Almorox J., V. H. Quej, and P. Marti, 2015: Global performance ranking of temperature-based approaches for evapotranspiration estimation considering Köppen climate classes. J. Hydrol., 528, 514- 522.
Baker B., H. Diaz, W. Hargrove, and F. Hoffman, 2010: Use of the Köppen-Trewartha climate classification to evaluate climatic refugia in statistically derived ecoregions for the People's Republic of China. Climatic Change, 98, 113- 131.10.1007/s10584-009-9622-21eed3899-dddd-4e13-a4a2-1171660c0754slarticleid_20063814f91a6a1887caa325dcf73f33b2db14http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1007%2Fs10584-009-9622-2refpaperuri:(69a26da470ed1caea01b0ef00dc1e558)http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1007/s10584-009-9622-2<a name="Abs1"></a>Changes in climate as projected by state-of-the-art climate models are likely to result in novel combinations of climate and topo-edaphic factors that will have substantial impacts on the distribution and persistence of natural vegetation and animal species. We have used multivariate techniques to quantify some of these changes; the method employed was the Multivariate Spatio-Temporal Clustering (MSTC) algorithm. We used the MSTC to quantitatively define ecoregions for the People&#8217;s Republic of China for historical and projected future climates. Using the K?ppen&#8211;Trewartha classification system we were able to quantify some of the temperature and precipitation relationships of the ecoregions. We then tested the hypothesis that impacts to environments will be lower for ecoregions that retain their approximate geographic locations. Our results showed that climate in 2050, as projected from anthropogenic forcings using the Hadley Centre HadCM3 general circulation model, were sufficient to create novel environmental conditions even where ecoregions remained spatially stable; cluster number was found to be of paramount importance in detecting novelty. Continental-scale analyses are generally able to locate potentially static ecoregions but they may be insufficient to define the position of those reserves at a grid cell-by-grid cell basis.
Chan D., Q. G. Wu, 2015: Significant anthropogenic-induced changes of climate classes since 1950. Sci. Rep., 5, 13487.10.1038/srep13487c1cf899092698a3e14827e4cb7bbac48http%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC4551970%2Fhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4551970/ABSTRACT Anthropogenic forcings have contributed to global and regional warming in the last few decades and likely affected terrestrial precipitation. Here we examine changes in major K&ouml;ppen climate classes from gridded observed data and their uncertainties due to internal climate variability using control simulations from Coupled Model Intercomparison Project 5 (CMIP5). About 5.7% of the global total land area has shifted toward warmer and drier climate types from 1950&ndash;2010, and significant changes include expansion of arid and high-latitude continental climate zones, shrinkage in polar and midlatitude continental climates, poleward shifts in temperate, continental and polar climates, and increasing average elevation of tropical and polar climates. Using CMIP5 multi-model averaged historical simulations forced by observed anthropogenic and natural, or natural only, forcing components, we find that these changes of climate types since 1950 cannot be explained as natural variations but are driven by anthropogenic factors.
Cho M.-H., K. -O. Boo, G. M. Martin, J. Lee, and G.-H. Lim, 2015: The impact of land cover generated by a dynamic vegetation model on climate over East Asia in present and possible future climate. Earth Sys. Dynam., 6( 1), 147- 160.10.5194/esdd-5-1319-20141f88f7a7b79eec9f4ede396efc78f874http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F275220407_The_impact_of_land_cover_generated_by_a_dynamic_vegetation_model_on_climate_over_east_Asia_in_present_and_possible_future_climatehttp://www.researchgate.net/publication/275220407_The_impact_of_land_cover_generated_by_a_dynamic_vegetation_model_on_climate_over_east_Asia_in_present_and_possible_future_climateThis study investigates the impacts of land cover change, as simulated by a dynamic vegetation model, on the summertime climatology over Asia. The climate model used in this study has systematic biases of underestimated rainfall around Korea and overestimation over the South China Sea. When coupled to a dynamic vegetation model, the resulting change in land cover is accompanied by an additional direct radiative effect over dust-producing regions. The direct radiative effect of the additional dust contributes to increasing the rainfall biases, while the land surface physical processes are related to local temperature biases such as warm biases over North China. In time-slice runs for future climate, as the dust loading changes, anomalous anticyclonic flows are simulated over South China Sea, resulting in reduced rainfall over the South China Sea and more rainfall toward around Korea and South China. In contrast with the rainfall changes, the influence of land cover change and the associated dust radiative effects are very small for future projection of temperature, which is dominated by atmospheric COincrease. The results in this study suggest that the land cover simulated by a dynamic vegetation model can affect, and be affected by, model systematic biases on regional scales over dust emission source regions such as Asia. In particular, analysis of the radiative effects of dust changes associated with land cover change is important in order to understand future changes of regional precipitation in global warming.
De Castro, M., C. Gallardo, K. Jylha, H. Tuomenvirta, 2007: The use of a climate-type classification for assessing climate change effects in Europe from an ensemble of nine regional climate models. Climatic Change, 81, 329- 341.10.1007/s10584-006-9224-177b15dba-8b9b-4279-85a7-03428d49f258slarticleid_17203049c35808861b1983f0d3f349886701e3http%3A%2F%2Fwww.springerlink.com%2Findex%2FT44523273087Q071.pdfrefpaperuri:(3670f77af89a2da363636f3bb62ccc82)http://www.springerlink.com/index/T44523273087Q071.pdf<a name="Abs1"></a>Making use of the K?ppen&#8211;Trewartha (K&#8211;T) climate classification, we have found that a set of nine high-resolution regional climate models (RCM) are fairly capable of reproducing the current climate in Europe. The percentage of grid-point to grid-point coincidences between climate subtypes based on the control simulations and those of the Climate Research Unit (CRU) climatology varied between 73 and 82%. The best agreement with the CRU climatology corresponds to the RCM &#8220;ensemble mean&#8221;. The K&#8211;T classification was then used to elucidate scenarios of climate change for 2071&#8211;2100 under the SRES A2 emission scenario. The percentage of land grid-points with unchanged K&#8211;T subtypes ranged from 41 to 49%, while those with a shift from the current climate subtypes towards warmer or drier ones ranged from 51 to 59%. As a first approximation, one may assume that in regions with a shift of two or more climate subtypes, ecosystems might be at risk. Excluding northern Scandinavia, such regions were projected to cover about 12% of the European land area.
Engelbrecht C. J., F. A. Engelbrecht, 2015: Shifts in Köppen-Geiger climate zones over southern Africa in relation to key global temperature goals. Theor. Appl. Climatol., doi: 10.1007/s00704-014-1354-1.
Feng S., Q. Hu, W. Huang, C.-H. Ho, R. P. Li, and Z. H. Tang, 2014: Projected climate regime shift under future global warming from multi-model, multi-scenario CMIP5 simulations. Global Planet.Change, 112, 41- 52.10.1016/j.gloplacha.2013.11.00273a7c2d335e15b1abcc5ca1cce34cd82http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0921818113002403http://www.sciencedirect.com/science/article/pii/S0921818113002403ABSTRACT
Fraedrich K., F.-W. Gerstengarbe, and P. C. Werner, 2001: Climate shifts during the last century. Climatic Change, 50, 405- 417.10.1023/A:10106994288638a28a579d5373a831b4b34dec2a65aefhttp%3A%2F%2Flink.springer.com%2Farticle%2F10.1023%2FA%3A1010699428863http://link.springer.com/article/10.1023/A:1010699428863Fluctuations of the land surface areas covered by Koeppen climates are analysed for the 1901 to 1995 period using trends and outliers as indicators of climate shift. Only the extreme climate zones of the global Tropics and of the Tundra (with the highly correlated northern hemisphere temperature) realise statistically significant shifts and outliers. There are nosignificant trends and outliers in the fluctuating ocean-atmosphere patterns (Pacific Decadal and North Atlantic Oscillations) and the highly correlated intermediate climate zones (dry, subtropical and boreal) of the surrounding continents.
Gnanadesikan A., R. J. Stouffer, 2006: Diagnosing atmosphere-ocean general circulation model errors relevant to the terrestrial biosphere using the Köppen climate classification. Geophys. Res. Lett., 33, L22701.
Hou X. Y., S. Sun, J. Zhang, M. He, Y. Wang, D. Kong, and S. Wang, 1982: Vegetation Map of the People's Republic of China. China Cartography Press, Beijing. (in Chinese)5a26a6232eff3823a392cb74c01280fbhttp%3A%2F%2Flib.ugent.be%2Fen%2Fcatalog%2Frug01%3A001665769http://lib.ugent.be/en/catalog/rug01:001665769
Köppen, W., 1936: Das geographisca system der klimate. Handbuch der Klimatologie, W. Köppen, G. Geiger, Eds.Borntraeger, 1- 44.10.1126/science.34.866.155519fae98cfab0cf62e5e8cb2d3952f7ahttp%3A%2F%2Fwww.nature.com%2Fnature%2Fjournal%2Fv79%2Fn2048%2Fpdf%2F079363a0.pdfhttp://www.nature.com/nature/journal/v79/n2048/pdf/079363a0.pdfTHIS is the first part of vol. ii. of the third edition of Prof. Hann's “Handbuch der Klimatologie.” Vol. i. dealt with general principles, and we now come to the detailed consideration of the climates of different parts of the world. The volume before us concerns itself with the tropics, the consideration of temperate and polar regions being reserved for subsequent volumes. The author has not confined himself strictly to the area lying between 2305° north and south of the Equator. When desirable he has gone outside this region. Roughly speaking, he discusses that portion of the earth's surface which has an annual mean temperature of 20° C. or above. The isotherm of this value may be taken as marking the polar limits of the trade winds, when definable, and of the palm tree.
Legates D. R., C. J. Willmott, 1990a: Mean seasonal and spatial variability in global surface air temperature. Theor. Appl. Climatol., 41, 11- 21.10.1007/BF00866198947dcc02-3836-49a7-952c-d3824871e72b3bd9b287e1e7c055c432445a90e3227bhttp%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2FBF00866198refpaperuri:(13ec70797f1725af0cfcc5599ed4600a)http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1007/BF00866198Using terrestrial observations of shelter-height air temperature and shipboard measurements, a global climatology of mean monthly surface air temperature has been compiled. Data were obtained from ten sources, screened for coding errors, and redundant station records were removed. The combined data base consists of 17 986 independent terrestrial station records and 6 955 oceanic grid-point records. These data were then interpolated to a 0.5 of latitude by 0.5 of longitude lattice using a spherically-based interpolation algorithm. Spatial distributions of the annual mean and intra-annual variance are presented along with a harmonic decomposition of the intra-annual variance.
Legates D. R., C. J. Willmott, 1990b: Mean seasonal and spatial variability in gauge-corrected, global precipitation. Int. J. Climatol., 10, 111- 127.10.1002/joc.33701002020da23a9e-4ef4-4226-8f6e-6b300c5e961588d31a95602b5e5b85c6cc24e43fd42dhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fjoc.3370100202%2Ffullrefpaperuri:(70ae89c462aaf37d16a7f834fed8818f)http://onlinelibrary.wiley.com/doi/10.1002/joc.3370100202/fullAbstract Using traditional land-based gauge measurements and shipboard estimates, a global climatology of mean monthly precipitation has been developed. Data were obtained from ten existing sources, screened for coding errors, and redundant station records were removed. The edited data base contains 24,635 spatially independent terrestrial station records and 2223 oceanic grid-point records. A procedure for correcting gauge-induced biases is presented and used to remove systematic errors caused by wind, wetting on the interior walls of the gauge, and evaporation from the gauge. These ‘corrected’ monthly precipitation observations were then interpolated to a 0·5° of latitude by 0·5° of longitude grid using a spherically based interpolation procedure. Bias-corrected spatial distributions of the annual mean and intraannual variance are presented along with a harmonic decomposition of the intra-annual variance.
Leng W. F., H. S. He, R. C. Bu, L. M. Dai, Y. M. Hu, and X. G. Wang, 2008: Predicting the distributions of suitable habitat for three larch species under climate warming in Northeastern China. For. Eco. Manag., 254, 420- 428.10.1016/j.foreco.2007.08.031188ff77f4ee4afc23878695bd1e2c36chttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0378112707006433http://www.sciencedirect.com/science/article/pii/S0378112707006433Under the current climate regime, in general, the prediction accuracy for the training dataset is much higher than that of testing dataset. The prediction accuracy for Dahurian larch is much higher than that of other two larch species. Under three climate warming scenarios, the southeast boundary of suitable habitat of Dahurian Larch was modeled to retreat northwestward by 9002km (CGCM3-B1) via 10502km (CGCM3-A1B) to 14002km (CGCM3-A2) scenario. The potential area would thus decrease from 25.502million02ha currently to 13, 9.5 and 7.202million02ha, correspondingly. The northwest boundary of suitable habitat for Korean larch was modeled move northwestward by 10002km (CGCM3-B1) via 12502km (CGCM3-A1B) to 34002km (CGCM3-A2), while the southern boundary may move northeastward 12502km via 170–20002km, respectively. The modeled potential area thus decreased from 14.602million02ha to 14.5, 12.6 and 9.702million02ha, correspondingly. The suitable habitat of Prince Rupprecht Larch was modeled to disappear under each of the three scenarios.
Ma J., Y. M. Hu, R. C. Bu, Y. Chang, H. W. Deng, and Q. Qin, 2014: Predicting impacts of climate change on the aboveground carbon sequestration rate of a temperate forest in northeastern China. PLoS one, 2014, 9( 4), e96157.10.1371/journal.pone.009615724763409d9c897b11155646b4b9516087f5234abhttp%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F24763409http://www.ncbi.nlm.nih.gov/pubmed/24763409The aboveground carbon sequestration rate (ACSR) reflects the influence of climate change on forest dynamics. To reveal the long-term effects of climate change on forest succession and carbon sequestration, a forest landscape succession and disturbance model (LANDIS Pro7.0) was used to simulate the ACSR of a temperate forest at the community and species levels in northeastern China based on both current and predicted climatic data. On the community level, the ACSR of mixed Korean pine hardwood forests and mixed larch hardwood forests, fluctuated during the entire simulation, while a large decline of ACSR emerged in interim of simulation in spruce-fir forest and aspen-white birch forests, respectively. On the species level, the ACSR of all conifers declined greatly around 2070s except for Korean pine. The ACSR of dominant hardwoods in the Lesser Khingan Mountains area, such as Manchurian ash, Amur cork, black elm, and ribbed birch fluctuated with broad ranges, respectively. Pioneer species experienced a sharp decline around 2080s, and they would finally disappear in the simulation. The differences of the ACSR among various climates were mainly identified in mixed Korean pine hardwood forests, in all conifers, and in a few hardwoods in the last quarter of simulation. These results indicate that climate warming can influence the ACSR in the Lesser Khingan Mountains area, and the largest impact commonly emerged in the A2 scenario. The ACSR of coniferous species experienced higher impact by climate change than that of deciduous species.
Mahlstein I., J. S. Daniel, and S. Solomon, 2013: Pace of shifts in climate regions increases with global temperature. Nature Climate Change, 3, 739- 743.10.1038/nclimate18761213740b51dfdc9d1c484616b015e006http%3A%2F%2Fwww.nature.com%2Fnclimate%2Fjournal%2Fv3%2Fn8%2Ffull%2Fnclimate1876.htmlhttp://www.nature.com/nclimate/journal/v3/n8/full/nclimate1876.htmlHuman-induced climate change causes significant changes in local climates, which in turn lead to changes in regional climate zones. Large shifts in the world distribution of K0109ppen-Geiger climate classifications by the end of this century have been projected. However, only a few studies have analysed the pace of these shifts in climate zones, and none has analysed whether the pace itself changes with increasing global mean temperature. In this study, pace refers to the rate at which climate zones change as a function of amount of global warming. Here we show that present climate projections suggest that the pace of shifting climate zones increases approximately linearly with increasing global temperature. Using the RCP8.5 emissions pathway, the pace nearly doubles by the end of this century and about 20% of all land area undergoes a change in its original climate. This implies that species will have increasingly less time to adapt to K0109ppen zone changes in the future, which is expected to increase the risk of extinction.
Ni J., 2011: Impacts of climate change on Chinese ecosystems: Key vulnerable regions and potential thresholds. Reg. Environ.Change, 11, 49- 64.10.1007/s10113-010-0170-0f33339badebd757bff80191c28ebc62ahttp%3A%2F%2Flink.springer.com%2F10.1007%2Fs10113-010-0170-0http://link.springer.com/10.1007/s10113-010-0170-0China is a key vulnerable region of climate change in the world. Climate warming and general increase in precipitation with strong temporal and spatial variations have happened in China during the past century. Such changes in climate associated with the human disturbances have influenced natural ecosystems of China, leading to the advanced plant phenology in spring, lengthened growing season of vegetation, modified composition and geographical pattern of vegetation, especially in ecotone and tree-lines, and the increases in vegetation cover, vegetation activity and net primary productivity. Increases in temperature, changes in precipitation regime and CO 2 concentration enrichment will happen in the future in China according to climate model simulations. The projected climate scenarios (associated with land use changes again) will significantly influence Chinese ecosystems, resulting in a northward shift of all forests, disappearance of boreal forest from northeastern China, new tropical forests and woodlands move into the tropics, an eastward shift of grasslands (expansion) and deserts (shrinkage), a reduction in alpine vegetation and an increase in net primary productivity of most vegetation types. Ecosystems in northern and western parts of China are more vulnerable to climate changes than those in eastern China, while ecosystems in the east are more vulnerable to land use changes other than climate changes. Such assessment could be helpful to address the ultimate objective of the United Nations Framework Convention on Climate Change (UNFCCC Article 2).
Ni J., M. T. Sykes, I. C. Prentice, and W. Cramer, 2000: Modelling the vegetation of China using the process-based equilibrium terrestrial biosphere model BIOME3. Global Ecol. Biogeogr., 9, 463- 479.10.1046/j.1365-2699.2000.00206.xa0abfdb3-9938-4614-90a9-3cde275373a2f3246439d6ac7a534593a25aada741eahttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1046%2Fj.1365-2699.2000.00206.x%2Ffullrefpaperuri:(79f122345aedfc03946e8f3c41838b0e)http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2699.2000.00206.x/fullAbstract Top of page Abstract Introduction METHODS RESULTS DISCUSSION Acknowledgments References 168We model the potential vegetation and annual net primary production (NPP) of China on a 10′ grid under the present climate using the processed-based equilibrium terrestrial biosphere model BIOME3. The simulated distribution of the vegetation was in general in good agreement with the potential natural vegetation based on a numerical comparison between the two maps using the ΔV statistic (ΔV=0.23). Predicted and measured NPP were also similar, especially in terms of biome-averages. 268A coupled ocean–atmosphere general circulation model including sulphate aerosols was used to drive a double greenhouse gas scenario for 2070–2099. Simulated vegetation maps from two different CO 2 scenarios (340 and 500 p.p.m.v.) were compared to the baseline biome map using ΔV. Climate change alone produced a large reduction in desert, alpine tundra and ice/polar desert, and a general pole-ward shift of the boreal, temperate deciduous, warm–temperate evergreen and tropical forest belts, a decline in boreal deciduous forest and the appearance of tropical deciduous forest. The inclusion of CO 2 physiological effects led to a marked decrease in moist savannas and desert, a general decrease for grasslands and steppe, and disappearance of xeric woodland/scrub. Temperate deciduous broadleaved forest, however, shifted north to occupy nearly half the area of previously temperate mixed forest. 368The impact of climate change and increasing CO 2 is not only on biogeography, but also on potential NPP. The NPP values for most of the biomes in the scenarios with CO 2 set at 340 p.p.m.v. and 500 p.p.m.v. are greater than those under the current climate, except for the temperate deciduous forest, temperate evergreen broadleaved forest, tropical rain forest, tropical seasonal forest, and xeric woodland/scrub biomes. Total vegetation and total carbon is simulated to increase significantly in the future climate scenario, both with and without the CO 2 direct physiological effect. 468Our results show that the global process-based equilibrium terrestrial biosphere model BIOME3 can be used successfully at a regional scale.
Pan S., H. Q. Tian, C. Q. Lu, S. R. S. Dangal, and M. L. Liu, 2015: Net primary production of major plant functional types in China: Vegetation classification and ecosystem simulation. Acta Ecol. Sin., 35( 2), 28- 36.10.1016/j.chnaes.2015.03.00159941c3d62339b190cb0e33c6997f012http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1872203215000050http://www.sciencedirect.com/science/article/pii/S1872203215000050The characteristics and distribution of vegetation are essential information for understanding the structure and functioning of terrestrial ecosystems across a large region. In this study, we developed the contemporary and potential vegetation maps of China with a spatial resolution of 165km65×65165km. The vegetation classification scheme includes 17 types of vegetation and 3 non-vegetated land cover types. For cropland, we further provide spatial information on three major cropping systems across China, i.e., single, double and triple cropping system. In addition, we further evaluate the accuracy of this classification against field survey. As a case study, we used this vegetation data set combined with other environmental factors (climate, atmospheric CO 2 and nitrogen deposition) to drive the Dynamic Land Ecosystem Model (DLEM) for estimating terrestrial net primary production at both plant functional type and national levels. DLEM simulations indicate that net primary productivity (NPP) in China's terrestrial ecosystem has substantially increased by 51%, from 2.5065Pg C y 611 in the 1900s to 3.7965Pg C y 611 during the first decade of the 21st century. Among major plant functional types across China, cropland shows the largest NPP increase by nearly 3–4 fold during 1901–2010 primarily due to cropland expansion as well as increased nitrogen fertilizer use and irrigation. The NPP increase is estimated to be 48065 and 69265gC m 61265 y 611 for upland crops and rice fields, respectively. This vegetation distribution data set was originally developed for driving the Dynamic Land Ecosystem model (DLEM), but it can be used for other purposes such as driving hydrological and climate models.
Peel M. C., B. L. Finlayson, and T. A. McMahon, 2007: Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci., 4, 439- 473.
Peterson T. C., R. Vose, R. Schmoyer, and V. Razuva\"ev, 1998: Global historical climatology network (GHCN) quality control of monthly temperature data. Int. J. Climatol., 18, 1169- 1179.10.1002/(SICI)1097-0088(199809)18:11<1169::AID-JOC309>3.0.CO;2-U49c9207a9cc18bdad28d1b3abb8ba417http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F%28SICI%291097-0088%28199809%2918%3A11%3C1169%3A%3AAID-JOC309%3E3.0.CO%3B2-U%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1002/(SICI)1097-0088(199809)18:11<1169::AID-JOC309>3.0.CO;2-U/fullAbstract All geophysical data bases need some form of quality assurance. Otherwise, erroneous data points may produce faulty analyses. However, simplistic quality control procedures have been known to contribute to erroneous conclusions by removing valid data points that were more extreme than the data set compilers expected. In producing version 2 of the global historical climatology network's (GHCN's) temperature data sets, a variety of quality control tests were evaluated and a specialized suite of procedures was developed. Quality control traditionally relies primarily on checks for outliers from both a time series and spatial perspective, the latter accomplished by comparisons with neighbouring stations. This traditional approach was used, and it was determined that there are many data problems that require additional tests to detect. In this paper a suite of quality control tests are justified and documented and applied to this global temperature data base, emphasizing the logic and limitations of each test. 1998 Royal Meteorological Society
Phillips T. J., C. J. W. Bonfils, 2015: Köppen bioclimatic evaluation of CMIP historical climate simulations. Environ. Res. Lett., 10, 064005.
Rohli R. V., T. A. Joyner, S. J. Reynolds, C. Shaw, and J. R. Vãzquez, 2015: Globally extended Köppen-Geiger climate classification and temporal shifts in terrestrial climatic types. Phys. Geogr., 36, 142- 157.
Rubel F., M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z., 19, 135- 141.
Shi Y., X. J. Gao, and J. Wu, 2012: Projected changes in Köppen climate types in the 21st century over China. Atmos. Oceanic Sci. Lett., 5, 495- 498.
Song M. H., C. P. Zhou, and H. Ouyang, 2005: Simulated distribution of vegetation types in response to climate change on the Tibetan Plateau. J. Veg. Sci., 16, 341- 350.10.1111/j.1654-1103.2005.tb02372.xe0aed2e6acb381e4f30e05a69a7bc6cehttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.1654-1103.2005.tb02372.x%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1111/j.1654-1103.2005.tb02372.x/abstractAbstract Questions: What is the relationship between alpine vegetation patterns and climate? And how do alpine vegetation patterns respond to climate changes? Location: Tibetan Plateau, southwestern China. The total area is 2 500 000 km2 with an average altitude over 4000 m. Methods: The geographic distribution of vegetation types on the Tibetan Plateau was simulated based on climatology using a small set of plant functional types (PFTs) embedded in the biogeochemistry-biography model BIOME4. The paleoclimate for the early Holocene was used to explore the possibility of simulating past vegetation patterns. Changes in vegetation patterns were simulated assuming continuous exponential increase in atmospheric CO2 concentration, based on a transient ocean-atmosphere simulation including sulfate aerosol effects during the 21st century. Results: Forest, shrub steppe, alpine steppe and alpine meadow extended while no desert vegetation developed under the warmer and humid climate of the early Holocene. In the fut...
Taylor K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485- 498.10.1175/BAMS-D-11-00094.102496a28-fd74-494f-9dd0-772d832581a7d378bae55de68ca8b37ba4ba57a3c0b9http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F235793806_An_Overview_of_CMIP5_and_the_Experiment_Design%3Fev%3Dauth_pubrefpaperuri:(102c64f576f0dc49ca552e6df691421b)http://www.researchgate.net/publication/235793806_An_Overview_of_CMIP5_and_the_Experiment_Design?ev=auth_pubThe fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.
Van Vuuren, D. P., Coauthors, 2011: The representative concentration pathways: An overview. Climatic Change, 109, 5- 31.10.1007/s10584-011-0148-z8bb571b08a5c08a22377509f6eb0986fhttp%3A%2F%2Fwww.springerlink.com%2Fcontent%2Ff296645337804p75%2Fhttp://www.springerlink.com/content/f296645337804p75/This paper summarizes the development process and main characteristics of the Representative Concentration Pathways (RCPs), a set of four new scenarios developed for the climate modeling community as a basis for long-term and near-term modeling experiments. The four RCPs together span the range of year 2100 radiative forcing values found in the open literature, i.e. from 2.6 to 8.5 W/m2. The RCPs are the product of an innovative collaboration between integrated assessment modelers, climate modelers, terrestrial ecosystem modelers and emission inventory experts. The resulting product forms a comprehensive data set with high spatial and sectoral resolutions for the period extending to 2100. Land use and emissions of air pollutants and greenhouse gases are reported mostly at a 0.5 x 0.5 degree spatial resolution, with air pollutants also provided per sector (for well-mixed gases, a coarser resolution is used). The underlying integrated assessment model outputs for land use, atmospheric emissions and concentration data were harmonized across models and scenarios to ensure consistency with historical observations while preserving individual scenario trends. For most variables, the RCPs cover a wide range of the existing literature. The RCPs are supplemented with extensions (Extended Concentration Pathways, ECPs), which allow climate modeling experiments through the year more&raquo; 2300. The RCPs are an important development in climate research and provide a potential foundation for further research and assessment, including emissions mitigation and impact analysis. 芦less
Wang H., 2014: A multi-model assessment of climate change impacts on the distribution and productivity of ecosystems in China. Reg. Environ.Change, 14, 133- 144.10.1007/s10113-013-0469-848c60fa5bdf2cb485174c008e09da6bdhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1007%2Fs10113-013-0469-8http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1007/s10113-013-0469-8Potential twenty-first century changes in vegetation distribution and net primary production in China were assessed using three different vegetation models, including new process-oriented but observat
Wang H., J. Ni, and I. C. Prentice, 2011: Sensitivity of potential natural vegetation in China to projected changes in temperature, precipitation and atmospheric CO2. Reg. Environ.Change, 11, 715- 727.10.1007/s10113-011-0204-2230d4bbccba1b94e056860a15480e17fhttp%3A%2F%2Flink.springer.com%2F10.1007%2Fs10113-011-0204-2http://link.springer.com/10.1007/s10113-011-0204-2A sensitivity study was performed to investigate the responses of potential natural vegetation distribution in China to the separate and combined effects of temperature, precipitation and [CO 2 ], using the process-based equilibrium terrestrial biosphere model BIOME4. The model shows a generally good agreement with a map of the potential natural vegetation distribution based on a numerical comparison using the Δ V statistic (Δ V =0.25). Mean temperature of each month was increased uniformly by 0–5K, in 0.5- or 1-K intervals. Mean precipitation of each month was increased and decreased uniformly by 0–30%, in 10% intervals. The analyses were run at fixed CO 2 concentrations of 360 and 720ppm. Temperature increases shifted most forest boundaries northward and westward, expanded the distribution of xeric biomes, and confined the tundra to progressively higher elevations. Precipitation increases led to a greater area occupied by mesic biomes at the expense of xeric biomes. Most vegetation types in the temperate regions, and on the Tibetan Plateau, expanded westward into the dry continental interior with increasing precipitation. Precipitation decreases had opposite effects. The modelled effect of CO 2 doubling was to partially compensate for the negative effect of drought on the mesic biomes and to increase potential ecosystem carbon storage by about 40%. Warming tended to counteract this effect, by reducing soil carbon storage. Forest biomes showed substantial resilience to climate change, especially when the effects of increasing [CO 2 ] were taken into account. Savannas, dry woodland and tundra biomes proved sensitive to temperature increases. The transition region of grassland and forest, and the Tibetan plateau, was the most vulnerable region.
Wang M. Y., J. E. Overland, 2004: Detecting Arctic climate change using Köppen climate classification. Climatic Change, 67, 43- 62.
Xie Z. H., F. Yuan, Q. Y. Duan, J. Zheng, M. L. Liang, and F. Chen, 2007: Regional parameter estimation of the VIC land surface model: Methodology and application to river basins in China. J. Hydrometeorol., 8, 447- 468.10.1175/JHM568.1cae65da92a89d39e499da9155c7a1018http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F200471980_Regional_Parameter_Estimation_of_the_VIC_Land_Surface_Model_Methodology_and_Application_to_River_Basins_in_Chinahttp://www.researchgate.net/publication/200471980_Regional_Parameter_Estimation_of_the_VIC_Land_Surface_Model_Methodology_and_Application_to_River_Basins_in_ChinaAbstract This paper presents a methodology for regional parameter estimation of the three-layer Variable Infiltration Capacity (VIC-3L) land surface model with the goal of improving the streamflow simulation for river basins in China. This methodology is designed to obtain model parameter estimates from a limited number of calibrated basins and then regionalize them to uncalibrated basins based on climate characteristics and large river basin domains, and ultimately to continental China. Fourteen basins from different climatic zones and large river basins were chosen for model calibration. For each of these basins, seven runoff-related model parameters were calibrated using a systematic manual calibration approach. These calibrated parameters were then transferred within the climate and large river basin zones or climatic zones to the uncalibrated basins. To test the efficiency of the parameter regionalization method, a verification study was conducted on 19 independent river basins in China. Overall, the regionalized parameters, when evaluated against the a priori parameter estimates, were able to reduce the model bias by 0.4%–249.8% and relative root-mean-squared error by 0.2%–119.1% and increase the Nash–Sutcliffe efficiency of the streamflow simulation by 1.9%–31.7% for most of the tested basins. The transferred parameters were then used to perform a hydrological simulation over all of China so as to test the applicability of the regionalized parameters on a continental scale. The continental simulation results agree well with the observations at regional scales, indicating that the tested regionalization method is a promising scheme for parameter estimation for ungauged basins in China.
Xu C. H., Y. Xu, 2012: The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 multi-model ensemble. Atmos. Oceanic Sci. Lett., 5, 527- 533.ff3adcac1613ff11156ee6650aa81976http%3A%2F%2Fwww.cqvip.com%2FQK%2F89435X%2F201206%2F43967880.htmlhttp://d.wanfangdata.com.cn/Periodical/dqhhykxkb201206016
Yu L., M. K. Cao, and K. R. Li, 2006: Climate-induced changes in the vegetation pattern of China in the 21st century. Eco. Res., 21, 912- 919.10.1007/s11284-006-0042-8e4f6a48dd9d68ca7dbc4544ba3d3668fhttp%3A%2F%2Flink.springer.com%2F10.1007%2Fs11284-006-0042-8http://link.springer.com/10.1007/s11284-006-0042-8Quantifying climate-induced changes in vegetation patterns is essential to understanding land09 limate interactions and ecosystem changes. In the present study, we estimated various distributional changes of vegetation under different climate-change scenarios in the 21st century. Both hypothetical scenarios and Hedley RCM scenarios show that the transitional vegetation types, such as shrubland and grassland, have higher sensitivity to climatic change compared to vegetation under extreme climatic conditions, such as the evergreen broadleaf forest or desert, barren lands. Mainly, the sensitive areas in China lie in the Tibetan Plateau, Yunnan-Guizhou Plateau, northeastern plain of China and eco-zones between different vegetations. As the temperature increases, mixed forests and deciduous broadleaf forests will shift towards northern China. Grassland, shrubland and wooded grassland will extend to southeastern China. The RCM-project climate changes generally have caused positive vegetation changes; vegetation cover will probably improve 19% relative to baseline, and the forest will expand to 8% relative to baseline, while the desert and bare ground will reduce by about 13%.
Zhang Y. J., G. S. Zhou, 2008: Terrestrial transect study on driving mechanism of vegetation changes. Sci. China Ser.D, 51, 984- 991.10.1007/s11430-008-0065-90ad13f099747b7efa84a6450d40d3203http%3A%2F%2Flink.springer.com%2F10.1007%2Fs11430-008-0065-9http://www.cnki.com.cn/Article/CJFDTotal-JDXG200807008.htmIn terms of Chinese climate-vegetation model based on the classification of plant functional types, to- gether with climatic data from 1951 to 1980 and two future climatic scenarios (SRES-A2 and SRES-B2) in China from the highest and the lowest emission scenarios of greenhouse gases, the distribution patterns of vegetation types and their changes along the Northeast China Transect (NECT) and the North-South Transect of Eastern China (NSTEC) were simulated in order to understand the driving mechanisms of vegetation changes under climatic change. The results indicated that the vegetation distribution patterns would change significantly under future climate, and the major factors driving the vegetation changes were water and heat. However, the responses of various vegetation types to the changes in water and heat factors were obviously different. The vegetation changes were more sensi- tive to heat factors than to water factors. Thus, in the future climate warming will significantly affect vegetation distribution patterns.
Zhao D. S., S. H. Wu, 2014: Responses of vegetation distribution to climate change in China. Theor. Appl. Climatol., 117, 15- 28.10.1007/s00704-013-0971-447836a19c7fea08456a85645035c3569http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs00704-013-0971-4http://link.springer.com/article/10.1007/s00704-013-0971-4Climate plays a crucial role in controlling vegetation distribution and climate change may therefore cause extended changes. A coupled biogeography and biogeochemistry model called BIOME4 was modified by redefining the bioclimatic limits of key plant function types on the basis of the regional vegetation limate relationships in China. Compared to existing natural vegetation distribution, BIOME4 is proven more reliable in simulating the overall vegetation distribution in China. Possible changes in vegetation distribution were simulated under climate change scenarios by using the improved model. Simulation results suggest that regional climate change would result in dramatic changes in vegetation distribution. Climate change may increase the areas covered by tropical forests, warm-temperate forests, savannahs/dry woodlands and grasslands/dry shrublands, but decrease the areas occupied by temperate forests, boreal forests, deserts, dry tundra and tundra across China. Most vegetation in east China, specifically the boreal forests and the tropical forests, may shift their boundaries northwards. The tundra and dry tundra on the Tibetan Plateau may be progressively confined to higher elevation.