climates have been observed and projected in the last 2
centuries. Current accuracy, temporal coverage and spatial and temporal
resolution of historical and future climate classification maps cannot
sufficiently fulfill the current needs of climate change research.
Comprehensive assessment of climate change impacts requires a more accurate
depiction of fine-grained climatic conditions and continuous long-term time
coverage. Here, we present a series of improved 1 km Köppen–Geiger
climate classification maps for six historical periods in 1979–2013 and four future periods in 2020–2099 under RCP2.6, 4.5, 6.0, and 8.5. The historical
maps are derived from multiple downscaled observational datasets, and the
future maps are derived from an ensemble of bias-corrected downscaled CMIP5
projections. In addition to climate classification maps, we calculate 12
bioclimatic variables at 1 km resolution, providing detailed descriptions of annual averages, seasonality, and stressful conditions of climates. The new maps offer higher classification accuracy than existing climate map products
and demonstrate the ability to capture recent and future projected changes
in spatial distributions of climate zones. On regional and continental
scales, the new maps show accurate depictions of topographic features and
correspond closely with vegetation distributions. We also provide a
heuristic application example to detect long-term global-scale area changes
of climate zones. This high-resolution dataset of the Köppen–Geiger
climate classification and bioclimatic variables can be used in conjunction
with species distribution models to promote biodiversity conservation and to
analyze and identify recent and future interannual or interdecadal changes
in climate zones on a global or regional scale. The dataset referred to as
KGClim is publicly available via http://glass.umd.edu/KGClim (Cui et al., 2021d) and can also be downloaded at https://doi.org/10.5281/zenodo.5347837 (Cui et al., 2021c) for historical climate and https://doi.org/10.5281/zenodo.4542076 (Cui et al., 2021b) for future climate.
... 气候模式(包括全球气候模式和区域气候模式)是 气候预估的基础工具. 基于参加耦合模式比较计划第 五阶段(CMIP5)全球模式集合结果, 使用柯本气候分类 法, Mahlstein等人 [17] 指出全球气候带随全球气温的升 高而线性变化; Feng等人 [18] 预估全球的温带、热带和 干旱带的面积将扩张, 而极地带、亚寒带和亚热带的 面积将减少; Belda等人 [13] 预估到21世纪末, 在RCP4.5 ...
... For the PMIP4 models, the simulated 1961-1990 climatology is derived from the corresponding historical experiments. Such a method for dealing with the model biases has been widely used in calculating the indicators that are sensitive to the threshold values in previous studies (Belda et al., 2016;Chan et al., 2016;Déqué, 2007;Mahlstein et al., 2013), but this correction does not completely reduce the uncertainties in the climate simulations (Déqué, 2007). Additionally, the biases caused by the interpolation are inevitable due to the coarse horizontal resolution of the TraCE-21ka. ...
Investigating the past evolution of the northern boundary of the East Asian summer monsoon (EASM) is important in understanding the East Asian climate of the past, present, and future. In this study, using a set of transient simulations from TraCE‐21ka, we investigate the migration of the EASM northern boundary over eastern China via three metrics and its responses to individual external forcings during the last 21,000 years. The northern boundary was located farther southeastward from 21 ka to 18 ka and more northwestward since 15 ka compared with the present boundary. For two key time slices, the boundary exhibited a southeastward migration ranging from 120 to 140 km in the last glacial maximum (LGM) and a 110–140 km northwestward shift in the mid‐Holocene relative to the present boundary. Temporally, the evolution of the boundary was characterized first by a poleward migration and then an equatorward shift from the LGM to the present, which was mainly driven by orbital insolation. This long‐term trend was punctuated by several millennial fluctuations in response to meltwater fluxes. The boundary migration during the LGM and the mid‐Holocene generally lies within the range of the multimodel results from the Paleoclimate Modeling Intercomparison Project Phase 4 (PMIP4). In terms of dynamic mechanisms, an orbitally induced increase of summer insolation led to an enhanced land‐sea thermal contrast between the East Asian continent and adjacent ocean and, in turn, intensified low‐level southerly winds, which facilitated the northwestward migration of the EASM northern boundary and more precipitation over eastern China. The boundary responses to the changing meltwater fluxes were similar but weak. Additionally, the EASM northern limit was closely linked to the forest‐steppe boundary. The reconstructed forest‐steppe boundary estimated from pollen records and the simulated EASM northern edge display spatially consistent positions, orientations, and change trends.
