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Gong H, Zhang M, Xiang X, Liu H. 1 km monthly precipitation and temperatures dataset for China from 1952 to 2019 based on new baseline climatology surfaces. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167613. [PMID: 37813268 DOI: 10.1016/j.scitotenv.2023.167613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 10/04/2023] [Accepted: 10/04/2023] [Indexed: 10/11/2023]
Abstract
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 >2000 freely available weather stations. Then, climatologically aided interpolation (CAI) was employed to generate a 1 km 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- 52.307 mm, and 0.461-0.939 °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 1 km spatial resolution is appropriate for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China.
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Affiliation(s)
- Haibo Gong
- Key Laboratory of Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China; College of Geography Science, Nanjing Normal University, Nanjing 210023, China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing 210023, China; Jiangsu Key Laboratory of Environmental Change and Ecological Construction, Nanjing Normal University, Nanjing 210023, China
| | - Mingyang Zhang
- Key Laboratory of Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences, Huanjiang, Hechi 547100, China.
| | - Xueqiao Xiang
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China; College of Geography Science, Nanjing Normal University, Nanjing 210023, China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing 210023, China; Jiangsu Key Laboratory of Environmental Change and Ecological Construction, Nanjing Normal University, Nanjing 210023, China
| | - Huiyu Liu
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China; College of Geography Science, Nanjing Normal University, Nanjing 210023, China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing 210023, China; Jiangsu Key Laboratory of Environmental Change and Ecological Construction, Nanjing Normal University, Nanjing 210023, China.
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Is New Always Better? Frontiers in Global Climate Datasets for Modeling Treeline Species in the Himalayas. ATMOSPHERE 2021. [DOI: 10.3390/atmos12050543] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Comparing and evaluating global climate datasets and their effect on model performance in regions with limited data availability has received little attention in ecological modeling studies so far. In this study, we aim at comparing the interpolated climate dataset Worldclim 1.4, which is the most widely used in ecological modeling studies, and the quasi-mechanistical downscaled climate dataset Chelsa, as well as their latest versions Worldclim 2.1 and Chelsa 1.2, with regard to their suitability for modeling studies. To evaluate the effect of these global climate datasets at the meso-scale, the ecological niche of Betula utilis in Nepal is modeled under current and future climate conditions. We underline differences regarding methodology and bias correction between Chelsa and Worldclim versions and highlight potential drawbacks for ecological models in remote high mountain regions. Regarding model performance and prediction plausibility under current climatic conditions, Chelsa-based models significantly outperformed Worldclim-based models, however, the latest version of Chelsa contains partially inherent distorted precipitation amounts. This study emphasizes that unmindful usage of climate data may have severe consequences for modeling treeline species in high-altitude regions as well as for future projections, if based on flawed current model predictions. The results illustrate the inevitable need for interdisciplinary investigations and collaboration between climate scientists and ecologists to enhance climate-based ecological model quality at meso- to local-scales by accounting for local-scale physical features at high temporal and spatial resolution.
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Bazzato E, Rosati L, Canu S, Fiori M, Farris E, Marignani M. High spatial resolution bioclimatic variables to support ecological modelling in a Mediterranean biodiversity hotspot. Ecol Modell 2021. [DOI: 10.1016/j.ecolmodel.2020.109354] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Noce S, Caporaso L, Santini M. A new global dataset of bioclimatic indicators. Sci Data 2020; 7:398. [PMID: 33199736 PMCID: PMC7670417 DOI: 10.1038/s41597-020-00726-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 10/21/2020] [Indexed: 11/17/2022] Open
Abstract
This study presents a new global gridded dataset of bioclimatic indicators at 0.5° by 0.5° resolution for historical and future conditions. The dataset, called CMCC-BioClimInd, provides a set of 35 bioclimatic indices, expressed as mean values over each time interval, derived from post-processing both climate reanalysis for historical period (1960-1999) and an ensemble of 11 bias corrected CMIP5 simulations under two greenhouse gas concentration scenarios for future climate projections along two periods (2040-2079 and 2060-2099). This new dataset complements the availability of spatialized bioclimatic information, crucial aspect in many ecological and environmental wide scale applications and for several disciplines, including forestry, biodiversity conservation, plant and landscape ecology. The data of individual indicators are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format.
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Affiliation(s)
- Sergio Noce
- Division on Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Viterbo, Italy.
| | - Luca Caporaso
- Division on Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Viterbo, Italy
- Institute of Marine Sciences (ISMAR), Centro Nazionale delle Ricerche (CNR), Rome, Italy
| | - Monia Santini
- Division on Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Viterbo, Italy
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Potential Impact of Climate Change on the Forest Coverage and the Spatial Distribution of 19 Key Forest Tree Species in Italy under RCP4.5 IPCC Trajectory for 2050s. FORESTS 2020. [DOI: 10.3390/f11090934] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Forests provide a range of ecosystem services essential for human wellbeing. In a changing climate, forest management is expected to play a fundamental role by preserving the functioning of forest ecosystems and enhancing the adaptive processes. Understanding and quantifying the future forest coverage in view of climate changes is therefore crucial in order to develop appropriate forest management strategies. However, the potential impacts of climate change on forest ecosystems remain largely unknown due to the uncertainties lying behind the future prediction of models. To fill this knowledge gap, here we aim to provide an uncertainty assessment of the potential impact of climate change on the forest coverage in Italy using species distribution modelling technique. The spatial distribution of 19 forest tree species in the country was extracted from the last national forest inventory and modelled using nine Species Distribution Models algorithms, six different Global Circulation Models (GCMs), and one Regional Climate Models (RCMs) for 2050s under an intermediate forcing scenario (RCP 4.5). The single species predictions were then compared and used to build a future forest cover map for the country. Overall, no sensible variation in the spatial distribution of the total forested area was predicted with compensatory effects in forest coverage of different tree species, whose magnitude and patters appear largely modulated by the driving climate models. The analyses reported an unchanged amount of total land suitability to forest growth in mountain areas while smaller values were predicted for valleys and floodplains than high-elevation areas. Pure woods were predicted as the most influenced when compared with mixed stands which are characterized by a greater species richness and, therefore, a supposed higher level of biodiversity and resilience to climate change threatens. Pure softwood stands along the Apennines chain in central Italy (e.g., Pinus, Abies) were more sensitive than hardwoods (e.g., Fagus, Quercus) and generally characterized by pure and even-aged planted forests, much further away from their natural structure where admixture with other tree species is more likely. In this context a sustainable forest management strategy may reduce the potential impact of climate change on forest ecosystems. Silvicultural practices should be aimed at increasing the species richness and favoring hardwoods currently growing as dominating species under conifers canopy, stimulating the natural regeneration, gene flow, and supporting (spatial) migration processes.
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Cuervo-Robayo AP, Ureta C, Gómez-Albores MA, Meneses-Mosquera AK, Téllez-Valdés O, Martínez-Meyer E. One hundred years of climate change in Mexico. PLoS One 2020; 15:e0209808. [PMID: 32673306 PMCID: PMC7365465 DOI: 10.1371/journal.pone.0209808] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 06/19/2020] [Indexed: 11/18/2022] Open
Abstract
Spatial assessments of historical climate change provide information that can be used by scientists to analyze climate variation over time and evaluate, for example, its effects on biodiversity, in order to focus their research and conservation efforts. Despite the fact that there are global climatic databases available at high spatial resolution, they represent a short temporal window that impedes evaluating historical changes of climate and their impacts on biodiversity. To fill this gap, we developed climate gridded surfaces for Mexico for three periods that cover most of the 20th and early 21st centuries: t1-1940 (1910–1949), t2-1970 (1950–1979) and t3-2000 (1980–2009), and used these interpolated surfaces to describe how climate has changed over time, both countrywide and in its 19 biogeographic provinces. Results from our characterization of climate change indicate that the mean annual temperature has increased by nearly 0.2°C on average across the whole country from t2-1970 to t3-2000. However, changes have not been spatially uniform: Nearctic provinces in the north have suffered higher temperature increases than southern tropical regions. Central and southern provinces cooled at the beginning of the 20th century but warmed consistently since the 1970s. Precipitation increased between t1-1940 and t2-1970 across the country, more notably in the northern provinces, and it decreased between t2-1970 and t3-2000 in most of the country. Results on the historical climate conditions in Mexico may be useful for climate change analyses for both environmental and social sciences. Nonetheless, our climatology was based on information from climate stations for which 9.4–36.2% presented inhomogeneities over time probably owing to non-climatic factors, and climate station density changed over time. Therefore, the estimated changes observed in our analysis need to be interpreted cautiously.
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Affiliation(s)
- Angela P. Cuervo-Robayo
- Departamento de Zoología, Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México, México
- Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (Conabio), Ciudad de México, México
| | - Carolina Ureta
- Cátedras-Departamento de Ciencias Atmosféricas, Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Miguel A. Gómez-Albores
- Instituto Interamericano de Tecnología y Ciencias del Agua, Universidad Autónoma del Estado de México, Toluca, Estado de México, México
| | - Anny K. Meneses-Mosquera
- Departamento de Zoología, Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Oswaldo Téllez-Valdés
- Facultad de Estudios Superiores Iztacala, Unidad de Biotecnología y Prototipos, Laboratorio de Recursos Naturales, Universidad Nacional Autónoma de México, Tlalnepantla, México
| | - Enrique Martínez-Meyer
- Departamento de Zoología, Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México, México
- * E-mail:
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Picazo F, Vilmi A, Aalto J, Soininen J, Casamayor EO, Liu Y, Wu Q, Ren L, Zhou J, Shen J, Wang J. Climate mediates continental scale patterns of stream microbial functional diversity. MICROBIOME 2020; 8:92. [PMID: 32534595 PMCID: PMC7293791 DOI: 10.1186/s40168-020-00873-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 05/20/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Understanding the large-scale patterns of microbial functional diversity is essential for anticipating climate change impacts on ecosystems worldwide. However, studies of functional biogeography remain scarce for microorganisms, especially in freshwater ecosystems. Here we study 15,289 functional genes of stream biofilm microbes along three elevational gradients in Norway, Spain and China. RESULTS We find that alpha diversity declines towards high elevations and assemblage composition shows increasing turnover with greater elevational distances. These elevational patterns are highly consistent across mountains, kingdoms and functional categories and exhibit the strongest trends in China due to its largest environmental gradients. Across mountains, functional gene assemblages differ in alpha diversity and composition between the mountains in Europe and Asia. Climate, such as mean temperature of the warmest quarter or mean precipitation of the coldest quarter, is the best predictor of alpha diversity and assemblage composition at both mountain and continental scales, with local non-climatic predictors gaining more importance at mountain scale. Under future climate, we project substantial variations in alpha diversity and assemblage composition across the Eurasian river network, primarily occurring in northern and central regions, respectively. CONCLUSIONS We conclude that climate controls microbial functional gene diversity in streams at large spatial scales; therefore, the underlying ecosystem processes are highly sensitive to climate variations, especially at high latitudes. This biogeographical framework for microbial functional diversity serves as a baseline to anticipate ecosystem responses and biogeochemical feedback to ongoing climate change. Video Abstract.
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Affiliation(s)
- Félix Picazo
- State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008 China
| | - Annika Vilmi
- State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008 China
| | - Juha Aalto
- Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland
- Department of Geosciences and Geography, University of Helsinki, 00014 Helsinki, Finland
| | - Janne Soininen
- Department of Geosciences and Geography, University of Helsinki, 00014 Helsinki, Finland
| | - Emilio O. Casamayor
- Integrative Freshwater Ecology Group, Centre of Advanced Studies of Blanes-Spanish Council for Research CEAB-CSIC, E-17300 Blanes, Spain
| | - Yongqin Liu
- University of Chinese Academy of Sciences, Beijing, 1000049 China
- Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101 China
| | - Qinglong Wu
- State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008 China
| | - Lijuan Ren
- Department of Ecology, Jinan University, Guangzhou, 510632 China
| | - Jizhong Zhou
- Department of Microbiology and Plant Biology, Institute for Environmental Genomics, University of Oklahoma, Norman, OK 73019 USA
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084 China
- Earth Science Division, Lawrence Berkeley National Laboratory, California, 94270 USA
| | - Ji Shen
- State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008 China
| | - Jianjun Wang
- State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008 China
- University of Chinese Academy of Sciences, Beijing, 1000049 China
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Exploring Nonlinear Intra-Annual Growth Dynamics in Fagus sylvatica L. Trees at the Italian ICP-Forests Level II Network. FORESTS 2019. [DOI: 10.3390/f10070584] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The European beech (Fagus sylvatica L.) is a widely distributed tree species across Europe, highly sensitive to climate change and global warming. This study illustrates results of a 5-year monitoring time period from eight sites of the ICP-Forests Level II (intensive monitoring network) along the Italian latitudinal gradient. The tree-level relationship between tree growth dynamics and environmental factors, including seasonal climate fluctuations were investigated by means of tree-level Generalized Additive Mixed Models (GAMMs). Model results revealed that climate was responsible for just a portion of the variability in beech growth dynamics. Even if climatic predictors were highly significant in almost all sites, the model explained nearly 30% of the total variance (with just a maximum value of 71.6%), leaving the remaining variance unexplained and likely connected with forest management trajectories applied to each site (e.g., aged coppice and fully grown high forest). Climate change scenarios were then applied to predict site-specific future responses. By applying climate change scenarios, it was predicted that central and northern Italy would face similar climatic conditions to those currently detected at southern latitudes. A special case study was represented by VEN1 plot (Veneto, Northern Italy) whose current and future climate regimes were grouped in a unique and separated cluster.
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